awesome-systematic-trading
github.com/paperswithbacktest/awesome-systematic-trading ↗A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
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Installation instructions →What's inside
Machine Learning
Backtesting and Live Trading
- aatGeneral - Event Driven Frameworks
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.
- backtesting.pyGeneral - Event Driven Frameworks
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.
- backtraderGeneral - Event Driven Frameworks
Event driven Python Backtesting library for trading strategies
- btGeneral - Vector Based Frameworks
Flexible backtesting for Python based on Algo and Strategy Tree
- bTraderCryptocurrencies
Triangle arbitrage trading bot for Binance
- crypto-crawler-rsCryptocurrencies
Crawl orderbook and trade messages from crypto exchanges
Beginner
- A Beginner’s Guide to the Stock Market: Everything You Need to Start Making Money Today - Matthew R. Kratter
- Algorithmic Trading and DMA: An introduction to direct access trading strategies - Barry Johnson
- Day Trading QuickStart Guide: The Simplified Beginner’s Guide to Winning Trade Plans, Conquering the Markets, and Becoming a Successful Day Trader - Troy Noonan
- How to Day Trade for a Living: A Beginner’s Guide to Trading Tools and Tactics, Money Management, Discipline and Trading Psychology - Andrew Aziz
- Introduction To Algo Trading: How Retail Traders Can Successfully Compete With Professional Traders - Kevin J Davey
- Investing QuickStart Guide: The Simplified Beginner’s Guide to Successfully Navigating the Stock Market, Growing Your Wealth & Creating a Secure Financial Future - Ted D. Snow
General
- Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk - Richard Grinold, Ronald Kahn
- Advances in Active Portfolio Management: New Developments in Quantitative Investing - Richard Grinold, Ronald Kahn
- Algorithmic Trading and Quantitative Strategies (Chapman and Hall/CRC Financial Mathematics Series) - Raja Velu, Maxence Hardy, Daniel Nehren
- Algorithmic Trading: Winning Strategies and Their Rationale - Ernest P. Chan
- Building Winning Algorithmic Trading Systems: A Trader’s Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading) - Kevin J Davey
- How I Invest My Money: Finance experts reveal how they save, spend, and invest - Joshua Brown, Brian Portnoy
Data Sources
- AkShareGeneral
AKShare is an elegant and simple financial data interface library for Python, built for human beings!
- CryptofeedCryptocurrencies
Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio
- CryptoInscriberCryptocurrencies
A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange.
- Crypto LakeCryptocurrencies
High frequency order book & trade data for crypto
- findatapyGeneral
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.
- Fundamental Analysis DataGeneral
Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.
High Frequency Trading
- Algorithmic and High-Frequency Trading (Mathematics, Finance and Risk) - Álvaro Cartea, Sebastian Jaimungal, José Penalva
- An Introduction to High-Frequency Finance - Ramazan Gençay, Michel Dacorogna, Ulrich A. Muller, Olivier Pictet, Richard Olsen
- High-Frequency Trading - Maureen O’Hara, David Easley, Marcos M López de Prado
- Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading - Rishi K. Narang
- Market Microstructure in Practice - Charles-Albert Lehalle, Sophie Laruelle
- The Financial Mathematics of Market Liquidity - Olivier Gueant
Coding
- Algorithmic Trading with Python: Quantitative Methods and Strategy Development - Chris Conlan
- Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis - Sebastien Donadio
- Python for Algorithmic Trading: From Idea to Cloud Deployment - Yves Hilpisch
- Python for Finance: Mastering Data-Driven Finance - Yves Hilpisch
Trading bots
- analyzingalpha
Implementation of simple strategies
- bitcoin-arbitrage
Bitcoin arbitrage - opportunity detector
- Blackbird
Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy
- czsc
缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易
- PyTrendFollow
PyTrendFollow - systematic futures trading using trend following
- R2 Bitcoin Arbitrager
R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript
Showing a sample of 200 resources. View the full list on GitHub →