playniuniu/catalyst repository overview

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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 <https://www.catalystcrypto.io>_ to learn more about Catalyst.

Catalyst builds on top of the well-established Zipline <https://github.com/quantopian/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 <https://forum.catalystcrypto.io/>_ for questions around Catalyst, algorithmic trading and technical support. We also have a Discord <https://discord.gg/SJK32GY>_ 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 <https://github.com/enigmampc/catalyst/tree/master/catalyst/examples>_ provided.
• Support for several of the top crypto-exchanges by trading volume: Bitfinex <https://www.bitfinex.com>, Bittrex <http://www.bittrex.com>, Poloniex <https://www.poloniex.com>_ and Binance <https://www.binance.com/>_.
• 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 <https://www.enigma.co/catalyst/status>_.
• 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 <https://enigmampc.github.io/catalyst/>_.

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