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https://github.com/scrtlabs/catalyst

An Algorithmic Trading Library for Crypto-Assets in Python
https://github.com/scrtlabs/catalyst

algorithmic-trading cryptocurrencies cryptocurrency trading

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An Algorithmic Trading Library for Crypto-Assets in Python

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README 

        
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    :target: https://enigmampc.github.io/catalyst

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    :alt: Enigma | Catalyst



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⚠️ DEPRECATION WARNING ⚠️

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This repo is no longer actively maintained since the end of 2018. If you wish to use this project or get support for it, there are many forks that may be more active. If any of those is still active, please get in touch with them, as we can no longer provide support for it.



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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 (with daily and minute resolution), providing analytics and 

insights regarding a particular strategy's performance. Catalyst also supports

live-trading of crypto-assets starting with four exchanges (Binance, Bitfinex, Bittrex,

and Poloniex) with more being added over time. Catalyst empowers users to share 

and curate data and build profitable, data-driven investment strategies. Please 

visit `catalystcrypto.io `_ to learn more about Catalyst.



Catalyst builds on top of the well-established 

`Zipline `_ project. We did our best to 

minimize structural changes to the general API to maximize compatibility with 

existing trading algorithms, developer knowledge, and tutorials. Join us on the 

`Catalyst Forum `_ for questions around Catalyst,

algorithmic trading and technical support. We also have a 

`Discord `_ group with the *#catalyst_dev* and 

*#catalyst_setup* dedicated channels.



Overview

========



-  Ease of use: Catalyst tries to get out of your way so that you can 

   focus on algorithm development. See 

   `examples of trading strategies `_ 

   provided.

-  Support for several of the top crypto-exchanges by trading volume:

   `Bitfinex `_, `Bittrex `_,

   `Poloniex `_ and `Binance `_.

-  Secure: You and only you have access to each exchange API keys for your accounts.

-  Input of historical pricing data of all crypto-assets by exchange, 

   with daily and minute resolution. See 

   `Catalyst Market Coverage Overview `_.

-  Backtesting and live-trading functionality, with a seamless transition

   between the two modes.

-  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.

-  Addition of Bitcoin price (btc_usdt) as a benchmark for comparing 

   performance across trading algorithms.



Go to our `Documentation Website `_.



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   :target: https://pypi.python.org/pypi/enigma-catalyst



.. |version status| image:: https://img.shields.io/pypi/pyversions/enigma-catalyst.svg

   :target: https://pypi.python.org/pypi/enigma-catalyst

   

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   :target: https://forum.catalystcrypto.io/



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   :target: https://discordapp.com/invite/SJK32GY



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   :target: https://twitter.com/catalystcrypto



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   :target: https://travis-ci.com/enigmampc/catalyst



.. |travis-master| image:: https://travis-ci.com/enigmampc/catalyst.svg?branch=master

   :target: https://travis-ci.com/enigmampc/catalyst