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:
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.
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What will you find here?
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.
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 |
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zipline dormant since 2024-02 |
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. |
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backtrader dormant since 2024-08 |
Event driven Python Backtesting library for trading strategies |
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| 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 |
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| QuantConnect |
Lean Algorithmic Trading Engine by QuantConnect (Python, C#) |
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| Rqalpha |
A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities |
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| finmarketpy |
Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians) |
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|
| 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. |
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| zvt |
Modular quant framework |
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| WonderTrader |
WonderTrader——量化研发交易一站式框架 |
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| nautilus_trader |
A high-performance algorithmic trading platform and event-driven backtester |
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| PandoraTrader |
High-frequency quantitative trading platform based on c++ development, supporting multiple trading APIs and cross-platform |
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| HFTBacktest |
Highly precise backtest on HFT data in Python+Numba |
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| PyBroker |
Algorithmic trading in Python with machine learning: rule based and model driven strategies, walkforward analysis and bootstrapped significance tests on the results |
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| Hikyuu |
C++/Python quantitative research framework built around reusable strategy components, with its own bar and indicator engine |
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| 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 |
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| Investing Algorithm Framework |
Framework for developing, backtesting and deploying automated trading algorithms and trading bots |
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| qf-lib |
Modular event driven backtester with data vendor and broker integrations, portfolio construction tools and automated PDF reporting |
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| trade-frame |
C++17 library and sample applications for automated trading of equities, futures, currencies, ETFs and options on IQFeed and Interactive Brokers data |
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| QuantFabric |
Linux/C++ mid and high frequency trading system for the Chinese futures, stock and bond exchanges |
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| 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. |
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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. |
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| lumibot |
A very simple yet useful backtesting and sample based live trading framework (a bit slow to run…) |
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quanttrader dormant since 2024-06 |
Backtest and live trading in Python. Event based. Similar to backtesting.py. |
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gobacktest archived |
A Go implementation of event-driven backtesting framework |
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| PineForge |
Transpiles PineScript v6 strategies to C++ and runs deterministic offline backtests on user-provided OHLCV data. |
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| FlashFunk |
High Performance Runtime in Rust |
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General - Vector Based Frameworks
| Repository |
Description |
Stars |
Made with |
| QTradeX |
A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots |
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| 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. |
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| pysystemtrade |
Systematic Trading in python from book Systematic Trading by Rob Carver |
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| bt |
Flexible backtesting for Python based on Algo and Strategy Tree |
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| ml-quant-trading |
PyTorch research stack for ML multi-factor trading with 213 factors, bias correction, portfolio optimization, vectorized backtesting, and public validation reports |
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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. |
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| Jesse |
Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies. |
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| OctoBot |
Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface |
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Kelp archived |
Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges |
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| basana |
Python async and event driven framework for algorithmic trading, with a focus on crypto currencies |
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openlimits dormant since 2022-07 |
A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers. |
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bTrader archived |
Triangle arbitrage trading bot for Binance |
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crypto-crawler-rs dormant since 2023-03 |
Crawl orderbook and trade messages from crypto exchanges |
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| Hummingbot |
A client for crypto market making |
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cryptotrader-core dormant since 2019-06 |
Simple to use Crypto Exchange REST API client in rust. |
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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 |
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| bitcoin-arbitrage |
Bitcoin arbitrage - opportunity detector |
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| ThetaGang |
ThetaGang is an IBKR bot for collecting money |
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| czsc |
缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易 |
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R2 Bitcoin Arbitrager dormant since 2023-04 |
R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript |
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| Intelligent Trading Bot |
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering |
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analyzingalpha dormant since 2023-08 |
Implementation of simple strategies |
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PyTrendFollow dormant since 2018-04 |
PyTrendFollow - systematic futures trading using trend following |
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| TradeSight |
AI-powered algorithmic trading platform with RSI/MACD signals, overnight strategy tournaments, paper trading via Alpaca, multi-stock scanning, and web dashboard |
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| PRISM-INSIGHT |
AI-powered stock analysis with 13 specialized agents, automated trading via KIS API (Korean & US markets) |
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Analytics
Indicators
Libraries of indicators to predict future price movements.
| Repository |
Description |
Stars |
Made with |
| ta-lib |
Perform technical analysis of financial market data |
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go-tart dormant since 2021-06 |
A Go implementation of the [ta-lib]((https://github.com/mrjbq7/ta-lib) with streaming update support |
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pandas-ta no longer available |
Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns |
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finta archived |
Common financial technical indicators implemented in Pandas |
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ta-rust dormant since 2024-07 |
Technical analysis library for Rust language |
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| kand |
Technical analysis library written in Rust with Python and WASM bindings, exposing both batch and incremental streaming updates |
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| wickra |
Streaming-first technical-analysis library with a Rust core and native Python/Node/WASM bindings plus a C ABI (C, C++, C#/.NET, Go, Java, R); 514 O(1)-per-tick indicators across 24 families, bit-exact batch and streaming |
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Metrics computation
Librairies of financial metrics.
| Repository |
Description |
Stars |
Made with |
| quantstats |
Portfolio analytics for quants, written in Python |
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| ffn |
A financial function library for Python |
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Optimization
| Repository |
Description |
Stars |
Made with |
| skfolio |
Portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models. |
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| PyPortfolioOpt |
Financial portfolio optimizations in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity |
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| Riskfolio-Lib |
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python |
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| empyrial |
Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021 |
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| cvxportfolio |
Portfolio optimization and back-testing from the Stanford convex optimization group, implementing the multi-period framework of Boyd et al. |
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Deepdow dormant since 2024-01 |
Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass. |
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| spectre |
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python |
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Pricing
| Repository |
Description |
Stars |
Made with |
| tf-quant-finance |
High-performance TensorFlow library for quantitative finance from Google |
 |
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| FinancePy |
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives |
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| PyQL |
Python wrapper of the famous pricing library QuantLib |
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Risk
| Repository |
Description |
Stars |
Made with |
pyfolio dormant since 2023-12 |
Portfolio and risk analytics in Python |
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Broker APIs
| Repository |
Description |
Stars |
Made with |
| ccxt |
A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges |
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Ib_insync archived |
Python sync/async framework for Interactive Brokers. |
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| pmxt |
Unified prediction market trading API across Polymarket, Kalshi and other venues, in the spirit of ccxt |
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Coinnect dormant since 2021-11 |
Coinnect is a Rust library aiming to provide a complete access to main crypto currencies exchanges via REST API. |
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PENDAX dormant since 2024-05 |
Javascript SDK for Trading, Data, and Websockets for FTX, FTXUS, OKX, Bybit, & More. |
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Data Sources
General
| Repository |
Description |
Stars |
Made with |
| Fincept Terminal |
Fincept Terminal is a comprehensive CLI tool that provides financial insights, market analysis, and a host of other financial services such as technical analysis, fundamental analysis, sentiment analysis, quantitative analysis, and economic data services. |
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| OpenBB Terminal |
Investment Research for Everyone, Anywhere. |
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TuShare dormant since 2024-03 |
TuShare is a utility for crawling historical data of China stocks |
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| yfinance |
yfinance offers a threaded and Pythonic way to download market data from Yahoo!Ⓡ finance. |
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| AkShare |
AKShare is an elegant and simple financial data interface library for Python, built for human beings! |
 |
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| FinanceDatabase |
Database of 300,000+ symbols covering equities, ETFs, funds, indices, currencies, cryptocurrencies and money markets |
 |
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| FinanceToolkit |
200+ financial metrics, ratios, technical indicators and risk measures computed from Financial Modeling Prep and Yahoo Finance data |
 |
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| pandas-datareader |
Up to date remote data access for pandas, works for multiple versions of pandas. |
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| edgartools |
SEC EDGAR filings in Python: XBRL fundamentals, 13F institutional holdings, insider transactions (Forms 3/4/5) and 8-K events |
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Quandl archived |
Get millions of financial and economic dataset from hundreds of publishers via a single free API. |
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| findatapy |
findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using a unified high level interface. |
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| Investpy |
Financial Data Extraction from Investing.com with Python |
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| Fundamental Analysis Data |
Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies. |
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Wallstreet dormant since 2024-07 |
Wallstreet: Real time Stock and Option tools |
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| pwb-toolbox |
Loader for the 32 Papers With Backtest datasets on Hugging Face: daily prices back to 1962 for stocks, ETFs, indices, currencies and commodities, sovereign yield curves, quarterly fundamentals, FRED-MD macro series, and 5.7 billion rows of 1-minute US equity bars. Cards and schemas are open to read, downloads are gated. |
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Cryptocurrencies
| Repository |
Description |
Stars |
Made with |
| Cryptofeed |
Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio |
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Gekko-Datasets dormant since 2018-05 |
Gekko trading bot dataset dumps. Download and use history files in SQLite format. |
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CryptoInscriber dormant since 2018-03 |
A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange. |
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| Crypto Lake |
High frequency order book & trade data for crypto |
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Data Science
| Repository |
Description |
Stars |
Made with |
| TensorFlow |
Fundamental algorithms for scientific computing in Python |
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| Pytorch |
Tensors and Dynamic neural networks in Python with strong GPU acceleration |
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| Keras |
The most user friendly Deep Learning for humans in Python |
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| Scikit-learn |
Machine learning in Python |
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| Pandas |
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more |
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| JAX |
Composable transformations of Python+NumPy programs: automatic differentiation, vectorization and JIT compilation to GPU/TPU |
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| Numpy |
The fundamental package for scientific computing with Python |
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| Scipy |
Fundamental algorithms for scientific computing in Python |
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| PyMC |
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara |
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| Cvxpy |
A Python-embedded modeling language for convex optimization problems. |
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Databases
| Repository |
Description |
Stars |
Made with |
| DuckDB |
In-process analytical SQL database that queries Parquet and Arrow files directly, a common backend for research datasets |
 |
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Marketstore no longer available |
DataFrame Server for Financial Timeseries Data |
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Tectonicdb dormant since 2024-01 |
Tectonicdb is a fast, highly compressed standalone database and streaming protocol for order book ticks. |
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| ArcticDB (Man Group) |
High performance datastore for time series and tick data |
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| PyStore |
Fast datastore for Pandas time series data, built on Dask and Parquet |
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Graph Computation
| Repository |
Description |
Stars |
Made with |
| Ray |
An open source framework that provides a simple, universal API for building distributed applications. |
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| Dask |
Parallel computing with task scheduling in Python with a Pandas like API |
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| Incremental (JaneStreet) |
Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications |
 |
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| csp (Point72) |
High performance reactive stream processing library written in C++ and Python, where the same graph runs in backtest and in real time |
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Man MDF dormant since 2016-12 |
Data-flow programming toolkit for Python |
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GraphKit dormant since 2023-03 |
A lightweight Python module for creating and running ordered graphs of computations. |
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| Tributary |
Streaming reactive and dataflow graphs in Python |
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Machine Learning
| Repository |
Description |
Stars |
Made with |
| AI Hedge Fund |
Educational hedge fund simulator where a team of LLM agents modelled on well known investors debates and takes positions |
 |
|
| QLib (Microsoft) |
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. |
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| FinGPT |
Open source financial large language models, with the fine-tuned weights released on HuggingFace |
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| Machine Learning for Trading |
Code for Machine Learning for Trading (3rd edition), from data sourcing and alpha factor research to live execution |
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| Qbot |
AI powered quantitative investment platform covering data collection, strategy research, backtesting and live trading |
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| FinRL |
FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance. |
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MlFinLab (Hudson & Thames) dormant since 2023-10 |
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. |
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TradingGym dormant since 2024-02 |
Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo. |
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| AlphaGen |
Generating sets of formulaic alpha factors with reinforcement learning |
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Stock Trading Bot using Deep Q-Learning dormant since 2023-12 |
Stock Trading Bot using Deep Q-Learning |
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TimeSeries Analysis
| Repository |
Description |
Stars |
Made with |
| Facebook Prophet |
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. |
 |
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| statsmodels |
Python module that allows users to explore data, estimate statistical models, and perform statistical tests. |
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| tsfresh |
Automatic extraction of relevant features from time series. |
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| pmdarima |
A statistical library designed to fill the void in Python’s time series analysis capabilities, including the equivalent of R’s auto.arima function. |
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Visualization
| Repository |
Description |
Stars |
Made with |
| Perspective |
Data visualization and analytics component built for large and streaming datasets, originally open sourced by J.P. Morgan |
 |
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| D-Tale (Man Group) |
D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures. |
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mplfinance dormant since 2024-08 |
Financial Markets Data Visualization using Matplotlib |
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| btplotting |
btplotting provides plotting for backtests, optimization results and live data from backtrader. |
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Strategies
Every strategy below is a published paper that has been coded and run over its own full history. The table is regenerated from the replication catalogue by scripts/build_strategies_table.py, so the numbers move when the catalogue does.
Showing the 61 strongest of 1,687 replications that clear a t-statistic of 1.96 over at least 10 years, up to 12 per asset class. Sharpe ratios are measured on each strategy’s own active window, not on a common calendar, and are gross of trading costs. Series with an annualised volatility outside 1% to 100% are treated as degenerate and dropped. The t-statistic is shown because a Sharpe ratio without one says very little: half the catalogue does not clear it.
Equities
Bonds
Commodities
Currencies
Cryptocurrencies
Derivatives
README 内容过长，已截取前 64 KB。完整内容请前往 GitHub 仓库查看。
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