Awesome Systematic Trading

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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.

Run your first paper with Papers With Backtest

Start with a published strategy, its Python code and its data. Clone it into your workspace and run a backtest in your browser.

Try the value-and-size example →

No card or local setup required. A free account includes one full strategy unlock, one clone and $1 of research-agent credit, once per account. The strategy you unlock stays readable; the allowance does not reset.

Choose one starting point for your free unlock:

What will you find here?

• 136 libraries and packages for research and live trading, with dead and dormant projects flagged
• Strategies from published papers, with the Sharpe ratio each one produced when it was coded and run
• 55 books for beginners and professionals
• 22 videos and interviews
• And also some blogs and courses

What the replication record looks like

We have coded and run 4,843 of these papers over their own full history. Some numbers worth knowing before you pick one to implement:

• The median replication returns a Sharpe ratio of 0.37, and 48% clear a t-statistic of 1.96. Half the published record cannot be distinguished from zero on its own sample.
• Median test window: 34 years. A strategy needs roughly (1.96 / Sharpe)² years to prove itself, so a Sharpe of 0.4 needs about 24 of them.
• The median strategy carries a beta of +0.17 to the S&P 500. Removing it takes the median information ratio down to 0.21, so a meaningful slice of the published edge is index exposure rather than skill.
• Across 2,838 papers with a record on both sides of their publication date, we could find no measurable decay after publication once the market period is controlled for, to within a fifth of a percentage point a year.

Method and caveats are written up on the wiki.

How can I help?

You can help by submitting an issue with suggestions and by sharing on Twitter:

Libraries and packages

List of 136 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 dormant since 2024-02	Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.
backtrader dormant since 2024-08	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