Refactoring exchanges

# Conflicts:
#	catalyst/__main__.py
#	catalyst/utils/run_algo.py
#	catalyst/utils/run_algo_original.py
#	zipline/utils/run_algo.py

.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 +1,84 @@
+.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
+
+# 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 @@
+All the documentation for `Catalyst <https://github.com/enigmampc/catalyst>`_ can be found in the `catalyst-docs wiki <https://github.com/enigmampc/catalyst-docs/wiki>`_.

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

_sources/appendix.rst.txt

@@ -1,371 +0,0 @@
-API Reference
--------------
-
-Running a Backtest
-~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: catalyst.run_algorithm(...)
-
-Algorithm API
-~~~~~~~~~~~~~
-
-The following methods are available for use in the ``initialize``,
-``handle_data``, and ``before_trading_start`` API functions.
-
-In all listed functions, the ``self`` argument is implicitly the
-currently-executing :class:`~zipline.algorithm.TradingAlgorithm` instance.
-
-Data Object
-```````````
-
-.. autoclass:: catalyst.protocol.BarData
-   :members:
-
-Scheduling Functions
-````````````````````
-
-.. autofunction:: catalyst.api.schedule_function
-
-.. autoclass:: catalyst.api.date_rules
-   :members:
-   :undoc-members:
-
-.. autoclass:: catalyst.api.time_rules
-   :members:
-
-Orders
-``````
-
-.. autofunction:: catalyst.api.order
-
-.. autofunction:: catalyst.api.order_value
-
-.. autofunction:: catalyst.api.order_percent
-
-.. autofunction:: catalyst.api.order_target
-
-.. autofunction:: catalyst.api.order_target_value
-
-.. autofunction:: catalyst.api.order_target_percent
-
-.. autoclass:: catalyst.finance.execution.ExecutionStyle
-   :members:
-
-.. autoclass:: catalyst.finance.execution.MarketOrder
-
-.. autoclass:: catalyst.finance.execution.LimitOrder
-
-.. autoclass:: catalyst.finance.execution.StopOrder
-
-.. autoclass:: catalyst.finance.execution.StopLimitOrder
-
-.. autofunction:: catalyst.api.get_order
-
-.. autofunction:: catalyst.api.get_open_orders
-
-.. autofunction:: catalyst.api.cancel_order
-
-Order Cancellation Policies
-'''''''''''''''''''''''''''
-
-.. autofunction:: catalyst.api.set_cancel_policy
-
-.. autoclass:: catalyst.finance.cancel_policy.CancelPolicy
-   :members:
-
-.. autofunction:: catalyst.api.EODCancel
-
-.. autofunction:: catalyst.api.NeverCancel
-
-
-Assets
-``````
-
-.. autofunction:: catalyst.api.symbol
-
-.. autofunction:: catalyst.api.symbols
-
-.. autofunction:: catalyst.api.set_symbol_lookup_date
-
-.. autofunction:: catalyst.api.sid
-
-
-Trading Controls
-````````````````
-
-zipline provides trading controls to help ensure that the algorithm is
-performing as expected. The functions help protect the algorithm from certian
-bugs that could cause undesirable behavior when trading with real money.
-
-.. autofunction:: catalyst.api.set_do_not_order_list
-
-.. autofunction:: catalyst.api.set_long_only
-
-.. autofunction:: catalyst.api.set_max_leverage
-
-.. autofunction:: catalyst.api.set_max_order_count
-
-.. autofunction:: catalyst.api.set_max_order_size
-
-.. autofunction:: catalyst.api.set_max_position_size
-
-
-Simulation Parameters
-`````````````````````
-
-.. autofunction:: catalyst.api.set_benchmark
-
-Commission Models
-'''''''''''''''''
-
-.. autofunction:: catalyst.api.set_commission
-
-.. autoclass:: catalyst.finance.commission.CommissionModel
-   :members:
-
-.. autoclass:: catalyst.finance.commission.PerShare
-
-.. autoclass:: catalyst.finance.commission.PerTrade
-
-.. autoclass:: catalyst.finance.commission.PerDollar
-
-Slippage Models
-'''''''''''''''
-
-.. autofunction:: catalyst.api.set_slippage
-
-.. autoclass:: catalyst.finance.slippage.SlippageModel
-   :members:
-
-.. autoclass:: catalyst.finance.slippage.FixedSlippage
-
-.. autoclass:: catalyst.finance.slippage.VolumeShareSlippage
-
-Pipeline
-````````
-
-Not supported yet.
-
-.. For more information, see :ref:`pipeline-api`
-
-.. .. autofunction:: catalyst.api.attach_pipeline
-
-.. .. autofunction:: catalyst.api.pipeline_output
-
-
-Miscellaneous
-`````````````
-
-.. autofunction:: catalyst.api.record
-
-.. autofunction:: catalyst.api.get_environment
-
-.. autofunction:: catalyst.api.fetch_csv
-
-
-.. _pipeline-api:
-
-.. Pipeline API
-.. ~~~~~~~~~~~~
-
-.. .. autoclass:: zipline.pipeline.Pipeline
-..    :members:
-..    :member-order: groupwise
-
-.. .. autoclass:: zipline.pipeline.CustomFactor
-..    :members:
-..    :member-order: groupwise
-
-.. .. autoclass:: zipline.pipeline.filters.Filter
-..    :members: __and__, __or__
-..    :exclude-members: dtype
-
-.. .. autoclass:: zipline.pipeline.factors.Factor
-..    :members: bottom, deciles, demean, linear_regression, pearsonr,
-..              percentile_between, quantiles, quartiles, quintiles, rank,
-..              spearmanr, top, winsorize, zscore, isnan, notnan, isfinite, eq,
-..              \__add__, \__sub__, \__mul__, \__div__, \__mod__, \__pow__, 
-..              \__lt__, \__le__, \__ne__, \__ge__, \__gt__
-..    :exclude-members: dtype
-..    :member-order: bysource
-
-.. .. autoclass:: zipline.pipeline.term.Term
-..    :members:
-..    :exclude-members: compute_extra_rows, dependencies, inputs, mask, windowed
-
-.. .. autoclass:: zipline.pipeline.data.USEquityPricing
-..    :members: open, high, low, close, volume
-..    :undoc-members:
-
-.. Built-in Factors
-.. ````````````````
-
-.. .. autoclass:: zipline.pipeline.factors.AverageDollarVolume
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.BollingerBands
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.BusinessDaysSincePreviousEvent
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.BusinessDaysUntilNextEvent
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingAverage
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.ExponentialWeightedMovingStdDev
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.Latest
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.MaxDrawdown
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.Returns
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.RollingLinearRegressionOfReturns
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.RollingPearsonOfReturns
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.RollingSpearmanOfReturns
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.RSI
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.SimpleMovingAverage
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.VWAP
-..    :members:
-
-.. .. autoclass:: zipline.pipeline.factors.WeightedAverageValue
-..    :members:
-
-.. Pipeline Engine
-.. ```````````````
-
-.. .. autoclass:: zipline.pipeline.engine.PipelineEngine
-..    :members: run_pipeline, run_chunked_pipeline
-..    :member-order: bysource
-
-.. .. autoclass:: zipline.pipeline.engine.SimplePipelineEngine
-..    :members: __init__, run_pipeline, run_chunked_pipeline
-..    :member-order: bysource
-
-.. .. autofunction:: zipline.pipeline.engine.default_populate_initial_workspace
-
-.. Data Loaders
-.. ````````````
-
-.. .. autoclass:: zipline.pipeline.loaders.equity_pricing_loader.USEquityPricingLoader
-..    :members: __init__, from_files, load_adjusted_array
-..    :member-order: bysource
-
-Asset Metadata
-~~~~~~~~~~~~~~
-
-.. autoclass:: catalyst.assets.Asset
-   :members:
-
-.. autoclass:: catalyst.assets.AssetConvertible
-   :members:
-
-
-Trading Calendar API
-~~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: catalyst.utils.calendars.get_calendar
-
-.. autoclass:: catalyst.utils.calendars.TradingCalendar
-   :members:
-
-.. autofunction:: catalyst.utils.calendars.register_calendar
-
-.. autofunction:: catalyst.utils.calendars.register_calendar_type
-
-.. autofunction:: catalyst.utils.calendars.deregister_calendar
-
-.. autofunction:: catalyst.utils.calendars.clear_calendars
-
-
-Data API
-~~~~~~~~
-
-.. Writers
-.. ```````
-.. .. autoclass:: zipline.data.minute_bars.BcolzMinuteBarWriter
-..    :members:
-
-.. .. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarWriter
-..    :members:
-
-.. .. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentWriter
-..    :members:
-
-.. .. autoclass:: zipline.assets.AssetDBWriter
-..    :members:
-
-.. Readers
-.. ```````
-.. .. autoclass:: zipline.data.minute_bars.BcolzMinuteBarReader
-..    :members:
-
-.. .. autoclass:: zipline.data.us_equity_pricing.BcolzDailyBarReader
-..    :members:
-
-.. .. autoclass:: zipline.data.us_equity_pricing.SQLiteAdjustmentReader
-..    :members:
-
-.. .. autoclass:: zipline.assets.AssetFinder
-..    :members:
-
-.. .. autoclass:: zipline.data.data_portal.DataPortal
-..    :members:
-
-.. Bundles
-.. ```````
-.. .. autofunction:: zipline.data.bundles.register
-
-.. .. autofunction:: zipline.data.bundles.ingest(name, environ=os.environ, date=None, show_progress=True)
-
-.. .. autofunction:: zipline.data.bundles.load(name, environ=os.environ, date=None)
-
-.. .. autofunction:: zipline.data.bundles.unregister
-
-.. .. data:: zipline.data.bundles.bundles
-
-..    The bundles that have been registered as a mapping from bundle name to bundle
-..    data. This mapping is immutable and should only be updated through
-..    :func:`~zipline.data.bundles.register` or
-..    :func:`~zipline.data.bundles.unregister`.
-
-.. .. autofunction:: zipline.data.bundles.yahoo_equities
-
-
-
-Utilities
-~~~~~~~~~
-
-Caching
-```````
-
-.. autoclass:: catalyst.utils.cache.CachedObject
-
-.. autoclass:: catalyst.utils.cache.ExpiringCache
-
-.. autoclass:: catalyst.utils.cache.dataframe_cache
-
-.. autoclass:: catalyst.utils.cache.working_file
-
-.. autoclass:: catalyst.utils.cache.working_dir
-
-Command Line
-````````````
-.. autofunction:: catalyst.utils.cli.maybe_show_progress

_sources/development-guidelines.rst.txt

@@ -1,144 +0,0 @@
-Development Guidelines
-======================
-This page is intended for developers of Catalyst, people who want to contribute to the Catalyst codebase or documentation, or people who want to install from source and make local changes to their copy of Catalyst.
-
-All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We `track issues <https://github.com/enigmampc/catalyst/issues>`_ on `GitHub <https://github.com/enigmampc/catalyst>`_ and also have a `discord group <https://discord.gg/SJK32GY>`_ where you can ask questions.
-
-Creating a Development Environment
-----------------------------------
-
-First, you'll need to clone Catalyst by running:
-
-.. code-block:: bash
-
-   $ git clone git@github.com:enigmampc/catalyst.git
-
-Then check out to a new branch where you can make your changes:
-
-.. code-block:: bash
-		
-   $ git checkout -b some-short-descriptive-name
-
-If you don't already have them, you'll need some C library dependencies. You can follow the `install guide <install.html>`_ to get the appropriate dependencies.
-
-The following section assumes you already have virtualenvwrapper and pip installed on your system. Suggested installation of Python library dependencies used for development:
-
-.. code-block:: bash
-
-   $ mkvirtualenv catalyst
-   $ ./etc/ordered_pip.sh ./etc/requirements.txt
-   $ pip install -r ./etc/requirements_dev.txt
-   $ pip install -r ./etc/requirements_blaze.txt 
-
-Finally, you can build the C extensions by running:
-
-.. code-block:: bash
-
-   $ python setup.py build_ext --inplace
-
-Development with Docker
------------------------
-
-If you want to work with zipline using a `Docker`__ container, you'll need to 
-build the ``Dockerfile`` in the Zipline root directory, and then build 
-``Dockerfile-dev``. Instructions for building both containers can be found in 
-``Dockerfile`` and ``Dockerfile-dev``, respectively.
-
-__ https://docs.docker.com/get-started/
-   
-Git Branching Structure
------------------------
-
-If you want to contribute to the codebase of Catalyst, familiarize yourself with our branching structure, a fairly standardized one for that matter, that follows what is documented in the following article: `A successful Git branching model <http://nvie.com/posts/a-successful-git-branching-model/>`_. To contribute, create your local branch and submit a Pull Request (PR) to the **develop** branch.
-
-.. image:: https://camo.githubusercontent.com/9bde6fb64a9542a572e0e2017cbb58d9d2c440ac/687474703a2f2f6e7669652e636f6d2f696d672f6769742d6d6f64656c4032782e706e67
-
-Contributing to the Docs
-------------------------
-
-If you'd like to contribute to the documentation on enigmampc.github.io, you can navigate to ``docs/source/`` where each `reStructuredText <https://en.wikipedia.org/wiki/ReStructuredText>`_ file is a separate section there. To add a section, create a new file called ``some-descriptive-name.rst`` and add ``some-descriptive-name`` to ``index.rst``. To edit a section, simply open up one of the existing files, make your changes, and save them.
-
-We use `Sphinx <http://www.sphinx-doc.org/en/stable/>`_ to generate documentation for Catalyst, which you will need to install by running:
-
-.. code-block:: bash
-
-   $ pip install -r ./etc/requirements_docs.txt
-
-To build and view the docs locally, run:
-
-.. code-block:: bash
-
-   # assuming you're in the Catalyst root directory
-   $ cd docs
-   $ make html
-   $ {BROWSER} build/html/index.html
-
-
-There is a `documented issue <https://github.com/sphinx-doc/sphinx/issues/3212>`_ 
-with ``sphinx`` and ``docutils`` that causes the error below when trying to build 
-the docs.
-
-.. code-block:: text
-
-   Exception occurred:
-     File "(...)/env-c/lib/python2.7/site-packages/docutils/writers/_html_base.py", line 671, in depart_document
-       assert not self.context, 'len(context) = %s' % len(self.context)
-   AssertionError: len(context) = 3
-
-If you get this error, you need to downgrade your version of ``docutils`` as 
-follows, and build the docs again:
-
-.. code-block:: bash
-
-   $ pip install docutils==0.12
-
-
-Commit messages
----------------
-
-Standard prefixes to start a commit message:
-
-.. code-block:: text
-
-   BLD: change related to building Catalyst
-   BUG: bug fix
-   DEP: deprecate something, or remove a deprecated object
-   DEV: development tool or utility
-   DOC: documentation
-   ENH: enhancement
-   MAINT: maintenance commit (refactoring, typos, etc)
-   REV: revert an earlier commit
-   STY: style fix (whitespace, PEP8, flake8, etc)
-   TST: addition or modification of tests
-   REL: related to releasing Catalyst
-   PERF: performance enhancements
-
-
-Some commit style guidelines:
-
-Commit lines should be no longer than `72 characters <https://git-scm.com/book/en/v2/Distributed-Git-Contributing-to-a-Project>`_. The first line of the commit should include one of the above prefixes. There should be an empty line between the commit subject and the body of the commit. In general, the message should be in the imperative tense. Best practice is to include not only what the change is, but why the change was made.
-
-**Example:**
-
-.. code-block:: text
-
-   MAINT: Remove unused calculations of max_leverage, et al.
-
-   In the performance period the max_leverage, max_capital_used,
-   cumulative_capital_used were calculated but not used.
-
-   At least one of those calculations, max_leverage, was causing a
-   divide by zero error.
-   
-   Instead of papering over that error, the entire calculation was
-   a bit suspect so removing, with possibility of adding it back in
-   later with handling the case (or raising appropriate errors) when
-   the algorithm has little cash on hand.
-
-
-Formatting Docstrings
----------------------
-
-When adding or editing docstrings for classes, functions, etc, we use `numpy <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`_ as the canonical reference.
-
-

_sources/example-algos.rst.txt

@@ -1,213 +0,0 @@
-|
-
-Example Algorithms
-==================
-
-This section documents a number of example algorithms to complement the 
-beginner tutorial, and show how other trading algorithms can be implemented 
-using Catalyst.
-
-Overview
-~~~~~~~~
-
-- :ref:`Buy BTC Simple<buy_btc_simple>`: The simplest algorithm that introduces
-  the ``initialize()`` and ``handle_data()`` functions, and is used in the 
-  :doc:`beginner tutorial<beginner-tutorial>` to show how to run catalyst 
-  for the first time.
-
-- :ref:`Buy and Hodl <buy_and_hodl>`: A very straightforward *buy and hold* that 
-  makes one single buy at the very beginning. Introduces the notions of 
-  ``cash``, management of outstanding ``orders``, and ``order_target_value`` 
-  to place orders. It also introduces the ``analyze()`` function to visualize 
-  the performance of our strategy using the external library ``matplotlib``.
-
-- :ref:`Dual Moving Average Crossover<dual_moving_average>`: A classic momentum 
-  strategy used in the second part of the 
-  `beginner tutorial <beginner-tutorial.html#history>`_ to introduce the 
-  ``data.history()`` function. It makes a heavy use of ``matplotlib`` library 
-  in the ``analyze()`` function to chart the performance of the algorithm.
-
-- :ref:`Mean Reversion Algorithm <mean_reversion>`: Another simple momentum 
-  strategy that is used in our 
-  `two-part video tutorial <videos.html#backtesting-a-strategy>`_ to show how 
-  to get started in backtesting and live trading with Catalyst.
-
-- :ref:`Simple Universe <simple_universe>`: This code provides the 'universe' 
-  of available trading pairs on a given exchange on any given day. You can use 
-  this code to dynamically select which currency pairs you want to trade each 
-  day of your strategy. This example does not make any trades. 
-
-- :ref:`Portfolio Optimization <portfolio_optimization>`: Use this code to 
-  execute a portfolio optimization model. This strategy will select the 
-  portfolio with the maximum Sharpe Ratio. The parameters are set to use 180 
-  days of historical data and rebalance every 30 days. This code was used in 
-  writting the following article: 
-  `Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.
-
-
-.. _buy_btc_simple:
-
-Buy BTC Simple Algorithm
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-Source code: `examples/buy_btc_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_btc_simple.py>`_
-
-.. literalinclude:: ../../catalyst/examples/buy_btc_simple.py
-   :language: python
-
-This simple algorithm does not produce any output nor displays any chart.
-
-
-.. _buy_and_hodl:
-
-Buy and Hodl Algorithm
-~~~~~~~~~~~~~~~~~~~~~~
-
-First ingest the historical pricing data needed to run this algorithm:
-
-.. code-block:: bash
-
-   catalyst ingest-exchange -x bitfinex -f daily -i btc_usd
-
-Then, you can run the code below with the following command:
-
-.. code-block:: bash
-
-   catalyst run -f buy_and_hodl.py --start 2015-3-1 --end 2017-10-31 --capital-base 100000 -x bitfinex -c btc -o bah.pickle
-
-or using the same parameters specified in the run_algorithm() function at the 
-end of the file:
-
-.. code-block:: bash
-
-  python buy_and_hodl.py
-
-
-This command will run the trading algorithm in the specified time range and 
-plot the resulting performance using the matplotlib library. You can choose any 
-date interval with the ``--start`` and ``--end`` parameters, but bear in mind 
-that 2015-3-1 is the earliest date that Catalyst supports (if you choose an 
-earlier date, you'll get an error), and the most recent date you can choose is 
-one day prior to the current date. 
-
-Source code: `examples/buy_and_hodl.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_and_hodl.py>`_
-
-.. literalinclude:: ../../catalyst/examples/buy_and_hodl.py
-   :language: python
-
-.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_buy_and_hodl.png
-
-.. _dual_moving_average:
-
-Dual Moving Average Crossover
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This strategy is covered in detail in the last part of 
-`this tutorial <beginner-tutorial.html#history>`_.
-
-Source Code: `examples/dual_moving_average.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/dual_moving_average.py>`_
-
-.. literalinclude:: ../../catalyst/examples/dual_moving_average.py
-   :language: python
-
-.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/tutorial_dual_moving_average.png
-
-
-.. _mean_reversion:
-
-Mean Reversion Algorithm
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-This algorithm is based on a simple momentum strategy. When the cryptoasset goes
-up quickly, we're going to buy; when it goes down quickly, we're going to sell. 
-Hopefully, we'll ride the waves.
-
-We are choosing to backtest this trading algorithm with the ``neo_usd`` currency 
-pairon the ``Bitfinex`` exchange. Thus, first ingest the historical pricing data
-that we need, with minute resolution:
-
-.. code-block:: bash
-
-   catalyst ingest-exchange -x bitfinex -f minute -i neo_usd
-
-To run this algorithm, we are opting for the Python interpreter, instead of the 
-command line (CLI). All of the parameters for the simulation are specified in 
-lines 218-245, so in order to run the algorithm we just type:
-
-.. code-block:: bash
-
-   python mean_reversion_simple.py
-
-Source code: `examples/mean_reversion_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/mean_reversion_simple.py>`_
-
-.. literalinclude:: ../../catalyst/examples/mean_reversion_simple.py
-   :language: python
-
-.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/example_mean_reversion_simple.png
-
-Notice the difference in performance between the charts above and those seen on 
-`this video tutorial <https://youtu.be/JOBRwst9jUY>`_ at 
-minute 8:10. The buy and sell orders are triggered at the same exact times, but
-the differences result from a more realistic slippage model 
-implemented after the video was recorded, which executes the orders at slighlty
-different prices, but resulting in significant changes in performance of our 
-strategy.
-
-.. _simple_universe:
-
-Simple Universe
-~~~~~~~~~~~~~~~
-
-This example aims to provide an easy way for users to learn how to 
-collect data from any given exchange and select a subset of the available
-currency pairs for trading. You simply need to specify the exchange and 
-the market (base_currency) that you want to focus on. You will then see 
-how to create a universe of assets, and filter it based the market you 
-desire.
-
-The example prints out the closing price of all the pairs for a given 
-market in a given exchange every 30 minutes. The example also contains 
-the OHLCV data with minute-resolution for the past seven days which 
-could be used to create indicators. Use this code as the backbone to 
-create your own trading strategy.
-
-The lookback_date variable is used to ensure data for a coin existed on 
-the lookback period specified.
-
-To run, execute the following two commands in a terminal (inside catalyst 
-environment). The first one retrieves all the pricing data needed for this
-script to run (only needs to be run once), and the second one executes this
-script with the parameters specified in the run_algorithm() call at the end 
-of the file:
-
-.. code-block:: bash
-  
-  catalyst ingest-exchange -x bitfinex -f minute
-
-Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py>`_
-
-.. literalinclude:: ../../catalyst/examples/simple_universe.py
-   :language: python
-
-
-.. _portfolio_optimization:
-
-Portfolio Optimization
-~~~~~~~~~~~~~~~~~~~~~~
-
-Use this code to execute a portfolio optimization model. This strategy will 
-select the portfolio with the maximum Sharpe Ratio. The parameters are set to 
-use 180 days of historical data and rebalance every 30 days. This code was used 
-in writting the following article: 
-`Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.
-
-Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/portfolio_optimization.py>`_
-
-.. literalinclude:: ../../catalyst/examples/portfolio_optimization.py
-   :language: python
-
-.. image:: https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ.
-   :align: center
-
-
-

_sources/features.rst.txt

@@ -1,122 +0,0 @@
-Features
-========
-
-This page describes the features that Catalyst provides in the current version,
-and what is planned for future releases.
-
-Current Functionality
-~~~~~~~~~~~~~~~~~~~~~
-
-* Backtesting and live-trading modes to run your trading algorithms, with a 
-  seamless transition between the two.
-* Paper trading simulates order in live-trading mode.
-* Support for 3 exchanges: Bitfinex, Bittrex and Poloniex in both modes 
-  (backtesting and live-trading). Historical data for backtesting is provided 
-  with daily resolution for all three exchanges, and minute resolution for 
-  Bitfinex and Poloniex. No minute-resolution data is currently available for 
-  Bittrex. Refer to 
-  `Catalyst Market Coverage <https://www.enigma.co/catalyst/status>`_ for 
-  details.
-* Interface with over 90 exchanges available in live and paper trading modes.
-* Granular commission models which closely simulates each exchange fee
-  structure in backtesting and paper trading.
-* Standardized naming convention for all asset pairs trading on any exchange in 
-  the form ``{market_currency}_{base_currency}``. See 
-  :ref:`naming`.
-* Output of performance statistics based on Pandas DataFrames to integrate 
-  nicely into the existing PyData ecosystem.
-* Support for accessing multiple exchanges per algorithm, which opens the door
-  to cross-exchange arbitrage opportunities.
-* Support for running multiple algorithms on the same exchange independently of
-  one another. Catalyst performance tracker stores just enough data to allow 
-  algorithms to run independently while still sharing critical data through 
-  exchanges.
-* Benchmark defaults to Bitcoin price (btc_usdt in Poloniex exchange) for the 
-  purpose of comparing performance across trading algorithms. A custom benchmark
-  can be specified through ``set_benchmark()`` (but see 
-  `issue #86 <https://github.com/enigmampc/catalyst/issues/86>`_). 
-* Support for MacOS, Linux and Windows installations.
-* Support for Python2 and Python3.
-
-For additional details on the functionality added on recent releases, see the
-:doc:`Release Notes<releases>`.
-
-Upcoming features
-~~~~~~~~~~~~~~~~~
-
-* Additional datasets beyond pricing data (Q1 2018)
-* API documentation (Q1 2018)
-* Support for decentralized exchanges (Q1 2018)
-* Support for data ingestion of community-contributed data sets (Q1 2018)
-* Pipeline support (Q1 2018)
-* Web UI (Q2 2018)
-
-
- .. _naming:
-
-Naming Convention
-~~~~~~~~~~~~~~~~~
-
-Catalyst introduces a standardized naming convention for all asset pairs 
-trading on any exchange in the following form:
-
-
-    **{market_currency}_{base_currency}**
-
-Where {market_currency} is the asset to be traded using {base_currency} as 
-the reference, both written in lowercase and separated with an underscore.
-
-This standardization is needed to overcome the lack of consistency in the 
-naming of assets across different exchanges, and making it easier to the user
-to refer to the asset pairs that you want to trade.
-
-Catalyst maintains a `Market Coverage Overview <https://www.enigma.co/catalyst/status>`_ 
-where you can check the mapping between Catalyst naming pairs and that of each 
-exchange. Catalyst will always expect in all its functions that you will refer to 
-the asset pairs by using the Catalyst naming convention.
-
-If at any point, you input the wrong name for an asset pair, you will get an error 
-of that pair not found in the given exchange, and a list of pairs available on that exchange:
-
-.. code-block:: bash
-
-   $ catalyst ingest-exchange -x poloniex -i btc_usd
-
-.. parsed-literal::
-
-	Ingesting exchange bundle poloniex...
-	Error traceback: /Volumes/Data/Users/victoris/Desktop/Enigma/user-install/catalyst-dev/catalyst/exchange/exchange.py (line 175)
-	SymbolNotFoundOnExchange:  Symbol btc_usd not found on exchange Poloniex. 
-	Choose from: ['rep_usdt', 'gno_btc', 'xvc_btc', 'pink_btc', 'sys_btc', 
-	'emc2_btc', 'rads_btc', 'note_btc', 'maid_btc', 'bch_btc', 'gnt_btc', 
-	'bcn_btc', 'rep_btc', 'bcy_btc', 'cvc_btc', 'nxt_xmr', 'zec_usdt', 
-	'fct_btc', 'gas_btc', 'pot_btc', 'eth_usdt', 'btc_usdt', 'lbc_btc', 
-	'dcr_btc', 'etc_usdt', 'omg_eth', 'amp_btc', 'xpm_btc', 'nxt_btc', 
-	'vtc_btc', 'steem_eth', 'blk_xmr', 'pasc_btc', 'zec_xmr', 'grc_btc', 
-	'nxc_btc', 'btcd_btc', 'ltc_btc', 'dash_btc', 'naut_btc', 'zec_eth', 
-	'zec_btc', 'burst_btc', 'zrx_eth', 'bela_btc', 'steem_btc', 'etc_btc', 
-	'eth_btc', 'huc_btc', 'strat_btc', 'lsk_btc', 'exp_btc', 'clam_btc', 
-	'rep_eth', 'dash_xmr', 'cvc_eth', 'bch_usdt', 'zrx_btc', 'dash_usdt', 
-	'blk_btc', 'xrp_btc', 'nxt_usdt', 'neos_btc', 'omg_btc', 'bts_btc', 
-	'doge_btc', 'gnt_eth', 'sbd_btc', 'gno_eth', 'xcp_btc', 'ltc_usdt', 
-	'btm_btc', 'xmr_usdt', 'lsk_eth', 'omni_btc', 'nav_btc', 'fldc_btc', 
-	'ppc_btc', 'xbc_btc', 'dgb_btc', 'sc_btc', 'btcd_xmr', 'vrc_btc', 
-	'ric_btc', 'str_btc', 'maid_xmr', 'xmr_btc', 'sjcx_btc', 'via_btc', 
-	'xem_btc', 'nmc_btc', 'etc_eth', 'ltc_xmr', 'ardr_btc', 'gas_eth', 
-	'flo_btc', 'xrp_usdt', 'game_btc', 'bch_eth', 'bcn_xmr', 'str_usdt']
-
-In the example above, exchange Poloniex does not use USD, but uses instead the 
-USDT cryptocurrency asset that is issued on the Bitcoin blockchain via the Omni
-Layer Protocol. Each USDT unit is backed by a U.S Dollar held in the reserves of 
-Tether Limited. USDT can be transferred, stored, and spent, just like bitcoins 
-or any other cryptocurrency. Given its 1:1 mapping to the USD, is a viable alternative.
-
-.. code-block:: bash
-
-   $ catalyst ingest-exchange -x poloniex -i btc_usdt
-
-.. parsed-literal::
-
-	Ingesting exchange bundle poloniex...
-	    [====================================]  Fetching poloniex daily candles: :  100%
-

_sources/index.rst.txt

@@ -1,25 +0,0 @@
-.. include:: ../../README.rst
-
-|
-|
-
-Table of Contents
------------------
-
-.. toctree::
-   :maxdepth: 1
-
-   install
-   beginner-tutorial
-   live-trading
-   features
-   example-algos
-   utilities
-   videos
-   resources
-   development-guidelines
-   releases
-..   bundles
-..   appendix
-..   release-process
-

_sources/install.rst.txt

@@ -1,588 +0,0 @@
-Install
-=======
-
-To get started with Catalyst, you will need to install it in your computer. 
-Like any other piece of software, Catalyst has a number of dependencies 
-(other software on which it depends to run) that you will need to install, as 
-well. We recommend using a software named ``Conda`` that will manage all 
-these dependencies for you, and set up the environment needed to get you up 
-and running as easily as possible. This is the recommended installation method
-for Windows, MacOS and Linux. See :ref:`Installing with Conda <conda>`.
-
-What conda does is create a pre-configured environment, and inside that 
-environment install Catalyst using ``pip``, Python's package manager. Thus, 
-as an alternative installation method for MacOS and Linux, you can install 
-Catalyst directly with ``pip`` (we recommend in combination with a virtual 
-environemnt). See :ref:`Installing with pip <pip>`.
-
-Alternatively you can install Catalyst using ``pipenv`` which is a mix of pip
-and virtualenv. See :ref:`Installing with pipenv <pipenv>`.
-
-Regardless of the method, each operating system (OS), has its own 
-prerequisites, make sure to review the corresponding sections for your system:
-:ref:`Linux <linux>`, :ref:`MacOS <macos>` and :ref:`Windows <windows>`.
-
-.. _conda:
-
-Installing with ``conda``
--------------------------
-
-The preferred method to install Catalyst is via the ``conda`` package manager, 
-which comes as part of Continuum Analytics' `Anaconda
-<http://continuum.io/downloads>`_ distribution.
-
-The primary advantage of using Conda over ``pip`` is that conda natively
-understands the complex binary dependencies of packages like ``numpy`` and
-``scipy``.  This means that ``conda`` can install Catalyst and its 
-dependencies without requiring the use of a second tool to acquire Catalyst's 
-non-Python dependencies.
-
-  For Windows, you will first need to install the *Microsoft Visual C++ 
-  Compiler for Python 2.7*. Follow the instructions on the :ref:`Windows 
-  <windows>` section and come back here.
-
-For instructions on how to install ``conda``, see the `Conda Installation
-Documentation <http://conda.pydata.org/docs/download.html>`_. Alternatively, 
-you can install MiniConda, which is a smaller footprint (fewer packages and 
-smaller size) than its big brother Anaconda, but it still contains all the 
-main packages needed. To install MiniConda, you can follow these steps:
-
-1. Download `MiniConda <https://conda.io/miniconda.html>`_. Select either 
-   Python 3.6 (recommended) or Python 2.7 for your Operating System. The 
-   `Enigma Data Marketplace <https://enigmampc.github.io/marketplace/>`_ will 
-   require Python3, that's why we are recommending to opt for the newer version.
-2. Install MiniConda. See the `Installation Instructions 
-   <https://conda.io/docs/user-guide/install/index.html>`_ if you need help.
-3. Ensure the correct installation by running ``conda list`` in a Terminal 
-   window, which should print the list of packages installed with Conda.
-
-  For Windows, if you accepted the default installation options, you didn't 
-  check an option to add Conda to the PATH, so trying to run ``conda`` from
-  a regular ``Command Prompt`` will result in the following error: ``'conda' 
-  is no recognized as an internal or external command, operatble program or 
-  batch file``. That's to be expected. You will nee to launch an ``Anaconda 
-  Prompt`` that was added at installation time to your list of programs 
-  available from the Start menu. 
-
-Once either Conda or MiniConda has been set up you can install Catalyst:
-
-1. Download the file `python3.6-environment.yml 
-   <https://github.com/enigmampc/catalyst/blob/master/etc/python3.6-environment.yml>`_ 
-   (recommended) or `python2.7-environment.yml 
-   <https://github.com/enigmampc/catalyst/blob/master/etc/python2.7-environment.yml>`_ 
-   matching your Conda installation from step #1 above.
-
-     To download, simply click on the 'Raw' button and save the file locally 
-     to a folder you can remember. Make sure that the file gets saved with the
-     ``.yml`` extension, and nothing like a ``.txt`` file or anything else.
-
-2. Open a Terminal window and enter [``cd/dir``] into the directory where you 
-   saved the above ``.yml`` file.
-
-3. Install using this file. This step can take about 5-10 minutes to install.
-
-   .. code-block:: bash
-
-      conda env create -f python3.6-environment.yml
-
-  or
-
-   .. code-block:: bash
-
-      conda env create -f python2.7-environment.yml
-
-4. Activate the environment (which you need to do every time you start a new 
-   session to run Catalyst):
-
-   **Linux or MacOS:**
-
-   .. code-block:: bash
-
-      source activate catalyst
-
-   **Windows:**
-
-   .. code-block:: bash
-
-      activate catalyst
-
-5. Verify that Catalyst is install correctly:
-
-   .. code-block:: bash
-
-     catalyst --version
-
-   which should display the current version.
-
-Congratulations! You now have Catalyst installed.
-
-Troubleshooting ``conda`` Install
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-If the command  ``conda env create -f python2.7-environment.yml`` in step 3 
-above failed for any reason, you can try setting up the environment manually 
-with the following steps:
-
-1. If the above installation failed, and you have a partially set up catalyst
-   environment, remove it first. If you are starting from scratch, proceed to 
-   step #2:
-
-   .. code-block:: bash
-
-      conda env remove --name catalyst
-
-2. Create the environment:
-
-   for python 2.7:
-
-   .. code-block:: bash
-
-      conda create --name catalyst python=2.7 scipy zlib
-
-  or for python 3.6:
-
-   .. code-block:: bash
-
-      conda create --name catalyst python=3.6 scipy zlib
-
-3. Activate the environment:
-
-   **Linux or MacOS:**
-
-   .. code-block:: bash
-
-      source activate catalyst
-
-   **Windows:**
-
-   .. code-block:: bash
-
-      activate catalyst
-
-4. Install the Catalyst inside the environment:
-
-   .. code-block:: bash
-
-      pip install enigma-catalyst matplotlib
-
-5. Verify that Catalyst is installed correctly:
-
-   .. code-block:: bash
-
-     catalyst --version
-
-   which should display the current version.
-
-Congratulations! You now have Catalyst properly installed.
-
-.. _pip:
-
-Installing with ``pip``
------------------------
-
-Installing Catalyst via ``pip`` is slightly more involved than the average
-Python package.
-
-There are two reasons for the additional complexity:
-
-1. Catalyst ships several C extensions that require access to the CPython C 
-   API. In order to build the C extensions, ``pip`` needs access to the 
-   CPython header files for your Python installation.
-
-2. Catalyst depends on `numpy <http://www.numpy.org/>`_, the core library for
-   numerical array computing in Python.  Numpy depends on having the `LAPACK
-   <http://www.netlib.org/lapack>`_ linear algebra routines available.
-
-Because LAPACK and the CPython headers are non-Python dependencies, the 
-correctway to install them varies from platform to platform.  If you'd rather 
-use a single tool to install Python and non-Python dependencies, or if you're 
-already using `Anaconda <http://continuum.io/downloads>`_ as your Python 
-distribution, refer to the :ref:`Installing with Conda <conda>` section.
-
-If you use Python for anything other than Catalyst, we **strongly** recommend
-that you install in a `virtualenv
-<https://virtualenv.readthedocs.org/en/latest>`_.  The `Hitchhiker's Guide to
-Python`_ provides an `excellent tutorial on virtualenv
-<http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_. Here's a 
-summarized version:
-
-.. code-block:: bash
-
-   $ pip install virtualenv
-   $ virtualenv catalyst-venv
-   $ source ./catalyst-venv/bin/activate
-
-Once you've installed the necessary additional dependencies for your system 
-(:ref:`Linux`, :ref:`MacOS` or :ref:`Windows`) **and have activated your virtualenv**, you should be able to simply run
-
-.. code-block:: bash
-
-   $ pip install enigma-catalyst matplotlib
-
-Note that in the command above we install two different packages. The second 
-one, ``matplotlib`` is a visualization library. While it's not strictly 
-required to run catalyst simulations or live trading, it comes in very handy
-to visualize the performance of your algorithms, and for this reason we 
-recommend you install it, as well.
-
-
-Troubleshooting ``pip`` Install
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-**Issue**: 
-   Package enigma-catalyst cannot be found 
- 
-**Solution**: 
-   Make sure you have the most up-to-date version of pip installed, by running: 
-
-   .. code-block:: bash
-
-      $ pip install --upgrade pip
-
-   On Windows, the recommended command is:
-
-   .. code-block:: bash
-
-      $ python -m pip install --upgrade pip
-
-----
-
-**Issue**: 
-   Package enigma-catalyst cannot still be found, even after upgrading pip 
-   (see above), with an error similar to:
-
-   .. code-block:: bash
-
-      Downloading/unpacking enigma-catalyst
-      Could not find a version that satisfies the requirement enigma-catalyst 
-      (from versions: 0.1.dev9, 0.2.dev2, 0.1.dev4, 0.1.dev5, 0.1.dev3, 
-      0.2.dev1, 0.1.dev8, 0.1.dev6)
-      Cleaning up...
-      No distributions matching the version for enigma-catalyst
-
-**Solution**:
-   In some systems (this error has been reported in Ubuntu), pip is configured 
-   to only find stable versions by default. Since Catalyst is in alpha 
-   version, pip cannot find a matching version that satisfies the installation 
-   requirements. The solution is to include the `--pre` flag to include 
-   pre-release and development versions:
-
-   .. code-block:: bash
-
-      $ pip install --pre enigma-catalyst
-
-----
-
-**Issue**: 
-   Package enigma-catalyst fails to install because of outdated setuptools
-
-**Solution**: 
-   Upgrade to the most up-to-date setuptools package by running: 
-
-   .. code-block:: bash
-
-      $ pip install --upgrade pip setuptools
-
-----
-
-**Issue**:
-   Missing required packages  
-
-**Solution**:
-   Download `requirements.txt 
-   <https://github.com/enigmampc/catalyst/blob/master/etc/requirements.txt>`_ 
-   (click on the *Raw* button and Right click -> Save As...) and use it to
-   install all the required dependencies by running:
-
-   .. code-block:: bash
-
-      $ pip install -r requirements.txt
-
-----
-
-**Issue**: 
-   Installation fails with error: 
-   ``fatal error: Python.h: No such file or directory``
-
-**Solution**: 
-   Some systems (this issue has been reported in Ubuntu) require `python-dev` 
-   for the proper build and installation of package dependencies. The solution 
-   is to install python-dev, which is independent of the virtual environment. 
-   In Ubuntu, you would need to run:
-
-   .. code-block:: bash
-
-      $ sudo apt-get install python-dev
-
-----
-
-**Issue**:
-   Missing TA_Lib
-
-**Solution**:
-   Follow `these instructions
-   <https://mrjbq7.github.io/ta-lib/install.html>`_ to install the TA_Lib Python wrapper
-   (and if needed, its underlying C library as well).
-
-.. _pipenv:
-
-Installing with ``pipenv``
---------------------------
-
-Installing Catalyst via ``pipenv`` is perhaps easier that installing it via
-``pip`` itself but you need to install ``pipenv`` first via ``pip``.
-
-.. code-block:: bash
-
-   $ pip install pipenv
-
-Once ``pipenv`` is installed you can proceed by creating a project folder and
-installing Catalyst on that project automagically as follows:
-
-.. code-block:: bash
-
-   $ mkdir project
-   $ cd project
-   $ pipenv --two
-   $ pipenv install enigma-catalyst matplotlib
-
-Until now the workflow compared to ``pip`` is almost identical, the difference
-is that you don't need to load manually any virtualenv however you need to use
-the `pipenv run` prefix to run the `catalyst` command as follows:
-
-.. code-block:: bash
-
-   $ pipenv run catalyst --version
-
-If you want to know more about ``pipenv`` go to the `pipenv github repo`_
-
-.. _`pipenv github repo`: https://github.com/pypa/pipenv
-
-.. _linux:
-
-GNU/Linux Requirements
-----------------------
-
-On `Debian-derived`_ Linux distributions, you can acquire all the necessary
-binary dependencies from ``apt`` by running:
-
-.. code-block:: bash
-
-   $ sudo apt-get install libatlas-base-dev python-dev gfortran pkg-config libfreetype6-dev
-
-On recent `RHEL-derived`_ derived Linux distributions (e.g. Fedora), the
-following should be sufficient to acquire the necessary additional
-dependencies:
-
-.. code-block:: bash
-
-   $ sudo dnf install atlas-devel gcc-c++ gcc-gfortran libgfortran python-devel redhat-rep-config
-
-On `Arch Linux`_, you can acquire the additional dependencies via ``pacman``:
-
-.. code-block:: bash
-
-   $ pacman -S lapack gcc gcc-fortran pkg-config
-
-.. Commenting it out until Catalyst fully supports Python 3.X
-..
-.. There are also AUR packages available for installing `Python 3.4
-.. <https://aur.archlinux.org/packages/python34/>`_ (Arch's default python is now
-.. 3.5, but Catalyst only currently supports 3.4), and `ta-lib
-.. <https://aur.archlinux.org/packages/ta-lib/>`_, an optional Catalyst dependency.
-.. Python 2 is also installable via:
-
-.. 
-
-..   $ pacman -S python2
-
-Amazon Linux AMI Notes
-~~~~~~~~~~~~~~~~~~~~~~
-
-The packages ``pip`` and ``setuptools`` that come shipped by default are very 
-outdated. Thus, you first need to run:
-
-.. code-block:: bash
-
-   $ pip install --upgrade pip setuptools
-
-The default installation is also missing the C and C++ compilers, which you 
-install by:
-
-.. code-block:: bash
-
-   $ sudo yum install gcc gcc-c++
-
-Then you should follow the regular installation instructions outlined at the 
-beginning of this page.
-
-
-.. _MacOS:
-
-MacOS Requirements
-------------------
-
-The version of Python shipped with MacOS by default is generally out of date, 
-and has a number of quirks because it's used directly by the operating system.
-For these reasons, many developers choose to install and use a separate Python
-installation. The `Hitchhiker's Guide to Python`_ provides an excellent guide
-to `Installing Python on MacOS <http://docs.python-guide.org/en/latest/>`_, 
-which explains how to install Python with the `Homebrew`_ manager.
-
-Assuming you've installed Python with Homebrew, you'll also likely need the
-following brew packages:
-
-.. code-block:: bash
-
-   $ brew install freetype pkg-config gcc openssl
-
-MacOS + virtualenv/conda + matplotlib
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The first time that you try to run an algorithm that loads the ``matplotlib`` 
-library, you may get the following error:
-
-.. code-block:: text
-
-  RuntimeError: Python is not installed as a framework. The Mac OS X backend 
-  will not be able to function correctly if Python is not installed as a 
-  framework. See the Python documentation for more information on installing 
-  Python as a framework on Mac OS X. Please either reinstall Python as a 
-  framework, or try one of the other backends. If you are using (Ana)Conda 
-  please install python.app and replace the use of 'python' with 'pythonw'. 
-  See 'Working with Matplotlib on OSX' in the Matplotlib FAQ for more 
-  information.
-
-This is a ``matplotlib``-specific error, that will go away once you run the 
-following command:
-
-.. code-block:: bash
-
-   $ echo "backend: TkAgg" > ~/.matplotlib/matplotlibrc
-
-in order to override the default ``MacOS`` backend for your system, which 
-may not be accessible from inside the virtual or conda environment. This will 
-allow Catalyst to open matplotlib charts from within a virtual environment, 
-which is useful for displaying the performance of your backtests.  To learn more 
-about matplotlib backends, please refer to the
-`matplotlib backend documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
-
-.. _windows:
-
-Windows Requirements
---------------------
-
-In Windows, you will first need to install the Microsoft Visual C++ Compiler, 
-which is different depending on the version of Python that you plan to use:
-
-* Python 3.5, 3.6: `Visual C++ 2015 Build Tools 
-  <http://landinghub.visualstudio.com/visual-cpp-build-tools>`_, 
-  which installs Visual C++ version 14.0. **This is the recommended version**
-
-* Python 2.7: `Microsoft Visual C++ Compiler for Python 2.7 
-  <https://www.microsoft.com/en-us/download/details.aspx?id=44266>`_, which 
-  installs version Visual C++ version 9.0
-
-This package contains the compiler and the set of system headers necessary for 
-producing binary wheels for Python packages. If it's not already in your 
-system, download it and install it before proceeding to the next step. If you 
-need additional help, or are looking for other versions of Visual C++ for 
-Windows (only advanced users), follow `this link <https://wiki.python.org/moin/WindowsCompilers>`_.
-
-Once you have the above compiler installed, the easiest and best supported way 
-to install Catalyst in Windows is to use :ref:`Conda <conda>`. If you didn't 
-any problems installing the compiler, jump to the :ref:`Conda <conda>` section, 
-otherwise keep on reading to troubleshoot the C++ compiler installtion.
-
-Some problems we have encountered installing the **Visual C++ Compiler** 
-mentioned above are as follows:
-
-- **The system administrator has set policies to prevent this installation**.
-  
-  In some systems, there is a default *Windows Software Restriction* policy 
-  that prevents the installation of some software packages like this one. 
-  You'll have to change the Registry to circumvent this:
-
-  - Click ``Start``, and search for ``regedit`` and launch the 
-    ``Registry Editor``
-  - Navigate to the following folder:
-    ``HKEY_LOCAL_MACHINE\SOFTWARE\Policies\Microsoft\Windows\Installer``
-  - If the last folder does not exist, create it by right-clicking on the 
-    parent folder and choosing -> ``New`` -> ``Key`` and typing ``Installer``
-  - If there is an entry for ``DisableMSI``, set the Value data to 0.
-  - If there is no such entry, click on the ``Edit`` menu -> ``New`` -> 
-    ``DWORD (32-bit) Value`` and enter ``DisableMSI`` as the Name (and by 
-    default you get 0 as the Value Data)
-
-|
-
-- **The installer has encountered an unexpected error installing this package. 
-  This may indicate a problem with this package. The error code is 2503.**
-
-  We have observed this when trying to install a package without enough 
-  administrator permissions. Even when you are logged in as an Administrator, 
-  you have to explictily install this package with administrator privileges:
-
-  - Click ``Start`` and find ``CMD`` or ``Command Prompt``
-  - Right click on it and choose ``Run as administrator``
-  - ``cd`` into the folder where you downloaded ``VCForPython27.msi``
-  - Run ``msiexec /i VCForPython27.msi``
-
-Updating Catalyst
------------------
-
-Catalyst is currently in alpha and in under very active development. We release
-new minor versions every few days in response to the thorough battle testing 
-that our user community puts Catalyst in. As a result, you should expect to 
-update Catalyst frequently. Once installed, Catalyst can easily be updated as a 
-``pip`` package regardless of the environemnt used for installation. Make sure 
-you activate your environment first as you did in your first install, and then 
-execute:
-
-.. code-block:: bash
-
-   $ pip uninstall enigma-catalyst
-   $ pip install enigma-catalyst
-
-Alternatively, you could update Catalyst issuing the following command:
-
-.. code-block:: bash
-
-   $ pip install -U enigma-catalyst
-
-but this command will also upgrade all the Catalyst dependencies to the latest 
-versions available, and may have unexpected side effects if a newer version of a 
-dependency inadvertently breaks some functionality that Catalyst relies on. 
-Thus, the first method is the recommended one.
-
-Getting Help
-------------
-
-If after following the instructions above, and going through the 
-*Troubleshooting* sections, you still experience problems installing Catalyst,
-you can seek additional help through the following channels:
-
-- Join our `Catalyst Forum <https://catalyst.enigma.co/>`_, and browse a variety
-  of topics and conversations around common issues that others face when using
-  Catalyst, and how to resolve them. And join the conversation!
-
-- Join our `Discord community <https://discord.gg/SJK32GY>`_, and head over 
-  the #catalyst_dev channel where many other users (as well as the project 
-  developers) hang out, and can assist you with your particular issue. The 
-  more descriptive and the more information you can provide, the easiest will 
-  be for others to help you out.
-
-- Report the problem you are experiencing on our 
-  `GitHub repository <https://github.com/enigmampc/catalyst/issues>`_ 
-  following the guidelines provided therein. Before you do so, take a moment 
-  to browse through all `previous reported issues
-  <https://github.com/enigmampc/catalyst/issues?utf8=%E2%9C%93&q=is%3Aissue>`_ 
-  in the likely case that someone else experienced that same issue before, 
-  and you get a hint on how to solve it.
-
-
-.. _`Debian-derived`: https://www.debian.org/misc/children-distros
-.. _`RHEL-derived`: https://en.wikipedia.org/wiki/Red_Hat_Enterprise_Linux_derivatives
-.. _`Arch Linux` : https://www.archlinux.org/
-.. _`Hitchhiker's Guide to Python` : http://docs.python-guide.org/en/latest/
-.. _`Homebrew` : http://brew.sh

_sources/live-trading.rst.txt

@@ -1,197 +0,0 @@
-Live Trading
-============
-This document explains how to get started with live trading.
-
-Supported Exchanges
-^^^^^^^^^^^^^^^^^^^
-
-Since version 0.4, Catalyst integrated with `CCXT <https://github.com/ccxt/ccxt>`_,
-a cryptocurrency trading library with support for more than 90 exchanges. The 
-range of CCXT and Catalyst support for each of those exchanges varies greatly. 
-The most supported exchanges are as follows:
-
-The exchanges available for backtesting are fully supported in live mode:
-
-- Bitfinex, id = ``bitfinex``
-- Bittrex, id = ``bittrex``
-- Poloniex, id = ``poloniex``
-
-Additionally, we have successfully tested the following exchanges:
-
-- Binance, id = ``binance``
-- Bitmex, id = ``bitmex``
-- GDAX, id = ``gdax``
-
-As Catalyst is currently in Alpha and in under active development, you are 
-encouraged to throughly test any exchange in *paper trading* mode before trading
-*live* with it.
-
-Paper Trading vs Live Trading modes
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Catalyst currently supports three different modes in which you can execute your
-trading algorithm. The first is **backtesting**, which is covered extensively in
-the tutorial, and uses historical data to run your algorithm. There is no 
-interaction with the exchange in backtesting mode, and this is the first mode 
-that you should test any new algorithm.
-
-Once you are confident with the simulations that you have obtained with your
-algorithm in backtesting, you may switch to live trading, where you have two
-different modes:
-
-* **Paper Trading**: The simulated algorithm runs in real time, and fetches 
-  pricing data in real time from the exchange, but the orders never reach the 
-  exchange, and are instead kept within Catalyst and simulated. No real currency
-  is bought or sold. Think of it as a `backtesting happening in real time`.
-
-* **Live Trading**: This is the proper live trading mode in which an algorithm
-  runs in real time, fetching pricing data from live exchanges and placing 
-  orders against the exchange. Real currency is transacted on the exchange 
-  driven by the algorithm.
-
-These three modes are controlled by the following variables:
-
-+---------------+-------------------------+
-|    Mode       |       Parameters        |
-+               +-------+-----------------+
-|               | live  | simulate_orders |
-+---------------+-------+-----------------+
-| backtesting   | False | True (default)  |
-+---------------+-------+-----------------+
-| paper trading | True  | True            |
-+---------------+-------+-----------------+
-| live trading  | True  | False           |
-+---------------+-------+-----------------+
-
-
-Authentication
-^^^^^^^^^^^^^^
-Most exchanges require token key/secret combination for authentication. By
-convention, Catalyst uses an ``auth.json`` file to hold this data.
-
-This example illustrates the convention using the *Bitfinex* exchange.
-Here is how to generate key and secret values for the Bitfinex exchange:
-https://docs.bitfinex.com/v1/docs/api-access. Most exchanges follow
-a similar process.
-
-The auth.json file:
-
-.. code-block:: json
-
-  {
-    "name": "bitfinex",
-    "key": "my-key",
-    "secret": "my-secret"
-  }
-
-
-The file goes here: ``~/.catalyst/data/exchanges/bitfinex/auth.json``
-
-Note that the `bitfinex` part in the directory above corresponds to the id of the Bitfinex
-exchange as defined in the "Supported Exchanges" section above.
-Attempting to run an algorithm where the targeted exchange is missing
-its ``auth.json`` file will create the directory structure and create an empty
-auth.json file, but will result in an error.
-
-Currency Symbols
-^^^^^^^^^^^^^^^^
-Catalyst introduces a universal convention to reference
-trading pairs and individual currencies. This
-is required to ensure that the ``symbol()`` api predictably
-returns the correct asset regardless of the targeted exchange.
-
-Exchanges tend to use their own convention to represent currencies
-(e.g. XBT and BTC both represent Bitcoin on different exchanges).
-Trading pairs are also inconsistent. 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]* all lowercase.
-
-Currency symbols (e.g. btc, eth, ltc) follow the Bittrex convention.
-
-Here are some examples:
-
-.. code:: python
-
-  # With Bitfinex
-  bitcoin_usd_asset = symbol('btc_usd')
-  ethereum_bitcoin_asset = symbol('eth_btc')
-
-  # With Bittrex
-  ethereum_bitcoin_asset = symbol('eth_btc')
-  neo_ethereum_asset = symbol('neo_eth)
-
-Note that the trading pairs are always referenced in the same manner.
-However, not all trading pairs are available on all exchanges. An
-error will occur if the specified trading pair is not trading
-on the exchange. To check which currency pairs are available on each 
-of the supported exchanges, see 
-`Catalyst Market Coverage <https://www.enigma.co/catalyst/status>`_.
-
-Trading an Algorithm
-^^^^^^^^^^^^^^^^^^^^
-There is no special convention to follow when writing an
-algorithm for live trading. The same algorithm should work in
-backtest and live execution mode without modification.
-
-What differs are the arguments provided to the catalyst client or
-`run_algorithm()` interface. Here is the same example in both interfaces:
-
-.. code-block:: bash
-
-  catalyst live -f my_algo_code -x bitfinex -c btc -n my_algo_name 
-
-.. code-block:: python
-
-  run_algorithm(
-      initialize=initialize,
-      handle_data=handle_data,
-      analyze=analyze,
-      exchange_name='bitfinex',
-      live=True,
-      algo_namespace='my_algo_name',
-      base_currency='btc'
-  )
-
-
-Here is the breakdown of the new arguments:
-
-- ``live``: Boolean flag which enables live trading. It defaults to ``False``.
-- ``capital_base``: The amount of base_currency assigned to the strategy.
-  It has to be lower or equal to the amount of base currency available for
-  trading on the exchange. For illustration, order_target_percent(asset, 1)
-  will order the capital_base amount specified here of the specified asset.
-- ``exchange_name``: The name of the targeted exchange. See the 
-  `CCXT Supported Exchanges <https://github.com/ccxt/ccxt/wiki/Exchange-Markets>`_ 
-  for the full list.
-- ``algo_namespace``: A arbitrary label assigned to your algorithm for
-  data storage purposes.
-- ``base_currency``: The base currency used to calculate the
-  statistics of your algorithm. Currently, the base currency of all
-  trading pairs of your algorithm must match this value.
-- ``simulate_orders``: Enables the paper trading mode, in which orders are
-  simulated in Catalyst instead of processed on the exchange. It defaults to 
-  ``True``.
-- ``end_date``: When setting the end_date to a time in the **future**,
-  it will schedule the live algo to finish gracefully at the specified date.
-- ``start_date``: (**Will be implemented in the future**)
-  The live algo starts by default in the present, as mentioned above.
-  by setting the start_date to a time in the future, the algorithm would
-  essentially sleep and when the predefined time comes, it would start executing.
-
-
-
-The `catalyst live` command offers additional parameters.
-You can learn more by running the following from the command line:
-
-.. code-block:: bash
-
-    catalyst live --help
-
-
-Here is a complete algorithm for reference:
-`Buy Low and Sell High <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_low_sell_high_live.py>`_

_sources/releases.rst.txt

@@ -1,485 +0,0 @@
-=============
-Release Notes
-=============
-
-Version 0.5.8
-^^^^^^^^^^^^^
-**Release Date**: 2018-03-29
-
-Bug Fixes
-~~~~~~~~~
-- Fix Data Marketplace release on mainnet
-
-Version 0.5.7
-^^^^^^^^^^^^^
-**Release Date**: 2018-03-29
-
-Build
-~~~~~
-- Data Marketplace deployed on mainnet.
-- Added progress indicators for publishing data, and made the data publishing
-  synchronous to provide feedback to the publisher.
-
-Bug Fixes
-~~~~~~~~~
-- fixes in storing and loading the state :issue:`214`, 
-  :issue:`287`
-
-Version 0.5.6
-^^^^^^^^^^^^^
-**Release Date**: 2018-03-22
-
-Build
-~~~~~
-- Data Marketplace: ensures compatibility across wallets, now fully supporting 
-  ``ledger``, ``trezor``, ``keystore``, ``private key``. Partial support for 
-  ``metamask`` (includes sign_msg, but not sign_tx). Current support for 
-  ``Digital Bitbox`` is unknown, but believed to be supported.
-- Data Marketplace: Switched online provider from MyEtherWallet to MyCrypto.
-- Data Marketplace: Added progress indicator for data ingestion.
-
-Bug Fixes
-~~~~~~~~~
-- Changed benchmark to be constant, so it doesn't ingest data at all. Temporary
-  fix for :issue:`271`, :issue:`285`
-
-Version 0.5.5
-^^^^^^^^^^^^^
-**Release Date**: 2018-03-19
-
-Bug Fixes
-~~~~~~~~~
-- Fixed an issue with the data history in daily frequency :issue:`274`
-- Fix hourly frequency issues :issue:`227` and :issue:`114`
-
-Version 0.5.4
-^^^^^^^^^^^^^
-**Release Date**: 2018-03-14
-
-Build
-~~~~~
-- Switched Data Marketplace from Ropstein testnet to Rinkeby testnet after 
-  incorporating changes resulting from the marketplace contract audit
-- Several usability improvements of the Data Marketplace that make the 
-  `--dataset` parameter optional. If it is not included in the command line, 
-  will list available datasets, and let you choose interactively.
-
-Bug Fixes
-~~~~~~~~~
-- Fix Binance requirement of symbol to be included in the cancelled order 
-  :issue:`204`
-- Fix `notenoughcasherror` when an open order is filled minutes later 
-  :issue:`237`
-- Properly handle of empty candles received from exchanges :issue:`236`
-- Added a function to reduce open orders amount from calculated target/amount 
-  for target orders :issue:`243`
-- Fix missing file in live trading mode on date change :issue:`252`, 
-  :issue:`253`
-- Upgraded Data Marketplace to Web3==4.0.0b11, which was breaking some 
-  functionality from prior version 4.0.0b7 :issue:`257`
-- Always request more data to avoid empty bars and always give the exact bar
-  number :issue:`260`
-
-Documentation
-~~~~~~~~~~~~~
-- PyCharm documentation :issue:`195`
-- Added TA-Lib troubleshooting instructions
-- Added instructions on how to create a Conda environment for Python 3.6, and
-  updated Visual C++ instructions for Windows and Python 3
-- Linking example algorithms in the documentation to their sources
-
-
-Version 0.5.3
-^^^^^^^^^^^^^
-**Release Date**: 2018-02-09
-
-Bug Fixes
-~~~~~~~~~
-- Fixed an issue with last candle in backtesting :issue:`219`
-
-Version 0.5.2
-^^^^^^^^^^^^^
-**Release Date**: 2018-02-08
-
-Bug Fixes
-~~~~~~~~~
-- Fixed an issue with live candle values :issue:`216` and :issue:`199`
-
-Version 0.5.1
-^^^^^^^^^^^^^
-**Release Date**: 2018-02-07
-
-Bug Fixes
-~~~~~~~~~
-- Fixed an issue with orders that stay open :issue:`211`
-- Fixed Jupyter issues :issue:`179`
-- Fetching multiple tickers in one call to minimize rate limit risks :issue:`174`
-- Improved live state presentation :issue:`171`
-
-
-Build
-~~~~~
-- Introducing the Enigma Marketplace
-
-Version 0.4.7
-^^^^^^^^^^^^^
-**Release Date**: 2018-01-19
-
-Bug Fixes
-~~~~~~~~~
-- Fixing issue :issue:`137` impacting the CLI
-
-Build
-~~~~~
-- Implemented authentication aliases (:issue:`60`)
-
-Version 0.4.6
-^^^^^^^^^^^^^
-**Release Date**: 2018-01-18
-
-Bug Fixes
-~~~~~~~~~
-- Fixed some Python3 issues
-- Reading the trade log to get executed order prices on exchanges like Binance (:issue:`151`)
-- Fixed issue with market order executing price (:issue:`150` and :issue:`111`)
-- Implemented standardized symbol mapping (:issue:`157`)
-- Improved error handling for unsupported timeframes (:issue:`159`)
-- Using Bitfinex instead of Poloniex to fetch btc_usdt benchmark (:issue:`161`)
-
-
-Build
-~~~~~
-- Added a `context.state` dict to keep arbitrary state values between runs
-- Added ability to stop live algo at specified end date
-
-Version 0.4.5
-^^^^^^^^^^^^^
-**Release Date**: 2018-01-12
-
-Bug Fixes
-~~~~~~~~~
-- Improved order execution for exchanges supporting trade lists (:issue:`151`)
-- Fixed an issue where requesting history of multiple assets repeats values
-- Raising an error for order amounts smaller than exchange lots
-- Handling multiple req errors with tickers more gracefully (:issue:`160`)
-
-Version 0.4.4
-^^^^^^^^^^^^^
-**Release Date**: 2018-01-09
-
-Bug Fixes
-~~~~~~~~~
-- Removed redundant capital_base validation (:issue:`142`)
-- Fixed portfolio update issue with restored state (:issue:`111`)
-- Skipping cash validation where there are open orders (:issue:`144`)
-
-Version 0.4.3
-^^^^^^^^^^^^^
-**Release Date**: 2018-01-05
-
-Bug Fixes
-~~~~~~~~~
-- Fixed CLI issue (:issue:`137`)
-- Upgraded CCXT
-
-Version 0.4.2
-^^^^^^^^^^^^^
-**Release Date**: 2018-01-03
-
-Bug Fixes
-~~~~~~~~~
-- Fixed cash synchronization issue (:issue:`133`)
-- Fixed positions synchronization issue (:issue:`132`)
-- Patched empyrical to resolve a np.log1p issue (:issue:`126`)
-- Fixed a paper trading issue (:issue:`124`)
-- Fixed a commission issue (:issue:`104`)
-- Fixed a poloniex specific issue in live trading (:issue:`103`)
-
-Build
-~~~~~
-- Caching CCXT market info to limit round-trips (:issue:`99`)
-- Tentative support for Pipeline (:issue:`96`)
-
-Version 0.4.0
-^^^^^^^^^^^^^
-**Release Date**: 2017-12-12
-
-Bug Fixes
-~~~~~~~~~
-
-- Changed Poloniex interface (should solve :issue:`95` and :issue:`94`)
-- Solved issue with overriding commission and slippage (:issue:`87`)
-- Fixed inefficiency with Bittrex current prices (:issue:`76`)
-
-Build
-~~~~~
-- Integrated with CCXT
-- Added paper trading capability (`simulate_orders=True` param in live mode)
-- More granular commissions (:issue:`82`)
-- Added market orders in live mode (:issue:`81`)
-
-Version 0.3.10
-~~~~~~~~~~~~~~
-**Release Date**: 2017-11-28
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed issue with fetching assets with daily frequency
-
-Version 0.3.9
-^^^^^^^^^^^^^
-**Release Date**: 2017-11-28
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed sortino warning issues (:issue:`77`)
-- Adjusted computation of last candle of data.history (:issue:`71`)
-
-Build
-~~~~~
-- Added capital_base parameter to live mode to limit cash (:issue:`79`)
-- Added support for csv ingestion (:issue:`65`)
-- Improved cash display in running stats (:issue:`80`)
-
-
-Version 0.3.8
-^^^^^^^^^^^^^
-**Release Date**: 2017-11-14
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed a warning filter issue introduced with the latest release
-
-Version 0.3.7
-^^^^^^^^^^^^^
-**Release Date**: 2017-11-14
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed an SSL cert issue (:issue:`64`)
-- Fixed cumulative stats warnings (:issue:`63`)
-- Disabled auto-ingestion because of unresolved caching issues (:issue:`47`)
-- Standardized live-trading stats (:issue:`61`)
-
-Build
-~~~~~
-
-- Added a mean-reversion sample algo
-- Added minutely stats in the analyze() function (:issue:`62`)
-- Added specificity to some error messages
-
-Version 0.3.6
-^^^^^^^^^^^^^
-**Release Date**: 2017-11-4
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed an issue with single bar data.history() (:issue:`55`)
-
-Version 0.3.5
-^^^^^^^^^^^^^
-**Release Date**: 2017-11-4
-
-Bug Fixes
-~~~~~~~~~
-
-- Added workaround for: KeyError: Timestamp error (:issue:`53`)
-
-Version 0.3.4
-^^^^^^^^^^^^^
-**Release Date**: 2017-11-2
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed issue with auto-ingestion of minute data (:issue:`47`)
-- Fixed issue with sell orders in backtesting
-- Fixed data frequency issues with data.history() in backtesting
-- Fixed an issue with can_trade()
-- Reduced the commission and slippage values to account for lower volume
-  transactions
-
-Build
-~~~~~
-
-- Added more unit tests
-
-Documentation
-~~~~~~~~~~~~~
-
-- Improved installation notes for Windows C++ compiler and Conda
-- Addition of
-  `Jupyter Notebook guide <https://enigmampc.github.io/catalyst/jupyter.html>`_
-- Addition of
-  `Live Trading page <https://enigmampc.github.io/catalyst/live-trading.html>`_
-- Addition of
-  `Videos page <https://enigmampc.github.io/catalyst/videos.html>`_
-- Addition of
-  `Resources page <https://enigmampc.github.io/catalyst/resources.html>`_
-- Addition of `Development Guidelines
-  <https://enigmampc.github.io/catalyst/development-guidelines.html>`_
-- Addition of
-  `Release Notes <https://enigmampc.github.io/catalyst/releases.html>`_
-- Updated code docstrings
-
-
-Version 0.3.3
-^^^^^^^^^^^^^
-**Release Date**: 2017-10-26
-
-Bug Fixes
-~~~~~~~~~
-
-- Fix missing -x in ingest-exchange
-- Fix issue with daily chunks end date (data bundles)
-- Fix issue in the prepare_chunk logic (data bundles)
-
-Build
-~~~~~
-
-- Added data validation unit tests
-
-
-Version 0.3.2
-^^^^^^^^^^^^^
-**Release Date**: 2017-10-25
-
-Bug Fixes
-~~~~~~~~~
-
-- Fix to work with empty data bundles
-- Fix Windows path of ``$HOME/.catalyst`` folder
-- Fix ``etc/python2.7-environment.yml`` for Windows Conda install
-- Fix hash method to create sid numbers compatible across platforms
-- Fix an issue with asset date in chunks
-
-Build
-~~~~~
-
-- Python3 adjustments
-- Added method to clean bundle folders, and remove symbols.json
-- Implemented and improved unit tests
-
-
-Version 0.3.1
-^^^^^^^^^^^^^
-**Release Date**: 2017-10-22
-
-Bug Fixes
-~~~~~~~~~
-
-- Fixed OS-dependent path issue in data bundle
-- Changed handling of empty ``auth.json``, instead of throwing an error for
-  missing file
-- Updated ``etc/python2.7-environment.yml`` to work with Catalyst version 0.3
-- Updated ``catalyst/examples/buy_and_hodl.py``  and
-  ``catalyst/examples/buy_low_sell_high.py`` to work with Catalyst version 0.3
-
-
-Version 0.3
-^^^^^^^^^^^
-**Release Date**: 2017-10-20
-
-- Standardized live and backtesting syntax
-- Added a repository for historical data
-- Added supported for multiple exchanges per algorithm
-- Added a standardized dictionary of symbols for each exchange
-- Added auto-ingestion of bundle data while backtesting
-- Bug fixes
-
-
-Version 0.2.dev5
-^^^^^^^^^^^^^^^^
-**Release Date**: 2017-10-03
-
-- Fixes bug in data.history function that was formatting 'volume' data as
-  integers, now they are returned as floats with up to 9 decimals of precision.
-  Data bundles redone.
-
-Version 0.2.dev4
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-09-20
-
-- Fixes bug in the pricing resolution of 1-minute data, now set to 8 decimal
-  places. Pricing resolution of daily data remains set to 9 decimal places.
-- The current data bundle takes 340MB compressed for download, and 460MB
-  uncompressed on disk for Catalyst to use.
-
-Version 0.2.dev3
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-09-20
-
-- 1-minute resolution OHLCV data bundle for backtesting from Poloniex exchange
-- Implementation of trading of fractional crypto assets (i.e. 0.01 BTC)
-- Minimum trade size of a coin can be configured on a per-coin basis, defaults
-  to 0.00000001 in backtesting (most exchanges set the minimum trade to larger
-  amounts, which will impact live trading)
-- Increased pricing resolution from 3 to 9 decimal places
-- The current data bundle takes 40MB compressed for download, and 99MB
-  uncompressed on disk for Catalyst to use.
-
-Version 0.2.dev2
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-09-07
-
-- Fix path issue
-
-Version 0.2.dev1
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-09-03
-
-- Implementation of live trading:
-
-  - Comprehensive trading functionality against exchanges Bitfinex and Bittrex.
-  - Support for all trading pairs available on each exchange.
-  - Multiple algorithms can trade simultaneously against a single exchange
-    using the same account.
-  - Each algorithm has a persisted state (i.e. algorithm can be stopped and
-    restarted preserving the state without data loss) that tracks all open
-    orders, executed transactions and portfolio positions.
-
-- Minute by minute portfolio performance metrics.
-
-  - Daily summary performance statistics compatible with pyfolio, a Python
-    library for performance and risk analysis of financial portfolios
-
-Version 0.1.dev9
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-08-28
-
-- Retrieval of crypto benchmark from bundle, instead of hitting Poloniex
-  exchange directly
-- Change of bundle storage provider from Dropbox to AWS
-- Fix issue with 1/1000 scaling issue of prices in bundle
-
-Version 0.1.dev8
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-08-18
-
-- Fixes issue in the creation of bundles (:issue:`27`)
-
-
-Version 0.1.dev7
-^^^^^^^^^^^^^^^^
-- Fixes issues in empty benchmark (:issue:`16`)
-- Fixes issue of normalizing timestamps before comparison (:issue:`24`)
-- Generic data bundles
-- CLI UI improvements
-
-Version 0.1.dev6
-^^^^^^^^^^^^^^^^
-
-**Release Date**: 2017-07-13
-
-- Initial public release

_sources/resources.rst.txt

@@ -1,26 +0,0 @@
-Resources
-=========
-
--  `Catalyst Whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ 
-
-
-Related 3rd Party APIs
-^^^^^^^^^^^^^^^^^^^^^^
-
-- `Zipline <http://www.zipline.io/appendix.html>`_ is a Pythonic Algorithmic 
-  Trading Library, and the project Catalyst forked off in the spring of 2017.
-- `Quantopian <https://www.quantopian.com/help>`_ provides a platform for 
-  freelance quantitative analysts develop, test, and use trading algorithms to 
-  buy and sell securities. They aim to create a crowd-sourced hedge fund by
-  fostering their community of freelance traders. Quantopian's backtesting and 
-  live-trading engine is powered by *Zipline*.
-- `Pandas <https://pandas.pydata.org/pandas-docs/stable/api.html>`_ is a Python
-  library providing high-performance, easy-to-use data structures and data 
-  analysis tools. Catalyst relies heavily on pandas, and many API functions 
-  return data as Pandas dataframes.
-- `Numpy <https://docs.scipy.org/doc/numpy/reference/>`_ is the fundamental 
-  package for scientific computing with Python. Some of the data computation 
-  that your algorithms will need, will be optimized leveraging Numpy.
-- `Matplotlib <https://matplotlib.org/1.5.3/api/index.html>`_ is a Python 2D 
-  plotting library that many of examples rely on to plot the performance of 
-  trading algorithms

_sources/unit-tests.rst.txt

@@ -1,88 +0,0 @@
-==========
-Unit Tests
-==========
-
-Exchanges
-~~~~~~~~~
-
-Markets
--------
-Sample:
-    All markets in 3 random exchanges
-Test:
-    Fetch all TradingPair instances
-Assert:
-    No error
-
-Current Ticker
-------------------
-Sample:
-    3 random markets in each of the 3 random exchanges
-Test:
-    Fetch current price and volume
-Assert:
-    Not null and no error
-
-Historical Price Data
----------------------
-Sample:
-    - 3 random markets for each of the 3 random exchanges supporting historical data
-    - For each market, randomly select one supported frequency
-Test:
-    Fetch historical data for each market using the selected frequency
-Assert:
-    - No error and not blank
-    - Date of each candle is consistent with the Catalyst desired pattern,
-        - All candle start at fix intervals
-        - Last candle partial and forward looking from the end date
-
-Authentication and Orders
--------------------------
-Sample:
-    1 random market for each of 3 random authenticated exchanges
-Test:
-    - Create one limit order randomly buying or selling at least 10% out from the current price
-    - Retrieve the open order from the exchange
-    - Cancel the open order
-Assert:
-    No error
-
-
-Bundles
-~~~~~~~
-
-Validate Bundle Data
---------------------
-Sample:
-    - 3 random market in bundles for exchanges supporting historical data
-    - For each market, randomly selected data range available in the exchange historical data
-Test:
-    - Clean the target exchange bundle
-    - Ingest the selected market data for the selected data range
-    - Retrieve the bundle data into a dataframe
-    - Retrieve the equivalent OHLCV data from the exchange into a dataframe
-Assert:
-    Matching data for the bundle and exchange
-
-
-Algo Stats
-----------
-Sample:
-    - 2 sample algorithms with built-in stats calculator
-    - 2 KPIs both calculated by each algo and by Catalyst
-Test:
-    - Run each algorithm
-    - Compare the results of the two methods or calculating stats
-Assert:
-    - Matching stats
-
-CSV Ingestion
--------------
-Sample:
-    3 random CSV files containing price data
-Test:
-    - Ingest each CSV files
-    - Validate with the exchange like in the 'Validate Bundle Data' test
-Assert:
-    Matching data between the bundle and the exchange
-

_sources/utilities.rst.txt

@@ -1,149 +0,0 @@
-Utilities
-=========
-
-This section covers a variety of utilites that provide complimentary 
-functionality to your trading algorithms. These are code snippets that you can
-add to any algorithm to add the desired functionality.
-
-If you are looking for example trading algorithms, see the corresponding section.
-
-Output to CSV file
-~~~~~~~~~~~~~~~~~~
-
-Add this script to the analyze method to create and save a CSV file with the 
-results from the trading algorithm. This file will include the default 
-parameters of the results DataFrame plus any recorded variables and will be 
-saved in the same location where your trading algorithm is saved. The exact 
-script that you need to use depends on the interface that you are using to run 
-your trading algorithm, which could be the CLI or a Python Interpreter.
-
-1. Script to use with CLI:
-
-   .. code-block:: python
-
-      def analyze(context=None, results=None):
-          import sys
-          import os
-          from os.path import basename
-          
-          # Save results in CSV file
-          filename = os.path.splitext(basename(sys.argv[3]))[0]
-          results.to_csv(filename + '.csv')
-
-2. Script to use with Python Interpreter:
-
-   .. code-block:: python
-
-      def analyze(context=None, results=None):
-          import os
-          from os.path import basename
-          
-          # Save results in CSV file
-          filename = os.path.splitext(os.path.basename(__file__))[0]
-          results.to_csv(filename + '.csv')
-
-Extracting market data
-~~~~~~~~~~~~~~~~~~~~~~
-
-Use this script to save the price and volume data of one cryptoasset in a CSV 
-file, which will be saved in the same location and with the same name as your 
-Python file. To get custom data, simply modify the asset's symbol and the dates. 
-Run this script directly from your development environment: python scriptname.py, 
-where the contents of 'scriptname.py' are as follows. Two different version are 
-provided as an example for daily- and minute-resolution data respectively:
-
-Simpler case for daily data
-
-.. code-block:: python
-
-   import os
-   import pytz
-   from datetime import datetime
-
-   from catalyst.api import record, symbol, symbols
-   from catalyst.utils.run_algo import run_algorithm
-
-   def initialize(context):
-      # Portfolio assets list
-      context.asset = symbol('btc_usdt') # Bitcoin on Poloniex
-                          
-   def handle_data(context, data):
-      # Variables to record for a given asset: price and volume
-      price = data.current(context.asset, 'price')
-      volume = data.current(context.asset, 'volume')
-      record(price=price, volume=volume)
-      
-   def analyze(context=None, results=None):
-      
-      # Generate DataFrame with Price and Volume only
-      data = results[['price','volume']]
-      
-      # Save results in CSV file
-      filename = os.path.splitext(os.path.basename(__file__))[0]
-      data.to_csv(filename + '.csv')
-      
-   ''' Bitcoin data is available on Poloniex since 2015-3-1. 
-       Dates vary for other tokens. In the example below, we choose the 
-       full month of July of 2017.
-   '''
-   start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
-   end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc) 
-   results = run_algorithm(initialize=initialize, 
-                           handle_data=handle_data, 
-                           analyze=analyze, 
-                           start=start, 
-                           end=end, 
-                           exchange_name='poloniex', 
-                           capital_base=10000, 
-                           base_currency = 'usdt')
-
-More versatile case for minute data
-
-.. code-block:: python
-
-   import os
-   import csv
-   import pytz
-   from datetime import datetime
-
-   from catalyst.api import record, symbol, symbols
-   from catalyst.utils.run_algo import run_algorithm
-
-
-   def initialize(context):
-   # Portfolio assets list
-   context.asset     = symbol('btc_usdt') # Bitcoin on Poloniex
-
-   # Creates a .CSV file with the same name as this script to store results
-   context.csvfile   = open(os.path.splitext(
-                            os.path.basename(__file__))[0]+'.csv', 'w+')
-   context.csvwriter = csv.writer(context.csvfile)
-
-   def handle_data(context, data):
-   # Variables to record for a given asset: price and volume
-   # Other options include 'open', 'high', 'open', 'close'
-   # Please note that 'price' equals 'close'
-   date   = context.blotter.current_dt     # current time in each iteration
-   price  = data.current(context.asset, 'price')
-   volume = data.current(context.asset, 'volume')
-
-   # Writes one line to CSV on each iteration with the chosen variables    
-   context.csvwriter.writerow([date,price,volume])
-
-   def analyze(context=None, results=None):
-   # Close open file properly at the end
-   context.csvfile.close()
-
-
-   # Bitcoin data is available from 2015-3-2. Dates vary for other tokens.    
-   start = datetime(2017, 7, 30, 0, 0, 0, 0, pytz.utc)
-   end = datetime(2017, 7, 31, 0, 0, 0, 0, pytz.utc) 
-   results = run_algorithm(initialize=initialize, 
-                           handle_data=handle_data,
-                           analyze=analyze, 
-                           start=start, 
-                           end=end,
-                           exchange_name='poloniex', 
-                           data_frequency='minute',
-                           base_currency ='usdt', 
-                           capital_base=10000 )

_sources/videos.rst.txt

@@ -1,63 +0,0 @@
-Videos
-======
-
-
-Installation: MacOS
--------------------
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/ZnsslmHljvw" frameborder="0" allowfullscreen></iframe>
-
-|
-|
-
-Installation: Windows
----------------------
-
-Where things go smoothly:
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/H8HqcEbZmkk" frameborder="0" allowfullscreen></iframe>
-
-|
-
-Where things don't:
-
-.. raw:: html
-   
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/qLkQcWlUBy8" frameborder="0" allowfullscreen></iframe>
-
-|
-|
-
-Backtesting a Strategy
-----------------------
-
-This is the first video of a two-part series on using Catalyst for algorithmic 
-trading. This video implements a simple momentum strategy based on 
-`mean reversion <example-algos.html#mean-reversion>`_: when the cryptoasset
-goes up quickly, we’re going to buy; when it goes down quickly, we’re going to 
-sell. Hopefully, we’ll ride the waves.
-
-.. raw:: html
-
-   <iframe width="560" height="315" src="https://www.youtube.com/embed/JOBRwst9jUY" frameborder="0" allowfullscreen></iframe>
-
-|
-|
-
-Live Trading a Strategy
------------------------
-
-This is the second part of the two-part series on using Catalyst for algorithmic
-trading. Having backtested `our strategy <example-algos.html#mean-reversion>`_ 
-in the previous video, we now take it to trade live against the Bittrex exchange.
-
-.. raw:: html
-
-	<iframe width="560" height="315" src="https://www.youtube.com/embed/NupiE-Xuglw" frameborder="0" allowfullscreen></iframe>
-
-|
-|

_static/basic.css

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_static/css/badge_only.css

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-.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../font/fontawesome_webfont.eot");src:url("../font/fontawesome_webfont.eot?#iefix") format("embedded-opentype"),url("../font/fontawesome_webfont.woff") format("woff"),url("../font/fontawesome_webfont.ttf") format("truetype"),url("../font/fontawesome_webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a .fa{display:inline-block;text-decoration:inherit}li .fa{display:inline-block}li .fa-large:before,li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-0.8em}ul.fas li .fa{width:0.8em}ul.fas li .fa-large:before,ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before{content:""}.icon-book:before{content:""}.fa-caret-down:before{content:""}.icon-caret-down:before{content:""}.fa-caret-up:before{content:""}.icon-caret-up:before{content:""}.fa-caret-left:before{content:""}.icon-caret-left:before{content:""}.fa-caret-right:before{content:""}.icon-caret-right:before{content:""}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;border-top:solid 10px #343131;font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;z-index:400}.rst-versions a{color:#2980B9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27AE60;*zoom:1}.rst-versions .rst-current-version:before,.rst-versions .rst-current-version:after{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book{float:left}.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#E74C3C;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#F1C40F;color:#000}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:gray;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:solid 1px #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px}.rst-versions.rst-badge .icon-book{float:none}.rst-versions.rst-badge .fa-book{float:none}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book{float:left}.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge .rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width: 768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
-/*# sourceMappingURL=badge_only.css.map */

_static/doctools.js

@@ -1,311 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for all documentation.
- *
- * :copyright: Copyright 2007-2018 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
-  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
-    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
-    "profile", "profileEnd"];
-  window.console = {};
-  for (var i = 0; i < names.length; ++i)
-    window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- */
-jQuery.urldecode = function(x) {
-  return decodeURIComponent(x).replace(/\+/g, ' ');
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-  if (typeof s === 'undefined')
-    s = document.location.search;
-  var parts = s.substr(s.indexOf('?') + 1).split('&');
-  var result = {};
-  for (var i = 0; i < parts.length; i++) {
-    var tmp = parts[i].split('=', 2);
-    var key = jQuery.urldecode(tmp[0]);
-    var value = jQuery.urldecode(tmp[1]);
-    if (key in result)
-      result[key].push(value);
-    else
-      result[key] = [value];
-  }
-  return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-  function highlight(node, addItems) {
-    if (node.nodeType === 3) {
-      var val = node.nodeValue;
-      var pos = val.toLowerCase().indexOf(text);
-      if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
-        var span;
-        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-        if (isInSVG) {
-          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-        } else {
-          span = document.createElement("span");
-          span.className = className;
-        }
-        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling));
-        node.nodeValue = val.substr(0, pos);
-        if (isInSVG) {
-          var bbox = span.getBBox();
-          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-       	  rect.x.baseVal.value = bbox.x;
-          rect.y.baseVal.value = bbox.y;
-          rect.width.baseVal.value = bbox.width;
-          rect.height.baseVal.value = bbox.height;
-          rect.setAttribute('class', className);
-          var parentOfText = node.parentNode.parentNode;
-          addItems.push({
-              "parent": node.parentNode,
-              "target": rect});
-        }
-      }
-    }
-    else if (!jQuery(node).is("button, select, textarea")) {
-      jQuery.each(node.childNodes, function() {
-        highlight(this, addItems);
-      });
-    }
-  }
-  var addItems = [];
-  var result = this.each(function() {
-    highlight(this, addItems);
-  });
-  for (var i = 0; i < addItems.length; ++i) {
-    jQuery(addItems[i].parent).before(addItems[i].target);
-  }
-  return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-  jQuery.uaMatch = function(ua) {
-    ua = ua.toLowerCase();
-
-    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-      /(msie) ([\w.]+)/.exec(ua) ||
-      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-      [];
-
-    return {
-      browser: match[ 1 ] || "",
-      version: match[ 2 ] || "0"
-    };
-  };
-  jQuery.browser = {};
-  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
-/**
- * Small JavaScript module for the documentation.
- */
-var Documentation = {
-
-  init : function() {
-    this.fixFirefoxAnchorBug();
-    this.highlightSearchWords();
-    this.initIndexTable();
-    
-  },
-
-  /**
-   * i18n support
-   */
-  TRANSLATIONS : {},
-  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
-  LOCALE : 'unknown',
-
-  // gettext and ngettext don't access this so that the functions
-  // can safely bound to a different name (_ = Documentation.gettext)
-  gettext : function(string) {
-    var translated = Documentation.TRANSLATIONS[string];
-    if (typeof translated === 'undefined')
-      return string;
-    return (typeof translated === 'string') ? translated : translated[0];
-  },
-
-  ngettext : function(singular, plural, n) {
-    var translated = Documentation.TRANSLATIONS[singular];
-    if (typeof translated === 'undefined')
-      return (n == 1) ? singular : plural;
-    return translated[Documentation.PLURALEXPR(n)];
-  },
-
-  addTranslations : function(catalog) {
-    for (var key in catalog.messages)
-      this.TRANSLATIONS[key] = catalog.messages[key];
-    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
-    this.LOCALE = catalog.locale;
-  },
-
-  /**
-   * add context elements like header anchor links
-   */
-  addContextElements : function() {
-    $('div[id] > :header:first').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this headline')).
-      appendTo(this);
-    });
-    $('dt[id]').each(function() {
-      $('<a class="headerlink">\u00B6</a>').
-      attr('href', '#' + this.id).
-      attr('title', _('Permalink to this definition')).
-      appendTo(this);
-    });
-  },
-
-  /**
-   * workaround a firefox stupidity
-   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
-   */
-  fixFirefoxAnchorBug : function() {
-    if (document.location.hash && $.browser.mozilla)
-      window.setTimeout(function() {
-        document.location.href += '';
-      }, 10);
-  },
-
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords : function() {
-    var params = $.getQueryParameters();
-    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
-    if (terms.length) {
-      var body = $('div.body');
-      if (!body.length) {
-        body = $('body');
-      }
-      window.setTimeout(function() {
-        $.each(terms, function() {
-          body.highlightText(this.toLowerCase(), 'highlighted');
-        });
-      }, 10);
-      $('<p class="highlight-link"><a href="javascript:Documentation.' +
-        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
-          .appendTo($('#searchbox'));
-    }
-  },
-
-  /**
-   * init the domain index toggle buttons
-   */
-  initIndexTable : function() {
-    var togglers = $('img.toggler').click(function() {
-      var src = $(this).attr('src');
-      var idnum = $(this).attr('id').substr(7);
-      $('tr.cg-' + idnum).toggle();
-      if (src.substr(-9) === 'minus.png')
-        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
-      else
-        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
-    }).css('display', '');
-    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
-        togglers.click();
-    }
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords : function() {
-    $('#searchbox .highlight-link').fadeOut(300);
-    $('span.highlighted').removeClass('highlighted');
-  },
-
-  /**
-   * make the url absolute
-   */
-  makeURL : function(relativeURL) {
-    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
-  },
-
-  /**
-   * get the current relative url
-   */
-  getCurrentURL : function() {
-    var path = document.location.pathname;
-    var parts = path.split(/\//);
-    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
-      if (this === '..')
-        parts.pop();
-    });
-    var url = parts.join('/');
-    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
-  },
-
-  initOnKeyListeners: function() {
-    $(document).keyup(function(event) {
-      var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box or textarea
-      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
-        switch (event.keyCode) {
-          case 37: // left
-            var prevHref = $('link[rel="prev"]').prop('href');
-            if (prevHref) {
-              window.location.href = prevHref;
-              return false;
-            }
-          case 39: // right
-            var nextHref = $('link[rel="next"]').prop('href');
-            if (nextHref) {
-              window.location.href = nextHref;
-              return false;
-            }
-        }
-      }
-    });
-  }
-};
-
-// quick alias for translations
-_ = Documentation.gettext;
-
-$(document).ready(function() {
-  Documentation.init();
-});

_static/fonts/fontawesome-webfont.svg

@@ -1,685 +0,0 @@
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-<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
-<svg xmlns="http://www.w3.org/2000/svg">
-<metadata></metadata>
-<defs>
-<font id="fontawesomeregular" horiz-adv-x="1536" >
-<font-face units-per-em="1792" ascent="1536" descent="-256" />
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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 +1,87 @@
+#
+# 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
+
+
+# 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.
+from catalyst.utils.calendars.calendar_utils import global_calendar_dispatcher
+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
+
+
+__version__ = get_versions()['version']
+del get_versions
+
+
+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',
+    'exchange',
+]
+
+from ._version import get_versions
+__version__ = get_versions()['version']
+del get_versions

catalyst/__main__.py

@@ -0,0 +1,443 @@
+import errno
+import os
+from functools import wraps
+
+import click
+import logbook
+import pandas as pd
+from six import text_type
+
+from catalyst.data import bundles as bundles_module
+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 $ZIPLINE_HOME.",
+)
+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', '5-minute', 'minute'}),
+    default='daily',
+    show_default=True,
+    help='The data frequency of the simulation.',
+)
+@click.option(
+    '--capital-base',
+    type=float,
+    default=10e6,
+    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.',
+)
+@click.option(
+    '-s',
+    '--start',
+    type=Date(tz='utc', as_timestamp=True),
+    help='The start date of the simulation.',
+)
+@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(
+    '--live/--no-live',
+    is_flag=True,
+    default=False,
+    help='Enable live trading.',
+)
+@click.option(
+    '-x',
+    '--exchange-name',
+    type=click.Choice({'bitfinex'}),
+    help='The name of the exchange (supported: bitfinex).',
+)
+@click.option(
+    '-n',
+    '--algo-name',
+    help='A label assigned to the algorithm for tracking purposes.',
+)
+@click.option(
+    '-c',
+    '--reference-currency',
+    help='The reference 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,
+        live,
+        exchange_name,
+        algo_namespace,
+        base_currency):
+    """Run a backtest for the given algorithm.
+    """
+
+    if live:
+        if exchange_name is None:
+            ctx.fail("must specify an exchange name '-x' in live execution "
+                     "mode '--live'")
+        if algo_namespace is None:
+            ctx.fail("must specify an algorithm name '-n' in live execution "
+                     "mode '--live'")
+        if base_currency is None:
+            ctx.fail("must specify a reference currency '-c' in live "
+                     "execution mode '--live'")
+    else:
+        # 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'",
+            )
+        if start is None:
+            ctx.fail("must specify a start date with '-s' / '--start'")
+        if end is None:
+            ctx.fail("must specify an end date with '-e' / '--end'")
+
+    if (algotext is not None) == (algofile is not None):
+        ctx.fail(
+            "must specify exactly one of '-f' / '--algofile' or"
+            " '-t' / '--algotext'",
+        )
+
+    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=live,
+        exchange=exchange_name,
+        algo_namespace=algo_namespace,
+        base_currency=base_currency
+    )
+
+    if output == '-':
+        click.echo(str(perf))
+    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(
+    '-b',
+    '--bundle',
+    default='poloniex',
+    metavar='BUNDLE-NAME',
+    show_default=True,
+    help='The data 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.'
+)
+def ingest(bundle, 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(
+    '-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 data downloaded with the ingest command.
+    """
+    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))
+
+
+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 +1,775 @@
+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`
+    """
+

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 +1,395 @@
+# 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.
+"""
+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 numpy as np
+from numpy cimport int64_t
+import warnings
+cimport numpy as np
+
+from catalyst.utils.calendars import get_calendar
+
+
+# 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
+
+    _kwargnames = frozenset({
+        'sid',
+        'symbol',
+        'asset_name',
+        'start_date',
+        'end_date',
+        'first_traded',
+        'auto_close_date',
+        'exchange',
+        'exchange_full',
+    })
+
+    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):
+
+        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
+
+    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')
+        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))
+
+    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,
+        }
+
+    @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')
+        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
+
+
+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 +1,200 @@
+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)
+)
+
+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 +1,737 @@
+#
+# 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,
+}
+
+# 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.
+
+            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/curate/poloniex.py

@@ -0,0 +1,144 @@
+import json, time, csv
+from datetime import datetime
+import pandas as pd
+import os
+import time
+import requests
+import logbook
+
+DT_START        = time.mktime(datetime(2010, 1, 1, 0, 0).timetuple())
+CSV_OUT_FOLDER  = '/var/tmp/catalyst/data/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: %s' % CSV_OUT_FOLDER)
+                log.exception(e)
+
+    def get_currency_pairs(self):
+        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: %d' % (len(self.currency_pairs)))
+
+    def _get_start_date(self, csv_fn):
+        ''' Function returns latest appended date, if the file has been previously written
+            the last line is an empty one, so we have to read the second to last line 
+        '''
+        try:
+            with open(csv_fn, 'ab+') as f: 
+                f.seek(0, os.SEEK_END)              # First check file is not zero size
+                if(f.tell() > 2):
+                    f.seek(-2, os.SEEK_END)         # Jump to the second last byte.
+                    while f.read(1) != b"\n":       # Until EOL is found...
+                        f.seek(-2, os.SEEK_CUR)     # ...jump back the read byte plus one more.
+                    lastrow = f.readline() 
+                    return int(lastrow.split(',')[0]) + 300
+
+        except Exception as e:
+            log.error('Error opening file: %s' % csv_fn)
+            log.exception(e)
+
+        return DT_START
+
+    def get_data(self, currencyPair, start, end=9999999999, period=300):
+        url = self._api_path + 'command=returnChartData&currencyPair=' + currencyPair + '&start=' + str(start) + '&end=' + str(end) + '&period=' + str(period)
+
+        try:
+            response = requests.get(url)
+        except Exception as e:
+            log.error('Failed to retrieve candlestick chart data for %s' % currencyPair)
+            log.exception(e)
+            return None
+
+        return response.json()
+
+    '''
+    Pulls latest data for a single pair
+    '''
+    def append_data_single_pair(self, currencyPair, repeat=0):
+        log.debug('Getting data for %s' % currencyPair)
+        csv_fn = CSV_OUT_FOLDER + 'crypto_prices-' + currencyPair + '.csv'
+        start  = self._get_start_date(csv_fn)
+        # Only fetch data if more than 5min have passed since last fetch
+        if (time.time() > start):
+            data   = self.get_data(currencyPair, start)
+            if data is not None:
+                try: 
+                    with open(csv_fn, 'ab') as csvfile:
+                        csvwriter = csv.writer(csvfile)
+                        for item in data:
+                            if item['date'] == 0:
+                                continue
+                            csvwriter.writerow([
+                                item['date'],
+                                item['open'],
+                                item['high'],
+                                item['low'],
+                                item['close'],
+                                item['volume'],
+                            ])
+                except Exception as e:
+                    log.error('Error opening %s' % csv_fn)
+                    log.exception(e)
+            elif (repeat < CONN_RETRIES):
+                    log.debug('Retrying: attemt %d' % (repeat+1) )
+                    self.append_data_single_pair(currencyPair, repeat + 1)    
+
+    '''
+    Pulls latest data for all currency pairs
+    '''
+    def append_data(self):
+        for currencyPair in self.currency_pairs:
+            self.append_data_single_pair(currencyPair)
+            # Rate limit is 6 calls per second, sleep 1sec/6 to be safe
+            time.sleep(0.17)
+
+    '''
+    Returns a data frame for all pairs, or for the requests currency pair.
+    Makes sure data is up to date
+    '''
+    def to_dataframe(self, start, end, currencyPair=None):
+        csv_fn = CSV_OUT_FOLDER + 'crypto_prices-' + currencyPair + '.csv'
+        last_date = self._get_start_date(csv_fn)
+        if last_date + 300 < end or not os.path.exists(csv_fn):
+            # get latest data
+            self.append_data_single_pair(currencyPair)
+
+        # CSV holds the latest snapshot
+        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[datetime.fromtimestamp(start):datetime.fromtimestamp(end-1)]
+
+if __name__ == '__main__':
+    pc = PoloniexCurator()
+    pc.get_currency_pairs()
+    pc.append_data()

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']:
+            where_nan = (outbuf == 0)
+            outbuf_as_float = outbuf.astype(float64) * .000001
+            outbuf_as_float[where_nan] = NAN
+            results.append(outbuf_as_float)
+        elif column_name != 'volume':
+            results.append(outbuf.astype(uint32))
+        else:
+            results.append(outbuf)
+    return results

catalyst/data/_minute_bar_internal.pyx

@@ -0,0 +1,238 @@
+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
+
+@cython.cdivision(True)
+def five_minute_value(ndarray[long_t, ndim=1] market_opens,
+                      Py_ssize_t pos,
+                      short five_minutes_per_day):
+
+    cdef short q, r
+    q = cython.cdiv(pos, five_minutes_per_day)
+    r = cython.cmod(pos, five_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_position_of_five_minute(ndarray[long_t, ndim=1] market_opens,
+                                 ndarray[long_t, ndim=1] market_closes,
+                                 long_t five_minute_val,
+                                 short five_minutes_per_day,
+                                 bool forward_fill):
+
+    cdef Py_ssize_t market_open_loc, market_open, delta
+
+    market_open_loc = \
+        searchsorted(market_opens, five_minute_val, side='right') - 1
+    market_open = market_opens[market_open_loc]
+    market_close = market_closes[market_open_loc]
+
+    if not forward_fill and ((five_minute_val - market_open) >= five_minutes_per_day):
+        raise ValueError("Given five minutes is not between an open and a close")
+
+    delta = int_min(five_minute_val - market_open, market_close - market_open)
+
+    return (market_open_loc * five_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
+
+def find_last_traded_five_minute_position_internal(
+        ndarray[long_t, ndim=1] market_opens,
+        ndarray[long_t, ndim=1] market_closes,
+        long_t end_five_minute,
+        long_t start_five_minute,
+        volumes,
+        short five_minutes_per_day):
+    cdef Py_ssize_t minute_pos, current_minute, q
+
+    five_minute_pos = int_min(
+        find_position_of_five_minute(
+            market_opens,
+            market_closes,
+            end_five_minute,
+            five_minutes_per_day,
+            True,
+        ),
+        len(volumes) - 1,
+    )
+
+    while five_minute_pos >= 0:
+        current_five_minute = five_minute_value(
+            market_opens, five_minute_pos, five_minutes_per_day
+        )
+
+        q = cython.cdiv(five_minute_pos, five_minutes_per_day)
+        if current_five_minute > market_closes[q]:
+            five_minute_pos = find_position_of_five_minute(
+                market_opens,
+                market_closes,
+                market_closes[q],
+                five_minutes_per_day,
+                False,
+            )
+            continue
+
+        if current_five_minute < start_five_minute:
+            return -1
+
+        if volumes[five_minute_pos] != 0:
+            return five_minute_pos
+
+        five_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 +1,31 @@
+# These imports are necessary to force module-scope register calls to happen.
+from . import quandl  # noqa
+from . import poloniex
+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 +1,524 @@
+#
+# 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 time, 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
+
+logbook.StderrHandler().push_application()
+log = logbook.Logger(__name__)
+
+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 five_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,
+               five_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 and 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
+                # either minute or 5-minute data.
+                metadata = self._post_process_metadata(
+                    raw_metadata,
+                    cache,
+                    show_progress=show_progress,
+                )
+                asset_db_writer.write(metadata)
+
+                # Compile 5-minute symbol data if bundle supports 5-minute mode and
+                # persist the dataset to disk.
+                '''
+                if '5-minute' in self.frequencies:
+                    five_minute_bar_writer.write(
+                        self._fetch_symbol_iter(
+                            api_key,
+                            cache,
+                            symbol_map,
+                            calendar,
+                            start_session,
+                            end_session,
+                            '5-minute',
+                            retries,
+                        ),
+                        length=len(symbol_map),
+                        show_progress=show_progress,
+                    )
+                '''
+
+                # 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 as e:
+                        raw = pd.DataFrame([])
+                        break
+                    except Exception as e:
+                        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)
+                raw_data.index = raw_data.index.tz_localize('UTC')
+
+                # 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 as e:
+                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 +1,80 @@
+#
+# 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]'),
+        ]
+
+    @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
+
+    @lazyval
+    def five_minutes_per_day(self):
+        return 288
+
+    @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
+
+    @lazyval
+    def five_minutes_per_day(self):
+        return 78
+
+    @property
+    def splits(self):
+        return self._splits
+
+    @property
+    def dividends(self):
+        return self._dividends

catalyst/data/bundles/core.py

@@ -0,0 +1,738 @@
+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 ..five_minute_bars import (
+    BcolzFiveMinuteBarReader,
+    BcolzFiveMinuteBarWriter,
+)
+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 five_minute_path(bundle_name, timestr, environ=None):
+    return pth.data_path(
+        five_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 five_minute_relative(bundle_name, timestr, environ=None):
+    return bundle_name, timestr, 'five_minute.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',
+     'five_minutes_per_day',
+     'ingest',
+     'create_writers']
+)
+
+BundleData = namedtuple(
+    'BundleData',
+    'asset_finder minute_bar_reader five_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,
+            five_minutes_per_day=bundle.five_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,
+                 five_minutes_per_day=288,
+                 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,
+            five_minutes_per_day=five_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(())
+
+                five_minute_bar_writer = BcolzFiveMinuteBarWriter(
+                    wd.ensure_dir(*five_minute_relative(
+                        name, timestr, environ=environ)
+                    ),
+                    calendar,
+                    start_session,
+                    end_session,
+                    five_minutes_per_day=bundle.five_minutes_per_day,
+                )
+
+                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
+                five_minute_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,
+                five_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),
+            ),
+            five_minute_bar_reader=BcolzFiveMinuteBarReader(
+                five_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 +1,167 @@
+#
+# 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 BaseCryptoPricingBundle
+from catalyst.utils.memoize import lazyval
+
+class PoloniexBundle(BaseCryptoPricingBundle):
+    @lazyval
+    def name(self):
+        return 'poloniex'
+
+    @lazyval
+    def exchange(self):
+        return 'POLO'
+
+    @lazyval
+    def frequencies(self):
+        return set((
+            'daily',
+            #'5-minute',
+        ))
+
+    @lazyval
+    def tar_url(self):
+        return (
+            'https://www.dropbox.com/s/9naqffawnq8o4r2/'
+            'poloniex-bundle.tar?dl=1'
+        )
+
+    @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)
+
+        return (
+            sym_md.symbol,
+            start_date,
+            end_date,
+            ac_date,
+        )
+
+    def fetch_raw_symbol_frame(self,
+                               api_key,
+                               symbol,
+                               calendar,
+                               start_date,
+                               end_date,
+                               frequency):
+        raw = pd.read_json(
+            self._format_data_url(
+                api_key,
+                symbol,
+                start_date,
+                end_date,
+                frequency,
+            ),
+            orient='records',
+        )
+        raw.set_index('date', inplace=True)
+
+        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,
+#            '5-minute': 300,
+        }
+
+        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):
+        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)
+'''
+register_bundle(PoloniexBundle)

catalyst/data/bundles/quandl.py

@@ -0,0 +1,231 @@
+#
+# 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 itertools import count
+import tarfile
+from time import time, sleep
+from datetime import datetime
+
+from logbook import Logger
+import pandas as pd
+from six.moves.urllib.parse import urlencode
+
+from catalyst.utils.calendars import register_calendar_alias
+from catalyst.utils.cli import maybe_show_progress
+
+from . import core as bundles
+
+log = Logger(__name__)
+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