Looking for reliable open-source libraries for quantitative and algorithmic trading? The online resources are vast and messy, making it hard to find what actually works. Awesome-systematic-trading is a curated list of free quantitative libraries, organized by language, so you can jump straight to the relevant resources. It includes historical data backtesting frameworks, real-time trading bots for stocks/crypto/futures/options/forex, market data scrapers and API clients, as well as materials for strategy research and machine learning. It contains only the truly worthwhile ones, all free and open-source.
Portal: https://github.com/wangzhe3224/awesome-systematic-trading

wangzhe3224/awesome-systematic-trading

Source: https://github.com/wangzhe3224/awesome-systematic-trading

Awesome Systematic Trading

or Quantitative Trading + a bit data science infra

Awesome (https://awesome.re)

GitHub last commit (branch)

Python Java C++ JavaScript Rust Go Jupyter Notebook

Star History

Star History Chart

Want to read in Chinese? Click here

Interested in systematic trading? Check QuantBox

A curated list of awesome libraries, packages and resources for Systematic Trading (Quantitative Trading)

Open access: all rights granted for use and re-use of any kind, by anyone, at no cost, under your choice of either the free MIT License or Creative Commons CC-BY International Public License.

How do we pick the projects?

• Fit in Systematic Trading / Quantitative Trading domain
• Good coding style and software architecture
• (Optional) Under active development
• (Optional) Reasonable test coverage

Overall, I tend to pick decent or promising libraries that closely related to systematic trading instead of including as many libraries as possible.

Please raise a PR if you found some good fit projects for this repo or remove some outdated projects. Thanks!

Search page by languages you are interested in to find related libraries. For example: Ctrl+F, Rust

And I count crypto as whole new category: >> Click ME to Systematic Crypto.

• Awesome Systematic Trading
• Star History
• 🔥 AI Powered Systematic Trading Systems
• Backtest + live trading
  • General purpose
  • Crypto currency focus
  • Machine Learning / Reinforcement Learning Focused
• Alpha Collections
  • General Alpha
  • Expression based alpha
  • Stock picking
  • Orderbook
  • Arbitrage (Crypto)
• Basic Components
  • Fundamental libraries
  • Computation
  • Python Performance Booster
  • Python Profilers
  • Alternative libraries
  • Numpy Alternatives
  • Pandas Alternatives
  • Analytic tools
  • Metrics computation
  • Indicators
  • Pricing
  • Risk
  • Optimization
  • TimeSeries Analysis
  • Visualization
  • Message Queues
  • Databases
• Data Source
  • Stocks and General
  • Alternative
  • Crypto
  • Broker APIs
• Quant Shops Code and Blog
• Resources
  • Research
  • Books
  • Blogs
  • Tutorials
  • Courses
  • Relevant Projects

🔥 AI Powered Systematic Trading Systems

• DepthSight | Python TypeScript | - Self-hosted visual algo-trading platform featuring a drag-and-drop strategy builder, an AI co-pilot for automated strategy design, and integrated billing.
• TradeSight | Python | - AI-powered trading intelligence platform with automated strategy tournaments, multi-market scanning (stocks + prediction markets), 15+ technical indicators, paper trading, and web dashboard. Self-evolving strategies via overnight optimization cron.
• AI Hedge Fund | Python | - An AI Hedge Fund Team
• FinRL | Python | - FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance.
• FinGPT | - FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.
• QLib (Microsoft) | Python, Cython | - 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.
• Qbot | Python | - AI 自动量化交易机器人 AI-powered Quantitative Investment Research Platform.
• VARRD | Python | - AI-powered trading research platform that validates any trading idea with event studies, statistical tests, and real market data across 15,000+ instruments. CLI, Python SDK, and MCP server.
• InvicTrade | - AI-powered trading signals with 74% historical win rate, combining strategies from legendary investors using multi-model AI intelligence.
• BullBear | TypeScript | - Open-source AI agent stock trading battle platform. AI agents get $100K virtual cash, trade real US stock prices, and compete on a public leaderboard with social feed.
• FinClaw | Python | - AI-native quantitative finance engine with genetic algorithm strategy evolution. 484 built-in factors, walk-forward validation, multi-market support (A-shares, US, crypto). Strategies evolve themselves via GA — no manual parameter tuning needed.
• OpenFinClaw | TypeScript | - AI-native systematic trading framework. Natural language strategy generation, multi-market execution (US/HK/CN/Crypto), self-evolving strategy pipeline with community leaderboard. Built on OpenClaw (68K+ stars).
• StockKit (GitHub) | TypeScript | - Free AI-powered stock research reports delivered daily. Wall Street-grade analysis for US, China & HK stocks using Claude Opus and multi-model AI engine. 20+ technical indicators, automated email delivery.
• stock-analysis | Python | - Evidence-driven market recap CLI for A/HK/US stocks and funds, producing Markdown reports and JSON Evidence Packs for AI agent workflows.
• oracle3 | Python | - Autonomous trading agent for Kalshi, Polymarket, and Solana DFlow with Wang Transform pricing engine calibrated on 291,309 resolved contracts (λ̂ = 0.183), eight constraint-based arbitrage strategies, hierarchical MLE, model Greeks, and Kelly-sized execution. Backed by SSRN working paper.
• Eterna | MCP | - Launch your own autonomous perp trading AI via Claude Code in 60 seconds. Hybrid exchange MCP with $10B+ aggregated liquidity across 500+ pairs — Claude executes your strategies 24/7. Endpoint: https://mcp.eterna.exchange/mcp
• Inalpha | Python TypeScript | - AI agent framework for quant research: agents pick the factors working now to time entries (time-series rank IC), write full Python strategies audited in sandboxes, and evolve them under multi-objective fitness. Every order passes machine approval — the LLM has no direct order path. Multi-market: crypto, US/CN/HK equities, global indices, FRED macro.
• TraderHarness | Python | - Contamination-resistant A-share backtesting environment for LLM trading agents: point-in-time masking, entity/date anonymization, progressive 5-minute execution, fingerprinted replay, and full-fidelity trajectory (SFT) export.

Backtest + live trading

General purpose

Event Driven Frameworks

Note: the one marked as Live Trading has reasonable live trading support for at least 1 broker. Otherwise, backtest function only.

• the0 | Python, TypeScript, Rust, C++, C#, Scala, Haskell, Live Trading | - Self-hosted execution engine for algorithmic trading bots. Each bot runs in an isolated container. No framework imposed — your bot is just normal code.
• aat | Python, C++, Live Trading| - 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.
• * barter-rs | Rust | - Open-source Rust framework for building event-driven live-trading & backtesting systems. Algorithmic trade with the peace of mind that comes from knowing your strategies have been backtested with a near-identical trading Engine.
• * bt | Python | - Flexible backtesting for Python based on Algo and Strategy Tree
• Better Quant | C++, Live Trading | - Better quant today, best quant tomorrow. 💪
• Botvana | Rust | - high-performance and event-driven trading system built using Rust
• backtrader | Python, Live Trading | - Event driven Python Backtesting library for trading strategies
• backtesting.py | Python | - 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.
• FlashFunk | Rust | - High Performance Runtime in Rust
• QuantFabric | C++ | - QuantFabric is a mid-to-high frequency quantitative trading system developed based on Linux/C++, supporting futures business varieties of CFFEX, ZCE, DCE, SHFE, INE, as well as stocks and bonds of SSE and SZSE.
• gobacktest | Go | - A Go implementation of event-driven backtesting framework
• Hikyuu | C++, Python| - Hikyuu Quant Framework 基于C++/Python的开源量化交易研究框架 (Open-source quantitative trading research framework based on C++/Python)
• Investing Algorithm Framework | Python | - Framework for developing, backtesting, and deploying automated trading algorithms and trading bots.
• lumibot | Python | - A very simple yet useful backtesting and sample based live trading framework (a bit slow to run…)
• * nautilus_trader | Python, Cython, Rust, Live Trading | - A high-performance algorithmic trading platform and event-driven backtester
• PyBroker | Python | - Algorithmic Trading in Python with Machine Learning
• QuantConnect | C#, .NET, Live Trading | - Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
• QUANTAXIS | Python, Rust, Live Trading | - 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 (QUANTAXIS supports task scheduling and distributed deployment, a pure local quantitative solution for stocks/futures/options/HK stocks/virtual currencies with data/backtesting/simulation/trading/visualization/multi-account support)
• Rqalpha | Python | - A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
• quanttrader | Python | - Backtest and live trading in Python. Event based. Similar to backtesting.py.
• qf-lib | Python | - Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.
• sdoosa-algo-trade-python | Python | - This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter.
• * vnpy | Python, Stock, Futures, Crypto, Live Trading | - 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
• WonderTrader | C++, Python | - WonderTrader——量化研发交易一站式框架 (Quantitative R&D and trading one-stop framework)
• zvt | Python, Stock, Backtest | - Modular quant framework
• zipline | Python | - Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting.
• PandoraTrader | C++ | - CTP 高频量化交易平台 C++ Trade Platform for quant developer
• hftbacktest | Python, numba | - A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
• flashalpha-fill-simulator | Python, Options | - Realistic limit-order fill simulator for options credit/debit spreads. Models post-and-wait limits, stale-quotation, and partial fills, with latency modeling and realistic queue priority. Designed for strategy research and backtesting.