mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-27 17:29:56 +08:00
DOC: use sphinx docs
This commit is contained in:
llllllllll
@@ -1,158 +0,0 @@
|
||||
Zipline
|
||||
=======
|
||||
[](https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
|
||||
[](https://pypi.python.org/pypi/zipline)
|
||||
[](https://pypi.python.org/pypi/zipline)
|
||||
[](https://travis-ci.org/quantopian/zipline)
|
||||
[](https://coveralls.io/r/quantopian/zipline)
|
||||
[](https://scrutinizer-ci.com/g/quantopian/zipline/)
|
||||
|
||||
Zipline is a Pythonic algorithmic trading library. The system is
|
||||
fundamentally event-driven and a close approximation of how
|
||||
live-trading systems operate.
|
||||
|
||||
Zipline is currently used in production as the backtesting engine
|
||||
powering [Quantopian Inc.](https://www.quantopian.com) -- a free,
|
||||
community-centered platform that allows development and real-time
|
||||
backtesting of trading algorithms in the web browser.
|
||||
|
||||
[*Join our community!*](https://groups.google.com/forum/#!forum/zipline)
|
||||
|
||||
Want to contribute? See our [open requests](https://github.com/quantopian/zipline/wiki/Contribution-Requests)
|
||||
and our [general guidelines](https://github.com/quantopian/zipline#contributions) below.
|
||||
|
||||
Features
|
||||
========
|
||||
|
||||
* Ease of use: Zipline tries to get out of your way so that you can
|
||||
focus on algorithm development. See below for a code example.
|
||||
|
||||
* Zipline comes "batteries included" as many common statistics like
|
||||
moving average and linear regression can be readily accessed from
|
||||
within a user-written algorithm.
|
||||
|
||||
* Input of historical data and output of performance statistics is
|
||||
based on Pandas DataFrames to integrate nicely into the existing
|
||||
Python eco-system.
|
||||
|
||||
* Statistic and machine learning libraries like matplotlib, scipy,
|
||||
statsmodels, and sklearn support development, analysis and
|
||||
visualization of state-of-the-art trading systems.
|
||||
|
||||
Installation
|
||||
============
|
||||
|
||||
The easiest way to install Zipline is via `conda` which comes as part of [Anaconda](http://continuum.io/downloads) or can be installed via `pip install conda`.
|
||||
|
||||
Once set up, you can install Zipline from our Quantopian channel:
|
||||
|
||||
```
|
||||
conda install -c Quantopian zipline
|
||||
```
|
||||
|
||||
Currently supported platforms include:
|
||||
|
||||
* Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)
|
||||
|
||||
* OSX 64-bit
|
||||
|
||||
* Linux 64-bit
|
||||
|
||||
PIP
|
||||
---
|
||||
|
||||
Alternatively you can install Zipline via the more traditional `pip`
|
||||
command. Since zipline is pure-python code it should be very easy to
|
||||
install and set up:
|
||||
|
||||
```
|
||||
pip install numpy # Pre-install numpy to handle dependency chain quirk
|
||||
pip install zipline
|
||||
```
|
||||
|
||||
If there are problems installing the dependencies or zipline we
|
||||
recommend installing these packages via some other means. For Windows,
|
||||
the [Enthought Python Distribution](http://www.enthought.com/products/epd.php)
|
||||
includes most of the necessary dependencies. On OSX, the
|
||||
[Scipy Superpack](http://fonnesbeck.github.com/ScipySuperpack/)
|
||||
works very well.
|
||||
|
||||
Dependencies
|
||||
------------
|
||||
|
||||
* Python (2.7 or 3.3)
|
||||
* numpy (>= 1.6.0)
|
||||
* pandas (>= 0.9.0)
|
||||
* pytz
|
||||
* Logbook
|
||||
* requests
|
||||
* [python-dateutil](https://pypi.python.org/pypi/python-dateutil) (>= 2.1)
|
||||
* ta-lib
|
||||
|
||||
|
||||
Quickstart
|
||||
==========
|
||||
|
||||
See our [getting started tutorial](http://www.zipline.io/#quickstart).
|
||||
|
||||
The following code implements a simple dual moving average algorithm.
|
||||
|
||||
```python
|
||||
from zipline.api import order_target, record, symbol, history, add_history
|
||||
|
||||
|
||||
def initialize(context):
|
||||
# Register 2 histories that track daily prices,
|
||||
# one with a 100 window and one with a 300 day window
|
||||
add_history(100, '1d', 'price')
|
||||
add_history(300, '1d', 'price')
|
||||
|
||||
context.i = 0
|
||||
|
||||
|
||||
def handle_data(context, data):
|
||||
# Skip first 300 days to get full windows
|
||||
context.i += 1
|
||||
if context.i < 300:
|
||||
return
|
||||
|
||||
# Compute averages
|
||||
# history() has to be called with the same params
|
||||
# from above and returns a pandas dataframe.
|
||||
short_mavg = history(100, '1d', 'price').mean()
|
||||
long_mavg = history(300, '1d', 'price').mean()
|
||||
|
||||
sym = symbol('AAPL')
|
||||
|
||||
# Trading logic
|
||||
if short_mavg[sym] > long_mavg[sym]:
|
||||
# order_target orders as many shares as needed to
|
||||
# achieve the desired number of shares.
|
||||
order_target(sym, 100)
|
||||
elif short_mavg[sym] < long_mavg[sym]:
|
||||
order_target(sym, 0)
|
||||
|
||||
# Save values for later inspection
|
||||
record(AAPL=data[sym].price,
|
||||
short_mavg=short_mavg[sym],
|
||||
long_mavg=long_mavg[sym])
|
||||
```
|
||||
|
||||
You can then run this algorithm using the Zipline CLI. From the
|
||||
command line, run:
|
||||
|
||||
```bash
|
||||
python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
|
||||
```
|
||||
|
||||
This will download the AAPL price data from Yahoo! Finance in the
|
||||
specified time range and stream it through the algorithm and save the
|
||||
resulting performance dataframe to dma.pickle which you can then load
|
||||
and analyze from within python.
|
||||
|
||||
You can find other examples in the zipline/examples directory.
|
||||
|
||||
Contributions
|
||||
============
|
||||
|
||||
If you would like to contribute, please see our Contribution Requests: https://github.com/quantopian/zipline/wiki/Contribution-Requests
|
||||
+181
@@ -0,0 +1,181 @@
|
||||
Zipline
|
||||
=======
|
||||
|
||||
|Gitter|
|
||||
|version status|
|
||||
|downloads|
|
||||
|build status|
|
||||
|Coverage Status|
|
||||
|Code quality|
|
||||
|
||||
Zipline is a Pythonic algorithmic trading library. The system is
|
||||
fundamentally event-driven and a close approximation of how live-trading
|
||||
systems operate.
|
||||
|
||||
Zipline is currently used in production as the backtesting engine
|
||||
powering `Quantopian Inc. <https://www.quantopian.com>`__ -- a free,
|
||||
community-centered platform that allows development and real-time
|
||||
backtesting of trading algorithms in the web browser.
|
||||
|
||||
`Join our
|
||||
community! <https://groups.google.com/forum/#!forum/zipline>`__
|
||||
|
||||
Want to contribute? See our `open
|
||||
requests <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__
|
||||
and our `general
|
||||
guidelines <https://github.com/quantopian/zipline#contributions>`__
|
||||
below.
|
||||
|
||||
Features
|
||||
========
|
||||
|
||||
- Ease of use: Zipline tries to get out of your way so that you can
|
||||
focus on algorithm development. See below for a code example.
|
||||
|
||||
- Zipline comes "batteries included" as many common statistics like
|
||||
moving average and linear regression can be readily accessed from
|
||||
within a user-written algorithm.
|
||||
|
||||
- Input of historical data and output of performance statistics is
|
||||
based on Pandas DataFrames to integrate nicely into the existing
|
||||
Python eco-system.
|
||||
|
||||
- Statistic and machine learning libraries like matplotlib, scipy,
|
||||
statsmodels, and sklearn support development, analysis and
|
||||
visualization of state-of-the-art trading systems.
|
||||
|
||||
Installation
|
||||
============
|
||||
|
||||
The easiest way to install Zipline is via ``conda`` which comes as part
|
||||
of `Anaconda <http://continuum.io/downloads>`__ or can be installed via
|
||||
``pip install conda``.
|
||||
|
||||
Once set up, you can install Zipline from our Quantopian channel:
|
||||
|
||||
::
|
||||
|
||||
conda install -c Quantopian zipline
|
||||
|
||||
Currently supported platforms include:
|
||||
|
||||
- Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)
|
||||
|
||||
- OSX 64-bit
|
||||
|
||||
- Linux 64-bit
|
||||
|
||||
PIP
|
||||
---
|
||||
|
||||
Alternatively you can install Zipline via the more traditional ``pip``
|
||||
command. Since zipline is pure-python code it should be very easy to
|
||||
install and set up:
|
||||
|
||||
::
|
||||
|
||||
pip install numpy # Pre-install numpy to handle dependency chain quirk
|
||||
pip install zipline
|
||||
|
||||
If there are problems installing the dependencies or zipline we
|
||||
recommend installing these packages via some other means. For Windows,
|
||||
the `Enthought Python
|
||||
Distribution <http://www.enthought.com/products/epd.php>`__ includes
|
||||
most of the necessary dependencies. On OSX, the `Scipy
|
||||
Superpack <http://fonnesbeck.github.com/ScipySuperpack/>`__ works very
|
||||
well.
|
||||
|
||||
Dependencies
|
||||
------------
|
||||
|
||||
- Python (2.7 or 3.3)
|
||||
- numpy (>= 1.6.0)
|
||||
- pandas (>= 0.9.0)
|
||||
- pytz
|
||||
- Logbook
|
||||
- requests
|
||||
- `python-dateutil <https://pypi.python.org/pypi/python-dateutil>`__
|
||||
(>= 2.1)
|
||||
- ta-lib
|
||||
|
||||
Quickstart
|
||||
==========
|
||||
|
||||
See our `getting started
|
||||
tutorial <http://www.zipline.io/#quickstart>`__.
|
||||
|
||||
The following code implements a simple dual moving average algorithm.
|
||||
|
||||
.. code:: python
|
||||
|
||||
from zipline.api import order_target, record, symbol, history, add_history
|
||||
|
||||
|
||||
def initialize(context):
|
||||
# Register 2 histories that track daily prices,
|
||||
# one with a 100 window and one with a 300 day window
|
||||
add_history(100, '1d', 'price')
|
||||
add_history(300, '1d', 'price')
|
||||
|
||||
context.i = 0
|
||||
|
||||
|
||||
def handle_data(context, data):
|
||||
# Skip first 300 days to get full windows
|
||||
context.i += 1
|
||||
if context.i < 300:
|
||||
return
|
||||
|
||||
# Compute averages
|
||||
# history() has to be called with the same params
|
||||
# from above and returns a pandas dataframe.
|
||||
short_mavg = history(100, '1d', 'price').mean()
|
||||
long_mavg = history(300, '1d', 'price').mean()
|
||||
|
||||
sym = symbol('AAPL')
|
||||
|
||||
# Trading logic
|
||||
if short_mavg[sym] > long_mavg[sym]:
|
||||
# order_target orders as many shares as needed to
|
||||
# achieve the desired number of shares.
|
||||
order_target(sym, 100)
|
||||
elif short_mavg[sym] < long_mavg[sym]:
|
||||
order_target(sym, 0)
|
||||
|
||||
# Save values for later inspection
|
||||
record(AAPL=data[sym].price,
|
||||
short_mavg=short_mavg[sym],
|
||||
long_mavg=long_mavg[sym])
|
||||
|
||||
You can then run this algorithm using the Zipline CLI. From the command
|
||||
line, run:
|
||||
|
||||
.. code:: bash
|
||||
|
||||
python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
|
||||
|
||||
This will download the AAPL price data from Yahoo! Finance in the
|
||||
specified time range and stream it through the algorithm and save the
|
||||
resulting performance dataframe to dma.pickle which you can then load
|
||||
and analyze from within python.
|
||||
|
||||
You can find other examples in the zipline/examples directory.
|
||||
|
||||
Contributions
|
||||
=============
|
||||
|
||||
If you would like to contribute, please see our Contribution Requests:
|
||||
https://github.com/quantopian/zipline/wiki/Contribution-Requests
|
||||
|
||||
.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
|
||||
:target: https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
|
||||
.. |version status| image:: https://img.shields.io/pypi/pyversions/zipline.svg
|
||||
:target: https://pypi.python.org/pypi/zipline
|
||||
.. |downloads| image:: https://img.shields.io/pypi/dd/zipline.svg
|
||||
:target: https://pypi.python.org/pypi/zipline
|
||||
.. |build status| image:: https://travis-ci.org/quantopian/zipline.png?branch=master
|
||||
:target: https://travis-ci.org/quantopian/zipline
|
||||
.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.png
|
||||
:target: https://coveralls.io/r/quantopian/zipline
|
||||
.. |Code quality| image:: https://scrutinizer-ci.com/g/quantopian/zipline/badges/quality-score.png?b=master
|
||||
:target: https://scrutinizer-ci.com/g/quantopian/zipline/
|
||||
+192
@@ -0,0 +1,192 @@
|
||||
# Makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line.
|
||||
SPHINXOPTS =
|
||||
SPHINXBUILD = sphinx-build
|
||||
PAPER =
|
||||
BUILDDIR = build
|
||||
|
||||
# User-friendly check for sphinx-build
|
||||
ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
|
||||
$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
|
||||
endif
|
||||
|
||||
# Internal variables.
|
||||
PAPEROPT_a4 = -D latex_paper_size=a4
|
||||
PAPEROPT_letter = -D latex_paper_size=letter
|
||||
ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
|
||||
# the i18n builder cannot share the environment and doctrees with the others
|
||||
I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
|
||||
|
||||
.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext
|
||||
|
||||
help:
|
||||
@echo "Please use \`make <target>' where <target> is one of"
|
||||
@echo " html to make standalone HTML files"
|
||||
@echo " dirhtml to make HTML files named index.html in directories"
|
||||
@echo " singlehtml to make a single large HTML file"
|
||||
@echo " pickle to make pickle files"
|
||||
@echo " json to make JSON files"
|
||||
@echo " htmlhelp to make HTML files and a HTML help project"
|
||||
@echo " qthelp to make HTML files and a qthelp project"
|
||||
@echo " applehelp to make an Apple Help Book"
|
||||
@echo " devhelp to make HTML files and a Devhelp project"
|
||||
@echo " epub to make an epub"
|
||||
@echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
|
||||
@echo " latexpdf to make LaTeX files and run them through pdflatex"
|
||||
@echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
|
||||
@echo " text to make text files"
|
||||
@echo " man to make manual pages"
|
||||
@echo " texinfo to make Texinfo files"
|
||||
@echo " info to make Texinfo files and run them through makeinfo"
|
||||
@echo " gettext to make PO message catalogs"
|
||||
@echo " changes to make an overview of all changed/added/deprecated items"
|
||||
@echo " xml to make Docutils-native XML files"
|
||||
@echo " pseudoxml to make pseudoxml-XML files for display purposes"
|
||||
@echo " linkcheck to check all external links for integrity"
|
||||
@echo " doctest to run all doctests embedded in the documentation (if enabled)"
|
||||
@echo " coverage to run coverage check of the documentation (if enabled)"
|
||||
|
||||
clean:
|
||||
rm -rf $(BUILDDIR)/*
|
||||
|
||||
html:
|
||||
$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
|
||||
|
||||
dirhtml:
|
||||
$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
|
||||
@echo
|
||||
@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
|
||||
|
||||
singlehtml:
|
||||
$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
|
||||
@echo
|
||||
@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
|
||||
|
||||
pickle:
|
||||
$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
|
||||
@echo
|
||||
@echo "Build finished; now you can process the pickle files."
|
||||
|
||||
json:
|
||||
$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
|
||||
@echo
|
||||
@echo "Build finished; now you can process the JSON files."
|
||||
|
||||
htmlhelp:
|
||||
$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
|
||||
@echo
|
||||
@echo "Build finished; now you can run HTML Help Workshop with the" \
|
||||
".hhp project file in $(BUILDDIR)/htmlhelp."
|
||||
|
||||
qthelp:
|
||||
$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
|
||||
@echo
|
||||
@echo "Build finished; now you can run "qcollectiongenerator" with the" \
|
||||
".qhcp project file in $(BUILDDIR)/qthelp, like this:"
|
||||
@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/zipline.qhcp"
|
||||
@echo "To view the help file:"
|
||||
@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/zipline.qhc"
|
||||
|
||||
applehelp:
|
||||
$(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
|
||||
@echo
|
||||
@echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
|
||||
@echo "N.B. You won't be able to view it unless you put it in" \
|
||||
"~/Library/Documentation/Help or install it in your application" \
|
||||
"bundle."
|
||||
|
||||
devhelp:
|
||||
$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
|
||||
@echo
|
||||
@echo "Build finished."
|
||||
@echo "To view the help file:"
|
||||
@echo "# mkdir -p $$HOME/.local/share/devhelp/zipline"
|
||||
@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/zipline"
|
||||
@echo "# devhelp"
|
||||
|
||||
epub:
|
||||
$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
|
||||
@echo
|
||||
@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
|
||||
|
||||
latex:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo
|
||||
@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
|
||||
@echo "Run \`make' in that directory to run these through (pdf)latex" \
|
||||
"(use \`make latexpdf' here to do that automatically)."
|
||||
|
||||
latexpdf:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo "Running LaTeX files through pdflatex..."
|
||||
$(MAKE) -C $(BUILDDIR)/latex all-pdf
|
||||
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
|
||||
|
||||
latexpdfja:
|
||||
$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
|
||||
@echo "Running LaTeX files through platex and dvipdfmx..."
|
||||
$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
|
||||
@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
|
||||
|
||||
text:
|
||||
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
|
||||
@echo
|
||||
@echo "Build finished. The text files are in $(BUILDDIR)/text."
|
||||
|
||||
man:
|
||||
$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
|
||||
@echo
|
||||
@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
|
||||
|
||||
texinfo:
|
||||
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
|
||||
@echo
|
||||
@echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
|
||||
@echo "Run \`make' in that directory to run these through makeinfo" \
|
||||
"(use \`make info' here to do that automatically)."
|
||||
|
||||
info:
|
||||
$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
|
||||
@echo "Running Texinfo files through makeinfo..."
|
||||
make -C $(BUILDDIR)/texinfo info
|
||||
@echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
|
||||
|
||||
gettext:
|
||||
$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
|
||||
@echo
|
||||
@echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
|
||||
|
||||
changes:
|
||||
$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
|
||||
@echo
|
||||
@echo "The overview file is in $(BUILDDIR)/changes."
|
||||
|
||||
linkcheck:
|
||||
$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
|
||||
@echo
|
||||
@echo "Link check complete; look for any errors in the above output " \
|
||||
"or in $(BUILDDIR)/linkcheck/output.txt."
|
||||
|
||||
doctest:
|
||||
$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
|
||||
@echo "Testing of doctests in the sources finished, look at the " \
|
||||
"results in $(BUILDDIR)/doctest/output.txt."
|
||||
|
||||
coverage:
|
||||
$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
|
||||
@echo "Testing of coverage in the sources finished, look at the " \
|
||||
"results in $(BUILDDIR)/coverage/python.txt."
|
||||
|
||||
xml:
|
||||
$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
|
||||
@echo
|
||||
@echo "Build finished. The XML files are in $(BUILDDIR)/xml."
|
||||
|
||||
pseudoxml:
|
||||
$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
|
||||
@echo
|
||||
@echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
|
||||
@@ -1,10 +0,0 @@
|
||||
#!/bin/bash
|
||||
NBDIR=notebooks
|
||||
|
||||
for fullfile in $NBDIR/*.ipynb; do
|
||||
echo "Processing $fullfile file..";
|
||||
filename=$(basename "$fullfile")
|
||||
extension="${filename##*.}"
|
||||
filename="${filename%.*}"
|
||||
ipython nbconvert $fullfile --to markdown --output $filename
|
||||
done
|
||||
@@ -1 +0,0 @@
|
||||
../README.md
|
||||
+263
@@ -0,0 +1,263 @@
|
||||
@ECHO OFF
|
||||
|
||||
REM Command file for Sphinx documentation
|
||||
|
||||
if "%SPHINXBUILD%" == "" (
|
||||
set SPHINXBUILD=sphinx-build
|
||||
)
|
||||
set BUILDDIR=build
|
||||
set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source
|
||||
set I18NSPHINXOPTS=%SPHINXOPTS% source
|
||||
if NOT "%PAPER%" == "" (
|
||||
set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
|
||||
set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS%
|
||||
)
|
||||
|
||||
if "%1" == "" goto help
|
||||
|
||||
if "%1" == "help" (
|
||||
:help
|
||||
echo.Please use `make ^<target^>` where ^<target^> is one of
|
||||
echo. html to make standalone HTML files
|
||||
echo. dirhtml to make HTML files named index.html in directories
|
||||
echo. singlehtml to make a single large HTML file
|
||||
echo. pickle to make pickle files
|
||||
echo. json to make JSON files
|
||||
echo. htmlhelp to make HTML files and a HTML help project
|
||||
echo. qthelp to make HTML files and a qthelp project
|
||||
echo. devhelp to make HTML files and a Devhelp project
|
||||
echo. epub to make an epub
|
||||
echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter
|
||||
echo. text to make text files
|
||||
echo. man to make manual pages
|
||||
echo. texinfo to make Texinfo files
|
||||
echo. gettext to make PO message catalogs
|
||||
echo. changes to make an overview over all changed/added/deprecated items
|
||||
echo. xml to make Docutils-native XML files
|
||||
echo. pseudoxml to make pseudoxml-XML files for display purposes
|
||||
echo. linkcheck to check all external links for integrity
|
||||
echo. doctest to run all doctests embedded in the documentation if enabled
|
||||
echo. coverage to run coverage check of the documentation if enabled
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "clean" (
|
||||
for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
|
||||
del /q /s %BUILDDIR%\*
|
||||
goto end
|
||||
)
|
||||
|
||||
|
||||
REM Check if sphinx-build is available and fallback to Python version if any
|
||||
%SPHINXBUILD% 2> nul
|
||||
if errorlevel 9009 goto sphinx_python
|
||||
goto sphinx_ok
|
||||
|
||||
:sphinx_python
|
||||
|
||||
set SPHINXBUILD=python -m sphinx.__init__
|
||||
%SPHINXBUILD% 2> nul
|
||||
if errorlevel 9009 (
|
||||
echo.
|
||||
echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
|
||||
echo.installed, then set the SPHINXBUILD environment variable to point
|
||||
echo.to the full path of the 'sphinx-build' executable. Alternatively you
|
||||
echo.may add the Sphinx directory to PATH.
|
||||
echo.
|
||||
echo.If you don't have Sphinx installed, grab it from
|
||||
echo.http://sphinx-doc.org/
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
:sphinx_ok
|
||||
|
||||
|
||||
if "%1" == "html" (
|
||||
%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The HTML pages are in %BUILDDIR%/html.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "dirhtml" (
|
||||
%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "singlehtml" (
|
||||
%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "pickle" (
|
||||
%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished; now you can process the pickle files.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "json" (
|
||||
%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished; now you can process the JSON files.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "htmlhelp" (
|
||||
%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished; now you can run HTML Help Workshop with the ^
|
||||
.hhp project file in %BUILDDIR%/htmlhelp.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "qthelp" (
|
||||
%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished; now you can run "qcollectiongenerator" with the ^
|
||||
.qhcp project file in %BUILDDIR%/qthelp, like this:
|
||||
echo.^> qcollectiongenerator %BUILDDIR%\qthelp\zipline.qhcp
|
||||
echo.To view the help file:
|
||||
echo.^> assistant -collectionFile %BUILDDIR%\qthelp\zipline.ghc
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "devhelp" (
|
||||
%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "epub" (
|
||||
%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The epub file is in %BUILDDIR%/epub.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "latex" (
|
||||
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "latexpdf" (
|
||||
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
|
||||
cd %BUILDDIR%/latex
|
||||
make all-pdf
|
||||
cd %~dp0
|
||||
echo.
|
||||
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "latexpdfja" (
|
||||
%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
|
||||
cd %BUILDDIR%/latex
|
||||
make all-pdf-ja
|
||||
cd %~dp0
|
||||
echo.
|
||||
echo.Build finished; the PDF files are in %BUILDDIR%/latex.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "text" (
|
||||
%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The text files are in %BUILDDIR%/text.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "man" (
|
||||
%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The manual pages are in %BUILDDIR%/man.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "texinfo" (
|
||||
%SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "gettext" (
|
||||
%SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The message catalogs are in %BUILDDIR%/locale.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "changes" (
|
||||
%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.The overview file is in %BUILDDIR%/changes.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "linkcheck" (
|
||||
%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Link check complete; look for any errors in the above output ^
|
||||
or in %BUILDDIR%/linkcheck/output.txt.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "doctest" (
|
||||
%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Testing of doctests in the sources finished, look at the ^
|
||||
results in %BUILDDIR%/doctest/output.txt.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "coverage" (
|
||||
%SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Testing of coverage in the sources finished, look at the ^
|
||||
results in %BUILDDIR%/coverage/python.txt.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "xml" (
|
||||
%SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The XML files are in %BUILDDIR%/xml.
|
||||
goto end
|
||||
)
|
||||
|
||||
if "%1" == "pseudoxml" (
|
||||
%SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml
|
||||
if errorlevel 1 exit /b 1
|
||||
echo.
|
||||
echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml.
|
||||
goto end
|
||||
)
|
||||
|
||||
:end
|
||||
+379
-2162
File diff suppressed because one or more lines are too long
@@ -1,359 +0,0 @@
|
||||
# Zipline 0.6.1 Release Notes
|
||||
|
||||
**Highlights**
|
||||
|
||||
- **Major fixes to risk calculations, see BUG section.**
|
||||
- **Port of `history()` function, see ENH section**
|
||||
- **Start of support for Quantopian algorithm script-syntax, see ENH section.**
|
||||
- **conda package manager support, see BLD section.**
|
||||
|
||||
## Enhancements (ENH)
|
||||
|
||||
### Always process new orders.
|
||||
|
||||
i.e. on bars where `handle_data` isn't called, but there is 'clock' data e.g. a
|
||||
consistent benchmark, process orders.
|
||||
|
||||
### Empty positions are now filtered from the portfolio container.
|
||||
|
||||
To help prevent algorithms from operating on positions that are not in the
|
||||
existing universe of stocks.
|
||||
|
||||
Formerly, iterating over positions would return positions for stocks which had
|
||||
zero shares held. (Where an explicit check in algorithm code for `pos.amount !=
|
||||
0` could prevent from using a non-existent position.)
|
||||
|
||||
### Add trading calendar for BMF&Bovespa.
|
||||
### Add beginning of algo script support.
|
||||
|
||||
Starts on the path of parity with the script syntax in Quantopian's IDE on
|
||||
<https://quantopian.com>
|
||||
|
||||
Example:
|
||||
from datetime import datetime
|
||||
import pytz
|
||||
|
||||
from zipline import TradingAlgorithm
|
||||
from zipline.utils.factory import load_from_yahoo
|
||||
|
||||
from zipline.api import order
|
||||
|
||||
def initialize(context):
|
||||
context.test = 10
|
||||
|
||||
def handle_date(context, data):
|
||||
order('AAPL', 10)
|
||||
print(context.test)
|
||||
|
||||
if __name__ == '__main__':
|
||||
import pylab as pl
|
||||
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
end = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
data = load_from_yahoo(
|
||||
stocks=['AAPL'],
|
||||
indexes={},
|
||||
start=start,
|
||||
end=end)
|
||||
data = data.dropna()
|
||||
algo = TradingAlgorithm(
|
||||
initialize=initialize,
|
||||
handle_data=handle_date)
|
||||
results = algo.run(data)
|
||||
results.portfolio_value.plot()
|
||||
pl.show()
|
||||
|
||||
### Add HDF5 and CSV sources.
|
||||
|
||||
### Limit `handle_data` to times with market data.
|
||||
|
||||
To prevent cases where custom data types had unaligned timestamps, only call
|
||||
`handle_data` when market data passes through.
|
||||
|
||||
Custom data that comes before market data will still update the data bar. But
|
||||
the handling of that data will only be done when there is actionable market
|
||||
data.
|
||||
|
||||
### Extended commission PerShare method to allow a minimum cost per trade.
|
||||
|
||||
### Add symbol api function
|
||||
|
||||
A `symbol()` lookup feature was added to Quantopian. By adding the same API
|
||||
function to zipline we can make copy&pasting of a Zipline algo to Quantopian
|
||||
easier.
|
||||
|
||||
### Add simulated random trade source.
|
||||
|
||||
Added a new data source that emits events with certain user-specified
|
||||
frequency (minute or daily).
|
||||
|
||||
This allows users to backtest and debug an algorithm in minute mode to
|
||||
provide a cleaner path towards Quantopian.
|
||||
|
||||
### Remove dependency on benchmark for trading day calendar.
|
||||
|
||||
Instead of the benchmarks' index, the trading calendar is now used to populate
|
||||
the environment's trading days.
|
||||
|
||||
Remove `extra_date` field, since unlike the benchmarks list, the trading
|
||||
calendar can generate future dates, so dates for current day trading do not need
|
||||
to be appended.
|
||||
|
||||
Motivations:
|
||||
|
||||
- The source for the open and close/early close calendar and the trading day
|
||||
calendar is now the same, which should help prevent potential issues due to
|
||||
misalignment.
|
||||
- Allows configurations where the benchmark is provided as a generator based
|
||||
data source to need to supply a second benchmark list just to populate dates.
|
||||
|
||||
### Port `history()` API method from Quantopian.
|
||||
|
||||
Opens the core of the `history()` function that was previously only available on
|
||||
the Quantopian platform.
|
||||
|
||||
The history method is analoguous to the `batch_transform` function/decorator,
|
||||
but with a hopefully more precise specification of the frequency and period of
|
||||
the previous bar data that is captured.
|
||||
|
||||
Example usage:
|
||||
|
||||
from zipline.api import history, add_history
|
||||
|
||||
def initialize(context):
|
||||
add_history(bar_count=2, frequency='1d', field='price')
|
||||
|
||||
def handle_data(context, data):
|
||||
prices = history(bar_count=2, frequency='1d', field='price')
|
||||
context.last_prices = prices
|
||||
|
||||
N.B. this version of history lacks the backfilling capability that allows the
|
||||
return a full DataFrame on the first bar.
|
||||
|
||||
## Bug Fixes (BUG)
|
||||
|
||||
### Adjust benchmark events to match market hours (#241)
|
||||
|
||||
Previously benchmark events were emitted at 0:00 on the day the
|
||||
benchmark related to: in 'minute' emission mode this meant that
|
||||
the benchmarks were emitted before any intra-day trades were
|
||||
processed.
|
||||
|
||||
### Ensure perf stats are generated for all days
|
||||
|
||||
When running with minutely emissions the simulator would report to the
|
||||
user that it simulated 'n - 1' days (where n is the number of days
|
||||
specified in the simulation params). Now the correct number of trading
|
||||
days are reported as being simulated.
|
||||
|
||||
### Fix repr for cumulative risk metrics.
|
||||
|
||||
The `__repr__` for RiskMetricsCumulative was referring to an older structure of
|
||||
the class, causing an exception when printed.
|
||||
|
||||
Also, now prints the last values in the metrics DataFrame.
|
||||
|
||||
### Prevent minute emission from crashing at end of available data.
|
||||
|
||||
The next day calculation was causing an error when a minute emission algorithm
|
||||
reached the end of available data.
|
||||
|
||||
Instead of a generic exception when available data is reached, raise and catch a
|
||||
named exception so that the tradesimulation loop can skip over, since the next
|
||||
market close is not needed at the end.
|
||||
|
||||
### Fix pandas indexing in trading calendar.
|
||||
|
||||
This could alternatively be filed under PERF. Index using loc instead of the
|
||||
inefficient index-ing of day, then time.
|
||||
|
||||
### Prevent crash in vwap transform due to non-existent member.
|
||||
|
||||
The WrongDataForTransform was referencing a `self.fields` member,
|
||||
which did not exist.
|
||||
|
||||
Add a self.fields member set to `price` and `volume` and use
|
||||
it to iterate over during the check.
|
||||
|
||||
### Fix max drawdown calculation.
|
||||
|
||||
The input into max drawdown was incorrect, causing the bad results. i.e. the
|
||||
`compounded_log_returns` were not values representative of the algorithms total
|
||||
return at a given time, though `calculate_max_drawdown` was treating the values
|
||||
as if they were. Instead, the `algorithm_period_returns` series is now used,
|
||||
which does provide the total return.
|
||||
|
||||
### Fix cost basis calculation.
|
||||
|
||||
Cost basis calculation now takes direction of txn into account.
|
||||
|
||||
Closing a long position or covering a short shouldn't affect the cost basis.
|
||||
|
||||
### Fix floating point error in order()
|
||||
|
||||
Where order amounts that were near an integer could accidentally be floored or
|
||||
ceilinged (depending on being postive or negative) to the wrong integer.
|
||||
|
||||
e.g. an amount stored internally as -27.99999 was converted to -27 instead of
|
||||
-28.
|
||||
|
||||
### Update perf period state when positions are changed by splits
|
||||
|
||||
Otherwise, `self._position_amounts` will be out of sync with position.amount,
|
||||
etc.
|
||||
|
||||
### Fix misalignment of downside series calc when using exact dates.
|
||||
|
||||
An oddity that was exposed while working on making the return series passed to
|
||||
the risk module more exact, the series comparison between the returns and mean
|
||||
returns was unbalanced, because the mean returns were not masked down to the
|
||||
downside data points; however, in most, if not all cases this was papered over
|
||||
by the call to `.valid()` which was removed in this change set.
|
||||
|
||||
### Check that self.logger exists before using it.
|
||||
|
||||
`self.logger` is initialized as `None` and there is no guarantee that users have
|
||||
set it, so check that it exists before trying to pass messages to it.
|
||||
|
||||
### Prevent out of sync market closes in performance tracker.
|
||||
|
||||
In situations where the performance tracker has been reset or patched to handle
|
||||
state juggling with warming up live data, the `market_close` member of the
|
||||
performance tracker could end up out of sync with the current algo time as
|
||||
determined by the
|
||||
|
||||
The symptom was dividends never triggering, because the end of day checks would
|
||||
not match the current time.
|
||||
|
||||
Fix by having the tradesimulation loop be responsible, in minute/minute mode,
|
||||
for advancing the market close and passing that value to the performance
|
||||
tracker, instead of having the market close advanced by the performance tracker
|
||||
as well.
|
||||
|
||||
### Fix numerous cumulative and period risk calculations.
|
||||
|
||||
The calculations that are expected to change are:
|
||||
- cumulative.beta
|
||||
- cumulative.alpha
|
||||
- cumulative.information
|
||||
- cumulative.sharpe
|
||||
- period.sortino
|
||||
|
||||
#### How Risk Calculations Are Changing
|
||||
|
||||
##### Risk Fixes for Both Period and Cumulative
|
||||
|
||||
###### Downside Risk
|
||||
|
||||
Use sample instead of population for standard deviation.
|
||||
|
||||
Add a rounding factor, so that if the two values are close for a given dt, that
|
||||
they do not count as a downside value, which would throw off the denominator of
|
||||
the standard deviation of the downside diffs.
|
||||
|
||||
###### Standard Deviation Type
|
||||
|
||||
|
||||
Across the board the standard deviation has been standardized to using a
|
||||
'sample' calculation, whereas before cumulative risk was mostly using
|
||||
'population'. Using `ddof=1` with `np.std` calculates as if the values are a
|
||||
sample.
|
||||
|
||||
##### Cumulative Risk Fixes
|
||||
|
||||
###### Beta
|
||||
|
||||
Use the daily algorithm returns and benchmarks instead of annualized mean
|
||||
returns.
|
||||
|
||||
###### Volatility
|
||||
|
||||
Use sample instead of population with standard deviation.
|
||||
|
||||
The volatility is an input to other calculations so this change affects Sharpe
|
||||
and Information ratio calculations.
|
||||
|
||||
###### Information Ratio
|
||||
|
||||
The benchmark returns input is changed from annualized benchmark returns to the
|
||||
annualized mean returns.
|
||||
|
||||
###### Alpha
|
||||
|
||||
The benchmark returns input is changed from annualized benchmark returns to the
|
||||
annualized mean returns.
|
||||
|
||||
##### Period Risk Fixes
|
||||
|
||||
###### Sortino
|
||||
|
||||
Now uses the downside risk of the daily return vs. the mean algorithm returns
|
||||
for the minimum acceptable return instead of the treasury return.
|
||||
|
||||
The above required adding the calculation of the mean algorithm returns for
|
||||
period risk.
|
||||
|
||||
Also, uses `algorithm_period_returns` and `tresaury_period_return` as the
|
||||
cumulative Sortino does, instead of using algorithm returns for both inputs into
|
||||
the Sortino calculation.
|
||||
|
||||
## Performance (PERF)
|
||||
|
||||
### Removed `alias_dt` transform in favor of property on SIDData.
|
||||
|
||||
Adding a copy of the Event's dt field as datetime via the `alias_dt` generator,
|
||||
so that the API was forgiving and allowed both datetime and dt on a SIDData
|
||||
object, was creating noticeable overhead, even on an noop algorithms.
|
||||
|
||||
Instead of incurring the cost of copying the datetime value and assigning it
|
||||
to the Event object on every event that is passed through the system, add a
|
||||
property to SIDData which acts as an alias `datetime` to `dt`.
|
||||
|
||||
Eventually support for `data['foo'].datetime` may be removed, and could be
|
||||
considered deprecated.
|
||||
|
||||
### Remove the drop of 'null return' from cumulative returns.
|
||||
|
||||
The check of existence of the null return key, and the drop of said return
|
||||
on every single bar was adding unneeded CPU time when an algorithm was run
|
||||
with minute emissions.
|
||||
|
||||
Instead, add the 0.0 return with an index of the trading day before the
|
||||
start date.
|
||||
|
||||
The removal of the `null return` was mainly in place so that the period
|
||||
calculation was not crashing on a non-date index value; with the index as a
|
||||
date, the period return can also approximate volatility (even though the
|
||||
that volatility has high noise-to-signal strength because it uses only two
|
||||
values as an input.)
|
||||
|
||||
## Maintenance and Refactorings (MAINT)
|
||||
|
||||
### Allow `sim_params` to provide data frequency for the algorithm.
|
||||
|
||||
In the case that `data_frequency` of the algorithm is None, allow the
|
||||
`sim_params` to provide the `data_frequency`.
|
||||
|
||||
Also, defer to the algorithms data frequency, if provided.
|
||||
|
||||
## Build (BLD)
|
||||
|
||||
### Added support for building and releasing via conda
|
||||
|
||||
For those who prefer building with <http://conda.pydata.org/> to compiling
|
||||
locally with pip.
|
||||
|
||||
The following should install Zipline on many systems.
|
||||
|
||||
conda install -c quantopian zipline
|
||||
|
||||
# Contributors
|
||||
|
||||
- Eddie Hebert \<ehebert@quantopian.com\>, @ehebert, 49
|
||||
- Thomas Wiecki \<thomas.wiecki@gmail.com\>, @twiecki, 28
|
||||
- Richard Frank \<rich@quantopian.com\>, @richafrank, 11
|
||||
- Jamie Kirkpatrick \<jkp@spotify.com\>, @jkp, 2
|
||||
- Jeremiah Lowin \<jlowin@gmail.com\>, @jlowin, 2
|
||||
- Colin Alexander \<colin.1.alexander@gmail.com\>, @colin1alexander, 1
|
||||
- Michael Schatzow \<michael.schatzow@gmail.com\>, @MichaelWS, 1
|
||||
- Moises Trovo \<moises.trovo@gmail.com\>, @mtrovo, 1
|
||||
- Suminda Dharmasena \<sirinath1978m@gmail.com\>, @sirinath, 1
|
||||
@@ -1,189 +0,0 @@
|
||||
# Zipline 0.7.0 Release Notes
|
||||
|
||||
**Highlights**
|
||||
|
||||
* Command line interface to run algorithms directly.
|
||||
|
||||
* IPython Magic %%zipline that runs algorithm defined in an IPython
|
||||
notebook cell.
|
||||
|
||||
* API methods for building safeguards against runaway ordering and undesired
|
||||
short positions.
|
||||
|
||||
* New history() function to get a moving DataFrame of past market data
|
||||
(replaces BatchTransform).
|
||||
|
||||
* A new [beginner tutorial](http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb).
|
||||
|
||||
|
||||
## Enhancements (ENH)
|
||||
|
||||
* CLI: Adds a CLI and IPython magic for zipline. [PR325](https://github.com/quantopian/zipline/pull/325)
|
||||
|
||||
> Example:
|
||||
|
||||
> ```
|
||||
> python run_algo.py -f dual_moving_avg.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
|
||||
> ```
|
||||
|
||||
> Grabs the data from yahoo finance, runs the file
|
||||
dual_moving_avg.py (and looks for `dual_moving_avg_analyze.py`
|
||||
which, if found, will be executed after the algorithm has been run),
|
||||
and outputs the perf `DataFrame` to `dma.pickle`.
|
||||
|
||||
* IPython magic command (at the top of an IPython notebook cell). [PR325](https://github.com/quantopian/zipline/pull/325)
|
||||
|
||||
> ```
|
||||
> %%zipline --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o perf
|
||||
> ```
|
||||
|
||||
> Does the same as above except instead of executing the file looks
|
||||
> for the algorithm in the cell and instead of outputting the perf df
|
||||
> to a file, creates a variable in the namespace called perf.
|
||||
|
||||
* Adds Trading Controls to the algorithm API. [PR329](https://github.com/quantopian/zipline/pull/329)
|
||||
|
||||
> The following functions are now available on ```TradingAlgorithm``` and for algo scripts:
|
||||
> - `set_max_order_size(self, sid=None, max_shares=None, max_notional=None)`
|
||||
- Set a limit on the absolute magnitude, in shares and/or total
|
||||
dollar value, of any single order placed by this algorithm for a
|
||||
given sid. If `sid` is None, then the rule is applied to any order
|
||||
placed by the algorithm.
|
||||
- Example:
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if we attempt to place an
|
||||
# order which would cause us to hold more than 10 shares
|
||||
# or 1000 dollars worth of sid(24).
|
||||
set_max_order_size(sid(24), max_shares=10, max_notional=1000.0)
|
||||
|
||||
> - `set_max_position_size(self, sid=None, max_shares=None, max_notional=None)`
|
||||
- Set a limit on the absolute magnitude, in either shares or dollar
|
||||
value, of any position held by the algorithm for a given sid. If `sid`
|
||||
is None, then the rule is applied to any position held by the
|
||||
algorithm.
|
||||
- Example:
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if we attempt to order more than
|
||||
# 10 shares or 1000 dollars worth of sid(24) in a single order.
|
||||
set_max_order_size(sid(24), max_shares=10, max_notional=1000.0)
|
||||
|
||||
> - `set_max_order_count(self, max_count)`
|
||||
- Set a limit on the number of orders that can be placed by the
|
||||
algorithm in a single trading day.
|
||||
- Example:
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if more than 50 orders are placed in a day.
|
||||
set_max_order_count(50)
|
||||
|
||||
> - `set_long_only(self)`
|
||||
- Set a rule specifying that the algorithm may not hold short positions.
|
||||
- Example:
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if it attempts to place
|
||||
# an order that would cause it to hold a short position.
|
||||
set_long_only()
|
||||
|
||||
* Adds an `all_api_methods` classmethod on `TradingAlgorithm` that returns a
|
||||
list of all `TradingAlgorithm` API methods. [PR333](https://github.com/quantopian/zipline/pull/333)
|
||||
|
||||
* Expanded record() functionality for dynamic naming. [PR325](https://github.com/quantopian/zipline/pull/355)
|
||||
|
||||
> The record() function can now take positional args before the kwargs.
|
||||
> All original usage and functionality is the same, but now these
|
||||
> extra usages will work:
|
||||
>
|
||||
|
||||
name = 'Dynamically_Generated_String'
|
||||
record( name, value, ... )
|
||||
record( name, value1, 'name2', value2, name3=value3, name4=value4 )
|
||||
|
||||
> The requirements are simply that the poritional args occur only before the
|
||||
> kwargs.
|
||||
|
||||
* history() has been ported from Quantopian to Zipline and provides moving window of market data. [PR345](https://github.com/quantopian/zipline/pull/345) and [PR357](https://github.com/quantopian/zipline/pull/357)
|
||||
|
||||
> history() replaces BatchTransform. It is faster, works for minute level data and has a superior interface.
|
||||
> To use it, call `add_history()` inside of `initialize()` and then receive a pandas `DataFrame` by calling
|
||||
> history() from inside `handle_data()`. Check out the [tutorial](http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb) and an [example](https://github.com/quantopian/zipline/blob/master/zipline/examples/dual_moving_average.py).
|
||||
|
||||
* history() now supports `1m` window lengths [PR345](https://github.com/quantopian/zipline/pull/345)
|
||||
|
||||
## Bug Fixes (BUG)
|
||||
|
||||
* Fix alignment of trading days and open and closes in trading environment.
|
||||
[PR331](https://github.com/quantopian/zipline/pull/331)
|
||||
|
||||
* RollingPanel fix when adding/dropping new fields [PR349](https://github.com/quantopian/zipline/pull/349)
|
||||
|
||||
## Performance (PERF)
|
||||
|
||||
## Maintenance and Refactorings (MAINT)
|
||||
|
||||
* Removed undocumented and untested HDF5 and CSV data sources. [PR267](https://github.com/quantopian/zipline/issues/267)
|
||||
|
||||
* Refactor sim_params [PR352](https://github.com/quantopian/zipline/pull/352)
|
||||
|
||||
* Refactoring of history [PR340](https://github.com/quantopian/zipline/pull/340)
|
||||
|
||||
## Build (BLD)
|
||||
|
||||
* The following dependencies have been updated (zipline might work with other versions too):
|
||||
```diff
|
||||
-pytz==2013.9
|
||||
-numpy==1.8.0
|
||||
+pytz==2014.4
|
||||
+numpy==1.8.1
|
||||
|
||||
+scipy==0.12.0
|
||||
+patsy==0.2.1
|
||||
+statsmodels==0.5.0
|
||||
-six==1.5.2
|
||||
+six==1.6.1
|
||||
|
||||
-Cython==0.20
|
||||
-TA-Lib==0.4.8
|
||||
+Cython==0.20.1
|
||||
+Cython==0.20.1
|
||||
+--allow-external TA-Lib --allow-unverified TA-Lib TA-Lib==0.4.8
|
||||
|
||||
-requests==2.2.0
|
||||
+requests==2.3.0
|
||||
|
||||
-nose==1.3.0
|
||||
+nose==1.3.3
|
||||
-xlrd==0.9.2
|
||||
+xlrd==0.9.3
|
||||
|
||||
-pep8==1.4.6
|
||||
-pyflakes==0.7.3
|
||||
-pip-tools==0.3.4
|
||||
+pep8==1.5.7
|
||||
+pyflakes==0.8.1
|
||||
|
||||
-scipy==0.13.2
|
||||
-tornado==3.2
|
||||
-pyparsing==2.0.1
|
||||
-patsy==0.2.1
|
||||
-statsmodels==0.4.3
|
||||
+tornado==3.2.1
|
||||
+pyparsing==2.0.2
|
||||
|
||||
q-Markdown==2.3.1
|
||||
+Markdown==2.4.1
|
||||
```
|
||||
|
||||
# Contributors
|
||||
38 Scott Sanderson
|
||||
29 Thomas Wiecki
|
||||
26 Eddie Hebert
|
||||
6 Delaney Granizo-Mackenzie
|
||||
3 David Edwards
|
||||
3 Richard Frank
|
||||
2 Jonathan Kamens
|
||||
1 Pankaj Garg
|
||||
1 Tony Lambiris
|
||||
1 fawce
|
||||
@@ -1,118 +0,0 @@
|
||||
# Zipline 0.8.0 Release Notes
|
||||
|
||||
## Highlights
|
||||
|
||||
* New documentation system with a new website at [zipline.io](http://www.zipline.io)
|
||||
* Major performance enhancements.
|
||||
* Dynamic history.
|
||||
|
||||
## Bug Fixes (BUG)
|
||||
|
||||
### Fix a bug where the reported returns could sharply dip for random periods of time. [PR378](https://github.com/quantopian/zipline/pull/378)
|
||||
|
||||
## Enhancements (ENH)
|
||||
|
||||
### Account object: Adds an account object to conext to track information about the trading account. [PR396](https://github.com/quantopian/zipline/pull/396)
|
||||
|
||||
> Example:
|
||||
|
||||
> ```
|
||||
> context.account.settled_cash
|
||||
> ```
|
||||
|
||||
> Returns the settled cash value that is stored on the account object. This
|
||||
> value is updated accordingly as the algorithm is run.
|
||||
|
||||
### HistoryContainer can now grow dynamically. [PR412](https://github.com/quantopian/zipline/pull/412)
|
||||
|
||||
> Calls to `history` will now be able to increase the size or change the shape
|
||||
> of the history container to be able to service the call. `add_history` now
|
||||
> acts as a preformance hint to pre-allocate sufficient space in the
|
||||
> container. This change is backwards compatible with `history`, all existing
|
||||
> algorithms should continue to work as intended.
|
||||
|
||||
### Simple transforms ported from quantopian and use history. [PR429](https://github.com/quantopian/zipline/pull/429)
|
||||
|
||||
> SIDData now has methods for:
|
||||
|
||||
> - `stddev`
|
||||
> - `mavg`
|
||||
> - `vwap`
|
||||
> - `returns`
|
||||
|
||||
> These methods, except for `returns`, accept a number of days. If you are
|
||||
> running with minute data, then this will calculate the number of minutes in
|
||||
> those days, accounting for early closes and the current time and apply the
|
||||
> transform over the set of minutes. `returns` takes no parameters and will
|
||||
> return the daily returns of the given asset.
|
||||
|
||||
> Example:
|
||||
```python
|
||||
data[security].stddev(3)
|
||||
```
|
||||
|
||||
### New fields in Performance Period [PR464](https://github.com/quantopian/zipline/pull/464)
|
||||
|
||||
> Performance Period has new fields accessible in return value of to_dict:
|
||||
|
||||
> - gross leverage
|
||||
> - net leverage
|
||||
> - short exposure
|
||||
> - long exposure
|
||||
> - shorts count
|
||||
> - longs count
|
||||
|
||||
|
||||
### Allow order_percent to work with various market values (by Jeremiah Lowin) [PR477](https://github.com/quantopian/zipline/pull/477)
|
||||
|
||||
> Currently, `order_percent()` and `order_target_percent()` both operate as a percentage of `self.portfolio.portfolio_value`. This PR lets them operate as percentages of other important MVs.
|
||||
|
||||
> Also adds `context.get_market_value()`, which enables this functionality.
|
||||
|
||||
> For example:
|
||||
> ```python
|
||||
# this is how it works today (and this still works)
|
||||
# put 50% of my portfolio in AAPL
|
||||
order_percent('AAPL', 0.5)
|
||||
# note that if this were a fully invested portfolio, it would become 150% levered.
|
||||
>
|
||||
# take half of my available cash and buy AAPL
|
||||
order_percent('AAPL', 0.5, percent_of='cash')
|
||||
>
|
||||
# rebalance my short position, as a percentage of my current short book
|
||||
order_target_percent('MSFT', 0.1, percent_of='shorts')
|
||||
>
|
||||
# rebalance within a custom group of stocks
|
||||
tech_stocks = ('AAPL', 'MSFT', 'GOOGL')
|
||||
tech_filter = lambda p: p.sid in tech_stocks
|
||||
for stock in tech_stocks:
|
||||
order_target_percent(stock, 1/3, percent_of_fn=tech_filter)
|
||||
```
|
||||
|
||||
### Major performance enhancements to history (by Dale Jung) [PR488](https://github.com/quantopian/zipline/commit/38e8d5214d46f089020703712dc6b3f4f6ee084d)
|
||||
|
||||
### Command line option to for printing algo to stdout (by Andrea D'Amore) [PR545](https://github.com/quantopian/zipline/pull/545)
|
||||
|
||||
## Contributors
|
||||
|
||||
The following people have contributed to this release, ordered by numbers of commit:
|
||||
```
|
||||
39 Thomas Wiecki
|
||||
36 Joe Jevnik
|
||||
26 John Fawcett
|
||||
24 Scott Sanderson
|
||||
11 Delaney Granizo-Mackenzie
|
||||
8 John Ricklefs
|
||||
5 Brian Fink
|
||||
5 Eddie Hebert
|
||||
2 Dale Jung
|
||||
2 Jeremiah Lowin
|
||||
2 Jonathan Kamens
|
||||
2 Richard Frank
|
||||
1 David Edwards
|
||||
1 Luke Schiefelbein
|
||||
1 Mete Atamel
|
||||
1 Nicholas Pezolano
|
||||
1 Philipp Kosel
|
||||
1 Andrea D'Amore
|
||||
```
|
||||
@@ -0,0 +1,8 @@
|
||||
Zipline API
|
||||
-----------
|
||||
|
||||
For each of these api functions, the ``self`` argument is implicitly the current
|
||||
:class:`~zipline.algorithm.TradingAlgorithm`
|
||||
|
||||
.. automodule:: zipline.api
|
||||
:members:
|
||||
@@ -0,0 +1,818 @@
|
||||
Zipline beginner tutorial
|
||||
-------------------------
|
||||
|
||||
Basics
|
||||
~~~~~~
|
||||
|
||||
Zipline is an open-source algorithmic trading simulator written in
|
||||
Python.
|
||||
|
||||
The source can be found at: https://github.com/quantopian/zipline
|
||||
|
||||
Some benefits include:
|
||||
|
||||
- Realistic: slippage, transaction costs, order delays.
|
||||
- Stream-based: Process each event individually, avoids look-ahead
|
||||
bias.
|
||||
- Batteries included: Common transforms (moving average) as well as
|
||||
common risk calculations (Sharpe).
|
||||
- Developed and continuously updated by
|
||||
`Quantopian <https://www.quantopian.com>`__ which provides an
|
||||
easy-to-use web-interface to Zipline, 10 years of minute-resolution
|
||||
historical US stock data, and live-trading capabilities. This
|
||||
tutorial is directed at users wishing to use Zipline without using
|
||||
Quantopian. If you instead want to get started on Quantopian, see
|
||||
`here <https://www.quantopian.com/faq#get-started>`__.
|
||||
|
||||
This tutorial assumes that you have zipline correctly installed, see the
|
||||
`installation
|
||||
instructions <https://github.com/quantopian/zipline#installation>`__ if
|
||||
you haven't set up zipline yet.
|
||||
|
||||
Every ``zipline`` algorithm consists of two functions you have to
|
||||
define: \* ``initialize(context)`` \* ``handle_data(context, data)``
|
||||
|
||||
Before the start of the algorithm, ``zipline`` calls the
|
||||
``initialize()`` function and passes in a ``context`` variable.
|
||||
``context`` is a persistent namespace for you to store variables you
|
||||
need to access from one algorithm iteration to the next.
|
||||
|
||||
After the algorithm has been initialized, ``zipline`` calls the
|
||||
``handle_data()`` function once for each event. At every call, it passes
|
||||
the same ``context`` variable and an event-frame called ``data``
|
||||
containing the current trading bar with open, high, low, and close
|
||||
(OHLC) prices as well as volume for each stock in your universe. For
|
||||
more information on these functions, see the `relevant part of the
|
||||
Quantopian docs <https://www.quantopian.com/help#api-toplevel>`__.
|
||||
|
||||
My first algorithm
|
||||
~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Lets take a look at a very simple algorithm from the ``examples``
|
||||
directory, ``buyapple.py``:
|
||||
|
||||
.. code:: python
|
||||
|
||||
!tail ../../zipline/examples/buyapple.py
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
# Load price data from yahoo.
|
||||
data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
|
||||
end=end)
|
||||
|
||||
# Create and run the algorithm.
|
||||
algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data,
|
||||
identifiers=['AAPL'])
|
||||
results = algo.run(data)
|
||||
|
||||
analyze(results=results)
|
||||
|
||||
|
||||
As you can see, we first have to import some functions we would like to
|
||||
use. All functions commonly used in your algorithm can be found in
|
||||
``zipline.api``. Here we are using ``order()`` which takes two arguments
|
||||
-- a security object, and a number specifying how many stocks you would
|
||||
like to order (if negative, ``order()`` will sell/short stocks). In this
|
||||
case we want to order 10 shares of Apple at each iteration. For more
|
||||
documentation on ``order()``, see the `Quantopian
|
||||
docs <https://www.quantopian.com/help#api-order>`__.
|
||||
|
||||
You don't have to use the ``symbol()`` function and could just pass in
|
||||
``AAPL`` directly but it is good practice as this way your code will be
|
||||
Quantopian compatible.
|
||||
|
||||
Finally, the ``record()`` function allows you to save the value of a
|
||||
variable at each iteration. You provide it with a name for the variable
|
||||
together with the variable itself: ``varname=var``. After the algorithm
|
||||
finished running you will have access to each variable value you tracked
|
||||
with ``record()`` under the name you provided (we will see this further
|
||||
below). You also see how we can access the current price data of the
|
||||
AAPL stock in the ``data`` event frame (for more information see
|
||||
`here <https://www.quantopian.com/help#api-event-properties>`__.
|
||||
|
||||
Running the algorithm
|
||||
~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
To now test this algorithm on financial data, ``zipline`` provides two
|
||||
interfaces. A command-line interface and an ``IPython Notebook``
|
||||
interface.
|
||||
|
||||
Command line interface
|
||||
^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
After you installed zipline you should be able to execute the following
|
||||
from your command line (e.g. ``cmd.exe`` on Windows, or the Terminal app
|
||||
on OSX):
|
||||
|
||||
.. code:: python
|
||||
|
||||
!run_algo.py --help
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
usage: run_algo.py [-h] [-c FILE] [--algofile ALGOFILE] [--data-frequency {minute,daily}] [--start START] [--end END]
|
||||
[--capital_base CAPITAL_BASE] [--source {yahoo}] [--source_time_column SOURCE_TIME_COLUMN] [--symbols SYMBOLS]
|
||||
[--output OUTPUT] [--metadata_path METADATA_PATH] [--metadata_index METADATA_INDEX] [--print-algo] [--no-print-algo]
|
||||
|
||||
Zipline version 0.8.0rc1.
|
||||
|
||||
optional arguments:
|
||||
-h, --help show this help message and exit
|
||||
-c FILE, --conf_file FILE
|
||||
Specify config file
|
||||
--algofile ALGOFILE, -f ALGOFILE
|
||||
--data-frequency {minute,daily}
|
||||
--start START, -s START
|
||||
--end END, -e END
|
||||
--capital_base CAPITAL_BASE
|
||||
--source {yahoo}, -d {yahoo}
|
||||
--source_time_column SOURCE_TIME_COLUMN, -t SOURCE_TIME_COLUMN
|
||||
--symbols SYMBOLS
|
||||
--output OUTPUT, -o OUTPUT
|
||||
--metadata_path METADATA_PATH, -m METADATA_PATH
|
||||
--metadata_index METADATA_INDEX, -x METADATA_INDEX
|
||||
--print-algo, -p
|
||||
--no-print-algo, -q
|
||||
|
||||
|
||||
Note that you have to omit the preceding '!' when you call
|
||||
``run_algo.py``, this is only required by the IPython Notebook in which
|
||||
this tutorial was written.
|
||||
|
||||
As you can see there are a couple of flags that specify where to find
|
||||
your algorithm (``-f``) as well as parameters specifying which stock
|
||||
data to load from Yahoo! finance (``--symbols``) and the time-range
|
||||
(``--start`` and ``--end``). Finally, you'll want to save the
|
||||
performance metrics of your algorithm so that you can analyze how it
|
||||
performed. This is done via the ``--output`` flag and will cause it to
|
||||
write the performance ``DataFrame`` in the pickle Python file format.
|
||||
Note that you can also define a configuration file with these parameters
|
||||
that you can then conveniently pass to the ``-c`` option so that you
|
||||
don't have to supply the command line args all the time (see the .conf
|
||||
files in the examples directory).
|
||||
|
||||
Thus, to execute our algorithm from above and save the results to
|
||||
``buyapple_out.pickle`` we would call ``run_algo.py`` as follows:
|
||||
|
||||
.. code:: python
|
||||
|
||||
!run_algo.py -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o buyapple_out.pickle
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
AAPL
|
||||
[2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.
|
||||
[2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00
|
||||
[2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 21:00:00+00:00
|
||||
|
||||
|
||||
``run_algo.py`` first outputs the algorithm contents. It then fetches
|
||||
historical price and volume data of Apple from Yahoo! finance in the
|
||||
desired time range, calls the ``initialize()`` function, and then
|
||||
streams the historical stock price day-by-day through ``handle_data()``.
|
||||
After each call to ``handle_data()`` we instruct ``zipline`` to order 10
|
||||
stocks of AAPL. After the call of the ``order()`` function, ``zipline``
|
||||
enters the ordered stock and amount in the order book. After the
|
||||
``handle_data()`` function has finished, ``zipline`` looks for any open
|
||||
orders and tries to fill them. If the trading volume is high enough for
|
||||
this stock, the order is executed after adding the commission and
|
||||
applying the slippage model which models the influence of your order on
|
||||
the stock price, so your algorithm will be charged more than just the
|
||||
stock price \* 10. (Note, that you can also change the commission and
|
||||
slippage model that ``zipline`` uses, see the `Quantopian
|
||||
docs <https://www.quantopian.com/help#ide-slippage>`__ for more
|
||||
information).
|
||||
|
||||
Note that there is also an ``analyze()`` function printed.
|
||||
``run_algo.py`` will try and look for a file with the ending with
|
||||
``_analyze.py`` and the same name of the algorithm (so
|
||||
``buyapple_analyze.py``) or an ``analyze()`` function directly in the
|
||||
script. If an ``analyze()`` function is found it will be called *after*
|
||||
the simulation has finished and passed in the performance ``DataFrame``.
|
||||
(The reason for allowing specification of an ``analyze()`` function in a
|
||||
separate file is that this way ``buyapple.py`` remains a valid
|
||||
Quantopian algorithm that you can copy&paste to the platform).
|
||||
|
||||
Lets take a quick look at the performance ``DataFrame``. For this, we
|
||||
use ``pandas`` from inside the IPython Notebook and print the first ten
|
||||
rows. Note that ``zipline`` makes heavy usage of ``pandas``, especially
|
||||
for data input and outputting so it's worth spending some time to learn
|
||||
it.
|
||||
|
||||
.. code:: python
|
||||
|
||||
import pandas as pd
|
||||
perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame
|
||||
perf.head()
|
||||
|
||||
|
||||
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div style="max-height:1000px;max-width:1500px;overflow:auto;">
|
||||
<table border="1" class="dataframe">
|
||||
<thead>
|
||||
<tr style="text-align: right;">
|
||||
<th></th>
|
||||
<th>AAPL</th>
|
||||
<th>algo_volatility</th>
|
||||
<th>algorithm_period_return</th>
|
||||
<th>alpha</th>
|
||||
<th>benchmark_period_return</th>
|
||||
<th>benchmark_volatility</th>
|
||||
<th>beta</th>
|
||||
<th>capital_used</th>
|
||||
<th>ending_cash</th>
|
||||
<th>ending_exposure</th>
|
||||
<th>...</th>
|
||||
<th>short_exposure</th>
|
||||
<th>short_value</th>
|
||||
<th>shorts_count</th>
|
||||
<th>sortino</th>
|
||||
<th>starting_cash</th>
|
||||
<th>starting_exposure</th>
|
||||
<th>starting_value</th>
|
||||
<th>trading_days</th>
|
||||
<th>transactions</th>
|
||||
<th>treasury_period_return</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<th>2000-01-03 21:00:00</th>
|
||||
<td>3.738314</td>
|
||||
<td>0.000000e+00</td>
|
||||
<td>0.000000e+00</td>
|
||||
<td>-0.065800</td>
|
||||
<td>-0.009549</td>
|
||||
<td>0.000000</td>
|
||||
<td>0.000000</td>
|
||||
<td>0.00000</td>
|
||||
<td>10000000.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0.000000</td>
|
||||
<td>10000000.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>1</td>
|
||||
<td>[]</td>
|
||||
<td>0.0658</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-04 21:00:00</th>
|
||||
<td>3.423135</td>
|
||||
<td>3.367492e-07</td>
|
||||
<td>-3.000000e-08</td>
|
||||
<td>-0.064897</td>
|
||||
<td>-0.047528</td>
|
||||
<td>0.323229</td>
|
||||
<td>0.000001</td>
|
||||
<td>-34.53135</td>
|
||||
<td>9999965.46865</td>
|
||||
<td>34.23135</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0.000000</td>
|
||||
<td>10000000.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>2</td>
|
||||
<td>[{u'order_id': u'513357725cb64a539e3dd02b47da7...</td>
|
||||
<td>0.0649</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-05 21:00:00</th>
|
||||
<td>3.473229</td>
|
||||
<td>4.001918e-07</td>
|
||||
<td>-9.906000e-09</td>
|
||||
<td>-0.066196</td>
|
||||
<td>-0.045697</td>
|
||||
<td>0.329321</td>
|
||||
<td>0.000001</td>
|
||||
<td>-35.03229</td>
|
||||
<td>9999930.43636</td>
|
||||
<td>69.46458</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0.000000</td>
|
||||
<td>9999965.46865</td>
|
||||
<td>34.23135</td>
|
||||
<td>34.23135</td>
|
||||
<td>3</td>
|
||||
<td>[{u'order_id': u'd7d4ad03cfec4d578c0d817dc3829...</td>
|
||||
<td>0.0662</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-06 21:00:00</th>
|
||||
<td>3.172661</td>
|
||||
<td>4.993979e-06</td>
|
||||
<td>-6.410420e-07</td>
|
||||
<td>-0.065758</td>
|
||||
<td>-0.044785</td>
|
||||
<td>0.298325</td>
|
||||
<td>-0.000006</td>
|
||||
<td>-32.02661</td>
|
||||
<td>9999898.40975</td>
|
||||
<td>95.17983</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>-12731.780516</td>
|
||||
<td>9999930.43636</td>
|
||||
<td>69.46458</td>
|
||||
<td>69.46458</td>
|
||||
<td>4</td>
|
||||
<td>[{u'order_id': u'1fbf5e9bfd7c4d9cb2e8383e1085e...</td>
|
||||
<td>0.0657</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-07 21:00:00</th>
|
||||
<td>3.322945</td>
|
||||
<td>5.977002e-06</td>
|
||||
<td>-2.201900e-07</td>
|
||||
<td>-0.065206</td>
|
||||
<td>-0.018908</td>
|
||||
<td>0.375301</td>
|
||||
<td>0.000005</td>
|
||||
<td>-33.52945</td>
|
||||
<td>9999864.88030</td>
|
||||
<td>132.91780</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>-12629.274583</td>
|
||||
<td>9999898.40975</td>
|
||||
<td>95.17983</td>
|
||||
<td>95.17983</td>
|
||||
<td>5</td>
|
||||
<td>[{u'order_id': u'9ea6b142ff09466b9113331a37437...</td>
|
||||
<td>0.0652</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>5 rows × 39 columns</p>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
As you can see, there is a row for each trading day, starting on the
|
||||
first business day of 2000. In the columns you can find various
|
||||
information about the state of your algorithm. The very first column
|
||||
``AAPL`` was placed there by the ``record()`` function mentioned earlier
|
||||
and allows us to plot the price of apple. For example, we could easily
|
||||
examine now how our portfolio value changed over time compared to the
|
||||
AAPL stock price.
|
||||
|
||||
.. code:: python
|
||||
|
||||
%pylab inline
|
||||
figsize(12, 12)
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
ax1 = plt.subplot(211)
|
||||
perf.portfolio_value.plot(ax=ax1)
|
||||
ax1.set_ylabel('portfolio value')
|
||||
ax2 = plt.subplot(212, sharex=ax1)
|
||||
perf.AAPL.plot(ax=ax2)
|
||||
ax2.set_ylabel('AAPL stock price')
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
Populating the interactive namespace from numpy and matplotlib
|
||||
|
||||
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
<matplotlib.text.Text at 0x7ff5c6147f90>
|
||||
|
||||
|
||||
|
||||
|
||||
.. image:: tutorial_files/tutorial_11_2.png
|
||||
|
||||
|
||||
As you can see, our algorithm performance as assessed by the
|
||||
``portfolio_value`` closely matches that of the AAPL stock price. This
|
||||
is not surprising as our algorithm only bought AAPL every chance it got.
|
||||
|
||||
IPython Notebook
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
The `IPython Notebook <http://ipython.org/notebook.html>`__ is a very
|
||||
powerful browser-based interface to a Python interpreter (this tutorial
|
||||
was written in it). As it is already the de-facto interface for most
|
||||
quantitative researchers ``zipline`` provides an easy way to run your
|
||||
algorithm inside the Notebook without requiring you to use the CLI.
|
||||
|
||||
To use it you have to write your algorithm in a cell and let ``zipline``
|
||||
know that it is supposed to run this algorithm. This is done via the
|
||||
``%%zipline`` IPython magic command that is available after you
|
||||
``import zipline`` from within the IPython Notebook. This magic takes
|
||||
the same arguments as the command line interface described above. Thus
|
||||
to run the algorithm from above with the same parameters we just have to
|
||||
execute the following cell after importing ``zipline`` to register the
|
||||
magic.
|
||||
|
||||
.. code:: python
|
||||
|
||||
import zipline
|
||||
|
||||
.. code:: python
|
||||
|
||||
%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o perf_ipython
|
||||
|
||||
from zipline.api import symbol, order, record
|
||||
|
||||
def initialize(context):
|
||||
pass
|
||||
|
||||
def handle_data(context, data):
|
||||
order(symbol('AAPL'), 10)
|
||||
record(AAPL=data[symbol('AAPL')].price)
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
AAPL
|
||||
|
||||
|
||||
Note that we did not have to specify an input file as above since the
|
||||
magic will use the contents of the cell and look for your algorithm
|
||||
functions there. Also, instead of defining an output file we are
|
||||
specifying a variable name with ``-o`` that will be created in the name
|
||||
space and contain the performance ``DataFrame`` we looked at above.
|
||||
|
||||
.. code:: python
|
||||
|
||||
perf_ipython.head()
|
||||
|
||||
|
||||
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<div style="max-height:1000px;max-width:1500px;overflow:auto;">
|
||||
<table border="1" class="dataframe">
|
||||
<thead>
|
||||
<tr style="text-align: right;">
|
||||
<th></th>
|
||||
<th>AAPL</th>
|
||||
<th>algo_volatility</th>
|
||||
<th>algorithm_period_return</th>
|
||||
<th>alpha</th>
|
||||
<th>benchmark_period_return</th>
|
||||
<th>benchmark_volatility</th>
|
||||
<th>beta</th>
|
||||
<th>capital_used</th>
|
||||
<th>ending_cash</th>
|
||||
<th>ending_exposure</th>
|
||||
<th>...</th>
|
||||
<th>short_exposure</th>
|
||||
<th>short_value</th>
|
||||
<th>shorts_count</th>
|
||||
<th>sortino</th>
|
||||
<th>starting_cash</th>
|
||||
<th>starting_exposure</th>
|
||||
<th>starting_value</th>
|
||||
<th>trading_days</th>
|
||||
<th>transactions</th>
|
||||
<th>treasury_period_return</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<th>2000-01-03 21:00:00</th>
|
||||
<td>3.738314</td>
|
||||
<td>0.000000e+00</td>
|
||||
<td>0.000000e+00</td>
|
||||
<td>-0.065800</td>
|
||||
<td>-0.009549</td>
|
||||
<td>0.000000</td>
|
||||
<td>0.000000</td>
|
||||
<td>0.00000</td>
|
||||
<td>10000000.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0.000000</td>
|
||||
<td>10000000.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>1</td>
|
||||
<td>[]</td>
|
||||
<td>0.0658</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-04 21:00:00</th>
|
||||
<td>3.423135</td>
|
||||
<td>3.367492e-07</td>
|
||||
<td>-3.000000e-08</td>
|
||||
<td>-0.064897</td>
|
||||
<td>-0.047528</td>
|
||||
<td>0.323229</td>
|
||||
<td>0.000001</td>
|
||||
<td>-34.53135</td>
|
||||
<td>9999965.46865</td>
|
||||
<td>34.23135</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0.000000</td>
|
||||
<td>10000000.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>0.00000</td>
|
||||
<td>2</td>
|
||||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||||
<td>0.0649</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-05 21:00:00</th>
|
||||
<td>3.473229</td>
|
||||
<td>4.001918e-07</td>
|
||||
<td>-9.906000e-09</td>
|
||||
<td>-0.066196</td>
|
||||
<td>-0.045697</td>
|
||||
<td>0.329321</td>
|
||||
<td>0.000001</td>
|
||||
<td>-35.03229</td>
|
||||
<td>9999930.43636</td>
|
||||
<td>69.46458</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0.000000</td>
|
||||
<td>9999965.46865</td>
|
||||
<td>34.23135</td>
|
||||
<td>34.23135</td>
|
||||
<td>3</td>
|
||||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||||
<td>0.0662</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-06 21:00:00</th>
|
||||
<td>3.172661</td>
|
||||
<td>4.993979e-06</td>
|
||||
<td>-6.410420e-07</td>
|
||||
<td>-0.065758</td>
|
||||
<td>-0.044785</td>
|
||||
<td>0.298325</td>
|
||||
<td>-0.000006</td>
|
||||
<td>-32.02661</td>
|
||||
<td>9999898.40975</td>
|
||||
<td>95.17983</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>-12731.780516</td>
|
||||
<td>9999930.43636</td>
|
||||
<td>69.46458</td>
|
||||
<td>69.46458</td>
|
||||
<td>4</td>
|
||||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||||
<td>0.0657</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>2000-01-07 21:00:00</th>
|
||||
<td>3.322945</td>
|
||||
<td>5.977002e-06</td>
|
||||
<td>-2.201900e-07</td>
|
||||
<td>-0.065206</td>
|
||||
<td>-0.018908</td>
|
||||
<td>0.375301</td>
|
||||
<td>0.000005</td>
|
||||
<td>-33.52945</td>
|
||||
<td>9999864.88030</td>
|
||||
<td>132.91780</td>
|
||||
<td>...</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>0</td>
|
||||
<td>-12629.274583</td>
|
||||
<td>9999898.40975</td>
|
||||
<td>95.17983</td>
|
||||
<td>95.17983</td>
|
||||
<td>5</td>
|
||||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||||
<td>0.0652</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>5 rows × 39 columns</p>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
Manual (advanced)
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
If you are happy with either way above you can safely skip this passage.
|
||||
To provide a closer look at how ``zipline`` actually works it is
|
||||
instructive to see how we run an algorithm without any of the interfaces
|
||||
demonstrated above which hide the actual ``zipline`` API.
|
||||
|
||||
.. code:: python
|
||||
|
||||
import pytz
|
||||
from datetime import datetime
|
||||
|
||||
from zipline.algorithm import TradingAlgorithm
|
||||
from zipline.utils.factory import load_bars_from_yahoo
|
||||
|
||||
# Load data manually from Yahoo! finance
|
||||
start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
end = datetime(2012, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
data = load_bars_from_yahoo(stocks=['AAPL'], start=start,
|
||||
end=end)
|
||||
|
||||
# Define algorithm
|
||||
def initialize(context):
|
||||
pass
|
||||
|
||||
def handle_data(context, data):
|
||||
order(symbol('AAPL'), 10)
|
||||
record(AAPL=data[symbol('AAPL')].price)
|
||||
|
||||
# Create algorithm object passing in initialize and
|
||||
# handle_data functions
|
||||
algo_obj = TradingAlgorithm(initialize=initialize,
|
||||
handle_data=handle_data)
|
||||
|
||||
# Run algorithm
|
||||
perf_manual = algo_obj.run(data)
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
AAPL
|
||||
|
||||
|
||||
As you can see, we again define the functions as above but we manually
|
||||
pass them to the ``TradingAlgorithm`` class which is the main
|
||||
``zipline`` class for running algorithms. We also manually load the data
|
||||
using ``load_bars_from_yahoo()`` and pass it to the
|
||||
``TradingAlgorithm.run()`` method which kicks off the backtest
|
||||
simulation.
|
||||
|
||||
Access to previous prices using ``history``
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Working example: Dual Moving Average Cross-Over
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
The Dual Moving Average (DMA) is a classic momentum strategy. It's
|
||||
probably not used by any serious trader anymore but is still very
|
||||
instructive. The basic idea is that we compute two rolling or moving
|
||||
averages (mavg) -- one with a longer window that is supposed to capture
|
||||
long-term trends and one shorter window that is supposed to capture
|
||||
short-term trends. Once the short-mavg crosses the long-mavg from below
|
||||
we assume that the stock price has upwards momentum and long the stock.
|
||||
If the short-mavg crosses from above we exit the positions as we assume
|
||||
the stock to go down further.
|
||||
|
||||
As we need to have access to previous prices to implement this strategy
|
||||
we need a new concept: History
|
||||
|
||||
``history()`` is a convenience function that keeps a rolling window of
|
||||
data for you. The first argument is the number of bars you want to
|
||||
collect, the second argument is the unit (either ``'1d'`` for ``'1m'``
|
||||
but note that you need to have minute-level data for using ``1m``). For
|
||||
a more detailed description ``history()``'s features, see the
|
||||
`Quantopian docs <https://www.quantopian.com/help#ide-history>`__. While
|
||||
you can directly use the ``history()`` function on Quantopian, in
|
||||
``zipline`` you have to register each history container you want to use
|
||||
with ``add_history()`` and pass it the same arguments as the history
|
||||
function below. Lets look at the strategy which should make this clear:
|
||||
|
||||
.. code:: python
|
||||
|
||||
%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o perf_dma
|
||||
|
||||
|
||||
from zipline.api import order_target, record, symbol, history, add_history
|
||||
import numpy as np
|
||||
|
||||
def initialize(context):
|
||||
# Register 2 histories that track daily prices,
|
||||
# one with a 100 window and one with a 300 day window
|
||||
add_history(100, '1d', 'price')
|
||||
add_history(300, '1d', 'price')
|
||||
|
||||
context.i = 0
|
||||
|
||||
|
||||
def handle_data(context, data):
|
||||
# Skip first 300 days to get full windows
|
||||
context.i += 1
|
||||
if context.i < 300:
|
||||
return
|
||||
|
||||
# Compute averages
|
||||
# history() has to be called with the same params
|
||||
# from above and returns a pandas dataframe.
|
||||
short_mavg = history(100, '1d', 'price').mean()
|
||||
long_mavg = history(300, '1d', 'price').mean()
|
||||
|
||||
# Trading logic
|
||||
if short_mavg[0] > long_mavg[0]:
|
||||
# order_target orders as many shares as needed to
|
||||
# achieve the desired number of shares.
|
||||
order_target(symbol('AAPL'), 100)
|
||||
elif short_mavg[0] < long_mavg[0]:
|
||||
order_target(symbol('AAPL'), 0)
|
||||
|
||||
# Save values for later inspection
|
||||
record(AAPL=data[symbol('AAPL')].price,
|
||||
short_mavg=short_mavg[0],
|
||||
long_mavg=long_mavg[0])
|
||||
|
||||
|
||||
def analyze(context, perf):
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(211)
|
||||
perf.portfolio_value.plot(ax=ax1)
|
||||
ax1.set_ylabel('portfolio value in $')
|
||||
|
||||
ax2 = fig.add_subplot(212)
|
||||
perf['AAPL'].plot(ax=ax2)
|
||||
perf[['short_mavg', 'long_mavg']].plot(ax=ax2)
|
||||
|
||||
perf_trans = perf.ix[[t != [] for t in perf.transactions]]
|
||||
buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]
|
||||
sells = perf_trans.ix[
|
||||
[t[0]['amount'] < 0 for t in perf_trans.transactions]]
|
||||
ax2.plot(buys.index, perf.short_mavg.ix[buys.index],
|
||||
'^', markersize=10, color='m')
|
||||
ax2.plot(sells.index, perf.short_mavg.ix[sells.index],
|
||||
'v', markersize=10, color='k')
|
||||
ax2.set_ylabel('price in $')
|
||||
plt.legend(loc=0)
|
||||
plt.show()
|
||||
|
||||
|
||||
.. parsed-literal::
|
||||
|
||||
AAPL
|
||||
|
||||
|
||||
|
||||
.. image:: tutorial_files/tutorial_22_1.png
|
||||
|
||||
|
||||
Here we are explicitly defining an ``analyze()`` function that gets
|
||||
automatically called once the backtest is done (this is not possible on
|
||||
Quantopian currently).
|
||||
|
||||
Although it might not be directly apparent, the power of ``history()``
|
||||
(pun intended) can not be under-estimated as most algorithms make use of
|
||||
prior market developments in one form or another. You could easily
|
||||
devise a strategy that trains a classifier with
|
||||
`scikit-learn <http://scikit-learn.org/stable/>`__ which tries to
|
||||
predict future market movements based on past prices (note, that most of
|
||||
the ``scikit-learn`` functions require ``numpy.ndarray``\ s rather than
|
||||
``pandas.DataFrame``\ s, so you can simply pass the underlying
|
||||
``ndarray`` of a ``DataFrame`` via ``.values``).
|
||||
|
||||
We also used the ``order_target()`` function above. This and other
|
||||
functions like it can make order management and portfolio rebalancing
|
||||
much easier. See the `Quantopian documentation on order
|
||||
functions <https://www.quantopian.com/help#api-order-methods>`__ fore
|
||||
more details.
|
||||
|
||||
Conclusions
|
||||
~~~~~~~~~~~
|
||||
|
||||
We hope that this tutorial gave you a little insight into the
|
||||
architecture, API, and features of ``zipline``. For next steps, check
|
||||
out some of the
|
||||
`examples <https://github.com/quantopian/zipline/tree/master/zipline/examples>`__.
|
||||
|
||||
Feel free to ask questions on `our mailing
|
||||
list <https://groups.google.com/forum/#!forum/zipline>`__, report
|
||||
problems on our `GitHub issue
|
||||
tracker <https://github.com/quantopian/zipline/issues?state=open>`__,
|
||||
`get
|
||||
involved <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__,
|
||||
and `checkout Quantopian <https://quantopian.com>`__.
|
||||
@@ -0,0 +1,91 @@
|
||||
import sys
|
||||
import os
|
||||
|
||||
from zipline import __version__ as version
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
sys.path.insert(0, os.path.abspath('.'))
|
||||
sys.path.insert(0, os.path.abspath('..'))
|
||||
|
||||
extensions = [
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.intersphinx',
|
||||
'sphinx.ext.doctest',
|
||||
'sphinx.ext.extlinks',
|
||||
'sphinx.ext.autosummary',
|
||||
]
|
||||
|
||||
|
||||
extlinks = dict(issue=('https://github.com/quantopian/zipline/issues/%s', '#'))
|
||||
|
||||
# -- Docstrings ---------------------------------------------------------------
|
||||
|
||||
extensions += ['numpydoc']
|
||||
numpydoc_show_class_members = False
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ['.templates']
|
||||
|
||||
# The suffix of source filenames.
|
||||
source_suffix = '.rst'
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = 'index'
|
||||
|
||||
# General information about the project.
|
||||
project = u'Zipline'
|
||||
copyright = u'2015, Quantopian Inc.'
|
||||
|
||||
# The full version, including alpha/beta/rc tags.
|
||||
release = version
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
exclude_patterns = []
|
||||
|
||||
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
|
||||
if not on_rtd: # only import and set the theme if we're building docs locally
|
||||
try:
|
||||
import sphinx_rtd_theme
|
||||
except ImportError:
|
||||
html_theme = 'default'
|
||||
html_theme_path = []
|
||||
else:
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
highlight_language = 'python'
|
||||
|
||||
# The name of an image file (within the static path) to use as favicon of the
|
||||
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
|
||||
# pixels large.
|
||||
# html_favicon = os.path.join('svg', 'zipline.ico')
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['.static']
|
||||
|
||||
# If false, no index is generated.
|
||||
html_use_index = True
|
||||
|
||||
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
|
||||
html_show_sphinx = False
|
||||
|
||||
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
|
||||
html_show_copyright = True
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'ziplinedoc'
|
||||
|
||||
intersphinx_mapping = {
|
||||
'http://docs.python.org/dev': None,
|
||||
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
|
||||
'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
|
||||
'pandas': ('http://pandas.pydata.org/pandas-docs/stable/', None),
|
||||
}
|
||||
|
||||
doctest_global_setup = "import zipline"
|
||||
@@ -0,0 +1,11 @@
|
||||
.. include:: ../../README.rst
|
||||
|
||||
Index
|
||||
-----
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
|
||||
beginner-tutorial
|
||||
releases
|
||||
appendix
|
||||
@@ -0,0 +1,9 @@
|
||||
=============
|
||||
Release Notes
|
||||
=============
|
||||
|
||||
.. include:: whatsnew/0.8.0.txt
|
||||
|
||||
.. include:: whatsnew/0.7.0.txt
|
||||
|
||||
.. include:: whatsnew/0.6.1.txt
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 40 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 53 KiB |
@@ -0,0 +1,327 @@
|
||||
Release 0.6.1
|
||||
-------------
|
||||
|
||||
:Release: 0.6.1
|
||||
:Date: April 23, 2014
|
||||
|
||||
|
||||
Highlights
|
||||
~~~~~~~~~~
|
||||
|
||||
* Major fixes to risk calculations, see Bug Fixes section.
|
||||
* Port of ``history()`` function, see Enhancements section
|
||||
* Start of support for Quantopian algorithm script-syntax, see ENH
|
||||
section.
|
||||
* conda package manager support, see Build section.
|
||||
|
||||
Enhancements
|
||||
~~~~~~~~~~~~
|
||||
|
||||
* Always process new orders
|
||||
i.e. on bars where ``handle_data`` isn't called, but there is 'clock'
|
||||
data e.g. a consistent benchmark, process orders.
|
||||
* Empty positions are now filtered from the portfolio container.
|
||||
To help prevent algorithms from operating on positions that are not in
|
||||
the existing universe of stocks.
|
||||
Formerly, iterating over positions would return positions for stocks
|
||||
which had zero shares held. (Where an explicit check in algorithm code
|
||||
for ``pos.amount != 0`` could prevent from using a non-existent
|
||||
position.)
|
||||
* Add trading calendar for BMF&Bovespa.
|
||||
* Add beginning of algo script support.
|
||||
* Starts on the path of parity with the script syntax in Quantopian's IDE
|
||||
on https://quantopian.com
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from datetime import datetime import pytz
|
||||
from zipline import TradingAlgorithm
|
||||
from zipline.utils.factory import load_from_yahoo
|
||||
|
||||
from zipline.api import order
|
||||
|
||||
def initialize(context):
|
||||
context.test = 10
|
||||
|
||||
def handle_date(context, data):
|
||||
order('AAPL', 10)
|
||||
print(context.test)
|
||||
|
||||
if __name__ == '__main__':
|
||||
import pylab as pl
|
||||
start = datetime(2008, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
end = datetime(2010, 1, 1, 0, 0, 0, 0, pytz.utc)
|
||||
data = load_from_yahoo(
|
||||
stocks=['AAPL'],
|
||||
indexes={},
|
||||
start=start,
|
||||
end=end)
|
||||
data = data.dropna()
|
||||
algo = TradingAlgorithm(
|
||||
initialize=initialize,
|
||||
handle_data=handle_date)
|
||||
results = algo.run(data)
|
||||
results.portfolio_value.plot()
|
||||
pl.show()
|
||||
|
||||
* Add HDF5 and CSV sources.
|
||||
* Limit ``handle_data`` to times with market data.
|
||||
To prevent cases where custom data types had unaligned timestamps, only
|
||||
call ``handle_data`` when market data passes through.
|
||||
Custom data that comes before market data will still update the data
|
||||
bar. But the handling of that data will only be done when there is
|
||||
actionable market data.
|
||||
* Extended commission PerShare method to allow a minimum cost per trade.
|
||||
* Add symbol api function
|
||||
A ``symbol()`` lookup feature was added to Quantopian. By adding the
|
||||
same API function to zipline we can make copy&pasting of a Zipline algo
|
||||
to Quantopian easier.
|
||||
* Add simulated random trade source.
|
||||
Added a new data source that emits events with certain user-specified
|
||||
frequency (minute or daily).
|
||||
This allows users to backtest and debug an algorithm in minute mode to
|
||||
provide a cleaner path towards Quantopian.
|
||||
* Remove dependency on benchmark for trading day calendar.
|
||||
Instead of the benchmarks' index, the trading calendar is now used to
|
||||
populate the environment's trading days.
|
||||
Remove ``extra_date`` field, since unlike the benchmarks list, the
|
||||
trading calendar can generate future dates, so dates for current day
|
||||
trading do not need to be appended.
|
||||
Motivations:
|
||||
|
||||
- The source for the open and close/early close calendar and the
|
||||
trading day calendar is now the same, which should help prevent
|
||||
potential issues due to misalignment.
|
||||
- Allows configurations where the benchmark is provided as a generator
|
||||
based data source to need to supply a second benchmark list just to
|
||||
populate dates.
|
||||
* Port ``history()`` API method from Quantopian.
|
||||
Opens the core of the ``history()`` function that was previously only
|
||||
available on the Quantopian platform.
|
||||
|
||||
The history method is analoguous to the ``batch_transform``
|
||||
function/decorator, but with a hopefully more precise specification of
|
||||
the frequency and period of the previous bar data that is captured.
|
||||
Example usage:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from zipline.api import history, add_history
|
||||
|
||||
def initialize(context):
|
||||
add_history(bar_count=2, frequency='1d', field='price')
|
||||
|
||||
def handle_data(context, data):
|
||||
prices = history(bar_count=2, frequency='1d', field='price')
|
||||
context.last_prices = prices
|
||||
|
||||
N.B. this version of history lacks the backfilling capability that
|
||||
allows the return a full DataFrame on the first bar.
|
||||
|
||||
|
||||
Bug Fixes
|
||||
~~~~~~~~~
|
||||
|
||||
* Adjust benchmark events to match market hours (:issue:`241).
|
||||
Previously benchmark events were emitted at 0:00 on the day the
|
||||
benchmark related to: in 'minute' emission mode this meant that the
|
||||
benchmarks were emitted before any intra-day trades were processed.
|
||||
* Ensure perf stats are generated for all days
|
||||
When running with minutely emissions the simulator would report to the
|
||||
user that it simulated 'n - 1' days (where n is the number of days
|
||||
specified in the simulation params). Now the correct number of trading
|
||||
days are reported as being simulated.
|
||||
* Fix repr for cumulative risk metrics.
|
||||
The ``__repr__`` for RiskMetricsCumulative was referring to an older
|
||||
structure of the class, causing an exception when printed.
|
||||
Also, now prints the last values in the metrics DataFrame.
|
||||
* Prevent minute emission from crashing at end of available data.
|
||||
The next day calculation was causing an error when a minute emission
|
||||
algorithm reached the end of available data.
|
||||
Instead of a generic exception when available data is reached, raise and
|
||||
catch a named exception so that the tradesimulation loop can skip over,
|
||||
since the next market close is not needed at the end.
|
||||
* Fix pandas indexing in trading calendar. This could alternatively be filed
|
||||
under Performance. Index using loc instead of the inefficient index-ing of
|
||||
day, then time.
|
||||
* Prevent crash in vwap transform due to non-existent member.
|
||||
The WrongDataForTransform was referencing a ``self.fields`` member,
|
||||
which did not exist.
|
||||
Add a self.fields member set to ``price`` and ``volume`` and use it to
|
||||
iterate over during the check.
|
||||
* Fix max drawdown calculation.
|
||||
The input into max drawdown was incorrect, causing the bad results. i.e.
|
||||
the ``compounded_log_returns`` were not values representative of the
|
||||
algorithms total return at a given time, though
|
||||
``calculate_max_drawdown`` was treating the values as if they were.
|
||||
Instead, the ``algorithm_period_returns`` series is now used, which does
|
||||
provide the total return.
|
||||
* Fix cost basis calculation.
|
||||
Cost basis calculation now takes direction of txn into account.
|
||||
Closing a long position or covering a short shouldn't affect the cost
|
||||
basis.
|
||||
* Fix floating point error in ``order()``.
|
||||
Where order amounts that were near an integer could accidentally be
|
||||
floored or ceilinged (depending on being postive or negative) to the
|
||||
wrong integer.
|
||||
e.g. an amount stored internally as -27.99999 was converted to -27
|
||||
instead of -28.
|
||||
* Update perf period state when positions are changed by splits.
|
||||
Otherwise, ``self._position_amounts`` will be out of sync with
|
||||
position.amount, etc.
|
||||
* Fix misalignment of downside series calc when using exact dates.
|
||||
An oddity that was exposed while working on making the return series
|
||||
passed to the risk module more exact, the series comparison between the
|
||||
returns and mean returns was unbalanced, because the mean returns were
|
||||
not masked down to the downside data points; however, in most, if not
|
||||
all cases this was papered over by the call to ``.valid()`` which was
|
||||
removed in this change set.
|
||||
* Check that self.logger exists before using it.
|
||||
``self.logger`` is initialized as ``None`` and there is no guarantee
|
||||
that users have set it, so check that it exists before trying to pass
|
||||
messages to it.
|
||||
* Prevent out of sync market closes in performance tracker.
|
||||
In situations where the performance tracker has been reset or patched to
|
||||
handle state juggling with warming up live data, the ``market_close``
|
||||
member of the performance tracker could end up out of sync with the
|
||||
current algo time as determined by the performance tracker.
|
||||
The symptom was dividends never triggering, because the end of day
|
||||
checks would not match the current time.
|
||||
Fix by having the tradesimulation loop be responsible, in minute/minute
|
||||
mode, for advancing the market close and passing that value to the
|
||||
performance tracker, instead of having the market close advanced by the
|
||||
performance tracker as well.
|
||||
* Fix numerous cumulative and period risk calculations.
|
||||
The calculations that are expected to change are:
|
||||
|
||||
- ``cumulative.beta``
|
||||
- ``cumulative.alpha``
|
||||
- ``cumulative.information``
|
||||
- ``cumulative.sharpe``
|
||||
- ``period.sortino``
|
||||
|
||||
How Risk Calculations Are Changing
|
||||
Risk Fixes for Both Period and Cumulative
|
||||
|
||||
Downside Risk
|
||||
|
||||
Use sample instead of population for standard deviation.
|
||||
|
||||
Add a rounding factor, so that if the two values are close for a given
|
||||
dt, that they do not count as a downside value, which would throw off
|
||||
the denominator of the standard deviation of the downside diffs.
|
||||
|
||||
Standard Deviation Type
|
||||
|
||||
Across the board the standard deviation has been standardized to using a
|
||||
'sample' calculation, whereas before cumulative risk was mostly using
|
||||
'population'. Using ``ddof=1`` with ``np.std`` calculates as if the
|
||||
values are a sample.
|
||||
|
||||
Cumulative Risk Fixes
|
||||
|
||||
Beta
|
||||
|
||||
Use the daily algorithm returns and benchmarks instead of annualized
|
||||
mean returns.
|
||||
|
||||
Volatility
|
||||
|
||||
Use sample instead of population with standard deviation.
|
||||
|
||||
The volatility is an input to other calculations so this change affects
|
||||
Sharpe and Information ratio calculations.
|
||||
|
||||
Information Ratio
|
||||
|
||||
The benchmark returns input is changed from annualized benchmark returns
|
||||
to the annualized mean returns.
|
||||
|
||||
Alpha
|
||||
|
||||
The benchmark returns input is changed from annualized benchmark returns
|
||||
to the annualized mean returns.
|
||||
|
||||
Period Risk Fixes
|
||||
|
||||
Sortino
|
||||
|
||||
Now uses the downside risk of the daily return vs. the mean algorithm
|
||||
returns for the minimum acceptable return instead of the treasury
|
||||
return.
|
||||
|
||||
The above required adding the calculation of the mean algorithm returns
|
||||
for period risk.
|
||||
|
||||
Also, uses ``algorithm_period_returns`` and ``tresaury_period_return``
|
||||
as the cumulative Sortino does, instead of using algorithm returns for
|
||||
both inputs into the Sortino calculation.
|
||||
|
||||
|
||||
Performance
|
||||
~~~~~~~~~~~
|
||||
|
||||
* Removed ``alias_dt`` transform in favor of property on SIDData.
|
||||
Adding a copy of the Event's dt field as datetime via the ``alias_dt``
|
||||
generator, so that the API was forgiving and allowed both datetime and
|
||||
dt on a SIDData object, was creating noticeable overhead, even on an
|
||||
noop algorithms.
|
||||
Instead of incurring the cost of copying the datetime value and
|
||||
assigning it to the Event object on every event that is passed through
|
||||
the system, add a property to SIDData which acts as an alias
|
||||
``datetime`` to ``dt``.
|
||||
Eventually support for ``data['foo'].datetime`` may be removed, and
|
||||
could be considered deprecated.
|
||||
* Remove the drop of 'null return' from cumulative returns.
|
||||
The check of existence of the null return key, and the drop of said
|
||||
return on every single bar was adding unneeded CPU time when an
|
||||
algorithm was run with minute emissions.
|
||||
Instead, add the 0.0 return with an index of the trading day before the
|
||||
start date.
|
||||
The removal of the ``null return`` was mainly in place so that the
|
||||
period calculation was not crashing on a non-date index value; with the
|
||||
index as a date, the period return can also approximate volatility (even
|
||||
though the that volatility has high noise-to-signal strength because it
|
||||
uses only two values as an input.)
|
||||
|
||||
|
||||
Maintenance and Refactorings
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
* Allow ``sim_params`` to provide data frequency for the algorithm.
|
||||
In the case that ``data_frequency`` of the algorithm is None, allow the
|
||||
``sim_params`` to provide the ``data_frequency``.
|
||||
|
||||
Also, defer to the algorithms data frequency, if provided.
|
||||
|
||||
|
||||
Build
|
||||
~~~~~
|
||||
|
||||
* Added support for building and releasing via conda
|
||||
For those who prefer building with http://conda.pydata.org/ to compiling
|
||||
locally with pip.
|
||||
The following should install Zipline on many systems.
|
||||
|
||||
::
|
||||
|
||||
conda install -c quantopian zipline
|
||||
|
||||
Contributors
|
||||
~~~~~~~~~~~~
|
||||
|
||||
The following people have contributed to this release, ordered by
|
||||
numbers of commit:
|
||||
|
||||
::
|
||||
|
||||
49 Eddie Hebert
|
||||
28 Thomas Wiecki
|
||||
11 Richard Frank
|
||||
2 Jamie Kirkpatrick
|
||||
2 Jeremiah Lowin
|
||||
1 Colin Alexander
|
||||
1 Michael Schatzow
|
||||
1 Moises Trovo
|
||||
1 Suminda Dharmasena
|
||||
@@ -0,0 +1,213 @@
|
||||
Release 0.7.0
|
||||
-------------
|
||||
|
||||
:Release: 0.7.0
|
||||
:Date: July 25, 2014
|
||||
|
||||
Highlights
|
||||
~~~~~~~~~~
|
||||
|
||||
|
||||
* Command line interface to run algorithms directly.
|
||||
* IPython Magic ``%%zipline`` that runs algorithm defined in an IPython
|
||||
notebook cell.
|
||||
* API methods for building safeguards against runaway ordering and
|
||||
undesired short positions.
|
||||
* New history() function to get a moving DataFrame of past market data
|
||||
(replaces BatchTransform).
|
||||
* A new `beginner
|
||||
tutorial <http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb>`__.
|
||||
|
||||
|
||||
Enhancements
|
||||
~~~~~~~~~~~~
|
||||
|
||||
* CLI: Adds a CLI and IPython magic for zipline.
|
||||
Example:
|
||||
|
||||
::
|
||||
|
||||
python run_algo.py -f dual_moving_avg.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
|
||||
|
||||
Grabs the data from yahoo finance, runs the file
|
||||
dual\_moving\_avg.py (and looks for ``dual_moving_avg_analyze.py``
|
||||
which, if found, will be executed after the algorithm has been run),
|
||||
and outputs the perf ``DataFrame`` to ``dma.pickle`` (:issue:`325`).
|
||||
|
||||
- IPython magic command (at the top of an IPython notebook cell).
|
||||
Example:
|
||||
|
||||
::
|
||||
|
||||
%%zipline --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o perf
|
||||
|
||||
Does the same as above except instead of executing the file looks
|
||||
for the algorithm in the cell and instead of outputting the perf df
|
||||
to a file, creates a variable in the namespace called perf (:issue:`325`).
|
||||
|
||||
* Adds Trading Controls to the algorithm API.
|
||||
|
||||
The following functions are now available on ``TradingAlgorithm``
|
||||
and for algo scripts:
|
||||
|
||||
``set_max_order_size(self, sid=None, max_shares=None, max_notional=None)``
|
||||
Set a limit on the absolute magnitude, in shares and/or total
|
||||
dollar value, of any single order placed by this algorithm for a
|
||||
given sid. If ``sid`` is None, then the rule is applied to any order
|
||||
placed by the algorithm.
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if we attempt to place an
|
||||
# order which would cause us to hold more than 10 shares
|
||||
# or 1000 dollars worth of sid(24).
|
||||
set_max_order_size(sid(24), max_shares=10, max_notional=1000.0)
|
||||
|
||||
``set_max_position_size(self, sid=None, max_shares=None, max_notional=None)``
|
||||
-Set a limit on the absolute magnitude, in either shares or
|
||||
dollar value, of any position held by the algorithm for a given
|
||||
sid. If ``sid`` is None, then the rule is applied to any position
|
||||
held by the algorithm.
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if we attempt to order more than
|
||||
# 10 shares or 1000 dollars worth of sid(24) in a single order.
|
||||
set_max_order_size(sid(24), max_shares=10, max_notional=1000.0)
|
||||
|
||||
``set_max_order_count(self, max_count)``
|
||||
Set a limit on the number of orders that can be placed by the algorithm in
|
||||
a single trading day.
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if more than 50 orders are placed in a day.
|
||||
set_max_order_count(50)
|
||||
|
||||
``set_long_only(self)``
|
||||
Set a rule specifying that the
|
||||
algorithm may not hold short positions.
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def initialize(context):
|
||||
# Algorithm will raise an exception if it attempts to place
|
||||
# an order that would cause it to hold a short position.
|
||||
set_long_only()
|
||||
|
||||
(:issue:`329`).
|
||||
|
||||
* Adds an ``all_api_methods`` classmethod on ``TradingAlgorithm`` that
|
||||
returns a list of all ``TradingAlgorithm`` API methods (:issue:`333`).
|
||||
|
||||
* Expanded record() functionality for dynamic naming.
|
||||
The record() function can now take positional args before the
|
||||
kwargs. All original usage and functionality is the same, but now
|
||||
these extra usages will work:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
name = 'Dynamically_Generated_String'
|
||||
record( name, value, ... )
|
||||
record( name, value1, 'name2', value2, name3=value3, name4=value4 )
|
||||
|
||||
The requirements are simply that the poritional args occur only
|
||||
before the kwargs (:issue:`355`).
|
||||
|
||||
* history() has been ported from Quantopian to Zipline and provides
|
||||
moving window of market data.
|
||||
history() replaces BatchTransform. It is faster, works for minute level data
|
||||
and has a superior interface. To use it, call ``add_history()`` inside of
|
||||
``initialize()`` and then receive a pandas ``DataFrame`` by calling history()
|
||||
from inside ``handle_data()``. Check out the `tutorial
|
||||
<http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb>`__
|
||||
and an `example
|
||||
<https://github.com/quantopian/zipline/blob/master/zipline/examples/dual_moving_average.py>`__.
|
||||
(:issue:`345` and :issue:`357`).
|
||||
|
||||
* history() now supports ``1m`` window lengths (:issue:`345`).
|
||||
|
||||
Bug Fixes
|
||||
~~~~~~~~~
|
||||
|
||||
* Fix alignment of trading days and open and closes in trading
|
||||
environment (:issue:`331`).
|
||||
* RollingPanel fix when adding/dropping new fields (:issue:`349`).
|
||||
|
||||
Performance
|
||||
~~~~~~~~~~~
|
||||
|
||||
Maintenance and Refactorings
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
* Removed undocumented and untested HDF5 and CSV data sources (:issue:`267`).
|
||||
* Refactor sim\_params (:issue:`352`).
|
||||
* Refactoring of history (:issue:`340`).
|
||||
|
||||
Build
|
||||
~~~~~
|
||||
|
||||
* The following dependencies have been updated (zipline might work with
|
||||
other versions too):
|
||||
|
||||
.. code-block:: diff
|
||||
|
||||
-pytz==2013.9
|
||||
+pytz==2014.4
|
||||
+numpy==1.8.1
|
||||
-numpy==1.8.0
|
||||
+scipy==0.12.0
|
||||
+patsy==0.2.1
|
||||
+statsmodels==0.5.0
|
||||
-six==1.5.2
|
||||
+six==1.6.1
|
||||
-Cython==0.20
|
||||
+Cython==0.20.1
|
||||
-TA-Lib==0.4.8
|
||||
+--allow-external TA-Lib --allow-unverified TA-Lib TA-Lib==0.4.8
|
||||
-requests==2.2.0
|
||||
+requests==2.3.0
|
||||
-nose==1.3.0
|
||||
+nose==1.3.3
|
||||
-xlrd==0.9.2
|
||||
+xlrd==0.9.3
|
||||
-pep8==1.4.6
|
||||
+pep8==1.5.7
|
||||
-pyflakes==0.7.3
|
||||
-pip-tools==0.3.4
|
||||
+pyflakes==0.8.1`
|
||||
-scipy==0.13.2
|
||||
-tornado==3.2
|
||||
-pyparsing==2.0.1
|
||||
-patsy==0.2.1
|
||||
-statsmodels==0.4.3
|
||||
+tornado==3.2.1
|
||||
+pyparsing==2.0.2
|
||||
-Markdown==2.3.1
|
||||
+Markdown==2.4.1
|
||||
|
||||
Contributors
|
||||
~~~~~~~~~~~~
|
||||
|
||||
The following people have contributed to this release, ordered by
|
||||
numbers of commit:
|
||||
|
||||
::
|
||||
|
||||
38 Scott Sanderson
|
||||
29 Thomas Wiecki
|
||||
26 Eddie Hebert
|
||||
6 Delaney Granizo-Mackenzie
|
||||
3 David Edwards
|
||||
3 Richard Frank
|
||||
2 Jonathan Kamens
|
||||
1 Pankaj Garg
|
||||
1 Tony Lambiris
|
||||
1 fawce
|
||||
@@ -0,0 +1,122 @@
|
||||
Release 0.8.0
|
||||
-------------
|
||||
|
||||
:Release: 0.8.0
|
||||
:Date: TBD
|
||||
|
||||
Highlights
|
||||
~~~~~~~~~~
|
||||
|
||||
* New documentation system with a new website at
|
||||
`zipline.io <http://www.zipline.io>`__
|
||||
* Major performance enhancements.
|
||||
* Dynamic history.
|
||||
|
||||
Enhancements
|
||||
~~~~~~~~~~~~
|
||||
|
||||
* Account object: Adds an account object to conext to track information about
|
||||
the trading account.
|
||||
Example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
context.account.settled_cash
|
||||
|
||||
Returns the settled cash value that is stored on the account object.
|
||||
This value is updated accordingly as the algorithm is run (:issue:`396`).
|
||||
* :class:`~zipline.history.history_container.HistoryContainer` can now grow
|
||||
dynamically. Calls to :func:`~zipline.api.history` will now be able to increase
|
||||
the size or change the shape of the history container to be able to service the
|
||||
call. :func:`~zipline.api.add_history` now acts as a preformance hint to
|
||||
pre-allocate sufficient space in the container. This change is backwards
|
||||
compatible with ``history``, all existing algorithms should continue to work as
|
||||
intended (:issue:`412`).
|
||||
* Simple transforms ported from quantopian and use history.
|
||||
:class:`~zipline.protocol.SIDData` now has methods for:
|
||||
|
||||
- ``stddev``
|
||||
- ``mavg``
|
||||
- ``vwap``
|
||||
- ``returns``
|
||||
|
||||
These methods, except for ``returns``, accept a number of days. If
|
||||
you are running with minute data, then this will calculate the
|
||||
number of minutes in those days, accounting for early closes and the
|
||||
current time and apply the transform over the set of minutes.
|
||||
``returns`` takes no parameters and will return the daily returns of
|
||||
the given asset.
|
||||
Example:
|
||||
|
||||
.. code:: python
|
||||
|
||||
data[security].stddev(3)
|
||||
|
||||
(:issue:`429`).
|
||||
* New fields in Performance Period.
|
||||
Performance Period has new fields accessible in return value of
|
||||
``to_dict``:
|
||||
- gross leverage
|
||||
- net leverage
|
||||
- short exposure
|
||||
- long exposure
|
||||
- shorts count
|
||||
- longs count
|
||||
(:issue:`464`).
|
||||
* Allow :func:`~zipline.api.order_percent` to work with various market values
|
||||
(by Jeremiah Lowin).
|
||||
|
||||
Currently, :func:`~zipline.api.order_percent` and
|
||||
:func:`~zipline.api.order_target_percent` both operate as a percentage of
|
||||
``self.portfolio.portfolio_value``. This PR lets them operate as percentages
|
||||
of other important MVs.
|
||||
Also adds ``context.get_market_value()``, which enables this
|
||||
functionality.
|
||||
For example:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
# this is how it works today (and this still works)
|
||||
# put 50% of my portfolio in AAPL
|
||||
order_percent('AAPL', 0.5)
|
||||
# note that if this were a fully invested portfolio, it would become 150% levered.
|
||||
|
||||
# take half of my available cash and buy AAPL
|
||||
order_percent('AAPL', 0.5, percent_of='cash')
|
||||
|
||||
# rebalance my short position, as a percentage of my current short
|
||||
book_target_percent('MSFT', 0.1, percent_of='shorts')
|
||||
|
||||
# rebalance within a custom group of stocks
|
||||
tech_stocks = ('AAPL', 'MSFT', 'GOOGL')
|
||||
tech_filter = lambda p: p.sid in tech_stocks
|
||||
for stock in tech_stocks:
|
||||
order_target_percent(stock, 1/3, percent_of_fn=tech_filter)
|
||||
|
||||
(:issue:`477`).
|
||||
* Command line option to for printing algo to stdout (by Andrea D'Amore)
|
||||
(:issue:`545`).
|
||||
|
||||
Bug Fixes
|
||||
~~~~~~~~~
|
||||
|
||||
* Fix a bug where the reported returns could sharply dip for random periods of
|
||||
time (:issue:`378`).
|
||||
|
||||
Performance
|
||||
~~~~~~~~~~~
|
||||
* Major performance enhancements to history (by Dale Jung) (:issue:`488`).
|
||||
|
||||
Maintenance and Refactorings
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Build
|
||||
~~~~~
|
||||
|
||||
Documentation
|
||||
~~~~~~~~~~~~~
|
||||
|
||||
* Switched to sphinx for the documentation (:issue:`816`).
|
||||
|
||||
Contributors
|
||||
~~~~~~~~~~~~
|
||||
Reference in New Issue
Block a user