Awesome Systematic Trading
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:
| Research question | Open the example |
|---|---|
| How do value and size affect equity returns? | Value and Size Effect |
| Can correlation improve commodity momentum? | Commodity Momentum |
| How do trend and mean reversion behave in Bitcoin? | Bitcoin Seasonality, Trend and Mean Reversion |
Walk through your first backtest
- Open an example, create a free account and unlock that strategy's code and results.
- Clone it into your workspace. Its backtest starts in the strategy chat.
- Inspect the result and the code. Use the available agent credit to ask which trading costs the backtest includes and how they affect the result.
Further research uses the remaining agent credit or a top-up. Backtester includes 100 strategy unlocks per month, market datasets, the course, $10/month of browser-agent credit and catalog access from your own Claude, Codex or Cursor over MCP. Compare free and paid access · Connect your own assistant
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)²years 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.
Click here to see the full table of content
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 APIs and cross-platform |  |
|
| HFTBacktest | Highly precise backtest on HFT data in Python+Numba | |
|
| PyBroker | Algorithmic trading in Python with machine learning: rule based and model driven strategies, walkforward analysis and bootstrapped significance tests on the results | |
|
| Hikyuu | C++/Python quantitative research framework built around reusable strategy components, with its own bar and indicator engine | |
|
| barter-rs | Open source Rust framework for building event driven live trading and backtesting systems, running strategies on a near identical engine on both sides |  |
|
| Investing Algorithm Framework | Framework for developing, backtesting and deploying automated trading algorithms and trading bots | |
|
| qf-lib | Modular event driven backtester with data vendor and broker integrations, portfolio construction tools and automated PDF reporting | |
|
| trade-frame | C++17 library and sample applications for automated trading of equities, futures, currencies, ETFs and options on IQFeed and Interactive Brokers data | |
|
| QuantFabric | Linux/C++ mid and high frequency trading system for the Chinese futures, stock and bond exchanges | |
|
| 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 dormant since 2023-09 |
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 dormant since 2024-06 |
Backtest and live trading in Python. Event based. Similar to backtesting.py. | |
|
gobacktest archived |
A Go implementation of event-driven backtesting framework |  |
|
| PineForge | Transpiles PineScript v6 strategies to C++ and runs deterministic offline backtests on user-provided OHLCV data. | |
|
| FlashFunk | High Performance Runtime in Rust | |
General - Vector Based Frameworks
| Repository | Description | Stars | Made with |
|---|---|---|---|
| QTradeX | A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots | |
|
| vectorbt | vectorbt takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds. | |
|
| pysystemtrade | Systematic Trading in python from book Systematic Trading by Rob Carver | |
|
| bt | Flexible backtesting for Python based on Algo and Strategy Tree | |
|
| ml-quant-trading | PyTorch research stack for ML multi-factor trading with 213 factors, bias correction, portfolio optimization, vectorized backtesting, and public validation reports | |
Cryptocurrencies
| Repository | Description | Stars | Made with |
|---|---|---|---|
| Freqtrade | Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning. | |
|
| Jesse | Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies. | |
|
| OctoBot | Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface | |
|
Kelp archived |
Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges | |
|
| basana | Python async and event driven framework for algorithmic trading, with a focus on crypto currencies | |
|
openlimits dormant since 2022-07 |
A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers. | |
|
bTrader archived |
Triangle arbitrage trading bot for Binance | |
|
crypto-crawler-rs dormant since 2023-03 |
Crawl orderbook and trade messages from crypto exchanges | |
|
| Hummingbot | A client for crypto market making | |
|
cryptotrader-core dormant since 2019-06 |
Simple to use Crypto Exchange REST API client in rust. | |
Trading bots
Trading bots and alpha models. Some of them are old and not maintained.
| Repository | Description | Stars | Made with |
|---|---|---|---|
Blackbird no longer available |
Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy | |
|
| bitcoin-arbitrage | Bitcoin arbitrage - opportunity detector | |
|
| ThetaGang | ThetaGang is an IBKR bot for collecting money | |
|
| czsc | 缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易 | |
|
R2 Bitcoin Arbitrager dormant since 2023-04 |
R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript |  |
|
| Intelligent Trading Bot | Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering | |
|
analyzingalpha dormant since 2023-08 |
Implementation of simple strategies | |
|
PyTrendFollow dormant since 2018-04 |
PyTrendFollow - systematic futures trading using trend following | |
|
| TradeSight | AI-powered algorithmic trading platform with RSI/MACD signals, overnight strategy tournaments, paper trading via Alpaca, multi-stock scanning, and web dashboard | |
|
| PRISM-INSIGHT | AI-powered stock analysis with 13 specialized agents, automated trading via KIS API (Korean & US markets) | |
Analytics
Indicators
Libraries of indicators to predict future price movements.
