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Merge pull request #21 from snth/docs-snth
High level documentation organisation
This commit is contained in:
Thomas Wiecki
@@ -0,0 +1,40 @@
|
||||
***************************
|
||||
Contributing to the project
|
||||
***************************
|
||||
|
||||
Style Guide
|
||||
===========
|
||||
|
||||
To ensure that changes and patches are focused on behavior changes,
|
||||
the zipline codebase adheres to PEP-8,
|
||||
`<http://www.python.org/dev/peps/pep-0008/>`_.
|
||||
|
||||
The maintainers check the code using the flake8 script,
|
||||
`<https://github.com/jcrocholl/pep8/>`_, which is included in the
|
||||
requirements_dev.txt.
|
||||
|
||||
Before submitting patches or pull requests, please ensure that your
|
||||
changes pass
|
||||
|
||||
::
|
||||
|
||||
flake8 --ignore=E124,E125,E126 zipline tests
|
||||
|
||||
Discussion and Help
|
||||
===================
|
||||
|
||||
Discussion of the project is held at the Google Group,
|
||||
`<zipline@googlegroups.com>`_,
|
||||
`<https://groups.google.com/forum/#!forum/zipline>`_.
|
||||
|
||||
Source
|
||||
======
|
||||
|
||||
The source for Zipline is hosted at
|
||||
`<https://github.com/quantopian/zipline>`_.
|
||||
|
||||
Contact
|
||||
=======
|
||||
|
||||
For other questions, please contact `<opensource@quantopian.com>`_.
|
||||
|
||||
@@ -1,3 +1,7 @@
|
||||
**********
|
||||
Extensions
|
||||
**********
|
||||
|
||||
.. highlight:: cython
|
||||
|
||||
Philosophy
|
||||
|
||||
+49
-47
@@ -3,56 +3,60 @@
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Contents:
|
||||
.. module:: zipline
|
||||
|
||||
****************************************************
|
||||
Zipline: Financial Backtester for Trading Algorithms
|
||||
****************************************************
|
||||
|
||||
Python is quickly becoming the glue language which holds together data science
|
||||
and related fields like quantitative finance. Zipline is a new, BSD-licensed
|
||||
quantitative trading system which allows easy backtesting of investment
|
||||
algorithms on historical data. The system is fundamentally event-driven and a
|
||||
close approximation of how live-trading systems operate. Moreover, 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. Furthermore, statistic and machine learning libraries like
|
||||
matplotlib, scipy, statsmodels, and sklearn support development, analysis and
|
||||
visualization of state-of-the-art trading systems.
|
||||
|
||||
Zipline is currently used in production as the backtesting engine
|
||||
powering `quantopian.com <https://app.quantopian.com>`_ -- a free, community-centered
|
||||
platform that allows development and real-time backtesting of trading
|
||||
algorithms in the web browser.
|
||||
|
||||
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.
|
||||
|
||||
Contents
|
||||
========
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
notes.rst
|
||||
manifesto.rst
|
||||
installation.rst
|
||||
quickstart.rst
|
||||
contributing.rst
|
||||
overview.rst
|
||||
modules.rst
|
||||
messaging.rst
|
||||
|
||||
Zipline
|
||||
=======
|
||||
|
||||
Zipline runs backtests using asynchronous components and zeromq messaging for communication and coordination.
|
||||
|
||||
Simulator is the heart of Zipline, and the primary access point for creating, launching, and tracking simulations. You can find it in :py:class:`~zipline.core.Simulator`
|
||||
|
||||
Simulator Sub-Components
|
||||
========================
|
||||
|
||||
Each simulation contains numerous subcomponents, each operating asynchronously from all others, and communicating
|
||||
via zeromq.
|
||||
|
||||
DataSources
|
||||
--------------------
|
||||
|
||||
A DataSource represents a historical event record, which will be played back during simulation. A simulation may have one or more DataSources, which will be combined in DataFeed. Generally, datasources read records from a persistent store (db, csv file, remote service), format the messages for downstream simulation components, and send them to a PUSH socket. See the base class for all datasources :py:class:`~zipline.messaging.DataSource` and the module holding all datasources :py:mod:`zipline.sources`
|
||||
|
||||
DataFeed
|
||||
--------------------
|
||||
|
||||
All simulations start with a collection of :py:class:`~zipline.messaging.DataSource`, which need to be fed to an algorithm. Each :py:class:`~zipline.sources.DataSource`can contain events of differing content (trades, quotes, corporate event) and frequency (quarterly, intraday). To simplify the process of managing the data sources, :py:class:`~zipline.core.DataFeed` can receive events from multiple :py:class:`DataSources <zipline.sources.DataSource>` and combine them into a serial chronological stream.
|
||||
|
||||
Transforms
|
||||
--------------------
|
||||
|
||||
Often, an algorithm will require a running calculation on top of a :py:class:`~zipline.messaging.DataSource`, or on the consolidated feed. A simple example is a technical indicator or a moving average. Transforms can be described in :py:class:`~zipline.core.Simulator`'s configuration. Subclass :py:class:`~zipline.transforms.core.Transform` to add your own Transform. Transforms must hold their own state between events, and serialize their current values into messages.
|
||||
|
||||
|
||||
Data Alignment
|
||||
--------------------
|
||||
|
||||
Like Datasources, Transforms have differing frequencies and results. Simulator manages the results of parallel transforms and **aligns** transform results with the raw DataFeed. Client algorithms simply receive a map of data, which includes the current event and all the transformed values.
|
||||
|
||||
Time Compression
|
||||
--------------------
|
||||
|
||||
|
||||
Review the unit test coverage_.
|
||||
|
||||
|
||||
extensions.rst
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
@@ -60,5 +64,3 @@ Indices and tables
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
|
||||
.. _coverage: cover/index.html
|
||||
|
||||
@@ -1,20 +1,32 @@
|
||||
System Setup
|
||||
==============
|
||||
|
||||
You need to have zeromq installed - http://www.zeromq.org/intro:get-the-software.
|
||||
************
|
||||
Installation
|
||||
************
|
||||
|
||||
Running
|
||||
-------
|
||||
Since zipline is pure-python code it should be very easy to install
|
||||
and set up with pip:
|
||||
|
||||
Initial `virtualenv` setup::
|
||||
::
|
||||
|
||||
$ mkvirtualenv zipline
|
||||
$ workon zipline
|
||||
#go get coffee, this will compile a heap of C/C++ code
|
||||
$ ./etc/ordered_pip.sh requirements_sci.txt
|
||||
$ ./etc/ordered_pip.sh requirements.txt
|
||||
#optionally
|
||||
$ ./etc/ordered_pip.sh requirements_dev.txt
|
||||
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 (>= 2.7.2)
|
||||
* numpy (>= 1.6.0)
|
||||
* pandas (>= 0.9.0)
|
||||
* pytz
|
||||
* msgpack-python
|
||||
* iso8601
|
||||
* Logbook
|
||||
* blist
|
||||
|
||||
|
||||
Develop
|
||||
@@ -51,7 +63,7 @@ For building distributable egg::
|
||||
|
||||
Tooling hints
|
||||
================
|
||||
QBT relies heavily on scientific python components (numpy, scikit, pandas, matplotlib, ipython, etc). Tooling up can be a pain, and it often involves managing a configuration including your OS, c/c++/fortran compilers, python version, and versions of numerous modules. I've found the following tools absolutely indispensable:
|
||||
:mod:`zipline` relies heavily on scientific python components (numpy, scikit, pandas, matplotlib, ipython, etc). Tooling up can be a pain, and it often involves managing a configuration including your OS, c/c++/fortran compilers, python version, and versions of numerous modules. I've found the following tools absolutely indispensable:
|
||||
|
||||
- some kind of package manager for your platform. package managers generally give you a way to search, install, uninstall, and check currently installed packages. They also do a great job of managing dependencies.
|
||||
- linux: yum/apt-get
|
||||
@@ -78,11 +90,22 @@ Scientific python on the Mac can be a bit confusing because of the many independ
|
||||
|
||||
Data Sources
|
||||
=============
|
||||
The Backtest can handle multiple concurrent data sources. QBT will start a subprocess to run each datasource, and merge all events from all sources into a single serial feed, ordered by date.
|
||||
|
||||
Data sources have events with very different frequencies. For example, liquid stocks will trade many times per minute, while illiquid stocks may trade just once a day. In order to serialize events from all sources into a single feed, qbt loads events from all sources into memory, then sorts. The communication happens like this:
|
||||
1. QBT requests the next event from each data source, ignoring date (i.e. just next in sequence for all)
|
||||
2. Using the earliest date from all the events from all sources, QBT then asks for "next after <date>" from all sources.
|
||||
3. All datasources send all events in their history from before <date>, moving their internal pointer forward to the next unsent event.
|
||||
4. QBT merges all events in memory
|
||||
5. goto 1!
|
||||
The Backtest can handle multiple concurrent data sources. QBT will start a
|
||||
subprocess to run each datasource, and merge all events from all sources into a
|
||||
single serial feed, ordered by date.
|
||||
|
||||
Data sources have events with very different frequencies. For example, liquid
|
||||
stocks will trade many times per minute, while illiquid stocks may trade just
|
||||
once a day. In order to serialize events from all sources into a single feed,
|
||||
qbt loads events from all sources into memory, then sorts. The communication
|
||||
happens like this:
|
||||
|
||||
1. QBT requests the next event from each data source, ignoring date (i.e.
|
||||
just next in sequence for all)
|
||||
2. Using the earliest date from all the events from all sources, QBT then
|
||||
asks for "next after <date>" from all sources.
|
||||
3. All datasources send all events in their history from before <date>,
|
||||
moving their internal pointer forward to the next unsent event.
|
||||
4. QBT merges all events in memory
|
||||
5. goto 1!
|
||||
@@ -0,0 +1,140 @@
|
||||
********************
|
||||
Quantopian Manifesto
|
||||
********************
|
||||
|
||||
Wall Street's culture was born in an age of information scarcity.
|
||||
Hoarding information and keeping secrets were the norm. The world has changed.
|
||||
Today's world is defined by information that wants to be free. The new
|
||||
scarcity is people: people with the talent and drive to wring insight from all
|
||||
of that data.
|
||||
|
||||
Quantopian's mission is to attract the world's algorithmic and
|
||||
financial talent. We want to attract today's quants, and we want to
|
||||
attract talent that hasn't yet had the opportunity to be a quant. We
|
||||
want to bring this talent together, provide them with the tools that they
|
||||
require, and help them build a community. First and foremost, our community is
|
||||
rooted in openness and sharing. Members share code, know-how, and data.
|
||||
Quantopian sets the tone by providing open-sourced code, discussing our
|
||||
techniques, and supplying the historical data needed for algorithmic investing.
|
||||
|
||||
By educating more people about statistical arbitrage and data mining for
|
||||
finance, we aim to dispense with the secrecy and raise the state of the art.
|
||||
Rather than hoard data, we relentlessly push data to our community. We want to
|
||||
diversify the data that can be mined, and permit our members to explore as much
|
||||
as they like. Our members' success in analyzing and investing will help
|
||||
us draw more data and more members to our community. Every individual's
|
||||
success will also help other Quantopians.
|
||||
|
||||
The Evolution of Algorithmic Finance
|
||||
====================================
|
||||
|
||||
Charting
|
||||
--------
|
||||
|
||||
Algorithmic finance originated as chart reading. Chartists would look for
|
||||
certain patterns in price history charts. The patterns were always graced with
|
||||
artfully chosen names like 'head and shoulders,' 'spinning top', or 'morning
|
||||
star'. Chart reading looks a lot like palm reading, and for the skeptics among
|
||||
us the similarities don't end with appearances. Still, chart reading is an
|
||||
attempt to infer the balance of buying and selling appetites in the markets
|
||||
from a stock's history. Viewed that way, chart reading pursues the noble goal
|
||||
of prediction. Charting is so common that certain events can trigger market
|
||||
responses, possibly because so many participants infer the same meaning from a
|
||||
stock's price chart.
|
||||
|
||||
Technical Analysis
|
||||
------------------
|
||||
|
||||
Analysis grew more sophisticated as chartists gave way to
|
||||
computer scientists writing algorithms. These algorithms have more scientific
|
||||
sounding names like Moving Averages, Volume Weighted Moving Averages, Bollinger
|
||||
Bands, Relative Strength Indicators, and Pearson's Correlation
|
||||
Coefficient. Building technical analysis algorithms looks a lot like modern
|
||||
statistics, and the optimists among us would say the similarities run deep.
|
||||
Technical analysts take algorithmic approaches to the same concept: inferring
|
||||
future behavior from trailing data. In addition to greater sophistication,
|
||||
technical analysts can also test their algorithms over historic data. Imperfect
|
||||
to be sure, but a giant leap from staring at a chart.
|
||||
|
||||
Reasonable people can disagree about the 'correctness' of
|
||||
inferring future events from past behavior. Rather than dwell on that question,
|
||||
we choose to point out a different limitation of both charting and modern
|
||||
technical analysis: **both interpret the movement of a single stock in
|
||||
isolation**. This limitation is both a blessing and a curse.
|
||||
|
||||
On the one hand, there is little room for sophisticated statistics or machine
|
||||
learning when you have just a single time series for both your signal and your
|
||||
prediction target.
|
||||
|
||||
On the other, technical analysis can still be intuitive, which makes it easier
|
||||
to get acquainted with the idea of automated trading. Often there is a mental
|
||||
leap for people to make from understanding the interpretation of a price series
|
||||
to issuing orders. Because the signals are easy to understand, technical
|
||||
analysis makes for a good initial learning experience to explore risk and
|
||||
performance evaluation as well as order management: the price going above its
|
||||
30 day moving average is something you can visualize. So, you can focus your
|
||||
attention on the financial and trading aspects of the problem.
|
||||
|
||||
Statistical Arbitrage
|
||||
---------------------
|
||||
|
||||
Statistical Arbitrage is the grandchild of chart reading.
|
||||
Like technical analysis it relies on algorithms and statistics, but it departs
|
||||
in one very significant way: 'stat arb' looks for relationships
|
||||
among many stocks. The challenge with stat arb is twofold:
|
||||
|
||||
* visualizing the relationships can be quite difficult, since the relationships
|
||||
can have high dimensionality
|
||||
* the data processing load is quite high - a simple linear regression for all
|
||||
stocks results in 32 million individual regressions. Assuming a 10-day
|
||||
window, that can be 320 million individual calculations. To prepare,
|
||||
backtest, and trade a stat arb strategy required both familiarity with the
|
||||
mechanics of trading, knowledge of statistics, and a strong computer science
|
||||
background.
|
||||
|
||||
As stat arb matured, the competition to find stat arb strategies that work
|
||||
became a two part race:
|
||||
|
||||
1. execute the trades faster
|
||||
2. find new ways to identify relationships within
|
||||
market data
|
||||
|
||||
We think the pursuit of faster trades reached diminishing returns when the
|
||||
market hit sub-millisecond trade execution. We think that the resulting high
|
||||
level of liquidity is a good thing, but we agree with Thomas Petterffy that
|
||||
`pursuing even faster trades
|
||||
<http://www.npr.org/blogs/money/2012/08/27/159992076/a-father-of-high-speed-trading-thinks-we-should-slow-down>`_
|
||||
"has absolutely no social value".
|
||||
|
||||
Finding new relationships in the market data is possible and more important now
|
||||
than ever. In the summer of 2007, there was a sudden meltdown in quantitative
|
||||
trading firms. Subsequent analysis points to quants crowding into the same
|
||||
arbitrage bets, and an unforeseen fund liquidation driving all the quants to
|
||||
unwind those bets concurrently. We believe finding new relationships should
|
||||
permit investments with lower correlation and lower risks.
|
||||
|
||||
Algorithmic Investing and the Future
|
||||
====================================
|
||||
|
||||
A revolution in market understanding happens next. We want Quantopian to enable
|
||||
more quants than all of Wall Street combined. We want quants, new and old, to
|
||||
explore and share new ways to view the market. We want to clear away the
|
||||
obstacles that have so far kept all but a few from doing algorithmic investing
|
||||
by:
|
||||
|
||||
* simulating with clean, high-quality market data for free
|
||||
* easy access to markets through trusted brokers
|
||||
* providing a robust, flexible open-source backtester to permit evaluation and
|
||||
iteration of algorithms
|
||||
* supporting a community that fosters the exchange of knowledge, ideas, code
|
||||
solutions, and data sources
|
||||
|
||||
The community will find new ways to identify market opportunities. It may take
|
||||
the form of new, non-market data sources, like news feeds or Twitter. It may be
|
||||
new algorithmic techniques. Most likely, it will be something we
|
||||
haven't heard of yet: your idea. The one you keep coming back to. The
|
||||
idea you couldn't test without data. The idea that needs backtesting,
|
||||
and iteration, and encouragement from other quants.
|
||||
|
||||
Do you want to unleash your idea? This is your chance. `Come hack Wall Street
|
||||
<http://www.quantopian.com>`_.
|
||||
@@ -1,26 +0,0 @@
|
||||
qbt runs backtests using multiple processes and zeromq messaging for communication and coordination.
|
||||
|
||||
Backtest is the primary process. It maintains both server and client sockets:
|
||||
zmq sockets for internal processing::
|
||||
|
||||
- data sink, ZMQ.REQ. Port = port_start + 1
|
||||
- backtest will connect to socket, and then spawn one process per datasource, passing the data sink url as a startup arg. Each
|
||||
datasource process will bind to the socket, and start processing
|
||||
- backtest is responsible for merging the data events from all sources into a serialized stream and relaying it to the
|
||||
aggregators, merging agg results, and transmitting consolidated stream to event feed.
|
||||
- agg source, ZMQ.PUSH. Port = port_start + 2
|
||||
- agg sink, ZMQ.PULL. Port = port_start + 3
|
||||
- control source, ZMQ.PUB. Port = port_start + 4
|
||||
- all child processes must subscribe to this socket. Control commands:
|
||||
- START -- begin processing
|
||||
- TIME -- current simulated time in backtest
|
||||
- KILL -- exit immediately
|
||||
|
||||
zmq sockets for backtest clients:
|
||||
=================================
|
||||
- orders sink, ZMQ.RESP. Port = port_start + 5
|
||||
- backtest will connect (can you bind?) to this socket and await orders from the client. Order data will be processed against the streaming datafeed.
|
||||
- event feed, ZMQ.RESP. Port = port_start + 6
|
||||
- backtest will bind to this socket and respond to requests from client for more data. Response data will be the queue of events that
|
||||
transpired since the last request.
|
||||
|
||||
+3
-2
@@ -1,5 +1,6 @@
|
||||
zipline
|
||||
====
|
||||
***********************
|
||||
Packages and Modules
|
||||
***********************
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
@@ -0,0 +1,77 @@
|
||||
*******************************************
|
||||
Overview
|
||||
*******************************************
|
||||
|
||||
Simulations
|
||||
===========
|
||||
|
||||
:mod:`zipline` runs backtests using asynchronous components and zeromq messaging for
|
||||
communication and coordination.
|
||||
|
||||
:class:`.algorithm.TradingAlgorithm` is the heart of :mod:`zipline`, and the primary access point for creating,
|
||||
launching, and tracking simulations. You can find it in
|
||||
:py:class:`~zipline.algorithm.TradingAlgorithm`
|
||||
|
||||
Simulator Sub-Components
|
||||
========================
|
||||
|
||||
Each simulation contains numerous subcomponents, each operating asynchronously
|
||||
from all others, and communicating via zeromq.
|
||||
|
||||
DataSources
|
||||
--------------------
|
||||
|
||||
A DataSource represents a historical event record, which will be played back
|
||||
during simulation. A simulation may have one or more DataSources, which will be
|
||||
combined in DataFeed. Generally, datasources read records from a persistent
|
||||
store (db, csv file, remote service), format the messages for downstream
|
||||
simulation components, and send them to a PUSH socket. See the base class for
|
||||
all datasources :py:class:`~zipline.messaging.DataSource` and the module
|
||||
holding all datasources :py:mod:`zipline.sources`
|
||||
|
||||
DataFeed
|
||||
--------------------
|
||||
|
||||
All simulations start with a collection of
|
||||
:py:class:`~zipline.messaging.DataSource`, which need to be fed to an
|
||||
algorithm. Each :py:class:`~zipline.sources.DataSource`can contain events of
|
||||
differing content (trades, quotes, corporate event) and frequency (quarterly,
|
||||
intraday). To simplify the process of managing the data sources,
|
||||
:py:class:`~zipline.core.DataFeed` can receive events from multiple
|
||||
:py:class:`DataSources <zipline.sources.DataSource>` and combine them into a
|
||||
serial chronological stream.
|
||||
|
||||
Transforms
|
||||
--------------------
|
||||
|
||||
Often, an algorithm will require a running calculation on top of a
|
||||
:py:class:`~zipline.messaging.DataSource`, or on the consolidated feed. A
|
||||
simple example is a technical indicator or a moving average. Transforms can be
|
||||
described in :py:class:`~zipline.core.Simulator`'s configuration. Subclass
|
||||
:py:class:`~zipline.transforms.core.Transform` to add your own Transform.
|
||||
Transforms must hold their own state between events, and serialize their
|
||||
current values into messages.
|
||||
|
||||
|
||||
Data Alignment
|
||||
--------------------
|
||||
|
||||
Like Datasources, Transforms have differing frequencies and results. Simulator
|
||||
manages the results of parallel transforms and **aligns** transform results
|
||||
with the raw DataFeed. Client algorithms simply receive a map of data, which
|
||||
includes the current event and all the transformed values.
|
||||
|
||||
Time Compression
|
||||
--------------------
|
||||
|
||||
According to `this post
|
||||
<https://www.quantopian.com/posts/help-with-runtime-error>`_ on the Quantopian
|
||||
forums, time periods during which none of the selected SIDs were traded are
|
||||
skipped.
|
||||
|
||||
|
||||
Review the unit test coverage_.
|
||||
|
||||
|
||||
|
||||
.. _coverage: cover/index.html
|
||||
@@ -0,0 +1,55 @@
|
||||
**********
|
||||
Quickstart
|
||||
**********
|
||||
|
||||
Dual-Moving Average Example
|
||||
===========================
|
||||
|
||||
The following code implements a simple dual moving average algorithm
|
||||
and tests it on data extracted from yahoo finance.
|
||||
|
||||
.. code:: python
|
||||
|
||||
from zipline.algorithm import TradingAlgorithm
|
||||
from zipline.transforms import MovingAverage
|
||||
from zipline.utils.factory import load_from_yahoo
|
||||
|
||||
class DualMovingAverage(TradingAlgorithm):
|
||||
"""Dual Moving Average algorithm.
|
||||
"""
|
||||
def initialize(self, short_window=200, long_window=400):
|
||||
# Add 2 mavg transforms, one with a long window, one
|
||||
# with a short window.
|
||||
self.add_transform(MovingAverage, 'short_mavg', ['price'],
|
||||
market_aware=True,
|
||||
days=short_window)
|
||||
|
||||
self.add_transform(MovingAverage, 'long_mavg', ['price'],
|
||||
market_aware=True,
|
||||
days=long_window)
|
||||
|
||||
# To keep track of whether we invested in the stock or not
|
||||
self.invested = False
|
||||
|
||||
self.short_mavg = []
|
||||
self.long_mavg = []
|
||||
|
||||
|
||||
def handle_data(self, data):
|
||||
if (data['AAPL'].short_mavg['price'] > data['AAPL'].long_mavg['price']) and not self.invested:
|
||||
self.order('AAPL', 100)
|
||||
self.invested = True
|
||||
elif (data['AAPL'].short_mavg['price'] < data['AAPL'].long_mavg['price']) and self.invested:
|
||||
self.order('AAPL', -100)
|
||||
self.invested = False
|
||||
|
||||
# Save mavgs for later analysis.
|
||||
self.short_mavg.append(data['AAPL'].short_mavg['price'])
|
||||
self.long_mavg.append(data['AAPL'].long_mavg['price'])
|
||||
|
||||
data = load_from_yahoo()
|
||||
dma = DualMovingAverage()
|
||||
results = dma.run(data)
|
||||
|
||||
You can find other examples in the zipline/examples directory.
|
||||
|
||||
@@ -0,0 +1,40 @@
|
||||
:mod:`zipline.data` subpackage
|
||||
===============================
|
||||
|
||||
.. automodule:: zipline.data.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`benchmarks` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.data.benchmarks
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`loader` Module
|
||||
--------------------
|
||||
|
||||
.. automodule:: zipline.data.loader
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`loader_utils` Module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: zipline.data.loader_utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`treasuries` Module
|
||||
------------------------
|
||||
|
||||
.. automodule:: zipline.data.treasuries
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
@@ -1,5 +1,18 @@
|
||||
finance Package
|
||||
===============
|
||||
:mod:`zipline.finance` subpackage
|
||||
==================================
|
||||
|
||||
.. automodule:: zipline.finance.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`commission` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.finance.commission
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`performance` Module
|
||||
-------------------------
|
||||
@@ -17,6 +30,14 @@ finance Package
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`slippage` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.finance.slippage
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`trading` Module
|
||||
---------------------
|
||||
|
||||
|
||||
@@ -0,0 +1,40 @@
|
||||
:mod:`zipline.gens` subpackage
|
||||
==============================
|
||||
|
||||
.. automodule:: zipline.gens.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`composites` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.gens.composites
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sort` Module
|
||||
------------------
|
||||
|
||||
.. automodule:: zipline.gens.sort
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`tradesimulation` Module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: zipline.gens.tradesimulation
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`utils` Module
|
||||
---------------------
|
||||
|
||||
.. automodule:: zipline.gens.utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
+12
-8
@@ -1,18 +1,23 @@
|
||||
zipline Package
|
||||
===============
|
||||
|
||||
:mod:`zipline` Package
|
||||
----------------------
|
||||
=======================
|
||||
|
||||
.. automodule:: zipline.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`protocol` Module
|
||||
:mod:`algorithm` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.algorithm
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`sources` Module
|
||||
----------------------
|
||||
|
||||
.. automodule:: zipline.protocol
|
||||
.. automodule:: zipline.sources
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -38,10 +43,9 @@ Subpackages
|
||||
|
||||
.. toctree::
|
||||
|
||||
zipline.core
|
||||
zipline.data
|
||||
zipline.finance
|
||||
zipline.gens
|
||||
zipline.optimize
|
||||
zipline.transforms
|
||||
zipline.utils
|
||||
|
||||
|
||||
@@ -0,0 +1,47 @@
|
||||
:mod:`zipline.transforms` subpackage
|
||||
=====================================
|
||||
|
||||
.. automodule:: zipline.transforms.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`mavg` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.transforms.mavg
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`returns` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.transforms.returns
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`stddev` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.transforms.stddev
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`utils` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.transforms.utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`vwap` Module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: zipline.transforms.vwap
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -0,0 +1,56 @@
|
||||
:mod:`zipline.utils` subpackage
|
||||
===============================
|
||||
|
||||
.. automodule:: zipline.utils.__init__
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`date_utils` Module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: zipline.utils.date_utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`factory` Module
|
||||
---------------------
|
||||
|
||||
.. automodule:: zipline.utils.factory
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`protocol_units` Module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: zipline.utils.protocol_utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`simfactory` Module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: zipline.utils.simfactory
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`test_utils` Module
|
||||
------------------------
|
||||
|
||||
.. automodule:: zipline.utils.test_utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
:mod:`tradingcalendar` Module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: zipline.utils.tradingcalendar
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Reference in New Issue
Block a user