<div align="center"> <img src="static/images/awesome-systematic-trading.jpeg" height=200 alt=""/> <h1>Awesome Systematic Trading</h1> </div> <div align=center><img src="https://awesome.re/badge.svg" /></div>

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We are collecting a list of resources papers, softwares, books, articles for finding, developing, and running systematic trading (quantitative trading) strategies.

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What will you find here?

• 97 libraries and packages for research and live trading
• 40+ strategies described by institutionals and academics
• 55 books for beginners and professionals
• 23 videos and interviews
• And also some blogs and courses

<div align="center" style="margin-bottom: 50px; margin-top: 50px;"> <div style="border: 2px solid #007bff; border-radius: 10px; padding: 20px; margin-bottom: 20px;"> <h2>📈 Interested in trading strategies implemented in Python?</h2> <p>Visit our comprehensive collection at <a href="https://paperswithbacktest.com" target="_blank">paperswithbacktest.com</a> for exclusive content!</p> </div> </div> <details> <summary>Click here to see the full table of content</summary>

• Libraries and packages
  • Backtesting and Live Trading
    • General - Event Driven Frameworks
    • General - Vector Based Frameworks
    • Cryptocurrencies
  • Trading bots
  • Analytics
    • Indicators
    • Metrics computation
    • Optimization
    • Pricing
    • Risk
  • Broker APIs
  • Data Sources
    • General
    • Cryptocurrencies
  • Data Science
  • Databases
  • Graph Computation
  • Machine Learning
  • TimeSeries Analysis
  • Visualization
• Strategies
  • Bonds, commodities, currencies, equities
  • Bonds, commodities, equities, REITs
  • Bonds, equities
  • Bonds, equities, REITs
  • Commodities
  • Cryptos
  • Currencies
  • Equities
• Books
  • Beginner
  • Biography
  • Coding
  • Crypto
  • General
  • High Frequency Trading
  • Machine Learning
• Videos
• Blogs
• Courses

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Libraries and packages

List of 97 libraries and packages implementing trading bots, backtesters, indicators, pricers, etc. Each library is categorized by its programming language and ordered by descending populatrity (number of stars).

Backtesting and Live Trading

General - Event Driven Frameworks

| Repository | Description | Stars | Made with | |------------|-------------|-------|-----------| | vnpy | Python-based open source quantitative trading system development framework, officially released in January 2015, has grown step by step into a full-featured quantitative trading platform | | | | zipline | Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. | | | | backtrader | Event driven Python Backtesting library for trading strategies | | | | QUANTAXIS | QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权/港股/虚拟货币 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案 | | | | QuantConnect | Lean Algorithmic Trading Engine by QuantConnect (Python, C#) | | | | Rqalpha | A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities | | | | finmarketpy | Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians) | | | | backtesting.py | Backtesting.py is a Python framework for inferring viability of trading strategies on historical (past) data. Improved upon the vision of Backtrader, and by all means surpassingly comparable to other accessible alternatives, Backtesting.py is lightweight, fast, user-friendly, intuitive, interactive, intelligent and, hopefully, future-proof. | | | | zvt | Modular quant framework | | | | WonderTrader | WonderTrader——量化研发交易一站式框架 | | | | nautilus_trader | A high-performance algorithmic trading platform and event-driven backtester | | | | PandoraTrader | High-frequency quantitative trading platform based on c++ development, supporting multiple trading APIs and cross-platform | | | | HFTBacktest | Highly precise backtest on HFT data in Python+Numba | | | | aat | An asynchronous, event-driven framework for writing algorithmic trading strategies in python with optional acceleration in C++. It is designed to be modular and extensible, with support for a wide variety of instruments and strategies, live trading across (and between) multiple exchanges. | | | | sdoosa-algo-trade-python | This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter. | | | | lumibot | A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...) | | | | quanttrader | Backtest and live trading in Python. Event based. Similar to backtesting.py. | | | | gobacktest | A Go implementation of event-driven backtesting framework | | | | FlashFunk | High Performance Runtime in Rust | | |

General - Vector Based Frameworks

| Repository | Description | Stars | Made with | |------------|-------------|-------|-----------| | [vectorbt](https://github.