mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-17 11:25:55 +08:00
Update README.rst
This commit is contained in:
Conner Fromknecht
+136
-133
@@ -1,174 +1,177 @@
|
||||
.. image:: https://media.quantopian.com/logos/open_source/zipline-logo-03_.png
|
||||
:target: http://www.zipline.io
|
||||
:width: 212px
|
||||
:align: center
|
||||
:alt: Zipline
|
||||
|
||||
=============
|
||||
|
||||
|Gitter|
|
||||
|version status|
|
||||
|travis status|
|
||||
|appveyor status|
|
||||
|Coverage Status|
|
||||
|
||||
Zipline is a Pythonic algorithmic trading library. It is an event-driven
|
||||
system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading
|
||||
engine powering `Quantopian <https://www.quantopian.com>`_ -- a free,
|
||||
community-centered, hosted platform for building and executing trading
|
||||
strategies.
|
||||
|
||||
`Join our
|
||||
community! <https://groups.google.com/forum/#!forum/zipline>`_
|
||||
|
||||
`Documentation <http://www.zipline.io>`_
|
||||
|
||||
Want to contribute? See our `development guidelines`__
|
||||
|
||||
__ http://zipline.io/development-guidelines.html
|
||||
|
||||
Features
|
||||
Catalyst
|
||||
========
|
||||
|
||||
- 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 are
|
||||
based on Pandas DataFrames to integrate nicely into the existing
|
||||
PyData eco-system.
|
||||
- Statistic and machine learning libraries like matplotlib, scipy,
|
||||
statsmodels, and sklearn support development, analysis, and
|
||||
visualization of state-of-the-art trading systems.
|
||||
|version status|
|
||||
|
||||
Catalyst is an algorithmic trading library for crypto-assets written in Python.
|
||||
It allows trading strategies to be easily expressed and backtested against historical data, providing analytics and insights regarding a particular strategy's performance.
|
||||
Catalyst will be expanded to support live-trading of crypto-assets in the coming months.
|
||||
Please visit `<enigma.co/catalyst>`_ to learn about Catalyst, or refer to the
|
||||
`whitepaper <http://www.enigma.co/enigma_catalyst.pdf>`_ for further technical details.
|
||||
|
||||
Interested in getting involved?
|
||||
`Join our slack! <https://join.slack.com/enigmacatalyst/shared_invite/MTkzMjQ0MTg1NTczLTE0OTY3MjE3MDEtZGZmMTI5YzI3ZA>`_
|
||||
|
||||
|
||||
Installation
|
||||
============
|
||||
|
||||
Installing With ``pip``
|
||||
-----------------------
|
||||
|
||||
Assuming you have all required (see note below) non-Python dependencies, you
|
||||
can install Zipline with ``pip`` via:
|
||||
At the moment, Catalyst has some fairly specific and strict depedency requirements.
|
||||
We recommend the use of Python virtual environments if you wish to simplify the installation process, or otherwise isolate Catalyst's dependencies from your other projects.
|
||||
If you don't have ``virtualenv`` installed, see our later section on Virtual Environments.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ pip install zipline
|
||||
$ virtualenv catalyst-venv
|
||||
$ source ./catalyst-venv/bin/activate
|
||||
$ pip install enigma-catalyst
|
||||
|
||||
**Note:** Installing Zipline via ``pip`` is slightly more involved than the
|
||||
average Python package. Simply running ``pip install zipline`` will likely
|
||||
fail if you've never installed any scientific Python packages before.
|
||||
**Note:** A successful installation will require several minutes in order to compile dependencies that expose C APIs.
|
||||
|
||||
There are two reasons for the additional complexity:
|
||||
Dependencies
|
||||
------------
|
||||
|
||||
1. Zipline ships several C extensions that require access to the CPython C API.
|
||||
In order to build the C extensions, ``pip`` needs access to the CPython
|
||||
header files for your Python installation.
|
||||
|
||||
2. Zipline depends on `numpy <http://www.numpy.org/>`_, the core library for
|
||||
numerical array computing in Python. Numpy depends on having the `LAPACK
|
||||
<http://www.netlib.org/lapack>`_ linear algebra routines available.
|
||||
|
||||
Because LAPACK and the CPython headers are binary dependencies, the correct way
|
||||
to install them varies from platform to platform. On Linux, users generally
|
||||
acquire these dependencies via a package manager like ``apt``, ``yum``, or
|
||||
``pacman``. On OSX, `Homebrew <http://www.brew.sh>`_ is a popular choice
|
||||
providing similar functionality.
|
||||
|
||||
See the full `Zipline Install Documentation`_ for more information on acquiring
|
||||
binary dependencies for your specific platform.
|
||||
|
||||
conda
|
||||
-----
|
||||
|
||||
Another way to install Zipline is via the ``conda`` package manager, 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:
|
||||
Catalyst's depedencies can be found in the ``etc/requirements.txt`` file.
|
||||
If you need to install them outside of a typical ``pip install``, this is done using:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ conda install -c Quantopian zipline
|
||||
$ pip install -r etc/requirements.txt
|
||||
|
||||
Currently supported platforms include:
|
||||
Though not required by Catalyst directly, our example algorithms use matplotlib to visually display backtest results.
|
||||
If you wish to run any examples or use matplotlib during development, it can be installed using:
|
||||
|
||||
- GNU/Linux 64-bit
|
||||
- OSX 64-bit
|
||||
- Windows 64-bit
|
||||
.. code-block:: bash
|
||||
|
||||
.. note::
|
||||
$ pip install matplotlib
|
||||
|
||||
Windows 32-bit may work; however, it is not currently included in
|
||||
continuous integration tests.
|
||||
Virtual Environments
|
||||
--------------------
|
||||
|
||||
Here we will provide a brief tutorial for installing ``virtualenv`` and its basic usage.
|
||||
For more information regarding ``virtualenv``, please refer to this `virtualenv guide <http://python-guide-pt-br.readthedocs.io/en/latest/dev/virtualenvs/>`_.
|
||||
|
||||
The ``virtualenv`` command can be installed using:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ pip install virtualenv
|
||||
|
||||
To create a new virtual environment, choose a directory, e.g. ``/path/to/venv-dir``, where project-specific packages and files will be stored. The environment is created by running:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ virtualenv /path/to/venv-dir
|
||||
|
||||
To enter an environment, run the ``bin/activate`` script located in ``/path/to/venv-dir`` using:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ source /path/to/venv-dir/bin/activate
|
||||
|
||||
Exiting an environment is accomplished using ``deactivate``, and removing it entirely is done by deleting ``/path/to/venv-dir``.
|
||||
|
||||
Using virtualenv & matplotlib on OS X
|
||||
-------------------------------------
|
||||
|
||||
A note about using matplotlib in virtual enviroments on OS X: it may be necessary to add
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
backend : TkAgg
|
||||
|
||||
to your ``~/.matplotlib/matplotlibrc`` file, in order to override the default ``macosx`` backend for your system, which may not be accessible from inside the virtual environment.
|
||||
This will allow Catalyst to open matplotlib charts from within a virtual environment, which is useful for displaying the performance of your backtests. To learn more about matplotlib backends, please refer to the
|
||||
`matplotlib backend documentation <https://matplotlib.org/faq/usage_faq.html#what-is-a-backend>`_.
|
||||
|
||||
Quickstart
|
||||
==========
|
||||
|
||||
See our `getting started
|
||||
tutorial <http://www.zipline.io/#quickstart>`_.
|
||||
See our `getting started tutorial <http://www.zipline.io/#quickstart>`_.
|
||||
|
||||
The following code implements a simple dual moving average algorithm.
|
||||
The following code implements a simple buy and hodl algorithm.
|
||||
|
||||
.. code:: python
|
||||
|
||||
from zipline.api import order_target, record, symbol
|
||||
import numpy as np
|
||||
|
||||
from catalyst.api import (
|
||||
order_target_value,
|
||||
symbol,
|
||||
record,
|
||||
cancel_order,
|
||||
get_open_orders,
|
||||
)
|
||||
|
||||
ASSET = 'USDT_BTC'
|
||||
|
||||
TARGET_HODL_RATIO = 0.8
|
||||
RESERVE_RATIO = 1.0 - TARGET_HODL_RATIO
|
||||
|
||||
def initialize(context):
|
||||
context.i = 0
|
||||
context.asset = symbol('AAPL')
|
||||
|
||||
context.is_hodling = True
|
||||
context.asset = symbol(ASSET)
|
||||
|
||||
def handle_data(context, data):
|
||||
# Skip first 300 days to get full windows
|
||||
context.i += 1
|
||||
if context.i < 300:
|
||||
return
|
||||
|
||||
# Compute averages
|
||||
# data.history() has to be called with the same params
|
||||
# from above and returns a pandas dataframe.
|
||||
short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
|
||||
long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()
|
||||
|
||||
# Trading logic
|
||||
if short_mavg > long_mavg:
|
||||
# order_target orders as many shares as needed to
|
||||
# achieve the desired number of shares.
|
||||
order_target(context.asset, 100)
|
||||
elif short_mavg < long_mavg:
|
||||
order_target(context.asset, 0)
|
||||
|
||||
# Save values for later inspection
|
||||
record(AAPL=data.current(context.asset, 'price'),
|
||||
short_mavg=short_mavg,
|
||||
long_mavg=long_mavg)
|
||||
cash = context.portfolio.cash
|
||||
target_hodl_value = TARGET_HODL_RATIO * context.portfolio.starting_cash
|
||||
reserve_value = RESERVE_RATIO * context.portfolio.starting_cash
|
||||
|
||||
# Cancel any outstanding orders from the previous day
|
||||
orders = get_open_orders(context.asset) or []
|
||||
for order in orders:
|
||||
cancel_order(order)
|
||||
|
||||
# Stop hodling after passing reserve threshold
|
||||
if cash <= reserve_value:
|
||||
context.is_hodling = False
|
||||
|
||||
# Retrieve current price from pricing data
|
||||
price = data[context.asset].price
|
||||
|
||||
# Check if still hodling and could afford another purchase
|
||||
if context.is_hodling and cash > price:
|
||||
order_target_value(
|
||||
context.asset,
|
||||
target_hodl_value,
|
||||
limit_price=1.1 * price,
|
||||
stop_price=0.9 * price,
|
||||
)
|
||||
|
||||
# Record any state for later analysis
|
||||
record(
|
||||
price=price,
|
||||
cash=context.portfolio.cash,
|
||||
leverage=context.account.leverage,
|
||||
)
|
||||
|
||||
|
||||
You can then run this algorithm using the Zipline CLI. From the command
|
||||
You can then run this algorithm using the Catalyst CLI. From the command
|
||||
line, run:
|
||||
|
||||
.. code:: bash
|
||||
|
||||
$ zipline ingest
|
||||
$ zipline run -f dual_moving_average.py --start 2011-1-1 --end 2012-1-1 -o dma.pickle
|
||||
$ catalyst ingest
|
||||
$ catalyst run -f buy_and_hodl.py --start 2015-1-1 --end 2016-6-25 --captial-base 100000
|
||||
|
||||
This will download the AAPL price data from `quantopian-quandl` 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.
|
||||
This will download the crypto-asset price data from a poloniex bundle
|
||||
curated by Enigma in the specified time range and stream it through
|
||||
the algorithm and plot the resulting performance using matplotlib.
|
||||
|
||||
You can find other examples in the ``zipline/examples`` directory.
|
||||
You can find other examples in the ``catalyst/examples`` directory.
|
||||
|
||||
.. |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
|
||||
.. |travis status| image:: https://travis-ci.org/quantopian/zipline.png?branch=master
|
||||
:target: https://travis-ci.org/quantopian/zipline
|
||||
.. |appveyor status| image:: https://ci.appveyor.com/api/projects/status/3dg18e6227dvstw6/branch/master?svg=true
|
||||
:target: https://ci.appveyor.com/project/quantopian/zipline/branch/master
|
||||
.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.png
|
||||
:target: https://coveralls.io/r/quantopian/zipline
|
||||
Disclaimer
|
||||
==========
|
||||
|
||||
.. _`Zipline Install Documentation` : http://www.zipline.io/install.html
|
||||
Keep in mind that this project is still under active development, and is not recommended for production use in its current state.
|
||||
We are deeply committed to improving the overall user experience, reliability, and feature-set offered by Catalyst.
|
||||
If you have any suggestions, feedback, or general improvements regarding any of these topics, please let us know!
|
||||
|
||||
Hello World,
|
||||
|
||||
The Enigma Team
|
||||
|
||||
.. |version status| image:: https://img.shields.io/pypi/pyversions/catalyst-hf.svg
|
||||
:target: https://testpypi.python.org/pypi/catalyst-hf
|
||||
|
||||
.. _`Catalyst Install Documentation` : https://enigma.co/catalyst/install.html
|
||||
|
||||
Reference in New Issue
Block a user