awesome-systematic-trading README

Awesome Systematic Trading

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

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.

📈 Interested in trading strategies implemented in Python?

Visit our comprehensive collection at paperswithbacktest.com for exclusive content!

Click here to see the full table of content

Libraries and packages
Strategies
Books
Videos
Blogs
Courses

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

14.8K ·GitHub

Awesome-systematic-trading

Что это такое?

Тщательно подобранный список ресурсов для систематической (количественной) торговли: научные статьи, библиотеки, пакеты, книги, материалы, видео, блоги и курсы для поиска, разработки и запуска торговых стратегий.

Это инструмент, который нужно устанавливать, или список, который просто читают?

Просто список — устанавливать ничего не нужно. Вы читаете его на GitHub и переходите по ссылкам на библиотеки, статьи и книги; писать и запускать код предстоит вам самому.

Все перечисленные проекты до сих пор поддерживаются?

Нет — заброшенные и приостановленные проекты помечены явно, например zipline не развивается с 2024-02, а backtrader с 2024-08; некоторые записи заархивированы или больше недоступны.

Это для профессиональных квантов или для любителей?

И для тех, и для других — 55 книг охватывают уровень от новичка до профессионала, а разделы идут от вводных материалов до высокочастотной торговли и машинного обучения.

А криптоторговля там отдельно раскрыта?

Да — у крипты есть свои подразделы: специализированные фреймворки вроде Freqtrade, Jesse, OctoBot и Hummingbot, раздел Cryptocurrencies strategies и книги по крипте.

Есть ли сайт-компаньон с готовыми Python-стратегиями?

Да — в README упоминается paperswithbacktest.com как обширная коллекция торговых стратегий, реализованных на Python.

Можно почитать список на другом языке?

Да — переводы README на китайский и японский языки приведены ссылками в самом верху.

What are similar projects?

awesome-quant, quant-trading, abu, bytex, qlib, Lean, StockSharp, machine-learning-for-trading

Which topics is it in?

algotrading, trading-strategies, quantitative-trading, backtesting, trading-bot, quant, algorithmic-trading, crypto, quantitative-finance, papers, paper, finance

Creator: paperswithbacktest
Size: 2.3 MB
Stars: 14.8K
Updated: CommitSep 28
Age: 4y
Commits · 90 Days