Merge branch 'develop'

BUG: Error when ingesting marketcap on windows

.coveragerc

@@ -0,0 +1,6 @@
+[report]
+omit =
+    */python?.?/*
+    */site-packages/nose/*
+exclude_lines =
+    raise NotImplementedError

.dir-locals.el

@@ -0,0 +1,3 @@
+((nil . ((sentence-end-double-space . t)))
+ (python-mode . ((fill-column . 79)
+                 (python-fill-docstring-style . django))))

.dockerignore

@@ -0,0 +1,6 @@
+MANIFEST.in
+**/*pyc
+.eggs
+dist
+build
+*.egg-info

.gitattributes

@@ -0,0 +1,3 @@
+zipline/_version.py export-subst
+*.ipynb binary
+catalyst/_version.py export-subst

.github/ISSUE_TEMPLATE.md

@@ -0,0 +1,39 @@
+Dear Catalyst Maintainers,
+
+Before I tell you about my issue, let me describe my environment:
+
+# Environment
+
+* Operating System: (Windows Version or `$ uname --all`)
+* Python Version: `$ python --version`
+* Python Bitness: `$ python -c 'import math, sys;print(int(math.log(sys.maxsize + 1, 2) + 1))'`
+* How did you install Catalyst: (`pip`, `conda`, or `other (please explain)`)
+* Python packages: `$ pip freeze` or `$ conda list`
+
+Now that you know a little about me, let me tell you about the issue I am
+having:
+
+# Description of Issue
+
+* What did you expect to happen?
+* What happened instead?
+
+Here is how you can reproduce this issue on your machine:
+
+## Reproduction Steps
+
+1.
+2.
+3.
+...
+
+## What steps have you taken to resolve this already?
+
+...
+
+# Anything else?
+
+...
+
+Sincerely,
+`$ whoami`

.gitignore

@@ -0,0 +1,85 @@
+.bundle
+db/*.sqlite3
+log/*.log
+*.log
+tmp/**/*
+tmp/*
+*.swp
+*~
+#mac autosaving file
+.DS_Store
+*.py[co]
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+test.log
+.noseids
+*.xlsx
+
+# Compiled python files
+*.py[co]
+
+# Packages
+*.egg
+.eggs/*
+*.egg-info
+dist
+build
+eggs
+cover
+parts
+bin
+var
+sdist
+develop-eggs
+.installed.cfg
+coverage.xml
+htmlcov
+nosetests.xml
+.python-version
+
+# C Extensions
+*.o
+*.so
+*.out
+# git add -f if needed
+*.c
+
+# Vim
+*.swp
+*.swo
+
+# Built documentation
+docs/_build/*
+
+# Un-tarred example data input. We should only commit the tarball.
+tests/resources/example_data/*
+
+# database of vbench
+benchmarks.db
+
+# Vagrant temp folder
+.vagrant
+
+# Intellij IDE temp project files
+.project
+zipline.iml
+
+# PyCharm custom settings
+.idea
+
+# Pickle files
+*.pickle
+
+# data
+./data
+
+TAGS
+
+python2
+python3
+scratch

.travis.yml

@@ -0,0 +1,82 @@
+language: python
+sudo: false
+fast_finish: true
+python:
+  - 2.7
+  - 3.4
+  - 3.5
+env:
+  global:
+    # 1. Generated a token for travis at https://anaconda.org/quantopian/settings/access with scope api:write.
+    #   Can also be done via anaconda CLI with
+    #     $ TOKEN=$(anaconda auth --create --name my_travis_token)
+    # 2. Generated secure env var below with travis gem via
+    #     $ travis encrypt ANACONDA_TOKEN=$TOKEN
+    #   See https://github.com/travis-ci/travis.rb#installation.
+    #   If authenticating travis gem with github, a github token with the following scopes
+    #   is sufficient: ["read:org", "user:email", "repo_deployment", "repo:status", "write:repo_hook"]
+    #   See https://docs.travis-ci.com/api#external-apis.
+    - secure: "W2tTHoZYLuEjoIMI/K3adv7QW7yx4iVOIkVOn73jUkv3IlyZZ+BraL0hBw5Dh/iBA9PnO1qOKeRFLDDfDza/1S+2QxZMBmJ8HAkcZehbtTPdCgn/+CYSlauUlJ2izxgnXFw49qJDllQWtwsK2PEuvHrir6wbdElkXKvIJoD7jQ4="
+    - CONDA_ROOT_PYTHON_VERSION: "2.7"
+  matrix:
+    - NUMPY_VERSION=1.11.1 SCIPY_VERSION=0.17.1
+cache:
+  directories:
+    - $HOME/.cache/.pip/
+
+before_install:
+  - if [ ${CONDA_ROOT_PYTHON_VERSION:0:1} == "2" ]; then wget https://repo.continuum.io/miniconda/Miniconda-3.7.0-Linux-x86_64.sh -O miniconda.sh; else wget https://repo.continuum.io/miniconda/Miniconda3-3.7.0-Linux-x86_64.sh -O miniconda.sh; fi
+  - chmod +x miniconda.sh
+  - ./miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+install:
+  - conda info -a
+  - conda install conda=4.1.11 conda-build=1.21.11 anaconda-client=1.5.1 --yes
+
+  - TALIB_VERSION=$(cat ./etc/requirements_talib.txt | sed "s/TA-Lib==\(.*\)/\1/")
+  - IFS='.' read -r -a NPY_VERSION_ARR <<< "$NUMPY_VERSION"
+  - CONDA_NPY=${NPY_VERSION_ARR[0]}${NPY_VERSION_ARR[1]}
+  - CONDA_PY=$TRAVIS_PYTHON_VERSION
+
+  - if [[ "$TRAVIS_SECURE_ENV_VARS" = "true" && "$TRAVIS_BRANCH" = "master" && "$TRAVIS_PULL_REQUEST" = "false" ]]; then DO_UPLOAD="true"; else DO_UPLOAD="false"; fi
+  - |
+    for recipe in $(ls -d conda/*/ | xargs -I {} basename {}); do
+      if [[ "$recipe" = "catalyst" ]]; then continue; fi
+
+      conda build conda/$recipe --python=$CONDA_PY --numpy=$CONDA_NPY --skip-existing -c quantopian -c quantopian/label/ci
+      RECIPE_OUTPUT=$(conda build conda/$recipe --python=$CONDA_PY --numpy=$CONDA_NPY --output)
+      if [[ -f "$RECIPE_OUTPUT" && "$DO_UPLOAD" = "true" ]]; then anaconda -t $ANACONDA_TOKEN upload "$RECIPE_OUTPUT" -u quantopian --label ci; fi
+    done
+
+  - conda create -n testenv --use-local --yes -c quantopian pip python=$TRAVIS_PYTHON_VERSION numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION libgfortran=3.0 ta-lib=$TALIB_VERSION
+  - source activate testenv
+
+  - CACHE_DIR="$HOME/.cache/.pip/pip_np""$CONDA_NPY"
+  - pip install --upgrade pip coverage coveralls --cache-dir=$CACHE_DIR
+  - pip install -r etc/requirements.txt --cache-dir=$CACHE_DIR
+  - pip install -r etc/requirements_dev.txt --cache-dir=$CACHE_DIR
+  - pip install -r etc/requirements_blaze.txt --cache-dir=$CACHE_DIR  # this uses git requirements right now
+  - pip install -r etc/requirements_talib.txt --cache-dir=$CACHE_DIR
+  - pip install -e .[all] --cache-dir=$CACHE_DIR
+before_script:
+  - pip freeze | sort
+script:
+  - flake8 catalyst tests
+  - nosetests --with-coverage
+  # deactive env to get access to anaconda command
+  - source deactivate
+
+  # unshallow the clone so the conda build can clone it.
+  - git fetch --unshallow
+  - exec 3>&1; ZP_OUT=$(conda build conda/catalyst --python=$CONDA_PY --numpy=$CONDA_NPY -c quantopian -c quantopian/label/ci | tee >(cat - >&3))
+  - ZP_OUTPUT=$(echo "$ZP_OUT" | grep "anaconda upload" | awk '{print $NF}')
+  - if [[ "$DO_UPLOAD" = "true" ]]; then anaconda -t $ANACONDA_TOKEN upload $ZP_OUTPUT -u quantopian --label ci; fi
+  # reactivate env (necessary for coveralls)
+  - source activate testenv
+
+after_success:
+  - coveralls
+
+branches:
+  only:
+    - master

AUTHORS

@@ -0,0 +1,49 @@
+Eddie Hebert
+fawce
+Thomas Wiecki
+Stephen Diehl
+scottsanderson
+Scott Sanderson
+Richard Frank
+Jonathan Kamens
+twiecki
+Joe Jevnik
+Delaney Granizo-Mackenzie
+Tobias Brandt
+Ben McCann
+John Ricklefs
+Jenkins T. Quantopian, III
+Jeremiah Lowin
+jbredeche
+Brian Fink
+David Edwards
+Matti Hanninen
+Ryan Day
+llllllllll
+David Stephens
+Tim
+Dale Jung
+Jamie Kirkpatrick
+Jean Bredeche
+Wes McKinney
+jikamens
+Aidan
+Colin Alexander
+Elektra58
+Jason Kölker
+Jeremi Joslin
+Luke Schiefelbein
+Martin Dengler
+Mete Atamel
+Michael Schatzow
+Moises Trovo
+Nicholas Pezolano
+Pankaj Garg
+Paolo Bernardi
+Peter Cawthron
+Philipp Kosel
+Suminda Dharmasena
+The Gitter Badger
+Tony Lambiris
+Tony Worm
+stanh

Dockerfile

@@ -0,0 +1,93 @@
+#
+# Dockerfile for an image with the currently checked out version of catalyst installed. To build:
+#
+#    docker build -t quantopian/catalyst .
+#
+# To run the container:
+#
+#    docker run -v /path/to/your/notebooks:/projects -v ~/.catalyst:/root/.catalyst -p 8888:8888/tcp --name catalyst -it quantopian/catalyst
+#
+# To access Jupyter when running docker locally (you may need to add NAT rules):
+#
+#    https://127.0.0.1
+#
+# Default password is 'jupyter'. To provide another, see:
+#    http://jupyter-notebook.readthedocs.org/en/latest/public_server.html#preparing-a-hashed-password
+#
+# Once generated, you can pass the new value via `docker run --env` the first time
+# you start the container.
+#
+# You can also run an algo using the docker exec command. For example:
+#
+#    docker exec -it catalyst catalyst run -f /projects/my_algo.py --start 2015-1-1 --end 2016-1-1 /projects/result.pickle
+#
+FROM python:3.5
+
+#
+# set up environment
+#
+ENV TINI_VERSION v0.10.0
+ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /tini
+RUN chmod +x /tini
+ENTRYPOINT ["/tini", "--"]
+
+ENV PROJECT_DIR=/projects \
+    NOTEBOOK_PORT=8888 \
+    SSL_CERT_PEM=/root/.jupyter/jupyter.pem \
+    SSL_CERT_KEY=/root/.jupyter/jupyter.key \
+    PW_HASH="u'sha1:31cb67870a35:1a2321318481f00b0efdf3d1f71af523d3ffc505'" \
+    CONFIG_PATH=/root/.jupyter/jupyter_notebook_config.py
+
+#
+# install TA-Lib and other prerequisites
+#
+
+RUN mkdir ${PROJECT_DIR} \
+    && apt-get -y update \
+    && apt-get -y install libfreetype6-dev libpng-dev libopenblas-dev liblapack-dev gfortran \
+    && curl -L https://downloads.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz | tar xvz
+
+#
+# build and install catalyst from source.  install TA-Lib after to ensure
+# numpy is available.
+#
+
+WORKDIR /ta-lib
+
+RUN pip install 'numpy>=1.11.1,<2.0.0' \
+  && pip install 'scipy>=0.17.1,<1.0.0' \
+  && pip install 'pandas>=0.18.1,<1.0.0' \
+  && ./configure --prefix=/usr \
+  && make \
+  && make install \
+  && pip install TA-Lib \
+  && pip install matplotlib \
+  && pip install jupyter
+
+#
+# This is then only file we need from source to remain in the
+# image after build and install.
+#
+
+ADD ./etc/docker_cmd.sh /
+
+#
+# make port available. /catalyst is made a volume
+# for developer testing.
+#
+EXPOSE ${NOTEBOOK_PORT}
+
+#
+# build and install the catalyst package into the image
+#
+
+ADD . /catalyst
+WORKDIR /catalyst
+RUN pip install -e .
+
+#
+# start the jupyter server
+#
+
+WORKDIR ${PROJECT_DIR}
+CMD /docker_cmd.sh

Dockerfile-dev

@@ -0,0 +1,34 @@
+#
+# Dockerfile for an image with the currently checked out version of catalyst installed. To build:
+#
+#    docker build -t quantopian/catalystdev -f Dockerfile-dev .
+#
+# Note: the dev build requires a quantopian/catalyst image, which you can build as follows:
+#
+#    docker build -t quantopian/catalyst -f Dockerfile .
+#
+# To run the container:
+#
+#    docker run -v /path/to/your/notebooks:/projects -v ~/.catalyst:/root/.catalyst -p 8888:8888/tcp --name catalystdev -it quantopian/catalystdev
+#
+# To access Jupyter when running docker locally (you may need to add NAT rules):
+#
+#    https://127.0.0.1
+#
+# Default password is 'jupyter'. To provide another, see:
+#    http://jupyter-notebook.readthedocs.org/en/latest/public_server.html#preparing-a-hashed-password
+#
+# Once generated, you can pass the new value via `docker run --env` the first time
+# you start the container.
+#
+# You can also run an algo using the docker exec command. For example:
+#
+#    docker exec -it catalystdev catalyst run -f /projects/my_algo.py --start 2015-1-1 --end 2016-1-1 /projects/result.pickle
+#
+FROM quantopian/catalyst
+
+WORKDIR /catalyst
+
+RUN pip install -r etc/requirements_dev.txt -r etc/requirements_blaze.txt
+# Clean out any cython assets. The pip install re-builds them.
+RUN find . -type f -name '*.c' -exec rm {} + && pip install -e .[all]

LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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MANIFEST.in

@@ -0,0 +1,9 @@
+include LICENSE
+
+include etc/requirements*.txt
+recursive-include catalyst *.pyi
+recursive-include catalyst *.pxi
+
+recursive-include catalyst/resources *.*
+include versioneer.py
+include catalyst/_version.py

README.rst

@@ -0,0 +1,70 @@
+.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.png
+    :target: https://enigmampc.github.io/catalyst
+    :align: center
+    :alt: Enigma | Catalyst
+
+|version tag|
+|version status|
+|discord|
+|twitter|
+
+|
+
+Catalyst is an algorithmic trading library for crypto-assets written in Python.
+It allows trading strategies to be easily expressed and backtested against 
+historical data (with daily and minute resolution), providing analytics and 
+insights regarding a particular strategy's performance. Catalyst also supports
+live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex, 
+and Poloniex) with more being added over time. Catalyst empowers users to share 
+and curate data and build profitable, data-driven investment strategies. Please 
+visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst.
+
+Catalyst builds on top of the well-established 
+`Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to 
+minimize structural changes to the general API to maximize compatibility with 
+existing trading algorithms, developer knowledge, and tutorials. Join us on 
+`Discord <https://discord.gg/SJK32GY>`_ where we have a *#catalyst_dev* channel 
+for questions around Catalyst, algorithmic trading and technical support.
+
+Overview
+========
+
+-  Ease of use: Catalyst tries to get out of your way so that you can 
+   focus on algorithm development. See 
+   `examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_ 
+   provided.
+-  Support for several of the top crypto-exchanges by trading volume:
+   `Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
+   and `Poloniex <https://www.poloniex.com>`_. 
+-  Secure: You and only you have access to each exchange API keys for your accounts.
+-  Input of historical pricing data of all crypto-assets by exchange, 
+   with daily and minute resolution. See 
+   `Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
+-  Backtesting and live-trading functionality, with a seamless transition
+   between the two modes.
+-  Output of performance statistics are based on Pandas DataFrames to 
+   integrate nicely into the existing PyData eco-system.
+-  Statistic and machine learning libraries like matplotlib, scipy, 
+   statsmodels, and sklearn support development, analysis, and 
+   visualization of state-of-the-art trading systems.
+-  Addition of Bitcoin price (btc_usdt) as a benchmark for comparing 
+   performance across trading algorithms.
+
+Go to our `Documentation Website <https://enigmampc.github.io/catalyst/>`_.
+
+
+
+
+.. |version tag| image:: https://img.shields.io/pypi/v/enigma-catalyst.svg
+   :target: https://pypi.python.org/pypi/enigma-catalyst
+
+.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
+   :target: https://pypi.python.org/pypi/enigma-catalyst
+
+.. |discord| image:: https://img.shields.io/badge/discord-join%20chat-green.svg
+   :target: https://discordapp.com/invite/SJK32GY
+
+.. |twitter| image:: https://img.shields.io/twitter/follow/enigmampc.svg?style=social&label=Follow&style=flat-square
+   :target: https://twitter.com/enigmampc
+
+

Vagrantfile

@@ -0,0 +1,10 @@
+# -*- mode: ruby -*-
+# vi: set ft=ruby :
+
+Vagrant.configure("2") do |config|
+  config.vm.box = "ubuntu/trusty64"
+  config.vm.provider :virtualbox do |vb|
+    vb.customize ["modifyvm", :id, "--memory", 2048, "--cpus", 2]
+  end
+  config.vm.provision "shell", path: "vagrant_init.sh"
+end

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_static/js/theme.js

@@ -1,169 +0,0 @@
-require=(function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({"sphinx-rtd-theme":[function(require,module,exports){
-var jQuery = (typeof(window) != 'undefined') ? window.jQuery : require('jquery');
-
-// Sphinx theme nav state
-function ThemeNav () {
-
-    var nav = {
-        navBar: null,
-        win: null,
-        winScroll: false,
-        winResize: false,
-        linkScroll: false,
-        winPosition: 0,
-        winHeight: null,
-        docHeight: null,
-        isRunning: false
-    };
-
-    nav.enable = function () {
-        var self = this;
-
-        if (!self.isRunning) {
-            self.isRunning = true;
-            jQuery(function ($) {
-                self.init($);
-
-                self.reset();
-                self.win.on('hashchange', self.reset);
-
-                // Set scroll monitor
-                self.win.on('scroll', function () {
-                    if (!self.linkScroll) {
-                        self.winScroll = true;
-                    }
-                });
-                setInterval(function () { if (self.winScroll) self.onScroll(); }, 25);
-
-                // Set resize monitor
-                self.win.on('resize', function () {
-                    self.winResize = true;
-                });
-                setInterval(function () { if (self.winResize) self.onResize(); }, 25);
-                self.onResize();
-            });
-        };
-    };
-
-    nav.init = function ($) {
-        var doc = $(document),
-            self = this;
-
-        this.navBar = $('div.wy-side-scroll:first');
-        this.win = $(window);
-
-        // Set up javascript UX bits
-        $(document)
-            // Shift nav in mobile when clicking the menu.
-            .on('click', "[data-toggle='wy-nav-top']", function() {
-                $("[data-toggle='wy-nav-shift']").toggleClass("shift");
-                $("[data-toggle='rst-versions']").toggleClass("shift");
-            })
-
-            // Nav menu link click operations
-            .on('click', ".wy-menu-vertical .current ul li a", function() {
-                var target = $(this);
-                // Close menu when you click a link.
-                $("[data-toggle='wy-nav-shift']").removeClass("shift");
-                $("[data-toggle='rst-versions']").toggleClass("shift");
-                // Handle dynamic display of l3 and l4 nav lists
-                self.toggleCurrent(target);
-                self.hashChange();
-            })
-            .on('click', "[data-toggle='rst-current-version']", function() {
-                $("[data-toggle='rst-versions']").toggleClass("shift-up");
-            })
-
-        // Make tables responsive
-        $("table.docutils:not(.field-list)")
-            .wrap("<div class='wy-table-responsive'></div>");
-
-        // Add expand links to all parents of nested ul
-        $('.wy-menu-vertical ul').not('.simple').siblings('a').each(function () {
-            var link = $(this);
-                expand = $('<span class="toctree-expand"></span>');
-            expand.on('click', function (ev) {
-                self.toggleCurrent(link);
-                ev.stopPropagation();
-                return false;
-            });
-            link.prepend(expand);
-        });
-    };
-
-    nav.reset = function () {
-        // Get anchor from URL and open up nested nav
-        var anchor = encodeURI(window.location.hash);
-        if (anchor) {
-            try {
-                var link = $('.wy-menu-vertical')
-                    .find('[href="' + anchor + '"]');
-                // If we didn't find a link, it may be because we clicked on
-                // something that is not in the sidebar (eg: when using
-                // sphinxcontrib.httpdomain it generates headerlinks but those
-                // aren't picked up and placed in the toctree). So let's find
-                // the closest header in the document and try with that one.
-                if (link.length === 0) {
-                  var doc_link = $('.document a[href="' + anchor + '"]');
-                  var closest_section = doc_link.closest('div.section');
-                  // Try again with the closest section entry.
-                  link = $('.wy-menu-vertical')
-                    .find('[href="#' + closest_section.attr("id") + '"]');
-
-                }
-                $('.wy-menu-vertical li.toctree-l1 li.current')
-                    .removeClass('current');
-                link.closest('li.toctree-l2').addClass('current');
-                link.closest('li.toctree-l3').addClass('current');
-                link.closest('li.toctree-l4').addClass('current');
-            }
-            catch (err) {
-                console.log("Error expanding nav for anchor", err);
-            }
-        }
-    };
-
-    nav.onScroll = function () {
-        this.winScroll = false;
-        var newWinPosition = this.win.scrollTop(),
-            winBottom = newWinPosition + this.winHeight,
-            navPosition = this.navBar.scrollTop(),
-            newNavPosition = navPosition + (newWinPosition - this.winPosition);
-        if (newWinPosition < 0 || winBottom > this.docHeight) {
-            return;
-        }
-        this.navBar.scrollTop(newNavPosition);
-        this.winPosition = newWinPosition;
-    };
-
-    nav.onResize = function () {
-        this.winResize = false;
-        this.winHeight = this.win.height();
-        this.docHeight = $(document).height();
-    };
-
-    nav.hashChange = function () {
-        this.linkScroll = true;
-        this.win.one('hashchange', function () {
-            this.linkScroll = false;
-        });
-    };
-
-    nav.toggleCurrent = function (elem) {
-        var parent_li = elem.closest('li');
-        parent_li.siblings('li.current').removeClass('current');
-        parent_li.siblings().find('li.current').removeClass('current');
-        parent_li.find('> ul li.current').removeClass('current');
-        parent_li.toggleClass('current');
-    }
-
-    return nav;
-};
-
-module.exports.ThemeNav = ThemeNav();
-
-if (typeof(window) != 'undefined') {
-    window.SphinxRtdTheme = { StickyNav: module.exports.ThemeNav };
-}
-
-},{"jquery":"jquery"}]},{},["sphinx-rtd-theme"]);

_static/pygments.css

@@ -1,2 +0,0 @@
-.highlight .hll { background-color: #ffffcc }
-.highlight  { background: #ffffff; }

_static/searchtools.js

@@ -1,761 +0,0 @@
-/*
- * searchtools.js_t
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-
-/* Non-minified version JS is _stemmer.js if file is provided */ 
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
-  var step2list = {
-    ational: 'ate',
-    tional: 'tion',
-    enci: 'ence',
-    anci: 'ance',
-    izer: 'ize',
-    bli: 'ble',
-    alli: 'al',
-    entli: 'ent',
-    eli: 'e',
-    ousli: 'ous',
-    ization: 'ize',
-    ation: 'ate',
-    ator: 'ate',
-    alism: 'al',
-    iveness: 'ive',
-    fulness: 'ful',
-    ousness: 'ous',
-    aliti: 'al',
-    iviti: 'ive',
-    biliti: 'ble',
-    logi: 'log'
-  };
-
-  var step3list = {
-    icate: 'ic',
-    ative: '',
-    alize: 'al',
-    iciti: 'ic',
-    ical: 'ic',
-    ful: '',
-    ness: ''
-  };
-
-  var c = "[^aeiou]";          // consonant
-  var v = "[aeiouy]";          // vowel
-  var C = c + "[^aeiouy]*";    // consonant sequence
-  var V = v + "[aeiou]*";      // vowel sequence
-
-  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
-  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
-  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
-  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
-
-  this.stemWord = function (w) {
-    var stem;
-    var suffix;
-    var firstch;
-    var origword = w;
-
-    if (w.length < 3)
-      return w;
-
-    var re;
-    var re2;
-    var re3;
-    var re4;
-
-    firstch = w.substr(0,1);
-    if (firstch == "y")
-      w = firstch.toUpperCase() + w.substr(1);
-
-    // Step 1a
-    re = /^(.+?)(ss|i)es$/;
-    re2 = /^(.+?)([^s])s$/;
-
-    if (re.test(w))
-      w = w.replace(re,"$1$2");
-    else if (re2.test(w))
-      w = w.replace(re2,"$1$2");
-
-    // Step 1b
-    re = /^(.+?)eed$/;
-    re2 = /^(.+?)(ed|ing)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      re = new RegExp(mgr0);
-      if (re.test(fp[1])) {
-        re = /.$/;
-        w = w.replace(re,"");
-      }
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1];
-      re2 = new RegExp(s_v);
-      if (re2.test(stem)) {
-        w = stem;
-        re2 = /(at|bl|iz)$/;
-        re3 = new RegExp("([^aeiouylsz])\\1$");
-        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-        if (re2.test(w))
-          w = w + "e";
-        else if (re3.test(w)) {
-          re = /.$/;
-          w = w.replace(re,"");
-        }
-        else if (re4.test(w))
-          w = w + "e";
-      }
-    }
-
-    // Step 1c
-    re = /^(.+?)y$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(s_v);
-      if (re.test(stem))
-        w = stem + "i";
-    }
-
-    // Step 2
-    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step2list[suffix];
-    }
-
-    // Step 3
-    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      suffix = fp[2];
-      re = new RegExp(mgr0);
-      if (re.test(stem))
-        w = stem + step3list[suffix];
-    }
-
-    // Step 4
-    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
-    re2 = /^(.+?)(s|t)(ion)$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      if (re.test(stem))
-        w = stem;
-    }
-    else if (re2.test(w)) {
-      var fp = re2.exec(w);
-      stem = fp[1] + fp[2];
-      re2 = new RegExp(mgr1);
-      if (re2.test(stem))
-        w = stem;
-    }
-
-    // Step 5
-    re = /^(.+?)e$/;
-    if (re.test(w)) {
-      var fp = re.exec(w);
-      stem = fp[1];
-      re = new RegExp(mgr1);
-      re2 = new RegExp(meq1);
-      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
-      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
-        w = stem;
-    }
-    re = /ll$/;
-    re2 = new RegExp(mgr1);
-    if (re.test(w) && re2.test(w)) {
-      re = /.$/;
-      w = w.replace(re,"");
-    }
-
-    // and turn initial Y back to y
-    if (firstch == "y")
-      w = firstch.toLowerCase() + w.substr(1);
-    return w;
-  }
-}
-
-
-
-/**
- * Simple result scoring code.
- */
-var Scorer = {
-  // Implement the following function to further tweak the score for each result
-  // The function takes a result array [filename, title, anchor, descr, score]
-  // and returns the new score.
-  /*
-  score: function(result) {
-    return result[4];
-  },
-  */
-
-  // query matches the full name of an object
-  objNameMatch: 11,
-  // or matches in the last dotted part of the object name
-  objPartialMatch: 6,
-  // Additive scores depending on the priority of the object
-  objPrio: {0:  15,   // used to be importantResults
-            1:  5,   // used to be objectResults
-            2: -5},  // used to be unimportantResults
-  //  Used when the priority is not in the mapping.
-  objPrioDefault: 0,
-
-  // query found in title
-  title: 15,
-  // query found in terms
-  term: 5
-};
-
-
-
-
-
-var splitChars = (function() {
-    var result = {};
-    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
-         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
-         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
-         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
-         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
-         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
-         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
-         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
-         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
-         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
-    var i, j, start, end;
-    for (i = 0; i < singles.length; i++) {
-        result[singles[i]] = true;
-    }
-    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
-         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
-         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
-         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
-         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
-         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
-         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
-         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
-         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
-         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
-         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
-         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
-         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
-         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
-         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
-         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
-         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
-         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
-         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
-         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
-         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
-         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
-         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
-         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
-         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
-         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
-         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
-         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
-         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
-         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
-         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
-         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
-         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
-         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
-         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
-         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
-         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
-         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
-         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
-         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
-         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
-         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
-         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
-         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
-         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
-         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
-         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
-         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
-         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
-    for (i = 0; i < ranges.length; i++) {
-        start = ranges[i][0];
-        end = ranges[i][1];
-        for (j = start; j <= end; j++) {
-            result[j] = true;
-        }
-    }
-    return result;
-})();
-
-function splitQuery(query) {
-    var result = [];
-    var start = -1;
-    for (var i = 0; i < query.length; i++) {
-        if (splitChars[query.charCodeAt(i)]) {
-            if (start !== -1) {
-                result.push(query.slice(start, i));
-                start = -1;
-            }
-        } else if (start === -1) {
-            start = i;
-        }
-    }
-    if (start !== -1) {
-        result.push(query.slice(start));
-    }
-    return result;
-}
-
-
-
-
-/**
- * Search Module
- */
-var Search = {
-
-  _index : null,
-  _queued_query : null,
-  _pulse_status : -1,
-
-  init : function() {
-      var params = $.getQueryParameters();
-      if (params.q) {
-          var query = params.q[0];
-          $('input[name="q"]')[0].value = query;
-          this.performSearch(query);
-      }
-  },
-
-  loadIndex : function(url) {
-    $.ajax({type: "GET", url: url, data: null,
-            dataType: "script", cache: true,
-            complete: function(jqxhr, textstatus) {
-              if (textstatus != "success") {
-                document.getElementById("searchindexloader").src = url;
-              }
-            }});
-  },
-
-  setIndex : function(index) {
-    var q;
-    this._index = index;
-    if ((q = this._queued_query) !== null) {
-      this._queued_query = null;
-      Search.query(q);
-    }
-  },
-
-  hasIndex : function() {
-      return this._index !== null;
-  },
-
-  deferQuery : function(query) {
-      this._queued_query = query;
-  },
-
-  stopPulse : function() {
-      this._pulse_status = 0;
-  },
-
-  startPulse : function() {
-    if (this._pulse_status >= 0)
-        return;
-    function pulse() {
-      var i;
-      Search._pulse_status = (Search._pulse_status + 1) % 4;
-      var dotString = '';
-      for (i = 0; i < Search._pulse_status; i++)
-        dotString += '.';
-      Search.dots.text(dotString);
-      if (Search._pulse_status > -1)
-        window.setTimeout(pulse, 500);
-    }
-    pulse();
-  },
-
-  /**
-   * perform a search for something (or wait until index is loaded)
-   */
-  performSearch : function(query) {
-    // create the required interface elements
-    this.out = $('#search-results');
-    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
-    this.dots = $('<span></span>').appendTo(this.title);
-    this.status = $('<p style="display: none"></p>').appendTo(this.out);
-    this.output = $('<ul class="search"/>').appendTo(this.out);
-
-    $('#search-progress').text(_('Preparing search...'));
-    this.startPulse();
-
-    // index already loaded, the browser was quick!
-    if (this.hasIndex())
-      this.query(query);
-    else
-      this.deferQuery(query);
-  },
-
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query : function(query) {
-    var i;
-    var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
-
-    // stem the searchterms and add them to the correct list
-    var stemmer = new Stemmer();
-    var searchterms = [];
-    var excluded = [];
-    var hlterms = [];
-    var tmp = splitQuery(query);
-    var objectterms = [];
-    for (i = 0; i < tmp.length; i++) {
-      if (tmp[i] !== "") {
-          objectterms.push(tmp[i].toLowerCase());
-      }
-
-      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
-          tmp[i] === "") {
-        // skip this "word"
-        continue;
-      }
-      // stem the word
-      var word = stemmer.stemWord(tmp[i].toLowerCase());
-      // prevent stemmer from cutting word smaller than two chars
-      if(word.length < 3 && tmp[i].length >= 3) {
-        word = tmp[i];
-      }
-      var toAppend;
-      // select the correct list
-      if (word[0] == '-') {
-        toAppend = excluded;
-        word = word.substr(1);
-      }
-      else {
-        toAppend = searchterms;
-        hlterms.push(tmp[i].toLowerCase());
-      }
-      // only add if not already in the list
-      if (!$u.contains(toAppend, word))
-        toAppend.push(word);
-    }
-    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
-    // console.debug('SEARCH: searching for:');
-    // console.info('required: ', searchterms);
-    // console.info('excluded: ', excluded);
-
-    // prepare search
-    var terms = this._index.terms;
-    var titleterms = this._index.titleterms;
-
-    // array of [filename, title, anchor, descr, score]
-    var results = [];
-    $('#search-progress').empty();
-
-    // lookup as object
-    for (i = 0; i < objectterms.length; i++) {
-      var others = [].concat(objectterms.slice(0, i),
-                             objectterms.slice(i+1, objectterms.length));
-      results = results.concat(this.performObjectSearch(objectterms[i], others));
-    }
-
-    // lookup as search terms in fulltext
-    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
-
-    // let the scorer override scores with a custom scoring function
-    if (Scorer.score) {
-      for (i = 0; i < results.length; i++)
-        results[i][4] = Scorer.score(results[i]);
-    }
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort(function(a, b) {
-      var left = a[4];
-      var right = b[4];
-      if (left > right) {
-        return 1;
-      } else if (left < right) {
-        return -1;
-      } else {
-        // same score: sort alphabetically
-        left = a[1].toLowerCase();
-        right = b[1].toLowerCase();
-        return (left > right) ? -1 : ((left < right) ? 1 : 0);
-      }
-    });
-
-    // for debugging
-    //Search.lastresults = results.slice();  // a copy
-    //console.info('search results:', Search.lastresults);
-
-    // print the results
-    var resultCount = results.length;
-    function displayNextItem() {
-      // results left, load the summary and display it
-      if (results.length) {
-        var item = results.pop();
-        var listItem = $('<li style="display:none"></li>');
-        if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
-          // dirhtml builder
-          var dirname = item[0] + '/';
-          if (dirname.match(/\/index\/$/)) {
-            dirname = dirname.substring(0, dirname.length-6);
-          } else if (dirname == 'index/') {
-            dirname = '';
-          }
-          listItem.append($('<a/>').attr('href',
-            DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
-            highlightstring + item[2]).html(item[1]));
-        } else {
-          // normal html builders
-          listItem.append($('<a/>').attr('href',
-            item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
-            highlightstring + item[2]).html(item[1]));
-        }
-        if (item[3]) {
-          listItem.append($('<span> (' + item[3] + ')</span>'));
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
-          var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
-          if (suffix === undefined) {
-            suffix = '.txt';
-          }
-          $.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
-                  dataType: "text",
-                  complete: function(jqxhr, textstatus) {
-                    var data = jqxhr.responseText;
-                    if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
-                    }
-                    Search.output.append(listItem);
-                    listItem.slideDown(5, function() {
-                      displayNextItem();
-                    });
-                  }});
-        } else {
-          // no source available, just display title
-          Search.output.append(listItem);
-          listItem.slideDown(5, function() {
-            displayNextItem();
-          });
-        }
-      }
-      // search finished, update title and status message
-      else {
-        Search.stopPulse();
-        Search.title.text(_('Search Results'));
-        if (!resultCount)
-          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
-        else
-            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
-        Search.status.fadeIn(500);
-      }
-    }
-    displayNextItem();
-  },
-
-  /**
-   * search for object names
-   */
-  performObjectSearch : function(object, otherterms) {
-    var filenames = this._index.filenames;
-    var docnames = this._index.docnames;
-    var objects = this._index.objects;
-    var objnames = this._index.objnames;
-    var titles = this._index.titles;
-
-    var i;
-    var results = [];
-
-    for (var prefix in objects) {
-      for (var name in objects[prefix]) {
-        var fullname = (prefix ? prefix + '.' : '') + name;
-        if (fullname.toLowerCase().indexOf(object) > -1) {
-          var score = 0;
-          var parts = fullname.split('.');
-          // check for different match types: exact matches of full name or
-          // "last name" (i.e. last dotted part)
-          if (fullname == object || parts[parts.length - 1] == object) {
-            score += Scorer.objNameMatch;
-          // matches in last name
-          } else if (parts[parts.length - 1].indexOf(object) > -1) {
-            score += Scorer.objPartialMatch;
-          }
-          var match = objects[prefix][name];
-          var objname = objnames[match[1]][2];
-          var title = titles[match[0]];
-          // If more than one term searched for, we require other words to be
-          // found in the name/title/description
-          if (otherterms.length > 0) {
-            var haystack = (prefix + ' ' + name + ' ' +
-                            objname + ' ' + title).toLowerCase();
-            var allfound = true;
-            for (i = 0; i < otherterms.length; i++) {
-              if (haystack.indexOf(otherterms[i]) == -1) {
-                allfound = false;
-                break;
-              }
-            }
-            if (!allfound) {
-              continue;
-            }
-          }
-          var descr = objname + _(', in ') + title;
-
-          var anchor = match[3];
-          if (anchor === '')
-            anchor = fullname;
-          else if (anchor == '-')
-            anchor = objnames[match[1]][1] + '-' + fullname;
-          // add custom score for some objects according to scorer
-          if (Scorer.objPrio.hasOwnProperty(match[2])) {
-            score += Scorer.objPrio[match[2]];
-          } else {
-            score += Scorer.objPrioDefault;
-          }
-          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
-        }
-      }
-    }
-
-    return results;
-  },
-
-  /**
-   * search for full-text terms in the index
-   */
-  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
-    var docnames = this._index.docnames;
-    var filenames = this._index.filenames;
-    var titles = this._index.titles;
-
-    var i, j, file;
-    var fileMap = {};
-    var scoreMap = {};
-    var results = [];
-
-    // perform the search on the required terms
-    for (i = 0; i < searchterms.length; i++) {
-      var word = searchterms[i];
-      var files = [];
-      var _o = [
-        {files: terms[word], score: Scorer.term},
-        {files: titleterms[word], score: Scorer.title}
-      ];
-
-      // no match but word was a required one
-      if ($u.every(_o, function(o){return o.files === undefined;})) {
-        break;
-      }
-      // found search word in contents
-      $u.each(_o, function(o) {
-        var _files = o.files;
-        if (_files === undefined)
-          return
-
-        if (_files.length === undefined)
-          _files = [_files];
-        files = files.concat(_files);
-
-        // set score for the word in each file to Scorer.term
-        for (j = 0; j < _files.length; j++) {
-          file = _files[j];
-          if (!(file in scoreMap))
-            scoreMap[file] = {}
-          scoreMap[file][word] = o.score;
-        }
-      });
-
-      // create the mapping
-      for (j = 0; j < files.length; j++) {
-        file = files[j];
-        if (file in fileMap)
-          fileMap[file].push(word);
-        else
-          fileMap[file] = [word];
-      }
-    }
-
-    // now check if the files don't contain excluded terms
-    for (file in fileMap) {
-      var valid = true;
-
-      // check if all requirements are matched
-      if (fileMap[file].length != searchterms.length)
-          continue;
-
-      // ensure that none of the excluded terms is in the search result
-      for (i = 0; i < excluded.length; i++) {
-        if (terms[excluded[i]] == file ||
-            titleterms[excluded[i]] == file ||
-            $u.contains(terms[excluded[i]] || [], file) ||
-            $u.contains(titleterms[excluded[i]] || [], file)) {
-          valid = false;
-          break;
-        }
-      }
-
-      // if we have still a valid result we can add it to the result list
-      if (valid) {
-        // select one (max) score for the file.
-        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
-        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
-        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
-      }
-    }
-    return results;
-  },
-
-  /**
-   * helper function to return a node containing the
-   * search summary for a given text. keywords is a list
-   * of stemmed words, hlwords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
-   */
-  makeSearchSummary : function(text, keywords, hlwords) {
-    var textLower = text.toLowerCase();
-    var start = 0;
-    $.each(keywords, function() {
-      var i = textLower.indexOf(this.toLowerCase());
-      if (i > -1)
-        start = i;
-    });
-    start = Math.max(start - 120, 0);
-    var excerpt = ((start > 0) ? '...' : '') +
-      $.trim(text.substr(start, 240)) +
-      ((start + 240 - text.length) ? '...' : '');
-    var rv = $('<div class="context"></div>').text(excerpt);
-    $.each(hlwords, function() {
-      rv = rv.highlightText(this, 'highlighted');
-    });
-    return rv;
-  }
-};
-
-$(document).ready(function() {
-  Search.init();
-});

_static/underscore-1.3.1.js

@@ -1,999 +0,0 @@
-//     Underscore.js 1.3.1
-//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
-//     Underscore is freely distributable under the MIT license.
-//     Portions of Underscore are inspired or borrowed from Prototype,
-//     Oliver Steele's Functional, and John Resig's Micro-Templating.
-//     For all details and documentation:
-//     http://documentcloud.github.com/underscore
-
-(function() {
-
-  // Baseline setup
-  // --------------
-
-  // Establish the root object, `window` in the browser, or `global` on the server.
-  var root = this;
-
-  // Save the previous value of the `_` variable.
-  var previousUnderscore = root._;
-
-  // Establish the object that gets returned to break out of a loop iteration.
-  var breaker = {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var slice            = ArrayProto.slice,
-      unshift          = ArrayProto.unshift,
-      toString         = ObjProto.toString,
-      hasOwnProperty   = ObjProto.hasOwnProperty;
-
-  // All **ECMAScript 5** native function implementations that we hope to use
-  // are declared here.
-  var
-    nativeForEach      = ArrayProto.forEach,
-    nativeMap          = ArrayProto.map,
-    nativeReduce       = ArrayProto.reduce,
-    nativeReduceRight  = ArrayProto.reduceRight,
-    nativeFilter       = ArrayProto.filter,
-    nativeEvery        = ArrayProto.every,
-    nativeSome         = ArrayProto.some,
-    nativeIndexOf      = ArrayProto.indexOf,
-    nativeLastIndexOf  = ArrayProto.lastIndexOf,
-    nativeIsArray      = Array.isArray,
-    nativeKeys         = Object.keys,
-    nativeBind         = FuncProto.bind;
-
-  // Create a safe reference to the Underscore object for use below.
-  var _ = function(obj) { return new wrapper(obj); };
-
-  // Export the Underscore object for **Node.js**, with
-  // backwards-compatibility for the old `require()` API. If we're in
-  // the browser, add `_` as a global object via a string identifier,
-  // for Closure Compiler "advanced" mode.
-  if (typeof exports !== 'undefined') {
-    if (typeof module !== 'undefined' && module.exports) {
-      exports = module.exports = _;
-    }
-    exports._ = _;
-  } else {
-    root['_'] = _;
-  }
-
-  // Current version.
-  _.VERSION = '1.3.1';
-
-  // Collection Functions
-  // --------------------
-
-  // The cornerstone, an `each` implementation, aka `forEach`.
-  // Handles objects with the built-in `forEach`, arrays, and raw objects.
-  // Delegates to **ECMAScript 5**'s native `forEach` if available.
-  var each = _.each = _.forEach = function(obj, iterator, context) {
-    if (obj == null) return;
-    if (nativeForEach && obj.forEach === nativeForEach) {
-      obj.forEach(iterator, context);
-    } else if (obj.length === +obj.length) {
-      for (var i = 0, l = obj.length; i < l; i++) {
-        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
-      }
-    } else {
-      for (var key in obj) {
-        if (_.has(obj, key)) {
-          if (iterator.call(context, obj[key], key, obj) === breaker) return;
-        }
-      }
-    }
-  };
-
-  // Return the results of applying the iterator to each element.
-  // Delegates to **ECMAScript 5**'s native `map` if available.
-  _.map = _.collect = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
-    each(obj, function(value, index, list) {
-      results[results.length] = iterator.call(context, value, index, list);
-    });
-    if (obj.length === +obj.length) results.length = obj.length;
-    return results;
-  };
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
-  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
-    var initial = arguments.length > 2;
-    if (obj == null) obj = [];
-    if (nativeReduce && obj.reduce === nativeReduce) {
-      if (context) iterator = _.bind(iterator, context);
-      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
-    }
-    each(obj, function(value, index, list) {
-      if (!initial) {
-        memo = value;
-        initial = true;
-      } else {
-        memo = iterator.call(context, memo, value, index, list);
-      }
-    });
-    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
-    return memo;
-  };
-
-  // The right-associative version of reduce, also known as `foldr`.
-  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
-  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
-    var initial = arguments.length > 2;
-    if (obj == null) obj = [];
-    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
-      if (context) iterator = _.bind(iterator, context);
-      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
-    }
-    var reversed = _.toArray(obj).reverse();
-    if (context && !initial) iterator = _.bind(iterator, context);
-    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
-  };
-
-  // Return the first value which passes a truth test. Aliased as `detect`.
-  _.find = _.detect = function(obj, iterator, context) {
-    var result;
-    any(obj, function(value, index, list) {
-      if (iterator.call(context, value, index, list)) {
-        result = value;
-        return true;
-      }
-    });
-    return result;
-  };
-
-  // Return all the elements that pass a truth test.
-  // Delegates to **ECMAScript 5**'s native `filter` if available.
-  // Aliased as `select`.
-  _.filter = _.select = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
-    each(obj, function(value, index, list) {
-      if (iterator.call(context, value, index, list)) results[results.length] = value;
-    });
-    return results;
-  };
-
-  // Return all the elements for which a truth test fails.
-  _.reject = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    each(obj, function(value, index, list) {
-      if (!iterator.call(context, value, index, list)) results[results.length] = value;
-    });
-    return results;
-  };
-
-  // Determine whether all of the elements match a truth test.
-  // Delegates to **ECMAScript 5**'s native `every` if available.
-  // Aliased as `all`.
-  _.every = _.all = function(obj, iterator, context) {
-    var result = true;
-    if (obj == null) return result;
-    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
-    each(obj, function(value, index, list) {
-      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
-    });
-    return result;
-  };
-
-  // Determine if at least one element in the object matches a truth test.
-  // Delegates to **ECMAScript 5**'s native `some` if available.
-  // Aliased as `any`.
-  var any = _.some = _.any = function(obj, iterator, context) {
-    iterator || (iterator = _.identity);
-    var result = false;
-    if (obj == null) return result;
-    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
-    each(obj, function(value, index, list) {
-      if (result || (result = iterator.call(context, value, index, list))) return breaker;
-    });
-    return !!result;
-  };
-
-  // Determine if a given value is included in the array or object using `===`.
-  // Aliased as `contains`.
-  _.include = _.contains = function(obj, target) {
-    var found = false;
-    if (obj == null) return found;
-    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
-    found = any(obj, function(value) {
-      return value === target;
-    });
-    return found;
-  };
-
-  // Invoke a method (with arguments) on every item in a collection.
-  _.invoke = function(obj, method) {
-    var args = slice.call(arguments, 2);
-    return _.map(obj, function(value) {
-      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
-    });
-  };
-
-  // Convenience version of a common use case of `map`: fetching a property.
-  _.pluck = function(obj, key) {
-    return _.map(obj, function(value){ return value[key]; });
-  };
-
-  // Return the maximum element or (element-based computation).
-  _.max = function(obj, iterator, context) {
-    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
-    if (!iterator && _.isEmpty(obj)) return -Infinity;
-    var result = {computed : -Infinity};
-    each(obj, function(value, index, list) {
-      var computed = iterator ? iterator.call(context, value, index, list) : value;
-      computed >= result.computed && (result = {value : value, computed : computed});
-    });
-    return result.value;
-  };
-
-  // Return the minimum element (or element-based computation).
-  _.min = function(obj, iterator, context) {
-    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
-    if (!iterator && _.isEmpty(obj)) return Infinity;
-    var result = {computed : Infinity};
-    each(obj, function(value, index, list) {
-      var computed = iterator ? iterator.call(context, value, index, list) : value;
-      computed < result.computed && (result = {value : value, computed : computed});
-    });
-    return result.value;
-  };
-
-  // Shuffle an array.
-  _.shuffle = function(obj) {
-    var shuffled = [], rand;
-    each(obj, function(value, index, list) {
-      if (index == 0) {
-        shuffled[0] = value;
-      } else {
-        rand = Math.floor(Math.random() * (index + 1));
-        shuffled[index] = shuffled[rand];
-        shuffled[rand] = value;
-      }
-    });
-    return shuffled;
-  };
-
-  // Sort the object's values by a criterion produced by an iterator.
-  _.sortBy = function(obj, iterator, context) {
-    return _.pluck(_.map(obj, function(value, index, list) {
-      return {
-        value : value,
-        criteria : iterator.call(context, value, index, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria, b = right.criteria;
-      return a < b ? -1 : a > b ? 1 : 0;
-    }), 'value');
-  };
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  _.groupBy = function(obj, val) {
-    var result = {};
-    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
-    each(obj, function(value, index) {
-      var key = iterator(value, index);
-      (result[key] || (result[key] = [])).push(value);
-    });
-    return result;
-  };
-
-  // Use a comparator function to figure out at what index an object should
-  // be inserted so as to maintain order. Uses binary search.
-  _.sortedIndex = function(array, obj, iterator) {
-    iterator || (iterator = _.identity);
-    var low = 0, high = array.length;
-    while (low < high) {
-      var mid = (low + high) >> 1;
-      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
-    }
-    return low;
-  };
-
-  // Safely convert anything iterable into a real, live array.
-  _.toArray = function(iterable) {
-    if (!iterable)                return [];
-    if (iterable.toArray)         return iterable.toArray();
-    if (_.isArray(iterable))      return slice.call(iterable);
-    if (_.isArguments(iterable))  return slice.call(iterable);
-    return _.values(iterable);
-  };
-
-  // Return the number of elements in an object.
-  _.size = function(obj) {
-    return _.toArray(obj).length;
-  };
-
-  // Array Functions
-  // ---------------
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. Aliased as `head`. The **guard** check allows it to work
-  // with `_.map`.
-  _.first = _.head = function(array, n, guard) {
-    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
-  };
-
-  // Returns everything but the last entry of the array. Especcialy useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N. The **guard** check allows it to work with
-  // `_.map`.
-  _.initial = function(array, n, guard) {
-    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
-  };
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  _.last = function(array, n, guard) {
-    if ((n != null) && !guard) {
-      return slice.call(array, Math.max(array.length - n, 0));
-    } else {
-      return array[array.length - 1];
-    }
-  };
-
-  // Returns everything but the first entry of the array. Aliased as `tail`.
-  // Especially useful on the arguments object. Passing an **index** will return
-  // the rest of the values in the array from that index onward. The **guard**
-  // check allows it to work with `_.map`.
-  _.rest = _.tail = function(array, index, guard) {
-    return slice.call(array, (index == null) || guard ? 1 : index);
-  };
-
-  // Trim out all falsy values from an array.
-  _.compact = function(array) {
-    return _.filter(array, function(value){ return !!value; });
-  };
-
-  // Return a completely flattened version of an array.
-  _.flatten = function(array, shallow) {
-    return _.reduce(array, function(memo, value) {
-      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
-      memo[memo.length] = value;
-      return memo;
-    }, []);
-  };
-
-  // Return a version of the array that does not contain the specified value(s).
-  _.without = function(array) {
-    return _.difference(array, slice.call(arguments, 1));
-  };
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // Aliased as `unique`.
-  _.uniq = _.unique = function(array, isSorted, iterator) {
-    var initial = iterator ? _.map(array, iterator) : array;
-    var result = [];
-    _.reduce(initial, function(memo, el, i) {
-      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
-        memo[memo.length] = el;
-        result[result.length] = array[i];
-      }
-      return memo;
-    }, []);
-    return result;
-  };
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  _.union = function() {
-    return _.uniq(_.flatten(arguments, true));
-  };
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays. (Aliased as "intersect" for back-compat.)
-  _.intersection = _.intersect = function(array) {
-    var rest = slice.call(arguments, 1);
-    return _.filter(_.uniq(array), function(item) {
-      return _.every(rest, function(other) {
-        return _.indexOf(other, item) >= 0;
-      });
-    });
-  };
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  _.difference = function(array) {
-    var rest = _.flatten(slice.call(arguments, 1));
-    return _.filter(array, function(value){ return !_.include(rest, value); });
-  };
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  _.zip = function() {
-    var args = slice.call(arguments);
-    var length = _.max(_.pluck(args, 'length'));
-    var results = new Array(length);
-    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
-    return results;
-  };
-
-  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
-  // we need this function. Return the position of the first occurrence of an
-  // item in an array, or -1 if the item is not included in the array.
-  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  _.indexOf = function(array, item, isSorted) {
-    if (array == null) return -1;
-    var i, l;
-    if (isSorted) {
-      i = _.sortedIndex(array, item);
-      return array[i] === item ? i : -1;
-    }
-    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
-    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
-    return -1;
-  };
-
-  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
-  _.lastIndexOf = function(array, item) {
-    if (array == null) return -1;
-    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
-    var i = array.length;
-    while (i--) if (i in array && array[i] === item) return i;
-    return -1;
-  };
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](http://docs.python.org/library/functions.html#range).
-  _.range = function(start, stop, step) {
-    if (arguments.length <= 1) {
-      stop = start || 0;
-      start = 0;
-    }
-    step = arguments[2] || 1;
-
-    var len = Math.max(Math.ceil((stop - start) / step), 0);
-    var idx = 0;
-    var range = new Array(len);
-
-    while(idx < len) {
-      range[idx++] = start;
-      start += step;
-    }
-
-    return range;
-  };
-
-  // Function (ahem) Functions
-  // ------------------
-
-  // Reusable constructor function for prototype setting.
-  var ctor = function(){};
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally). Binding with arguments is also known as `curry`.
-  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
-  // We check for `func.bind` first, to fail fast when `func` is undefined.
-  _.bind = function bind(func, context) {
-    var bound, args;
-    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
-    if (!_.isFunction(func)) throw new TypeError;
-    args = slice.call(arguments, 2);
-    return bound = function() {
-      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
-      ctor.prototype = func.prototype;
-      var self = new ctor;
-      var result = func.apply(self, args.concat(slice.call(arguments)));
-      if (Object(result) === result) return result;
-      return self;
-    };
-  };
-
-  // Bind all of an object's methods to that object. Useful for ensuring that
-  // all callbacks defined on an object belong to it.
-  _.bindAll = function(obj) {
-    var funcs = slice.call(arguments, 1);
-    if (funcs.length == 0) funcs = _.functions(obj);
-    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
-    return obj;
-  };
-
-  // Memoize an expensive function by storing its results.
-  _.memoize = function(func, hasher) {
-    var memo = {};
-    hasher || (hasher = _.identity);
-    return function() {
-      var key = hasher.apply(this, arguments);
-      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
-    };
-  };
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  _.delay = function(func, wait) {
-    var args = slice.call(arguments, 2);
-    return setTimeout(function(){ return func.apply(func, args); }, wait);
-  };
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  _.defer = function(func) {
-    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
-  };
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time.
-  _.throttle = function(func, wait) {
-    var context, args, timeout, throttling, more;
-    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
-    return function() {
-      context = this; args = arguments;
-      var later = function() {
-        timeout = null;
-        if (more) func.apply(context, args);
-        whenDone();
-      };
-      if (!timeout) timeout = setTimeout(later, wait);
-      if (throttling) {
-        more = true;
-      } else {
-        func.apply(context, args);
-      }
-      whenDone();
-      throttling = true;
-    };
-  };
-
-  // Returns a function, that, as long as it continues to be invoked, will not
-  // be triggered. The function will be called after it stops being called for
-  // N milliseconds.
-  _.debounce = function(func, wait) {
-    var timeout;
-    return function() {
-      var context = this, args = arguments;
-      var later = function() {
-        timeout = null;
-        func.apply(context, args);
-      };
-      clearTimeout(timeout);
-      timeout = setTimeout(later, wait);
-    };
-  };
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  _.once = function(func) {
-    var ran = false, memo;
-    return function() {
-      if (ran) return memo;
-      ran = true;
-      return memo = func.apply(this, arguments);
-    };
-  };
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  _.wrap = function(func, wrapper) {
-    return function() {
-      var args = [func].concat(slice.call(arguments, 0));
-      return wrapper.apply(this, args);
-    };
-  };
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  _.compose = function() {
-    var funcs = arguments;
-    return function() {
-      var args = arguments;
-      for (var i = funcs.length - 1; i >= 0; i--) {
-        args = [funcs[i].apply(this, args)];
-      }
-      return args[0];
-    };
-  };
-
-  // Returns a function that will only be executed after being called N times.
-  _.after = function(times, func) {
-    if (times <= 0) return func();
-    return function() {
-      if (--times < 1) { return func.apply(this, arguments); }
-    };
-  };
-
-  // Object Functions
-  // ----------------
-
-  // Retrieve the names of an object's properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`
-  _.keys = nativeKeys || function(obj) {
-    if (obj !== Object(obj)) throw new TypeError('Invalid object');
-    var keys = [];
-    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
-    return keys;
-  };
-
-  // Retrieve the values of an object's properties.
-  _.values = function(obj) {
-    return _.map(obj, _.identity);
-  };
-
-  // Return a sorted list of the function names available on the object.
-  // Aliased as `methods`
-  _.functions = _.methods = function(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (_.isFunction(obj[key])) names.push(key);
-    }
-    return names.sort();
-  };
-
-  // Extend a given object with all the properties in passed-in object(s).
-  _.extend = function(obj) {
-    each(slice.call(arguments, 1), function(source) {
-      for (var prop in source) {
-        obj[prop] = source[prop];
-      }
-    });
-    return obj;
-  };
-
-  // Fill in a given object with default properties.
-  _.defaults = function(obj) {
-    each(slice.call(arguments, 1), function(source) {
-      for (var prop in source) {
-        if (obj[prop] == null) obj[prop] = source[prop];
-      }
-    });
-    return obj;
-  };
-
-  // Create a (shallow-cloned) duplicate of an object.
-  _.clone = function(obj) {
-    if (!_.isObject(obj)) return obj;
-    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
-  };
-
-  // Invokes interceptor with the obj, and then returns obj.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  _.tap = function(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  };
-
-  // Internal recursive comparison function.
-  function eq(a, b, stack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
-    if (a === b) return a !== 0 || 1 / a == 1 / b;
-    // A strict comparison is necessary because `null == undefined`.
-    if (a == null || b == null) return a === b;
-    // Unwrap any wrapped objects.
-    if (a._chain) a = a._wrapped;
-    if (b._chain) b = b._wrapped;
-    // Invoke a custom `isEqual` method if one is provided.
-    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
-    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className != toString.call(b)) return false;
-    switch (className) {
-      // Strings, numbers, dates, and booleans are compared by value.
-      case '[object String]':
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-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
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-        return a.source == b.source &&
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-    var length = stack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
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-    }
-    // Add the first object to the stack of traversed objects.
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-      result = size == b.length;
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-          // Ensure commutative equality for sparse arrays.
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-      }
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-          // Count the expected number of properties.
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-          // Deep compare each member.
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-      }
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-        }
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-      }
-    }
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-    return result;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
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-  };
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-  // Is a given array, string, or object empty?
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-  _.isEmpty = function(obj) {
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-    for (var key in obj) if (_.has(obj, key)) return false;
-    return true;
-  };
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-  // Is a given value a DOM element?
-  _.isElement = function(obj) {
-    return !!(obj && obj.nodeType == 1);
-  };
-
-  // Is a given value an array?
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-  _.isArray = nativeIsArray || function(obj) {
-    return toString.call(obj) == '[object Array]';
-  };
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-  // Is a given variable an object?
-  _.isObject = function(obj) {
-    return obj === Object(obj);
-  };
-
-  // Is a given variable an arguments object?
-  _.isArguments = function(obj) {
-    return toString.call(obj) == '[object Arguments]';
-  };
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-    _.isArguments = function(obj) {
-      return !!(obj && _.has(obj, 'callee'));
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-  // Is a given value a date?
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-    return toString.call(obj) == '[object Date]';
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-  // Is the given value a regular expression?
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-  };
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-  // Add your own custom functions to the Underscore object, ensuring that
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-  // Generate a unique integer id (unique within the entire client session).
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-  _.uniqueId = function(prefix) {
-    var id = idCounter++;
-    return prefix ? prefix + id : id;
-  };
-
-  // By default, Underscore uses ERB-style template delimiters, change the
-  // following template settings to use alternative delimiters.
-  _.templateSettings = {
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-    interpolate : /<%=([\s\S]+?)%>/g,
-    escape      : /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `templateSettings`, if you don't want to define an
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-  // Within an interpolation, evaluation, or escaping, remove HTML escaping
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-  };
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-}).call(this);

_static/underscore.js

@@ -1,31 +0,0 @@
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_static/websupport.js

@@ -1,808 +0,0 @@
-/*
- * websupport.js
- * ~~~~~~~~~~~~~
- *
- * sphinx.websupport utilities for all documentation.
- *
- * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-(function($) {
-  $.fn.autogrow = function() {
-    return this.each(function() {
-    var textarea = this;
-
-    $.fn.autogrow.resize(textarea);
-
-    $(textarea)
-      .focus(function() {
-        textarea.interval = setInterval(function() {
-          $.fn.autogrow.resize(textarea);
-        }, 500);
-      })
-      .blur(function() {
-        clearInterval(textarea.interval);
-      });
-    });
-  };
-
-  $.fn.autogrow.resize = function(textarea) {
-    var lineHeight = parseInt($(textarea).css('line-height'), 10);
-    var lines = textarea.value.split('\n');
-    var columns = textarea.cols;
-    var lineCount = 0;
-    $.each(lines, function() {
-      lineCount += Math.ceil(this.length / columns) || 1;
-    });
-    var height = lineHeight * (lineCount + 1);
-    $(textarea).css('height', height);
-  };
-})(jQuery);
-
-(function($) {
-  var comp, by;
-
-  function init() {
-    initEvents();
-    initComparator();
-  }
-
-  function initEvents() {
-    $(document).on("click", 'a.comment-close', function(event) {
-      event.preventDefault();
-      hide($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.vote', function(event) {
-      event.preventDefault();
-      handleVote($(this));
-    });
-    $(document).on("click", 'a.reply', function(event) {
-      event.preventDefault();
-      openReply($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.close-reply', function(event) {
-      event.preventDefault();
-      closeReply($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.sort-option', function(event) {
-      event.preventDefault();
-      handleReSort($(this));
-    });
-    $(document).on("click", 'a.show-proposal', function(event) {
-      event.preventDefault();
-      showProposal($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.hide-proposal', function(event) {
-      event.preventDefault();
-      hideProposal($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.show-propose-change', function(event) {
-      event.preventDefault();
-      showProposeChange($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.hide-propose-change', function(event) {
-      event.preventDefault();
-      hideProposeChange($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.accept-comment', function(event) {
-      event.preventDefault();
-      acceptComment($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.delete-comment', function(event) {
-      event.preventDefault();
-      deleteComment($(this).attr('id').substring(2));
-    });
-    $(document).on("click", 'a.comment-markup', function(event) {
-      event.preventDefault();
-      toggleCommentMarkupBox($(this).attr('id').substring(2));
-    });
-  }
-
-  /**
-   * Set comp, which is a comparator function used for sorting and
-   * inserting comments into the list.
-   */
-  function setComparator() {
-    // If the first three letters are "asc", sort in ascending order
-    // and remove the prefix.
-    if (by.substring(0,3) == 'asc') {
-      var i = by.substring(3);
-      comp = function(a, b) { return a[i] - b[i]; };
-    } else {
-      // Otherwise sort in descending order.
-      comp = function(a, b) { return b[by] - a[by]; };
-    }
-
-    // Reset link styles and format the selected sort option.
-    $('a.sel').attr('href', '#').removeClass('sel');
-    $('a.by' + by).removeAttr('href').addClass('sel');
-  }
-
-  /**
-   * Create a comp function. If the user has preferences stored in
-   * the sortBy cookie, use those, otherwise use the default.
-   */
-  function initComparator() {
-    by = 'rating'; // Default to sort by rating.
-    // If the sortBy cookie is set, use that instead.
-    if (document.cookie.length > 0) {
-      var start = document.cookie.indexOf('sortBy=');
-      if (start != -1) {
-        start = start + 7;
-        var end = document.cookie.indexOf(";", start);
-        if (end == -1) {
-          end = document.cookie.length;
-          by = unescape(document.cookie.substring(start, end));
-        }
-      }
-    }
-    setComparator();
-  }
-
-  /**
-   * Show a comment div.
-   */
-  function show(id) {
-    $('#ao' + id).hide();
-    $('#ah' + id).show();
-    var context = $.extend({id: id}, opts);
-    var popup = $(renderTemplate(popupTemplate, context)).hide();
-    popup.find('textarea[name="proposal"]').hide();
-    popup.find('a.by' + by).addClass('sel');
-    var form = popup.find('#cf' + id);
-    form.submit(function(event) {
-      event.preventDefault();
-      addComment(form);
-    });
-    $('#s' + id).after(popup);
-    popup.slideDown('fast', function() {
-      getComments(id);
-    });
-  }
-
-  /**
-   * Hide a comment div.
-   */
-  function hide(id) {
-    $('#ah' + id).hide();
-    $('#ao' + id).show();
-    var div = $('#sc' + id);
-    div.slideUp('fast', function() {
-      div.remove();
-    });
-  }
-
-  /**
-   * Perform an ajax request to get comments for a node
-   * and insert the comments into the comments tree.
-   */
-  function getComments(id) {
-    $.ajax({
-     type: 'GET',
-     url: opts.getCommentsURL,
-     data: {node: id},
-     success: function(data, textStatus, request) {
-       var ul = $('#cl' + id);
-       var speed = 100;
-       $('#cf' + id)
-         .find('textarea[name="proposal"]')
-         .data('source', data.source);
-
-       if (data.comments.length === 0) {
-         ul.html('<li>No comments yet.</li>');
-         ul.data('empty', true);
-       } else {
-         // If there are comments, sort them and put them in the list.
-         var comments = sortComments(data.comments);
-         speed = data.comments.length * 100;
-         appendComments(comments, ul);
-         ul.data('empty', false);
-       }
-       $('#cn' + id).slideUp(speed + 200);
-       ul.slideDown(speed);
-     },
-     error: function(request, textStatus, error) {
-       showError('Oops, there was a problem retrieving the comments.');
-     },
-     dataType: 'json'
-    });
-  }
-
-  /**
-   * Add a comment via ajax and insert the comment into the comment tree.
-   */
-  function addComment(form) {
-    var node_id = form.find('input[name="node"]').val();
-    var parent_id = form.find('input[name="parent"]').val();
-    var text = form.find('textarea[name="comment"]').val();
-    var proposal = form.find('textarea[name="proposal"]').val();
-
-    if (text == '') {
-      showError('Please enter a comment.');
-      return;
-    }
-
-    // Disable the form that is being submitted.
-    form.find('textarea,input').attr('disabled', 'disabled');
-
-    // Send the comment to the server.
-    $.ajax({
-      type: "POST",
-      url: opts.addCommentURL,
-      dataType: 'json',
-      data: {
-        node: node_id,
-        parent: parent_id,
-        text: text,
-        proposal: proposal
-      },
-      success: function(data, textStatus, error) {
-        // Reset the form.
-        if (node_id) {
-          hideProposeChange(node_id);
-        }
-        form.find('textarea')
-          .val('')
-          .add(form.find('input'))
-          .removeAttr('disabled');
-	var ul = $('#cl' + (node_id || parent_id));
-        if (ul.data('empty')) {
-          $(ul).empty();
-          ul.data('empty', false);
-        }
-        insertComment(data.comment);
-        var ao = $('#ao' + node_id);
-        ao.find('img').attr({'src': opts.commentBrightImage});
-        if (node_id) {
-          // if this was a "root" comment, remove the commenting box
-          // (the user can get it back by reopening the comment popup)
-          $('#ca' + node_id).slideUp();
-        }
-      },
-      error: function(request, textStatus, error) {
-        form.find('textarea,input').removeAttr('disabled');
-        showError('Oops, there was a problem adding the comment.');
-      }
-    });
-  }
-
-  /**
-   * Recursively append comments to the main comment list and children
-   * lists, creating the comment tree.
-   */
-  function appendComments(comments, ul) {
-    $.each(comments, function() {
-      var div = createCommentDiv(this);
-      ul.append($(document.createElement('li')).html(div));
-      appendComments(this.children, div.find('ul.comment-children'));
-      // To avoid stagnating data, don't store the comments children in data.
-      this.children = null;
-      div.data('comment', this);
-    });
-  }
-
-  /**
-   * After adding a new comment, it must be inserted in the correct
-   * location in the comment tree.
-   */
-  function insertComment(comment) {
-    var div = createCommentDiv(comment);
-
-    // To avoid stagnating data, don't store the comments children in data.
-    comment.children = null;
-    div.data('comment', comment);
-
-    var ul = $('#cl' + (comment.node || comment.parent));
-    var siblings = getChildren(ul);
-
-    var li = $(document.createElement('li'));
-    li.hide();
-
-    // Determine where in the parents children list to insert this comment.
-    for(i=0; i < siblings.length; i++) {
-      if (comp(comment, siblings[i]) <= 0) {
-        $('#cd' + siblings[i].id)
-          .parent()
-          .before(li.html(div));
-        li.slideDown('fast');
-        return;
-      }
-    }
-
-    // If we get here, this comment rates lower than all the others,
-    // or it is the only comment in the list.
-    ul.append(li.html(div));
-    li.slideDown('fast');
-  }
-
-  function acceptComment(id) {
-    $.ajax({
-      type: 'POST',
-      url: opts.acceptCommentURL,
-      data: {id: id},
-      success: function(data, textStatus, request) {
-        $('#cm' + id).fadeOut('fast');
-        $('#cd' + id).removeClass('moderate');
-      },
-      error: function(request, textStatus, error) {
-        showError('Oops, there was a problem accepting the comment.');
-      }
-    });
-  }
-
-  function deleteComment(id) {
-    $.ajax({
-      type: 'POST',
-      url: opts.deleteCommentURL,
-      data: {id: id},
-      success: function(data, textStatus, request) {
-        var div = $('#cd' + id);
-        if (data == 'delete') {
-          // Moderator mode: remove the comment and all children immediately
-          div.slideUp('fast', function() {
-            div.remove();
-          });
-          return;
-        }
-        // User mode: only mark the comment as deleted
-        div
-          .find('span.user-id:first')
-          .text('[deleted]').end()
-          .find('div.comment-text:first')
-          .text('[deleted]').end()
-          .find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
-                ', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
-          .remove();
-        var comment = div.data('comment');
-        comment.username = '[deleted]';
-        comment.text = '[deleted]';
-        div.data('comment', comment);
-      },
-      error: function(request, textStatus, error) {
-        showError('Oops, there was a problem deleting the comment.');
-      }
-    });
-  }
-
-  function showProposal(id) {
-    $('#sp' + id).hide();
-    $('#hp' + id).show();
-    $('#pr' + id).slideDown('fast');
-  }
-
-  function hideProposal(id) {
-    $('#hp' + id).hide();
-    $('#sp' + id).show();
-    $('#pr' + id).slideUp('fast');
-  }
-
-  function showProposeChange(id) {
-    $('#pc' + id).hide();
-    $('#hc' + id).show();
-    var textarea = $('#pt' + id);
-    textarea.val(textarea.data('source'));
-    $.fn.autogrow.resize(textarea[0]);
-    textarea.slideDown('fast');
-  }
-
-  function hideProposeChange(id) {
-    $('#hc' + id).hide();
-    $('#pc' + id).show();
-    var textarea = $('#pt' + id);
-    textarea.val('').removeAttr('disabled');
-    textarea.slideUp('fast');
-  }
-
-  function toggleCommentMarkupBox(id) {
-    $('#mb' + id).toggle();
-  }
-
-  /** Handle when the user clicks on a sort by link. */
-  function handleReSort(link) {
-    var classes = link.attr('class').split(/\s+/);
-    for (var i=0; i<classes.length; i++) {
-      if (classes[i] != 'sort-option') {
-	by = classes[i].substring(2);
-      }
-    }
-    setComparator();
-    // Save/update the sortBy cookie.
-    var expiration = new Date();
-    expiration.setDate(expiration.getDate() + 365);
-    document.cookie= 'sortBy=' + escape(by) +
-                     ';expires=' + expiration.toUTCString();
-    $('ul.comment-ul').each(function(index, ul) {
-      var comments = getChildren($(ul), true);
-      comments = sortComments(comments);
-      appendComments(comments, $(ul).empty());
-    });
-  }
-
-  /**
-   * Function to process a vote when a user clicks an arrow.
-   */
-  function handleVote(link) {
-    if (!opts.voting) {
-      showError("You'll need to login to vote.");
-      return;
-    }
-
-    var id = link.attr('id');
-    if (!id) {
-      // Didn't click on one of the voting arrows.
-      return;
-    }
-    // If it is an unvote, the new vote value is 0,
-    // Otherwise it's 1 for an upvote, or -1 for a downvote.
-    var value = 0;
-    if (id.charAt(1) != 'u') {
-      value = id.charAt(0) == 'u' ? 1 : -1;
-    }
-    // The data to be sent to the server.
-    var d = {
-      comment_id: id.substring(2),
-      value: value
-    };
-
-    // Swap the vote and unvote links.
-    link.hide();
-    $('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
-      .show();
-
-    // The div the comment is displayed in.
-    var div = $('div#cd' + d.comment_id);
-    var data = div.data('comment');
-
-    // If this is not an unvote, and the other vote arrow has
-    // already been pressed, unpress it.
-    if ((d.value !== 0) && (data.vote === d.value * -1)) {
-      $('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
-      $('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
-    }
-
-    // Update the comments rating in the local data.
-    data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
-    data.vote = d.value;
-    div.data('comment', data);
-
-    // Change the rating text.
-    div.find('.rating:first')
-      .text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
-
-    // Send the vote information to the server.
-    $.ajax({
-      type: "POST",
-      url: opts.processVoteURL,
-      data: d,
-      error: function(request, textStatus, error) {
-        showError('Oops, there was a problem casting that vote.');
-      }
-    });
-  }
-
-  /**
-   * Open a reply form used to reply to an existing comment.
-   */
-  function openReply(id) {
-    // Swap out the reply link for the hide link
-    $('#rl' + id).hide();
-    $('#cr' + id).show();
-
-    // Add the reply li to the children ul.
-    var div = $(renderTemplate(replyTemplate, {id: id})).hide();
-    $('#cl' + id)
-      .prepend(div)
-      // Setup the submit handler for the reply form.
-      .find('#rf' + id)
-      .submit(function(event) {
-        event.preventDefault();
-        addComment($('#rf' + id));
-        closeReply(id);
-      })
-      .find('input[type=button]')
-      .click(function() {
-        closeReply(id);
-      });
-    div.slideDown('fast', function() {
-      $('#rf' + id).find('textarea').focus();
-    });
-  }
-
-  /**
-   * Close the reply form opened with openReply.
-   */
-  function closeReply(id) {
-    // Remove the reply div from the DOM.
-    $('#rd' + id).slideUp('fast', function() {
-      $(this).remove();
-    });
-
-    // Swap out the hide link for the reply link
-    $('#cr' + id).hide();
-    $('#rl' + id).show();
-  }
-
-  /**
-   * Recursively sort a tree of comments using the comp comparator.
-   */
-  function sortComments(comments) {
-    comments.sort(comp);
-    $.each(comments, function() {
-      this.children = sortComments(this.children);
-    });
-    return comments;
-  }
-
-  /**
-   * Get the children comments from a ul. If recursive is true,
-   * recursively include childrens' children.
-   */
-  function getChildren(ul, recursive) {
-    var children = [];
-    ul.children().children("[id^='cd']")
-      .each(function() {
-        var comment = $(this).data('comment');
-        if (recursive)
-          comment.children = getChildren($(this).find('#cl' + comment.id), true);
-        children.push(comment);
-      });
-    return children;
-  }
-
-  /** Create a div to display a comment in. */
-  function createCommentDiv(comment) {
-    if (!comment.displayed && !opts.moderator) {
-      return $('<div class="moderate">Thank you!  Your comment will show up '
-               + 'once it is has been approved by a moderator.</div>');
-    }
-    // Prettify the comment rating.
-    comment.pretty_rating = comment.rating + ' point' +
-      (comment.rating == 1 ? '' : 's');
-    // Make a class (for displaying not yet moderated comments differently)
-    comment.css_class = comment.displayed ? '' : ' moderate';
-    // Create a div for this comment.
-    var context = $.extend({}, opts, comment);
-    var div = $(renderTemplate(commentTemplate, context));
-
-    // If the user has voted on this comment, highlight the correct arrow.
-    if (comment.vote) {
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-      $(this)
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-          $(document.createElement('a')).attr({
-            href: '#',
-            'class': 'sphinx-comment-open' + addcls,
-            id: 'ao' + id
-          })
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-              alt: 'comment',
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-            }))
-            .click(function(event) {
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-              show($(this).attr('id').substring(2));
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-  };
-
-  var opts = {
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-    addCommentURL: '/_add_comment',
-    getCommentsURL: '/_get_comments',
-    acceptCommentURL: '/_accept_comment',
-    deleteCommentURL: '/_delete_comment',
-    commentImage: '/static/_static/comment.png',
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-      <div class="comment-loading" id="cn<%id%>">\
-        loading comments... <img src="<%loadingImage%>" alt="" /></div>\
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-        reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
-        <code>``code``</code>, \
-        code blocks: <code>::</code> and an indented block after blank line</div>\
-      <form method="post" id="cf<%id%>" class="comment-form" action="">\
-        <textarea name="comment" cols="80"></textarea>\
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-});

appveyor.yml

@@ -0,0 +1,109 @@
+#matrix:
+#  fast_finish: true
+
+environment:
+  global:
+    # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
+    # /E:ON and /V:ON options are not enabled in the batch script intepreter
+    # See: http://stackoverflow.com/a/13751649/163740
+    CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\ci\\appveyor\\run_with_env.cmd"
+
+    # 1. Generated a token for appveyor at https://anaconda.org/quantopian/settings/access with scope api:write.
+    #   Can also be done via anaconda CLI with
+    #     $ anaconda auth --create --name my_appveyor_token
+    # 2. Generated secure env var below via appveyor's Encrypt data tool at https://ci.appveyor.com/tools/encrypt.
+    #   See https://www.appveyor.com/docs/build-configuration/#secure-variables.
+    ANACONDA_TOKEN:
+      secure: "mfGCRgiY6D5BmJkH4HFPDJUH+6W9zH0cxKZ5o/cv9grfRkfu5YRG26No88H1q7Ua"
+
+    CONDA_ROOT_PYTHON_VERSION: "2.7"
+
+  matrix:
+    - PYTHON_VERSION: "2.7"
+      PYTHON_ARCH: "64"
+      PANDAS_VERSION: "0.18.1"
+      NUMPY_VERSION: "1.11.1"
+      SCIPY_VERSION: "0.17.1"
+
+    - PYTHON_VERSION: "3.4"
+      PYTHON_ARCH: "64"
+      PANDAS_VERSION: "0.18.1"
+      NUMPY_VERSION: "1.11.1"
+      SCIPY_VERSION: "0.17.1"
+
+    - PYTHON_VERSION: "3.5"
+      PYTHON_ARCH: "64"
+      PANDAS_VERSION: "0.18.1"
+      NUMPY_VERSION: "1.11.1"
+      SCIPY_VERSION: "0.17.1"
+
+# We always use a 64-bit machine, but can build x86 distributions
+# with the PYTHON_ARCH variable (which is used by CMD_IN_ENV).
+platform:
+  - x64
+
+cache:
+  - '%LOCALAPPDATA%\pip\Cache'
+
+# all our python builds have to happen in tests_script...
+build: false
+
+init:
+  - "ECHO %PYTHON_VERSION% %PYTHON_ARCH% %PYTHON%"
+  - "ECHO %NUMPY_VERSION%"
+
+install:
+  # If there is a newer build queued for the same PR, cancel this one.
+  # The AppVeyor 'rollout builds' option is supposed to serve the same
+  # purpose but it is problematic because it tends to cancel builds pushed
+  # directly to master instead of just PR builds (or the converse).
+  # credits: JuliaLang developers.
+  - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod `
+        https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | `
+        Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { `
+          throw "There are newer queued builds for this pull request, failing early." }
+
+  - ps: $NPY_VERSION_ARR=$env:NUMPY_VERSION -split '.', 0, 'simplematch'
+  - ps: $env:CONDA_NPY=$NPY_VERSION_ARR[0..1] -join ""
+  - ps: $PY_VERSION_ARR=$env:PYTHON_VERSION -split '.', 0, 'simplematch'
+  - ps: $env:CONDA_PY=$PY_VERSION_ARR[0..1] -join ""
+  - SET PYTHON=C:\Python%CONDA_PY%_64
+  # Get cygwin's git out of our PATH.  See https://github.com/omnia-md/conda-dev-recipes/pull/16/files#diff-180360612c6b8c4ed830919bbb4dd459
+  - "del C:\\cygwin\\bin\\git.exe"
+  # this installs the appropriate Miniconda (Py2/Py3, 32/64 bit),
+  - powershell .\ci\appveyor\install.ps1
+  - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PATH%
+  - sed -i "s/numpy==.*/numpy==%NUMPY_VERSION%/" etc/requirements.txt
+  - sed -i "s/pandas==.*/pandas==%PANDAS_VERSION%/" etc/requirements.txt
+  - sed -i "s/scipy==.*/scipy==%SCIPY_VERSION%/" etc/requirements.txt
+
+  - conda info -a
+  - conda install conda=4.1.11 conda-build=1.21.11 anaconda-client=1.5.1 --yes -q
+  # https://blog.ionelmc.ro/2014/12/21/compiling-python-extensions-on-windows/ for 64bit C compilation
+  - ps: copy .\ci\appveyor\vcvars64.bat "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin\amd64"
+  - "%CMD_IN_ENV% python .\\ci\\make_conda_packages.py"
+
+  # test that we can conda install catalyst in a new env
+  - conda create -n installenv --yes -q --use-local python=%PYTHON_VERSION% numpy=%NUMPY_VERSION% catalyst -c quantopian -c https://conda.anaconda.org/quantopian/label/ci
+
+  - ps: $env:BCOLZ_VERSION=(sls "bcolz==(.*)" .\etc\requirements.txt -ca).matches.groups[1].value
+  - ps: $env:NUMEXPR_VERSION=(sls "numexpr==(.*)" .\etc\requirements.txt -ca).matches.groups[1].value
+  - ps: $env:TALIB_VERSION=(sls "TA-Lib==(.*)" .\etc\requirements_talib.txt -ca).matches.groups[1].value
+  - conda create -n testenv --yes -q --use-local pip python=%PYTHON_VERSION% numpy=%NUMPY_VERSION% scipy=%SCIPY_VERSION% ta-lib=%TALIB_VERSION% bcolz=%BCOLZ_VERSION% numexpr=%NUMEXPR_VERSION% -c quantopian -c https://conda.anaconda.org/quantopian/label/ci
+  - activate testenv
+  - SET CACHE_DIR=%LOCALAPPDATA%\pip\Cache\pip_np%CONDA_NPY%py%CONDA_PY%
+  - pip install -r etc/requirements.txt --cache-dir=%CACHE_DIR%
+  - pip install -r etc/requirements_dev.txt --cache-dir=%CACHE_DIR%
+  # this uses git requirements right now
+  - pip install -r etc/requirements_blaze.txt --cache-dir=%CACHE_DIR%
+  - pip install -r etc/requirements_talib.txt --cache-dir=%CACHE_DIR%
+  - pip install -e .[all] --cache-dir=%CACHE_DIR%
+  - pip freeze | sort
+
+test_script:
+  - nosetests -e catalyst.utils.numpy_utils
+  - flake8 catalyst tests
+
+branches:
+  only:
+    - master

bundles.html

@@ -1,524 +0,0 @@
-
-
-<!DOCTYPE html>
-<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
-<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
-<head>
-  <meta charset="utf-8">
-  
-  <meta name="viewport" content="width=device-width, initial-scale=1.0">
-  
-  <title>Data Bundles &mdash; Catalyst 0.4 documentation</title>
-  
-
-  
-  
-  
-  
-
-  
-
-  
-  
-    
-
-  
-
-  
-  
-    <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-  
-
-  
-
-  
-        <link rel="index" title="Index"
-              href="genindex.html"/>
-        <link rel="search" title="Search" href="search.html"/>
-    <link rel="top" title="Catalyst 0.4 documentation" href="index.html"/> 
-
-  
-  <script src="_static/js/modernizr.min.js"></script>
-
-</head>
-
-<body class="wy-body-for-nav" role="document">
-
-   
-  <div class="wy-grid-for-nav">
-
-    
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-side-nav-search">
-          
-
-          
-            <a href="index.html" class="icon icon-home"> Catalyst
-          
-
-          
-          </a>
-
-          
-            
-            
-          
-
-          
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" />
-    <input type="hidden" name="check_keywords" value="yes" />
-    <input type="hidden" name="area" value="default" />
-  </form>
-</div>
-
-          
-        </div>
-
-        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
-          
-            
-            
-              
-            
-            
-              <ul>
-<li class="toctree-l1"><a class="reference internal" href="install.html">Install</a></li>
-<li class="toctree-l1"><a class="reference internal" href="beginner-tutorial.html">Catalyst Beginner Tutorial</a></li>
-<li class="toctree-l1"><a class="reference internal" href="live-trading.html">Live Trading</a></li>
-<li class="toctree-l1"><a class="reference internal" href="features.html">Features</a></li>
-<li class="toctree-l1"><a class="reference internal" href="example-algos.html">Example Algorithms</a></li>
-<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a></li>
-<li class="toctree-l1"><a class="reference internal" href="videos.html">Videos</a></li>
-<li class="toctree-l1"><a class="reference internal" href="resources.html">Resources</a></li>
-<li class="toctree-l1"><a class="reference internal" href="development-guidelines.html">Development Guidelines</a></li>
-<li class="toctree-l1"><a class="reference internal" href="releases.html">Release Notes</a></li>
-</ul>
-
-            
-          
-        </div>
-      </div>
-    </nav>
-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
-
-      
-      <nav class="wy-nav-top" role="navigation" aria-label="top navigation">
-        
-          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="index.html">Catalyst</a>
-        
-      </nav>
-
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-      
-      <div class="wy-nav-content">
-        <div class="rst-content">
-          
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-<div role="navigation" aria-label="breadcrumbs navigation">
-
-  <ul class="wy-breadcrumbs">
-    
-      <li><a href="index.html">Docs</a> &raquo;</li>
-        
-      <li>Data Bundles</li>
-    
-    
-      <li class="wy-breadcrumbs-aside">
-        
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-            <a href="_sources/bundles.rst.txt" rel="nofollow"> View page source</a>
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-  </ul>
-
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-  <hr/>
-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-            
-  <div class="section" id="data-bundles">
-<span id="id1"></span><h1>Data Bundles<a class="headerlink" href="#data-bundles" title="Permalink to this headline">¶</a></h1>
-<p>A data bundle is a collection of pricing data, adjustment data, and an asset
-database. Bundles allow us to preload all of the data we will need to run
-backtests and store the data for future runs.</p>
-<div class="section" id="discovering-available-bundles">
-<span id="bundles-command"></span><h2>Discovering Available Bundles<a class="headerlink" href="#discovering-available-bundles" title="Permalink to this headline">¶</a></h2>
-<p>Zipline comes with a few bundles by default as well as the ability to register
-new bundles. To see which bundles we have have available, we may run the
-<code class="docutils literal"><span class="pre">bundles</span></code> command, for example:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ zipline bundles
-my-custom-bundle <span class="m">2016</span>-05-05 <span class="m">20</span>:35:19.809398
-my-custom-bundle <span class="m">2016</span>-05-05 <span class="m">20</span>:34:53.654082
-my-custom-bundle <span class="m">2016</span>-05-05 <span class="m">20</span>:34:48.401767
-quandl &lt;no ingestions&gt;
-quantopian-quandl <span class="m">2016</span>-05-05 <span class="m">20</span>:06:40.894956
-</pre></div>
-</div>
-<p>The output here shows that there are 3 bundles available:</p>
-<ul class="simple">
-<li><code class="docutils literal"><span class="pre">my-custom-bundle</span></code> (added by the user)</li>
-<li><code class="docutils literal"><span class="pre">quandl</span></code> (provided by zipline)</li>
-<li><code class="docutils literal"><span class="pre">quantopian-quandl</span></code> (provided by zipline)</li>
-</ul>
-<p>The dates and times next to the name show the times when the data for this
-bundle was ingested. We have run three different ingestions for
-<code class="docutils literal"><span class="pre">my-custom-bundle</span></code>. We have never ingested any data for the <code class="docutils literal"><span class="pre">quandl</span></code> bundle
-so it just shows <code class="docutils literal"><span class="pre">&lt;no</span> <span class="pre">ingestions&gt;</span></code> instead. Finally, there is only one
-ingestion for <code class="docutils literal"><span class="pre">quantopian-quandl</span></code>.</p>
-</div>
-<div class="section" id="ingesting-data">
-<span id="id2"></span><h2>Ingesting Data<a class="headerlink" href="#ingesting-data" title="Permalink to this headline">¶</a></h2>
-<p>The first step to using a data bundle is to ingest the data. The ingestion
-process will invoke some custom bundle command and then write the data to a
-standard location that zipline can find. By default the location where ingested
-data will be written is <code class="docutils literal"><span class="pre">$ZIPLINE_ROOT/data/&lt;bundle&gt;</span></code> where by default
-<code class="docutils literal"><span class="pre">ZIPLINE_ROOT=~/.zipline</span></code>. The ingestion step may take some time as it could
-involve downloading and processing a lot of data. This can be run with:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ zipline ingest <span class="o">[</span>-b &lt;bundle&gt;<span class="o">]</span>
-</pre></div>
-</div>
-<p>where <code class="docutils literal"><span class="pre">&lt;bundle&gt;</span></code> is the name of the bundle to ingest, defaulting to
-<a class="reference internal" href="#quantopian-quandl-mirror"><span class="std std-ref">quantopian-quandl</span></a>.</p>
-</div>
-<div class="section" id="old-data">
-<h2>Old Data<a class="headerlink" href="#old-data" title="Permalink to this headline">¶</a></h2>
-<p>When the <code class="docutils literal"><span class="pre">ingest</span></code> command is used it will write the new data to a subdirectory
-of <code class="docutils literal"><span class="pre">$ZIPLINE_ROOT/data/&lt;bundle&gt;</span></code> which is named with the current date. This
-makes it possible to look at older data or even run backtests with the older
-copies. Running a backtest with an old ingestion makes it easier to reproduce
-backtest results later.</p>
-<p>One drawback of saving all of the data by default is that the data directory
-may grow quite large even if you do not want to use the data. As shown earlier,
-we can list all of the ingestions with the <a class="reference internal" href="#bundles-command"><span class="std std-ref">bundles command</span></a>. To solve the problem of leaking old data there is another
-command: <code class="docutils literal"><span class="pre">clean</span></code>, which will clear data bundles based on some time
-constraints.</p>
-<p>For example:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span><span class="c1"># clean everything older than &lt;date&gt;</span>
-$ zipline clean <span class="o">[</span>-b &lt;bundle&gt;<span class="o">]</span> --before &lt;date&gt;
-
-<span class="c1"># clean everything newer than &lt;date&gt;</span>
-$ zipline clean <span class="o">[</span>-b &lt;bundle&gt;<span class="o">]</span> --after &lt;date&gt;
-
-<span class="c1"># keep everything in the range of [before, after] and delete the rest</span>
-$ zipline clean <span class="o">[</span>-b &lt;bundle&gt;<span class="o">]</span> --before &lt;date&gt; --after &lt;after&gt;
-
-<span class="c1"># clean all but the last &lt;int&gt; runs</span>
-$ zipline clean <span class="o">[</span>-b &lt;bundle&gt;<span class="o">]</span> --keep-last &lt;int&gt;
-</pre></div>
-</div>
-</div>
-<div class="section" id="running-backtests-with-data-bundles">
-<h2>Running Backtests with Data Bundles<a class="headerlink" href="#running-backtests-with-data-bundles" title="Permalink to this headline">¶</a></h2>
-<p>Now that the data has been ingested we can use it to run backtests with the
-<code class="docutils literal"><span class="pre">run</span></code> command. The bundle to use can be specified with the <code class="docutils literal"><span class="pre">--bundle</span></code> option
-like:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ zipline run --bundle &lt;bundle&gt; --algofile algo.py ...
-</pre></div>
-</div>
-<p>We may also specify the date to use to look up the bundle data with the
-<code class="docutils literal"><span class="pre">--bundle-date</span></code> option. Setting the <code class="docutils literal"><span class="pre">--bundle-date</span></code> will cause run to use
-the most recent bundle ingestion that is less than or equal to the
-<code class="docutils literal"><span class="pre">bundle-date</span></code>. This is how we can run backtests with older data. The reason
-that <code class="docutils literal"><span class="pre">-bundle-date</span></code> uses a less than or equal to relationship is that we can
-specify the date that we ran an old backtest and get the same data that would
-have been available to us on that date. The <code class="docutils literal"><span class="pre">bundle-date</span></code> defaults to the
-current day to use the most recent data.</p>
-</div>
-<div class="section" id="default-data-bundles">
-<h2>Default Data Bundles<a class="headerlink" href="#default-data-bundles" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="quandl-wiki-bundle">
-<span id="quandl-data-bundle"></span><h3>Quandl WIKI Bundle<a class="headerlink" href="#quandl-wiki-bundle" title="Permalink to this headline">¶</a></h3>
-<p>By default zipline comes with the <code class="docutils literal"><span class="pre">quandl</span></code> data bundle which uses quandl’s
-<a class="reference external" href="https://www.quandl.com/data/WIKI">WIKI dataset</a>. The quandl data bundle
-includes daily pricing data, splits, cash dividends, and asset metadata. To
-ingest the <code class="docutils literal"><span class="pre">quandl</span></code> data bundle we recommend creating an account on quandl.com
-to get an API key to be able to make more API requests per day. Once we have an
-API key we may run:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ <span class="nv">QUANDL_API_KEY</span><span class="o">=</span>&lt;api-key&gt; zipline ingest -b quandl
-</pre></div>
-</div>
-<p>though we may still run <code class="docutils literal"><span class="pre">ingest</span></code> as an anonymous quandl user (with no API
-key). We may also set the <code class="docutils literal"><span class="pre">QUANDL_DOWNLOAD_ATTEMPTS</span></code> environment variable to
-an integer which is the number of attempts that should be made to download data
-from quandls servers. By default <code class="docutils literal"><span class="pre">QUANDL_DOWNLOAD_ATTEMPTS</span></code> will be 5, meaning
-that we will retry each attempt 5 times.</p>
-<div class="admonition note">
-<p class="first admonition-title">Note</p>
-<p class="last"><code class="docutils literal"><span class="pre">QUANDL_DOWNLOAD_ATTEMPTS</span></code> is not the total number of allowed failures,
-just the number of allowed failures per request. The quandl loader will make
-one request per 100 equities for the metadata followed by one request per
-equity.</p>
-</div>
-<div class="section" id="quantopian-quandl-wiki-mirror">
-<span id="quantopian-quandl-mirror"></span><h4>Quantopian Quandl WIKI Mirror<a class="headerlink" href="#quantopian-quandl-wiki-mirror" title="Permalink to this headline">¶</a></h4>
-<p>Quantopian provides a mirror of the quandl WIKI dataset with the data in the
-formats that zipline expects. This is available under the name:
-<code class="docutils literal"><span class="pre">quantopian-quandl</span></code> and is the default bundle for zipline.</p>
-</div>
-</div>
-<div class="section" id="yahoo-bundle-factories">
-<h3>Yahoo Bundle Factories<a class="headerlink" href="#yahoo-bundle-factories" title="Permalink to this headline">¶</a></h3>
-<p>Zipline also ships with a factory function for creating a data bundle out of a
-set of tickers from yahoo: <code class="xref py py-func docutils literal"><span class="pre">yahoo_equities()</span></code>.
-<code class="xref py py-func docutils literal"><span class="pre">yahoo_equities()</span></code> makes it easy to pre-download and
-cache the data for a set of equities from yahoo. The yahoo bundles include daily
-pricing data along with splits, cash dividends, and inferred asset metadata. To
-create a bundle from a set of equities, add the following to your
-<code class="docutils literal"><span class="pre">~/.zipline/extensions.py</span></code> file:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">zipline.data.bundles</span> <span class="kn">import</span> <span class="n">register</span><span class="p">,</span> <span class="n">yahoo_equities</span>
-
-<span class="c1"># these are the tickers you would like data for</span>
-<span class="n">equities</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s1">&#39;AAPL&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;MSFT&#39;</span><span class="p">,</span>
-    <span class="s1">&#39;GOOG&#39;</span><span class="p">,</span>
-<span class="p">}</span>
-<span class="n">register</span><span class="p">(</span>
-    <span class="s1">&#39;my-yahoo-equities-bundle&#39;</span><span class="p">,</span>  <span class="c1"># name this whatever you like</span>
-    <span class="n">yahoo_equities</span><span class="p">(</span><span class="n">equities</span><span class="p">),</span>
-<span class="p">)</span>
-</pre></div>
-</div>
-<p>This may now be used like:</p>
-<div class="highlight-bash"><div class="highlight"><pre><span></span>$ zipline ingest -b my-yahoo-equities-bundle
-$ zipline run -f algo.py --bundle my-yahoo-equities-bundle
-</pre></div>
-</div>
-<p>More than one yahoo equities bundle may be registered as long as they use
-different names.</p>
-</div>
-</div>
-<div class="section" id="writing-a-new-bundle">
-<h2>Writing a New Bundle<a class="headerlink" href="#writing-a-new-bundle" title="Permalink to this headline">¶</a></h2>
-<p>Data bundles exist to make it easy to use different data sources with
-zipline. To add a new bundle, one must implement an <code class="docutils literal"><span class="pre">ingest</span></code> function.</p>
-<p>The <code class="docutils literal"><span class="pre">ingest</span></code> function is responsible for loading the data into memory and
-passing it to a set of writer objects provided by zipline to convert the data to
-zipline’s internal format. The ingest function may work by downloading data from
-a remote location like the <code class="docutils literal"><span class="pre">quandl</span></code> bundle or yahoo bundles or it may just
-load files that are already on the machine. The function is provided with
-writers that will write the data to the correct location transactionally. If an
-ingestion fails part way through the bundle will not be written in an incomplete
-state.</p>
-<p>The signature of the ingest function should be:</p>
-<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">ingest</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span>
-       <span class="n">asset_db_writer</span><span class="p">,</span>
-       <span class="n">minute_bar_writer</span><span class="p">,</span>
-       <span class="n">daily_bar_writer</span><span class="p">,</span>
-       <span class="n">adjustment_writer</span><span class="p">,</span>
-       <span class="n">calendar</span><span class="p">,</span>
-       <span class="n">start_session</span><span class="p">,</span>
-       <span class="n">end_session</span><span class="p">,</span>
-       <span class="n">cache</span><span class="p">,</span>
-       <span class="n">show_progress</span><span class="p">,</span>
-       <span class="n">output_dir</span><span class="p">)</span>
-</pre></div>
-</div>
-<div class="section" id="environ">
-<h3><code class="docutils literal"><span class="pre">environ</span></code><a class="headerlink" href="#environ" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">environ</span></code> is a mapping representing the environment variables to use. This is
-where any custom arguments needed for the ingestion should be passed, for
-example: the <code class="docutils literal"><span class="pre">quandl</span></code> bundle uses the enviornment to pass the API key and the
-download retry attempt count.</p>
-</div>
-<div class="section" id="asset-db-writer">
-<h3><code class="docutils literal"><span class="pre">asset_db_writer</span></code><a class="headerlink" href="#asset-db-writer" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">asset_db_writer</span></code> is an instance of <code class="xref py py-class docutils literal"><span class="pre">AssetDBWriter</span></code>.
-This is the writer for the asset metadata which provides the asset lifetimes and
-the symbol to asset id (sid) mapping. This may also contain the asset name,
-exchange and a few other columns. To write data, invoke
-<code class="xref py py-meth docutils literal"><span class="pre">write()</span></code> with dataframes for the various
-pieces of metadata. More information about the format of the data exists in the
-docs for write.</p>
-</div>
-<div class="section" id="minute-bar-writer">
-<h3><code class="docutils literal"><span class="pre">minute_bar_writer</span></code><a class="headerlink" href="#minute-bar-writer" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">minute_bar_writer</span></code> is an instance of
-<code class="xref py py-class docutils literal"><span class="pre">BcolzMinuteBarWriter</span></code>. This writer is used to
-convert data to zipline’s internal bcolz format to later be read by a
-<code class="xref py py-class docutils literal"><span class="pre">BcolzMinuteBarReader</span></code>. If minute data is
-provided, users should call
-<code class="xref py py-meth docutils literal"><span class="pre">write()</span></code> with an iterable of
-(sid, dataframe) tuples. The <code class="docutils literal"><span class="pre">show_progress</span></code> argument should also be forwarded
-to this method. If the data source does not provide minute level data, then
-there is no need to call the write method. It is also acceptable to pass an
-empty iterator to <code class="xref py py-meth docutils literal"><span class="pre">write()</span></code>
-to signal that there is no minutely data.</p>
-<div class="admonition note">
-<p class="first admonition-title">Note</p>
-<p class="last">The data passed to
-<code class="xref py py-meth docutils literal"><span class="pre">write()</span></code> may be a lazy
-iterator or generator to avoid loading all of the minute data into memory at
-a single time. A given sid may also appear multiple times in the data as long
-as the dates are strictly increasing.</p>
-</div>
-</div>
-<div class="section" id="daily-bar-writer">
-<h3><code class="docutils literal"><span class="pre">daily_bar_writer</span></code><a class="headerlink" href="#daily-bar-writer" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">daily_bar_writer</span></code> is an instance of
-<code class="xref py py-class docutils literal"><span class="pre">BcolzDailyBarWriter</span></code>. This writer is
-used to convert data into zipline’s internal bcolz format to later be read by a
-<code class="xref py py-class docutils literal"><span class="pre">BcolzDailyBarReader</span></code>. If daily data is
-provided, users should call
-<code class="xref py py-meth docutils literal"><span class="pre">write()</span></code> with an iterable of
-(sid dataframe) tuples. The <code class="docutils literal"><span class="pre">show_progress</span></code> argument should also be forwarded
-to this method. If the data shource does not provide daily data, then there is
-no need to call the write method. It is also acceptable to pass an empty
-iterable to <code class="xref py py-meth docutils literal"><span class="pre">write()</span></code> to
-signal that there is no daily data. If no daily data is provided but minute data
-is provided, a daily rollup will happen to service daily history requests.</p>
-<div class="admonition note">
-<p class="first admonition-title">Note</p>
-<p class="last">Like the <code class="docutils literal"><span class="pre">minute_bar_writer</span></code>, the data passed to
-<code class="xref py py-meth docutils literal"><span class="pre">write()</span></code> may be a lazy
-iterable or generator to avoid loading all of the data into memory at once.
-Unlike the <code class="docutils literal"><span class="pre">minute_bar_writer</span></code>, a sid may only appear once in the data
-iterable.</p>
-</div>
-</div>
-<div class="section" id="adjustment-writer">
-<h3><code class="docutils literal"><span class="pre">adjustment_writer</span></code><a class="headerlink" href="#adjustment-writer" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">adjustment_writer</span></code> is an instance of
-<code class="xref py py-class docutils literal"><span class="pre">SQLiteAdjustmentWriter</span></code>. This writer is
-used to store splits, mergers, dividends, and stock dividends. The data should
-be provided as dataframes and passed to
-<code class="xref py py-meth docutils literal"><span class="pre">write()</span></code>. Each of
-these fields are optional, but the writer can accept as much of the data as you
-have.</p>
-</div>
-<div class="section" id="calendar">
-<h3><code class="docutils literal"><span class="pre">calendar</span></code><a class="headerlink" href="#calendar" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">calendar</span></code> is an instance of
-<code class="xref py py-class docutils literal"><span class="pre">zipline.utils.calendars.TradingCalendar</span></code>. The calendar is provided to
-help some bundles generate queries for the days needed.</p>
-</div>
-<div class="section" id="start-session">
-<h3><code class="docutils literal"><span class="pre">start_session</span></code><a class="headerlink" href="#start-session" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">start_session</span></code> is a <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v0.22.0)"><code class="xref py py-class docutils literal"><span class="pre">pandas.Timestamp</span></code></a> object indicating the first
-day that the bundle should load data for.</p>
-</div>
-<div class="section" id="end-session">
-<h3><code class="docutils literal"><span class="pre">end_session</span></code><a class="headerlink" href="#end-session" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">end_session</span></code> is a <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v0.22.0)"><code class="xref py py-class docutils literal"><span class="pre">pandas.Timestamp</span></code></a> object indicating the last day
-that the bundle should load data for.</p>
-</div>
-<div class="section" id="cache">
-<h3><code class="docutils literal"><span class="pre">cache</span></code><a class="headerlink" href="#cache" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">cache</span></code> is an instance of <code class="xref py py-class docutils literal"><span class="pre">dataframe_cache</span></code>. This
-object is a mapping from strings to dataframes. This object is provided in case
-an ingestion crashes part way through. The idea is that the ingest function
-should check the cache for raw data, if it doesn’t exist in the cache, it should
-acquire it and then store it in the cache. Then it can parse and write the
-data. The cache will be cleared only after a successful load, this prevents the
-ingest function from needing to redownload all the data if there is some bug in
-the parsing. If it is very fast to get the data, for example if it is coming
-from another local file, then there is no need to use this cache.</p>
-</div>
-<div class="section" id="show-progress">
-<h3><code class="docutils literal"><span class="pre">show_progress</span></code><a class="headerlink" href="#show-progress" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">show_progress</span></code> is a boolean indicating that the user would like to receive
-feedback about the ingest function’s progress fetching and writing the
-data. Some examples for where to show how many files you have downloaded out of
-the total needed, or how far into some data conversion the ingest function
-is. One tool that may help with implementing <code class="docutils literal"><span class="pre">show_progress</span></code> for a loop is
-<code class="xref py py-class docutils literal"><span class="pre">maybe_show_progress</span></code>. This argument should always be
-forwarded to <code class="docutils literal"><span class="pre">minute_bar_writer.write</span></code> and <code class="docutils literal"><span class="pre">daily_bar_writer.write</span></code>.</p>
-</div>
-<div class="section" id="output-dir">
-<h3><code class="docutils literal"><span class="pre">output_dir</span></code><a class="headerlink" href="#output-dir" title="Permalink to this headline">¶</a></h3>
-<p><code class="docutils literal"><span class="pre">output_dir</span></code> is a string representing the file path where all the data will be
-written. <code class="docutils literal"><span class="pre">output_dir</span></code> will be some subdirectory of <code class="docutils literal"><span class="pre">$ZIPLINE_ROOT</span></code> and will
-contain the time of the start of the current ingestion. This can be used to
-directly move resources here if for some reason your ingest function can produce
-it’s own outputs without the writers. For example, the <code class="docutils literal"><span class="pre">quantopian:quandl</span></code>
-bundle uses this to directly untar the bundle into the <code class="docutils literal"><span class="pre">output_dir</span></code>.</p>
-</div>
-</div>
-</div>
-
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catalyst/__init__.py

@@ -0,0 +1,80 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+
+
+# This is *not* a place to dump arbitrary classes/modules for convenience,
+# it is a place to expose the public interfaces.
+from . import data
+from . import finance
+from . import gens
+from . import utils
+from .utils.calendars import get_calendar
+from .utils.run_algo import run_algorithm
+from ._version import get_versions
+
+# These need to happen after the other imports.
+from . algorithm import TradingAlgorithm
+from . import api
+
+from catalyst.utils.calendars.calendar_utils import global_calendar_dispatcher
+
+__version__ = get_versions()['version']
+del get_versions
+
+# PERF: Fire a warning if calendars were instantiated during catalyst import.
+# Having calendars doesn't break anything per-se, but it makes catalyst imports
+# noticeably slower, which becomes particularly noticeable in the Zipline CLI.
+if global_calendar_dispatcher._calendars:
+    import warnings
+    warnings.warn(
+        "Found TradingCalendar instances after catalyst import.\n"
+        "Zipline startup will be much slower until this is fixed!",
+    )
+    del warnings
+del global_calendar_dispatcher
+
+
+def load_ipython_extension(ipython):
+    from .__main__ import catalyst_magic
+    ipython.register_magic_function(catalyst_magic, 'line_cell', 'catalyst')
+
+
+if os.name == 'nt':
+    # we need to be able to write to our temp directoy on windows so we
+    # create a subdir in %TMP% that has write access and use that as %TMP%
+    def _():
+        import atexit
+        import tempfile
+
+        tempfile.tempdir = tempdir = tempfile.mkdtemp()
+
+        @atexit.register
+        def cleanup_tempdir():
+            import shutil
+            shutil.rmtree(tempdir)
+    _()
+    del _
+
+__all__ = [
+    'TradingAlgorithm',
+    'api',
+    'data',
+    'finance',
+    'get_calendar',
+    'gens',
+    'run_algorithm',
+    'utils',
+]

catalyst/__main__.py

@@ -0,0 +1,893 @@
+import errno
+import os
+from functools import wraps
+
+import click
+import sys
+import logbook
+import pandas as pd
+from catalyst.marketplace.marketplace import Marketplace
+from six import text_type
+
+from catalyst.data import bundles as bundles_module
+from catalyst.exchange.exchange_bundle import ExchangeBundle
+from catalyst.exchange.utils.exchange_utils import delete_algo_folder
+from catalyst.utils.cli import Date, Timestamp
+from catalyst.utils.run_algo import _run, load_extensions
+
+try:
+    __IPYTHON__
+except NameError:
+    __IPYTHON__ = False
+
+
+@click.group()
+@click.option(
+    '-e',
+    '--extension',
+    multiple=True,
+    help='File or module path to a catalyst extension to load.',
+)
+@click.option(
+    '--strict-extensions/--non-strict-extensions',
+    is_flag=True,
+    help='If --strict-extensions is passed then catalyst will not run '
+         'if it cannot load all of the specified extensions. If this is '
+         'not passed or --non-strict-extensions is passed then the '
+         'failure will be logged but execution will continue.',
+)
+@click.option(
+    '--default-extension/--no-default-extension',
+    is_flag=True,
+    default=True,
+    help="Don't load the default catalyst extension.py file "
+         "in $CATALYST_HOME.",
+)
+@click.version_option()
+def main(extension, strict_extensions, default_extension):
+    """Top level catalyst entry point.
+    """
+    # install a logbook handler before performing any other operations
+    logbook.StderrHandler().push_application()
+    load_extensions(
+        default_extension,
+        extension,
+        strict_extensions,
+        os.environ,
+    )
+
+
+def extract_option_object(option):
+    """Convert a click.option call into a click.Option object.
+
+    Parameters
+    ----------
+    option : decorator
+        A click.option decorator.
+
+    Returns
+    -------
+    option_object : click.Option
+        The option object that this decorator will create.
+    """
+
+    @option
+    def opt():
+        pass
+
+    return opt.__click_params__[0]
+
+
+def ipython_only(option):
+    """Mark that an option should only be exposed in IPython.
+
+    Parameters
+    ----------
+    option : decorator
+        A click.option decorator.
+
+    Returns
+    -------
+    ipython_only_dec : decorator
+        A decorator that correctly applies the argument even when not
+        using IPython mode.
+    """
+    if __IPYTHON__:
+        return option
+
+    argname = extract_option_object(option).name
+
+    def d(f):
+        @wraps(f)
+        def _(*args, **kwargs):
+            kwargs[argname] = None
+            return f(*args, **kwargs)
+
+        return _
+
+    return d
+
+
+@main.command()
+@click.option(
+    '-f',
+    '--algofile',
+    default=None,
+    type=click.File('r'),
+    help='The file that contains the algorithm to run.',
+)
+@click.option(
+    '-t',
+    '--algotext',
+    help='The algorithm script to run.',
+)
+@click.option(
+    '-D',
+    '--define',
+    multiple=True,
+    help="Define a name to be bound in the namespace before executing"
+         " the algotext. For example '-Dname=value'. The value may be"
+         " any python expression. These are evaluated in order so they"
+         " may refer to previously defined names.",
+)
+@click.option(
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute'}),
+    default='daily',
+    show_default=True,
+    help='The data frequency of the simulation.',
+)
+@click.option(
+    '--capital-base',
+    type=float,
+    show_default=True,
+    help='The starting capital for the simulation.',
+)
+@click.option(
+    '-b',
+    '--bundle',
+    default='poloniex',
+    metavar='BUNDLE-NAME',
+    show_default=True,
+    help='The data bundle to use for the simulation.',
+)
+@click.option(
+    '--bundle-timestamp',
+    type=Timestamp(),
+    default=pd.Timestamp.utcnow(),
+    show_default=False,
+    help='The date to lookup data on or before.\n'
+         '[default: <current-time>]'
+)
+@click.option(
+    '-s',
+    '--start',
+    type=Date(tz='utc', as_timestamp=True),
+    help='The start date of the simulation.',
+)
+@click.option(
+    '-e',
+    '--end',
+    type=Date(tz='utc', as_timestamp=True),
+    help='The end date of the simulation.',
+)
+@click.option(
+    '-o',
+    '--output',
+    default='-',
+    metavar='FILENAME',
+    show_default=True,
+    help="The location to write the perf data. If this is '-' the perf"
+         " will be written to stdout.",
+)
+@click.option(
+    '--print-algo/--no-print-algo',
+    is_flag=True,
+    default=False,
+    help='Print the algorithm to stdout.',
+)
+@ipython_only(click.option(
+    '--local-namespace/--no-local-namespace',
+    is_flag=True,
+    default=None,
+    help='Should the algorithm methods be resolved in the local namespace.'
+))
+@click.option(
+    '-x',
+    '--exchange-name',
+    help='The name of the targeted exchange.',
+)
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='A label assigned to the algorithm for data storage purposes.'
+)
+@click.option(
+    '-c',
+    '--base-currency',
+    help='The base currency used to calculate statistics '
+         '(e.g. usd, btc, eth).',
+)
+@click.pass_context
+def run(ctx,
+        algofile,
+        algotext,
+        define,
+        data_frequency,
+        capital_base,
+        bundle,
+        bundle_timestamp,
+        start,
+        end,
+        output,
+        print_algo,
+        local_namespace,
+        exchange_name,
+        algo_namespace,
+        base_currency):
+    """Run a backtest for the given algorithm.
+    """
+
+    if (algotext is not None) == (algofile is not None):
+        ctx.fail(
+            "must specify exactly one of '-f' / '--algofile' or"
+            " '-t' / '--algotext'",
+        )
+
+    # check that the start and end dates are passed correctly
+    if start is None and end is None:
+        # check both at the same time to avoid the case where a user
+        # does not pass either of these and then passes the first only
+        # to be told they need to pass the second argument also
+        ctx.fail(
+            "must specify dates with '-s' / '--start' and '-e' / '--end'"
+            " in backtest mode",
+        )
+    if start is None:
+        ctx.fail("must specify a start date with '-s' / '--start'"
+                 " in backtest mode")
+    if end is None:
+        ctx.fail("must specify an end date with '-e' / '--end'"
+                 " in backtest mode")
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+
+    if base_currency is None:
+        ctx.fail("must specify a base currency with '-c' in backtest mode")
+
+    if capital_base is None:
+        ctx.fail("must specify a capital base with '--capital-base'")
+
+    click.echo('Running in backtesting mode.', sys.stdout)
+
+    perf = _run(
+        initialize=None,
+        handle_data=None,
+        before_trading_start=None,
+        analyze=None,
+        algofile=algofile,
+        algotext=algotext,
+        defines=define,
+        data_frequency=data_frequency,
+        capital_base=capital_base,
+        data=None,
+        bundle=bundle,
+        bundle_timestamp=bundle_timestamp,
+        start=start,
+        end=end,
+        output=output,
+        print_algo=print_algo,
+        local_namespace=local_namespace,
+        environ=os.environ,
+        live=False,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency,
+        analyze_live=None,
+        live_graph=False,
+        simulate_orders=True,
+        auth_aliases=None,
+        stats_output=None,
+    )
+
+    if output == '-':
+        click.echo(str(perf), sys.stdout)
+    elif output != os.devnull:  # make the catalyst magic not write any data
+        perf.to_pickle(output)
+
+    return perf
+
+
+def catalyst_magic(line, cell=None):
+    """The catalyst IPython cell magic.
+    """
+    load_extensions(
+        default=True,
+        extensions=[],
+        strict=True,
+        environ=os.environ,
+    )
+    try:
+        return run.main(
+            # put our overrides at the start of the parameter list so that
+            # users may pass values with higher precedence
+            [
+                '--algotext', cell,
+                '--output', os.devnull,  # don't write the results by default
+            ] + ([
+                     # these options are set when running in line magic mode
+                     # set a non None algo text to use the ipython user_ns
+                     '--algotext', '',
+                     '--local-namespace',
+                 ] if cell is None else []) + line.split(),
+            '%s%%catalyst' % ((cell or '') and '%'),
+            # don't use system exit and propogate errors to the caller
+            standalone_mode=False,
+        )
+    except SystemExit as e:
+        # https://github.com/mitsuhiko/click/pull/533
+        # even in standalone_mode=False `--help` really wants to kill us ;_;
+        if e.code:
+            raise ValueError('main returned non-zero status code: %d' % e.code)
+
+
+@main.command()
+@click.option(
+    '-f',
+    '--algofile',
+    default=None,
+    type=click.File('r'),
+    help='The file that contains the algorithm to run.',
+)
+@click.option(
+    '--capital-base',
+    type=float,
+    show_default=True,
+    help='The amount of capital (in base_currency) allocated to trading.',
+)
+@click.option(
+    '-t',
+    '--algotext',
+    help='The algorithm script to run.',
+)
+@click.option(
+    '-D',
+    '--define',
+    multiple=True,
+    help="Define a name to be bound in the namespace before executing"
+         " the algotext. For example '-Dname=value'. The value may be"
+         " any python expression. These are evaluated in order so they"
+         " may refer to previously defined names.",
+)
+@click.option(
+    '-o',
+    '--output',
+    default='-',
+    metavar='FILENAME',
+    show_default=True,
+    help="The location to write the perf data. If this is '-' the perf will"
+         " be written to stdout.",
+)
+@click.option(
+    '--print-algo/--no-print-algo',
+    is_flag=True,
+    default=False,
+    help='Print the algorithm to stdout.',
+)
+@ipython_only(click.option(
+    '--local-namespace/--no-local-namespace',
+    is_flag=True,
+    default=None,
+    help='Should the algorithm methods be resolved in the local namespace.'
+))
+@click.option(
+    '-x',
+    '--exchange-name',
+    help='The name of the targeted exchange.',
+)
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='A label assigned to the algorithm for data storage purposes.'
+)
+@click.option(
+    '-c',
+    '--base-currency',
+    help='The base currency used to calculate statistics '
+         '(e.g. usd, btc, eth).',
+)
+@click.option(
+    '-e',
+    '--end',
+    type=Date(tz='utc', as_timestamp=True),
+    help='An optional end date at which to stop the execution.',
+)
+@click.option(
+    '--live-graph/--no-live-graph',
+    is_flag=True,
+    default=False,
+    help='Display live graph.',
+)
+@click.option(
+    '--simulate-orders/--no-simulate-orders',
+    is_flag=True,
+    default=True,
+    help='Simulating orders enable the paper trading mode. No orders will be '
+         'sent to the exchange unless set to false.',
+)
+@click.option(
+    '--auth-aliases',
+    default=None,
+    help='Authentication file aliases for the specified exchanges. By default,'
+         'each exchange uses the "auth.json" file in the exchange folder. '
+         'Specifying an "auth2" alias would use "auth2.json". It should be '
+         'specified like this: "[exchange_name],[alias],..." For example, '
+         '"binance,auth2" or "binance,auth2,bittrex,auth2".',
+)
+@click.pass_context
+def live(ctx,
+         algofile,
+         capital_base,
+         algotext,
+         define,
+         output,
+         print_algo,
+         local_namespace,
+         exchange_name,
+         algo_namespace,
+         base_currency,
+         end,
+         live_graph,
+         auth_aliases,
+         simulate_orders):
+    """Trade live with the given algorithm.
+    """
+    if (algotext is not None) == (algofile is not None):
+        ctx.fail(
+            "must specify exactly one of '-f' / '--algofile' or"
+            " '-t' / '--algotext'",
+        )
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+
+    if algo_namespace is None:
+        ctx.fail("must specify an algorithm name '-n' in live execution mode")
+
+    if base_currency is None:
+        ctx.fail("must specify a base currency '-c' in live execution mode")
+
+    if capital_base is None:
+        ctx.fail("must specify a capital base with '--capital-base'")
+
+    if simulate_orders:
+        click.echo('Running in paper trading mode.', sys.stdout)
+
+    else:
+        click.echo('Running in live trading mode.', sys.stdout)
+
+    perf = _run(
+        initialize=None,
+        handle_data=None,
+        before_trading_start=None,
+        analyze=None,
+        algofile=algofile,
+        algotext=algotext,
+        defines=define,
+        data_frequency=None,
+        capital_base=capital_base,
+        data=None,
+        bundle=None,
+        bundle_timestamp=None,
+        start=None,
+        end=end,
+        output=output,
+        print_algo=print_algo,
+        local_namespace=local_namespace,
+        environ=os.environ,
+        live=True,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency,
+        live_graph=live_graph,
+        analyze_live=None,
+        simulate_orders=simulate_orders,
+        auth_aliases=auth_aliases,
+        stats_output=None,
+    )
+
+    if output == '-':
+        click.echo(str(perf), sys.stdout)
+    elif output != os.devnull:  # make the catalyst magic not write any data
+        perf.to_pickle(output)
+
+    return perf
+
+
+@main.command(name='ingest-exchange')
+@click.option(
+    '-x',
+    '--exchange-name',
+    help='The name of the exchange bundle to ingest.',
+)
+@click.option(
+    '-f',
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute', 'daily,minute', 'minute,daily'}),
+    default='daily',
+    show_default=True,
+    help='The data frequency of the desired OHLCV bars.',
+)
+@click.option(
+    '-s',
+    '--start',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The start date of the data range. (default: one year from end date)',
+)
+@click.option(
+    '-e',
+    '--end',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The end date of the data range. (default: today)',
+)
+@click.option(
+    '-i',
+    '--include-symbols',
+    default=None,
+    help='A list of symbols to ingest (optional comma separated list)',
+)
+@click.option(
+    '--exclude-symbols',
+    default=None,
+    help='A list of symbols to exclude from the ingestion '
+         '(optional comma separated list)',
+)
+@click.option(
+    '--csv',
+    default=None,
+    help='The path of a CSV file containing the data. If specified, start, '
+         'end, include-symbols and exclude-symbols will be ignored. Instead,'
+         'all data in the file will be ingested.',
+)
+@click.option(
+    '--show-progress/--no-show-progress',
+    default=True,
+    help='Print progress information to the terminal.'
+)
+@click.option(
+    '--verbose/--no-verbose`',
+    default=False,
+    help='Show a progress indicator for every currency pair.'
+)
+@click.option(
+    '--validate/--no-validate`',
+    default=False,
+    help='Report potential anomalies found in data bundles.'
+)
+@click.pass_context
+def ingest_exchange(ctx, exchange_name, data_frequency, start, end,
+                    include_symbols, exclude_symbols, csv, show_progress,
+                    verbose, validate):
+    """
+    Ingest data for the given exchange.
+    """
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+
+    exchange_bundle = ExchangeBundle(exchange_name)
+
+    click.echo('Trying to ingest exchange bundle {}...'.format(exchange_name),
+               sys.stdout)
+    exchange_bundle.ingest(
+        data_frequency=data_frequency,
+        include_symbols=include_symbols,
+        exclude_symbols=exclude_symbols,
+        start=start,
+        end=end,
+        show_progress=show_progress,
+        show_breakdown=verbose,
+        show_report=validate,
+        csv=csv
+    )
+
+
+@main.command(name='clean-algo')
+@click.option(
+    '-n',
+    '--algo-namespace',
+    help='The label of the algorithm to for which to clean the state.'
+)
+@click.pass_context
+def clean_algo(ctx, algo_namespace):
+    click.echo(
+        'Cleaning algo state: {}'.format(algo_namespace),
+        sys.stdout
+    )
+    delete_algo_folder(algo_namespace)
+    click.echo('Done', sys.stdout)
+
+
+@main.command(name='clean-exchange')
+@click.option(
+    '-x',
+    '--exchange-name',
+    help='The name of the exchange bundle to ingest.',
+)
+@click.option(
+    '-f',
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute'}),
+    default=None,
+    help='The bundle data frequency to remove. If not specified, it will '
+         'remove both daily and minute bundles.',
+)
+@click.pass_context
+def clean_exchange(ctx, exchange_name, data_frequency):
+    """Clean up bundles from 'ingest-exchange'.
+    """
+
+    if exchange_name is None:
+        ctx.fail("must specify an exchange name '-x'")
+
+    exchange_bundle = ExchangeBundle(exchange_name)
+
+    click.echo('Cleaning exchange bundle {}...'.format(exchange_name),
+               sys.stdout)
+    exchange_bundle.clean(
+        data_frequency=data_frequency,
+    )
+    click.echo('Done', sys.stdout)
+
+
+@main.command()
+@click.option(
+    '-b',
+    '--bundle',
+    metavar='BUNDLE-NAME',
+    default=None,
+    show_default=False,
+    help='The data bundle to ingest.',
+)
+@click.option(
+    '-x',
+    '--exchange-name',
+    help='The name of the exchange bundle to ingest.',
+)
+@click.option(
+    '-c',
+    '--compile-locally',
+    is_flag=True,
+    default=False,
+    help='Download dataset from source and compile bundle locally.',
+)
+@click.option(
+    '--assets-version',
+    type=int,
+    multiple=True,
+    help='Version of the assets db to which to downgrade.',
+)
+@click.option(
+    '--show-progress/--no-show-progress',
+    default=True,
+    help='Print progress information to the terminal.'
+)
+@click.pass_context
+def ingest(ctx, bundle, exchange_name, compile_locally, assets_version,
+           show_progress):
+    """Ingest the data for the given bundle.
+    """
+
+    bundles_module.ingest(
+        bundle,
+        os.environ,
+        pd.Timestamp.utcnow(),
+        assets_version,
+        show_progress,
+        compile_locally,
+    )
+
+
+@main.command()
+@click.option(
+    '-b',
+    '--bundle',
+    default='poloniex',
+    metavar='BUNDLE-NAME',
+    show_default=True,
+    help='The data bundle to clean.',
+)
+@click.option(
+    '-x',
+    '--exchange_name',
+    metavar='EXCHANGE-NAME',
+    show_default=True,
+    help='The exchange bundle name to clean.',
+)
+@click.option(
+    '-e',
+    '--before',
+    type=Timestamp(),
+    help='Clear all data before TIMESTAMP.'
+         ' This may not be passed with -k / --keep-last',
+)
+@click.option(
+    '-a',
+    '--after',
+    type=Timestamp(),
+    help='Clear all data after TIMESTAMP'
+         ' This may not be passed with -k / --keep-last',
+)
+@click.option(
+    '-k',
+    '--keep-last',
+    type=int,
+    metavar='N',
+    help='Clear all but the last N downloads.'
+         ' This may not be passed with -e / --before or -a / --after',
+)
+def clean(bundle, before, after, keep_last):
+    """Clean up bundles from 'ingest'.
+    """
+    bundles_module.clean(
+        bundle,
+        before,
+        after,
+        keep_last,
+    )
+
+
+@main.command()
+def bundles():
+    """List all of the available data bundles.
+    """
+    for bundle in sorted(bundles_module.bundles.keys()):
+        if bundle.startswith('.'):
+            # hide the test data
+            continue
+        try:
+            ingestions = list(
+                map(text_type, bundles_module.ingestions_for_bundle(bundle))
+            )
+        except OSError as e:
+            if e.errno != errno.ENOENT:
+                raise
+            ingestions = []
+
+        # If we got no ingestions, either because the directory didn't exist or
+        # because there were no entries, print a single message indicating that
+        # no ingestions have yet been made.
+        for timestamp in ingestions or ["<no ingestions>"]:
+            click.echo("%s %s" % (bundle, timestamp), sys.stdout)
+
+
+@main.group()
+@click.pass_context
+def marketplace(ctx):
+    """Access the Enigma Data Marketplace to:\n
+    - Register and Publish new datasets (seller-side)\n
+    - Subscribe and Ingest premium datasets (buyer-side)\n
+    """
+    pass
+
+
+@marketplace.command()
+@click.pass_context
+def ls(ctx):
+    """List all available datasets.
+    """
+    click.echo('Listing of available data sources on the marketplace:',
+               sys.stdout)
+    marketplace = Marketplace()
+    marketplace.list()
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the dataset to ingest from the Data Marketplace.',
+)
+@click.pass_context
+def subscribe(ctx, dataset):
+    """Subscribe to an existing dataset.
+    """
+    marketplace = Marketplace()
+    marketplace.subscribe(dataset)
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the dataset to ingest from the Data Marketplace.',
+)
+@click.option(
+    '-f',
+    '--data-frequency',
+    type=click.Choice({'daily', 'minute', 'daily,minute', 'minute,daily'}),
+    default='daily',
+    show_default=True,
+    help='The data frequency of the desired OHLCV bars.',
+)
+@click.option(
+    '-s',
+    '--start',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The start date of the data range. (default: one year from end date)',
+)
+@click.option(
+    '-e',
+    '--end',
+    default=None,
+    type=Date(tz='utc', as_timestamp=True),
+    help='The end date of the data range. (default: today)',
+)
+@click.pass_context
+def ingest(ctx, dataset, data_frequency, start, end):
+    """Ingest a dataset (requires subscription).
+    """
+    marketplace = Marketplace()
+    marketplace.ingest(dataset, data_frequency, start, end)
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the dataset to ingest from the Data Marketplace.',
+)
+@click.pass_context
+def clean(ctx, dataset):
+    """Clean/Remove local data for a given dataset.
+    """
+    marketplace = Marketplace()
+    marketplace.clean(dataset)
+
+
+@marketplace.command()
+@click.pass_context
+def register(ctx):
+    """Register a new dataset.
+    """
+    marketplace = Marketplace()
+    marketplace.register()
+
+
+@marketplace.command()
+@click.option(
+    '--dataset',
+    default=None,
+    help='The name of the Marketplace dataset to publish data for.',
+)
+@click.option(
+    '--datadir',
+    default=None,
+    help='The folder that contains the CSV data files to publish.',
+)
+@click.option(
+    '--watch/--no-watch',
+    is_flag=True,
+    default=False,
+    help='Whether to watch the datadir for live data.',
+)
+@click.pass_context
+def publish(ctx, dataset, datadir, watch):
+    """Publish data for a registered dataset.
+    """
+    marketplace = Marketplace()
+    if dataset is None:
+        ctx.fail("must specify a dataset to publish data for "
+                 " with '--dataset'\n")
+    if datadir is None:
+        ctx.fail("must specify a datadir where to find the files to publish "
+                 " with '--datadir'\n")
+    marketplace.publish(dataset, datadir, watch)
+
+
+if __name__ == '__main__':
+    main()

catalyst/_protocol.pyx

@@ -0,0 +1,936 @@
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import warnings
+from contextlib import contextmanager
+from functools import wraps
+
+from pandas.tslib import normalize_date
+import pandas as pd
+import numpy as np
+
+from six import iteritems, PY2, string_types
+from cpython cimport bool
+from collections import Iterable
+
+from catalyst.assets import (Asset,
+                            AssetConvertible,
+                            PricingDataAssociable,
+                            Future)
+from catalyst.assets.continuous_futures import ContinuousFuture
+from catalyst.catalyst_warnings import ZiplineDeprecationWarning
+
+
+cdef bool _is_iterable(obj):
+    return isinstance(obj, Iterable) and not isinstance(obj, string_types)
+
+
+# Wraps doesn't work for method objects in python2. Docs should be generated
+# with python3 so it is not a big deal.
+if PY2:
+    def no_wraps_py2(f):
+        def dec(g):
+            return g
+        return dec
+else:
+    no_wraps_py2 = wraps
+
+
+cdef class check_parameters(object):
+    """
+    Asserts that the keywords passed into the wrapped function are included
+    in those passed into this decorator. If not, raise a TypeError with a
+    meaningful message, unlike the one Cython returns by default.
+
+    Also asserts that the arguments passed into the wrapped function are
+    consistent with the types passed into this decorator. If not, raise a
+    TypeError with a meaningful message.
+    """
+    cdef tuple keyword_names
+    cdef tuple types
+    cdef dict keys_to_types
+
+    def __init__(self, keyword_names, types):
+        self.keyword_names = keyword_names
+        self.types = types
+
+        self.keys_to_types = dict(zip(keyword_names, types))
+
+    def __call__(self, func):
+        @no_wraps_py2(func)
+        def assert_keywords_and_call(*args, **kwargs):
+            cdef short i
+
+            # verify all the keyword arguments
+            for field in kwargs:
+                if field not in self.keyword_names:
+                    raise TypeError("%s() got an unexpected keyword argument"
+                                    " '%s'" % (func.__name__, field))
+
+            # verify type of each argument
+            for i, arg in enumerate(args[1:]):
+                expected_type = self.types[i]
+
+                if (i == 0 or i == 1) and _is_iterable(arg):
+                    if len(arg) == 0:
+                        continue
+                    arg = arg[0]
+
+                if not isinstance(arg, expected_type):
+                    expected_type_name = expected_type.__name__ \
+                        if not _is_iterable(expected_type) \
+                        else ', '.join([type_.__name__ for type_ in expected_type])
+
+                    raise TypeError("Expected %s argument to be of type %s%s" %
+                        (self.keyword_names[i],
+                         'or iterable of type ' if i in (0, 1) else '',
+                         expected_type_name)
+                    )
+
+            # verify type of each kwarg
+            for keyword, arg in iteritems(kwargs):
+                if keyword in ('assets', 'fields') and _is_iterable(arg):
+                    if len(arg) == 0:
+                        continue
+                    arg = arg[0]
+                if not isinstance(arg, self.keys_to_types[keyword]):
+                    expected_type = self.keys_to_types[keyword].__name__ \
+                        if not _is_iterable(self.keys_to_types[keyword]) \
+                        else ', '.join([type_.__name__ for type_ in
+                            self.keys_to_types[keyword]])
+
+                    raise TypeError("Expected %s argument to be of type %s%s" %
+                                    (keyword,
+                                     'or iterable of type ' if keyword in
+                                     ('assets', 'fields') else '',
+                                     expected_type)
+                    )
+
+            return func(*args, **kwargs)
+
+        return assert_keywords_and_call
+
+
+@contextmanager
+def handle_non_market_minutes(bar_data):
+    try:
+        bar_data._handle_non_market_minutes = True
+        yield
+    finally:
+        bar_data._handle_non_market_minutes = False
+
+
+cdef class BarData:
+    """
+    Provides methods to access spot value or history windows of price data.
+    Also provides some utility methods to determine if an asset is alive,
+    has recent trade data, etc.
+
+    This is what is passed as ``data`` to the ``handle_data`` function.
+
+    Parameters
+    ----------
+    data_portal : DataPortal
+        Provider for bar pricing data.
+    simulation_dt_func : callable
+        Function which returns the current simulation time.
+        This is usually bound to a method of TradingSimulation.
+    data_frequency : {'minute', 'daily'}
+        The frequency of the bar data; i.e. whether the data is
+        daily or minute bars
+    restrictions : catalyst.finance.asset_restrictions.Restrictions
+        Object that combines and returns restricted list information from
+        multiple sources
+    universe_func : callable, optional
+        Function which returns the current 'universe'.  This is for
+        backwards compatibility with older API concepts.
+    """
+    cdef object data_portal
+    cdef object simulation_dt_func
+    cdef object data_frequency
+    cdef object restrictions
+    cdef dict _views
+    cdef object _universe_func
+    cdef object _last_calculated_universe
+    cdef object _universe_last_updated_at
+    cdef bool _daily_mode
+    cdef object _trading_calendar
+    cdef object _is_restricted
+
+    cdef bool _adjust_minutes
+
+    def __init__(self, data_portal, simulation_dt_func, data_frequency,
+                 trading_calendar, restrictions, universe_func=None):
+        self.data_portal = data_portal
+        self.simulation_dt_func = simulation_dt_func
+        self.data_frequency = data_frequency
+        self._views = {}
+
+        self._daily_mode = (self.data_frequency == "daily")
+
+        self._universe_func = universe_func
+        self._last_calculated_universe = None
+        self._universe_last_updated_at = None
+
+        self._adjust_minutes = False
+
+        self._trading_calendar = trading_calendar
+        self._is_restricted = restrictions.is_restricted
+
+    cdef _get_equity_price_view(self, asset):
+        """
+        Returns a DataPortalSidView for the given asset.  Used to support the
+        data[sid(N)] public API.  Not needed if DataPortal is used standalone.
+
+        Parameters
+        ----------
+        asset : Asset
+            Asset that is being queried.
+
+        Returns
+        -------
+        SidView : Accessor into the given asset's data.
+        """
+        try:
+            self._warn_deprecated("`data[sid(N)]` is deprecated. Use "
+                            "`data.current`.")
+            view = self._views[asset]
+        except KeyError:
+            try:
+                asset = self.data_portal.asset_finder.retrieve_asset(asset)
+            except ValueError:
+                # assume fetcher
+                pass
+            view = self._views[asset] = self._create_sid_view(asset)
+
+        return view
+
+    cdef _create_sid_view(self, asset):
+        return SidView(
+            asset,
+            self.data_portal,
+            self.simulation_dt_func,
+            self.data_frequency
+        )
+
+    cdef _get_current_minute(self):
+        """
+        Internal utility method to get the current simulation time.
+
+        Possible answers are:
+        - whatever the algorithm's get_datetime() method returns (this is what
+            `self.simulation_dt_func()` points to)
+        - sometimes we're knowingly not in a market minute, like if we're in
+            before_trading_start.  In that case, `self._adjust_minutes` is
+            True, and we get the previous market minute.
+        - if we're in daily mode, get the session label for this minute.
+        """
+        dt = self.simulation_dt_func()
+
+        if self._adjust_minutes:
+            dt = \
+                self.data_portal.trading_calendar.previous_minute(dt)
+
+        if self._daily_mode:
+            # if we're in daily mode, take the given dt (which is the last
+            # minute of the session) and get the session label for it.
+            dt = self.data_portal.trading_calendar.minute_to_session_label(dt)
+
+        return dt
+
+    @check_parameters(('assets', 'fields'),
+                      ((Asset, ContinuousFuture) + string_types, string_types))
+    def current(self, assets, fields):
+        """
+        Returns the current value of the given assets for the given fields
+        at the current simulation time.  Current values are the as-traded price
+        and are usually not adjusted for events like splits or dividends (see
+        notes for more information).
+
+        Parameters
+        ----------
+        assets : Asset or iterable of Assets
+        fields : str or iterable[str].
+            Valid values are: "price",
+            "last_traded", "open", "high", "low", "close", "volume", or column
+            names in files read by ``fetch_csv``.
+
+        Returns
+        -------
+        current_value : Scalar, pandas Series, or pandas DataFrame.
+            See notes below.
+
+        Notes
+        -----
+        If a single asset and a single field are passed in, a scalar float
+        value is returned.
+
+        If a single asset and a list of fields are passed in, a pandas Series
+        is returned whose indices are the fields, and whose values are scalar
+        values for this asset for each field.
+
+        If a list of assets and a single field are passed in, a pandas Series
+        is returned whose indices are the assets, and whose values are scalar
+        values for each asset for the given field.
+
+        If a list of assets and a list of fields are passed in, a pandas
+        DataFrame is returned, indexed by asset.  The columns are the requested
+        fields, filled with the scalar values for each asset for each field.
+
+        If the current simulation time is not a valid market time, we use the
+        last market close instead.
+
+        "price" returns the last known close price of the asset.  If there is
+        no last known value (either because the asset has never traded, or
+        because it has delisted) NaN is returned.  If a value is found, and we
+        had to cross an adjustment boundary (split, dividend, etc) to get it,
+        the value is adjusted before being returned.
+
+        "last_traded" returns the date of the last trade event of the asset,
+        even if the asset has stopped trading. If there is no last known value,
+        pd.NaT is returned.
+
+        "volume" returns the trade volume for the current simulation time.  If
+        there is no trade this minute, 0 is returned.
+
+        "open", "high", "low", and "close" return the relevant information for
+        the current trade bar.  If there is no current trade bar, NaN is
+        returned.
+        """
+        multiple_assets = _is_iterable(assets)
+        multiple_fields = _is_iterable(fields)
+
+        # There's some overly verbose code in here, particularly around
+        # 'do something if self._adjust_minutes is False, otherwise do
+        # something else'. This could be less verbose, but the 99% case is that
+        # `self._adjust_minutes` is False, so it's important to keep that code
+        # path as fast as possible.
+
+        # There's probably a way to make this method (and `history`) less
+        # verbose, but this is OK for now.
+
+        if not multiple_assets:
+            asset = assets
+
+            if not multiple_fields:
+                field = fields
+
+                # return scalar value
+                if not self._adjust_minutes:
+                    return self.data_portal.get_spot_value(
+                        asset,
+                        field,
+                        self._get_current_minute(),
+                        self.data_frequency
+                    )
+                else:
+                    return self.data_portal.get_adjusted_value(
+                        asset,
+                        field,
+                        self._get_current_minute(),
+                        self.simulation_dt_func(),
+                        self.data_frequency
+                    )
+            else:
+                # assume fields is iterable
+                # return a Series indexed by field
+                if not self._adjust_minutes:
+                    return pd.Series(data={
+                        field: self.data_portal.get_spot_value(
+                                    asset,
+                                    field,
+                                    self._get_current_minute(),
+                                    self.data_frequency
+                               )
+                        for field in fields
+                    }, index=fields, name=assets.symbol)
+                else:
+                    return pd.Series(data={
+                        field: self.data_portal.get_adjusted_value(
+                                    asset,
+                                    field,
+                                    self._get_current_minute(),
+                                    self.simulation_dt_func(),
+                                    self.data_frequency
+                               )
+                        for field in fields
+                    }, index=fields, name=assets.symbol)
+        else:
+            if not multiple_fields:
+                field = fields
+
+                # assume assets is iterable
+                # return a Series indexed by asset
+                if not self._adjust_minutes:
+                    return pd.Series(data={
+                        asset: self.data_portal.get_spot_value(
+                                    asset,
+                                    field,
+                                    self._get_current_minute(),
+                                    self.data_frequency
+                               )
+                        for asset in assets
+                        }, index=assets, name=fields)
+                else:
+                    return pd.Series(data={
+                        asset: self.data_portal.get_adjusted_value(
+                                    asset,
+                                    field,
+                                    self._get_current_minute(),
+                                    self.simulation_dt_func(),
+                                    self.data_frequency
+                               )
+                        for asset in assets
+                        }, index=assets, name=fields)
+
+            else:
+                # both assets and fields are iterable
+                data = {}
+
+                if not self._adjust_minutes:
+                    for field in fields:
+                        series = pd.Series(data={
+                            asset: self.data_portal.get_spot_value(
+                                        asset,
+                                        field,
+                                        self._get_current_minute(),
+                                        self.data_frequency
+                                   )
+                            for asset in assets
+                            }, index=assets, name=field)
+                        data[field] = series
+                else:
+                    for field in fields:
+                        series = pd.Series(data={
+                            asset: self.data_portal.get_adjusted_value(
+                                        asset,
+                                        field,
+                                        self._get_current_minute(),
+                                        self.simulation_dt_func(),
+                                        self.data_frequency
+                                   )
+                            for asset in assets
+                            }, index=assets, name=field)
+                        data[field] = series
+
+                return pd.DataFrame(data)
+
+    @check_parameters(('continuous_future',),
+                      (ContinuousFuture,))
+    def current_chain(self, continuous_future):
+        return self.data_portal.get_current_future_chain(
+            continuous_future,
+            self.simulation_dt_func())
+
+    @check_parameters(('assets',), (Asset,))
+    def can_trade(self, assets):
+        """
+        For the given asset or iterable of assets, returns true if all of the
+        following are true:
+        1) the asset is alive for the session of the current simulation time
+          (if current simulation time is not a market minute, we use the next
+          session)
+        2) (if we are in minute mode) the asset's exchange is open at the
+          current simulation time or at the simulation calendar's next market
+          minute
+        3) there is a known last price for the asset.
+
+        Notes
+        -----
+        The second condition above warrants some further explanation.
+        - If the asset's exchange calendar is identical to the simulation
+        calendar, then this condition always returns True.
+        - If there are market minutes in the simulation calendar outside of
+        this asset's exchange's trading hours (for example, if the simulation
+        is running on the CME calendar but the asset is MSFT, which trades on
+        the NYSE), during those minutes, this condition will return false
+        (for example, 3:15 am Eastern on a weekday, during which the CME is
+        open but the NYSE is closed).
+
+        Parameters
+        ----------
+        assets: Asset or iterable of assets
+
+        Returns
+        -------
+        can_trade : bool or pd.Series[bool] indexed by asset.
+        """
+        dt = self.simulation_dt_func()
+
+        if self._adjust_minutes:
+            adjusted_dt = self._get_current_minute()
+        else:
+            adjusted_dt = dt
+
+        data_portal = self.data_portal
+
+        if isinstance(assets, Asset):
+            return self._can_trade_for_asset(
+                assets, dt, adjusted_dt, data_portal
+            )
+        else:
+            tradeable = [
+                self._can_trade_for_asset(
+                    asset, dt, adjusted_dt, data_portal
+                )
+                for asset in assets
+            ]
+            return pd.Series(data=tradeable, index=assets, dtype=bool)
+
+    cdef bool _can_trade_for_asset(self, asset, dt, adjusted_dt, data_portal):
+        cdef object session_label
+        cdef object dt_to_use_for_exchange_check,
+
+        if self._is_restricted(asset, adjusted_dt):
+            return False
+
+        session_label = self._trading_calendar.minute_to_session_label(dt)
+
+        if not asset.is_alive_for_session(session_label):
+            # asset isn't alive
+            return False
+
+        if asset.auto_close_date and session_label >= asset.auto_close_date:
+            return False
+
+        if not self._daily_mode:
+            # Find the next market minute for this calendar, and check if this
+            # asset's exchange is open at that minute.
+            if self._trading_calendar.is_open_on_minute(dt):
+                dt_to_use_for_exchange_check = dt
+            else:
+                dt_to_use_for_exchange_check = \
+                    self._trading_calendar.next_open(dt)
+
+            if not asset.is_exchange_open(dt_to_use_for_exchange_check):
+                return False
+
+        # is there a last price?
+        return not np.isnan(
+            data_portal.get_spot_value(
+                asset, "price", adjusted_dt, self.data_frequency
+            )
+        )
+
+    @check_parameters(('assets',), (Asset,))
+    def is_stale(self, assets):
+        """
+        For the given asset or iterable of assets, returns true if the asset
+        is alive and there is no trade data for the current simulation time.
+
+        If the asset has never traded, returns False.
+
+        If the current simulation time is not a valid market time, we use the
+        current time to check if the asset is alive, but we use the last
+        market minute/day for the trade data check.
+
+        Parameters
+        ----------
+        assets: Asset or iterable of assets
+
+        Returns
+        -------
+        boolean or Series of booleans, indexed by asset.
+        """
+        dt = self.simulation_dt_func()
+        if self._adjust_minutes:
+            adjusted_dt = self._get_current_minute()
+        else:
+            adjusted_dt = dt
+
+        data_portal = self.data_portal
+
+        if isinstance(assets, Asset):
+            return self._is_stale_for_asset(
+                assets, dt, adjusted_dt, data_portal
+            )
+        else:
+            return pd.Series(data={
+                asset: self._is_stale_for_asset(
+                    asset, dt, adjusted_dt, data_portal
+                )
+                for asset in assets
+            })
+
+    cdef bool _is_stale_for_asset(self, asset, dt, adjusted_dt, data_portal):
+        session_label = normalize_date(dt) # FIXME
+
+        if not asset.is_alive_for_session(session_label):
+            return False
+
+        current_volume = data_portal.get_spot_value(
+            asset, "volume",  adjusted_dt, self.data_frequency
+        )
+
+        if current_volume > 0:
+            # found a current value, so we know this asset is not stale.
+            return False
+        else:
+            # we need to distinguish between if this asset has ever traded
+            # (stale = True) or has never traded (stale = False)
+            last_traded_dt = \
+                data_portal.get_spot_value(asset, "last_traded", adjusted_dt,
+                                           self.data_frequency)
+
+            return not (last_traded_dt is pd.NaT)
+
+    @check_parameters(('assets', 'fields', 'bar_count',
+                       'frequency'),
+                      ((Asset, ContinuousFuture) + string_types, string_types,
+                       int,
+                       string_types))
+    def history(self, assets, fields, bar_count, frequency):
+        """
+        Returns a window of data for the given assets and fields.
+
+        This data is adjusted for splits, dividends, and mergers as of the
+        current algorithm time.
+
+        The semantics of missing data are identical to the ones described in
+        the notes for `get_spot_value`.
+
+        Parameters
+        ----------
+        assets: Asset or iterable of Asset
+
+        fields: string or iterable of string.  Valid values are "open", "high",
+            "low", "close", "volume", "price", and "last_traded".
+
+        bar_count: integer number of bars of trade data
+
+        frequency: string. "1m" for minutely data or "1d" for daily date
+
+        Returns
+        -------
+        history : Series or DataFrame or Panel
+            Return type depends on the dimensionality of the 'assets' and
+            'fields' parameters.
+
+            If single asset and field are passed in, the returned Series is
+            indexed by dt.
+
+            If multiple assets and single field are passed in, the returned
+            DataFrame is indexed by dt, and has assets as columns.
+
+            If a single asset and multiple fields are passed in, the returned
+            DataFrame is indexed by dt, and has fields as columns.
+
+            If multiple assets and multiple fields are passed in, the returned
+            Panel is indexed by field, has dt as the major axis, and assets
+            as the minor axis.
+
+        Notes
+        -----
+        If the current simulation time is not a valid market time, we use the
+        last market close instead.
+        """
+        if isinstance(fields, string_types):
+            single_asset = isinstance(assets, PricingDataAssociable)
+
+            if single_asset:
+                asset_list = [assets]
+            else:
+                asset_list = assets
+
+            df = self.data_portal.get_history_window(
+                asset_list,
+                self._get_current_minute(),
+                bar_count,
+                frequency,
+                fields,
+                self.data_frequency,
+            )
+
+            if self._adjust_minutes:
+                adjs = self.data_portal.get_adjustments(
+                    assets,
+                    fields,
+                    self._get_current_minute(),
+                    self.simulation_dt_func()
+                )
+
+                df = df * adjs
+
+            if single_asset:
+                # single asset, single field, return a series.
+                return df[assets]
+            else:
+                # multiple assets, single field, return a dataframe whose
+                # columns are the assets, indexed by dt.
+                return df
+        else:
+            if isinstance(assets, PricingDataAssociable):
+                # one asset, multiple fields. for now, just make multiple
+                # history calls, one per field, then stitch together the
+                # results. this can definitely be optimized!
+
+                df_dict = {
+                    field: self.data_portal.get_history_window(
+                        [assets],
+                        self._get_current_minute(),
+                        bar_count,
+                        frequency,
+                        field,
+                        self.data_frequency,
+                    )[assets] for field in fields
+                }
+
+                if self._adjust_minutes:
+                    adjs = {
+                        field: self.data_portal.get_adjustments(
+                            assets,
+                            field,
+                            self._get_current_minute(),
+                            self.simulation_dt_func()
+                        )[0] for field in fields
+                    }
+
+                    df_dict = {field: df * adjs[field]
+                               for field, df in iteritems(df_dict)}
+
+                # returned dataframe whose columns are the fields, indexed by
+                # dt.
+                return pd.DataFrame(df_dict)
+
+            else:
+                df_dict = {
+                    field: self.data_portal.get_history_window(
+                        assets,
+                        self._get_current_minute(),
+                        bar_count,
+                        frequency,
+                        field,
+                        self.data_frequency,
+                    ) for field in fields
+                }
+
+                if self._adjust_minutes:
+                    adjs = {
+                        field: self.data_portal.get_adjustments(
+                            assets,
+                            field,
+                            self._get_current_minute(),
+                            self.simulation_dt_func()
+                        ) for field in fields
+                    }
+
+                    df_dict = {field: df * adjs[field]
+                               for field, df in iteritems(df_dict)}
+
+                # returned panel has:
+                # items: fields
+                # major axis: dt
+                # minor axis: assets
+                return pd.Panel(df_dict)
+
+    property current_dt:
+        def __get__(self):
+            return self.simulation_dt_func()
+
+    @property
+    def fetcher_assets(self):
+        return self.data_portal.get_fetcher_assets(self.simulation_dt_func())
+
+    property _handle_non_market_minutes:
+        def __set__(self, val):
+            self._adjust_minutes = val
+
+    property current_session:
+        def __get__(self):
+            return self._trading_calendar.minute_to_session_label(
+                self.simulation_dt_func(),
+                direction="next"
+            )
+
+    property current_session_minutes:
+        def __get__(self):
+            return self._trading_calendar.minutes_for_session(
+                self.current_session
+            )
+
+    #################
+    # OLD API SUPPORT
+    #################
+    cdef _calculate_universe(self):
+        if self._universe_func is None:
+            return []
+
+        simulation_dt = self.simulation_dt_func()
+        if self._last_calculated_universe is None or \
+                self._universe_last_updated_at != simulation_dt:
+
+            self._last_calculated_universe = self._universe_func()
+            self._universe_last_updated_at = simulation_dt
+
+        return self._last_calculated_universe
+
+    def __iter__(self):
+        self._warn_deprecated("Iterating over the assets in `data` is "
+                        "deprecated.")
+        for asset in self._calculate_universe():
+            yield asset
+
+    def __contains__(self, asset):
+        self._warn_deprecated("Checking whether an asset is in data is "
+                        "deprecated.")
+        universe = self._calculate_universe()
+        return asset in universe
+
+    def items(self):
+        self._warn_deprecated("Iterating over the assets in `data` is "
+                        "deprecated.")
+        return [(asset, self[asset]) for asset in self._calculate_universe()]
+
+    def iteritems(self):
+        self._warn_deprecated("Iterating over the assets in `data` is "
+                        "deprecated.")
+        for asset in self._calculate_universe():
+            yield asset, self[asset]
+
+    def __len__(self):
+        self._warn_deprecated("Iterating over the assets in `data` is "
+                        "deprecated.")
+
+        return len(self._calculate_universe())
+
+    def keys(self):
+        self._warn_deprecated("Iterating over the assets in `data` is "
+                        "deprecated.")
+
+        return list(self._calculate_universe())
+
+    def iterkeys(self):
+        return iter(self.keys())
+
+    def __getitem__(self, name):
+        return self._get_equity_price_view(name)
+
+    cdef _warn_deprecated(self, msg):
+        warnings.warn(
+            msg,
+            category=ZiplineDeprecationWarning,
+            stacklevel=1
+        )
+
+cdef class SidView:
+    cdef object asset
+    cdef object data_portal
+    cdef object simulation_dt_func
+    cdef object data_frequency
+
+    """
+    This class exists to temporarily support the deprecated data[sid(N)] API.
+    """
+    def __init__(self, asset, data_portal, simulation_dt_func, data_frequency):
+        """
+        Parameters
+        ---------
+        asset : Asset
+            The asset for which the instance retrieves data.
+
+        data_portal : DataPortal
+            Provider for bar pricing data.
+
+        simulation_dt_func: function
+            Function which returns the current simulation time.
+            This is usually bound to a method of TradingSimulation.
+
+        data_frequency: string
+            The frequency of the bar data; i.e. whether the data is
+            'daily' or 'minute' bars
+        """
+        self.asset = asset
+        self.data_portal = data_portal
+        self.simulation_dt_func = simulation_dt_func
+        self.data_frequency = data_frequency
+
+    def __getattr__(self, column):
+        # backwards compatibility code for Q1 API
+        if column == "close_price":
+            column = "close"
+        elif column == "open_price":
+            column = "open"
+        elif column == "dt":
+            return self.dt
+        elif column == "datetime":
+            return self.datetime
+        elif column == "sid":
+            return self.sid
+
+        return self.data_portal.get_spot_value(
+            self.asset,
+            column,
+            self.simulation_dt_func(),
+            self.data_frequency
+        )
+
+    def __contains__(self, column):
+        return self.data_portal.contains(self.asset, column)
+
+    def __getitem__(self, column):
+        return self.__getattr__(column)
+
+    property sid:
+        def __get__(self):
+            return self.asset
+
+    property dt:
+        def __get__(self):
+            return self.datetime
+
+    property datetime:
+        def __get__(self):
+            return self.data_portal.get_last_traded_dt(
+                self.asset,
+                self.simulation_dt_func(),
+                self.data_frequency)
+
+    property current_dt:
+        def __get__(self):
+            return self.simulation_dt_func()
+
+    def mavg(self, num_minutes):
+        self._warn_deprecated("The `mavg` method is deprecated.")
+        return self.data_portal.get_simple_transform(
+            self.asset, "mavg", self.simulation_dt_func(),
+            self.data_frequency, bars=num_minutes
+        )
+
+    def stddev(self, num_minutes):
+        self._warn_deprecated("The `stddev` method is deprecated.")
+        return self.data_portal.get_simple_transform(
+            self.asset, "stddev", self.simulation_dt_func(),
+            self.data_frequency, bars=num_minutes
+        )
+
+    def vwap(self, num_minutes):
+        self._warn_deprecated("The `vwap` method is deprecated.")
+        return self.data_portal.get_simple_transform(
+            self.asset, "vwap", self.simulation_dt_func(),
+            self.data_frequency, bars=num_minutes
+        )
+
+    def returns(self):
+        self._warn_deprecated("The `returns` method is deprecated.")
+        return self.data_portal.get_simple_transform(
+            self.asset, "returns", self.simulation_dt_func(),
+            self.data_frequency
+        )
+
+    cdef _warn_deprecated(self, msg):
+        warnings.warn(
+            msg,
+            category=ZiplineDeprecationWarning,
+            stacklevel=1
+        )

catalyst/_version.py

@@ -0,0 +1,460 @@
+
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.15 (https://github.com/warner/python-versioneer)
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+
+def get_keywords():
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "$Format:%d$"
+    git_full = "$Format:%H$"
+    keywords = {"refnames": git_refnames, "full": git_full}
+    return keywords
+
+
+class VersioneerConfig:
+    pass
+
+
+def get_config():
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = ""
+    cfg.parentdir_prefix = "catalyst-"
+    cfg.versionfile_source = "catalyst/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    pass
+
+
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    def decorate(f):
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            dispcmd = str([c] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+        return None
+    return stdout
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    # Source tarballs conventionally unpack into a directory that includes
+    # both the project name and a version string.
+    dirname = os.path.basename(root)
+    if not dirname.startswith(parentdir_prefix):
+        if verbose:
+            print("guessing rootdir is '%s', but '%s' doesn't start with "
+                  "prefix '%s'" % (root, dirname, parentdir_prefix))
+        raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+    return {"version": dirname[len(parentdir_prefix):],
+            "full-revisionid": None,
+            "dirty": False, "error": None}
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    if not keywords:
+        raise NotThisMethod("no keywords at all, weird")
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs-tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None
+                    }
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags"}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
+    # this runs 'git' from the root of the source tree. This only gets called
+    # if the git-archive 'subst' keywords were *not* expanded, and
+    # _version.py hasn't already been rewritten with a short version string,
+    # meaning we're inside a checked out source tree.
+
+    if not os.path.exists(os.path.join(root, ".git")):
+        if verbose:
+            print("no .git in %s" % root)
+        raise NotThisMethod("no .git directory")
+
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    # if there is a tag, this yields TAG-NUM-gHEX[-dirty]
+    # if there are no tags, this yields HEX[-dirty] (no NUM)
+    describe_out = run_command(GITS, ["describe", "--tags", "--dirty",
+                                      "--always", "--long"],
+                               cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out = run_command(GITS, ["rev-list", "HEAD", "--count"],
+                                cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    # now build up version string, with post-release "local version
+    # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    # exceptions:
+    # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    # TAG[.post.devDISTANCE] . No -dirty
+
+    # exceptions:
+    # 1: no tags. 0.post.devDISTANCE
+
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post.dev%d" % pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that
+    # .dev0 sorts backwards (a dirty tree will appear "older" than the
+    # corresponding clean one), but you shouldn't be releasing software with
+    # -dirty anyways.
+
+    # exceptions:
+    # 1: no tags. 0.postDISTANCE[.dev0]
+
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_old(pieces):
+    # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty.
+
+    # exceptions:
+    # 1: no tags. 0.postDISTANCE[.dev0]
+
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty
+    # --always'
+
+    # exceptions:
+    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty
+    # --always -long'. The distance/hash is unconditional.
+
+    # exceptions:
+    # 1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"]}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None}
+
+
+def get_versions():
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for i in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree"}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version"}

catalyst/api.py

@@ -0,0 +1,59 @@
+#
+# Copyright 2014 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Note that part of the API is implemented in TradingAlgorithm as
+# methods (e.g. order). These are added to this namespace via the
+# decorator ``api_method`` inside of algorithm.py.
+from .finance.asset_restrictions import (
+    Restriction,
+    StaticRestrictions,
+    HistoricalRestrictions,
+    RESTRICTION_STATES,
+)
+from .finance import commission, execution, slippage, cancel_policy
+from .finance.cancel_policy import (
+    NeverCancel,
+    EODCancel
+)
+from .finance.slippage import (
+    FixedSlippage,
+    VolumeShareSlippage,
+)
+from .utils import math_utils, events
+from .utils.events import (
+    calendars,
+    date_rules,
+    time_rules
+)
+
+__all__ = [
+    'EODCancel',
+    'FixedSlippage',
+    'NeverCancel',
+    'VolumeShareSlippage',
+    'Restriction',
+    'StaticRestrictions',
+    'HistoricalRestrictions',
+    'RESTRICTION_STATES',
+    'cancel_policy',
+    'commission',
+    'date_rules',
+    'events',
+    'execution',
+    'math_utils',
+    'slippage',
+    'time_rules',
+    'calendars',
+]

catalyst/api.pyi

@@ -0,0 +1,832 @@
+import collections
+from catalyst.assets import Asset, Equity, Future
+from catalyst.assets.futures import FutureChain
+from catalyst.finance.asset_restrictions import Restrictions
+from catalyst.finance.cancel_policy import CancelPolicy
+from catalyst.pipeline import Pipeline
+from catalyst.protocol import Order
+from catalyst.utils.events import EventRule
+from catalyst.utils.security_list import SecurityList
+
+
+def attach_pipeline(pipeline, name, chunks=None):
+    """Register a pipeline to be computed at the start of each day.
+
+    Parameters
+    ----------
+    pipeline : Pipeline
+        The pipeline to have computed.
+    name : str
+        The name of the pipeline.
+    chunks : int or iterator, optional
+        The number of days to compute pipeline results for. Increasing
+        this number will make it longer to get the first results but
+        may improve the total runtime of the simulation. If an iterator
+        is passed, we will run in chunks based on values of the itereator.
+
+    Returns
+    -------
+    pipeline : Pipeline
+        Returns the pipeline that was attached unchanged.
+
+    See Also
+    --------
+    :func:`catalyst.api.pipeline_output`
+    """
+
+
+def batch_market_order(share_counts):
+    """Place a batch market order for multiple assets.
+
+    Parameters
+    ----------
+    share_counts : pd.Series[Asset -> int]
+        Map from asset to number of shares to order for that asset.
+
+    Returns
+    -------
+    order_ids : pd.Index[str]
+        Index of ids for newly-created orders.
+    """
+
+
+def cancel_order(order_param):
+    """Cancel an open order.
+
+    Parameters
+    ----------
+    order_param : str or Order
+        The order_id or order object to cancel.
+    """
+
+
+def continuous_future(root_symbol_str, offset=0, roll='volume',
+                      adjustment='mul'):
+    """Create a specifier for a continuous contract.
+
+    Parameters
+    ----------
+    root_symbol_str : str
+        The root symbol for the future chain.
+
+    offset : int, optional
+        The distance from the primary contract. Default is 0.
+
+    roll_style : str, optional
+        How rolls are determined. Default is 'volume'.
+
+    adjustment : str, optional
+        Method for adjusting lookback prices between rolls. Options are
+        'mul', 'add', and None. Default is 'mul'.
+
+    Returns
+    -------
+    continuous_future : ContinuousFuture
+        The continuous future specifier.
+    """
+
+
+def fetch_csv(url, pre_func=None, post_func=None, date_column='date',
+              date_format=None, timezone='UTC', symbol=None, mask=True,
+              symbol_column=None, special_params_checker=None, **kwargs):
+    """Fetch a csv from a remote url and register the data so that it is
+    queryable from the ``data`` object.
+
+    Parameters
+    ----------
+    url : str
+        The url of the csv file to load.
+    pre_func : callable[pd.DataFrame -> pd.DataFrame], optional
+        A callback to allow preprocessing the raw data returned from
+        fetch_csv before dates are paresed or symbols are mapped.
+    post_func : callable[pd.DataFrame -> pd.DataFrame], optional
+        A callback to allow postprocessing of the data after dates and
+        symbols have been mapped.
+    date_column : str, optional
+        The name of the column in the preprocessed dataframe containing
+        datetime information to map the data.
+    date_format : str, optional
+        The format of the dates in the ``date_column``. If not provided
+        ``fetch_csv`` will attempt to infer the format. For information
+        about the format of this string, see :func:`pandas.read_csv`.
+    timezone : tzinfo or str, optional
+        The timezone for the datetime in the ``date_column``.
+    symbol : str, optional
+        If the data is about a new asset or index then this string will
+        be the name used to identify the values in ``data``. For example,
+        one may use ``fetch_csv`` to load data for VIX, then this field
+        could be the string ``'VIX'``.
+    mask : bool, optional
+        Drop any rows which cannot be symbol mapped.
+    symbol_column : str
+        If the data is attaching some new attribute to each asset then this
+        argument is the name of the column in the preprocessed dataframe
+        containing the symbols. This will be used along with the date
+        information to map the sids in the asset finder.
+    **kwargs
+        Forwarded to :func:`pandas.read_csv`.
+
+    Returns
+    -------
+    csv_data_source : catalyst.sources.requests_csv.PandasRequestsCSV
+        A requests source that will pull data from the url specified.
+    """
+
+
+def future_symbol(symbol):
+    """Lookup a futures contract with a given symbol.
+
+    Parameters
+    ----------
+    symbol : str
+        The symbol of the desired contract.
+
+    Returns
+    -------
+    future : Future
+        The future that trades with the name ``symbol``.
+
+    Raises
+    ------
+    SymbolNotFound
+        Raised when no contract named 'symbol' is found.
+    """
+
+
+def get_datetime(tz=None):
+    """
+Returns the current simulation datetime.
+
+Parameters
+----------
+tz : tzinfo or str, optional
+    The timezone to return the datetime in. This defaults to utc.
+
+Returns
+-------
+dt : datetime
+    The current simulation datetime converted to ``tz``.
+    """
+
+
+def get_environment(field='platform'):
+    """Query the execution environment.
+
+    Parameters
+    ----------
+    field : {'platform', 'arena', 'data_frequency',
+             'start', 'end', 'capital_base', 'platform', '*'}
+        The field to query. The options have the following meanings:
+          arena : str
+              The arena from the simulation parameters. This will normally
+              be ``'backtest'`` but some systems may use this distinguish
+              live trading from backtesting.
+          data_frequency : {'daily', 'minute'}
+              data_frequency tells the algorithm if it is running with
+              daily data or minute data.
+          start : datetime
+              The start date for the simulation.
+          end : datetime
+              The end date for the simulation.
+          capital_base : float
+              The starting capital for the simulation.
+          platform : str
+              The platform that the code is running on. By default this
+              will be the string 'catalyst'. This can allow algorithms to
+              know if they are running on the Quantopian platform instead.
+          * : dict[str -> any]
+              Returns all of the fields in a dictionary.
+
+    Returns
+    -------
+    val : any
+        The value for the field queried. See above for more information.
+
+    Raises
+    ------
+    ValueError
+        Raised when ``field`` is not a valid option.
+    """
+
+
+def get_order(order_id):
+    """Lookup an order based on the order id returned from one of the
+    order functions.
+
+    Parameters
+    ----------
+    order_id : str
+        The unique identifier for the order.
+
+    Returns
+    -------
+    order : Order
+        The order object.
+    """
+
+
+def history(bar_count, frequency, field, ffill=True):
+    """DEPRECATED: use ``data.history`` instead.
+    """
+
+
+def order(asset, amount, limit_price=None, stop_price=None, style=None):
+    """Place an order.
+
+    Parameters
+    ----------
+    asset : Asset
+        The asset that this order is for.
+    amount : int
+        The amount of shares to order. If ``amount`` is positive, this is
+        the number of shares to buy or cover. If ``amount`` is negative,
+        this is the number of shares to sell or short.
+    limit_price : float, optional
+        The limit price for the order.
+    stop_price : float, optional
+        The stop price for the order.
+    style : ExecutionStyle, optional
+        The execution style for the order.
+
+    Returns
+    -------
+    order_id : str or None
+        The unique identifier for this order, or None if no order was
+        placed.
+
+    Notes
+    -----
+    The ``limit_price`` and ``stop_price`` arguments provide shorthands for
+    passing common execution styles. Passing ``limit_price=N`` is
+    equivalent to ``style=LimitOrder(N)``. Similarly, passing
+    ``stop_price=M`` is equivalent to ``style=StopOrder(M)``, and passing
+    ``limit_price=N`` and ``stop_price=M`` is equivalent to
+    ``style=StopLimitOrder(N, M)``. It is an error to pass both a ``style``
+    and ``limit_price`` or ``stop_price``.
+
+    See Also
+    --------
+    :class:`catalyst.finance.execution.ExecutionStyle`
+    :func:`catalyst.api.order_value`
+    :func:`catalyst.api.order_percent`
+    """
+
+
+def order_percent(asset, percent, limit_price=None, stop_price=None,
+                  style=None):
+    """Place an order in the specified asset corresponding to the given
+    percent of the current portfolio value.
+
+    Parameters
+    ----------
+    asset : Asset
+        The asset that this order is for.
+    percent : float
+        The percentage of the porfolio value to allocate to ``asset``.
+        This is specified as a decimal, for example: 0.50 means 50%.
+    limit_price : float, optional
+        The limit price for the order.
+    stop_price : float, optional
+        The stop price for the order.
+    style : ExecutionStyle
+        The execution style for the order.
+
+    Returns
+    -------
+    order_id : str
+        The unique identifier for this order.
+
+    Notes
+    -----
+    See :func:`catalyst.api.order` for more information about
+    ``limit_price``, ``stop_price``, and ``style``
+
+    See Also
+    --------
+    :class:`catalyst.finance.execution.ExecutionStyle`
+    :func:`catalyst.api.order`
+    :func:`catalyst.api.order_value`
+    """
+
+
+def order_target(asset, target, limit_price=None, stop_price=None, style=None):
+    """Place an order to adjust a position to a target number of shares. If
+    the position doesn't already exist, this is equivalent to placing a new
+    order. If the position does exist, this is equivalent to placing an
+    order for the difference between the target number of shares and the
+    current number of shares.
+
+    Parameters
+    ----------
+    asset : Asset
+        The asset that this order is for.
+    target : int
+        The desired number of shares of ``asset``.
+    limit_price : float, optional
+        The limit price for the order.
+    stop_price : float, optional
+        The stop price for the order.
+    style : ExecutionStyle
+        The execution style for the order.
+
+    Returns
+    -------
+    order_id : str
+        The unique identifier for this order.
+
+
+    Notes
+    -----
+    ``order_target`` does not take into account any open orders. For
+    example:
+
+    .. code-block:: python
+
+       order_target(sid(0), 10)
+       order_target(sid(0), 10)
+
+    This code will result in 20 shares of ``sid(0)`` because the first
+    call to ``order_target`` will not have been filled when the second
+    ``order_target`` call is made.
+
+    See :func:`catalyst.api.order` for more information about
+    ``limit_price``, ``stop_price``, and ``style``
+
+    See Also
+    --------
+    :class:`catalyst.finance.execution.ExecutionStyle`
+    :func:`catalyst.api.order`
+    :func:`catalyst.api.order_target_percent`
+    :func:`catalyst.api.order_target_value`
+    """
+
+
+def order_target_percent(asset, target, limit_price=None, stop_price=None,
+                         style=None):
+    """Place an order to adjust a position to a target percent of the
+    current portfolio value. If the position doesn't already exist, this is
+    equivalent to placing a new order. If the position does exist, this is
+    equivalent to placing an order for the difference between the target
+    percent and the current percent.
+
+    Parameters
+    ----------
+    asset : Asset
+        The asset that this order is for.
+    target : float
+        The desired percentage of the porfolio value to allocate to
+        ``asset``. This is specified as a decimal, for example:
+        0.50 means 50%.
+    limit_price : float, optional
+        The limit price for the order.
+    stop_price : float, optional
+        The stop price for the order.
+    style : ExecutionStyle
+        The execution style for the order.
+
+    Returns
+    -------
+    order_id : str
+        The unique identifier for this order.
+
+    Notes
+    -----
+    ``order_target_value`` does not take into account any open orders. For
+    example:
+
+    .. code-block:: python
+
+       order_target_percent(sid(0), 10)
+       order_target_percent(sid(0), 10)
+
+    This code will result in 20% of the portfolio being allocated to sid(0)
+    because the first call to ``order_target_percent`` will not have been
+    filled when the second ``order_target_percent`` call is made.
+
+    See :func:`catalyst.api.order` for more information about
+    ``limit_price``, ``stop_price``, and ``style``
+
+    See Also
+    --------
+    :class:`catalyst.finance.execution.ExecutionStyle`
+    :func:`catalyst.api.order`
+    :func:`catalyst.api.order_target`
+    :func:`catalyst.api.order_target_value`
+    """
+
+
+def order_target_value(asset, target, limit_price=None, stop_price=None,
+                       style=None):
+    """Place an order to adjust a position to a target value. If
+    the position doesn't already exist, this is equivalent to placing a new
+    order. If the position does exist, this is equivalent to placing an
+    order for the difference between the target value and the
+    current value.
+    If the Asset being ordered is a Future, the 'target value' calculated
+    is actually the target exposure, as Futures have no 'value'.
+
+    Parameters
+    ----------
+    asset : Asset
+        The asset that this order is for.
+    target : float
+        The desired total value of ``asset``.
+    limit_price : float, optional
+        The limit price for the order.
+    stop_price : float, optional
+        The stop price for the order.
+    style : ExecutionStyle
+        The execution style for the order.
+
+    Returns
+    -------
+    order_id : str
+        The unique identifier for this order.
+
+    Notes
+    -----
+    ``order_target_value`` does not take into account any open orders. For
+    example:
+
+    .. code-block:: python
+
+       order_target_value(sid(0), 10)
+       order_target_value(sid(0), 10)
+
+    This code will result in 20 dollars of ``sid(0)`` because the first
+    call to ``order_target_value`` will not have been filled when the
+    second ``order_target_value`` call is made.
+
+    See :func:`catalyst.api.order` for more information about
+    ``limit_price``, ``stop_price``, and ``style``
+
+    See Also
+    --------
+    :class:`catalyst.finance.execution.ExecutionStyle`
+    :func:`catalyst.api.order`
+    :func:`catalyst.api.order_target`
+    :func:`catalyst.api.order_target_percent`
+    """
+
+
+def order_value(asset, value, limit_price=None, stop_price=None, style=None):
+    """Place an order by desired value rather than desired number of
+    shares.
+
+    Parameters
+    ----------
+    asset : Asset
+        The asset that this order is for.
+    value : float
+        If the requested asset exists, the requested value is
+        divided by its price to imply the number of shares to transact.
+        If the Asset being ordered is a Future, the 'value' calculated
+        is actually the exposure, as Futures have no 'value'.
+
+        value > 0 :: Buy/Cover
+        value < 0 :: Sell/Short
+    limit_price : float, optional
+        The limit price for the order.
+    stop_price : float, optional
+        The stop price for the order.
+    style : ExecutionStyle
+        The execution style for the order.
+
+    Returns
+    -------
+    order_id : str
+        The unique identifier for this order.
+
+    Notes
+    -----
+    See :func:`catalyst.api.order` for more information about
+    ``limit_price``, ``stop_price``, and ``style``
+
+    See Also
+    --------
+    :class:`catalyst.finance.execution.ExecutionStyle`
+    :func:`catalyst.api.order`
+    :func:`catalyst.api.order_percent`
+    """
+
+
+def pipeline_output(name):
+    """Get the results of the pipeline that was attached with the name:
+    ``name``.
+
+    Parameters
+    ----------
+    name : str
+        Name of the pipeline for which results are requested.
+
+    Returns
+    -------
+    results : pd.DataFrame
+        DataFrame containing the results of the requested pipeline for
+        the current simulation date.
+
+    Raises
+    ------
+    NoSuchPipeline
+        Raised when no pipeline with the name `name` has been registered.
+
+    See Also
+    --------
+    :func:`catalyst.api.attach_pipeline`
+    :meth:`catalyst.pipeline.engine.PipelineEngine.run_pipeline`
+    """
+
+
+def record(*args, **kwargs):
+    """Track and record values each day.
+
+    Parameters
+    ----------
+    **kwargs
+        The names and values to record.
+
+    Notes
+    -----
+    These values will appear in the performance packets and the performance
+    dataframe passed to ``analyze`` and returned from
+    :func:`~catalyst.run_algorithm`.
+    """
+
+
+def schedule_function(func, date_rule=None, time_rule=None, half_days=True,
+                      calendar=None):
+    """Schedules a function to be called according to some timed rules.
+
+    Parameters
+    ----------
+    func : callable[(context, data) -> None]
+        The function to execute when the rule is triggered.
+    date_rule : EventRule, optional
+        The rule for the dates to execute this function.
+    time_rule : EventRule, optional
+        The rule for the times to execute this function.
+    half_days : bool, optional
+        Should this rule fire on half days?
+
+    See Also
+    --------
+    :class:`catalyst.api.date_rules`
+    :class:`catalyst.api.time_rules`
+    """
+
+
+def set_asset_restrictions(restrictions, on_error='fail'):
+    """Set a restriction on which assets can be ordered.
+
+    Parameters
+    ----------
+    restricted_list : Restrictions
+        An object providing information about restricted assets.
+
+    See Also
+    --------
+    catalyst.finance.asset_restrictions.Restrictions
+    """
+
+
+def set_benchmark(benchmark):
+    """Set the benchmark asset.
+
+    Parameters
+    ----------
+    benchmark : Asset
+        The asset to set as the new benchmark.
+
+    Notes
+    -----
+    Any dividends payed out for that new benchmark asset will be
+    automatically reinvested.
+    """
+
+
+def set_cancel_policy(cancel_policy):
+    """Sets the order cancellation policy for the simulation.
+
+    Parameters
+    ----------
+    cancel_policy : CancelPolicy
+        The cancellation policy to use.
+
+    See Also
+    --------
+    :class:`catalyst.api.EODCancel`
+    :class:`catalyst.api.NeverCancel`
+    """
+
+
+def set_commission(commission):
+    """Sets the commission model for the simulation.
+
+    Parameters
+    ----------
+    commission : CommissionModel
+        The commission model to use.
+
+    See Also
+    --------
+    :class:`catalyst.finance.commission.PerShare`
+    :class:`catalyst.finance.commission.PerTrade`
+    :class:`catalyst.finance.commission.PerDollar`
+    """
+
+
+def set_do_not_order_list(restricted_list, on_error='fail'):
+    """Set a restriction on which assets can be ordered.
+
+    Parameters
+    ----------
+    restricted_list : container[Asset], SecurityList
+        The assets that cannot be ordered.
+    """
+
+
+def set_long_only(on_error='fail'):
+    """Set a rule specifying that this algorithm cannot take short
+    positions.
+    """
+
+
+def set_max_leverage(max_leverage):
+    """Set a limit on the maximum leverage of the algorithm.
+
+    Parameters
+    ----------
+    max_leverage : float
+        The maximum leverage for the algorithm. If not provided there will
+        be no maximum.
+    """
+
+
+def set_max_order_count(max_count, on_error='fail'):
+    """Set a limit on the number of orders that can be placed in a single
+    day.
+
+    Parameters
+    ----------
+    max_count : int
+        The maximum number of orders that can be placed on any single day.
+    """
+
+
+def set_max_order_size(asset=None, max_shares=None, max_notional=None,
+                       on_error='fail'):
+    """Set a limit on the number of shares and/or dollar value of any single
+    order placed for sid.  Limits are treated as absolute values and are
+    enforced at the time that the algo attempts to place an order for sid.
+
+    If an algorithm attempts to place an order that would result in
+    exceeding one of these limits, raise a TradingControlException.
+
+    Parameters
+    ----------
+    asset : Asset, optional
+        If provided, this sets the guard only on positions in the given
+        asset.
+    max_shares : int, optional
+        The maximum number of shares that can be ordered at one time.
+    max_notional : float, optional
+        The maximum value that can be ordered at one time.
+    """
+
+
+def set_max_position_size(asset=None, max_shares=None, max_notional=None,
+                          on_error='fail'):
+    """Set a limit on the number of shares and/or dollar value held for the
+    given sid. Limits are treated as absolute values and are enforced at
+    the time that the algo attempts to place an order for sid. This means
+    that it's possible to end up with more than the max number of shares
+    due to splits/dividends, and more than the max notional due to price
+    improvement.
+
+    If an algorithm attempts to place an order that would result in
+    increasing the absolute value of shares/dollar value exceeding one of
+    these limits, raise a TradingControlException.
+
+    Parameters
+    ----------
+    asset : Asset, optional
+        If provided, this sets the guard only on positions in the given
+        asset.
+    max_shares : int, optional
+        The maximum number of shares to hold for an asset.
+    max_notional : float, optional
+        The maximum value to hold for an asset.
+    """
+
+
+def set_slippage(slippage):
+    """Set the slippage model for the simulation.
+
+    Parameters
+    ----------
+    slippage : SlippageModel
+        The slippage model to use.
+
+    See Also
+    --------
+    :class:`catalyst.finance.slippage.SlippageModel`
+    """
+
+
+def set_symbol_lookup_date(dt):
+    """Set the date for which symbols will be resolved to their assets
+    (symbols may map to different firms or underlying assets at
+    different times)
+
+    Parameters
+    ----------
+    dt : datetime
+        The new symbol lookup date.
+    """
+
+
+def sid(sid):
+    """Lookup an Asset by its unique asset identifier.
+
+    Parameters
+    ----------
+    sid : int
+        The unique integer that identifies an asset.
+
+    Returns
+    -------
+    asset : Asset
+        The asset with the given ``sid``.
+
+    Raises
+    ------
+    SidsNotFound
+        When a requested ``sid`` does not map to any asset.
+    """
+
+
+def symbol(symbol_str):
+    """Lookup an Equity by its ticker symbol.
+
+    Parameters
+    ----------
+    symbol_str : str
+        The ticker symbol for the equity to lookup.
+
+    Returns
+    -------
+    equity : Equity
+        The equity that held the ticker symbol on the current
+        symbol lookup date.
+
+    Raises
+    ------
+    SymbolNotFound
+        Raised when the symbols was not held on the current lookup date.
+
+    See Also
+    --------
+    :func:`catalyst.api.set_symbol_lookup_date`
+    """
+
+
+def symbols(*args):
+    """Lookup multuple Equities as a list.
+
+    Parameters
+    ----------
+    *args : iterable[str]
+        The ticker symbols to lookup.
+
+    Returns
+    -------
+    equities : list[Equity]
+        The equities that held the given ticker symbols on the current
+        symbol lookup date.
+
+    Raises
+    ------
+    SymbolNotFound
+        Raised when one of the symbols was not held on the current
+        lookup date.
+
+    See Also
+    --------
+    :func:`catalyst.api.set_symbol_lookup_date`
+    """
+
+
+def get_dataset(ds_name, start=None, end=None):
+    """
+    Lookup a data source from the marketplace
+
+    Parameters
+    ----------
+    ds_name: str
+    start: pd.Timestamp
+    end: pd.Timestamp
+
+    Returns
+    -------
+
+    """

catalyst/assets/__init__.py

@@ -0,0 +1,42 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ._assets import (
+    Asset,
+    Equity,
+    Future,
+    make_asset_array,
+    CACHE_FILE_TEMPLATE
+)
+from .assets import (
+    AssetFinder,
+    AssetConvertible,
+    PricingDataAssociable,
+)
+from .asset_db_schema import ASSET_DB_VERSION
+from .asset_writer import AssetDBWriter
+
+__all__ = [
+    'ASSET_DB_VERSION',
+    'Asset',
+    'AssetDBWriter',
+    'Equity',
+    'Future',
+    'AssetFinder',
+    'AssetConvertible',
+    'PricingDataAssociable',
+    'make_asset_array',
+    'CACHE_FILE_TEMPLATE'
+]

catalyst/assets/_assets.pyx

@@ -0,0 +1,654 @@
+# cython: embedsignature=True
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Cythonized Asset object.
+"""
+
+import hashlib
+
+cimport cython
+from cpython.number cimport PyNumber_Index
+from cpython.object cimport (
+Py_EQ,
+Py_NE,
+Py_GE,
+Py_LE,
+Py_GT,
+Py_LT,
+)
+from cpython cimport bool
+
+import pandas as pd
+from datetime import timedelta
+import numpy as np
+from numpy cimport int64_t
+import warnings
+cimport numpy as np
+
+from catalyst.exchange.utils.exchange_utils import get_sid
+from catalyst.utils.calendars import get_calendar
+from catalyst.exchange.exchange_errors import InvalidSymbolError, SidHashError
+
+# IMPORTANT NOTE: You must change this template if you change
+# Asset.__reduce__, or else we'll attempt to unpickle an old version of this
+# class
+CACHE_FILE_TEMPLATE = '/tmp/.%s-%s.v7.cache'
+
+cdef class Asset:
+    cdef readonly int sid
+    # Cached hash of self.sid
+    cdef int sid_hash
+
+    cdef readonly object symbol
+    cdef readonly object asset_name
+
+    cdef readonly object start_date
+    cdef readonly object end_date
+    cdef public object first_traded
+    cdef readonly object auto_close_date
+
+    cdef readonly object exchange
+    cdef readonly object exchange_full
+    cdef readonly object min_trade_size
+
+    _kwargnames = frozenset({
+        'sid',
+        'symbol',
+        'asset_name',
+        'start_date',
+        'end_date',
+        'first_traded',
+        'auto_close_date',
+        'exchange',
+        'exchange_full',
+        'min_trade_size',
+    })
+
+    def __init__(self,
+                 int sid,  # sid is required
+                 object exchange,  # exchange is required
+                 object symbol="",
+                 object asset_name="",
+                 object start_date=None,
+                 object end_date=None,
+                 object first_traded=None,
+                 object auto_close_date=None,
+                 object exchange_full=None,
+                 object min_trade_size=None):
+
+        self.sid = sid
+        self.sid_hash = hash(sid)
+        self.symbol = symbol
+        self.asset_name = asset_name
+        self.exchange = exchange
+        self.exchange_full = (exchange_full if exchange_full is not None
+                              else exchange)
+        self.start_date = start_date
+        self.end_date = end_date
+        self.first_traded = first_traded
+        self.auto_close_date = auto_close_date
+        self.min_trade_size = min_trade_size
+
+    def __int__(self):
+        return self.sid
+
+    def __index__(self):
+        return self.sid
+
+    def __hash__(self):
+        return self.sid_hash
+
+    def __richcmp__(x, y, int op):
+        """
+        Cython rich comparison method.  This is used in place of various
+        equality checkers in pure python.
+        """
+        cdef int x_as_int, y_as_int
+
+        try:
+            x_as_int = PyNumber_Index(x)
+        except (TypeError, OverflowError):
+            return NotImplemented
+
+        try:
+            y_as_int = PyNumber_Index(y)
+        except (TypeError, OverflowError):
+            return NotImplemented
+
+        compared = x_as_int - y_as_int
+
+        # Handle == and != first because they're significantly more common
+        # operations.
+        if op == Py_EQ:
+            return compared == 0
+        elif op == Py_NE:
+            return compared != 0
+        elif op == Py_LT:
+            return compared < 0
+        elif op == Py_LE:
+            return compared <= 0
+        elif op == Py_GT:
+            return compared > 0
+        elif op == Py_GE:
+            return compared >= 0
+        else:
+            raise AssertionError('%d is not an operator' % op)
+
+    def __str__(self):
+        if self.symbol:
+            return '%s(%d [%s])' % (type(self).__name__, self.sid, self.symbol)
+        else:
+            return '%s(%d)' % (type(self).__name__, self.sid)
+
+    def __repr__(self):
+        attrs = ('symbol', 'asset_name', 'exchange',
+                 'start_date', 'end_date', 'first_traded', 'auto_close_date',
+                 'min_trade_size')
+        tuples = ((attr, repr(getattr(self, attr, None)))
+                  for attr in attrs)
+        strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
+        params = ', '.join(strings)
+        return 'Asset(%d, %s)' % (self.sid, params)
+
+    cpdef __reduce__(self):
+        """
+        Function used by pickle to determine how to serialize/deserialize this
+        class.  Should return a tuple whose first element is self.__class__,
+        and whose second element is a tuple of all the attributes that should
+        be serialized/deserialized during pickling.
+        """
+        return (self.__class__, (self.sid,
+                                 self.exchange,
+                                 self.symbol,
+                                 self.asset_name,
+                                 self.start_date,
+                                 self.end_date,
+                                 self.first_traded,
+                                 self.auto_close_date,
+                                 self.exchange_full,
+                                 self.min_trade_size))
+
+    cpdef to_dict(self):
+        """
+        Convert to a python dict.
+        """
+        return {
+            'sid': self.sid,
+            'symbol': self.symbol,
+            'asset_name': self.asset_name,
+            'start_date': self.start_date,
+            'end_date': self.end_date,
+            'first_traded': self.first_traded,
+            'auto_close_date': self.auto_close_date,
+            'exchange': self.exchange,
+            'exchange_full': self.exchange_full,
+            'min_trade_size': self.min_trade_size
+        }
+
+    @classmethod
+    def from_dict(cls, dict_):
+        """
+        Build an Asset instance from a dict.
+        """
+        return cls(**dict_)
+
+    def is_alive_for_session(self, session_label):
+        """
+        Returns whether the asset is alive at the given dt.
+
+        Parameters
+        ----------
+        session_label: pd.Timestamp
+            The desired session label to check. (midnight UTC)
+
+        Returns
+        -------
+        boolean: whether the asset is alive at the given dt.
+        """
+        cdef int64_t ref_start
+        cdef int64_t ref_end
+
+        ref_start = self.start_date.value
+        ref_end = self.end_date.value
+
+        return ref_start <= session_label.value <= ref_end
+
+    def is_exchange_open(self, dt_minute):
+        """
+        Parameters
+        ----------
+        dt_minute: pd.Timestamp (UTC, tz-aware)
+            The minute to check.
+
+        Returns
+        -------
+        boolean: whether the asset's exchange is open at the given minute.
+        """
+        calendar = get_calendar(self.exchange)
+        return calendar.is_open_on_minute(dt_minute)
+
+cdef class Equity(Asset):
+    def __repr__(self):
+        attrs = ('symbol', 'asset_name', 'exchange',
+                 'start_date', 'end_date', 'first_traded', 'auto_close_date',
+                 'exchange_full', 'min_trade_size')
+        tuples = ((attr, repr(getattr(self, attr, None)))
+                  for attr in attrs)
+        strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
+        params = ', '.join(strings)
+        return 'Equity(%d, %s)' % (self.sid, params)
+
+    property security_start_date:
+        """
+        DEPRECATION: This property should be deprecated and is only present for
+        backwards compatibility
+        """
+        def __get__(self):
+            warnings.warn("The security_start_date property will soon be "
+                          "retired. Please use the start_date property instead.",
+                          DeprecationWarning)
+            return self.start_date
+
+    property security_end_date:
+        """
+        DEPRECATION: This property should be deprecated and is only present for
+        backwards compatibility
+        """
+        def __get__(self):
+            warnings.warn("The security_end_date property will soon be "
+                          "retired. Please use the end_date property instead.",
+                          DeprecationWarning)
+            return self.end_date
+
+    property security_name:
+        """
+        DEPRECATION: This property should be deprecated and is only present for
+        backwards compatibility
+        """
+        def __get__(self):
+            warnings.warn("The security_name property will soon be "
+                          "retired. Please use the asset_name property instead.",
+                          DeprecationWarning)
+            return self.asset_name
+
+cdef class Future(Asset):
+    cdef readonly object root_symbol
+    cdef readonly object notice_date
+    cdef readonly object expiration_date
+    cdef readonly object tick_size
+    cdef readonly float multiplier
+
+    _kwargnames = frozenset({
+        'sid',
+        'symbol',
+        'root_symbol',
+        'asset_name',
+        'start_date',
+        'end_date',
+        'notice_date',
+        'expiration_date',
+        'auto_close_date',
+        'first_traded',
+        'exchange',
+        'tick_size',
+        'multiplier',
+        'exchange_full',
+    })
+
+    def __init__(self,
+                 int sid,  # sid is required
+                 object exchange,  # exchange is required
+                 object symbol="",
+                 object root_symbol="",
+                 object asset_name="",
+                 object start_date=None,
+                 object end_date=None,
+                 object notice_date=None,
+                 object expiration_date=None,
+                 object auto_close_date=None,
+                 object first_traded=None,
+                 object tick_size="",
+                 float multiplier=1.0,
+                 object exchange_full=None):
+
+        super().__init__(
+            sid,
+            exchange,
+            symbol=symbol,
+            asset_name=asset_name,
+            start_date=start_date,
+            end_date=end_date,
+            first_traded=first_traded,
+            auto_close_date=auto_close_date,
+            exchange_full=exchange_full,
+        )
+        self.root_symbol = root_symbol
+        self.notice_date = notice_date
+        self.expiration_date = expiration_date
+        self.tick_size = tick_size
+        self.multiplier = multiplier
+
+        if auto_close_date is None:
+            if notice_date is None:
+                self.auto_close_date = expiration_date
+            elif expiration_date is None:
+                self.auto_close_date = notice_date
+            else:
+                self.auto_close_date = min(notice_date, expiration_date)
+
+    def __repr__(self):
+        attrs = ('symbol', 'root_symbol', 'asset_name', 'exchange',
+                 'start_date', 'end_date', 'first_traded', 'notice_date',
+                 'expiration_date', 'auto_close_date', 'tick_size',
+                 'multiplier', 'exchange_full')
+        tuples = ((attr, repr(getattr(self, attr, None)))
+                  for attr in attrs)
+        strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
+        params = ', '.join(strings)
+        return 'Future(%d, %s)' % (self.sid, params)
+
+    cpdef __reduce__(self):
+        """
+        Function used by pickle to determine how to serialize/deserialize this
+        class.  Should return a tuple whose first element is self.__class__,
+        and whose second element is a tuple of all the attributes that should
+        be serialized/deserialized during pickling.
+        """
+        return (self.__class__, (self.sid,
+                                 self.exchange,
+                                 self.symbol,
+                                 self.root_symbol,
+                                 self.asset_name,
+                                 self.start_date,
+                                 self.end_date,
+                                 self.notice_date,
+                                 self.expiration_date,
+                                 self.auto_close_date,
+                                 self.first_traded,
+                                 self.tick_size,
+                                 self.multiplier,
+                                 self.exchange_full))
+
+    cpdef to_dict(self):
+        """
+        Convert to a python dict.
+        """
+        super_dict = super(Future, self).to_dict()
+        super_dict['root_symbol'] = self.root_symbol
+        super_dict['notice_date'] = self.notice_date
+        super_dict['expiration_date'] = self.expiration_date
+        super_dict['tick_size'] = self.tick_size
+        super_dict['multiplier'] = self.multiplier
+        return super_dict
+
+cdef class TradingPair(Asset):
+    cdef readonly float leverage
+    cdef readonly object quote_currency
+    cdef readonly object base_currency
+    cdef readonly object end_daily
+    cdef readonly object end_minute
+    cdef readonly object exchange_symbol
+    cdef readonly float maker
+    cdef readonly float taker
+    cdef readonly int trading_state
+    cdef readonly object data_source
+    cdef readonly float max_trade_size
+    cdef readonly float lot
+    cdef readonly int decimals
+
+    _kwargnames = frozenset({
+        'sid',
+        'symbol',
+        'asset_name',
+        'start_date',
+        'end_date',
+        'first_traded',
+        'auto_close_date',
+        'exchange',
+        'exchange_full',
+        'leverage',
+        'quote_currency',
+        'base_currency',
+        'end_daily',
+        'end_minute',
+        'exchange_symbol',
+        'min_trade_size',
+        'max_trade_size',
+        'lot',
+        'maker',
+        'taker',
+        'trading_state',
+        'data_source',
+        'decimals'
+    })
+    def __init__(self,
+                 object symbol,
+                 object exchange,
+                 object start_date=None,
+                 object asset_name=None,
+                 int sid=0,
+                 float leverage=1.0,
+                 object end_daily=None,
+                 object end_minute=None,
+                 object end_date=None,
+                 object exchange_symbol=None,
+                 object first_traded=None,
+                 object auto_close_date=None,
+                 object exchange_full=None,
+                 float min_trade_size=0.0001,
+                 float max_trade_size=1000000,
+                 float maker=0.0015,
+                 float taker=0.0025,
+                 float lot=0,
+                 int decimals = 8,
+                 int trading_state=0,
+                 object data_source='catalyst'):
+        """
+        Replicates the Asset constructor with some built-in conventions
+        and adds properties for leverage and fees.
+
+        Symbol
+        ------
+        Catalyst defines its own set of "universal" symbols to reference
+        trading pairs across exchanges. This is required because exchanges
+        are not adhering to a universal symbolism. For example, Bitfinex
+        uses the BTC symbol for Bitcon while Kraken uses XBT. In addition,
+        pairs are sometimes presented differently. For example, Bitfinex
+        puts the market currency before the base currency without a
+        separator, Bittrex puts the base currency first and uses a dash
+        seperator.
+
+        Here is the Catalyst convention: [Market Currency]_[Base Currency]
+        For example: btc_usd, eth_btc, neo_eth, ltc_eur.
+
+        The symbol for each currency (e.g. btc, eth, ltc) is generally
+        aligned with the Bittrex exchange.
+
+        Sid
+        ---
+        The sid of each asset is calculated based on a numeric hash of the
+        universal symbol. This simple approach avoids maintaining a mapping
+        of sids.
+
+        Leverage
+        --------
+        In contrast with equities, crypto exchanges generally assign
+        leverage values to specific trading pairs. Pairs with the
+        highest volume and market cap generally benefit from high leverage.
+        New currencies from ICO generally cannot be leveraged.
+
+        Leverage allows you to open a larger position with a smaller amount
+        of funds. For example, if you open a $5,000 position in BTC/USD
+        with 5:1 leverage, only one-fifth of this amount, or $1000, will be
+        tied to the position from your balance. Your remaining balance will
+        be available for opening more positions. If you open this same
+        position with 2:1 leverage, $2,500 of your balance will be tied to
+        the position. If you open with 1:1 leverage, $5,000 of your balance
+        will be tied to the position.
+
+        Fees
+        ----
+        Exchanges generally charge a taker (taking from the order book) or
+        maker (adding to the order book) fee.
+
+        :param symbol:
+        :param exchange:
+        :param start_date:
+        :param asset_name:
+        :param sid:
+        :param leverage:
+        :param end_daily
+        :param end_minute
+        :param end_date:
+        :param exchange_symbol:
+        :param first_traded:
+        :param auto_close_date:
+        :param exchange_full:
+        :param min_trade_size:
+        :param max_trade_size:
+        :param maker:
+        :param taker:
+        :param data_source
+        :param decimals
+        :param lot
+        """
+
+        symbol = symbol.lower()
+        try:
+            self.base_currency, self.quote_currency = symbol.split('_')
+        except Exception as e:
+            raise InvalidSymbolError(symbol=symbol, error=e)
+
+        if sid == 0 or sid is None:
+            try:
+                sid = get_sid(symbol)
+            except Exception as e:
+                raise SidHashError(symbol=symbol)
+
+        if asset_name is None:
+            asset_name = ' / '.join(symbol.split('_')).upper()
+
+        if start_date is None:
+            start_date = pd.to_datetime('2009-1-1', utc=True)
+
+        if end_date is None:
+            end_date = pd.Timestamp.utcnow() + timedelta(days=365)
+
+        if lot == 0 and min_trade_size > 0:
+            lot = min_trade_size
+
+        super().__init__(
+            sid,
+            exchange,
+            symbol=symbol,
+            asset_name=asset_name,
+            start_date=start_date,
+            end_date=end_date,
+            first_traded=first_traded,
+            auto_close_date=auto_close_date,
+            exchange_full=exchange_full,
+            min_trade_size=min_trade_size,
+        )
+
+        self.maker = maker
+        self.taker = taker
+        self.leverage = leverage
+        self.end_daily = end_daily
+        self.end_minute = end_minute
+        self.exchange_symbol = exchange_symbol
+        self.trading_state = trading_state
+        self.data_source = data_source
+        self.max_trade_size = max_trade_size
+        self.lot = lot
+        self.decimals = decimals
+
+    def __repr__(self):
+        return 'Trading Pair {symbol}({sid}) Exchange: {exchange}, ' \
+               'Introduced On: {start_date}, ' \
+               'Base Currency: {base_currency}, ' \
+               'Quote Currency: {quote_currency}, ' \
+               'Exchange Leverage: {leverage}, ' \
+               'Minimum Trade Size: {min_trade_size} ' \
+               'Last daily ingestion: {end_daily} ' \
+               'Last minutely ingestion: {end_minute}'.format(
+            symbol=self.symbol,
+            sid=self.sid,
+            exchange=self.exchange,
+            start_date=self.start_date,
+            quote_currency=self.quote_currency,
+            base_currency=self.base_currency,
+            leverage=self.leverage,
+            min_trade_size=self.min_trade_size,
+            end_daily=self.end_daily,
+            end_minute=self.end_minute
+        )
+
+    cpdef to_dict(self):
+        """
+        Convert to a python dict.
+        """
+        #TODO: missing fields
+        super_dict = super(TradingPair, self).to_dict()
+        super_dict['end_daily'] = self.end_daily
+        super_dict['end_minute'] = self.end_minute
+        super_dict['leverage'] = self.leverage
+        super_dict['min_trade_size'] = self.min_trade_size
+        return super_dict
+
+    def is_exchange_open(self, dt_minute):
+        """
+        Parameters
+        ----------
+        dt_minute: pd.Timestamp (UTC, tz-aware)
+            The minute to check.
+
+        Returns
+        -------
+        boolean: whether the asset's exchange is open at the given minute.
+        """
+        #TODO: make more dymanic to catch holds
+        return True
+
+    cpdef __reduce__(self):
+        """
+        Function used by pickle to determine how to serialize/deserialize this
+        class.  Should return a tuple whose first element is self.__class__,
+        and whose second element is a tuple of all the attributes that should
+        be serialized/deserialized during pickling.
+        """
+        #TODO: make sure that all fields set there
+        return (self.__class__, (self.symbol,
+                                 self.exchange,
+                                 self.start_date,
+                                 self.asset_name,
+                                 self.sid,
+                                 self.leverage,
+                                 self.end_date,
+                                 self.first_traded,
+                                 self.auto_close_date,
+                                 self.exchange_full,
+                                 self.min_trade_size,
+                                 self.max_trade_size,
+                                 self.lot,
+                                 self.decimals,
+                                 self.taker,
+                                 self.maker))
+
+def make_asset_array(int size, Asset asset):
+    cdef np.ndarray out = np.empty([size], dtype=object)
+    out.fill(asset)
+    return out

catalyst/assets/asset_db_migrations.py

@@ -0,0 +1,316 @@
+from functools import wraps
+
+from alembic.migration import MigrationContext
+from alembic.operations import Operations
+import sqlalchemy as sa
+from toolz.curried import do, operator as op
+
+from catalyst.assets.asset_writer import write_version_info
+from catalyst.errors import AssetDBImpossibleDowngrade
+from catalyst.utils.preprocess import preprocess
+from catalyst.utils.sqlite_utils import coerce_string_to_eng
+
+
+@preprocess(engine=coerce_string_to_eng)
+def downgrade(engine, desired_version):
+    """Downgrades the assets db at the given engine to the desired version.
+
+    Parameters
+    ----------
+    engine : Engine
+        An SQLAlchemy engine to the assets database.
+    desired_version : int
+        The desired resulting version for the assets database.
+    """
+
+    # Check the version of the db at the engine
+    with engine.begin() as conn:
+        metadata = sa.MetaData(conn)
+        metadata.reflect()
+        version_info_table = metadata.tables['version_info']
+        starting_version = sa.select((version_info_table.c.version,)).scalar()
+
+        # Check for accidental upgrade
+        if starting_version < desired_version:
+            raise AssetDBImpossibleDowngrade(db_version=starting_version,
+                                             desired_version=desired_version)
+
+        # Check if the desired version is already the db version
+        if starting_version == desired_version:
+            # No downgrade needed
+            return
+
+        # Create alembic context
+        ctx = MigrationContext.configure(conn)
+        op = Operations(ctx)
+
+        # Integer keys of downgrades to run
+        # E.g.: [5, 4, 3, 2] would downgrade v6 to v2
+        downgrade_keys = range(desired_version, starting_version)[::-1]
+
+        # Disable foreign keys until all downgrades are complete
+        _pragma_foreign_keys(conn, False)
+
+        # Execute the downgrades in order
+        for downgrade_key in downgrade_keys:
+            _downgrade_methods[downgrade_key](op, conn, version_info_table)
+
+        # Re-enable foreign keys
+        _pragma_foreign_keys(conn, True)
+
+
+def _pragma_foreign_keys(connection, on):
+    """Sets the PRAGMA foreign_keys state of the SQLite database. Disabling
+    the pragma allows for batch modification of tables with foreign keys.
+
+    Parameters
+    ----------
+    connection : Connection
+        A SQLAlchemy connection to the db
+    on : bool
+        If true, PRAGMA foreign_keys will be set to ON. Otherwise, the PRAGMA
+        foreign_keys will be set to OFF.
+    """
+    connection.execute("PRAGMA foreign_keys=%s" % ("ON" if on else "OFF"))
+
+
+# This dict contains references to downgrade methods that can be applied to an
+# assets db. The resulting db's version is the key.
+# e.g. The method at key '0' is the downgrade method from v1 to v0
+_downgrade_methods = {}
+
+
+def downgrades(src):
+    """Decorator for marking that a method is a downgrade to a version to the
+    previous version.
+
+    Parameters
+    ----------
+    src : int
+        The version this downgrades from.
+
+    Returns
+    -------
+    decorator : callable[(callable) -> callable]
+        The decorator to apply.
+    """
+    def _(f):
+        destination = src - 1
+
+        @do(op.setitem(_downgrade_methods, destination))
+        @wraps(f)
+        def wrapper(op, conn, version_info_table):
+            conn.execute(version_info_table.delete())  # clear the version
+            f(op)
+            write_version_info(conn, version_info_table, destination)
+
+        return wrapper
+    return _
+
+
+@downgrades(1)
+def _downgrade_v1(op):
+    """
+    Downgrade assets db by removing the 'tick_size' column and renaming the
+    'multiplier' column.
+    """
+    # Drop indices before batch
+    # This is to prevent index collision when creating the temp table
+    op.drop_index('ix_futures_contracts_root_symbol')
+    op.drop_index('ix_futures_contracts_symbol')
+
+    # Execute batch op to allow column modification in SQLite
+    with op.batch_alter_table('futures_contracts') as batch_op:
+
+        # Rename 'multiplier'
+        batch_op.alter_column(column_name='multiplier',
+                              new_column_name='contract_multiplier')
+
+        # Delete 'tick_size'
+        batch_op.drop_column('tick_size')
+
+    # Recreate indices after batch
+    op.create_index('ix_futures_contracts_root_symbol',
+                    table_name='futures_contracts',
+                    columns=['root_symbol'])
+    op.create_index('ix_futures_contracts_symbol',
+                    table_name='futures_contracts',
+                    columns=['symbol'],
+                    unique=True)
+
+
+@downgrades(2)
+def _downgrade_v2(op):
+    """
+    Downgrade assets db by removing the 'auto_close_date' column.
+    """
+    # Drop indices before batch
+    # This is to prevent index collision when creating the temp table
+    op.drop_index('ix_equities_fuzzy_symbol')
+    op.drop_index('ix_equities_company_symbol')
+
+    # Execute batch op to allow column modification in SQLite
+    with op.batch_alter_table('equities') as batch_op:
+        batch_op.drop_column('auto_close_date')
+
+    # Recreate indices after batch
+    op.create_index('ix_equities_fuzzy_symbol',
+                    table_name='equities',
+                    columns=['fuzzy_symbol'])
+    op.create_index('ix_equities_company_symbol',
+                    table_name='equities',
+                    columns=['company_symbol'])
+
+
+@downgrades(3)
+def _downgrade_v3(op):
+    """
+    Downgrade assets db by adding a not null constraint on
+    ``equities.first_traded``
+    """
+    op.create_table(
+        '_new_equities',
+        sa.Column(
+            'sid',
+            sa.Integer,
+            unique=True,
+            nullable=False,
+            primary_key=True,
+        ),
+        sa.Column('symbol', sa.Text),
+        sa.Column('company_symbol', sa.Text),
+        sa.Column('share_class_symbol', sa.Text),
+        sa.Column('fuzzy_symbol', sa.Text),
+        sa.Column('asset_name', sa.Text),
+        sa.Column('start_date', sa.Integer, default=0, nullable=False),
+        sa.Column('end_date', sa.Integer, nullable=False),
+        sa.Column('first_traded', sa.Integer, nullable=False),
+        sa.Column('auto_close_date', sa.Integer),
+        sa.Column('exchange', sa.Text),
+    )
+    op.execute(
+        """
+        insert into _new_equities
+        select * from equities
+        where equities.first_traded is not null
+        """,
+    )
+    op.drop_table('equities')
+    op.rename_table('_new_equities', 'equities')
+    # we need to make sure the indices have the proper names after the rename
+    op.create_index(
+        'ix_equities_company_symbol',
+        'equities',
+        ['company_symbol'],
+    )
+    op.create_index(
+        'ix_equities_fuzzy_symbol',
+        'equities',
+        ['fuzzy_symbol'],
+    )
+
+
+@downgrades(4)
+def _downgrade_v4(op):
+    """
+    Downgrades assets db by copying the `exchange_full` column to `exchange`,
+    then dropping the `exchange_full` column.
+    """
+    op.drop_index('ix_equities_fuzzy_symbol')
+    op.drop_index('ix_equities_company_symbol')
+
+    op.execute("UPDATE equities SET exchange = exchange_full")
+
+    with op.batch_alter_table('equities') as batch_op:
+        batch_op.drop_column('exchange_full')
+
+    op.create_index('ix_equities_fuzzy_symbol',
+                    table_name='equities',
+                    columns=['fuzzy_symbol'])
+    op.create_index('ix_equities_company_symbol',
+                    table_name='equities',
+                    columns=['company_symbol'])
+
+
+@downgrades(5)
+def _downgrade_v5(op):
+    op.create_table(
+        '_new_equities',
+        sa.Column(
+            'sid',
+            sa.Integer,
+            unique=True,
+            nullable=False,
+            primary_key=True,
+        ),
+        sa.Column('symbol', sa.Text),
+        sa.Column('company_symbol', sa.Text),
+        sa.Column('share_class_symbol', sa.Text),
+        sa.Column('fuzzy_symbol', sa.Text),
+        sa.Column('asset_name', sa.Text),
+        sa.Column('start_date', sa.Integer, default=0, nullable=False),
+        sa.Column('end_date', sa.Integer, nullable=False),
+        sa.Column('first_traded', sa.Integer),
+        sa.Column('auto_close_date', sa.Integer),
+        sa.Column('exchange', sa.Text),
+        sa.Column('exchange_full', sa.Text)
+    )
+
+    op.execute(
+        """
+        insert into _new_equities
+        select
+            equities.sid as sid,
+            sym.symbol as symbol,
+            sym.company_symbol as company_symbol,
+            sym.share_class_symbol as share_class_symbol,
+            sym.company_symbol || sym.share_class_symbol as fuzzy_symbol,
+            equities.asset_name as asset_name,
+            equities.start_date as start_date,
+            equities.end_date as end_date,
+            equities.first_traded as first_traded,
+            equities.auto_close_date as auto_close_date,
+            equities.exchange as exchange,
+            equities.exchange_full as exchange_full
+        from
+            equities
+        inner join
+            -- Nested select here to take the most recently held ticker
+            -- for each sid. The group by with no aggregation function will
+            -- take the last element in the group, so we first order by
+            -- the end date ascending to ensure that the groupby takes
+            -- the last ticker.
+            (select
+                 *
+             from
+                 (select
+                      *
+                  from
+                      equity_symbol_mappings
+                  order by
+                      equity_symbol_mappings.end_date asc)
+             group by
+                 sid) sym
+        on
+            equities.sid == sym.sid
+        """,
+    )
+    op.drop_table('equity_symbol_mappings')
+    op.drop_table('equities')
+    op.rename_table('_new_equities', 'equities')
+    # we need to make sure the indicies have the proper names after the rename
+    op.create_index(
+        'ix_equities_company_symbol',
+        'equities',
+        ['company_symbol'],
+    )
+    op.create_index(
+        'ix_equities_fuzzy_symbol',
+        'equities',
+        ['fuzzy_symbol'],
+    )
+
+
+@downgrades(6)
+def _downgrade_v6(op):
+    op.drop_table('equity_supplementary_mappings')

catalyst/assets/asset_db_schema.py

@@ -0,0 +1,201 @@
+import sqlalchemy as sa
+
+
+# Define a version number for the database generated by these writers
+# Increment this version number any time a change is made to the schema of the
+# assets database
+# NOTE: When upgrading this remember to add a downgrade in:
+# .asset_db_migrations
+ASSET_DB_VERSION = 6
+
+# A frozenset of the names of all tables in the assets db
+# NOTE: When modifying this schema, update the ASSET_DB_VERSION value
+asset_db_table_names = frozenset({
+    'asset_router',
+    'equities',
+    'equity_symbol_mappings',
+    'equity_supplementary_mappings',
+    'futures_contracts',
+    'futures_exchanges',
+    'futures_root_symbols',
+    'version_info',
+})
+
+metadata = sa.MetaData()
+
+equities = sa.Table(
+    'equities',
+    metadata,
+    sa.Column(
+        'sid',
+        sa.Integer,
+        unique=True,
+        nullable=False,
+        primary_key=True,
+    ),
+    sa.Column('asset_name', sa.Text),
+    sa.Column('start_date', sa.Integer, default=0, nullable=False),
+    sa.Column('end_date', sa.Integer, nullable=False),
+    sa.Column('first_traded', sa.Integer),
+    sa.Column('auto_close_date', sa.Integer),
+    sa.Column('exchange', sa.Text),
+    sa.Column('exchange_full', sa.Text),
+    sa.Column('min_trade_size', sa.Float)
+)
+
+equity_symbol_mappings = sa.Table(
+    'equity_symbol_mappings',
+    metadata,
+    sa.Column(
+        'id',
+        sa.Integer,
+        unique=True,
+        nullable=False,
+        primary_key=True,
+    ),
+    sa.Column(
+        'sid',
+        sa.Integer,
+        sa.ForeignKey(equities.c.sid),
+        nullable=False,
+        index=True,
+    ),
+    sa.Column(
+        'symbol',
+        sa.Text,
+        nullable=False,
+    ),
+    sa.Column(
+        'company_symbol',
+        sa.Text,
+        index=True,
+    ),
+    sa.Column(
+        'share_class_symbol',
+        sa.Text,
+    ),
+    sa.Column(
+        'start_date',
+        sa.Integer,
+        nullable=False,
+    ),
+    sa.Column(
+        'end_date',
+        sa.Integer,
+        nullable=False,
+    ),
+)
+
+equity_supplementary_mappings = sa.Table(
+    'equity_supplementary_mappings',
+    metadata,
+    sa.Column(
+        'sid',
+        sa.Integer,
+        sa.ForeignKey(equities.c.sid),
+        nullable=False,
+        primary_key=True
+    ),
+    sa.Column('field', sa.Text, nullable=False, primary_key=True),
+    sa.Column('start_date', sa.Integer, nullable=False, primary_key=True),
+    sa.Column('end_date', sa.Integer, nullable=False),
+    sa.Column('value', sa.Text, nullable=False),
+)
+
+futures_exchanges = sa.Table(
+    'futures_exchanges',
+    metadata,
+    sa.Column(
+        'exchange',
+        sa.Text,
+        unique=True,
+        nullable=False,
+        primary_key=True,
+    ),
+    sa.Column('timezone', sa.Text),
+)
+
+futures_root_symbols = sa.Table(
+    'futures_root_symbols',
+    metadata,
+    sa.Column(
+        'root_symbol',
+        sa.Text,
+        unique=True,
+        nullable=False,
+        primary_key=True,
+    ),
+    sa.Column('root_symbol_id', sa.Integer),
+    sa.Column('sector', sa.Text),
+    sa.Column('description', sa.Text),
+    sa.Column(
+        'exchange',
+        sa.Text,
+        sa.ForeignKey('futures_exchanges.exchange'),
+    ),
+)
+
+futures_contracts = sa.Table(
+    'futures_contracts',
+    metadata,
+    sa.Column(
+        'sid',
+        sa.Integer,
+        unique=True,
+        nullable=False,
+        primary_key=True,
+    ),
+    sa.Column('symbol', sa.Text, unique=True, index=True),
+    sa.Column(
+        'root_symbol',
+        sa.Text,
+        sa.ForeignKey('futures_root_symbols.root_symbol'),
+        index=True
+    ),
+    sa.Column('asset_name', sa.Text),
+    sa.Column('start_date', sa.Integer, default=0, nullable=False),
+    sa.Column('end_date', sa.Integer, nullable=False),
+    sa.Column('first_traded', sa.Integer),
+    sa.Column(
+        'exchange',
+        sa.Text,
+        sa.ForeignKey('futures_exchanges.exchange'),
+    ),
+    sa.Column('notice_date', sa.Integer, nullable=False),
+    sa.Column('expiration_date', sa.Integer, nullable=False),
+    sa.Column('auto_close_date', sa.Integer, nullable=False),
+    sa.Column('multiplier', sa.Float),
+    sa.Column('tick_size', sa.Float),
+)
+
+asset_router = sa.Table(
+    'asset_router',
+    metadata,
+    sa.Column(
+        'sid',
+        sa.Integer,
+        unique=True,
+        nullable=False,
+        primary_key=True),
+    sa.Column('asset_type', sa.Text),
+)
+
+version_info = sa.Table(
+    'version_info',
+    metadata,
+    sa.Column(
+        'id',
+        sa.Integer,
+        unique=True,
+        nullable=False,
+        primary_key=True,
+    ),
+    sa.Column(
+        'version',
+        sa.Integer,
+        unique=True,
+        nullable=False,
+    ),
+    # This constraint ensures a single entry in this table
+    sa.CheckConstraint('id <= 1'),
+)

catalyst/assets/asset_writer.py

@@ -0,0 +1,740 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections import namedtuple
+import re
+
+from contextlib2 import ExitStack
+import numpy as np
+import pandas as pd
+import sqlalchemy as sa
+from toolz import first
+
+from catalyst.errors import AssetDBVersionError
+from catalyst.assets.asset_db_schema import (
+    ASSET_DB_VERSION,
+    asset_db_table_names,
+    asset_router,
+    equities as equities_table,
+    equity_symbol_mappings,
+    equity_supplementary_mappings as equity_supplementary_mappings_table,
+    futures_contracts as futures_contracts_table,
+    futures_exchanges,
+    futures_root_symbols,
+    metadata,
+    version_info,
+)
+
+from catalyst.utils.preprocess import preprocess
+from catalyst.utils.range import from_tuple, intersecting_ranges
+from catalyst.utils.sqlite_utils import coerce_string_to_eng
+
+# Define a namedtuple for use with the load_data and _load_data methods
+AssetData = namedtuple(
+    'AssetData', (
+        'equities',
+        'equities_mappings',
+        'futures',
+        'exchanges',
+        'root_symbols',
+        'equity_supplementary_mappings',
+    ),
+)
+
+SQLITE_MAX_VARIABLE_NUMBER = 999
+
+symbol_columns = frozenset({
+    'symbol',
+    'company_symbol',
+    'share_class_symbol',
+})
+mapping_columns = symbol_columns | {'start_date', 'end_date'}
+
+# Default values for the equities DataFrame
+_equities_defaults = {
+    'symbol': None,
+    'asset_name': None,
+    'start_date': 0,
+    'end_date': np.iinfo(np.int64).max,
+    'first_traded': None,
+    'auto_close_date': None,
+    # the canonical exchange name, like "NYSE"
+    'exchange': None,
+    # optional, something like "New York Stock Exchange"
+    'exchange_full': None,
+    'min_trade_size': 1
+}
+
+# Default values for the futures DataFrame
+_futures_defaults = {
+    'symbol': None,
+    'root_symbol': None,
+    'asset_name': None,
+    'start_date': 0,
+    'end_date': np.iinfo(np.int64).max,
+    'first_traded': None,
+    'exchange': None,
+    'notice_date': None,
+    'expiration_date': None,
+    'auto_close_date': None,
+    'tick_size': None,
+    'multiplier': 1,
+}
+
+# Default values for the exchanges DataFrame
+_exchanges_defaults = {
+    'timezone': None,
+}
+
+# Default values for the root_symbols DataFrame
+_root_symbols_defaults = {
+    'root_symbol_id': None,
+    'sector': None,
+    'description': None,
+    'exchange': None,
+}
+
+# Default values for the equity_supplementary_mappings DataFrame
+_equity_supplementary_mappings_defaults = {
+    'sid': None,
+    'value': None,
+    'field': None,
+    'start_date': 0,
+    'end_date': np.iinfo(np.int64).max,
+}
+
+
+# Fuzzy symbol delimiters that may break up a company symbol and share class
+_delimited_symbol_delimiters_regex = re.compile(r'[./\-_]')
+_delimited_symbol_default_triggers = frozenset({np.nan, None, ''})
+
+
+def split_delimited_symbol(symbol):
+    """
+    Takes in a symbol that may be delimited and splits it in to a company
+    symbol and share class symbol. Also returns the fuzzy symbol, which is the
+    symbol without any fuzzy characters at all.
+
+    Parameters
+    ----------
+    symbol : str
+        The possibly-delimited symbol to be split
+
+    Returns
+    -------
+    company_symbol : str
+        The company part of the symbol.
+    share_class_symbol : str
+        The share class part of a symbol.
+    """
+    # return blank strings for any bad fuzzy symbols, like NaN or None
+    if symbol in _delimited_symbol_default_triggers:
+        return '', ''
+
+    symbol = symbol.upper()
+
+    split_list = re.split(
+        pattern=_delimited_symbol_delimiters_regex,
+        string=symbol,
+        maxsplit=1,
+    )
+
+    # Break the list up in to its two components, the company symbol and the
+    # share class symbol
+    company_symbol = split_list[0]
+    if len(split_list) > 1:
+        share_class_symbol = split_list[1]
+    else:
+        share_class_symbol = ''
+
+    return company_symbol, share_class_symbol
+
+
+def _generate_output_dataframe(data_subset, defaults):
+    """
+    Generates an output dataframe from the given subset of user-provided
+    data, the given column names, and the given default values.
+
+    Parameters
+    ----------
+    data_subset : DataFrame
+        A DataFrame, usually from an AssetData object,
+        that contains the user's input metadata for the asset type being
+        processed
+    defaults : dict
+        A dict where the keys are the names of the columns of the desired
+        output DataFrame and the values are the default values to insert in the
+        DataFrame if no user data is provided
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame containing all user-provided metadata, and default values
+        wherever user-provided metadata was missing
+    """
+    # The columns provided.
+    cols = set(data_subset.columns)
+    desired_cols = set(defaults)
+
+    # Drop columns with unrecognised headers.
+    data_subset.drop(cols - desired_cols,
+                     axis=1,
+                     inplace=True)
+
+    # Get those columns which we need but
+    # for which no data has been supplied.
+    for col in desired_cols - cols:
+        # write the default value for any missing columns
+        data_subset[col] = defaults[col]
+
+    return data_subset
+
+
+def _check_asset_group(group):
+    row = group.sort_values('end_date').iloc[-1]
+    row.start_date = group.start_date.min()
+    row.end_date = group.end_date.max()
+    row.drop(list(symbol_columns), inplace=True)
+    return row
+
+
+def _format_range(r):
+    return (
+        str(pd.Timestamp(r.start, unit='ns')),
+        str(pd.Timestamp(r.stop, unit='ns')),
+    )
+
+
+def _split_symbol_mappings(df):
+    """Split out the symbol: sid mappings from the raw data.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe with multiple rows for each symbol: sid pair.
+
+    Returns
+    -------
+    asset_info : pd.DataFrame
+        The asset info with one row per asset.
+    symbol_mappings : pd.DataFrame
+        The dataframe of just symbol: sid mappings. The index will be
+        the sid, then there will be three columns: symbol, start_date, and
+        end_date.
+    """
+    mappings = df[list(mapping_columns)]
+    ambigious = {}
+    for symbol in mappings.symbol.unique():
+        persymbol = mappings[mappings.symbol == symbol]
+        intersections = list(intersecting_ranges(map(
+            from_tuple,
+            zip(persymbol.start_date, persymbol.end_date),
+        )))
+        if intersections:
+            ambigious[symbol] = (
+                intersections,
+                persymbol[['start_date', 'end_date']].astype('datetime64[ns]'),
+            )
+
+    if ambigious:
+        raise ValueError(
+            'Ambiguous ownership for %d symbol%s, multiple assets held the'
+            ' following symbols:\n%s' % (
+                len(ambigious),
+                '' if len(ambigious) == 1 else 's',
+                '\n'.join(
+                    '%s:\n  intersections: %s\n  %s' % (
+                        symbol,
+                        tuple(map(_format_range, intersections)),
+                        # indent the dataframe string
+                        '\n  '.join(str(df).splitlines()),
+                    )
+                    for symbol, (intersections, df) in sorted(
+                        ambigious.items(),
+                        key=first,
+                    ),
+                ),
+            )
+        )
+    return (
+        df.groupby(level=0).apply(_check_asset_group),
+        df[list(mapping_columns)],
+    )
+
+
+def _dt_to_epoch_ns(dt_series):
+    """Convert a timeseries into an Int64Index of nanoseconds since the epoch.
+
+    Parameters
+    ----------
+    dt_series : pd.Series
+        The timeseries to convert.
+
+    Returns
+    -------
+    idx : pd.Int64Index
+        The index converted to nanoseconds since the epoch.
+    """
+    index = pd.to_datetime(dt_series.values)
+    if index.tzinfo is None:
+        index = index.tz_localize('UTC')
+    else:
+        index = index.tz_convert('UTC')
+    return index.view(np.int64)
+
+
+def check_version_info(conn, version_table, expected_version):
+    """
+    Checks for a version value in the version table.
+
+    Parameters
+    ----------
+    conn : sa.Connection
+        The connection to use to perform the check.
+    version_table : sa.Table
+        The version table of the asset database
+    expected_version : int
+        The expected version of the asset database
+
+    Raises
+    ------
+    AssetDBVersionError
+        If the version is in the table and not equal to ASSET_DB_VERSION.
+    """
+
+    # Read the version out of the table
+    version_from_table = conn.execute(
+        sa.select((version_table.c.version,)),
+    ).scalar()
+
+    # A db without a version is considered v0
+    if version_from_table is None:
+        version_from_table = 0
+
+    # Raise an error if the versions do not match
+    if (version_from_table != expected_version):
+        raise AssetDBVersionError(db_version=version_from_table,
+                                  expected_version=expected_version)
+
+
+def write_version_info(conn, version_table, version_value):
+    """
+    Inserts the version value in to the version table.
+
+    Parameters
+    ----------
+    conn : sa.Connection
+        The connection to use to execute the insert.
+    version_table : sa.Table
+        The version table of the asset database
+    version_value : int
+        The version to write in to the database
+
+    """
+    conn.execute(sa.insert(version_table, values={'version': version_value}))
+
+
+class _empty(object):
+    columns = ()
+
+
+class AssetDBWriter(object):
+    """Class used to write data to an assets db.
+
+    Parameters
+    ----------
+    engine : Engine or str
+        An SQLAlchemy engine or path to a SQL database.
+    """
+    DEFAULT_CHUNK_SIZE = SQLITE_MAX_VARIABLE_NUMBER
+
+    @preprocess(engine=coerce_string_to_eng)
+    def __init__(self, engine):
+        self.engine = engine
+
+    def write(self,
+              equities=None,
+              futures=None,
+              exchanges=None,
+              root_symbols=None,
+              equity_supplementary_mappings=None,
+              chunk_size=DEFAULT_CHUNK_SIZE):
+        """Write asset metadata to a sqlite database.
+
+        Parameters
+        ----------
+        equities : pd.DataFrame, optional
+            The equity metadata. The columns for this dataframe are:
+
+              symbol : str
+                  The ticker symbol for this equity.
+              asset_name : str
+                  The full name for this asset.
+              start_date : datetime
+                  The date when this asset was created.
+              end_date : datetime, optional
+                  The last date we have trade data for this asset.
+              first_traded : datetime, optional
+                  The first date we have trade data for this asset.
+              auto_close_date : datetime, optional
+                  The date on which to close any positions in this asset.
+              exchange : str
+                  The exchange where this asset is traded.
+              min_trade_size: float, optional
+                  The minimum denomination this asset can be traded.
+
+            The index of this dataframe should contain the sids.
+        futures : pd.DataFrame, optional
+            The future contract metadata. The columns for this dataframe are:
+
+              symbol : str
+                  The ticker symbol for this futures contract.
+              root_symbol : str
+                  The root symbol, or the symbol with the expiration stripped
+                  out.
+              asset_name : str
+                  The full name for this asset.
+              start_date : datetime, optional
+                  The date when this asset was created.
+              end_date : datetime, optional
+                  The last date we have trade data for this asset.
+              first_traded : datetime, optional
+                  The first date we have trade data for this asset.
+              exchange : str
+                  The exchange where this asset is traded.
+              notice_date : datetime
+                  The date when the owner of the contract may be forced
+                  to take physical delivery of the contract's asset.
+              expiration_date : datetime
+                  The date when the contract expires.
+              auto_close_date : datetime
+                  The date when the broker will automatically close any
+                  positions in this contract.
+              tick_size : float
+                  The minimum price movement of the contract.
+              multiplier: float
+                  The amount of the underlying asset represented by this
+                  contract.
+        exchanges : pd.DataFrame, optional
+            The exchanges where assets can be traded. The columns of this
+            dataframe are:
+
+              exchange : str
+                  The name of the exchange.
+              timezone : str
+                  The timezone of the exchange.
+        root_symbols : pd.DataFrame, optional
+            The root symbols for the futures contracts. The columns for this
+            dataframe are:
+
+              root_symbol : str
+                  The root symbol name.
+              root_symbol_id : int
+                  The unique id for this root symbol.
+              sector : string, optional
+                  The sector of this root symbol.
+              description : string, optional
+                  A short description of this root symbol.
+              exchange : str
+                  The exchange where this root symbol is traded.
+        equity_supplementary_mappings : pd.DataFrame, optional
+            Additional mappings from values of abitrary type to assets.
+        chunk_size : int, optional
+            The amount of rows to write to the SQLite table at once.
+            This defaults to the default number of bind params in sqlite.
+            If you have compiled sqlite3 with more bind or less params you may
+            want to pass that value here.
+
+        See Also
+        --------
+        catalyst.assets.asset_finder
+        """
+        with self.engine.begin() as conn:
+            # Create SQL tables if they do not exist.
+            self.init_db(conn)
+
+            # Get the data to add to SQL.
+            data = self._load_data(
+                equities if equities is not None else pd.DataFrame(),
+                futures if futures is not None else pd.DataFrame(),
+                exchanges if exchanges is not None else pd.DataFrame(),
+                root_symbols if root_symbols is not None else pd.DataFrame(),
+                (
+                    equity_supplementary_mappings
+                    if equity_supplementary_mappings is not None
+                    else pd.DataFrame()
+                ),
+            )
+            # Write the data to SQL.
+            self._write_df_to_table(
+                futures_exchanges,
+                data.exchanges,
+                conn,
+                chunk_size,
+            )
+            self._write_df_to_table(
+                futures_root_symbols,
+                data.root_symbols,
+                conn,
+                chunk_size,
+            )
+            self._write_df_to_table(
+                equity_supplementary_mappings_table,
+                data.equity_supplementary_mappings,
+                conn,
+                chunk_size,
+                idx=False,
+            )
+            self._write_assets(
+                'future',
+                data.futures,
+                conn,
+                chunk_size,
+            )
+            self._write_assets(
+                'equity',
+                data.equities,
+                conn,
+                chunk_size,
+                mapping_data=data.equities_mappings,
+            )
+
+    def _write_df_to_table(
+        self,
+        tbl,
+        df,
+        txn,
+        chunk_size,
+        idx=True,
+        idx_label=None,
+    ):
+        df.to_sql(
+            tbl.name,
+            txn.connection,
+            index=idx,
+            index_label=(
+                idx_label
+                if idx_label is not None else
+                first(tbl.primary_key.columns).name
+            ),
+            if_exists='append',
+            chunksize=chunk_size,
+        )
+
+    def _write_assets(self,
+                      asset_type,
+                      assets,
+                      txn,
+                      chunk_size,
+                      mapping_data=None):
+        if asset_type == 'future':
+            tbl = futures_contracts_table
+            if mapping_data is not None:
+                raise TypeError('no mapping data expected for futures')
+
+        elif asset_type == 'equity':
+            tbl = equities_table
+            if mapping_data is None:
+                raise TypeError('mapping data required for equities')
+            # write the symbol mapping data.
+            self._write_df_to_table(
+                equity_symbol_mappings,
+                mapping_data,
+                txn,
+                chunk_size,
+                idx_label='sid',
+            )
+
+        else:
+            raise ValueError(
+                "asset_type must be in {'future', 'equity'}, got: %s" %
+                asset_type,
+            )
+
+        self._write_df_to_table(tbl, assets, txn, chunk_size)
+
+        pd.DataFrame({
+            asset_router.c.sid.name: assets.index.values,
+            asset_router.c.asset_type.name: asset_type,
+        }).to_sql(
+            asset_router.name,
+            txn.connection,
+            if_exists='append',
+            index=False,
+            chunksize=chunk_size
+        )
+
+    def _all_tables_present(self, txn):
+        """
+        Checks if any tables are present in the current assets database.
+
+        Parameters
+        ----------
+        txn : Transaction
+            The open transaction to check in.
+
+        Returns
+        -------
+        has_tables : bool
+            True if any tables are present, otherwise False.
+        """
+        conn = txn.connect()
+        for table_name in asset_db_table_names:
+            if txn.dialect.has_table(conn, table_name):
+                return True
+        return False
+
+    def init_db(self, txn=None):
+        """Connect to database and create tables.
+
+        Parameters
+        ----------
+        txn : sa.engine.Connection, optional
+            The transaction to execute in. If this is not provided, a new
+            transaction will be started with the engine provided.
+
+        Returns
+        -------
+        metadata : sa.MetaData
+            The metadata that describes the new assets db.
+        """
+        with ExitStack() as stack:
+            if txn is None:
+                txn = stack.enter_context(self.engine.begin())
+
+            tables_already_exist = self._all_tables_present(txn)
+
+            # Create the SQL tables if they do not already exist.
+            metadata.create_all(txn, checkfirst=True)
+
+            if tables_already_exist:
+                check_version_info(txn, version_info, ASSET_DB_VERSION)
+            else:
+                write_version_info(txn, version_info, ASSET_DB_VERSION)
+
+    def _normalize_equities(self, equities):
+        # HACK: If 'company_name' is provided, map it to asset_name
+        if ('company_name' in equities.columns and
+                'asset_name' not in equities.columns):
+            equities['asset_name'] = equities['company_name']
+
+        # remap 'file_name' to 'symbol' if provided
+        if 'file_name' in equities.columns:
+            equities['symbol'] = equities['file_name']
+
+        equities_output = _generate_output_dataframe(
+            data_subset=equities,
+            defaults=_equities_defaults,
+        )
+
+        # Split symbols to company_symbols and share_class_symbols
+        tuple_series = equities_output['symbol'].apply(split_delimited_symbol)
+        split_symbols = pd.DataFrame(
+            tuple_series.tolist(),
+            columns=['company_symbol', 'share_class_symbol'],
+            index=tuple_series.index
+        )
+        equities_output = pd.concat((equities_output, split_symbols), axis=1)
+
+        # Upper-case all symbol data
+        for col in symbol_columns:
+            equities_output[col] = equities_output[col].str.upper()
+
+        # Convert date columns to UNIX Epoch integers (nanoseconds)
+        for col in ('start_date',
+                    'end_date',
+                    'first_traded',
+                    'auto_close_date'):
+            equities_output[col] = _dt_to_epoch_ns(equities_output[col])
+
+        return _split_symbol_mappings(equities_output)
+
+    def _normalize_futures(self, futures):
+        futures_output = _generate_output_dataframe(
+            data_subset=futures,
+            defaults=_futures_defaults,
+        )
+        for col in ('symbol', 'root_symbol'):
+            futures_output[col] = futures_output[col].str.upper()
+
+        for col in ('start_date',
+                    'end_date',
+                    'first_traded',
+                    'notice_date',
+                    'expiration_date',
+                    'auto_close_date'):
+            futures_output[col] = _dt_to_epoch_ns(futures_output[col])
+
+        return futures_output
+
+    def _normalize_equity_supplementary_mappings(self, mappings):
+        mappings_output = _generate_output_dataframe(
+            data_subset=mappings,
+            defaults=_equity_supplementary_mappings_defaults,
+        )
+
+        for col in ('start_date', 'end_date'):
+            mappings_output[col] = _dt_to_epoch_ns(mappings_output[col])
+
+        return mappings_output
+
+    def _load_data(
+        self,
+        equities,
+        futures,
+        exchanges,
+        root_symbols,
+        equity_supplementary_mappings,
+    ):
+        """
+        Returns a standard set of pandas.DataFrames:
+        equities, futures, exchanges, root_symbols
+        """
+        # Check whether identifier columns have been provided.
+        # If they have, set the index to this column.
+        # If not, assume the index already cotains the identifier information.
+        for df, id_col in [(equities, 'sid'),
+                           (futures, 'sid'),
+                           (exchanges, 'exchange'),
+                           (root_symbols, 'root_symbol')]:
+            if id_col in df.columns:
+                df.set_index(id_col, inplace=True)
+
+        equities_output, equities_mappings = self._normalize_equities(equities)
+        futures_output = self._normalize_futures(futures)
+
+        equity_supplementary_mappings_output = (
+            self._normalize_equity_supplementary_mappings(
+                equity_supplementary_mappings,
+            )
+        )
+
+        exchanges_output = _generate_output_dataframe(
+            data_subset=exchanges,
+            defaults=_exchanges_defaults,
+        )
+
+        root_symbols_output = _generate_output_dataframe(
+            data_subset=root_symbols,
+            defaults=_root_symbols_defaults,
+        )
+
+        return AssetData(
+            equities=equities_output,
+            equities_mappings=equities_mappings,
+            futures=futures_output,
+            exchanges=exchanges_output,
+            root_symbols=root_symbols_output,
+            equity_supplementary_mappings=equity_supplementary_mappings_output,
+        )

catalyst/assets/continuous_futures.pyx

@@ -0,0 +1,423 @@
+# cython: embedsignature=True
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Cythonized ContinuousFutures object.
+"""
+cimport cython
+from cpython.number cimport PyNumber_Index
+from cpython.object cimport (
+    Py_EQ,
+    Py_NE,
+    Py_GE,
+    Py_LE,
+    Py_GT,
+    Py_LT,
+)
+from cpython cimport bool
+
+from functools import partial
+
+from numpy import array, empty, iinfo
+from numpy cimport long_t, int64_t
+from pandas import Timestamp
+import warnings
+
+from catalyst.utils.calendars import get_calendar
+
+
+def delivery_predicate(codes, contract):
+    # This relies on symbols that are construct following a pattern of
+    # root symbol + delivery code + year, e.g. PLF16
+    # This check would be more robust if the future contract class had
+    # a 'delivery_month' member.
+    delivery_code = contract.symbol[-3]
+    return delivery_code in codes
+
+march_cycle_delivery_predicate = partial(delivery_predicate,
+                                         set(['H', 'M', 'U', 'Z']))
+
+CHAIN_PREDICATES = {
+    'ME': march_cycle_delivery_predicate,
+    'PL': partial(delivery_predicate, set(['F', 'J', 'N', 'V'])),
+    'PA': march_cycle_delivery_predicate,
+
+    # The majority of trading in these currency futures is done on a
+    # March quarterly cycle (Mar, Jun, Sep, Dec) but contracts are
+    # listed for the first 3 consecutive months from the present day. We
+    # want the continuous futures to be composed of just the quarterly
+    # contracts.
+    'JY': march_cycle_delivery_predicate,
+    'CD': march_cycle_delivery_predicate,
+    'AD': march_cycle_delivery_predicate,
+    'BP': march_cycle_delivery_predicate,
+
+    # Gold and silver contracts trade on an unusual specific set of months.
+    'GC': partial(delivery_predicate, set(['G', 'J', 'M', 'Q', 'V', 'Z'])),
+    'XG': partial(delivery_predicate, set(['G', 'J', 'M', 'Q', 'V', 'Z'])),
+    'SV': partial(delivery_predicate, set(['H', 'K', 'N', 'U', 'Z'])),
+    'YS': partial(delivery_predicate, set(['H', 'K', 'N', 'U', 'Z'])),
+}
+
+ADJUSTMENT_STYLES = {'add', 'mul', None}
+
+
+cdef class ContinuousFuture:
+    """
+    Represents a specifier for a chain of future contracts, where the
+    coordinates for the chain are:
+    root_symbol : str
+        The root symbol of the contracts.
+    offset : int
+        The distance from the primary chain.
+        e.g. 0 specifies the primary chain, 1 the secondary, etc.
+    roll_style : str
+        How rolls from contract to contract should be calculated.
+        Currently supports 'calendar'.
+
+    Instances of this class are exposed to the algorithm.
+    """
+
+    cdef readonly long_t sid
+    # Cached hash of self.sid
+    cdef long_t sid_hash
+
+    cdef readonly object root_symbol
+    cdef readonly int offset
+    cdef readonly object roll_style
+
+    cdef readonly object start_date
+    cdef readonly object end_date
+
+    cdef readonly object exchange
+
+    cdef readonly object adjustment
+
+    _kwargnames = frozenset({
+        'sid',
+        'root_symbol',
+        'offset',
+        'start_date',
+        'end_date',
+        'exchange',
+    })
+
+    def __init__(self,
+                 long_t sid, # sid is required
+                 object root_symbol,
+                 int offset,
+                 object roll_style,
+                 object start_date,
+                 object end_date,
+                 object exchange,
+                 object adjustment=None):
+
+        self.sid = sid
+        self.sid_hash = hash(sid)
+        self.root_symbol = root_symbol
+        self.roll_style = roll_style
+        self.offset = offset
+        self.exchange = exchange
+        self.start_date = start_date
+        self.end_date = end_date
+        self.adjustment = adjustment
+
+
+    def __int__(self):
+        return self.sid
+
+    def __index__(self):
+        return self.sid
+
+    def __hash__(self):
+        return self.sid_hash
+
+    def __richcmp__(x, y, int op):
+        """
+        Cython rich comparison method.  This is used in place of various
+        equality checkers in pure python.
+        """
+        cdef long_t x_as_int, y_as_int
+
+        try:
+            x_as_int = PyNumber_Index(x)
+        except (TypeError, OverflowError):
+            return NotImplemented
+
+        try:
+            y_as_int = PyNumber_Index(y)
+        except (TypeError, OverflowError):
+            return NotImplemented
+
+        compared = x_as_int - y_as_int
+
+        # Handle == and != first because they're significantly more common
+        # operations.
+        if op == Py_EQ:
+            return compared == 0
+        elif op == Py_NE:
+            return compared != 0
+        elif op == Py_LT:
+            return compared < 0
+        elif op == Py_LE:
+            return compared <= 0
+        elif op == Py_GT:
+            return compared > 0
+        elif op == Py_GE:
+            return compared >= 0
+        else:
+            raise AssertionError('%d is not an operator' % op)
+
+    def __str__(self):
+        return '%s(%d [%s, %s, %s, %s])' % (
+            type(self).__name__,
+            self.sid,
+            self.root_symbol,
+            self.offset,
+            self.roll_style,
+            self.adjustment,
+        )
+
+    def __repr__(self):
+        attrs = ('root_symbol', 'offset', 'roll_style', 'adjustment')
+        tuples = ((attr, repr(getattr(self, attr, None)))
+                  for attr in attrs)
+        strings = ('%s=%s' % (t[0], t[1]) for t in tuples)
+        params = ', '.join(strings)
+        return 'ContinuousFuture(%d, %s)' % (self.sid, params)
+
+    cpdef __reduce__(self):
+        """
+        Function used by pickle to determine how to serialize/deserialize this
+        class.  Should return a tuple whose first element is self.__class__,
+        and whose second element is a tuple of all the attributes that should
+        be serialized/deserialized during pickling.
+        """
+        return (self.__class__, (self.sid,
+                                 self.root_symbol,
+                                 self.start_date,
+                                 self.end_date,
+                                 self.offset,
+                                 self.roll_style,
+                                 self.exchange))
+
+    cpdef to_dict(self):
+        """
+        Convert to a python dict.
+        """
+        return {
+            'sid': self.sid,
+            'root_symbol': self.root_symbol,
+            'start_date': self.start_date,
+            'end_date': self.end_date,
+            'offset': self.offset,
+            'roll_style': self.roll_style,
+            'exchange': self.exchange,
+        }
+
+    @classmethod
+    def from_dict(cls, dict_):
+        """
+        Build an ContinuousFuture instance from a dict.
+        """
+        return cls(**dict_)
+
+    def is_alive_for_session(self, session_label):
+        """
+        Returns whether the continuous future is alive at the given dt.
+
+        Parameters
+        ----------
+        session_label: pd.Timestamp
+            The desired session label to check. (midnight UTC)
+
+        Returns
+        -------
+        boolean: whether the continuous is alive at the given dt.
+        """
+        cdef int64_t ref_start
+        cdef int64_t ref_end
+
+        ref_start = self.start_date.value
+        ref_end = self.end_date.value
+
+        return ref_start <= session_label.value <= ref_end
+
+    def is_exchange_open(self, dt_minute):
+        """
+        Parameters
+        ----------
+        dt_minute: pd.Timestamp (UTC, tz-aware)
+            The minute to check.
+
+        Returns
+        -------
+        boolean: whether the continuous futures's exchange is open at the
+        given minute.
+        """
+        calendar = get_calendar(self.exchange)
+        return calendar.is_open_on_minute(dt_minute)
+
+
+cdef class ContractNode(object):
+
+    cdef readonly object contract
+    cdef public object prev
+    cdef public object next
+
+    def __init__(self, contract):
+        self.contract = contract
+        self.prev = None
+        self.next = None
+
+    def __rshift__(self, offset):
+        i = 0
+        curr = self
+        while i < offset and curr is not None:
+            curr = curr.next
+            i += 1
+        return curr
+
+    def __lshift__(self, offset):
+        i = 0
+        curr = self
+        while i < offset and curr is not None:
+            curr = curr.prev
+            i += 1
+        return curr
+
+
+cdef class OrderedContracts(object):
+    """
+    A container for aligned values of a future contract chain, in sorted order
+    of their occurrence.
+    Used to get answers about contracts in relation to their auto close
+    dates and start dates.
+
+    Members
+    -------
+    root_symbol : str
+        The root symbol of the future contract chain.
+    contracts : deque
+        The contracts in the chain in order of occurrence.
+    start_dates : long[:]
+        The start dates of the contracts in the chain.
+        Corresponds by index with contract_sids.
+    auto_close_dates : long[:]
+        The auto close dates of the contracts in the chain.
+        Corresponds by index with contract_sids.
+    future_chain_predicates : dict
+        A dict mapping root symbol to a predicate function which accepts a contract
+    as a parameter and returns whether or not the contract should be included in the
+    chain.
+
+    Instances of this class are used by the simulation engine, but not
+    exposed to the algorithm.
+    """
+
+    cdef readonly object root_symbol
+    cdef readonly object _head_contract
+    cdef readonly dict sid_to_contract
+    cdef readonly int64_t _start_date
+    cdef readonly int64_t _end_date
+    cdef readonly object chain_predicate
+
+    def __init__(self, object root_symbol, object contracts, object chain_predicate=None):
+
+        self.root_symbol = root_symbol
+
+        self.sid_to_contract = {}
+
+        self._start_date = iinfo('int64').max
+        self._end_date = 0
+
+        if chain_predicate is None:
+            chain_predicate = lambda x: True
+
+        self._head_contract = None
+        prev = None
+        while contracts:
+            contract = contracts.popleft()
+
+            # It is possible that the first contract in our list has a start
+            # date on or after its auto close date. In that case the contract
+            # is not tradable, so do not include it in the chain.
+            if prev is None and contract.start_date >= contract.auto_close_date:
+                continue
+
+            if not chain_predicate(contract):
+                continue
+
+            self._start_date = min(contract.start_date.value, self._start_date)
+            self._end_date = max(contract.end_date.value, self._end_date)
+
+            curr = ContractNode(contract)
+            self.sid_to_contract[contract.sid] = curr
+            if self._head_contract is None:
+                self._head_contract = curr
+                prev = curr
+                continue
+            curr.prev = prev
+            prev.next = curr
+            prev = curr
+
+    cpdef long_t contract_before_auto_close(self, long_t dt_value):
+        """
+        Get the contract with next upcoming auto close date.
+        """
+        curr = self._head_contract
+        while curr.next is not None:
+            if curr.contract.auto_close_date.value > dt_value:
+                break
+            curr = curr.next
+        return curr.contract.sid
+
+    cpdef contract_at_offset(self, long_t sid, Py_ssize_t offset, int64_t start_cap):
+        """
+        Get the sid which is the given sid plus the offset distance.
+        An offset of 0 should be reflexive.
+        """
+        cdef Py_ssize_t i
+        curr = self.sid_to_contract[sid]
+        i = 0
+        while i < offset:
+            if curr.next is None:
+                return None
+            curr = curr.next
+            i += 1
+        if curr.contract.start_date.value <= start_cap:
+            return curr.contract.sid
+        else:
+            return None
+
+    cpdef long_t[:] active_chain(self, long_t starting_sid, long_t dt_value):
+        curr = self.sid_to_contract[starting_sid]
+        cdef list contracts = []
+
+        while curr is not None:
+            if curr.contract.start_date.value <= dt_value:
+                contracts.append(curr.contract.sid)
+            curr = curr.next
+
+        return array(contracts, dtype='int64')
+
+    property start_date:
+        def __get__(self):
+            return Timestamp(self._start_date, tz='UTC')
+
+    property end_date:
+        def __get__(self):
+            return Timestamp(self._end_date, tz='UTC')

catalyst/assets/futures.py

@@ -0,0 +1,18 @@
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# http://www.cmegroup.com/product-codes-listing/month-codes.html
+CME_CODE_TO_MONTH = dict(zip('FGHJKMNQUVXZ', range(1, 13)))
+MONTH_TO_CME_CODE = dict(zip(range(1, 13), 'FGHJKMNQUVXZ'))

catalyst/assets/roll_finder.py

@@ -0,0 +1,201 @@
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from abc import ABCMeta, abstractmethod
+from six import with_metaclass
+
+
+class RollFinder(with_metaclass(ABCMeta, object)):
+    """
+    Abstract base class for calculating when futures contracts are the active
+    contract.
+    """
+    @abstractmethod
+    def _active_contract(self, oc, front, back, dt):
+        raise NotImplementedError
+
+    def get_contract_center(self, root_symbol, dt, offset):
+        """
+        Parameters
+        ----------
+        root_symbol : str
+            The root symbol for the contract chain.
+        dt : Timestamp
+            The datetime for which to retrieve the current contract.
+        offset : int
+            The offset from the primary contract.
+            0 is the primary, 1 is the secondary, etc.
+
+        Returns
+        -------
+        Future
+            The active future contract at the given dt.
+        """
+        oc = self.asset_finder.get_ordered_contracts(root_symbol)
+        session = self.trading_calendar.minute_to_session_label(dt)
+        front = oc.contract_before_auto_close(session.value)
+        back = oc.contract_at_offset(front, 1, dt.value)
+        if back is None:
+            return front
+        primary = self._active_contract(oc, front, back, session)
+        return oc.contract_at_offset(primary, offset, session.value)
+
+    def get_rolls(self, root_symbol, start, end, offset):
+        """
+        Get the rolls, i.e. the session at which to hop from contract to
+        contract in the chain.
+
+        Parameters
+        ----------
+        root_symbol : str
+            The root symbol for which to calculate rolls.
+        start : Timestamp
+            Start of the date range.
+        end : Timestamp
+            End of the date range.
+        offset : int
+            Offset from the primary.
+
+        Returns
+        -------
+        rolls - list[tuple(sid, roll_date)]
+            A list of rolls, where first value is the first active `sid`,
+        and the `roll_date` on which to hop to the next contract.
+            The last pair in the chain has a value of `None` since the roll
+            is after the range.
+        """
+        oc = self.asset_finder.get_ordered_contracts(root_symbol)
+        front = self.get_contract_center(root_symbol, end, 0)
+        back = oc.contract_at_offset(front, 1, end.value)
+        if back is not None:
+            end_session = self.trading_calendar.minute_to_session_label(end)
+            first = self._active_contract(oc, front, back, end_session)
+        else:
+            first = front
+        first_contract = oc.sid_to_contract[first]
+        rolls = [((first_contract >> offset).contract.sid, None)]
+        tc = self.trading_calendar
+        sessions = tc.sessions_in_range(tc.minute_to_session_label(start),
+                                        tc.minute_to_session_label(end))
+        freq = sessions.freq
+        if first == front:
+            curr = first_contract << 1
+        else:
+            curr = first_contract << 2
+        session = sessions[-1]
+
+        while session > start and curr is not None:
+            front = curr.contract.sid
+            back = rolls[0][0]
+            prev_c = curr.prev
+            while session > start:
+                prev = session - freq
+                if prev_c is not None:
+                    if prev < prev_c.contract.auto_close_date:
+                        break
+                if back != self._active_contract(oc, front, back, prev):
+                    # TODO: Instead of listing each contract with its roll date
+                    # as tuples, create a series which maps every day to the
+                    # active contract on that day.
+                    rolls.insert(0, ((curr >> offset).contract.sid, session))
+                    break
+                session = prev
+            curr = curr.prev
+            if curr is not None:
+                session = curr.contract.auto_close_date
+        return rolls
+
+
+class CalendarRollFinder(RollFinder):
+    """
+    The CalendarRollFinder calculates contract rolls based purely on the
+    contract's auto close date.
+    """
+
+    def __init__(self, trading_calendar, asset_finder):
+        self.trading_calendar = trading_calendar
+        self.asset_finder = asset_finder
+
+    def _active_contract(self, oc, front, back, dt):
+        contract = oc.sid_to_contract[front].contract
+        auto_close_date = contract.auto_close_date
+        auto_closed = dt >= auto_close_date
+        return back if auto_closed else front
+
+
+class VolumeRollFinder(RollFinder):
+    """
+    The CalendarRollFinder calculates contract rolls based on when
+    volume activity transfers from one contract to another.
+    """
+    GRACE_DAYS = 7
+    THRESHOLD = 0.10
+
+    def __init__(self, trading_calendar, asset_finder, session_reader):
+        self.trading_calendar = trading_calendar
+        self.asset_finder = asset_finder
+        self.session_reader = session_reader
+
+    def _active_contract(self, oc, front, back, dt):
+        """
+        Return the active contract based on the previous trading day's volume.
+
+        In the rare case that a double volume switch occurs we treat the first
+        switch as the roll. Take the following case for example:
+
+        | +++++             _____
+        |      +   __      /       <--- 'G'
+        |       ++/++\++++/++
+        |       _/    \__/   +
+        |      /              +
+        | ____/                +   <--- 'F'
+        |_________|__|___|________
+                  a  b   c         <--- Switches
+
+        We should treat 'a' as the roll date rather than 'c' because from the
+        perspective of 'a', if a switch happens and we are pretty close to the
+        auto-close date, we would probably assume it is time to roll. This
+        means that for every date after 'a', `data.current(cf, 'contract')`
+        should return the 'G' contract.
+        """
+        tc = self.trading_calendar
+        trading_day = tc.day
+        prev = dt - trading_day
+        get_value = self.session_reader.get_value
+        front_vol = get_value(front, prev, 'volume')
+        back_vol = get_value(back, prev, 'volume')
+        front_contract = oc.sid_to_contract[front].contract
+
+        if dt >= front_contract.auto_close_date or back_vol > front_vol:
+            return back
+
+        gap_start = \
+            front_contract.auto_close_date - (trading_day * self.GRACE_DAYS)
+        gap_end = prev - trading_day
+        if dt < gap_start:
+            return front
+
+        # If we are within `self.GRACE_DAYS` of the front contract's auto close
+        # date, and a volume flip happened during that period, return the back
+        # contract as the active one.
+        sessions = tc.sessions_in_range(
+            tc.minute_to_session_label(gap_start),
+            tc.minute_to_session_label(gap_end),
+        )
+        for session in sessions:
+            front_vol = get_value(front, session, 'volume')
+            back_vol = get_value(back, session, 'volume')
+            if back_vol > front_vol:
+                return back
+        return front

catalyst/assets/synthetic.py

@@ -0,0 +1,263 @@
+from itertools import product
+from string import ascii_uppercase
+
+import pandas as pd
+from pandas.tseries.offsets import MonthBegin
+from six import iteritems
+
+from .futures import CME_CODE_TO_MONTH
+
+
+def make_rotating_equity_info(num_assets,
+                              first_start,
+                              frequency,
+                              periods_between_starts,
+                              asset_lifetime):
+    """
+    Create a DataFrame representing lifetimes of assets that are constantly
+    rotating in and out of existence.
+
+    Parameters
+    ----------
+    num_assets : int
+        How many assets to create.
+    first_start : pd.Timestamp
+        The start date for the first asset.
+    frequency : str or pd.tseries.offsets.Offset (e.g. trading_day)
+        Frequency used to interpret next two arguments.
+    periods_between_starts : int
+        Create a new asset every `frequency` * `periods_between_new`
+    asset_lifetime : int
+        Each asset exists for `frequency` * `asset_lifetime` days.
+
+    Returns
+    -------
+    info : pd.DataFrame
+        DataFrame representing newly-created assets.
+    """
+    return pd.DataFrame(
+        {
+            'symbol': [chr(ord('A') + i) for i in range(num_assets)],
+            # Start a new asset every `periods_between_starts` days.
+            'start_date': pd.date_range(
+                first_start,
+                freq=(periods_between_starts * frequency),
+                periods=num_assets,
+            ),
+            # Each asset lasts for `asset_lifetime` days.
+            'end_date': pd.date_range(
+                first_start + (asset_lifetime * frequency),
+                freq=(periods_between_starts * frequency),
+                periods=num_assets,
+            ),
+            'exchange': 'TEST',
+            'exchange_full': 'TEST FULL',
+        },
+        index=range(num_assets),
+    )
+
+
+def make_simple_equity_info(sids,
+                            start_date,
+                            end_date,
+                            symbols=None):
+    """
+    Create a DataFrame representing assets that exist for the full duration
+    between `start_date` and `end_date`.
+
+    Parameters
+    ----------
+    sids : array-like of int
+    start_date : pd.Timestamp, optional
+    end_date : pd.Timestamp, optional
+    symbols : list, optional
+        Symbols to use for the assets.
+        If not provided, symbols are generated from the sequence 'A', 'B', ...
+
+    Returns
+    -------
+    info : pd.DataFrame
+        DataFrame representing newly-created assets.
+    """
+    num_assets = len(sids)
+    if symbols is None:
+        symbols = list(ascii_uppercase[:num_assets])
+    return pd.DataFrame(
+        {
+            'symbol': list(symbols),
+            'start_date': pd.to_datetime([start_date] * num_assets),
+            'end_date': pd.to_datetime([end_date] * num_assets),
+            'exchange': 'TEST',
+            'exchange_full': 'TEST FULL',
+        },
+        index=sids,
+        columns=(
+            'start_date',
+            'end_date',
+            'symbol',
+            'exchange',
+            'exchange_full',
+        ),
+    )
+
+
+def make_jagged_equity_info(num_assets,
+                            start_date,
+                            first_end,
+                            frequency,
+                            periods_between_ends,
+                            auto_close_delta):
+    """
+    Create a DataFrame representing assets that all begin at the same start
+    date, but have cascading end dates.
+
+    Parameters
+    ----------
+    num_assets : int
+        How many assets to create.
+    start_date : pd.Timestamp
+        The start date for all the assets.
+    first_end : pd.Timestamp
+        The date at which the first equity will end.
+    frequency : str or pd.tseries.offsets.Offset (e.g. trading_day)
+        Frequency used to interpret the next argument.
+    periods_between_ends : int
+        Starting after the first end date, end each asset every
+        `frequency` * `periods_between_ends`.
+
+    Returns
+    -------
+    info : pd.DataFrame
+        DataFrame representing newly-created assets.
+    """
+    frame = pd.DataFrame(
+        {
+            'symbol': [chr(ord('A') + i) for i in range(num_assets)],
+            'start_date': start_date,
+            'end_date': pd.date_range(
+                first_end,
+                freq=(periods_between_ends * frequency),
+                periods=num_assets,
+            ),
+            'exchange': 'TEST',
+            'exchange_full': 'TEST FULL',
+        },
+        index=range(num_assets),
+    )
+
+    # Explicitly pass None to disable setting the auto_close_date column.
+    if auto_close_delta is not None:
+        frame['auto_close_date'] = frame['end_date'] + auto_close_delta
+
+    return frame
+
+
+def make_future_info(first_sid,
+                     root_symbols,
+                     years,
+                     notice_date_func,
+                     expiration_date_func,
+                     start_date_func,
+                     month_codes=None):
+    """
+    Create a DataFrame representing futures for `root_symbols` during `year`.
+
+    Generates a contract per triple of (symbol, year, month) supplied to
+    `root_symbols`, `years`, and `month_codes`.
+
+    Parameters
+    ----------
+    first_sid : int
+        The first sid to use for assigning sids to the created contracts.
+    root_symbols : list[str]
+        A list of root symbols for which to create futures.
+    years : list[int or str]
+        Years (e.g. 2014), for which to produce individual contracts.
+    notice_date_func : (Timestamp) -> Timestamp
+        Function to generate notice dates from first of the month associated
+        with asset month code.  Return NaT to simulate futures with no notice
+        date.
+    expiration_date_func : (Timestamp) -> Timestamp
+        Function to generate expiration dates from first of the month
+        associated with asset month code.
+    start_date_func : (Timestamp) -> Timestamp, optional
+        Function to generate start dates from first of the month associated
+        with each asset month code.  Defaults to a start_date one year prior
+        to the month_code date.
+    month_codes : dict[str -> [1..12]], optional
+        Dictionary of month codes for which to create contracts.  Entries
+        should be strings mapped to values from 1 (January) to 12 (December).
+        Default is catalyst.futures.CME_CODE_TO_MONTH
+
+    Returns
+    -------
+    futures_info : pd.DataFrame
+        DataFrame of futures data suitable for passing to an AssetDBWriter.
+    """
+    if month_codes is None:
+        month_codes = CME_CODE_TO_MONTH
+
+    year_strs = list(map(str, years))
+    years = [pd.Timestamp(s, tz='UTC') for s in year_strs]
+
+    # Pairs of string/date like ('K06', 2006-05-01)
+    contract_suffix_to_beginning_of_month = tuple(
+        (month_code + year_str[-2:], year + MonthBegin(month_num))
+        for ((year, year_str), (month_code, month_num))
+        in product(
+            zip(years, year_strs),
+            iteritems(month_codes),
+        )
+    )
+
+    contracts = []
+    parts = product(root_symbols, contract_suffix_to_beginning_of_month)
+    for sid, (root_sym, (suffix, month_begin)) in enumerate(parts, first_sid):
+        contracts.append({
+            'sid': sid,
+            'root_symbol': root_sym,
+            'symbol': root_sym + suffix,
+            'start_date': start_date_func(month_begin),
+            'notice_date': notice_date_func(month_begin),
+            'expiration_date': notice_date_func(month_begin),
+            'multiplier': 500,
+            'exchange': "TEST",
+            'exchange_full': 'TEST FULL',
+        })
+    return pd.DataFrame.from_records(contracts, index='sid')
+
+
+def make_commodity_future_info(first_sid,
+                               root_symbols,
+                               years,
+                               month_codes=None):
+    """
+    Make futures testing data that simulates the notice/expiration date
+    behavior of physical commodities like oil.
+
+    Parameters
+    ----------
+    first_sid : int
+    root_symbols : list[str]
+    years : list[int]
+    month_codes : dict[str -> int]
+
+    Expiration dates are on the 20th of the month prior to the month code.
+    Notice dates are are on the 20th two months prior to the month code.
+    Start dates are one year before the contract month.
+
+    See Also
+    --------
+    make_future_info
+    """
+    nineteen_days = pd.Timedelta(days=19)
+    one_year = pd.Timedelta(days=365)
+    return make_future_info(
+        first_sid=first_sid,
+        root_symbols=root_symbols,
+        years=years,
+        notice_date_func=lambda dt: dt - MonthBegin(2) + nineteen_days,
+        expiration_date_func=lambda dt: dt - MonthBegin(1) + nineteen_days,
+        start_date_func=lambda dt: dt - one_year,
+        month_codes=month_codes,
+    )

catalyst/catalyst_warnings.py

@@ -0,0 +1,18 @@
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+class ZiplineDeprecationWarning(DeprecationWarning):
+    pass

catalyst/constants.py

@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+
+import os
+import logbook
+
+''' You can override the LOG level from your environment.
+    For example, if you want to see the DEBUG messages, run:
+    $ export CATALYST_LOG_LEVEL=10
+'''
+LOG_LEVEL = int(os.environ.get('CATALYST_LOG_LEVEL', logbook.INFO))
+
+SYMBOLS_URL = 'https://s3.amazonaws.com/enigmaco/catalyst-exchanges/' \
+              '{exchange}/symbols.json'
+
+DATE_TIME_FORMAT = '%Y-%m-%d %H:%M'
+DATE_FORMAT = '%Y-%m-%d'
+
+try:
+    ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+except Exception as e:
+    print('unable to get catalyst path: {}'.format(e))
+
+AUTO_INGEST = False
+
+AUTH_SERVER = 'https://data.enigma.co'
+
+# TODO: switch to mainnet
+ETH_REMOTE_NODE = 'https://rinkeby.infura.io/'
+
+MARKETPLACE_CONTRACT = 'https://raw.githubusercontent.com/enigmampc/' \
+                       'catalyst/master/catalyst/marketplace/' \
+                       'contract_marketplace_address.txt'
+
+MARKETPLACE_CONTRACT_ABI = 'https://raw.githubusercontent.com/enigmampc/' \
+                           'catalyst/master/catalyst/marketplace/' \
+                           'contract_marketplace_abi.json'
+
+# TODO: switch to mainnet
+ENIGMA_CONTRACT = 'https://raw.githubusercontent.com/enigmampc/' \
+                  'catalyst/master/catalyst/marketplace/' \
+                  'contract_enigma_address.txt'
+
+ENIGMA_CONTRACT_ABI = 'https://raw.githubusercontent.com/enigmampc/' \
+                      'catalyst/master/catalyst/marketplace/' \
+                      'contract_enigma_abi.json'
+
+SUPPORTED_WALLETS = ['metamask', 'ledger', 'trezor', 'bitbox', 'keystore',
+                     'key']

catalyst/curate/poloniex.py

@@ -0,0 +1,382 @@
+import csv
+import json
+import os
+import shutil
+import time
+from datetime import datetime
+
+import logbook
+import pandas as pd
+import requests
+
+from catalyst.exchange.utils.exchange_utils import \
+    get_exchange_symbols_filename
+
+DT_START = int(time.mktime(datetime(2010, 1, 1, 0, 0).timetuple()))
+DT_END = pd.to_datetime('today').value // 10 ** 9
+CSV_OUT_FOLDER = os.environ.get('CSV_OUT_FOLDER', '/efs/exchanges/poloniex/')
+CONN_RETRIES = 2
+
+logbook.StderrHandler().push_application()
+log = logbook.Logger(__name__)
+
+
+class PoloniexCurator(object):
+    '''
+    OHLCV data feed generator for crypto data. Based on Poloniex market data
+    '''
+
+    _api_path = 'https://poloniex.com/public?'
+    currency_pairs = []
+
+    def __init__(self):
+        if not os.path.exists(CSV_OUT_FOLDER):
+            try:
+                os.makedirs(CSV_OUT_FOLDER)
+            except Exception as e:
+                log.error('Failed to create data folder: {}'.format(
+                            CSV_OUT_FOLDER))
+                log.exception(e)
+
+    def get_currency_pairs(self):
+        '''
+        Retrieves and returns all currency pairs from the exchange
+        '''
+        url = self._api_path + 'command=returnTicker'
+
+        try:
+            response = requests.get(url)
+        except Exception as e:
+            log.error('Failed to retrieve list of currency pairs')
+            log.exception(e)
+            return None
+
+        data = response.json()
+        self.currency_pairs = []
+        for ticker in data:
+            self.currency_pairs.append(ticker)
+        self.currency_pairs.sort()
+
+        log.debug('Currency pairs retrieved successfully: {}'.format(
+                    len(self.currency_pairs)
+                    ))
+
+    def _retrieve_tradeID_date(self, row):
+        '''
+        Helper function that reads tradeID and date fields from CSV readline
+        '''
+        tId = int(row.split(',')[0])
+        d = pd.to_datetime(row.split(',')[1],
+                           infer_datetime_format=True).value // 10 ** 9
+        return tId, d
+
+    def retrieve_trade_history(self, currencyPair, start=DT_START,
+                               end=DT_END, temp=None):
+        '''
+        Retrieves TradeHistory from exchange for a given currencyPair
+        between start and end dates. If no start date is provided, uses
+        a system-wide one (beginning of time for cryptotrading).
+        If no end date is provided, 'now' is used.
+
+        Stores results in CSV file on disk.
+
+        This function is called recursively to work around the
+        limitations imposed by the provider API.
+        '''
+        csv_fn = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+
+        '''
+        Check what data we already have on disk, reading first and last
+        lines from file. Data is stored on file from NEWEST to OLDEST.
+        '''
+        try:
+            with open(csv_fn, 'ab+') as f:
+                f.seek(0, os.SEEK_END)
+                if(f.tell() > 2):                   # Check file size is not 0
+                    f.seek(0)                       # Go to start to read
+                    last_tradeID, end_file = self._retrieve_tradeID_date(
+                                                    f.readline())
+                    f.seek(-2, os.SEEK_END)         # Jump to the 2nd last byte
+                    while f.read(1) != b"\n":       # Until EOL is found...
+                        # ...jump back the read byte plus one more.
+                        f.seek(-2, os.SEEK_CUR)
+                    first_tradeID, start_file = self._retrieve_tradeID_date(
+                                                    f.readline())
+
+                    if(end_file + 3600 * 6 > DT_END
+                        and (first_tradeID == 1
+                             or (currencyPair == 'BTC_HUC'
+                                 and first_tradeID == 2)
+                             or (currencyPair == 'BTC_RIC'
+                                 and first_tradeID == 2)
+                             or (currencyPair == 'BTC_XCP'
+                                 and first_tradeID == 2)
+                             or (currencyPair == 'BTC_NAV'
+                                 and first_tradeID == 4569)
+                             or (currencyPair == 'BTC_POT'
+                                 and first_tradeID == 23511))):
+                        return
+
+        except Exception as e:
+            log.error('Error opening file: {}'.format(csv_fn))
+            log.exception(e)
+
+        '''
+        Poloniex API limits querying TradeHistory to intervals smaller
+        than 1 month, so we make sure that start date is never more than
+        1 month apart from end date
+        '''
+        if(end - start > 2419200):   # 60s/min * 60min/hr * 24hr/day * 28days
+            newstart = end - 2419200
+        else:
+            newstart = start
+
+        log.debug('{}: Retrieving from {} to {}\t {} - {}'.format(
+                    currencyPair, str(newstart), str(end),
+                    time.ctime(newstart), time.ctime(end)))
+
+        url = '{path}command=returnTradeHistory&currencyPair={pair}' \
+              '&start={start}&end={end}'.format(
+                    path=self._api_path,
+                    pair=currencyPair,
+                    start=str(newstart),
+                    end=str(end)
+                )
+
+        attempts = 0
+        success = 0
+        while attempts < CONN_RETRIES:
+            try:
+                response = requests.get(url)
+            except Exception as e:
+                log.error('Failed to retrieve trade history data'
+                          'for {}'.format(currencyPair))
+                log.exception(e)
+                attempts += 1
+            else:
+                try:
+                    if(isinstance(response.json(), dict)
+                       and response.json()['error']):
+                        log.error('Failed to to retrieve trade history data '
+                                  'for {}: {}'.format(
+                                    currencyPair,
+                                    response.json()['error']
+                                    ))
+                        attempts += 1
+                except Exception as e:
+                    log.exception(e)
+                    attempts += 1
+                else:
+                    success = 1
+                    break
+
+        if not success:
+            return None
+
+        '''
+        If we get to transactionId == 1, and we already have that on
+        disk, we got to the end of TradeHistory for this coin.
+        '''
+        if('first_tradeID' in locals()
+                and response.json()[-1]['tradeID'] == first_tradeID):
+            return
+
+        '''
+            There are primarily two scenarios:
+                a) There is newer data available that we need to add at
+                   the beginning of the file. We'll retrieve all what we
+                   need until we get to what we already have, writing it
+                   to a temporary file; and we will write that at the
+                   beginning of our existing file.
+                b) We are going back in time, appending at the end of
+                   our existing TradeHistory until the first transaction
+                   for this currencyPair
+        '''
+        try:
+            if(temp is not None
+                    or ('end_file' in locals() and end_file + 3600 < end)):
+                if (temp is None):
+                    temp = os.tmpfile()
+                tempcsv = csv.writer(temp)
+                for item in response.json():
+                    if(item['tradeID'] <= last_tradeID):
+                        continue
+                    tempcsv.writerow([
+                        item['tradeID'],
+                        item['date'],
+                        item['type'],
+                        item['rate'],
+                        item['amount'],
+                        item['total'],
+                        item['globalTradeID'],
+                    ])
+                if(response.json()[-1]['tradeID'] > last_tradeID):
+                    end = pd.to_datetime(response.json()[-1]['date'],
+                                         infer_datetime_format=True
+                                         ).value // 10**9
+                    self.retrieve_trade_history(currencyPair, start,
+                                                end, temp=temp)
+                else:
+                    with open(csv_fn, 'rb+') as f:
+                        shutil.copyfileobj(f, temp)
+                        f.seek(0)
+                        temp.seek(0)
+                        shutil.copyfileobj(temp, f)
+                    temp.close()
+                    end = start_file
+            else:
+                with open(csv_fn, 'ab') as csvfile:
+                    csvwriter = csv.writer(csvfile)
+                    for item in response.json():
+                        if('first_tradeID' in locals()
+                                and item['tradeID'] >= first_tradeID):
+                            continue
+                        csvwriter.writerow([
+                            item['tradeID'],
+                            item['date'],
+                            item['type'],
+                            item['rate'],
+                            item['amount'],
+                            item['total'],
+                            item['globalTradeID']
+                        ])
+                end = pd.to_datetime(response.json()[-1]['date'],
+                                     infer_datetime_format=True).value//10**9
+
+        except Exception as e:
+            log.error('Error opening {}'.format(csv_fn))
+            log.exception(e)
+
+        '''
+            If we got here, we aren't done yet. Call recursively with
+            'end' times that go sequentially back in time.
+        '''
+        self.retrieve_trade_history(currencyPair, start, end)
+
+    def generate_ohlcv(self, df):
+        '''
+        Generates OHLCV dataframe from a dataframe containing all TradeHistory
+        by resampling with 1-minute period
+        '''
+        df.set_index('date', inplace=True)             # Index by date
+        vol = df['total'].to_frame('volume')           # set Vol aside
+        df.drop('total', axis=1, inplace=True)         # Drop volume data
+        ohlc = df.resample('T').ohlc()                 # Resample OHLC 1min
+        ohlc.columns = ohlc.columns.map(lambda t: t[1])  # Rename cols
+        closes = ohlc['close'].fillna(method='pad')    # Pad fwd missing close
+        ohlc = ohlc.apply(lambda x: x.fillna(closes))  # Fill NA w/ last close
+        vol = vol.resample('T').sum().fillna(0)        # Add volumes by bin
+        ohlcv = pd.concat([ohlc, vol], axis=1)         # Concat OHLC + Vol
+        return ohlcv
+
+    def write_ohlcv_file(self, currencyPair):
+        '''
+        Generates OHLCV data file with 1minute bars from TradeHistory on disk
+        '''
+        csv_trades = CSV_OUT_FOLDER + 'crypto_trades-' + currencyPair + '.csv'
+        csv_1min = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        if(os.path.getmtime(csv_1min) > time.time() - 7200):
+            log.debug(currencyPair+': 1min data file already up to date. '
+                      'Delete the file if you want to rebuild it.')
+        else:
+            df = pd.read_csv(csv_trades,
+                             names=['tradeID',
+                                    'date',
+                                    'type',
+                                    'rate',
+                                    'amount',
+                                    'total',
+                                    'globalTradeID'],
+                             dtype={'tradeID': int,
+                                    'date': str,
+                                    'type': str,
+                                    'rate': float,
+                                    'amount': float,
+                                    'total': float,
+                                    'globalTradeID': int}
+                             )
+            df.drop(['tradeID', 'type', 'amount', 'globalTradeID'],
+                    axis=1, inplace=True)
+            df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True)
+            ohlcv = self.generate_ohlcv(df)
+            try:
+                with open(csv_1min, 'w') as csvfile:
+                    csvwriter = csv.writer(csvfile)
+                    for item in ohlcv.itertuples():
+                        if item.Index == 0:
+                            continue
+                        csvwriter.writerow([
+                            item.Index.value // 10 ** 9,
+                            item.open,
+                            item.high,
+                            item.low,
+                            item.close,
+                            item.volume,
+                        ])
+            except Exception as e:
+                log.error('Error opening {}'.format(csv_1min))
+                log.exception(e)
+            log.debug('{}: Generated 1min OHLCV data.'.format(currencyPair))
+
+    def onemin_to_dataframe(self, currencyPair, start, end):
+        '''
+        Returns a data frame for a given currencyPair from data on disk
+        '''
+        csv_fn = CSV_OUT_FOLDER + 'crypto_1min-' + currencyPair + '.csv'
+        df = pd.read_csv(csv_fn, names=['date',
+                                        'open',
+                                        'high',
+                                        'low',
+                                        'close',
+                                        'volume'])
+        df['date'] = pd.to_datetime(df['date'], unit='s')
+        df.set_index('date', inplace=True)
+        return df[start:end]
+
+    def generate_symbols_json(self, filename=None):
+        '''
+        Generates a symbols.json file with corresponding start_date
+        for each currencyPair
+        '''
+        symbol_map = {}
+
+        if(filename is None):
+            filename = get_exchange_symbols_filename('poloniex')
+
+        with open(filename, 'w') as symbols:
+            for currencyPair in self.currency_pairs:
+                start = None
+                csv_fn = '{}crypto_trades-{}.csv'.format(
+                                                    CSV_OUT_FOLDER,
+                                                    currencyPair)
+                with open(csv_fn, 'r') as f:
+                    f.seek(0, os.SEEK_END)
+                    if(f.tell() > 2):               # Check file size is not 0
+                        f.seek(-2, os.SEEK_END)     # Jump to 2nd last byte
+                        while f.read(1) != b"\n":   # Until EOL is found...
+                            # ...jump back the read byte plus one more.
+                            f.seek(-2, os.SEEK_CUR)
+                        start = pd.to_datetime(f.readline().split(',')[1],
+                                               infer_datetime_format=True)
+
+                if(start is None):
+                    start = time.gmtime()
+                base, market = currencyPair.lower().split('_')
+                symbol = '{market}_{base}'.format(market=market, base=base)
+                symbol_map[currencyPair] = dict(
+                    symbol=symbol,
+                    start_date=start.strftime("%Y-%m-%d")
+                )
+            json.dump(symbol_map, symbols, sort_keys=True, indent=2,
+                      separators=(',', ':'))
+
+
+if __name__ == '__main__':
+    pc = PoloniexCurator()
+    pc.get_currency_pairs()
+    # pc.generate_symbols_json()
+
+    for currencyPair in pc.currency_pairs:
+        pc.retrieve_trade_history(currencyPair)
+        log.debug('{} up to date.'.format(currencyPair))
+        pc.write_ohlcv_file(currencyPair)

catalyst/data/__init__.py

@@ -0,0 +1,16 @@
+from . import loader
+from .loader import (
+    load_from_yahoo,
+    load_bars_from_yahoo,
+    load_prices_from_csv,
+    load_prices_from_csv_folder,
+)
+
+
+__all__ = [
+    'load_bars_from_yahoo',
+    'load_from_yahoo',
+    'load_prices_from_csv',
+    'load_prices_from_csv_folder',
+    'loader',
+]

catalyst/data/_adjustments.pyx

@@ -0,0 +1,303 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from cpython cimport (
+    PyDict_Contains,
+    PySet_Add,
+)
+
+from numpy import (
+    int64,
+    uint32,
+    zeros,
+)
+from numpy cimport int64_t, ndarray
+from pandas import Timestamp
+
+ctypedef object Timestamp_t
+ctypedef object DatetimeIndex_t
+ctypedef object Int64Index_t
+
+from catalyst.lib.adjustment import Float64Multiply
+from catalyst.assets.asset_writer import (
+    SQLITE_MAX_VARIABLE_NUMBER as SQLITE_MAX_IN_STATEMENT,
+)
+from catalyst.utils.pandas_utils import timedelta_to_integral_seconds
+
+
+_SID_QUERY_TEMPLATE = """
+SELECT DISTINCT sid FROM {0}
+WHERE effective_date >= ? AND effective_date <= ?
+"""
+cdef dict SID_QUERIES = {
+    tablename: _SID_QUERY_TEMPLATE.format(tablename)
+    for tablename in ('splits', 'dividends', 'mergers')
+}
+
+ADJ_QUERY_TEMPLATE = """
+SELECT sid, ratio, effective_date
+FROM {0}
+WHERE sid IN ({1}) AND effective_date >= {2} AND effective_date <= {3}
+"""
+
+EPOCH = Timestamp(0, tz='UTC')
+
+cdef set _get_sids_from_table(object db,
+                              str tablename,
+                              int start_date,
+                              int end_date):
+    """
+    Get the unique sids for all adjustments between start_date and end_date
+    from table `tablename`.
+
+    Parameters
+    ----------
+    db : sqlite3.connection
+    tablename : str
+    start_date : int (seconds since epoch)
+    end_date : int (seconds since epoch)
+
+    Returns
+    -------
+    sids : set
+        Set of sets
+    """
+
+    cdef object cursor = db.execute(
+        SID_QUERIES[tablename],
+        (start_date, end_date),
+    )
+    cdef set out = set()
+    cdef tuple result
+    for result in cursor.fetchall():
+        PySet_Add(out, result[0])
+    return out
+
+
+cdef set _get_split_sids(object db, int start_date, int end_date):
+    return _get_sids_from_table(db, 'splits', start_date, end_date)
+
+
+cdef set _get_merger_sids(object db, int start_date, int end_date):
+    return _get_sids_from_table(db, 'mergers', start_date, end_date)
+
+
+cdef set _get_dividend_sids(object db, int start_date, int end_date):
+    return _get_sids_from_table(db, 'dividends', start_date, end_date)
+
+
+cdef _adjustments(object adjustments_db,
+                  set split_sids,
+                  set merger_sids,
+                  set dividends_sids,
+                  int start_date,
+                  int end_date,
+                  Int64Index_t assets):
+
+    c = adjustments_db.cursor()
+
+    splits_to_query = [str(a) for a in assets if a in split_sids]
+    splits_results = []
+    while splits_to_query:
+        query_len = min(len(splits_to_query), SQLITE_MAX_IN_STATEMENT)
+        query_assets = splits_to_query[:query_len]
+        t= [str(a) for a in query_assets]
+        statement = ADJ_QUERY_TEMPLATE.format('splits',
+            ",".join(['?' for _ in query_assets]), start_date, end_date)
+        c.execute(statement, t)
+        splits_to_query = splits_to_query[query_len:]
+        splits_results.extend(c.fetchall())
+
+    mergers_to_query = [str(a) for a in assets if a in merger_sids]
+    mergers_results = []
+    while mergers_to_query:
+        query_len = min(len(mergers_to_query), SQLITE_MAX_IN_STATEMENT)
+        query_assets = mergers_to_query[:query_len]
+        t= [str(a) for a in query_assets]
+        statement = ADJ_QUERY_TEMPLATE.format('mergers',
+            ",".join(['?' for _ in query_assets]), start_date, end_date)
+        c.execute(statement, t)
+        mergers_to_query = mergers_to_query[query_len:]
+        mergers_results.extend(c.fetchall())
+
+    dividends_to_query = [str(a) for a in assets if a in dividends_sids]
+    dividends_results = []
+    while dividends_to_query:
+        query_len = min(len(dividends_to_query), SQLITE_MAX_IN_STATEMENT)
+        query_assets = dividends_to_query[:query_len]
+        t= [str(a) for a in query_assets]
+        statement = ADJ_QUERY_TEMPLATE.format('dividends',
+            ",".join(['?' for _ in query_assets]), start_date, end_date)
+        c.execute(statement, t)
+        dividends_to_query = dividends_to_query[query_len:]
+        dividends_results.extend(c.fetchall())
+
+    return splits_results, mergers_results, dividends_results
+
+
+cpdef load_adjustments_from_sqlite(object adjustments_db,  # sqlite3.Connection
+                                   list columns,
+                                   DatetimeIndex_t dates,
+                                   Int64Index_t assets):
+    """
+    Load a dictionary of Adjustment objects from adjustments_db
+
+    Parameters
+    ----------
+    adjustments_db : sqlite3.Connection
+        Connection to a sqlite3 table in the format written by
+        SQLiteAdjustmentWriter.
+    columns : list[str]
+        List of column names for which adjustments are needed.
+    dates : pd.DatetimeIndex
+        Dates for which adjustments are needed
+    assets : pd.Int64Index
+        Assets for which adjustments are needed.
+
+    Returns
+    -------
+    adjustments : list[dict[int -> Adjustment]]
+        A list of mappings from index to adjustment objects to apply at that
+        index.
+    """
+
+    cdef int start_date = timedelta_to_integral_seconds(dates[0] - EPOCH)
+    cdef int end_date = timedelta_to_integral_seconds(dates[-1] - EPOCH)
+
+    cdef set split_sids = _get_split_sids(
+        adjustments_db,
+        start_date,
+        end_date,
+    )
+    cdef set merger_sids = _get_merger_sids(
+        adjustments_db,
+        start_date,
+        end_date,
+    )
+    cdef set dividend_sids = _get_dividend_sids(
+        adjustments_db,
+        start_date,
+        end_date,
+    )
+
+    cdef:
+        list splits, mergers, dividends
+    splits, mergers, dividends = _adjustments(
+        adjustments_db,
+        split_sids,
+        merger_sids,
+        dividend_sids,
+        start_date,
+        end_date,
+        assets,
+    )
+
+    cdef list results = [{} for column in columns]
+    cdef dict asset_ixs = {}  # Cache sid lookups here.
+    cdef dict date_ixs = {}
+    cdef:
+        int i
+        int dt
+        int sid
+        double ratio
+        int eff_date
+        int date_loc
+        Py_ssize_t asset_ix
+        dict col_adjustments
+
+    cdef ndarray[int64_t, ndim=1] _dates_seconds = \
+        dates.values.astype('datetime64[s]').view(int64)
+
+    # Pre-populate date index cache.
+    for i, dt in enumerate(_dates_seconds):
+        date_ixs[dt] = i
+
+    # splits affect prices and volumes, volumes is the inverse
+    for sid, ratio, eff_date in splits:
+        if eff_date < start_date:
+            continue
+
+        date_loc = _lookup_dt(date_ixs, eff_date, _dates_seconds)
+
+        if not PyDict_Contains(asset_ixs, sid):
+            asset_ixs[sid] = assets.get_loc(sid)
+        asset_ix = asset_ixs[sid]
+
+        price_adj = Float64Multiply(0, date_loc, asset_ix, asset_ix, ratio)
+        for i, column in enumerate(columns):
+            col_adjustments = results[i]
+            if column != 'volume':
+                try:
+                    col_adjustments[date_loc].append(price_adj)
+                except KeyError:
+                    col_adjustments[date_loc] = [price_adj]
+            else:
+                volume_adj = Float64Multiply(
+                    0, date_loc, asset_ix, asset_ix, 1.0 / ratio
+                )
+                try:
+                    col_adjustments[date_loc].append(volume_adj)
+                except KeyError:
+                    col_adjustments[date_loc] = [volume_adj]
+
+    # mergers affect prices only
+    for sid, ratio, eff_date in mergers:
+        if eff_date < start_date:
+            continue
+
+        date_loc = _lookup_dt(date_ixs, eff_date, _dates_seconds)
+
+        if not PyDict_Contains(asset_ixs, sid):
+            asset_ixs[sid] = assets.get_loc(sid)
+        asset_ix = asset_ixs[sid]
+
+        adj = Float64Multiply(0, date_loc, asset_ix, asset_ix, ratio)
+        for i, column in enumerate(columns):
+            col_adjustments = results[i]
+            if column != 'volume':
+                try:
+                    col_adjustments[date_loc].append(adj)
+                except KeyError:
+                    col_adjustments[date_loc] = [adj]
+
+    # dividends affect prices only
+    for sid, ratio, eff_date in dividends:
+        if eff_date < start_date:
+            continue
+
+        date_loc = _lookup_dt(date_ixs, eff_date, _dates_seconds)
+
+        if not PyDict_Contains(asset_ixs, sid):
+            asset_ixs[sid] = assets.get_loc(sid)
+        asset_ix = asset_ixs[sid]
+
+        adj = Float64Multiply(0, date_loc, asset_ix, asset_ix, ratio)
+        for i, column in enumerate(columns):
+            col_adjustments = results[i]
+            if column != 'volume':
+                try:
+                    col_adjustments[date_loc].append(adj)
+                except KeyError:
+                    col_adjustments[date_loc] = [adj]
+
+    return results
+
+
+cdef _lookup_dt(dict dt_cache,
+                int dt,
+                ndarray[int64_t, ndim=1] fallback):
+
+    if not PyDict_Contains(dt_cache, dt):
+        dt_cache[dt] = fallback.searchsorted(dt, side='right')
+    return dt_cache[dt]

catalyst/data/_equities.pyx

@@ -0,0 +1,227 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import bcolz
+cimport cython
+from cpython cimport bool
+
+from numpy import (
+    array,
+    float64,
+    intp,
+    uint32,
+    uint64,
+    zeros,
+)
+from numpy cimport (
+    float64_t,
+    intp_t,
+    ndarray,
+    uint32_t,
+    uint64_t,
+    uint8_t,
+)
+from numpy.math cimport NAN
+
+ctypedef object carray_t
+ctypedef object ctable_t
+ctypedef object Timestamp_t
+ctypedef object DatetimeIndex_t
+ctypedef object Int64Index_t
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef _compute_row_slices(dict asset_starts_absolute,
+                          dict asset_ends_absolute,
+                          dict asset_starts_calendar,
+                          intp_t query_start,
+                          intp_t query_end,
+                          Int64Index_t requested_assets):
+    """
+    Core indexing functionality for loading raw data from bcolz.
+
+    Parameters
+    ----------
+    asset_starts_absolute : dict
+        Dictionary containing the index of the first row of each asset in the
+        bcolz file from which we will query.
+
+    asset_ends_absolute : dict
+        Dictionary containing the index of the last row of each asset in the
+        bcolz file from which we will query.
+
+    asset_starts_calendar : dict
+        Dictionary containing the index of in our calendar corresponding to the
+        start date of each asset
+
+    query_start : intp
+    query_end : intp
+        Start and end indices in our calendar of the dates for which we're
+        querying.
+
+    requested_assets : pandas.Int64Index
+        The assets for which we want to load data.
+
+    For each asset in requested assets, computes three values:
+    1.) The index in the raw bcolz data of first row to load.
+    2.) The index in the raw bcolz data of the last row to load.
+    3.) The index in the dates of our query corresponding to the first row for
+        each asset. This is non-zero iff the asset's lifetime begins partway
+        through the requested query dates.
+
+    Returns
+    -------
+    first_rows, last_rows, offsets : 3-tuple of ndarrays
+    """
+    cdef:
+        intp_t nassets = len(requested_assets)
+
+        # For each sid, we need to compute the following:
+        ndarray[dtype=intp_t, ndim=1] first_row_a = zeros(nassets, dtype=intp)
+        ndarray[dtype=intp_t, ndim=1] last_row_a = zeros(nassets, dtype=intp)
+        ndarray[dtype=intp_t, ndim=1] offset_a = zeros(nassets, dtype=intp)
+
+        # Loop variables.
+        intp_t i
+        intp_t asset
+        intp_t asset_start_data
+        intp_t asset_end_data
+        intp_t asset_start_calendar
+        intp_t asset_end_calendar
+
+    for i, asset in enumerate(requested_assets):
+        asset_start_data = asset_starts_absolute[asset]
+        asset_end_data = asset_ends_absolute[asset]
+        asset_start_calendar = asset_starts_calendar[asset]
+        asset_end_calendar = (
+            asset_start_calendar + (asset_end_data - asset_start_data)
+        )
+
+        # If the asset started during the query, then start with the asset's
+        # first row.
+        # Otherwise start with the asset's first row + the number of rows
+        # before the query on which the asset existed.
+        first_row_a[i] = (
+            asset_start_data + max(0, (query_start - asset_start_calendar))
+        )
+        # If the asset ended during the query, the end with the asset's last
+        # row.
+        # Otherwise, end with the asset's last row minus the number of rows
+        # after the query for which the asset
+        last_row_a[i] = (
+            asset_end_data - max(0, asset_end_calendar - query_end)
+        )
+        # If the asset existed on or before the query, no offset.
+        # Otherwise, offset by the number of rows in the query in which the
+        # asset did not yet exist.
+        offset_a[i] = max(0, asset_start_calendar - query_start)
+
+    return first_row_a, last_row_a, offset_a
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef _read_bcolz_data(ctable_t table,
+                       tuple shape,
+                       list columns,
+                       intp_t[:] first_rows,
+                       intp_t[:] last_rows,
+                       intp_t[:] offsets,
+                       bool read_all):
+    """
+    Load raw bcolz data for the given columns and indices.
+
+    Parameters
+    ----------
+    table : bcolz.ctable
+        The table from which to read.
+    shape : tuple (length 2)
+        The shape of the expected output arrays.
+    columns : list[str]
+        List of column names to read.
+
+    first_rows : ndarray[intp]
+    last_rows : ndarray[intp]
+    offsets : ndarray[intp
+        Arrays in the format returned by _compute_row_slices.
+    read_all : bool
+        Whether to read_all sid data at once, or to read a silce from the
+        carray for each sid.
+
+    Returns
+    -------
+    results : list of ndarray
+        A 2D array of shape `shape` for each column in `columns`.
+    """
+    cdef:
+        int nassets
+        str column_name
+        carray_t carray
+        ndarray[dtype=uint64_t, ndim=1] raw_data
+        ndarray[dtype=uint64_t, ndim=2] outbuf
+        ndarray[dtype=uint8_t, ndim=2, cast=True] where_nan
+        ndarray[dtype=float64_t, ndim=2] outbuf_as_float
+        intp_t asset
+        intp_t out_idx
+        intp_t raw_idx
+        intp_t first_row
+        intp_t last_row
+        intp_t offset
+        list results = []
+
+    ndays = shape[0]
+    nassets = shape[1]
+    if not nassets== len(first_rows) == len(last_rows) == len(offsets):
+        raise ValueError("Incompatible index arrays.")
+
+    for column_name in columns:
+        outbuf = zeros(shape=shape, dtype=uint64)
+        if read_all:
+            raw_data = table[column_name][:]
+
+            for asset in range(nassets):
+                first_row = first_rows[asset]
+                last_row = last_rows[asset]
+                offset = offsets[asset]
+                if first_row <= last_row:
+                    outbuf[offset:offset + (last_row + 1 - first_row), asset] =\
+                        raw_data[first_row:last_row + 1]
+                else:
+                    continue
+        else:
+            carray = table[column_name]
+
+            for asset in range(nassets):
+                first_row = first_rows[asset]
+                last_row = last_rows[asset]
+                offset = offsets[asset]
+                out_start = offset
+                out_end = (last_row - first_row) + offset + 1
+                if first_row <= last_row:
+                    outbuf[offset:offset + (last_row + 1 - first_row), asset] =\
+                        carray[first_row:last_row + 1]
+                else:
+                    continue
+
+        if column_name in ['open', 'high', 'low', 'close', 'volume']:
+            where_nan = (outbuf == 0)
+            outbuf_as_float = outbuf.astype(float64) * .000000001
+            outbuf_as_float[where_nan] = NAN
+            results.append(outbuf_as_float)
+        elif column_name in ['volume']:
+            results.append(outbuf.astype(float64) * .000000001)
+        else:
+            results.append(outbuf)
+    return results

catalyst/data/_minute_bar_internal.pyx

@@ -0,0 +1,160 @@
+from numpy cimport ndarray, long_t
+from numpy import searchsorted
+from cpython cimport bool
+cimport cython
+
+cdef inline int int_min(int a, int b): return a if a <= b else b
+
+@cython.cdivision(True)
+def minute_value(ndarray[long_t, ndim=1] market_opens,
+                 Py_ssize_t pos,
+                 short minutes_per_day):
+    """
+    Finds the value of the minute represented by `pos` in the given array of
+    market opens.
+
+    Parameters
+    ----------
+    market_opens: numpy array of ints
+        Market opens, in minute epoch values.
+
+    pos: int
+        The index of the desired minute.
+
+    minutes_per_day: int
+        The number of minutes per day (e.g. 390 for NYSE).
+
+    Returns
+    -------
+    int: The minute epoch value of the desired minute.
+    """
+    cdef short q, r
+
+    q = cython.cdiv(pos, minutes_per_day)
+    r = cython.cmod(pos, minutes_per_day)
+
+    return market_opens[q] + r
+
+def find_position_of_minute(ndarray[long_t, ndim=1] market_opens,
+                            ndarray[long_t, ndim=1] market_closes,
+                            long_t minute_val,
+                            short minutes_per_day,
+                            bool forward_fill):
+    """
+    Finds the position of a given minute in the given array of market opens.
+    If not a market minute, adjusts to the last market minute.
+
+    Parameters
+    ----------
+    market_opens: numpy array of ints
+        Market opens, in minute epoch values.
+
+    market_closes: numpy array of ints
+        Market closes, in minute epoch values.
+
+    minute_val: int
+        The desired minute, as a minute epoch.
+
+    minutes_per_day: int
+        The number of minutes per day (e.g. 390 for NYSE).
+
+    forward_fill: bool
+        Whether to use the previous market minute if the given minute does
+        not fall within an open/close pair.
+
+    Returns
+    -------
+    int: The position of the given minute in the market opens array.
+
+    Raises
+    ------
+    ValueError
+        If the given minute is not between a single open/close pair AND
+        forward_fill is False.  For example, if minute_val is 17:00 Eastern
+        for a given day whose normal hours are 9:30 to 16:00, and we are not
+        forward filling, ValueError is raised.
+    """
+    cdef Py_ssize_t market_open_loc, market_open, delta
+
+    market_open_loc = \
+        searchsorted(market_opens, minute_val, side='right') - 1
+    market_open = market_opens[market_open_loc]
+    market_close = market_closes[market_open_loc]
+
+    if not forward_fill and ((minute_val - market_open) >= minutes_per_day):
+        raise ValueError("Given minute is not between an open and a close")
+
+    delta = int_min(minute_val - market_open, market_close - market_open)
+
+    return (market_open_loc * minutes_per_day) + delta
+
+def find_last_traded_position_internal(
+        ndarray[long_t, ndim=1] market_opens,
+        ndarray[long_t, ndim=1] market_closes,
+        long_t end_minute,
+        long_t start_minute,
+        volumes,
+        short minutes_per_day):
+
+    """
+    Finds the position of the last traded minute for the given volumes array.
+
+    Parameters
+    ----------
+    market_opens: numpy array of ints
+        Market opens, in minute epoch values.
+
+    market_closes: numpy array of ints
+        Market closes, in minute epoch values.
+
+    end_minute: int
+        The minute from which to start looking backwards, as a minute epoch.
+
+    start_minute: int
+        The asset's start date, as a minute epoch.  Acts as the bottom limit of
+        how far we can look backwards.
+
+    volumes: bcolz carray
+        The volume history for the given asset.
+
+    minutes_per_day: int
+        The number of minutes per day (e.g. 390 for NYSE).
+
+    Returns
+    -------
+    int: The position of the last traded minute, starting from `minute_val`
+    """
+    cdef Py_ssize_t minute_pos, current_minute, q
+
+    minute_pos = int_min(
+        find_position_of_minute(market_opens, market_closes, end_minute,
+                                minutes_per_day, True),
+        len(volumes) - 1
+    )
+
+    while minute_pos >= 0:
+        current_minute = minute_value(
+            market_opens, minute_pos, minutes_per_day
+        )
+
+        q = cython.cdiv(minute_pos, minutes_per_day)
+        if current_minute > market_closes[q]:
+            minute_pos = find_position_of_minute(market_opens,
+                                                 market_closes,
+                                                 market_closes[q],
+                                                 minutes_per_day,
+                                                 False)
+            continue
+
+        if current_minute < start_minute:
+            return -1
+
+        if volumes[minute_pos] != 0:
+            return minute_pos
+
+        minute_pos -= 1
+
+    # we've gone to the beginning of this asset's range, and still haven't
+    # found a trade event
+    return -1
+

catalyst/data/_resample.pyx

@@ -0,0 +1,116 @@
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from cython cimport boundscheck, wraparound
+from numpy import finfo, float64, nan, isnan
+from numpy cimport intp_t, float64_t, uint32_t
+
+@boundscheck(False)
+@wraparound(False)
+cpdef void _minute_to_session_open(intp_t[:] close_locs,
+                                   float64_t[:] data,
+                                   float64_t[:] out):
+    cdef intp_t i, close_loc, loc = 0
+    cdef float64_t val
+    for i, close_loc in enumerate(close_locs):
+        val = nan
+        # Start by getting the price value at the opening minute of each day.
+        # If the value is NaN, continue looking forward until we either find a
+        # valid value or reach the closing minute, at which point the value is
+        # just kept as a NaN. We increment 'loc' after obtaining the value to
+        # ensure we do not reach an out of bounds index.
+        while isnan(val) and loc <= close_loc:
+            val = data[loc]
+            loc += 1
+        out[i] = val
+        loc = close_loc + 1
+
+
+@boundscheck(False)
+@wraparound(False)
+cpdef void _minute_to_session_high(intp_t[:] close_locs,
+                                   float64_t[:] data,
+                                   float64_t[:] out):
+    cdef intp_t i, close_loc, loc = 0
+    cdef float64_t val
+    for i, close_loc in enumerate(close_locs):
+        val = -1
+        while loc <= close_loc:
+            val = max(val, data[loc])
+            loc += 1
+        if val == -1:
+            val = nan
+        out[i] = val
+        loc = close_loc + 1
+
+
+@boundscheck(False)
+@wraparound(False)
+cpdef void _minute_to_session_low(intp_t[:] close_locs,
+                                   float64_t[:] data,
+                                   float64_t[:] out):
+    cdef intp_t i, close_loc, loc = 0
+    cdef float64_t val
+    cdef float64_t max_float = finfo(float64).max
+    for i, close_loc in enumerate(close_locs):
+        val = max_float
+        while loc <= close_loc:
+            val = min(val, data[loc])
+            loc += 1
+        if val == max_float:
+            val = nan
+        out[i] = val
+        loc = close_loc + 1
+
+
+@boundscheck(False)
+@wraparound(False)
+cpdef void _minute_to_session_close(intp_t[:] close_locs,
+                                    float64_t[:] data,
+                                    float64_t[:] out):
+    cdef intp_t i, prev_close_loc, loc = 0
+    cdef float64_t val
+    num_out = len(out)
+    for i in range(num_out - 1, -1, -1):
+        if i > 0:
+            prev_close_loc = close_locs[i - 1]
+        else:
+            prev_close_loc = -1
+        loc = close_locs[i]
+        val = nan
+        # Start by getting the price value at the closing minute of each day.
+        # If the value is NaN, continue looking back until we either find a
+        # valid value or reach the closing minute of the previous day, at which
+        # point the value is just kept as a NaN. We decrement 'loc' after
+        # obtaining the value to ensure we do not reach a negative index.
+        while isnan(val) and loc > prev_close_loc:
+            val = data[loc]
+            loc -= 1
+        out[i] = val
+
+
+@boundscheck(False)
+@wraparound(False)
+cpdef void _minute_to_session_volume(intp_t[:] close_locs,
+                                     uint32_t[:] data,
+                                     uint32_t[:] out):
+    cdef intp_t i, close_loc, loc = 0
+    cdef uint32_t val
+    loc = 0
+    for i, close_loc in enumerate(close_locs):
+        val = 0
+        while loc <= close_loc:
+            val += data[loc]
+            loc += 1
+        out[i] = val
+        loc = close_loc + 1

catalyst/data/bar_reader.py

@@ -0,0 +1,138 @@
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from abc import ABCMeta, abstractmethod, abstractproperty
+from six import with_metaclass
+
+
+class NoDataOnDate(Exception):
+    """
+    Raised when a spot price cannot be found for the sid and date.
+    """
+    pass
+
+
+class NoDataBeforeDate(NoDataOnDate):
+    pass
+
+
+class NoDataAfterDate(NoDataOnDate):
+    pass
+
+
+class BarReader(with_metaclass(ABCMeta, object)):
+    @abstractproperty
+    def data_frequency(self):
+        pass
+
+    @abstractmethod
+    def load_raw_arrays(self, columns, start_date, end_date, assets):
+        """
+        Parameters
+        ----------
+        fields : list of str
+           'open', 'high', 'low', 'close', or 'volume'
+        start_dt: Timestamp
+           Beginning of the window range.
+        end_dt: Timestamp
+           End of the window range.
+        sids : list of int
+           The asset identifiers in the window.
+
+        Returns
+        -------
+        list of np.ndarray
+            A list with an entry per field of ndarrays with shape
+            (minutes in range, sids) with a dtype of float64, containing the
+            values for the respective field over start and end dt range.
+        """
+        pass
+
+    @abstractproperty
+    def last_available_dt(self):
+        """
+        Returns
+        -------
+        dt : pd.Timestamp
+            The last session for which the reader can provide data.
+        """
+        pass
+
+    @abstractproperty
+    def trading_calendar(self):
+        """
+        Returns the catalyst.utils.calendar.trading_calendar used to read
+        the data.  Can be None (if the writer didn't specify it).
+        """
+        pass
+
+    @abstractproperty
+    def first_trading_day(self):
+        """
+        Returns
+        -------
+        dt : pd.Timestamp
+            The first trading day (session) for which the reader can provide
+            data.
+        """
+        pass
+
+    @abstractmethod
+    def get_value(self, sid, dt, field):
+        """
+        Retrieve the value at the given coordinates.
+
+        Parameters
+        ----------
+        sid : int
+            The asset identifier.
+        dt : pd.Timestamp
+            The timestamp for the desired data point.
+        field : string
+            The OHLVC name for the desired data point.
+
+        Returns
+        -------
+        value : float|int
+            The value at the given coordinates, ``float`` for OHLC, ``int``
+            for 'volume'.
+
+        Raises
+        ------
+        NoDataOnDate
+            If the given dt is not a valid market minute (in minute mode) or
+            session (in daily mode) according to this reader's tradingcalendar.
+        """
+        pass
+
+    @abstractmethod
+    def get_last_traded_dt(self, asset, dt):
+        """
+        Get the latest minute on or before ``dt`` in which ``asset`` traded.
+
+        If there are no trades on or before ``dt``, returns ``pd.NaT``.
+
+        Parameters
+        ----------
+        asset : catalyst.asset.Asset
+            The asset for which to get the last traded minute.
+        dt : pd.Timestamp
+            The minute at which to start searching for the last traded minute.
+
+        Returns
+        -------
+        last_traded : pd.Timestamp
+            The dt of the last trade for the given asset, using the input
+            dt as a vantage point.
+        """
+        pass

catalyst/data/benchmarks.py

@@ -0,0 +1,64 @@
+#
+# Copyright 2013 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import pandas as pd
+
+import pandas_datareader.data as pd_reader
+
+
+def get_benchmark_returns(symbol, first_date, last_date):
+    """
+    Get a Series of benchmark returns from Google associated with `symbol`.
+    Default is `SPY`.
+
+    Parameters
+    ----------
+    symbol : str
+        Benchmark symbol for which we're getting the returns.
+    first_date : pd.Timestamp
+        First date for which we want to get data.
+    last_date : pd.Timestamp
+        Last date for which we want to get data.
+
+    The furthest date that Google goes back to is 1993-02-01. It has missing
+    data for 2008-12-15, 2009-08-11, and 2012-02-02, so we add data for the
+    dates for which Google is missing data.
+
+    We're also limited to 4000 days worth of data per request. If we make a
+    request for data that extends past 4000 trading days, we'll still only
+    receive 4000 days of data.
+
+    first_date is **not** included because we need the close from day N - 1 to
+    compute the returns for day N.
+    """
+    if symbol == '^GSPC':
+        symbol = 'spy'
+
+    data = pd_reader.DataReader(
+        symbol,
+        'google',
+        first_date,
+        last_date
+    )
+
+    data = data['Close']
+
+    data[pd.Timestamp('2008-12-15')] = np.nan
+    data[pd.Timestamp('2009-08-11')] = np.nan
+    data[pd.Timestamp('2012-02-02')] = np.nan
+
+    data = data.fillna(method='ffill')
+
+    return data.sort_index().tz_localize('UTC').pct_change(1).iloc[1:]

catalyst/data/bundles/__init__.py

@@ -0,0 +1,30 @@
+# These imports are necessary to force module-scope register calls to happen.
+from . import quandl  # noqa
+from .core import (
+    UnknownBundle,
+    bundles,
+    clean,
+    from_bundle_ingest_dirname,
+    ingest,
+    ingestions_for_bundle,
+    load,
+    register,
+    to_bundle_ingest_dirname,
+    unregister,
+)
+from .yahoo import yahoo_equities
+
+__all__ = [
+    'UnknownBundle',
+    'bundles',
+    'clean',
+    'from_bundle_ingest_dirname',
+    'ingest',
+    'ingestions_for_bundle',
+    'load',
+    'register',
+    'to_bundle_ingest_dirname',
+    'unregister',
+    'yahoo_equities',
+    'poloniex_cryptoassets',
+]

catalyst/data/bundles/base.py

@@ -0,0 +1,500 @@
+#
+# Copyright 2017 Enigma MPC, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from itertools import count
+import tarfile
+from time import sleep
+
+from abc import abstractmethod, abstractproperty
+import logbook
+import pandas as pd
+
+from . import core as bundles
+
+from catalyst.utils.cli import (
+    item_show_count,
+    maybe_show_progress
+)
+from catalyst.utils.memoize import lazyval
+
+from catalyst.constants import LOG_LEVEL
+
+logbook.StderrHandler().push_application()
+log = logbook.Logger(__name__, level=LOG_LEVEL)
+
+DEFAULT_RETRIES = 5
+
+
+class BaseBundle(object):
+    def __init__(self, asset_filter=[]):
+        self._asset_filter = asset_filter
+        self._reset()
+
+    def _reset(self):
+        self._splits = []
+        self._dividends = []
+
+    @lazyval
+    def name(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def exchange(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def calendar_name(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def minutes_per_day(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def frequencies(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def md_column_names(self):
+        return _dtypes_to_cols(self.md_dtypes)
+
+    @lazyval
+    def md_dtypes(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def column_names(self):
+        return _dtypes_to_cols(self.dtypes)
+
+    @lazyval
+    def dtypes(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def tar_url(self):
+        raise NotImplementedError()
+
+    @lazyval
+    def wait_time(self):
+        raise NotImplementedError()
+
+    @abstractproperty
+    def splits(self):
+        raise NotImplementedError()
+
+    @abstractproperty
+    def dividends(self):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def fetch_raw_metadata_frame(self, api_key, page_number):
+        raise NotImplementedError()
+
+    def post_process_symbol_metadata(self, metadata, data):
+        return metadata
+
+    @abstractmethod
+    def fetch_raw_symbol_frame(self, api_key, symbol, start_date, end_date):
+        raise NotImplementedError()
+
+    def ingest(self,
+               environ,
+               asset_db_writer,
+               minute_bar_writer,
+               daily_bar_writer,
+               adjustment_writer,
+               calendar,
+               start_session,
+               end_session,
+               cache,
+               show_progress,
+               is_compile,
+               output_dir):
+
+        try:
+            api_key = environ.get('CATALYST_API_KEY')
+            retries = environ.get('CATALYST_DOWNLOAD_ATTEMPTS', 5)
+
+            if is_compile:
+                # User has instructed local compilation & ingestion of bundle.
+                # Fetch raw metadata for all symbols.
+                raw_metadata = self._fetch_metadata_frame(
+                    api_key,
+                    cache=cache,
+                    retries=retries,
+                    environ=environ,
+                    show_progress=show_progress,
+                )
+
+                # Compile daily symbol data if bundle supports daily mode and
+                # persist the dataset to disk.
+                symbol_map = raw_metadata.symbol
+                if 'daily' in self.frequencies:
+                    daily_bar_writer.write(
+                        self._fetch_symbol_iter(
+                            api_key,
+                            cache,
+                            symbol_map,
+                            calendar,
+                            start_session,
+                            end_session,
+                            'daily',
+                            retries,
+                        ),
+                        assets=raw_metadata.index,
+                        show_progress=show_progress,
+                    )
+
+                # Post-process metadata using cached symbol frames, and write
+                # to disk.  This metadata must be written before any attempt
+                # to write minute data.
+                metadata = self._post_process_metadata(
+                    raw_metadata,
+                    cache,
+                    show_progress=show_progress,
+                )
+                asset_db_writer.write(metadata)
+
+                # Compile minute symbol data if bundle supports minute mode and
+                # persist the dataset to disk.
+                if 'minute' in self.frequencies:
+                    minute_bar_writer.write(
+                        self._fetch_symbol_iter(
+                            api_key,
+                            cache,
+                            symbol_map,
+                            calendar,
+                            start_session,
+                            end_session,
+                            'minute',
+                            retries,
+                        ),
+                        show_progress=show_progress,
+                    )
+
+                # For legacy purposes, this call is required to ensure the
+                # database contains an appropriately initialized file
+                # structure.  We don't forsee a usecase for adjustments at
+                # this time, but may later choose to expose this functionality
+                # in the future.
+                adjustment_writer.write(
+                    splits=(
+                        pd.concat(self.splits, ignore_index=True)
+                        if len(self.splits) > 0 else
+                        None
+                    ),
+                    dividends=(
+                        pd.concat(self.dividends, ignore_index=True)
+                        if len(self.dividends) > 0 else
+                        None
+                    ),
+                )
+            else:
+                # Otherwise, user has instructed to download and untar bundle
+                # directly from the bundles `tar_url`.
+                self._download_and_untar(show_progress, output_dir)
+        except Exception as e:
+            log.exception(
+                ' Failed to ingest {name}:\n{msg}'.format(
+                    name=self.name,
+                    msg=str(e),
+                )
+            )
+        else:
+            self._reset()
+
+    def _download_and_untar(self, show_progress, output_dir):
+        # Download bundle conditioned on whether the user would like progress
+        # information to be displayed in the CLI.
+        if show_progress:
+            data = bundles.download_with_progress(
+                self.tar_url,
+                chunk_size=bundles.ONE_MEGABYTE,
+                label='Downloading {name} bundle'.format(name=self.name),
+            )
+        else:
+            data = bundles.download_without_progress(self.tar_url)
+
+        # File transfer has completed, untar the bundle to the appropriate
+        # data directory.
+        with tarfile.open('r', fileobj=data) as tar:
+            tar.extractall(output_dir)
+
+    def _fetch_metadata_frame(self,
+                              api_key,
+                              cache,
+                              retries=DEFAULT_RETRIES,
+                              environ=None,
+                              show_progress=False):
+
+        # Setup raw metadata iterator to fetch pages if necessary.
+        raw_iter = self._fetch_metadata_iter(api_key, cache, retries, environ)
+
+        # Concatenate all frame in iterator to compute a single metadata frame.
+        with maybe_show_progress(
+            raw_iter,
+            show_progress,
+            label='Fetching symbol metadata',
+            item_show_func=item_show_count(),
+            length=3,
+            show_percent=False,
+        ) as blocks:
+            metadata = pd.concat(blocks, ignore_index=True)
+
+        return metadata
+
+    def _fetch_metadata_iter(self, api_key, cache, retries, environ):
+        for page_number in count(1):
+            # Attempt to load metadata page from cache.  If it does not exist,
+            # poll the API upto `retries` times in order to get raw DataFrame.
+            key = 'metadata-page-{pn}.frame'.format(pn=page_number)
+            try:
+                raw = cache[key]
+            except KeyError:
+                for _ in range(retries):
+                    try:
+                        raw = self.fetch_raw_metadata_frame(
+                            api_key,
+                            page_number,
+                        )
+                        break
+                    except ValueError:
+                        raw = pd.DataFrame([])
+                        break
+                    except Exception:
+                        log.exception(
+                            'Failed to load metadata from {}. '
+                            'Retrying.'.format(self.name)
+                        )
+                else:
+                    raise ValueError(
+                        'Failed to download metadata page {} after {} '
+                        'attempts.'.format(page_number, retries)
+                    )
+
+            if raw.empty:
+                # Empty DataFrame signals completion.
+                break
+
+            # Apply selective asset filtering, useful for benchmark
+            # ingestion.
+            if self._asset_filter:
+                raw = raw[raw.symbol.isin(self._asset_filter)]
+
+            # Update cached value for key.
+            cache[key] = raw
+
+            # Return metadata frame to application.
+            yield raw
+
+    def _post_process_metadata(self, metadata, cache, show_progress=False):
+        # Create empty data frame using target metadata column names and dtypes
+        final_metadata = pd.DataFrame(
+            columns=self.md_column_names,
+            index=metadata.index,
+        )
+
+        # Iterate over the available symbols, loading the asset's raw symbol
+        # data from the cache.  The final metadata is computed and recorded in
+        # the appropriate row depending on the asset's id.
+        with maybe_show_progress(
+            metadata.symbol.iteritems(),
+            show_progress,
+            label='Post-processing symbol metadata',
+            item_show_func=item_show_count(len(metadata)),
+            length=len(metadata),
+            show_percent=False,
+        ) as symbols_map:
+            for asset_id, symbol in symbols_map:
+                # Attempt to load data from disk, the cache should have an
+                # entry for each symbol at this point of the execution. If one
+                # does not exist, we should fail.
+                key = '{sym}.daily.frame'.format(sym=symbol)
+                try:
+                    raw_data = cache[key]
+                except KeyError:
+                    raise ValueError(
+                      'Unable to find cached data for symbol:'
+                      ' {0}'.format(symbol))
+
+                # Perform and require post-processing of metadata.
+                final_symbol_metadata = self.post_process_symbol_metadata(
+                    asset_id,
+                    metadata.iloc[asset_id],
+                    raw_data,
+                )
+
+                # Record symbol's final metadata.
+                final_metadata.iloc[asset_id] = final_symbol_metadata
+
+            # Register all assets with the bundle's default exchange.
+            final_metadata['exchange'] = self.exchange
+
+        return final_metadata
+
+    def _fetch_symbol_iter(self,
+                           api_key,
+                           cache,
+                           symbol_map,
+                           calendar,
+                           start_session,
+                           end_session,
+                           data_frequency,
+                           retries):
+
+        for asset_id, symbol in symbol_map.iteritems():
+            # Record start time of iteration, compare at end of iteration to
+            # adhere to the datas source's rate limit policy.
+            start_time = pd.Timestamp.utcnow()
+
+            # Fetch new data if cached data is absent or stale, otherwise
+            # returns the cached data unaltered.  The `should_sleep` flag
+            # indicates that an API call was attempted, and that we should be
+            # ensure aren't exceeding our rate limit before proceeding to the
+            # next symbol. If the raw_data is updated, it is cached before
+            # being returned.
+            raw_data, should_sleep = self._maybe_update_symbol_frame(
+                start_time,
+                api_key,
+                cache,
+                symbol,
+                calendar,
+                start_session,
+                end_session,
+                data_frequency,
+                retries,
+            )
+
+            # TODO(cfromknecht) further data validation?
+
+            # Pass asset_id and symbol data to writer.
+            yield asset_id, raw_data
+
+            # If an API call was made during this iteration and the time to
+            # reach this point was less than the inter-request `wait_time`,
+            # sleep until after enough time has elapsed to prevent getting rate
+            # limited.
+            if should_sleep:
+                remaining = pd.Timestamp.utcnow() - start_time + self.wait_time
+                if remaining.value > 0:
+                    sleep(remaining.value / 10**9)
+
+    def _maybe_update_symbol_frame(self,
+                                   start_time,
+                                   api_key,
+                                   cache,
+                                   symbol,
+                                   calendar,
+                                   start_session,
+                                   end_session,
+                                   data_frequency,
+                                   retries):
+
+        # Attempt to load pre-existing symbol data from cache.
+        key = '{sym}.{freq}.frame'.format(sym=symbol, freq=data_frequency)
+        try:
+            raw_data = cache[key]
+        except KeyError:
+            raw_data = None
+
+        # Select the most recent date in cached dataset if it exists,
+        # otherwise use the provided `start_session`.
+        last = start_session
+        if raw_data is not None and len(raw_data) > 0:
+            last = raw_data.index[-1].tz_localize('UTC')
+
+        should_sleep = False
+
+        # Determine time at which cached data will be considered stale.
+        cache_expiration = last + pd.Timedelta(days=2)
+        if start_time <= cache_expiration and raw_data is not None:
+            # Data is fresh enough to reuse, no need to update. Iterator can
+            # proceed to next symbol directly since no API call was required.
+            return raw_data, should_sleep
+
+        # If we arrive here, we must have attempted an API call.
+        # Setting this flag tells the iterator to pause before starting
+        # the next asset, that we don't exceed the data source's rate
+        # limit.
+        should_sleep = True
+
+        raw_data = self._fetch_symbol_frame(
+            api_key,
+            symbol,
+            calendar,
+            start_session,
+            end_session,
+            data_frequency,
+            retries=retries,
+        )
+
+        # Cache latest symbol data.
+        cache[key] = raw_data
+
+        return raw_data, should_sleep
+
+    def _fetch_symbol_frame(self,
+                            api_key,
+                            symbol,
+                            calendar,
+                            start_session,
+                            end_session,
+                            data_frequency,
+                            retries=DEFAULT_RETRIES):
+
+        # Data for symbol is old enough to attempt an update or is not
+        # present in the cache.  Fetch raw data for a single symbol
+        # with requested intervals and frequency. Retry as necessary.
+        for _ in range(retries):
+            try:
+                raw_data = self.fetch_raw_symbol_frame(
+                    api_key,
+                    symbol,
+                    calendar,
+                    start_session,
+                    end_session,
+                    data_frequency,
+                )
+                raw_data.index = pd.to_datetime(raw_data.index, utc=True)
+
+                # Filter incoming data to fit start and end sessions.
+                raw_data = raw_data[
+                    (raw_data.index >= start_session) &
+                    (raw_data.index <= end_session)
+                ]
+
+                # Filter out any duplicates entries, keep last one, since
+                # previous frame is probably an incomplete.
+                raw_data = raw_data[~raw_data.index.duplicated(keep='last')]
+
+                return raw_data
+
+            except Exception:
+                log.exception(
+                    'Exception raised fetching {name} data. Retrying.'
+                    .format(name=self.name)
+                )
+        else:
+            raise ValueError(
+                'Failed to download data for symbol {sym} '
+                'after {n} attempts.'.format(
+                    sym=symbol,
+                    n=retries,
+                )
+            )
+
+
+def _dtypes_to_cols(dtypes):
+    return [name for name, _ in dtypes]

catalyst/data/bundles/base_pricing.py

@@ -0,0 +1,76 @@
+#
+# Copyright 2017 Enigma MPC, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from catalyst.data.bundles.base import BaseBundle
+from catalyst.utils.memoize import lazyval
+
+
+class BasePricingBundle(BaseBundle):
+    @lazyval
+    def md_dtypes(self):
+        return [
+            ('symbol', 'object'),
+            ('start_date', 'datetime64[ns]'),
+            ('end_date', 'datetime64[ns]'),
+            ('ac_date', 'datetime64[ns]'),
+            ('min_trade_size', 'float'),
+        ]
+
+    @lazyval
+    def dtypes(self):
+        return [
+            ('date', 'datetime64[ns]'),
+            ('open', 'float64'),
+            ('high', 'float64'),
+            ('low', 'float64'),
+            ('close', 'float64'),
+            ('volume', 'float64'),
+        ]
+
+
+class BaseCryptoPricingBundle(BasePricingBundle):
+    @lazyval
+    def calendar_name(self):
+        return 'OPEN'
+
+    @lazyval
+    def minutes_per_day(self):
+        return 1440
+
+    @property
+    def splits(self):
+        return []
+
+    @property
+    def dividends(self):
+        return []
+
+
+class BaseEquityPricingBundle(BasePricingBundle):
+    @lazyval
+    def calendar_name(self):
+        return 'NYSE'
+
+    @lazyval
+    def minutes_per_day(self):
+        return 390
+
+    @property
+    def splits(self):
+        return self._splits
+
+    @property
+    def dividends(self):
+        return self._dividends

catalyst/data/bundles/core.py

@@ -0,0 +1,711 @@
+from collections import namedtuple
+import errno
+from io import BytesIO
+import os
+import requests
+import shutil
+import warnings
+
+from contextlib2 import ExitStack
+import click
+import pandas as pd
+from toolz import curry, complement, take
+
+from ..us_equity_pricing import (
+    BcolzDailyBarReader,
+    BcolzDailyBarWriter,
+    SQLiteAdjustmentReader,
+    SQLiteAdjustmentWriter,
+)
+from ..minute_bars import (
+    BcolzMinuteBarReader,
+    BcolzMinuteBarWriter,
+)
+from catalyst.assets import AssetDBWriter, AssetFinder, ASSET_DB_VERSION
+from catalyst.assets.asset_db_migrations import downgrade
+from catalyst.utils.cache import (
+    dataframe_cache,
+    working_dir,
+    working_file,
+)
+from catalyst.utils.compat import mappingproxy
+from catalyst.utils.input_validation import ensure_timestamp, optionally
+import catalyst.utils.paths as pth
+from catalyst.utils.preprocess import preprocess
+from catalyst.utils.calendars import get_calendar
+from catalyst.utils.cli import maybe_show_progress
+
+ONE_MEGABYTE = 1024 * 1024
+
+
+def asset_db_path(bundle_name, timestr, environ=None, db_version=None):
+    return pth.data_path(
+        asset_db_relative(bundle_name, timestr, environ, db_version),
+        environ=environ,
+    )
+
+
+def minute_path(bundle_name, timestr, environ=None):
+    return pth.data_path(
+        minute_relative(bundle_name, timestr, environ),
+        environ=environ,
+    )
+
+
+def daily_path(bundle_name, timestr, environ=None):
+    return pth.data_path(
+        daily_relative(bundle_name, timestr, environ),
+        environ=environ,
+    )
+
+
+def adjustment_db_path(bundle_name, timestr, environ=None):
+    return pth.data_path(
+        adjustment_db_relative(bundle_name, timestr, environ),
+        environ=environ,
+    )
+
+
+def cache_path(bundle_name, environ=None):
+    return pth.data_path(
+        cache_relative(bundle_name, environ),
+        environ=environ,
+    )
+
+
+def adjustment_db_relative(bundle_name, timestr, environ=None):
+    return bundle_name, timestr, 'adjustments.sqlite'
+
+
+def cache_relative(bundle_name, timestr, environ=None):
+    return bundle_name, '.cache'
+
+
+def daily_relative(bundle_name, timestr, environ=None):
+    return bundle_name, timestr, 'daily_equities.bcolz'
+
+
+def minute_relative(bundle_name, timestr, environ=None):
+    return bundle_name, timestr, 'minute_equities.bcolz'
+
+
+def asset_db_relative(bundle_name, timestr, environ=None, db_version=None):
+    db_version = ASSET_DB_VERSION if db_version is None else db_version
+
+    return bundle_name, timestr, 'assets-%d.sqlite' % db_version
+
+
+def to_bundle_ingest_dirname(ts):
+    """Convert a pandas Timestamp into the name of the directory for the
+    ingestion.
+
+    Parameters
+    ----------
+    ts : pandas.Timestamp
+        The time of the ingestions
+
+    Returns
+    -------
+    name : str
+        The name of the directory for this ingestion.
+    """
+    return ts.isoformat().replace(':', ';')
+
+
+def from_bundle_ingest_dirname(cs):
+    """Read a bundle ingestion directory name into a pandas Timestamp.
+
+    Parameters
+    ----------
+    cs : str
+        The name of the directory.
+
+    Returns
+    -------
+    ts : pandas.Timestamp
+        The time when this ingestion happened.
+    """
+    return pd.Timestamp(cs.replace(';', ':'))
+
+
+def ingestions_for_bundle(bundle, environ=None):
+    return sorted(
+        (from_bundle_ingest_dirname(ing)
+         for ing in os.listdir(pth.data_path([bundle], environ))
+         if not pth.hidden(ing)),
+        reverse=True,
+    )
+
+
+def download_with_progress(url, chunk_size, **progress_kwargs):
+    """
+    Download streaming data from a URL, printing progress information to the
+    terminal.
+
+    Parameters
+    ----------
+    url : str
+        A URL that can be understood by ``requests.get``.
+    chunk_size : int
+        Number of bytes to read at a time from requests.
+    **progress_kwargs
+        Forwarded to click.progressbar.
+
+    Returns
+    -------
+    data : BytesIO
+        A BytesIO containing the downloaded data.
+    """
+    resp = requests.get(url, stream=True)
+    resp.raise_for_status()
+
+    total_size = int(resp.headers['content-length'])
+    data = BytesIO()
+
+    progress_kwargs['length'] = total_size
+    with maybe_show_progress(None, True, **progress_kwargs) as pbar:
+        for chunk in resp.iter_content(chunk_size=chunk_size):
+            data.write(chunk)
+            pbar.update(len(chunk))
+
+    data.seek(0)
+    return data
+
+
+def download_without_progress(url):
+    """
+    Download data from a URL, returning a BytesIO containing the loaded data.
+
+    Parameters
+    ----------
+    url : str
+        A URL that can be understood by ``requests.get``.
+
+    Returns
+    -------
+    data : BytesIO
+        A BytesIO containing the downloaded data.
+    """
+    resp = requests.get(url)
+    resp.raise_for_status()
+    return BytesIO(resp.content)
+
+
+RegisteredBundle = namedtuple(
+    'RegisteredBundle',
+    ['calendar_name',
+     'start_session',
+     'end_session',
+     'minutes_per_day',
+     'ingest',
+     'create_writers']
+)
+
+BundleData = namedtuple(
+    'BundleData',
+    'asset_finder minute_bar_reader daily_bar_reader '
+    'adjustment_reader',
+)
+
+BundleCore = namedtuple(
+    'BundleCore',
+    'bundles register_bundle register unregister ingest load clean',
+)
+
+
+class UnknownBundle(click.ClickException, LookupError):
+    """Raised if no bundle with the given name was registered.
+    """
+    exit_code = 1
+
+    def __init__(self, name):
+        super(UnknownBundle, self).__init__(
+            'No bundle registered with the name %r' % name,
+        )
+        self.name = name
+
+    def __str__(self):
+        return self.message
+
+
+class BadClean(click.ClickException, ValueError):
+    """Exception indicating that an invalid argument set was passed to
+    ``clean``.
+
+    Parameters
+    ----------
+    before, after, keep_last : any
+        The bad arguments to ``clean``.
+
+    See Also
+    --------
+    clean
+    """
+    def __init__(self, before, after, keep_last):
+        super(BadClean, self).__init__(
+            'Cannot pass a combination of `before` and `after` with'
+            '`keep_last`. Got: before=%r, after=%r, keep_n=%r\n' % (
+                before,
+                after,
+                keep_last,
+            ),
+        )
+
+    def __str__(self):
+        return self.message
+
+
+def _make_bundle_core():
+    """Create a family of data bundle functions that read from the same
+    bundle mapping.
+
+    Returns
+    -------
+    bundles : mappingproxy
+        The mapping of bundles to bundle payloads.
+    register_bundle : Bundle
+        A bundle instance to add to the ``bundles`` mapping.
+    register : callable
+        The function which registers new bundles in the ``bundles`` mapping.
+    unregister : callable
+        The function which deregisters bundles from the ``bundles`` mapping.
+    ingest : callable
+        The function which downloads and write data for a given data bundle.
+    load : callable
+        The function which loads the ingested bundles back into memory.
+    clean : callable
+        The function which cleans up data written with ``ingest``.
+    """
+    _bundles = {}  # the registered bundles
+    # Expose _bundles through a proxy so that users cannot mutate this
+    # accidentally. Users may go through `register` to update this which will
+    # warn when trampling another bundle.
+    bundles = mappingproxy(_bundles)
+
+    def register_bundle(bundle_cls,
+                        asset_filter=None,
+                        start_session=None,
+                        end_session=None,
+                        create_writers=True):
+        bundle = bundle_cls(asset_filter=asset_filter)
+        return register(
+            bundle.name,
+            bundle.ingest,
+            calendar_name=bundle.calendar_name,
+            minutes_per_day=bundle.minutes_per_day,
+            start_session=start_session,
+            end_session=end_session,
+            create_writers=create_writers,
+        )
+
+    @curry
+    def register(name,
+                 f,
+                 calendar_name='OPEN',
+                 start_session=None,
+                 end_session=None,
+                 minutes_per_day=1440,
+                 create_writers=True):
+        """Register a data bundle ingest function.
+
+        Parameters
+        ----------
+        name : str
+            The name of the bundle.
+        f : callable
+            The ingest function. This function will be passed:
+
+              environ : mapping
+                  The environment this is being run with.
+              asset_db_writer : AssetDBWriter
+                  The asset db writer to write into.
+              minute_bar_writer : BcolzMinuteBarWriter
+                  The minute bar writer to write into.
+              daily_bar_writer : BcolzDailyBarWriter
+                  The daily bar writer to write into.
+              adjustment_writer : SQLiteAdjustmentWriter
+                  The adjustment db writer to write into.
+              calendar : catalyst.utils.calendars.TradingCalendar
+                  The trading calendar to ingest for.
+              start_session : pd.Timestamp
+                  The first session of data to ingest.
+              end_session : pd.Timestamp
+                  The last session of data to ingest.
+              cache : DataFrameCache
+                  A mapping object to temporarily store dataframes.
+                  This should be used to cache intermediates in case the load
+                  fails. This will be automatically cleaned up after a
+                  successful load.
+              show_progress : bool
+                  Show the progress for the current load where possible.
+        calendar_name : str, optional
+            The name of a calendar used to align bundle data.
+            Default is 'NYSE'.
+        start_session : pd.Timestamp, optional
+            The first session for which we want data. If not provided,
+            or if the date lies outside the range supported by the
+            calendar, the first_session of the calendar is used.
+        end_session : pd.Timestamp, optional
+            The last session for which we want data. If not provided,
+            or if the date lies outside the range supported by the
+            calendar, the last_session of the calendar is used.
+        minutes_per_day : int, optional
+            The number of minutes in each normal trading day.
+        create_writers : bool, optional
+            Should the ingest machinery create the writers for the ingest
+            function. This can be disabled as an optimization for cases where
+            they are not needed, like the ``quantopian-quandl`` bundle.
+
+        Notes
+        -----
+        This function my be used as a decorator, for example:
+
+        .. code-block:: python
+
+           @register('quandl')
+           def quandl_ingest_function(...):
+               ...
+
+        See Also
+        --------
+        catalyst.data.bundles.bundles
+        """
+        if name in bundles:
+            warnings.warn(
+                'Overwriting bundle with name %r' % name,
+                stacklevel=3,
+            )
+
+        # NOTE: We don't eagerly compute calendar values here because
+        # `register` is called at module scope in catalyst, and creating a
+        # calendar currently takes between 0.5 and 1 seconds, which causes a
+        # noticeable delay on the catalyst CLI.
+        _bundles[name] = RegisteredBundle(
+            calendar_name=calendar_name,
+            start_session=start_session,
+            end_session=end_session,
+            minutes_per_day=minutes_per_day,
+            ingest=f,
+            create_writers=create_writers,
+        )
+        return f
+
+    def unregister(name):
+        """Unregister a bundle.
+
+        Parameters
+        ----------
+        name : str
+            The name of the bundle to unregister.
+
+        Raises
+        ------
+        UnknownBundle
+            Raised when no bundle has been registered with the given name.
+
+        See Also
+        --------
+        catalyst.data.bundles.bundles
+        """
+        try:
+            del _bundles[name]
+        except KeyError:
+            raise UnknownBundle(name)
+
+    def ingest(name,
+               environ=os.environ,
+               timestamp=None,
+               assets_versions=(),
+               show_progress=False,
+               is_compile=False):
+        """Ingest data for a given bundle.
+
+        Parameters
+        ----------
+        name : str
+            The name of the bundle.
+        environ : mapping, optional
+            The environment variables. By default this is os.environ.
+        timestamp : datetime, optional
+            The timestamp to use for the load.
+            By default this is the current time.
+        assets_versions : Iterable[int], optional
+            Versions of the assets db to which to downgrade.
+        show_progress : bool, optional
+            Tell the ingest function to display the progress where possible.
+        """
+        try:
+            bundle = bundles[name]
+        except KeyError:
+            raise UnknownBundle(name)
+
+        calendar = get_calendar(bundle.calendar_name)
+
+        start_session = bundle.start_session
+        end_session = bundle.end_session
+
+        if start_session is None or start_session < calendar.first_session:
+            start_session = calendar.first_session
+
+        if end_session is None or end_session > calendar.last_session:
+            end_session = calendar.last_session
+
+        if timestamp is None:
+            timestamp = pd.Timestamp.utcnow()
+        timestamp = timestamp.tz_convert('utc').tz_localize(None)
+
+        timestr = to_bundle_ingest_dirname(timestamp)
+        cachepath = cache_path(name, environ=environ)
+        pth.ensure_directory(pth.data_path([name, timestr], environ=environ))
+        pth.ensure_directory(cachepath)
+        with dataframe_cache(cachepath, clean_on_failure=False) as cache, \
+                ExitStack() as stack:
+            # we use `cleanup_on_failure=False` so that we don't purge the
+            # cache directory if the load fails in the middle
+            if bundle.create_writers:
+                wd = stack.enter_context(working_dir(
+                    pth.data_path([], environ=environ))
+                )
+                daily_bars_path = wd.ensure_dir(
+                    *daily_relative(
+                        name, timestr, environ=environ,
+                    )
+                )
+                daily_bar_writer = BcolzDailyBarWriter(
+                    daily_bars_path,
+                    calendar,
+                    start_session,
+                    end_session,
+                )
+                # Do an empty write to ensure that the daily ctables exist
+                # when we create the SQLiteAdjustmentWriter below. The
+                # SQLiteAdjustmentWriter needs to open the daily ctables so
+                # that it can compute the adjustment ratios for the dividends.
+                daily_bar_writer.write(())
+
+                minute_bar_writer = BcolzMinuteBarWriter(
+                    wd.ensure_dir(*minute_relative(
+                        name, timestr, environ=environ)
+                    ),
+                    calendar,
+                    start_session,
+                    end_session,
+                    minutes_per_day=bundle.minutes_per_day,
+                )
+
+                assets_db_path = wd.getpath(*asset_db_relative(
+                    name, timestr, environ=environ,
+                ))
+                asset_db_writer = AssetDBWriter(assets_db_path)
+
+                adjustment_db_writer = stack.enter_context(
+                    SQLiteAdjustmentWriter(
+                        wd.getpath(*adjustment_db_relative(
+                            name, timestr, environ=environ)),
+                        BcolzDailyBarReader(daily_bars_path),
+                        calendar.all_sessions,
+                        overwrite=True,
+                    )
+                )
+            else:
+                daily_bar_writer = None
+                minute_bar_writer = None
+                asset_db_writer = None
+                adjustment_db_writer = None
+                if assets_versions:
+                    raise ValueError('Need to ingest a bundle that creates '
+                                     'writers in order to downgrade the assets'
+                                     ' db.')
+            bundle.ingest(
+                environ,
+                asset_db_writer,
+                minute_bar_writer,
+                daily_bar_writer,
+                adjustment_db_writer,
+                calendar,
+                start_session,
+                end_session,
+                cache,
+                show_progress,
+                is_compile,
+                pth.data_path([name, timestr], environ=environ),
+            )
+
+            for version in sorted(set(assets_versions), reverse=True):
+                version_path = wd.getpath(*asset_db_relative(
+                    name, timestr, environ=environ, db_version=version,
+                ))
+                with working_file(version_path) as wf:
+                    shutil.copy2(assets_db_path, wf.path)
+                    downgrade(wf.path, version)
+
+    def most_recent_data(bundle_name, timestamp, environ=None):
+        """Get the path to the most recent data after ``date``for the
+        given bundle.
+
+        Parameters
+        ----------
+        bundle_name : str
+            The name of the bundle to lookup.
+        timestamp : datetime
+            The timestamp to begin searching on or before.
+        environ : dict, optional
+            An environment dict to forward to catalyst_root.
+        """
+        if bundle_name not in bundles:
+            raise UnknownBundle(bundle_name)
+
+        try:
+            candidates = os.listdir(
+                pth.data_path([bundle_name], environ=environ),
+            )
+            return pth.data_path(
+                [bundle_name,
+                 max(
+                     filter(complement(pth.hidden), candidates),
+                     key=from_bundle_ingest_dirname,
+                 )],
+                environ=environ,
+            )
+        except (ValueError, OSError) as e:
+            if getattr(e, 'errno', errno.ENOENT) != errno.ENOENT:
+                raise
+            raise ValueError(
+                'no data for bundle {bundle!r} on or before {timestamp}\n'
+                'maybe you need to run: $ catalyst ingest -b {bundle}'.format(
+                    bundle=bundle_name,
+                    timestamp=timestamp,
+                ),
+            )
+
+    def load(name, environ=os.environ, timestamp=None):
+        """Loads a previously ingested bundle.
+
+        Parameters
+        ----------
+        name : str
+            The name of the bundle.
+        environ : mapping, optional
+            The environment variables. Defaults of os.environ.
+        timestamp : datetime, optional
+            The timestamp of the data to lookup.
+            Defaults to the current time.
+
+        Returns
+        -------
+        bundle_data : BundleData
+            The raw data readers for this bundle.
+        """
+        if timestamp is None:
+            timestamp = pd.Timestamp.utcnow()
+        timestr = most_recent_data(name, timestamp, environ=environ)
+        return BundleData(
+            asset_finder=AssetFinder(
+                asset_db_path(name, timestr, environ=environ),
+            ),
+            minute_bar_reader=BcolzMinuteBarReader(
+                minute_path(name, timestr, environ=environ),
+            ),
+            daily_bar_reader=BcolzDailyBarReader(
+                daily_path(name, timestr, environ=environ),
+            ),
+            adjustment_reader=SQLiteAdjustmentReader(
+                adjustment_db_path(name, timestr, environ=environ),
+            ),
+        )
+
+    @preprocess(
+        before=optionally(ensure_timestamp),
+        after=optionally(ensure_timestamp),
+    )
+    def clean(name,
+              before=None,
+              after=None,
+              keep_last=None,
+              environ=os.environ):
+        """Clean up data that was created with ``ingest`` or
+        ``$ python -m catalyst ingest``
+
+        Parameters
+        ----------
+        name : str
+            The name of the bundle to remove data for.
+        before : datetime, optional
+            Remove data ingested before this date.
+            This argument is mutually exclusive with: keep_last
+        after : datetime, optional
+            Remove data ingested after this date.
+            This argument is mutually exclusive with: keep_last
+        keep_last : int, optional
+            Remove all but the last ``keep_last`` ingestions.
+            This argument is mutually exclusive with:
+              before
+              after
+        environ : mapping, optional
+            The environment variables. Defaults of os.environ.
+
+        Returns
+        -------
+        cleaned : set[str]
+            The names of the runs that were removed.
+
+        Raises
+        ------
+        BadClean
+            Raised when ``before`` and or ``after`` are passed with
+            ``keep_last``. This is a subclass of ``ValueError``.
+        """
+        try:
+            all_runs = sorted(
+                filter(
+                    complement(pth.hidden),
+                    os.listdir(pth.data_path([name], environ=environ)),
+                ),
+                key=from_bundle_ingest_dirname,
+            )
+        except OSError as e:
+            if e.errno != errno.ENOENT:
+                raise
+            raise UnknownBundle(name)
+        if ((before is not None or after is not None) and
+                keep_last is not None):
+            raise BadClean(before, after, keep_last)
+
+        if keep_last is None:
+            def should_clean(name):
+                dt = from_bundle_ingest_dirname(name)
+                return (
+                    (before is not None and dt < before) or
+                    (after is not None and dt > after)
+                )
+
+        elif keep_last >= 0:
+            last_n_dts = set(take(keep_last, reversed(all_runs)))
+
+            def should_clean(name):
+                return name not in last_n_dts
+        else:
+            raise BadClean(before, after, keep_last)
+
+        cleaned = set()
+        for run in all_runs:
+            if should_clean(run):
+                path = pth.data_path([name, run], environ=environ)
+                shutil.rmtree(path)
+                cleaned.add(path)
+
+        return cleaned
+
+    return BundleCore(
+        bundles,
+        register_bundle,
+        register,
+        unregister,
+        ingest,
+        load,
+        clean,
+    )
+
+
+bundles, register_bundle, register, unregister, ingest, load, clean = \
+    _make_bundle_core()

catalyst/data/bundles/poloniex.py

@@ -0,0 +1,186 @@
+#
+# Copyright 2017 Enigma MPC, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+from six.moves.urllib.parse import urlencode
+
+import pandas as pd
+
+from catalyst.data.bundles.core import register_bundle
+from catalyst.data.bundles.base_pricing import BaseCryptoPricingBundle
+from catalyst.utils.memoize import lazyval
+
+from catalyst.curate.poloniex import PoloniexCurator
+
+
+class PoloniexBundle(BaseCryptoPricingBundle):
+    @lazyval
+    def name(self):
+        return 'poloniex'
+
+    @lazyval
+    def exchange(self):
+        return 'POLO'
+
+    @lazyval
+    def frequencies(self):
+        return set((
+            'daily',
+            'minute',
+        ))
+
+    @lazyval
+    def tar_url(self):
+        return (
+            'https://s3.amazonaws.com/enigmaco/catalyst-bundles/'
+            'poloniex/poloniex-bundle.tar.gz'
+        )
+
+    @lazyval
+    def wait_time(self):
+        return pd.Timedelta(milliseconds=170)
+
+    def fetch_raw_metadata_frame(self, api_key, page_number):
+        if page_number > 1:
+            return pd.DataFrame([])
+
+        raw = pd.read_json(
+            self._format_metadata_url(
+              api_key,
+              page_number,
+            ),
+            orient='index',
+        )
+
+        raw = raw.sort_index().reset_index()
+        raw.rename(
+            columns={'index': 'symbol'},
+            inplace=True,
+        )
+
+        raw = raw[raw['isFrozen'] == 0]
+        return raw
+
+    def post_process_symbol_metadata(self, asset_id, sym_md, sym_data):
+        start_date = sym_data.index[0]
+        end_date = sym_data.index[-1]
+        ac_date = end_date + pd.Timedelta(days=1)
+        min_trade_size = 0.00000001
+
+        return (
+            sym_md.symbol,
+            start_date,
+            end_date,
+            ac_date,
+            min_trade_size,
+        )
+
+    def fetch_raw_symbol_frame(self,
+                               api_key,
+                               symbol,
+                               calendar,
+                               start_date,
+                               end_date,
+                               frequency):
+
+        # TODO: replace this with direct exchange call
+        # The end date and frequency should be used to
+        # calculate the number of bars
+        if(frequency == 'minute'):
+            pc = PoloniexCurator()
+            raw = pc.onemin_to_dataframe(symbol, start_date, end_date)
+
+        else:
+            raw = pd.read_json(
+                self._format_data_url(
+                    api_key,
+                    symbol,
+                    start_date,
+                    end_date,
+                    frequency,
+                ),
+                orient='records',
+            )
+            raw.set_index('date', inplace=True)
+
+        # BcolzDailyBarReader introduces a 1/1000 factor in the way
+        # pricing is stored on disk, which we compensate here to get
+        # the right pricing amounts
+        # ref: data/us_equity_pricing.py
+        scale = 1
+        raw.loc[:, 'open'] /= scale
+        raw.loc[:, 'high'] /= scale
+        raw.loc[:, 'low'] /= scale
+        raw.loc[:, 'close'] /= scale
+        raw.loc[:, 'volume'] *= scale
+
+        return raw
+
+    '''
+    HELPER METHODS
+    '''
+
+    def _format_metadata_url(self, api_key, page_number):
+        query_params = [
+            ('command', 'returnTicker'),
+        ]
+
+        return self._format_polo_query(query_params)
+
+    def _format_data_url(self,
+                         api_key,
+                         symbol,
+                         start_date,
+                         end_date,
+                         data_frequency):
+        period_map = {
+            'daily': 86400,
+        }
+
+        try:
+            period = period_map[data_frequency]
+        except KeyError:
+            return None
+
+        query_params = [
+            ('command', 'returnChartData'),
+            ('currencyPair', symbol),
+            ('start', start_date.value / 10**9),
+            ('end', end_date.value / 10**9),
+            ('period', period),
+        ]
+
+        return self._format_polo_query(query_params)
+
+    def _format_polo_query(self, query_params):
+        # TODO: got against the exchange object
+        return 'https://poloniex.com/public?{query}'.format(
+            query=urlencode(query_params),
+        )
+
+
+'''
+As a second parameter, you can pass an array of currency pairs
+that will be processed as an asset_filter to only process that
+subset of assets in the bundle, such as:
+register_bundle(PoloniexBundle, ['USDT_BTC',])
+
+For a production environment make sure to use (to bundle all pairs):
+register_bundle(PoloniexBundle)
+'''
+if 'ingest' in sys.argv and '-c' in sys.argv:
+    register_bundle(PoloniexBundle)
+else:
+    register_bundle(PoloniexBundle, create_writers=False)

catalyst/data/bundles/quandl.py

@@ -0,0 +1,222 @@
+#
+# Copyright 2017 Enigma MPC, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datetime import datetime
+
+import pandas as pd
+from six.moves.urllib.parse import urlencode
+
+from catalyst.data.bundles.core import register_bundle
+from catalyst.data.bundles.base_pricing import BaseEquityPricingBundle
+from catalyst.utils.memoize import lazyval
+
+"""
+Module for building a complete daily dataset from Quandl's WIKI dataset.
+"""
+from logbook import Logger
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.utils.calendars import register_calendar_alias
+
+
+log = Logger(__name__, level=LOG_LEVEL)
+seconds_per_call = (pd.Timedelta('10 minutes') / 2000).total_seconds()
+
+
+class QuandlBundle(BaseEquityPricingBundle):
+    @lazyval
+    def name(self):
+        return 'quandl'
+
+    @lazyval
+    def exchange(self):
+        return 'QUANDL'
+
+    @lazyval
+    def frequencies(self):
+        return set(('daily',))
+
+    @lazyval
+    def tar_url(self):
+        return 'https://s3.amazonaws.com/quantopian-public-zipline-data/quandl'
+
+    @lazyval
+    def wait_time(self):
+        return pd.Timedelta(milliseconds=300)
+
+    @lazyval
+    def _excluded_symbols(self):
+        """
+        Invalid symbols that quandl has had in its metadata:
+        """
+        return frozenset({'TEST123456789'})
+
+    def fetch_raw_metadata_frame(self, api_key, page_number):
+        raw = pd.read_csv(
+            self._format_metadata_url(api_key, page_number),
+            date_parser=pd.tseries.tools.to_datetime,
+            parse_dates=[
+                'oldest_available_date',
+                'newest_available_date',
+            ],
+            dtype={
+                'dataset_code': 'str',
+                'name': 'str',
+                'oldest_available_date': 'str',
+                'newest_available_date': 'str',
+            },
+            usecols=[
+                'dataset_code',
+                'name',
+                'oldest_available_date',
+                'newest_available_date',
+            ],
+        ).rename(
+            columns={
+                'dataset_code': 'symbol',
+                'name': 'asset_name',
+                'oldest_available_date': 'start_date',
+                'newest_available_date': 'end_date',
+            },
+        )
+
+        raw['start_date'] = raw['start_date'].astype(datetime)
+        raw['end_date'] = raw['end_date'].astype(datetime)
+        raw['ac_date'] = raw['end_date'] + pd.Timedelta(days=1)
+
+        # Filter out invalid symbols
+        raw = raw[~raw.symbol.isin(self._excluded_symbols)]
+
+        # cut out all the other stuff in the name column. We need to
+        # escape the paren because it is actually splitting on a regex
+        raw.asset_name = raw.asset_name.str.split(r' \(', 1).str.get(0)
+
+        return raw
+
+    def fetch_raw_symbol_frame(self,
+                               api_key,
+                               symbol,
+                               calendar,
+                               start_session,
+                               end_session,
+                               data_frequency):
+        raw_data = pd.read_csv(
+            self._format_wiki_url(
+                api_key,
+                symbol,
+                start_session,
+                end_session,
+                data_frequency,
+            ),
+            parse_dates=['Date'],
+            index_col='Date',
+            usecols=[
+                'Open',
+                'High',
+                'Low',
+                'Close',
+                'Volume',
+                'Date',
+                'Ex-Dividend',
+                'Split Ratio',
+            ],
+            na_values=['NA'],
+        ).rename(columns={
+            'Open': 'open',
+            'High': 'high',
+            'Low': 'low',
+            'Close': 'close',
+            'Volume': 'volume',
+            'Date': 'date',
+            'Ex-Dividend': 'ex_dividend',
+            'Split Ratio': 'split_ratio',
+        })
+
+        sessions = calendar.sessions_in_range(start_session, end_session)
+
+        return raw_data.reindex(
+            sessions.tz_localize(None),
+            copy=False,
+        ).fillna(0.0)
+
+    def post_process_symbol_metadata(self, asset_id, sym_md, sym_data):
+        self._update_splits(asset_id, sym_data)
+        self._update_dividends(asset_id, sym_data)
+
+        return sym_md
+
+    def _update_splits(self, asset_id, raw_data):
+        split_ratios = raw_data.split_ratio
+        df = pd.DataFrame({'ratio': 1 / split_ratios[split_ratios != 1]})
+        df.index.name = 'effective_date'
+        df.reset_index(inplace=True)
+        df['sid'] = asset_id
+        self.splits.append(df)
+
+    def _update_dividends(self, asset_id, raw_data):
+        divs = raw_data.ex_dividend
+        df = pd.DataFrame({'amount': divs[divs != 0]})
+        df.index.name = 'ex_date'
+        df.reset_index(inplace=True)
+        df['sid'] = asset_id
+        # we do not have this data in the WIKI dataset
+        df['record_date'] = df['declared_date'] = df['pay_date'] = pd.NaT
+        self.dividends.append(df)
+
+    def _format_metadata_url(self, api_key, page_number):
+        """Build the query RL for the quandl WIKI metadata.
+        """
+        query_params = [
+            ('per_page', '100'),
+            ('sort_by', 'id'),
+            ('page', str(page_number)),
+            ('database_code', 'WIKI'),
+        ]
+        if api_key is not None:
+            query_params = [('api_key', api_key)] + query_params
+
+        return (
+            'https://www.quandl.com/api/v3/datasets.csv?'
+            + urlencode(query_params)
+        )
+
+    def _format_wiki_url(self,
+                         api_key,
+                         symbol,
+                         start_date,
+                         end_date,
+                         data_frequency):
+        """
+        Build a query URL for a quandl WIKI dataset.
+        """
+        query_params = [
+            ('start_date', start_date.strftime('%Y-%m-%d')),
+            ('end_date', end_date.strftime('%Y-%m-%d')),
+            ('order', 'asc'),
+        ]
+        if api_key is not None:
+            query_params = [('api_key', api_key)] + query_params
+
+        return (
+            "https://www.quandl.com/api/v3/datasets/WIKI/"
+            "{symbol}.csv?{query}".format(
+                symbol=symbol,
+                query=urlencode(query_params),
+            )
+        )
+
+
+register_calendar_alias('QUANDL', 'NYSE')
+register_bundle(QuandlBundle)

catalyst/data/bundles/yahoo.py

@@ -0,0 +1,203 @@
+import os
+
+import numpy as np
+import pandas as pd
+from pandas_datareader.data import DataReader
+import requests
+
+from catalyst.utils.calendars import register_calendar_alias
+from catalyst.utils.cli import maybe_show_progress
+from .core import register
+
+
+def _cachpath(symbol, type_):
+    return '-'.join((symbol.replace(os.path.sep, '_'), type_))
+
+
+def yahoo_equities(symbols, start=None, end=None):
+    """Create a data bundle ingest function from a set of symbols loaded from
+    yahoo.
+
+    Parameters
+    ----------
+    symbols : iterable[str]
+        The ticker symbols to load data for.
+    start : datetime, optional
+        The start date to query for. By default this pulls the full history
+        for the calendar.
+    end : datetime, optional
+        The end date to query for. By default this pulls the full history
+        for the calendar.
+
+    Returns
+    -------
+    ingest : callable
+        The bundle ingest function for the given set of symbols.
+
+    Examples
+    --------
+    This code should be added to ~/.catalyst/extension.py
+
+    .. code-block:: python
+
+       from catalyst.data.bundles import yahoo_equities, register
+
+       symbols = (
+           'AAPL',
+           'IBM',
+           'MSFT',
+       )
+       register('my_bundle', yahoo_equities(symbols))
+
+    Notes
+    -----
+    The sids for each symbol will be the index into the symbols sequence.
+    """
+    # strict this in memory so that we can reiterate over it
+    symbols = tuple(symbols)
+
+    def ingest(environ,
+               asset_db_writer,
+               minute_bar_writer,  # unused
+               daily_bar_writer,
+               adjustment_writer,
+               calendar,
+               start_session,
+               end_session,
+               cache,
+               show_progress,
+               output_dir,
+               # pass these as defaults to make them 'nonlocal' in py2
+               start=start,
+               end=end):
+        if start is None:
+            start = start_session
+        if end is None:
+            end = None
+
+        metadata = pd.DataFrame(np.empty(len(symbols), dtype=[
+            ('start_date', 'datetime64[ns]'),
+            ('end_date', 'datetime64[ns]'),
+            ('auto_close_date', 'datetime64[ns]'),
+            ('symbol', 'object'),
+        ]))
+
+        def _pricing_iter():
+            sid = 0
+            with maybe_show_progress(
+                    symbols,
+                    show_progress,
+                    label='Downloading Yahoo pricing data: ') as it, \
+                    requests.Session() as session:
+                for symbol in it:
+                    path = _cachpath(symbol, 'ohlcv')
+                    try:
+                        df = cache[path]
+                    except KeyError:
+                        df = cache[path] = DataReader(
+                            symbol,
+                            'yahoo',
+                            start,
+                            end,
+                            session=session,
+                        ).sort_index()
+
+                    # the start date is the date of the first trade and
+                    # the end date is the date of the last trade
+                    start_date = df.index[0]
+                    end_date = df.index[-1]
+                    # The auto_close date is the day after the last trade.
+                    ac_date = end_date + pd.Timedelta(days=1)
+                    metadata.iloc[sid] = start_date, end_date, ac_date, symbol
+
+                    df.rename(
+                        columns={
+                            'Open': 'open',
+                            'High': 'high',
+                            'Low': 'low',
+                            'Close': 'close',
+                            'Volume': 'volume',
+                        },
+                        inplace=True,
+                    )
+                    yield sid, df
+                    sid += 1
+
+        daily_bar_writer.write(_pricing_iter(), show_progress=show_progress)
+
+        symbol_map = pd.Series(metadata.symbol.index, metadata.symbol)
+
+        # Hardcode the exchange to "YAHOO" for all assets and (elsewhere)
+        # register "YAHOO" to resolve to the NYSE calendar, because these are
+        # all equities and thus can use the NYSE calendar.
+        metadata['exchange'] = "YAHOO"
+        asset_db_writer.write(equities=metadata)
+
+        adjustments = []
+        with maybe_show_progress(
+                symbols,
+                show_progress,
+                label='Downloading Yahoo adjustment data: ') as it, \
+                requests.Session() as session:
+            for symbol in it:
+                path = _cachpath(symbol, 'adjustment')
+                try:
+                    df = cache[path]
+                except KeyError:
+                    df = cache[path] = DataReader(
+                        symbol,
+                        'yahoo-actions',
+                        start,
+                        end,
+                        session=session,
+                    ).sort_index()
+
+                df['sid'] = symbol_map[symbol]
+                adjustments.append(df)
+
+        adj_df = pd.concat(adjustments)
+        adj_df.index.name = 'date'
+        adj_df.reset_index(inplace=True)
+
+        splits = adj_df[adj_df.action == 'SPLIT']
+        splits = splits.rename(
+            columns={'value': 'ratio', 'date': 'effective_date'},
+        )
+        splits.drop('action', axis=1, inplace=True)
+
+        dividends = adj_df[adj_df.action == 'DIVIDEND']
+        dividends = dividends.rename(
+            columns={'value': 'amount', 'date': 'ex_date'},
+        )
+        dividends.drop('action', axis=1, inplace=True)
+        # we do not have this data in the yahoo dataset
+        dividends['record_date'] = pd.NaT
+        dividends['declared_date'] = pd.NaT
+        dividends['pay_date'] = pd.NaT
+
+        adjustment_writer.write(splits=splits, dividends=dividends)
+
+    return ingest
+
+
+# bundle used when creating test data
+register(
+    '.test',
+    yahoo_equities(
+        (
+            'AMD',
+            'CERN',
+            'COST',
+            'DELL',
+            'GPS',
+            'INTC',
+            'MMM',
+            'AAPL',
+            'MSFT',
+        ),
+        pd.Timestamp('2004-01-02', tz='utc'),
+        pd.Timestamp('2015-01-01', tz='utc'),
+    ),
+)
+
+register_calendar_alias("YAHOO", "NYSE")

catalyst/data/continuous_future_reader.py

@@ -0,0 +1,359 @@
+import numpy as np
+import pandas as pd
+from catalyst.data.session_bars import SessionBarReader
+
+
+class ContinuousFutureSessionBarReader(SessionBarReader):
+
+    def __init__(self, bar_reader, roll_finders):
+        self._bar_reader = bar_reader
+        self._roll_finders = roll_finders
+
+    def load_raw_arrays(self, columns, start_date, end_date, assets):
+        """
+        Parameters
+        ----------
+        fields : list of str
+            'sid'
+        start_dt: Timestamp
+           Beginning of the window range.
+        end_dt: Timestamp
+           End of the window range.
+        sids : list of int
+           The asset identifiers in the window.
+
+        Returns
+        -------
+        list of np.ndarray
+            A list with an entry per field of ndarrays with shape
+            (minutes in range, sids) with a dtype of float64, containing the
+            values for the respective field over start and end dt range.
+        """
+        rolls_by_asset = {}
+        for asset in assets:
+            rf = self._roll_finders[asset.roll_style]
+            rolls_by_asset[asset] = rf.get_rolls(
+                asset.root_symbol, start_date, end_date, asset.offset)
+        num_sessions = len(
+            self.trading_calendar.sessions_in_range(start_date, end_date))
+        shape = num_sessions, len(assets)
+
+        results = []
+
+        tc = self._bar_reader.trading_calendar
+        sessions = tc.sessions_in_range(start_date, end_date)
+
+        # Get partitions
+        partitions_by_asset = {}
+        for asset in assets:
+            partitions = []
+            partitions_by_asset[asset] = partitions
+            rolls = rolls_by_asset[asset]
+            start = start_date
+            for roll in rolls:
+                sid, roll_date = roll
+                start_loc = sessions.get_loc(start)
+                if roll_date is not None:
+                    end = roll_date - sessions.freq
+                    end_loc = sessions.get_loc(end)
+                else:
+                    end = end_date
+                    end_loc = len(sessions) - 1
+                partitions.append((sid, start, end, start_loc, end_loc))
+                if roll[-1] is not None:
+                    start = sessions[end_loc + 1]
+
+        for column in columns:
+            if column != 'volume' and column != 'sid':
+                out = np.full(shape, np.nan)
+            else:
+                out = np.zeros(shape, dtype=np.int64)
+            for i, asset in enumerate(assets):
+                partitions = partitions_by_asset[asset]
+                for sid, start, end, start_loc, end_loc in partitions:
+                    if column != 'sid':
+                        result = self._bar_reader.load_raw_arrays(
+                            [column], start, end, [sid])[0][:, 0]
+                    else:
+                        result = int(sid)
+                    out[start_loc:end_loc + 1, i] = result
+            results.append(out)
+        return results
+
+    @property
+    def last_available_dt(self):
+        """
+        Returns
+        -------
+        dt : pd.Timestamp
+            The last session for which the reader can provide data.
+        """
+        return self._bar_reader.last_available_dt
+
+    @property
+    def trading_calendar(self):
+        """
+        Returns the catalyst.utils.calendar.trading_calendar used to read
+        the data.  Can be None (if the writer didn't specify it).
+        """
+        return self._bar_reader.trading_calendar
+
+    @property
+    def first_trading_day(self):
+        """
+        Returns
+        -------
+        dt : pd.Timestamp
+            The first trading day (session) for which the reader can provide
+            data.
+        """
+        return self._bar_reader.first_trading_day
+
+    def get_value(self, continuous_future, dt, field):
+        """
+        Retrieve the value at the given coordinates.
+
+        Parameters
+        ----------
+        sid : int
+            The asset identifier.
+        dt : pd.Timestamp
+            The timestamp for the desired data point.
+        field : string
+            The OHLVC name for the desired data point.
+
+        Returns
+        -------
+        value : float|int
+            The value at the given coordinates, ``float`` for OHLC, ``int``
+            for 'volume'.
+
+        Raises
+        ------
+        NoDataOnDate
+            If the given dt is not a valid market minute (in minute mode) or
+            session (in daily mode) according to this reader's tradingcalendar.
+        """
+        rf = self._roll_finders[continuous_future.roll_style]
+        sid = (rf.get_contract_center(continuous_future.root_symbol,
+                                      dt,
+                                      continuous_future.offset))
+        return self._bar_reader.get_value(sid, dt, field)
+
+    def get_last_traded_dt(self, asset, dt):
+        """
+        Get the latest minute on or before ``dt`` in which ``asset`` traded.
+
+        If there are no trades on or before ``dt``, returns ``pd.NaT``.
+
+        Parameters
+        ----------
+        asset : catalyst.asset.Asset
+            The asset for which to get the last traded minute.
+        dt : pd.Timestamp
+            The minute at which to start searching for the last traded minute.
+
+        Returns
+        -------
+        last_traded : pd.Timestamp
+            The dt of the last trade for the given asset, using the input
+            dt as a vantage point.
+        """
+        rf = self._roll_finders[asset.roll_style]
+        sid = (rf.get_contract_center(asset.root_symbol,
+                                      dt,
+                                      asset.offset))
+        if sid is None:
+            return pd.NaT
+        contract = rf.asset_finder.retrieve_asset(sid)
+        return self._bar_reader.get_last_traded_dt(contract, dt)
+
+    @property
+    def sessions(self):
+        """
+        Returns
+        -------
+        sessions : DatetimeIndex
+           All session labels (unionining the range for all assets) which the
+           reader can provide.
+        """
+        return self._bar_reader.sessions
+
+
+class ContinuousFutureMinuteBarReader(SessionBarReader):
+
+    def __init__(self, bar_reader, roll_finders):
+        self._bar_reader = bar_reader
+        self._roll_finders = roll_finders
+
+    def load_raw_arrays(self, columns, start_date, end_date, assets):
+        """
+        Parameters
+        ----------
+        fields : list of str
+           'open', 'high', 'low', 'close', or 'volume'
+        start_dt: Timestamp
+           Beginning of the window range.
+        end_dt: Timestamp
+           End of the window range.
+        sids : list of int
+           The asset identifiers in the window.
+
+        Returns
+        -------
+        list of np.ndarray
+            A list with an entry per field of ndarrays with shape
+            (minutes in range, sids) with a dtype of float64, containing the
+            values for the respective field over start and end dt range.
+        """
+        rolls_by_asset = {}
+
+        tc = self.trading_calendar
+        start_session = tc.minute_to_session_label(start_date)
+        end_session = tc.minute_to_session_label(end_date)
+
+        for asset in assets:
+            rf = self._roll_finders[asset.roll_style]
+            rolls_by_asset[asset] = rf.get_rolls(
+                asset.root_symbol,
+                start_session,
+                end_session, asset.offset)
+
+        sessions = tc.sessions_in_range(start_date, end_date)
+
+        minutes = tc.minutes_in_range(start_date, end_date)
+        num_minutes = len(minutes)
+        shape = num_minutes, len(assets)
+
+        results = []
+
+        # Get partitions
+        partitions_by_asset = {}
+        for asset in assets:
+            partitions = []
+            partitions_by_asset[asset] = partitions
+            rolls = rolls_by_asset[asset]
+            start = start_date
+            for roll in rolls:
+                sid, roll_date = roll
+                start_loc = minutes.searchsorted(start)
+                if roll_date is not None:
+                    _, end = tc.open_and_close_for_session(
+                        roll_date - sessions.freq)
+                    end_loc = minutes.searchsorted(end)
+                else:
+                    end = end_date
+                    end_loc = len(minutes) - 1
+                partitions.append((sid, start, end, start_loc, end_loc))
+                if roll[-1] is not None:
+                    start, _ = tc.open_and_close_for_session(
+                        tc.minute_to_session_label(minutes[end_loc + 1]))
+
+        for column in columns:
+            if column != 'volume':
+                out = np.full(shape, np.nan)
+            else:
+                out = np.zeros(shape, dtype=np.uint32)
+            for i, asset in enumerate(assets):
+                partitions = partitions_by_asset[asset]
+                for sid, start, end, start_loc, end_loc in partitions:
+                    if column != 'sid':
+                        result = self._bar_reader.load_raw_arrays(
+                            [column], start, end, [sid])[0][:, 0]
+                    else:
+                        result = int(sid)
+                    out[start_loc:end_loc + 1, i] = result
+            results.append(out)
+        return results
+
+    @property
+    def last_available_dt(self):
+        """
+        Returns
+        -------
+        dt : pd.Timestamp
+            The last session for which the reader can provide data.
+        """
+        return self._bar_reader.last_available_dt
+
+    @property
+    def trading_calendar(self):
+        """
+        Returns the catalyst.utils.calendar.trading_calendar used to read
+        the data.  Can be None (if the writer didn't specify it).
+        """
+        return self._bar_reader.trading_calendar
+
+    @property
+    def first_trading_day(self):
+        """
+        Returns
+        -------
+        dt : pd.Timestamp
+            The first trading day (session) for which the reader can provide
+            data.
+        """
+        return self._bar_reader.first_trading_day
+
+    def get_value(self, continuous_future, dt, field):
+        """
+        Retrieve the value at the given coordinates.
+
+        Parameters
+        ----------
+        sid : int
+            The asset identifier.
+        dt : pd.Timestamp
+            The timestamp for the desired data point.
+        field : string
+            The OHLVC name for the desired data point.
+
+        Returns
+        -------
+        value : float|int
+            The value at the given coordinates, ``float`` for OHLC, ``int``
+            for 'volume'.
+
+        Raises
+        ------
+        NoDataOnDate
+            If the given dt is not a valid market minute (in minute mode) or
+            session (in daily mode) according to this reader's tradingcalendar.
+        """
+        rf = self._roll_finders[continuous_future.roll_style]
+        sid = (rf.get_contract_center(continuous_future.root_symbol,
+                                      dt,
+                                      continuous_future.offset))
+        return self._bar_reader.get_value(sid, dt, field)
+
+    def get_last_traded_dt(self, asset, dt):
+        """
+        Get the latest minute on or before ``dt`` in which ``asset`` traded.
+
+        If there are no trades on or before ``dt``, returns ``pd.NaT``.
+
+        Parameters
+        ----------
+        asset : catalyst.asset.Asset
+            The asset for which to get the last traded minute.
+        dt : pd.Timestamp
+            The minute at which to start searching for the last traded minute.
+
+        Returns
+        -------
+        last_traded : pd.Timestamp
+            The dt of the last trade for the given asset, using the input
+            dt as a vantage point.
+        """
+        rf = self._roll_finders[asset.roll_style]
+        sid = (rf.get_contract_center(asset.root_symbol,
+                                      dt,
+                                      asset.offset))
+        if sid is None:
+            return pd.NaT
+        contract = rf.asset_finder.retrieve_asset(sid)
+        return self._bar_reader.get_last_traded_dt(contract, dt)
+
+    @property
+    def sessions(self):
+        return self._bar_reader.sessions

catalyst/data/dispatch_bar_reader.py

@@ -0,0 +1,152 @@
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from abc import ABCMeta, abstractmethod
+
+from numpy import (
+    full,
+    nan,
+    int64,
+    float64,
+    zeros
+)
+from six import iteritems, with_metaclass
+
+from catalyst.utils.memoize import lazyval
+
+
+class AssetDispatchBarReader(with_metaclass(ABCMeta)):
+    """
+
+    Parameters
+    ----------
+    - trading_calendar : catalyst.utils.trading_calendar.TradingCalendar
+    - asset_finder : catalyst.assets.AssetFinder
+    - readers : dict
+        A dict mapping Asset type to the corresponding
+        [Minute|Session]BarReader
+    - last_available_dt : pd.Timestamp or None, optional
+        If not provided, infers it by using the min of the
+        last_available_dt values of the underlying readers.
+    """
+    def __init__(
+        self,
+        trading_calendar,
+        asset_finder,
+        readers,
+        last_available_dt=None,
+    ):
+        self._trading_calendar = trading_calendar
+        self._asset_finder = asset_finder
+        self._readers = readers
+        self._last_available_dt = last_available_dt
+
+        for t, r in iteritems(self._readers):
+            assert trading_calendar == r.trading_calendar, \
+                "All readers must share target trading_calendar. " \
+                "Reader={0} for type={1} uses calendar={2} which does not " \
+                "match the desired shared calendar={3} ".format(
+                    r, t, r.trading_calendar, trading_calendar)
+
+    @abstractmethod
+    def _dt_window_size(self, start_dt, end_dt):
+        pass
+
+    @property
+    def _asset_types(self):
+        return self._readers.keys()
+
+    def _make_raw_array_shape(self, start_dt, end_dt, num_sids):
+        return self._dt_window_size(start_dt, end_dt), num_sids
+
+    def _make_raw_array_out(self, field, shape):
+        if field == 'volume':
+            out = zeros(shape, dtype=float64)
+        elif field != 'sid':
+            out = full(shape, nan)
+        else:
+            out = zeros(shape, dtype=int64)
+        return out
+
+    @property
+    def trading_calendar(self):
+        return self._trading_calendar
+
+    @lazyval
+    def last_available_dt(self):
+        if self._last_available_dt is not None:
+            return self._last_available_dt
+        else:
+            return min(r.last_available_dt for r in list(self._readers.values()))
+
+    @lazyval
+    def first_trading_day(self):
+        return max(r.first_trading_day for r in list(self._readers.values()))
+
+    def get_value(self, sid, dt, field):
+        asset = self._asset_finder.retrieve_asset(sid)
+        r = self._readers[type(asset)]
+        return r.get_value(asset, dt, field)
+
+    def get_last_traded_dt(self, asset, dt):
+        r = self._readers[type(asset)]
+        return r.get_last_traded_dt(asset, dt)
+
+    def load_raw_arrays(self, fields, start_dt, end_dt, sids):
+        asset_types = self._asset_types
+        sid_groups = {t: [] for t in asset_types}
+        out_pos = {t: [] for t in asset_types}
+
+        assets = self._asset_finder.retrieve_all(sids)
+
+        for i, asset in enumerate(assets):
+            t = type(asset)
+            sid_groups[t].append(asset)
+            out_pos[t].append(i)
+
+        batched_arrays = {
+            t: self._readers[t].load_raw_arrays(fields,
+                                                start_dt,
+                                                end_dt,
+                                                sid_groups[t])
+            for t in asset_types if sid_groups[t]}
+
+        results = []
+        shape = self._make_raw_array_shape(start_dt, end_dt, len(sids))
+
+        for i, field in enumerate(fields):
+            out = self._make_raw_array_out(field, shape)
+            for t, arrays in iteritems(batched_arrays):
+                out[:, out_pos[t]] = arrays[i]
+            results.append(out)
+
+        return results
+
+
+class AssetDispatchMinuteBarReader(AssetDispatchBarReader):
+
+    def _dt_window_size(self, start_dt, end_dt):
+        return len(self.trading_calendar.minutes_in_range(start_dt, end_dt))
+
+
+class AssetDispatchSessionBarReader(AssetDispatchBarReader):
+
+    def _dt_window_size(self, start_dt, end_dt):
+        return len(self.trading_calendar.sessions_in_range(start_dt, end_dt))
+
+    @lazyval
+    def sessions(self):
+        return self.trading_calendar.sessions_in_range(
+            self.first_trading_day,
+            self.last_available_dt)

catalyst/data/history_loader.py

@@ -0,0 +1,597 @@
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from abc import (
+    ABCMeta,
+    abstractmethod,
+    abstractproperty,
+)
+
+from numpy import concatenate
+from lru import LRU
+from pandas import isnull
+from pandas.tslib import normalize_date
+from toolz import sliding_window
+
+from six import with_metaclass
+
+from catalyst.assets import Equity, Future
+from catalyst.assets.continuous_futures import ContinuousFuture
+from catalyst.lib._int64window import AdjustedArrayWindow as Int64Window
+from catalyst.lib._float64window import AdjustedArrayWindow as Float64Window
+from catalyst.lib.adjustment import Float64Multiply, Float64Add
+from catalyst.utils.cache import ExpiringCache
+from catalyst.utils.math_utils import number_of_decimal_places
+from catalyst.utils.memoize import lazyval
+from catalyst.utils.numpy_utils import float64_dtype
+from catalyst.utils.pandas_utils import find_in_sorted_index
+
+# Default number of decimal places used for rounding asset prices.
+DEFAULT_ASSET_PRICE_DECIMALS = 9
+
+
+class HistoryCompatibleUSEquityAdjustmentReader(object):
+
+    def __init__(self, adjustment_reader):
+        self._adjustments_reader = adjustment_reader
+
+    def load_adjustments(self, columns, dts, assets):
+        """
+        Returns
+        -------
+        adjustments : list[dict[int -> Adjustment]]
+            A list, where each element corresponds to the `columns`, of
+            mappings from index to adjustment objects to apply at that index.
+        """
+        out = [None] * len(columns)
+        for i, column in enumerate(columns):
+            adjs = {}
+            for asset in assets:
+                adjs.update(self._get_adjustments_in_range(
+                    asset, dts, column))
+            out[i] = adjs
+        return out
+
+    def _get_adjustments_in_range(self, asset, dts, field):
+        """
+        Get the Float64Multiply objects to pass to an AdjustedArrayWindow.
+
+        For the use of AdjustedArrayWindow in the loader, which looks back
+        from current simulation time back to a window of data the dictionary is
+        structured with:
+        - the key into the dictionary for adjustments is the location of the
+        day from which the window is being viewed.
+        - the start of all multiply objects is always 0 (in each window all
+          adjustments are overlapping)
+        - the end of the multiply object is the location before the calendar
+          location of the adjustment action, making all days before the event
+          adjusted.
+
+        Parameters
+        ----------
+        asset : Asset
+            The assets for which to get adjustments.
+        dts : iterable of datetime64-like
+            The dts for which adjustment data is needed.
+        field : str
+            OHLCV field for which to get the adjustments.
+
+        Returns
+        -------
+        out : dict[loc -> Float64Multiply]
+            The adjustments as a dict of loc -> Float64Multiply
+        """
+        sid = int(asset)
+        start = normalize_date(dts[0])
+        end = normalize_date(dts[-1])
+        adjs = {}
+        if field != 'volume':
+            mergers = self._adjustments_reader.get_adjustments_for_sid(
+                'mergers', sid)
+            for m in mergers:
+                dt = m[0]
+                if start < dt <= end:
+                    end_loc = dts.searchsorted(dt)
+                    adj_loc = end_loc
+                    mult = Float64Multiply(0,
+                                           end_loc - 1,
+                                           0,
+                                           0,
+                                           m[1])
+                    try:
+                        adjs[adj_loc].append(mult)
+                    except KeyError:
+                        adjs[adj_loc] = [mult]
+            divs = self._adjustments_reader.get_adjustments_for_sid(
+                'dividends', sid)
+            for d in divs:
+                dt = d[0]
+                if start < dt <= end:
+                    end_loc = dts.searchsorted(dt)
+                    adj_loc = end_loc
+                    mult = Float64Multiply(0,
+                                           end_loc - 1,
+                                           0,
+                                           0,
+                                           d[1])
+                    try:
+                        adjs[adj_loc].append(mult)
+                    except KeyError:
+                        adjs[adj_loc] = [mult]
+        splits = self._adjustments_reader.get_adjustments_for_sid(
+            'splits', sid)
+        for s in splits:
+            dt = s[0]
+            if start < dt <= end:
+                if field == 'volume':
+                    ratio = 1.0 / s[1]
+                else:
+                    ratio = s[1]
+                end_loc = dts.searchsorted(dt)
+                adj_loc = end_loc
+                mult = Float64Multiply(0,
+                                       end_loc - 1,
+                                       0,
+                                       0,
+                                       ratio)
+                try:
+                    adjs[adj_loc].append(mult)
+                except KeyError:
+                    adjs[adj_loc] = [mult]
+        return adjs
+
+
+class ContinuousFutureAdjustmentReader(object):
+    """
+    Calculates adjustments for continuous futures, based on the
+    close and open of the contracts on the either side of each roll.
+    """
+
+    def __init__(self,
+                 trading_calendar,
+                 asset_finder,
+                 bar_reader,
+                 roll_finders,
+                 frequency):
+        self._trading_calendar = trading_calendar
+        self._asset_finder = asset_finder
+        self._bar_reader = bar_reader
+        self._roll_finders = roll_finders
+        self._frequency = frequency
+
+    def load_adjustments(self, columns, dts, assets):
+        """
+        Returns
+        -------
+        adjustments : list[dict[int -> Adjustment]]
+            A list, where each element corresponds to the `columns`, of
+            mappings from index to adjustment objects to apply at that index.
+        """
+        out = [None] * len(columns)
+        for i, column in enumerate(columns):
+            adjs = {}
+            for asset in assets:
+                adjs.update(self._get_adjustments_in_range(
+                    asset, dts, column))
+            out[i] = adjs
+        return out
+
+    def _make_adjustment(self,
+                         adjustment_type,
+                         front_close,
+                         back_close,
+                         end_loc):
+        adj_base = back_close - front_close
+        if adjustment_type == 'mul':
+            adj_value = 1.0 + adj_base / front_close
+            adj_class = Float64Multiply
+        elif adjustment_type == 'add':
+            adj_value = adj_base
+            adj_class = Float64Add
+        return adj_class(0,
+                         end_loc,
+                         0,
+                         0,
+                         adj_value)
+
+    def _get_adjustments_in_range(self, cf, dts, field):
+        if field == 'volume' or field == 'sid':
+            return {}
+        if cf.adjustment is None:
+            return {}
+        rf = self._roll_finders[cf.roll_style]
+        partitions = []
+
+        rolls = rf.get_rolls(cf.root_symbol, dts[0], dts[-1],
+                             cf.offset)
+
+        tc = self._trading_calendar
+
+        adjs = {}
+
+        for front, back in sliding_window(2, rolls):
+            front_sid, roll_dt = front
+            back_sid = back[0]
+            dt = tc.previous_session_label(roll_dt)
+            if self._frequency == 'minute':
+                dt = tc.open_and_close_for_session(dt)[1]
+                roll_dt = tc.open_and_close_for_session(roll_dt)[0]
+            partitions.append((front_sid,
+                               back_sid,
+                               dt,
+                               roll_dt))
+        for partition in partitions:
+            front_sid, back_sid, dt, roll_dt = partition
+            last_front_dt = self._bar_reader.get_last_traded_dt(
+                self._asset_finder.retrieve_asset(front_sid), dt)
+            last_back_dt = self._bar_reader.get_last_traded_dt(
+                self._asset_finder.retrieve_asset(back_sid), dt)
+            if isnull(last_front_dt) or isnull(last_back_dt):
+                continue
+            front_close = self._bar_reader.get_value(
+                front_sid, last_front_dt, 'close')
+            back_close = self._bar_reader.get_value(
+                back_sid, last_back_dt, 'close')
+            adj_loc = dts.searchsorted(roll_dt)
+            end_loc = adj_loc - 1
+            adj = self._make_adjustment(cf.adjustment,
+                                        front_close,
+                                        back_close,
+                                        end_loc)
+            try:
+                adjs[adj_loc].append(adj)
+            except KeyError:
+                adjs[adj_loc] = [adj]
+        return adjs
+
+
+class SlidingWindow(object):
+    """
+    Wrapper around an AdjustedArrayWindow which supports monotonically
+    increasing (by datetime) requests for a sized window of data.
+
+    Parameters
+    ----------
+    window : AdjustedArrayWindow
+       Window of pricing data with prefetched values beyond the current
+       simulation dt.
+    cal_start : int
+       Index in the overall calendar at which the window starts.
+    """
+
+    def __init__(self, window, size, cal_start, offset):
+        self.window = window
+        self.cal_start = cal_start
+        self.current = next(window)
+        self.offset = offset
+        self.most_recent_ix = self.cal_start + size
+
+    def get(self, end_ix):
+        """
+        Returns
+        -------
+        out : A np.ndarray of the equity pricing up to end_ix after adjustments
+              and rounding have been applied.
+        """
+        if self.most_recent_ix == end_ix:
+            return self.current
+
+        target = end_ix - self.cal_start - self.offset + 1
+        self.current = self.window.seek(target)
+
+        self.most_recent_ix = end_ix
+        return self.current
+
+
+class HistoryLoader(with_metaclass(ABCMeta)):
+    """
+    Loader for sliding history windows, with support for adjustments.
+
+    Parameters
+    ----------
+    trading_calendar: TradingCalendar
+        Contains the grouping logic needed to assign minutes to periods.
+    reader : DailyBarReader, MinuteBarReader
+        Reader for pricing bars.
+    adjustment_reader : SQLiteAdjustmentReader
+        Reader for adjustment data.
+    """
+    FIELDS = ('open', 'high', 'low', 'close', 'volume', 'sid')
+
+    def __init__(self, trading_calendar, reader, equity_adjustment_reader,
+                 asset_finder,
+                 roll_finders=None,
+                 sid_cache_size=1000,
+                 prefetch_length=0):
+        self.trading_calendar = trading_calendar
+        self._asset_finder = asset_finder
+        self._reader = reader
+        self._adjustment_readers = {}
+        if equity_adjustment_reader is not None:
+            self._adjustment_readers[Equity] = \
+                HistoryCompatibleUSEquityAdjustmentReader(
+                    equity_adjustment_reader)
+        if roll_finders:
+            self._adjustment_readers[ContinuousFuture] =\
+                ContinuousFutureAdjustmentReader(trading_calendar,
+                                                 asset_finder,
+                                                 reader,
+                                                 roll_finders,
+                                                 self._frequency)
+        self._window_blocks = {
+            field: ExpiringCache(LRU(sid_cache_size))
+            for field in self.FIELDS
+        }
+        self._prefetch_length = prefetch_length
+
+    @abstractproperty
+    def _frequency(self):
+        pass
+
+    @abstractproperty
+    def _calendar(self):
+        pass
+
+    @abstractmethod
+    def _array(self, start, end, assets, field):
+        pass
+
+    def _decimal_places_for_asset(self, asset, reference_date):
+        if isinstance(asset, Future) and asset.tick_size:
+            return number_of_decimal_places(asset.tick_size)
+        elif isinstance(asset, ContinuousFuture):
+            # Tick size should be the same for all contracts of a continuous
+            # future, so arbitrarily get the contract with next upcoming auto
+            # close date.
+            oc = self._asset_finder.get_ordered_contracts(asset.root_symbol)
+            contract_sid = oc.contract_before_auto_close(reference_date.value)
+            if contract_sid is not None:
+                contract = self._asset_finder.retrieve_asset(contract_sid)
+                if contract.tick_size:
+                    return number_of_decimal_places(contract.tick_size)
+        return DEFAULT_ASSET_PRICE_DECIMALS
+
+    def _ensure_sliding_windows(self, assets, dts, field,
+                                is_perspective_after):
+        """
+        Ensure that there is a Float64Multiply window for each asset that can
+        provide data for the given parameters.
+        If the corresponding window for the (assets, len(dts), field) does not
+        exist, then create a new one.
+        If a corresponding window does exist for (assets, len(dts), field), but
+        can not provide data for the current dts range, then create a new
+        one and replace the expired window.
+
+        Parameters
+        ----------
+        assets : iterable of Assets
+            The assets in the window
+        dts : iterable of datetime64-like
+            The datetimes for which to fetch data.
+            Makes an assumption that all dts are present and contiguous,
+            in the calendar.
+        field : str
+            The OHLCV field for which to retrieve data.
+        is_perspective_after : bool
+            see: `PricingHistoryLoader.history`
+
+        Returns
+        -------
+        out : list of Float64Window with sufficient data so that each asset's
+        window can provide `get` for the index corresponding with the last
+        value in `dts`
+        """
+        end = dts[-1]
+        size = len(dts)
+        asset_windows = {}
+        needed_assets = []
+        cal = self._calendar
+
+        assets = self._asset_finder.retrieve_all(assets)
+        end_ix = find_in_sorted_index(cal, end)
+
+        for asset in assets:
+            try:
+                window = self._window_blocks[field].get(
+                    (asset, size, is_perspective_after), end)
+            except KeyError:
+                needed_assets.append(asset)
+            else:
+                if end_ix < window.most_recent_ix:
+                    # Window needs reset. Requested end index occurs before the
+                    # end index from the previous history call for this window.
+                    # Grab new window instead of rewinding adjustments.
+                    needed_assets.append(asset)
+                else:
+                    asset_windows[asset] = window
+
+        if needed_assets:
+            offset = 0
+            start_ix = find_in_sorted_index(cal, dts[0])
+
+            prefetch_end_ix = min(end_ix + self._prefetch_length, len(cal) - 1)
+            prefetch_end = cal[prefetch_end_ix]
+            prefetch_dts = cal[start_ix:prefetch_end_ix + 1]
+            if is_perspective_after:
+                adj_end_ix = min(prefetch_end_ix + 1, len(cal) - 1)
+                adj_dts = cal[start_ix:adj_end_ix + 1]
+            else:
+                adj_dts = prefetch_dts
+            prefetch_len = len(prefetch_dts)
+            array = self._array(prefetch_dts, needed_assets, field)
+
+            if field == 'sid':
+                window_type = Int64Window
+            else:
+                window_type = Float64Window
+
+            view_kwargs = {}
+            if field == 'volume':
+                array = array.astype(float64_dtype)
+
+            for i, asset in enumerate(needed_assets):
+                adj_reader = None
+                try:
+                    adj_reader = self._adjustment_readers[type(asset)]
+                except KeyError:
+                    adj_reader = None
+                if adj_reader is not None:
+                    adjs = adj_reader.load_adjustments(
+                        [field], adj_dts, [asset])[0]
+                else:
+                    adjs = {}
+                window = window_type(
+                    array[:, i].reshape(prefetch_len, 1),
+                    view_kwargs,
+                    adjs,
+                    offset,
+                    size,
+                    int(is_perspective_after),
+                    self._decimal_places_for_asset(asset, dts[-1]),
+                )
+                sliding_window = SlidingWindow(window, size, start_ix, offset)
+                asset_windows[asset] = sliding_window
+                self._window_blocks[field].set(
+                    (asset, size, is_perspective_after),
+                    sliding_window,
+                    prefetch_end)
+
+        return [asset_windows[asset] for asset in assets]
+
+    def history(self, assets, dts, field, is_perspective_after):
+        """
+        A window of pricing data with adjustments applied assuming that the
+        end of the window is the day before the current simulation time.
+
+        Parameters
+        ----------
+        assets : iterable of Assets
+            The assets in the window.
+        dts : iterable of datetime64-like
+            The datetimes for which to fetch data.
+            Makes an assumption that all dts are present and contiguous,
+            in the calendar.
+        field : str
+            The OHLCV field for which to retrieve data.
+        is_perspective_after : bool
+            True, if the window is being viewed immediately after the last dt
+            in the sliding window.
+            False, if the window is viewed on the last dt.
+
+            This flag is used for handling the case where the last dt in the
+            requested window immediately precedes a corporate action, e.g.:
+
+            - is_perspective_after is True
+
+            When the viewpoint is after the last dt in the window, as when a
+            daily history window is accessed from a simulation that uses a
+            minute data frequency, the history call to this loader will not
+            include the current simulation dt. At that point in time, the raw
+            data for the last day in the window will require adjustment, so the
+            most recent adjustment with respect to the simulation time is
+            applied to the last dt in the requested window.
+
+            An example equity which has a 0.5 split ratio dated for 05-27,
+            with the dts for a history call of 5 bars with a '1d' frequency at
+            05-27 9:31. Simulation frequency is 'minute'.
+
+            (In this case this function is called with 4 daily dts, and the
+             calling function is responsible for stitching back on the
+             'current' dt)
+
+            |       |       |       |       | last dt | <-- viewer is here |
+            |       | 05-23 | 05-24 | 05-25 | 05-26   | 05-27 9:31         |
+            | raw   | 10.10 | 10.20 | 10.30 | 10.40   |                    |
+            | adj   |  5.05 |  5.10 |  5.15 |  5.25   |                    |
+
+            The adjustment is applied to the last dt, 05-26, and all previous
+            dts.
+
+            - is_perspective_after is False, daily
+
+            When the viewpoint is the same point in time as the last dt in the
+            window, as when a daily history window is accessed from a
+            simulation that uses a daily data frequency, the history call will
+            include the current dt. At that point in time, the raw data for the
+            last day in the window will be post-adjustment, so no adjustment
+            is applied to the last dt.
+
+            An example equity which has a 0.5 split ratio dated for 05-27,
+            with the dts for a history call of 5 bars with a '1d' frequency at
+            05-27 0:00. Simulation frequency is 'daily'.
+
+            |       |       |       |       |       | <-- viewer is here |
+            |       |       |       |       |       | last dt            |
+            |       | 05-23 | 05-24 | 05-25 | 05-26 | 05-27              |
+            | raw   | 10.10 | 10.20 | 10.30 | 10.40 | 5.25               |
+            | adj   |  5.05 |  5.10 |  5.15 |  5.20 | 5.25               |
+
+            Adjustments are applied 05-23 through 05-26 but not to the last dt,
+            05-27
+
+        Returns
+        -------
+        out : np.ndarray with shape(len(days between start, end), len(assets))
+        """
+        block = self._ensure_sliding_windows(assets,
+                                             dts,
+                                             field,
+                                             is_perspective_after)
+        end_ix = self._calendar.searchsorted(dts[-1])
+
+        return concatenate(
+            [window.get(end_ix) for window in block],
+            axis=1,
+        )
+
+
+class DailyHistoryLoader(HistoryLoader):
+
+    @property
+    def _frequency(self):
+        return 'daily'
+
+    @property
+    def _calendar(self):
+        return self._reader.sessions
+
+    def _array(self, dts, assets, field):
+        return self._reader.load_raw_arrays(
+            [field],
+            dts[0],
+            dts[-1],
+            assets,
+        )[0]
+
+
+class MinuteHistoryLoader(HistoryLoader):
+
+    @property
+    def _frequency(self):
+        return 'minute'
+
+    @lazyval
+    def _calendar(self):
+        mm = self.trading_calendar.all_minutes
+        start = mm.searchsorted(self._reader.first_trading_day)
+        end = mm.searchsorted(self._reader.last_available_dt, side='right')
+        return mm[start:end]
+
+    def _array(self, dts, assets, field):
+        return self._reader.load_raw_arrays(
+            [field],
+            dts[0],
+            dts[-1],
+            assets,
+        )[0]

catalyst/data/loader.py

@@ -0,0 +1,692 @@
+#
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+from collections import OrderedDict
+
+import logbook
+import pandas as pd
+import pytz
+from pandas_datareader.data import DataReader
+from six import iteritems
+from six.moves.urllib_error import HTTPError
+
+from catalyst.constants import LOG_LEVEL
+from catalyst.utils.calendars import get_calendar
+from . import treasuries, treasuries_can
+from .benchmarks import get_benchmark_returns
+from ..utils.deprecate import deprecated
+from ..utils.paths import (
+    cache_root,
+    data_root,
+)
+
+logger = logbook.Logger('Loader', level=LOG_LEVEL)
+
+# Mapping from index symbol to appropriate bond data
+INDEX_MAPPING = {
+    'SPY':
+        (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
+    '^GSPTSE':
+        (treasuries_can, 'treasury_curves_can.csv', 'bankofcanada.ca'),
+    '^FTSE':  # use US treasuries until UK bonds implemented
+        (treasuries, 'treasury_curves.csv', 'www.federalreserve.gov'),
+}
+
+ONE_HOUR = pd.Timedelta(hours=1)
+
+
+def last_modified_time(path):
+    """
+    Get the last modified time of path as a Timestamp.
+    """
+    return pd.Timestamp(os.path.getmtime(path), unit='s', tz='UTC')
+
+
+def get_data_filepath(name, environ=None):
+    """
+    Returns a handle to data file.
+
+    Creates containing directory, if needed.
+    """
+    dr = data_root(environ)
+
+    if not os.path.exists(dr):
+        os.makedirs(dr)
+
+    return os.path.join(dr, name)
+
+
+def get_cache_filepath(name):
+    cr = cache_root()
+    if not os.path.exists(cr):
+        os.makedirs(cr)
+
+    return os.path.join(cr, name)
+
+
+def get_benchmark_filename(symbol):
+    return "%s_benchmark.csv" % symbol
+
+
+def has_data_for_dates(series_or_df, first_date, last_date):
+    """
+    Does `series_or_df` have data on or before first_date and on or after
+    last_date?
+    """
+    dts = series_or_df.index
+    if not isinstance(dts, pd.DatetimeIndex):
+        raise TypeError("Expected a DatetimeIndex, but got %s." % type(dts))
+    first, last = dts[[0, -1]].tz_localize(None)
+    return (first <= first_date.tz_localize(None)) and (
+        last >= last_date.tz_localize(None))
+
+
+def load_crypto_market_data(trading_day=None, trading_days=None,
+                            bm_symbol=None, bundle=None, bundle_data=None,
+                            environ=None, exchange=None, start_dt=None,
+                            end_dt=None):
+    if trading_day is None:
+        trading_day = get_calendar('OPEN').trading_day
+
+    # TODO: consider making configurable
+    bm_symbol = 'btc_usd'
+    # if trading_days is None:
+    #    trading_days = get_calendar('OPEN').schedule
+
+    # if start_dt is None:
+    start_dt = get_calendar('OPEN').first_trading_session
+
+    if end_dt is None:
+        end_dt = pd.Timestamp.utcnow()
+
+    # We expect to have benchmark and treasury data that's current up until
+    # **two** full trading days prior to the most recently completed trading
+    # day.
+    # Example:
+    # On Thu Oct 22 2015, the previous completed trading day is Wed Oct 21.
+    # However, data for Oct 21 doesn't become available until the early morning
+    # hours of Oct 22.  This means that there are times on the 22nd at which we
+    # cannot reasonably expect to have data for the 21st available.  To be
+    # conservative, we instead expect that at any time on the 22nd, we can
+    # download data for Tuesday the 20th, which is two full trading days prior
+    # to the date on which we're running a test.
+
+    # We'll attempt to download new data if the latest entry in our cache is
+    # before this date.
+    '''
+    if(bundle_data):
+        # If we are using the bundle to retrieve the cryptobenchmark, find
+        # the last date for which there is trading data in the bundle
+        asset = bundle_data.asset_finder.lookup_symbol(
+                    symbol=bm_symbol,as_of_date=None)
+        ix = bundle_data.daily_bar_reader._last_rows[asset.sid]
+        last_date = pd.to_datetime(
+                    bundle_data.daily_bar_reader._spot_col('day')[ix],unit='s')
+    else:
+        last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
+    '''
+    last_date = trading_days[trading_days.get_loc(end_dt, method='ffill') - 1]
+
+    if exchange is None:
+        # This is exceptional, since placing the import at the module scope
+        #  breaks things and it's only needed here
+        from catalyst.exchange.utils.factory import get_exchange
+        exchange = get_exchange(
+            exchange_name='bitfinex', base_currency='usd'
+        )
+        exchange.init()
+
+    benchmark_asset = exchange.get_asset(bm_symbol)
+
+    # exchange.get_history_window() already ensures that we have the right data
+    # for the right dates
+    br = exchange.get_history_window_with_bundle(
+        assets=[benchmark_asset],
+        end_dt=last_date,
+        bar_count=pd.Timedelta(last_date - start_dt).days,
+        frequency='1d',
+        field='close',
+        data_frequency='daily',
+        force_auto_ingest=True)
+    br.columns = ['close']
+    br = br.pct_change(1).iloc[1:]
+    br.loc[start_dt] = 0
+    br = br.sort_index()
+
+    # Override first_date for treasury data since we have it for many more
+    # years and is independent of crypto data
+    first_date_treasury = pd.Timestamp('1990-01-02', tz='UTC')
+    tc = ensure_treasury_data(
+        bm_symbol,
+        first_date_treasury,
+        last_date,
+        end_dt,
+        environ,
+    )
+    benchmark_returns = br[br.index.slice_indexer(start_dt, last_date)]
+    treasury_curves = tc[
+        tc.index.slice_indexer(first_date_treasury, last_date)]
+    return benchmark_returns, treasury_curves
+
+
+def load_market_data(trading_day=None, trading_days=None, bm_symbol='SPY',
+                     environ=None):
+    """
+    Load benchmark returns and treasury yield curves for the given calendar and
+    benchmark symbol.
+
+    Benchmarks are downloaded as a Series from Google Finance.  Treasury curves
+    are US Treasury Bond rates and are downloaded from 'www.federalreserve.gov'
+    by default.  For Canadian exchanges, a loader for Canadian bonds from the
+    Bank of Canada is also available.
+
+    Results downloaded from the internet are cached in
+    ~/.catalyst/data. Subsequent loads will attempt to read from the cached
+    files before falling back to redownload.
+
+    Parameters
+    ----------
+    trading_day : pandas.CustomBusinessDay, optional
+        A trading_day used to determine the latest day for which we
+        expect to have data.  Defaults to an NYSE trading day.
+    trading_days : pd.DatetimeIndex, optional
+        A calendar of trading days.  Also used for determining what cached
+        dates we should expect to have cached. Defaults to the NYSE calendar.
+    bm_symbol : str, optional
+        Symbol for the benchmark index to load.  Defaults to 'SPY', the Google
+        ticker for the S&P 500.
+
+    Returns
+    -------
+    (benchmark_returns, treasury_curves) : (pd.Series, pd.DataFrame)
+
+    Notes
+    -----
+
+    Both return values are DatetimeIndexed with values dated to midnight in UTC
+    of each stored date.  The columns of `treasury_curves` are:
+
+    '1month', '3month', '6month',
+    '1year','2year','3year','5year','7year','10year','20year','30year'
+    """
+    if trading_day is None:
+        trading_day = get_calendar('NYSE').trading_day
+    if trading_days is None:
+        trading_days = get_calendar('NYSE').all_sessions
+
+    first_date = trading_days[0]
+    now = pd.Timestamp.utcnow()
+
+    # We expect to have benchmark and treasury data that's current up until
+    # **two** full trading days prior to the most recently completed trading
+    # day.
+    # Example:
+    # On Thu Oct 22 2015, the previous completed trading day is Wed Oct 21.
+    # However, data for Oct 21 doesn't become available until the early morning
+    # hours of Oct 22.  This means that there are times on the 22nd at which we
+    # cannot reasonably expect to have data for the 21st available.  To be
+    # conservative, we instead expect that at any time on the 22nd, we can
+    # download data for Tuesday the 20th, which is two full trading days prior
+    # to the date on which we're running a test.
+
+    # We'll attempt to download new data if the latest entry in our cache is
+    # before this date.
+    last_date = trading_days[trading_days.get_loc(now, method='ffill') - 2]
+
+    br = ensure_benchmark_data(
+        bm_symbol,
+        first_date,
+        last_date,
+        now,
+        # We need the trading_day to figure out the close prior to the first
+        # date so that we can compute returns for the first date.
+        trading_day,
+        environ,
+    )
+    tc = ensure_treasury_data(
+        bm_symbol,
+        first_date,
+        last_date,
+        now,
+        environ,
+    )
+    benchmark_returns = br[br.index.slice_indexer(first_date, last_date)]
+    treasury_curves = tc[tc.index.slice_indexer(first_date, last_date)]
+    return benchmark_returns, treasury_curves
+
+
+def ensure_crypto_benchmark_data(symbol,
+                                 first_date,
+                                 last_date,
+                                 now,
+                                 trading_day,
+                                 bundle,
+                                 bundle_data,
+                                 environ=None):
+    filename = get_benchmark_filename(symbol)
+
+    logger.info(
+        ('Loading benchmark data for {symbol!r} '
+         'from {first_date} to {last_date}'),
+        symbol=symbol,
+        first_date=first_date,
+        last_date=last_date
+    )
+
+    data = _load_cached_data(
+        filename,
+        first_date,
+        last_date,
+        now,
+        'benchmark',
+        environ,
+    )
+
+    if data is not None:
+        return data
+
+    # If no cached data was found or it was missing any dates then download the
+    # necessary data.
+
+    if (bundle == 'poloniex'):
+        '''
+        If we're using the Poloniex bundle, we'll get the benchmark from the
+        bundle instead of downloading it from Poloniex every time we need it.
+        Poloniex has a captcha for API queries originating from outside the US
+        that prevents users abroad from getting Catalyst to work
+        '''
+        logger.info(
+            ('Retrieving benchmark data from bundle for {symbol!r}'
+             ' from {first_date} to {last_date}'),
+            symbol=symbol, first_date=first_date, last_date=last_date)
+
+        asset = bundle_data.asset_finder.lookup_symbol(symbol=symbol,
+                                                       as_of_date=None)
+        fields = ['day', 'close']
+        raw = bundle_data.daily_bar_reader.load_raw_arrays(
+            columns=fields,
+            start_date=first_date - trading_day,
+            end_date=last_date,
+            assets=[asset, ])
+        bench_raw = pd.concat([pd.DataFrame(raw[0], columns=['date']),
+                               pd.DataFrame(raw[1], columns=['close'])],
+                              axis=1)
+        bench_raw['date'] = pd.to_datetime(bench_raw['date'], unit='s')
+        bench_raw.set_index('date', inplace=True)
+        bench_raw.sort_index(inplace=True)
+        bench_raw = bench_raw[
+                    pd.to_datetime(first_date - trading_day):pd.to_datetime(
+                        last_date)]
+
+    else:
+        # This is how it used to be: downloading the benchmark everytime.
+        # Leaving this code here to be repurposed in the future for
+        # other bundles.
+        logger.info(
+            ('Downloading benchmark data for {symbol!r}'
+             ' from {first_date} to {last_date}'),
+            symbol=symbol, first_date=first_date, last_date=last_date)
+
+        raise DeprecationWarning('poloniex bundle deprecated')
+        # Load benchmark symbol from Poloniex API
+        # try:
+        #     bundle = PoloniexBundle()
+        #     bench_raw = bundle._fetch_symbol_frame(
+        #         None,
+        #         symbol,
+        #         get_calendar(bundle.calendar_name),
+        #         first_date - trading_day,
+        #         last_date,
+        #         'daily',
+        #     )
+        # except (OSError, IOError, HTTPError):
+        #     logger.exception('Failed to fetch new crypto benchmark returns')
+        #     raise
+
+    # select close column and compute percent change between days
+    daily_close = bench_raw[['close']]
+    daily_close = daily_close.pct_change(1).iloc[1:]
+
+    try:
+        # write to benchmark csv cache
+        daily_close.to_csv(get_data_filepath(filename, environ))
+    except (OSError, IOError, HTTPError):
+        logger.exception('Failed to cache the new benchmark returns')
+        raise
+
+    if not has_data_for_dates(daily_close, first_date, last_date):
+        logger.warn("Still don't have expected data after redownload!")
+
+    return daily_close
+
+
+def ensure_benchmark_data(symbol, first_date, last_date, now, trading_day,
+                          environ=None):
+    """
+    Ensure we have benchmark data for `symbol` from `first_date` to `last_date`
+
+    Parameters
+    ----------
+    symbol : str
+        The symbol for the benchmark to load.
+    first_date : pd.Timestamp
+        First required date for the cache.
+    last_date : pd.Timestamp
+        Last required date for the cache.
+    now : pd.Timestamp
+        The current time.  This is used to prevent repeated attempts to
+        re-download data that isn't available due to scheduling quirks or other
+        failures.
+    trading_day : pd.CustomBusinessDay
+        A trading day delta.  Used to find the day before first_date so we can
+        get the close of the day prior to first_date.
+
+    We attempt to download data unless we already have data stored at the data
+    cache for `symbol` whose first entry is before or on `first_date` and whose
+    last entry is on or after `last_date`.
+
+    If we perform a download and the cache criteria are not satisfied, we wait
+    at least one hour before attempting a redownload.  This is determined by
+    comparing the current time to the result of os.path.getmtime on the cache
+    path.
+    """
+    filename = get_benchmark_filename(symbol)
+    data = _load_cached_data(filename, first_date, last_date, now, 'benchmark',
+                             environ)
+    if data is not None:
+        return data
+
+    # If no cached data was found or it was missing any dates then download the
+    # necessary data.
+    logger.info(
+        ('Downloading benchmark data for {symbol!r} '
+         'from {first_date} to {last_date}'),
+        symbol=symbol,
+        first_date=first_date - trading_day,
+        last_date=last_date
+    )
+
+    try:
+        data = get_benchmark_returns(
+            symbol,
+            first_date - trading_day,
+            last_date,
+        )
+        data.to_csv(get_data_filepath(filename, environ))
+    except (OSError, IOError, HTTPError):
+        logger.exception('Failed to cache the new benchmark returns')
+        raise
+    if not has_data_for_dates(data, first_date, last_date):
+        logger.warn("Still don't have expected data after redownload!")
+    return data
+
+
+def ensure_treasury_data(symbol, first_date, last_date, now, environ=None):
+    """
+    Ensure we have treasury data from treasury module associated with
+    `symbol`.
+
+    Parameters
+    ----------
+    symbol : str
+        Benchmark symbol for which we're loading associated treasury curves.
+    first_date : pd.Timestamp
+        First date required to be in the cache.
+    last_date : pd.Timestamp
+        Last date required to be in the cache.
+    now : pd.Timestamp
+        The current time.  This is used to prevent repeated attempts to
+        re-download data that isn't available due to scheduling quirks or other
+        failures.
+
+    We attempt to download data unless we already have data stored in the cache
+    for `module_name` whose first entry is before or on `first_date` and whose
+    last entry is on or after `last_date`.
+
+    If we perform a download and the cache criteria are not satisfied, we wait
+    at least one hour before attempting a redownload.  This is determined by
+    comparing the current time to the result of os.path.getmtime on the cache
+    path.
+    """
+    loader_module, filename, source = INDEX_MAPPING.get(
+        symbol, INDEX_MAPPING['SPY'],
+    )
+    first_date = max(first_date, loader_module.earliest_possible_date())
+
+    data = _load_cached_data(filename, first_date, last_date, now, 'treasury',
+                             environ)
+    if data is not None:
+        return data
+
+    # If no cached data was found or it was missing any dates then download the
+    # necessary data.
+    logger.info('Downloading treasury data for {symbol!r}.', symbol=symbol)
+
+    try:
+        data = loader_module.get_treasury_data(first_date, last_date)
+        data.to_csv(get_data_filepath(filename, environ))
+    except (OSError, IOError, HTTPError):
+        logger.exception('failed to cache treasury data')
+    if not has_data_for_dates(data, first_date, last_date):
+        logger.warn("Still don't have expected data after redownload!")
+    return data
+
+
+def _load_cached_data(filename, first_date, last_date, now, resource_name,
+                      environ=None):
+    # Path for the cache.
+    path = get_data_filepath(filename, environ)
+
+    # If the path does not exist, it means the first download has not happened
+    # yet, so don't try to read from 'path'.
+    if os.path.exists(path):
+        try:
+            data = pd.DataFrame.from_csv(path)
+            if data.empty:
+                raise ValueError("File is empty.")
+            data.index = pd.to_datetime(data.index, infer_datetime_format=True,
+                                        errors='coerce').tz_localize('UTC')
+            if has_data_for_dates(data, first_date, last_date):
+                return data
+
+            # Don't re-download if we've successfully downloaded and written a
+            # file in the last hour.
+            last_download_time = last_modified_time(path)
+            if (now - last_download_time) <= ONE_HOUR:
+                logger.warn(
+                    "Refusing to download new {resource} data because a "
+                    "download succeeded at {time}.",
+                    resource=resource_name,
+                    time=last_download_time,
+                )
+                return data
+
+        except (OSError, IOError, ValueError) as e:
+            # These can all be raised by various versions of pandas on various
+            # classes of malformed input.  Treat them all as cache misses.
+            logger.info(
+                "Loading data for {path} failed with error [{error}].",
+                path=path,
+                error=e,
+            )
+
+    logger.info(
+        "Cache at {path} does not have data from {start} to {end}.",
+        start=first_date,
+        end=last_date,
+        path=path,
+    )
+    return None
+
+
+def _load_raw_yahoo_data(indexes=None, stocks=None, start=None, end=None):
+    """Load closing prices from yahoo finance.
+
+    :Optional:
+        indexes : dict (Default: {'SPX': '^SPY'})
+            Financial indexes to load.
+        stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
+                                 'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
+            Stock closing prices to load.
+        start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
+            Retrieve prices from start date on.
+        end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
+            Retrieve prices until end date.
+
+    :Note:
+        This is based on code presented in a talk by Wes McKinney:
+        http://wesmckinney.com/files/20111017/notebook_output.pdf
+    """
+    assert indexes is not None or stocks is not None, """
+must specify stocks or indexes"""
+
+    if start is None:
+        start = pd.datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
+
+    if start is not None and end is not None:
+        assert start < end, "start date is later than end date."
+
+    data = OrderedDict()
+
+    if stocks is not None:
+        for stock in stocks:
+            logger.info('Loading stock: {}'.format(stock))
+            stock_pathsafe = stock.replace(os.path.sep, '--')
+            cache_filename = "{stock}-{start}-{end}.csv".format(
+                stock=stock_pathsafe,
+                start=start,
+                end=end).replace(':', '-')
+            cache_filepath = get_cache_filepath(cache_filename)
+            if os.path.exists(cache_filepath):
+                stkd = pd.DataFrame.from_csv(cache_filepath)
+            else:
+                stkd = DataReader(stock, 'yahoo', start, end).sort_index()
+                stkd.to_csv(cache_filepath)
+            data[stock] = stkd
+
+    if indexes is not None:
+        for name, ticker in iteritems(indexes):
+            logger.info('Loading index: {} ({})'.format(name, ticker))
+            stkd = DataReader(ticker, 'yahoo', start, end).sort_index()
+            data[name] = stkd
+
+    return data
+
+
+def load_from_yahoo(indexes=None,
+                    stocks=None,
+                    start=None,
+                    end=None,
+                    adjusted=True):
+    """
+    Loads price data from Yahoo into a dataframe for each of the indicated
+    assets.  By default, 'price' is taken from Yahoo's 'Adjusted Close',
+    which removes the impact of splits and dividends. If the argument
+    'adjusted' is False, then the non-adjusted 'close' field is used instead.
+
+    :param indexes: Financial indexes to load.
+    :type indexes: dict
+    :param stocks: Stock closing prices to load.
+    :type stocks: list
+    :param start: Retrieve prices from start date on.
+    :type start: datetime
+    :param end: Retrieve prices until end date.
+    :type end: datetime
+    :param adjusted: Adjust the price for splits and dividends.
+    :type adjusted: bool
+
+    """
+    data = _load_raw_yahoo_data(indexes, stocks, start, end)
+    if adjusted:
+        close_key = 'Adj Close'
+    else:
+        close_key = 'Close'
+    df = pd.DataFrame({key: d[close_key] for key, d in iteritems(data)})
+    df.index = df.index.tz_localize(pytz.utc)
+    return df
+
+
+@deprecated(
+    'load_bars_from_yahoo is deprecated, please register a'
+    ' yahoo_equities data bundle instead',
+)
+def load_bars_from_yahoo(indexes=None,
+                         stocks=None,
+                         start=None,
+                         end=None,
+                         adjusted=True):
+    """
+    Loads data from Yahoo into a panel with the following
+    column names for each indicated security:
+
+        - open
+        - high
+        - low
+        - close
+        - volume
+        - price
+
+    Note that 'price' is Yahoo's 'Adjusted Close', which removes the
+    impact of splits and dividends. If the argument 'adjusted' is True, then
+    the open, high, low, and close values are adjusted as well.
+
+    :param indexes: Financial indexes to load.
+    :type indexes: dict
+    :param stocks: Stock closing prices to load.
+    :type stocks: list
+    :param start: Retrieve prices from start date on.
+    :type start: datetime
+    :param end: Retrieve prices until end date.
+    :type end: datetime
+    :param adjusted: Adjust open/high/low/close for splits and dividends.
+        The 'price' field is always adjusted.
+    :type adjusted: bool
+
+    """
+    data = _load_raw_yahoo_data(indexes, stocks, start, end)
+    panel = pd.Panel(data)
+    # Rename columns
+    panel.minor_axis = ['open', 'high', 'low', 'close', 'volume', 'price']
+    panel.major_axis = panel.major_axis.tz_localize(pytz.utc)
+    # Adjust data
+    if adjusted:
+        adj_cols = ['open', 'high', 'low', 'close']
+        for ticker in panel.items:
+            ratio = (panel[ticker]['price'] / panel[ticker]['close'])
+            ratio_filtered = ratio.fillna(0).values
+            for col in adj_cols:
+                panel[ticker][col] *= ratio_filtered
+    return panel
+
+
+def load_prices_from_csv(filepath, identifier_col, tz='UTC'):
+    data = pd.read_csv(filepath, index_col=identifier_col)
+    data.index = pd.DatetimeIndex(data.index, tz=tz)
+    data.sort_index(inplace=True)
+    return data
+
+
+def load_prices_from_csv_folder(folderpath, identifier_col, tz='UTC'):
+    data = None
+    for file in os.listdir(folderpath):
+        if '.csv' not in file:
+            continue
+        raw = load_prices_from_csv(os.path.join(folderpath, file),
+                                   identifier_col, tz)
+        if data is None:
+            data = raw
+        else:
+            data = pd.concat([data, raw], axis=1)
+    return data

catalyst/data/resample.py

@@ -0,0 +1,735 @@
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections import OrderedDict
+from abc import ABCMeta, abstractmethod
+
+import numpy as np
+import pandas as pd
+from six import with_metaclass
+
+from catalyst.data._resample import (
+    _minute_to_session_open,
+    _minute_to_session_high,
+    _minute_to_session_low,
+    _minute_to_session_close,
+    _minute_to_session_volume,
+)
+from catalyst.data.bar_reader import NoDataOnDate
+from catalyst.data.minute_bars import MinuteBarReader
+from catalyst.data.session_bars import SessionBarReader
+from catalyst.utils.memoize import lazyval
+
+_MINUTE_TO_SESSION_OHCLV_HOW = OrderedDict((
+    ('open', 'first'),
+    ('high', 'max'),
+    ('low', 'min'),
+    ('close', 'last'),
+    ('volume', 'sum'),
+))
+
+
+def minute_frame_to_session_frame(minute_frame, calendar):
+
+    """
+    Resample a DataFrame with minute data into the frame expected by a
+    BcolzDailyBarWriter.
+
+    Parameters
+    ----------
+    minute_frame : pd.DataFrame
+        A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
+        and `dt` (minute dts)
+    calendar : catalyst.utils.calendars.trading_calendar.TradingCalendar
+        A TradingCalendar on which session labels to resample from minute
+        to session.
+
+    Return
+    ------
+    session_frame : pd.DataFrame
+        A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
+        and `day` (datetime-like).
+    """
+    how = OrderedDict((c, _MINUTE_TO_SESSION_OHCLV_HOW[c])
+                      for c in minute_frame.columns)
+    return minute_frame.groupby(calendar.minute_to_session_label).agg(how)
+
+
+def minute_to_session(column, close_locs, data, out):
+    """
+    Resample an array with minute data into an array with session data.
+
+    This function assumes that the minute data is the exact length of all
+    minutes in the sessions in the output.
+
+    Parameters
+    ----------
+    column : str
+        The `open`, `high`, `low`, `close`, or `volume` column.
+    close_locs : array[intp]
+        The locations in `data` which are the market close minutes.
+    data : array[float64|uint32]
+        The minute data to be sampled into session data.
+        The first value should align with the market open of the first session,
+        containing values for all minutes for all sessions. With the last value
+        being the market close of the last session.
+    out : array[float64|uint32]
+        The output array into which to write the sampled sessions.
+    """
+    if column == 'open':
+        _minute_to_session_open(close_locs, data, out)
+    elif column == 'high':
+        _minute_to_session_high(close_locs, data, out)
+    elif column == 'low':
+        _minute_to_session_low(close_locs, data, out)
+    elif column == 'close':
+        _minute_to_session_close(close_locs, data, out)
+    elif column == 'volume':
+        _minute_to_session_volume(close_locs, data, out)
+    return out
+
+
+class DailyHistoryAggregator(object):
+    """
+    Converts minute pricing data into a daily summary, to be used for the
+    last slot in a call to history with a frequency of `1d`.
+
+    This summary is the same as a daily bar rollup of minute data, with the
+    distinction that the summary is truncated to the `dt` requested.
+    i.e. the aggregation slides forward during a the course of simulation day.
+
+    Provides aggregation for `open`, `high`, `low`, `close`, and `volume`.
+    The aggregation rules for each price type is documented in their respective
+
+    """
+
+    def __init__(self, market_opens, minute_reader, trading_calendar):
+        self._market_opens = market_opens
+        self._minute_reader = minute_reader
+        self._trading_calendar = trading_calendar
+
+        # The caches are structured as (date, market_open, entries), where
+        # entries is a dict of asset -> (last_visited_dt, value)
+        #
+        # Whenever an aggregation method determines the current value,
+        # the entry for the respective asset should be overwritten with a new
+        # entry for the current dt.value (int) and aggregation value.
+        #
+        # When the requested dt's date is different from date the cache is
+        # flushed, so that the cache entries do not grow unbounded.
+        #
+        # Example cache:
+        # cache = (date(2016, 3, 17),
+        #          pd.Timestamp('2016-03-17 13:31', tz='UTC'),
+        #          {
+        #              1: (1458221460000000000, np.nan),
+        #              2: (1458221460000000000, 42.0),
+        #         })
+        self._caches = {
+            'open': None,
+            'high': None,
+            'low': None,
+            'close': None,
+            'volume': None
+        }
+
+        # The int value is used for deltas to avoid extra computation from
+        # creating new Timestamps.
+        self._one_min = pd.Timedelta('1 min').value
+
+    def _prelude(self, dt, field):
+        session = self._trading_calendar.minute_to_session_label(dt)
+        dt_value = dt.value
+        cache = self._caches[field]
+        if cache is None or cache[0] != session:
+            market_open = self._market_opens.loc[session]
+            cache = self._caches[field] = (session, market_open, {})
+
+        _, market_open, entries = cache
+        try:
+            market_open = market_open.tz_localize('UTC')
+        except TypeError:
+            market_open = market_open.tz_convert('UTC')
+        if dt != market_open:
+            prev_dt = dt_value - self._one_min
+        else:
+            prev_dt = None
+        return market_open, prev_dt, dt_value, entries
+
+    def opens(self, assets, dt):
+        """
+        The open field's aggregation returns the first value that occurs
+        for the day, if there has been no data on or before the `dt` the open
+        is `nan`.
+
+        Once the first non-nan open is seen, that value remains constant per
+        asset for the remainder of the day.
+
+        Returns
+        -------
+        np.array with dtype=float64, in order of assets parameter.
+        """
+        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'open')
+
+        opens = []
+        session_label = self._trading_calendar.minute_to_session_label(dt)
+
+        for asset in assets:
+            if not asset.is_alive_for_session(session_label):
+                opens.append(np.NaN)
+                continue
+
+            if prev_dt is None:
+                val = self._minute_reader.get_value(asset, dt, 'open')
+                entries[asset] = (dt_value, val)
+                opens.append(val)
+                continue
+            else:
+                try:
+                    last_visited_dt, first_open = entries[asset]
+                    if last_visited_dt == dt_value:
+                        opens.append(first_open)
+                        continue
+                    elif not pd.isnull(first_open):
+                        opens.append(first_open)
+                        entries[asset] = (dt_value, first_open)
+                        continue
+                    else:
+                        after_last = pd.Timestamp(
+                            last_visited_dt + self._one_min, tz='UTC')
+                        window = self._minute_reader.load_raw_arrays(
+                            ['open'],
+                            after_last,
+                            dt,
+                            [asset],
+                        )[0]
+                        nonnan = window[~pd.isnull(window)]
+                        if len(nonnan):
+                            val = nonnan[0]
+                        else:
+                            val = np.nan
+                        entries[asset] = (dt_value, val)
+                        opens.append(val)
+                        continue
+                except KeyError:
+                    window = self._minute_reader.load_raw_arrays(
+                        ['open'],
+                        market_open,
+                        dt,
+                        [asset],
+                    )[0]
+                    nonnan = window[~pd.isnull(window)]
+                    if len(nonnan):
+                        val = nonnan[0]
+                    else:
+                        val = np.nan
+                    entries[asset] = (dt_value, val)
+                    opens.append(val)
+                    continue
+        return np.array(opens)
+
+    def highs(self, assets, dt):
+        """
+        The high field's aggregation returns the largest high seen between
+        the market open and the current dt.
+        If there has been no data on or before the `dt` the high is `nan`.
+
+        Returns
+        -------
+        np.array with dtype=float64, in order of assets parameter.
+        """
+        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'high')
+
+        highs = []
+        session_label = self._trading_calendar.minute_to_session_label(dt)
+
+        for asset in assets:
+            if not asset.is_alive_for_session(session_label):
+                highs.append(np.NaN)
+                continue
+
+            if prev_dt is None:
+                val = self._minute_reader.get_value(asset, dt, 'high')
+                entries[asset] = (dt_value, val)
+                highs.append(val)
+                continue
+            else:
+                try:
+                    last_visited_dt, last_max = entries[asset]
+                    if last_visited_dt == dt_value:
+                        highs.append(last_max)
+                        continue
+                    elif last_visited_dt == prev_dt:
+                        curr_val = self._minute_reader.get_value(
+                            asset, dt, 'high')
+                        if pd.isnull(curr_val):
+                            val = last_max
+                        elif pd.isnull(last_max):
+                            val = curr_val
+                        else:
+                            val = max(last_max, curr_val)
+                        entries[asset] = (dt_value, val)
+                        highs.append(val)
+                        continue
+                    else:
+                        after_last = pd.Timestamp(
+                            last_visited_dt + self._one_min, tz='UTC')
+                        window = self._minute_reader.load_raw_arrays(
+                            ['high'],
+                            after_last,
+                            dt,
+                            [asset],
+                        )[0].T
+                        val = np.nanmax(np.append(window, last_max))
+                        entries[asset] = (dt_value, val)
+                        highs.append(val)
+                        continue
+                except KeyError:
+                    window = self._minute_reader.load_raw_arrays(
+                        ['high'],
+                        market_open,
+                        dt,
+                        [asset],
+                    )[0].T
+                    val = np.nanmax(window)
+                    entries[asset] = (dt_value, val)
+                    highs.append(val)
+                    continue
+        return np.array(highs)
+
+    def lows(self, assets, dt):
+        """
+        The low field's aggregation returns the smallest low seen between
+        the market open and the current dt.
+        If there has been no data on or before the `dt` the low is `nan`.
+
+        Returns
+        -------
+        np.array with dtype=float64, in order of assets parameter.
+        """
+        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'low')
+
+        lows = []
+        session_label = self._trading_calendar.minute_to_session_label(dt)
+
+        for asset in assets:
+            if not asset.is_alive_for_session(session_label):
+                lows.append(np.NaN)
+                continue
+
+            if prev_dt is None:
+                val = self._minute_reader.get_value(asset, dt, 'low')
+                entries[asset] = (dt_value, val)
+                lows.append(val)
+                continue
+            else:
+                try:
+                    last_visited_dt, last_min = entries[asset]
+                    if last_visited_dt == dt_value:
+                        lows.append(last_min)
+                        continue
+                    elif last_visited_dt == prev_dt:
+                        curr_val = self._minute_reader.get_value(
+                            asset, dt, 'low')
+                        val = np.nanmin([last_min, curr_val])
+                        entries[asset] = (dt_value, val)
+                        lows.append(val)
+                        continue
+                    else:
+                        after_last = pd.Timestamp(
+                            last_visited_dt + self._one_min, tz='UTC')
+                        window = self._minute_reader.load_raw_arrays(
+                            ['low'],
+                            after_last,
+                            dt,
+                            [asset],
+                        )[0].T
+                        val = np.nanmin(np.append(window, last_min))
+                        entries[asset] = (dt_value, val)
+                        lows.append(val)
+                        continue
+                except KeyError:
+                    window = self._minute_reader.load_raw_arrays(
+                        ['low'],
+                        market_open,
+                        dt,
+                        [asset],
+                    )[0].T
+                    val = np.nanmin(window)
+                    entries[asset] = (dt_value, val)
+                    lows.append(val)
+                    continue
+        return np.array(lows)
+
+    def closes(self, assets, dt):
+        """
+        The close field's aggregation returns the latest close at the given
+        dt.
+        If the close for the given dt is `nan`, the most recent non-nan
+        `close` is used.
+        If there has been no data on or before the `dt` the close is `nan`.
+
+        Returns
+        -------
+        np.array with dtype=float64, in order of assets parameter.
+        """
+        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'close')
+
+        closes = []
+        session_label = self._trading_calendar.minute_to_session_label(dt)
+
+        def _get_filled_close(asset):
+            """
+            Returns the most recent non-nan close for the asset in this
+            session. If there has been no data in this session on or before the
+            `dt`, returns `nan`
+            """
+            window = self._minute_reader.load_raw_arrays(
+                ['close'],
+                market_open,
+                dt,
+                [asset],
+            )[0]
+            try:
+                return window[~np.isnan(window)][-1]
+            except IndexError:
+                return np.NaN
+
+        for asset in assets:
+            if not asset.is_alive_for_session(session_label):
+                closes.append(np.NaN)
+                continue
+
+            if prev_dt is None:
+                val = self._minute_reader.get_value(asset, dt, 'close')
+                entries[asset] = (dt_value, val)
+                closes.append(val)
+                continue
+            else:
+                try:
+                    last_visited_dt, last_close = entries[asset]
+                    if last_visited_dt == dt_value:
+                        closes.append(last_close)
+                        continue
+                    elif last_visited_dt == prev_dt:
+                        val = self._minute_reader.get_value(
+                            asset, dt, 'close')
+                        if pd.isnull(val):
+                            val = last_close
+                        entries[asset] = (dt_value, val)
+                        closes.append(val)
+                        continue
+                    else:
+                        val = self._minute_reader.get_value(
+                            asset, dt, 'close')
+                        if pd.isnull(val):
+                            val = _get_filled_close(asset)
+                        entries[asset] = (dt_value, val)
+                        closes.append(val)
+                        continue
+                except KeyError:
+                    val = self._minute_reader.get_value(
+                        asset, dt, 'close')
+                    if pd.isnull(val):
+                        val = _get_filled_close(asset)
+                    entries[asset] = (dt_value, val)
+                    closes.append(val)
+                    continue
+        return np.array(closes)
+
+    def volumes(self, assets, dt):
+        """
+        The volume field's aggregation returns the sum of all volumes
+        between the market open and the `dt`
+        If there has been no data on or before the `dt` the volume is 0.
+
+        Returns
+        -------
+        np.array with dtype=int64, in order of assets parameter.
+        """
+        market_open, prev_dt, dt_value, entries = self._prelude(dt, 'volume')
+
+        volumes = []
+        session_label = self._trading_calendar.minute_to_session_label(dt)
+
+        for asset in assets:
+            if not asset.is_alive_for_session(session_label):
+                volumes.append(0)
+                continue
+
+            if prev_dt is None:
+                val = self._minute_reader.get_value(asset, dt, 'volume')
+                entries[asset] = (dt_value, val)
+                volumes.append(val)
+                continue
+            else:
+                try:
+                    last_visited_dt, last_total = entries[asset]
+                    if last_visited_dt == dt_value:
+                        volumes.append(last_total)
+                        continue
+                    elif last_visited_dt == prev_dt:
+                        val = self._minute_reader.get_value(
+                            asset, dt, 'volume')
+                        val += last_total
+                        entries[asset] = (dt_value, val)
+                        volumes.append(val)
+                        continue
+                    else:
+                        after_last = pd.Timestamp(
+                            last_visited_dt + self._one_min, tz='UTC')
+                        window = self._minute_reader.load_raw_arrays(
+                            ['volume'],
+                            after_last,
+                            dt,
+                            [asset],
+                        )[0]
+                        val = np.nansum(window) + last_total
+                        entries[asset] = (dt_value, val)
+                        volumes.append(val)
+                        continue
+                except KeyError:
+                    window = self._minute_reader.load_raw_arrays(
+                        ['volume'],
+                        market_open,
+                        dt,
+                        [asset],
+                    )[0]
+                    val = np.nansum(window)
+                    entries[asset] = (dt_value, val)
+                    volumes.append(val)
+                    continue
+        return np.array(volumes)
+
+
+class MinuteResampleSessionBarReader(SessionBarReader):
+
+    def __init__(self, calendar, minute_bar_reader):
+        self._calendar = calendar
+        self._minute_bar_reader = minute_bar_reader
+
+    def _get_resampled(self, columns, start_session, end_session, assets):
+        range_open = self._calendar.session_open(start_session)
+        range_close = self._calendar.session_close(end_session)
+
+        minute_data = self._minute_bar_reader.load_raw_arrays(
+            columns,
+            range_open,
+            range_close,
+            assets,
+        )
+
+        # Get the index of the close minute for each session in the range.
+        # If the range contains only one session, the only close in the range
+        # is the last minute in the data. Otherwise, we need to get all the
+        # session closes and find their indices in the range of minutes.
+        if start_session == end_session:
+            close_ilocs = np.array([len(minute_data[0]) - 1], dtype=np.int64)
+        else:
+            minutes = self._calendar.minutes_in_range(
+                range_open,
+                range_close,
+            )
+            session_closes = self._calendar.session_closes_in_range(
+                start_session,
+                end_session,
+            )
+            close_ilocs = minutes.searchsorted(session_closes.values)
+
+        results = []
+        shape = (len(close_ilocs), len(assets))
+
+        for col in columns:
+            if col != 'volume':
+                out = np.full(shape, np.nan)
+            else:
+                out = np.zeros(shape, dtype=np.uint32)
+            results.append(out)
+
+        for i in range(len(assets)):
+            for j, column in enumerate(columns):
+                data = minute_data[j][:, i]
+                minute_to_session(column, close_ilocs, data, results[j][:, i])
+
+        return results
+
+    @property
+    def trading_calendar(self):
+        return self._calendar
+
+    def load_raw_arrays(self, columns, start_dt, end_dt, sids):
+        return self._get_resampled(columns, start_dt, end_dt, sids)
+
+    def get_value(self, sid, session, colname):
+        # WARNING: This will need caching or other optimization if used in a
+        # tight loop.
+        # This was developed to complete interface, but has not been tuned
+        # for real world use.
+        return self._get_resampled([colname], session, session, [sid])[0][0][0]
+
+    @lazyval
+    def sessions(self):
+        cal = self._calendar
+        first = self._minute_bar_reader.first_trading_day
+        last = cal.minute_to_session_label(
+            self._minute_bar_reader.last_available_dt)
+        return cal.sessions_in_range(first, last)
+
+    @lazyval
+    def last_available_dt(self):
+        return self.trading_calendar.minute_to_session_label(
+            self._minute_bar_reader.last_available_dt
+        )
+
+    @property
+    def first_trading_day(self):
+        return self._minute_bar_reader.first_trading_day
+
+    def get_last_traded_dt(self, asset, dt):
+        return self.trading_calendar.minute_to_session_label(
+            self._minute_bar_reader.get_last_traded_dt(asset, dt))
+
+
+class ReindexBarReader(with_metaclass(ABCMeta)):
+    """
+    A base class for readers which reindexes results, filling in the additional
+    indices with empty data.
+
+    Used to align the reading assets which trade on different calendars.
+
+    Currently only supports a ``trading_calendar`` which is a superset of the
+    ``reader``'s calendar.
+
+    Parameters
+    ----------
+
+    - trading_calendar : catalyst.utils.trading_calendar.TradingCalendar
+       The calendar to use when indexing results from the reader.
+    - reader : MinuteBarReader|SessionBarReader
+       The reader which has a calendar that is a subset of the desired
+       ``trading_calendar``.
+    - first_trading_session : pd.Timestamp
+       The first trading session the reader should provide. Must be specified,
+       since the ``reader``'s first session may not exactly align with the
+       desired calendar. Specifically, in the case where the first session
+       on the target calendar is a holiday on the ``reader``'s calendar.
+    - last_trading_session : pd.Timestamp
+       The last trading session the reader should provide. Must be specified,
+       since the ``reader``'s last session may not exactly align with the
+       desired calendar. Specifically, in the case where the last session
+       on the target calendar is a holiday on the ``reader``'s calendar.
+    """
+
+    def __init__(self,
+                 trading_calendar,
+                 reader,
+                 first_trading_session,
+                 last_trading_session):
+        self._trading_calendar = trading_calendar
+        self._reader = reader
+        self._first_trading_session = first_trading_session
+        self._last_trading_session = last_trading_session
+
+    @property
+    def last_available_dt(self):
+        return self._reader.last_available_dt
+
+    def get_last_traded_dt(self, sid, dt):
+        return self._reader.get_last_traded_dt(sid, dt)
+
+    @property
+    def first_trading_day(self):
+        return self._reader.first_trading_day
+
+    def get_value(self, sid, dt, field):
+        # Give an empty result if no data is present.
+        try:
+            return self._reader.get_value(sid, dt, field)
+        except NoDataOnDate:
+            if field == 'volume':
+                return 0
+            else:
+                return np.nan
+
+    @abstractmethod
+    def _outer_dts(self, start_dt, end_dt):
+        raise NotImplementedError
+
+    @abstractmethod
+    def _inner_dts(self, start_dt, end_dt):
+        raise NotImplementedError
+
+    @property
+    def trading_calendar(self):
+        return self._trading_calendar
+
+    @lazyval
+    def sessions(self):
+        return self.trading_calendar.sessions_in_range(
+            self._first_trading_session,
+            self._last_trading_session
+        )
+
+    def load_raw_arrays(self, fields, start_dt, end_dt, sids):
+        outer_dts = self._outer_dts(start_dt, end_dt)
+        inner_dts = self._inner_dts(start_dt, end_dt)
+
+        indices = outer_dts.searchsorted(inner_dts)
+
+        shape = len(outer_dts), len(sids)
+
+        outer_results = []
+
+        if len(inner_dts) > 0:
+            inner_results = self._reader.load_raw_arrays(
+                fields, inner_dts[0], inner_dts[-1], sids)
+        else:
+            inner_results = None
+
+        for i, field in enumerate(fields):
+            if field != 'volume':
+                out = np.full(shape, np.nan)
+            else:
+                out = np.zeros(shape, dtype=np.uint32)
+
+            if inner_results is not None:
+                out[indices] = inner_results[i]
+
+            outer_results.append(out)
+
+        return outer_results
+
+
+class ReindexMinuteBarReader(ReindexBarReader, MinuteBarReader):
+    """
+    See: ``ReindexBarReader``
+    """
+
+    def _outer_dts(self, start_dt, end_dt):
+        return self._trading_calendar.minutes_in_range(start_dt, end_dt)
+
+    def _inner_dts(self, start_dt, end_dt):
+        return self._reader.calendar.minutes_in_range(start_dt, end_dt)
+
+
+class ReindexSessionBarReader(ReindexBarReader, SessionBarReader):
+    """
+    See: ``ReindexBarReader``
+    """
+
+    def _outer_dts(self, start_dt, end_dt):
+        return self.trading_calendar.sessions_in_range(start_dt, end_dt)
+
+    def _inner_dts(self, start_dt, end_dt):
+        return self._reader.trading_calendar.sessions_in_range(
+            start_dt, end_dt)

catalyst/data/session_bars.py

@@ -0,0 +1,36 @@
+# Copyright 2016 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from abc import abstractproperty
+
+from catalyst.data.bar_reader import BarReader
+
+
+class SessionBarReader(BarReader):
+    """
+    Reader for OHCLV pricing data at a session frequency.
+    """
+    @property
+    def data_frequency(self):
+        return 'session'
+
+    @abstractproperty
+    def sessions(self):
+        """
+        Returns
+        -------
+        sessions : DatetimeIndex
+           All session labels (unionining the range for all assets) which the
+           reader can provide.
+        """
+        pass

catalyst/data/treasuries.py

@@ -0,0 +1,101 @@
+#
+# Copyright 2013 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from operator import itemgetter
+import re
+
+import numpy as np
+import pandas as pd
+
+
+get_unit_and_periods = itemgetter('unit', 'periods')
+
+
+def parse_treasury_csv_column(column):
+    """
+    Parse a treasury CSV column into a more human-readable format.
+
+    Columns start with 'RIFLGFC', followed by Y or M (year or month), followed
+    by a two-digit number signifying number of years/months, followed by _N.B.
+    We only care about the middle two entries, which we turn into a string like
+    3month or 30year.
+    """
+    column_re = re.compile(
+        r"^(?P<prefix>RIFLGFC)"
+        "(?P<unit>[YM])"
+        "(?P<periods>[0-9]{2})"
+        "(?P<suffix>_N.B)$"
+    )
+
+    match = column_re.match(column)
+    if match is None:
+        raise ValueError("Couldn't parse CSV column %r." % column)
+    unit, periods = get_unit_and_periods(match.groupdict())
+
+    # Roundtrip through int to coerce '06' into '6'.
+    return str(int(periods)) + ('year' if unit == 'Y' else 'month')
+
+
+def earliest_possible_date():
+    """
+    The earliest date for which we can load data from this module.
+    """
+    # The US Treasury actually has data going back further than this, but it's
+    # pretty rare to find pricing data going back that far, and there's no
+    # reason to make people download benchmarks back to 1950 that they'll never
+    # be able to use.
+    return pd.Timestamp('1980', tz='UTC')
+
+
+def get_treasury_data(start_date, end_date):
+    return pd.read_csv(
+        "https://www.federalreserve.gov/datadownload/Output.aspx"
+        "?rel=H15"
+        "&series=bf17364827e38702b42a58cf8eaa3f78"
+        "&lastObs="
+        "&from="  # An unbounded query is ~2x faster than specifying dates.
+        "&to="
+        "&filetype=csv"
+        "&label=include"
+        "&layout=seriescolumn"
+        "&type=package",
+        skiprows=5,  # First 5 rows are useless headers.
+        parse_dates=['Time Period'],
+        na_values=['ND'],  # Presumably this stands for "No Data".
+        index_col=0,
+    ).loc[
+        start_date:end_date
+    ].dropna(
+        how='all'
+    ).rename(
+        columns=parse_treasury_csv_column
+    ).tz_localize('UTC') * 0.01  # Convert from 2.57% to 0.0257.
+
+
+def dataconverter(s):
+    try:
+        return float(s) / 100
+    except:
+        return np.nan
+
+
+def get_daily_10yr_treasury_data():
+    """Download daily 10 year treasury rates from the Federal Reserve and
+    return a pandas.Series."""
+    url = "https://www.federalreserve.gov/datadownload/Output.aspx?rel=H15" \
+          "&series=bcb44e57fb57efbe90002369321bfb3f&lastObs=&from=&to=" \
+          "&filetype=csv&label=include&layout=seriescolumn"
+    return pd.read_csv(url, header=5, index_col=0, names=['DATE', 'BC_10YEAR'],
+                       parse_dates=True, converters={1: dataconverter},
+                       squeeze=True)

catalyst/data/treasuries_can.py

@@ -0,0 +1,150 @@
+#
+# Copyright 2013 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pandas as pd
+import six
+from toolz import curry
+from toolz.curried.operator import add as prepend
+
+COLUMN_NAMES = {
+    "V39063": '1month',
+    "V39065": '3month',
+    "V39066": '6month',
+    "V39067": '1year',
+    "V39051": '2year',
+    "V39052": '3year',
+    "V39053": '5year',
+    "V39054": '7year',
+    "V39055": '10year',
+    # Bank of Canada refers to this as 'Long' Rate, approximately 30 years.
+    "V39056": '30year',
+}
+BILL_IDS = ['V39063', 'V39065', 'V39066', 'V39067']
+BOND_IDS = ['V39051', 'V39052', 'V39053', 'V39054', 'V39055', 'V39056']
+
+
+@curry
+def _format_url(instrument_type,
+                instrument_ids,
+                start_date,
+                end_date,
+                earliest_allowed_date):
+    """
+    Format a URL for loading data from Bank of Canada.
+    """
+    return (
+        "http://www.bankofcanada.ca/stats/results/csv"
+        "?lP=lookup_{instrument_type}_yields.php"
+        "&sR={restrict}"
+        "&se={instrument_ids}"
+        "&dF={start}"
+        "&dT={end}".format(
+            instrument_type=instrument_type,
+            instrument_ids='-'.join(map(prepend("L_"), instrument_ids)),
+            restrict=earliest_allowed_date.strftime("%Y-%m-%d"),
+            start=start_date.strftime("%Y-%m-%d"),
+            end=end_date.strftime("%Y-%m-%d"),
+        )
+    )
+
+
+format_bill_url = _format_url('tbill', BILL_IDS)
+format_bond_url = _format_url('bond', BOND_IDS)
+
+
+def load_frame(url, skiprows):
+    """
+    Load a DataFrame of data from a Bank of Canada site.
+    """
+    return pd.read_csv(
+        url,
+        skiprows=skiprows,
+        skipinitialspace=True,
+        na_values=["Bank holiday", "Not available"],
+        parse_dates=["Date"],
+        index_col="Date",
+    ).dropna(how='all') \
+     .tz_localize('UTC') \
+     .rename(columns=COLUMN_NAMES)
+
+
+def check_known_inconsistencies(bill_data, bond_data):
+    """
+    There are a couple quirks in the data provided by Bank of Canada.
+    Check that no new quirks have been introduced in the latest download.
+    """
+    inconsistent_dates = bill_data.index.sym_diff(bond_data.index)
+    known_inconsistencies = [
+        # bill_data has an entry for 2010-02-15, which bond_data doesn't.
+        # bond_data has an entry for 2006-09-04, which bill_data doesn't.
+        # Both of these dates are bank holidays (Flag Day and Labor Day,
+        # respectively).
+        pd.Timestamp('2006-09-04', tz='UTC'),
+        pd.Timestamp('2010-02-15', tz='UTC'),
+        # 2013-07-25 comes back as "Not available" from the bills endpoint.
+        # This date doesn't seem to be a bank holiday, but the previous
+        # calendar implementation dropped this entry, so we drop it as well.
+        # If someone cares deeply about the integrity of the Canadian trading
+        # calendar, they may want to consider forward-filling here rather than
+        # dropping the row.
+        pd.Timestamp('2013-07-25', tz='UTC'),
+    ]
+    unexpected_inconsistences = inconsistent_dates.drop(known_inconsistencies)
+    if len(unexpected_inconsistences):
+        in_bills = bill_data.index.difference(bond_data.index).difference(
+            known_inconsistencies
+        )
+        in_bonds = bond_data.index.difference(bill_data.index).difference(
+            known_inconsistencies
+        )
+        raise ValueError(
+            "Inconsistent dates for Canadian treasury bills vs bonds. \n"
+            "Dates with bills but not bonds: {in_bills}.\n"
+            "Dates with bonds but not bills: {in_bonds}.".format(
+                in_bills=in_bills,
+                in_bonds=in_bonds,
+            )
+        )
+
+
+def earliest_possible_date():
+    """
+    The earliest date for which we can load data from this module.
+    """
+    today = pd.Timestamp('now', tz='UTC').normalize()
+    # Bank of Canada only has the last 10 years of data at any given time.
+    return today.replace(year=today.year - 10)
+
+
+def get_treasury_data(start_date, end_date):
+    bill_data = load_frame(
+        format_bill_url(start_date, end_date, start_date),
+        # We skip fewer rows here because we query for fewer bill fields,
+        # which makes the header smaller.
+        skiprows=18,
+    )
+    bond_data = load_frame(
+        format_bond_url(start_date, end_date, start_date),
+        skiprows=22,
+    )
+    check_known_inconsistencies(bill_data, bond_data)
+
+    # dropna('any') removes the rows for which we only had data for one of
+    # bills/bonds.
+    out = pd.concat([bond_data, bill_data], axis=1).dropna(how='any')
+    assert set(out.columns) == set(six.itervalues(COLUMN_NAMES))
+
+    # Multiply by 0.01 to convert from percentages to expected output format.
+    return out * 0.01

catalyst/dispatch.py

@@ -0,0 +1,21 @@
+"""dispatcher object with a custom namespace.
+
+Anything that has been dispatched will also be put into this module.
+"""
+from functools import partial
+import sys
+
+from multipledispatch import dispatch
+
+try:
+    from datashape.dispatch import namespace
+except ImportError:
+    pass
+else:
+    globals().update(namespace)
+    del namespace
+
+dispatch = partial(dispatch, namespace=globals())
+
+del partial
+del sys

catalyst/errors.py

@@ -0,0 +1,798 @@
+#
+# Copyright 2015 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from textwrap import dedent
+
+from catalyst.utils.memoize import lazyval
+
+
+class ZiplineError(Exception):
+    msg = None
+
+    def __init__(self, **kwargs):
+        self.kwargs = kwargs
+
+    @lazyval
+    def message(self):
+        return str(self)
+
+    def __str__(self):
+        msg = self.msg.format(**self.kwargs)
+        return msg
+
+    __unicode__ = __str__
+    __repr__ = __str__
+
+
+class NoTradeDataAvailable(ZiplineError):
+    pass
+
+
+class NoTradeDataAvailableTooEarly(NoTradeDataAvailable):
+    msg = "{sid} does not exist on {dt}. It started trading on {start_dt}."
+
+
+class NoTradeDataAvailableTooLate(NoTradeDataAvailable):
+    msg = "{sid} does not exist on {dt}. It stopped trading on {end_dt}."
+
+
+class BenchmarkAssetNotAvailableTooEarly(NoTradeDataAvailableTooEarly):
+    pass
+
+
+class BenchmarkAssetNotAvailableTooLate(NoTradeDataAvailableTooLate):
+    pass
+
+
+class InvalidBenchmarkAsset(ZiplineError):
+    msg = """
+{sid} cannot be used as the benchmark because it has a stock \
+dividend on {dt}.  Choose another asset to use as the benchmark.
+""".strip()
+
+
+class WrongDataForTransform(ZiplineError):
+    """
+    Raised whenever a rolling transform is called on an event that
+    does not have the necessary properties.
+    """
+    msg = "{transform} requires {fields}. Event cannot be processed."
+
+
+class UnsupportedSlippageModel(ZiplineError):
+    """
+    Raised if a user script calls the set_slippage magic
+    with a slipage object that isn't a VolumeShareSlippage or
+    FixedSlipapge
+    """
+    msg = """
+You attempted to set slippage with an unsupported class. \
+Please use VolumeShareSlippage or FixedSlippage.
+""".strip()
+
+
+class IncompatibleSlippageModel(ZiplineError):
+    """
+    Raised if a user tries to set a futures slippage model for equities or vice
+    versa.
+    """
+    msg = """
+You attempted to set an incompatible slippage model for {asset_type}. \
+The slippage model '{given_model}' only supports {supported_asset_types}.
+""".strip()
+
+
+class SetSlippagePostInit(ZiplineError):
+    # Raised if a users script calls set_slippage magic
+    # after the initialize method has returned.
+    msg = """
+You attempted to set slippage outside of `initialize`. \
+You may only call 'set_slippage' in your initialize method.
+""".strip()
+
+
+class SetCancelPolicyPostInit(ZiplineError):
+    # Raised if a users script calls set_cancel_policy
+    # after the initialize method has returned.
+    msg = """
+You attempted to set the cancel policy outside of `initialize`. \
+You may only call 'set_cancel_policy' in your initialize method.
+""".strip()
+
+
+class RegisterTradingControlPostInit(ZiplineError):
+    # Raised if a user's script register's a trading control after initialize
+    # has been run.
+    msg = """
+You attempted to set a trading control outside of `initialize`. \
+Trading controls may only be set in your initialize method.
+""".strip()
+
+
+class RegisterAccountControlPostInit(ZiplineError):
+    # Raised if a user's script register's a trading control after initialize
+    # has been run.
+    msg = """
+You attempted to set an account control outside of `initialize`. \
+Account controls may only be set in your initialize method.
+""".strip()
+
+
+class UnsupportedCommissionModel(ZiplineError):
+    """
+    Raised if a user script calls the set_commission magic
+    with a commission object that isn't a PerShare, PerTrade or
+    PerDollar commission
+    """
+    msg = """
+You attempted to set commission with an unsupported class. \
+Please use PerShare or PerTrade.
+""".strip()
+
+
+class IncompatibleCommissionModel(ZiplineError):
+    """
+    Raised if a user tries to set a futures commission model for equities or
+    vice versa.
+    """
+    msg = """
+You attempted to set an incompatible commission model for {asset_type}. \
+The commission model '{given_model}' only supports {supported_asset_types}.
+""".strip()
+
+
+class UnsupportedCancelPolicy(ZiplineError):
+    """
+    Raised if a user script calls set_cancel_policy with an object that isn't
+    a CancelPolicy.
+    """
+    msg = """
+You attempted to set the cancel policy with an unsupported class.  Please use
+an instance of CancelPolicy.
+""".strip()
+
+
+class SetCommissionPostInit(ZiplineError):
+    """
+    Raised if a users script calls set_commission magic
+    after the initialize method has returned.
+    """
+    msg = """
+You attempted to override commission outside of `initialize`. \
+You may only call 'set_commission' in your initialize method.
+""".strip()
+
+
+class TransactionWithNoVolume(ZiplineError):
+    """
+    Raised if a transact call returns a transaction with zero volume.
+    """
+    msg = """
+Transaction {txn} has a volume of zero.
+""".strip()
+
+
+class TransactionWithWrongDirection(ZiplineError):
+    """
+    Raised if a transact call returns a transaction with a direction that
+    does not match the order.
+    """
+    msg = """
+Transaction {txn} not in same direction as corresponding order {order}.
+""".strip()
+
+
+class TransactionWithNoAmount(ZiplineError):
+    """
+    Raised if a transact call returns a transaction with zero amount.
+    """
+    msg = """
+Transaction {txn} has an amount of zero.
+""".strip()
+
+
+class TransactionVolumeExceedsOrder(ZiplineError):
+    """
+    Raised if a transact call returns a transaction with a volume greater than
+the corresponding order.
+    """
+    msg = """
+Transaction volume of {txn} exceeds the order volume of {order}.
+""".strip()
+
+
+class UnsupportedOrderParameters(ZiplineError):
+    """
+    Raised if a set of mutually exclusive parameters are passed to an order
+    call.
+    """
+    msg = "{msg}"
+
+
+class CannotOrderDelistedAsset(ZiplineError):
+    """
+    Raised if an order is for a delisted asset.
+    """
+    msg = "{msg}"
+
+
+class BadOrderParameters(ZiplineError):
+    """
+    Raised if any impossible parameters (nan, negative limit/stop)
+    are passed to an order call.
+    """
+    msg = "{msg}"
+
+
+class OrderDuringInitialize(ZiplineError):
+    """
+    Raised if order is called during initialize()
+    """
+    msg = "{msg}"
+
+
+class SetBenchmarkOutsideInitialize(ZiplineError):
+    """
+    Raised if set_benchmark is called outside initialize()
+    """
+    msg = "'set_benchmark' can only be called within initialize function."
+
+
+class AccountControlViolation(ZiplineError):
+    """
+    Raised if the account violates a constraint set by a AccountControl.
+    """
+    msg = """
+Account violates account constraint {constraint}.
+""".strip()
+
+
+class TradingControlViolation(ZiplineError):
+    """
+    Raised if an order would violate a constraint set by a TradingControl.
+    """
+    msg = """
+Order for {amount} shares of {asset} at {datetime} violates trading constraint
+{constraint}.
+""".strip()
+
+
+class IncompatibleHistoryFrequency(ZiplineError):
+    """
+    Raised when a frequency is given to history which is not supported.
+    At least, not yet.
+    """
+    msg = """
+Requested history at frequency '{frequency}' cannot be created with data
+at frequency '{data_frequency}'.
+""".strip()
+
+
+class HistoryInInitialize(ZiplineError):
+    """
+    Raised when an algorithm calls history() in initialize.
+    """
+    msg = "history() should only be called in handle_data()"
+
+
+class OrderInBeforeTradingStart(ZiplineError):
+    """
+    Raised when an algorithm calls an order method in before_trading_start.
+    """
+    msg = "Cannot place orders inside before_trading_start."
+
+
+class MultipleSymbolsFound(ZiplineError):
+    """
+    Raised when a symbol() call contains a symbol that changed over
+    time and is thus not resolvable without additional information
+    provided via as_of_date.
+    """
+    msg = """
+Multiple symbols with the name '{symbol}' found. Use the
+as_of_date' argument to to specify when the date symbol-lookup
+should be valid.
+
+Possible options: {options}
+    """.strip()
+
+
+class SymbolNotFound(ZiplineError):
+    """
+    Raised when a symbol() call contains a non-existant symbol.
+    """
+    msg = """
+Symbol '{symbol}' was not found.
+""".strip()
+
+
+class RootSymbolNotFound(ZiplineError):
+    """
+    Raised when a lookup_future_chain() call contains a non-existant symbol.
+    """
+    msg = """
+Root symbol '{root_symbol}' was not found.
+""".strip()
+
+
+class ValueNotFoundForField(ZiplineError):
+    """
+    Raised when a lookup_by_supplementary_mapping() call contains a
+    value does not exist for the specified mapping type.
+    """
+    msg = """
+Value '{value}' was not found for field '{field}'.
+""".strip()
+
+
+class MultipleValuesFoundForField(ZiplineError):
+    """
+    Raised when a lookup_by_supplementary_mapping() call contains a
+    value that changed over time for the specified field and is
+    thus not resolvable without additional information provided via
+    as_of_date.
+    """
+    msg = """
+Multiple occurrences of the value '{value}' found for field '{field}'.
+Use the as_of_date' argument to specify when the lookup should be valid.
+
+Possible options: {options}
+    """.strip()
+
+
+class NoValueForSid(ZiplineError):
+    """
+    Raised when a get_supplementary_field() call contains a sid that
+    does not have a value for the specified mapping type.
+    """
+    msg = """
+No '{field}' value found for sid '{sid}'.
+""".strip()
+
+
+class MultipleValuesFoundForSid(ZiplineError):
+    """
+    Raised when a get_supplementary_field() call contains a value that
+    changed over time for the specified field and is thus not resolvable
+    without additional information provided via as_of_date.
+    """
+    msg = """
+Multiple '{field}' values found for sid '{sid}'. Use the as_of_date' argument
+to specify when the lookup should be valid.
+
+Possible options: {options}
+""".strip()
+
+
+class SidsNotFound(ZiplineError):
+    """
+    Raised when a retrieve_asset() or retrieve_all() call contains a
+    non-existent sid.
+    """
+    @lazyval
+    def plural(self):
+        return len(self.sids) > 1
+
+    @lazyval
+    def sids(self):
+        return self.kwargs['sids']
+
+    @lazyval
+    def msg(self):
+        if self.plural:
+            return "No assets found for sids: {sids}."
+        return "No asset found for sid: {sids[0]}."
+
+
+class EquitiesNotFound(SidsNotFound):
+    """
+    Raised when a call to `retrieve_equities` fails to find an asset.
+    """
+    @lazyval
+    def msg(self):
+        if self.plural:
+            return "No equities found for sids: {sids}."
+        return "No equity found for sid: {sids[0]}."
+
+
+class FutureContractsNotFound(SidsNotFound):
+    """
+    Raised when a call to `retrieve_futures_contracts` fails to find an asset.
+    """
+    @lazyval
+    def msg(self):
+        if self.plural:
+            return "No future contracts found for sids: {sids}."
+        return "No future contract found for sid: {sids[0]}."
+
+
+class ConsumeAssetMetaDataError(ZiplineError):
+    """
+    Raised when AssetFinder.consume() is called on an invalid object.
+    """
+    msg = """
+AssetFinder can not consume metadata of type {obj}. Metadata must be a dict, a
+DataFrame, or a tables.Table. If the provided metadata is a Table, the rows
+must contain both or one of 'sid' or 'symbol'.
+""".strip()
+
+
+class MapAssetIdentifierIndexError(ZiplineError):
+    """
+    Raised when AssetMetaData.map_identifier_index_to_sids() is called on an
+    index of invalid objects.
+    """
+    msg = """
+AssetFinder can not map an index with values of type {obj}. Asset indices of
+DataFrames or Panels must be integer sids, string symbols, or Asset objects.
+""".strip()
+
+
+class SidAssignmentError(ZiplineError):
+    """
+    Raised when an AssetFinder tries to build an Asset that does not have a sid
+    and that AssetFinder is not permitted to assign sids.
+    """
+    msg = """
+AssetFinder metadata is missing a SID for identifier '{identifier}'.
+""".strip()
+
+
+class NoSourceError(ZiplineError):
+    """
+    Raised when no source is given to the pipeline
+    """
+    msg = """
+No data source given.
+""".strip()
+
+
+class PipelineDateError(ZiplineError):
+    """
+    Raised when only one date is passed to the pipeline
+    """
+    msg = """
+Only one simulation date given. Please specify both the 'start' and 'end' for
+the simulation, or neither. If neither is given, the start and end of the
+DataSource will be used. Given start = '{start}', end = '{end}'
+""".strip()
+
+
+class WindowLengthTooLong(ZiplineError):
+    """
+    Raised when a trailing window is instantiated with a lookback greater than
+    the length of the underlying array.
+    """
+    msg = (
+        "Can't construct a rolling window of length "
+        "{window_length} on an array of length {nrows}."
+    ).strip()
+
+
+class WindowLengthNotPositive(ZiplineError):
+    """
+    Raised when a trailing window would be instantiated with a length less than
+    1.
+    """
+    msg = (
+        "Expected a window_length greater than 0, got {window_length}."
+    ).strip()
+
+
+class NonWindowSafeInput(ZiplineError):
+    """
+    Raised when a Pipeline API term that is not deemed window safe is specified
+    as an input to another windowed term.
+
+    This is an error because it's generally not safe to compose windowed
+    functions on split/dividend adjusted data.
+    """
+    msg = (
+        "Can't compute windowed expression {parent} with "
+        "windowed input {child}."
+    )
+
+
+class TermInputsNotSpecified(ZiplineError):
+    """
+    Raised if a user attempts to construct a term without specifying inputs and
+    that term does not have class-level default inputs.
+    """
+    msg = "{termname} requires inputs, but no inputs list was passed."
+
+
+class TermOutputsEmpty(ZiplineError):
+    """
+    Raised if a user attempts to construct a term with an empty outputs list.
+    """
+    msg = (
+        "{termname} requires at least one output when passed an outputs "
+        "argument."
+    )
+
+
+class InvalidOutputName(ZiplineError):
+    """
+    Raised if a term's output names conflict with any of its attributes.
+    """
+    msg = (
+        "{output_name!r} cannot be used as an output name for {termname}. "
+        "Output names cannot start with an underscore or be contained in the "
+        "following list: {disallowed_names}."
+    )
+
+
+class WindowLengthNotSpecified(ZiplineError):
+    """
+    Raised if a user attempts to construct a term without specifying window
+    length and that term does not have a class-level default window length.
+    """
+    msg = (
+        "{termname} requires a window_length, but no window_length was passed."
+    )
+
+
+class InvalidTermParams(ZiplineError):
+    """
+    Raised if a user attempts to construct a Term using ParameterizedTermMixin
+    without specifying a `params` list in the class body.
+    """
+    msg = (
+        "Expected a list of strings as a class-level attribute for "
+        "{termname}.params, but got {value} instead."
+    )
+
+
+class DTypeNotSpecified(ZiplineError):
+    """
+    Raised if a user attempts to construct a term without specifying dtype and
+    that term does not have class-level default dtype.
+    """
+    msg = (
+        "{termname} requires a dtype, but no dtype was passed."
+    )
+
+
+class NotDType(ZiplineError):
+    """
+    Raised when a pipeline Term is constructed with a dtype that isn't a numpy
+    dtype object.
+    """
+    msg = (
+        "{termname} expected a numpy dtype "
+        "object for a dtype, but got {dtype} instead."
+    )
+
+
+class UnsupportedDType(ZiplineError):
+    """
+    Raised when a pipeline Term is constructed with a dtype that's not
+    supported.
+    """
+    msg = (
+        "Failed to construct {termname}.\n"
+        "Pipeline terms of dtype {dtype} are not yet supported."
+    )
+
+
+class BadPercentileBounds(ZiplineError):
+    """
+    Raised by API functions accepting percentile bounds when the passed bounds
+    are invalid.
+    """
+    msg = (
+        "Percentile bounds must fall between 0.0 and {upper_bound}, and min "
+        "must be less than max."
+        "\nInputs were min={min_percentile}, max={max_percentile}."
+    )
+
+
+class UnknownRankMethod(ZiplineError):
+    """
+    Raised during construction of a Rank factor when supplied a bad Rank
+    method.
+    """
+    msg = (
+        "Unknown ranking method: '{method}'. "
+        "`method` must be one of {choices}"
+    )
+
+
+class AttachPipelineAfterInitialize(ZiplineError):
+    """
+    Raised when a user tries to call add_pipeline outside of initialize.
+    """
+    msg = (
+        "Attempted to attach a pipeline after initialize()."
+        "attach_pipeline() can only be called during initialize."
+    )
+
+
+class PipelineOutputDuringInitialize(ZiplineError):
+    """
+    Raised when a user tries to call `pipeline_output` during initialize.
+    """
+    msg = (
+        "Attempted to call pipeline_output() during initialize. "
+        "pipeline_output() can only be called once initialize has completed."
+    )
+
+
+class NoSuchPipeline(ZiplineError, KeyError):
+    """
+    Raised when a user tries to access a non-existent pipeline by name.
+    """
+    msg = (
+        "No pipeline named '{name}' exists. Valid pipeline names are {valid}. "
+        "Did you forget to call attach_pipeline()?"
+    )
+
+
+class UnsupportedDataType(ZiplineError):
+    """
+    Raised by CustomFactors with unsupported dtypes.
+    """
+    msg = "{typename} instances with dtype {dtype} are not supported."
+
+
+class NoFurtherDataError(ZiplineError):
+    """
+    Raised by calendar operations that would ask for dates beyond the extent of
+    our known data.
+    """
+    # This accepts an arbitrary message string because it's used in more places
+    # that can be usefully templated.
+    msg = '{msg}'
+
+    @classmethod
+    def from_lookback_window(cls,
+                             initial_message,
+                             first_date,
+                             lookback_start,
+                             lookback_length):
+        return cls(
+            msg=dedent(
+                """
+                {initial_message}
+
+                lookback window started at {lookback_start}
+                earliest known date was {first_date}
+                {lookback_length} extra rows of data were required
+                """
+            ).format(
+                initial_message=initial_message,
+                first_date=first_date,
+                lookback_start=lookback_start,
+                lookback_length=lookback_length,
+            )
+        )
+
+
+class UnsupportedDatetimeFormat(ZiplineError):
+    """
+    Raised when an unsupported datetime is passed to an API method.
+    """
+    msg = ("The input '{input}' passed to '{method}' is not "
+           "coercible to a pandas.Timestamp object.")
+
+
+class AssetDBVersionError(ZiplineError):
+    """
+    Raised by an AssetDBWriter or AssetFinder if the version number in the
+    versions table does not match the ASSET_DB_VERSION in asset_writer.py.
+    """
+    msg = (
+        "The existing Asset database has an incorrect version: {db_version}. "
+        "Expected version: {expected_version}. Try rebuilding your asset "
+        "database or updating your version of Zipline."
+    )
+
+
+class AssetDBImpossibleDowngrade(ZiplineError):
+    msg = (
+        "The existing Asset database is version: {db_version} which is lower "
+        "than the desired downgrade version: {desired_version}."
+    )
+
+
+class HistoryWindowStartsBeforeData(ZiplineError):
+    msg = (
+        "History window extends before {first_trading_day}. To use this "
+        "history window, start the backtest on or after {suggested_start_day}."
+        )
+
+
+class NonExistentAssetInTimeFrame(ZiplineError):
+    msg = (
+        "The target asset '{asset}' does not exist for the entire timeframe "
+        "between {start_date} and {end_date}."
+    )
+
+
+class InvalidCalendarName(ZiplineError):
+    """
+    Raised when a calendar with an invalid name is requested.
+    """
+    msg = (
+        "The requested TradingCalendar, {calendar_name}, does not exist."
+    )
+
+
+class CalendarNameCollision(ZiplineError):
+    """
+    Raised when the static calendar registry already has a calendar with a
+    given name.
+    """
+    msg = (
+        "A calendar with the name {calendar_name} is already registered."
+    )
+
+
+class CyclicCalendarAlias(ZiplineError):
+    """
+    Raised when calendar aliases form a cycle.
+    """
+    msg = "Cycle in calendar aliases: [{cycle}]"
+
+
+class ScheduleFunctionWithoutCalendar(ZiplineError):
+    """
+    Raised when schedule_function is called but there is not a calendar to be
+    used in the construction of an event rule.
+    """
+    # TODO update message when new TradingSchedules are built
+    msg = (
+        "To use schedule_function, the TradingAlgorithm must be running on an "
+        "ExchangeTradingSchedule, rather than {schedule}."
+    )
+
+
+class ScheduleFunctionInvalidCalendar(ZiplineError):
+    """
+    Raised when schedule_function is called with an invalid calendar argument.
+    """
+    msg = (
+        "Invalid calendar '{given_calendar}' passed to schedule_function. "
+        "Allowed options are {allowed_calendars}."
+    )
+
+
+class UnsupportedPipelineOutput(ZiplineError):
+    """
+    Raised when a 1D term is added as a column to a pipeline.
+    """
+    msg = (
+        "Cannot add column {column_name!r} with term {term}. Adding slices or "
+        "single-column-output terms as pipeline columns is not currently "
+        "supported."
+    )
+
+
+class NonSliceableTerm(ZiplineError):
+    """
+    Raised when attempting to index into a non-sliceable term, e.g. instances
+    of `catalyst.pipeline.term.LoadableTerm`.
+    """
+    msg = "Taking slices of {term} is not currently supported."
+
+
+class IncompatibleTerms(ZiplineError):
+    """
+    Raised when trying to compute correlations/regressions between two 2D
+    factors with different masks.
+    """
+    msg = (
+        "{term_1} and {term_2} must have the same mask in order to compute "
+        "correlations and regressions asset-wise."
+    )