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Merge branch 'develop' of github.com:enigmampc/catalyst into develop
This commit is contained in:
Frederic Fortier
+72
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@@ -1,3 +1,72 @@
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All the documentation for `Catalyst <https://github.com/enigmampc/catalyst>`_
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can be found in the
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`documentation website <https://enigmampc.github.io/catalyst>`_.
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.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.jpg
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:target: https://enigmampc.github.io/catalyst
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:align: center
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:alt: Enigma | Catalyst
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|version tag|
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|version status|
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|discord|
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|twitter|
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Catalyst is an algorithmic trading library for crypto-assets written in Python.
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It allows trading strategies to be easily expressed and backtested against
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historical data (with daily and minute resolution), providing analytics and
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insights regarding a particular strategy's performance. Catalyst also supports
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live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex,
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and Poloniex) with more being added over time. Catalyst empowers users to share
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and curate data and build profitable, data-driven investment strategies. Please
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visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst, or
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refer to the `whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for
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further technical details.
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Catalyst builds on top of the well-established
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`Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to
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minimize structural changes to the general API to maximize compatibility with
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existing trading algorithms, developer knowledge, and tutorials. Join us on
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`Discord <https://discord.gg/SJK32GY>`_ where we have a *#catalyst_dev* channel
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for questions around Catalyst, algorithmic trading and technical support.
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Overview
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========
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- Ease of use: Catalyst tries to get out of your way so that you can
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focus on algorithm development. See
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`examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_
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provided.
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- Support for several of the top crypto-exchanges by trading volume:
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`Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
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and `Poloniex <https://www.poloniex.com>`_.
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- Secure: You and only you have access to each exchange API keys for your accounts.
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- Input of historical pricing data of all crypto-assets by exchange,
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with daily and minute resolution. See
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`Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
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- Backtesting and live-trading functionality, with a seamless transition
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between the two modes.
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- Output of performance statistics are based on Pandas DataFrames to
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integrate nicely into the existing PyData eco-system.
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- Statistic and machine learning libraries like matplotlib, scipy,
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statsmodels, and sklearn support development, analysis, and
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visualization of state-of-the-art trading systems.
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- Addition of Bitcoin price (btc_usdt) as a benchmark for comparing
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performance across trading algorithms.
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Go to our `Documentation Website <https://enigmampc.github.io/catalyst/>`_.
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.. |version tag| image:: https://img.shields.io/pypi/v/enigma-catalyst.svg
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:target: https://pypi.python.org/pypi/enigma-catalyst
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.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg
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:target: https://pypi.python.org/pypi/enigma-catalyst
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.. |discord| image:: https://img.shields.io/badge/discord-join%20chat-green.svg
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:target: https://discordapp.com/invite/SJK32GY
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.. |twitter| image:: https://img.shields.io/twitter/follow/enigmampc.svg?style=social&label=Follow&style=flat-square
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:target: https://twitter.com/enigmampc
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@@ -63,6 +63,7 @@ def handle_data(context, data):
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# get data every 30 minutes
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minutes = 30
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# get lookback_days of history data: that is 'lookback' number of bins
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lookback = one_day_in_minutes / minutes * lookback_days
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if not context.i % minutes and context.universe:
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@@ -1,4 +1,4 @@
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.. include:: welcome.rst
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.. include:: ../../README.rst
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Table of Contents
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+15
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Catalyst & Jupyter Notebook
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===========================
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(Feel free to check out the actual Notebook file
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`here <https://github.com/enigmampc/catalyst-docs/blob/master/docs/running_catalyst_in_jupyter_notebook.ipynb>`__)
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(`This is actual Notebook <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/running_catalyst_in_jupyter_notebook.ipynb>`_ referenced in the text below)
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The `Jupyter Notebook <https://jupyter.org/>`__ is a very powerful
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browser-based interface to a Python interpreter. As it is already the
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@@ -33,11 +32,19 @@ the interface through which you will interact with Jupyter Notebook.
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Running Algorithms
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^^^^^^^^^^^^^^^^^^
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To use it you have to write your algorithm in a cell and let
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``catalyst`` know that it is supposed to run this algorithm. This is
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done via the ``%%catalyst`` IPython magic command that is available
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after you import ``catalyst`` from within the Notebook. This magic takes
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the same arguments as the command line interface. Thus to run the
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Before running your algorithms inside the Jupyter Notebook, remember to ingest
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the data from the command line interface (CLI). In the example below, you would
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need to run first:
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.. code:: bash
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catalyst ingest-exchange -x bitfinex -i btc_usd
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To use Catalyst inside a Jupyter Noebook, you have to write your algorithm in a
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cell and let the Jupyter know that it is supposed to execute this algorithm with
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Catalyst. This is done via the ``%%catalyst`` IPython magic command that is
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available after you import ``catalyst`` from within the Notebook. This magic
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takes the same arguments as the command line interface. Thus to run the
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algorithm just supply the same parameters as the CLI but without the -f
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and -o arguments. We just have to execute the following cell after
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importing ``catalyst`` to register the magic.
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@@ -58,7 +65,7 @@ functions.
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.. code:: python
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%%catalyst --start 2015-3-2 --end 2017-6-28 --capital-base 100000 -x bitfinex
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%%catalyst --start 2015-3-2 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd
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from catalyst.finance.slippage import VolumeShareSlippage
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@@ -199,8 +206,6 @@ functions.
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.. figure:: https://i.imgur.com/DS5w47q.png
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:alt: png
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png
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.. raw:: html
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<div>
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@@ -1,43 +0,0 @@
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.. image:: https://s3.amazonaws.com/enigmaco-docs/enigma-catalyst.jpg
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|
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Catalyst is an algorithmic trading library for crypto-assets written in Python.
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It allows trading strategies to be easily expressed and backtested against
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historical data (with daily and minute resolution), providing analytics and
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insights regarding a particular strategy's performance. Catalyst also supports
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live-trading of crypto-assets starting with three exchanges (Bitfinex, Bittrex,
|
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and Poloniex) with more being added over time. Catalyst empowers users to share
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and curate data and build profitable, data-driven investment strategies. Please
|
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visit `enigma.co <https://www.enigma.co>`_ to learn more about Catalyst, or
|
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refer to the `whitepaper <https://www.enigma.co/enigma_catalyst.pdf>`_ for
|
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further technical details.
|
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Catalyst builds on top of the well-established
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`Zipline <https://github.com/quantopian/zipline>`_ project. We did our best to
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minimize structural changes to the general API to maximize compatibility with
|
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existing trading algorithms, developer knowledge, and tutorials. Join us on
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`Discord <https://discord.gg/SJK32GY>`_ where we have a *#catalyst_dev* channel
|
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for questions around Catalyst, algorithmic trading and technical support.
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Features
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========
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- Ease of use: Catalyst tries to get out of your way so that you can
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focus on algorithm development. See
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`examples of trading strategies <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>`_
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provided.
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- Support for several of the top crypto-exchanges by trading volume:
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`Bitfinex <https://www.bitfinex.com>`_, `Bittrex <http://www.bittrex.com>`_,
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and `Poloniex <https://www.poloniex.com>`_.
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- Secure: You and only you have access to each exchange API keys for your accounts.
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- Input of historical pricing data of all crypto-assets by exchange,
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with daily and minute resolution. See
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`Catalyst Market Coverage Overview <https://www.enigma.co/catalyst/status>`_.
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- Backtesting and live-trading functionality, with a seamless transition
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between the two modes.
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||||
- Output of performance statistics are based on Pandas DataFrames to
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integrate nicely into the existing PyData eco-system.
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- Statistic and machine learning libraries like matplotlib, scipy,
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statsmodels, and sklearn support development, analysis, and
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visualization of state-of-the-art trading systems.
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- Addition of Bitcoin price (btc_usdt) as a benchmark for comparing
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performance across trading algorithms.
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