Awesome Quant

Free, open source crypto trading bot

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

"Vibe-Trading: Your Personal Trading Agent"

FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.

Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.

Zipline, a Pythonic Algorithmic Trading Library

FinRL®: Financial Reinforcement Learning. 🔥

Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).

The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.

An advanced crypto trading bot written in Python

Portfolio analytics for quants, written in Python

Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit

Performance analysis of predictive (alpha) stock factors

modular quant framework.

Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.

Algorithmic Trading in Python with Machine Learning

Statistical and Algorithmic Investing Strategies for Everyone

An Algorithmic Trading Library for Crypto-Assets in Python

Open-source Rust framework for building event-driven live-trading & backtesting systems

QTPyLib, Pythonic Algorithmic Trading

Common financial technical indicators implemented in Pandas.

Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.

AI agents that actually place the trade. 12 brokers, real backtests, stocks options futures forex crypto and prediction markets.

Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering

Machine Learning in Asset Management (by @firmai)

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Quantitative Financial Modelling Framework

A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.

Asynchronous, event-driven algorithmic trading in Python and C++

GPU-accelerated Factors analysis library and Backtester

Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]

Python live trade execution library with zipline interface.

A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine

Open Source Algorithmic Trading Engine

Python interface to IEX and IEX cloud APIs

Python framework for quantitative financial analysis and trading algorithms on decentralised exchanges

Technical analysis and other functions to construct technical trading rules with R

Vectorized backtester and trading engine for QuantRocket

Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests

Start developing and backtesting your own automated trading strategies

The fastest way from backtest to live trading.

Feature Engineering and Feature Importance in Machine Learning for Financial Markets

Self-hosted Python strategy research and Alpaca paper trading with broker reconciliation, evidence gates, and live execution disabled.

High performance, low-latency backtesting engine for testing quantitative trading strategies on historical and live data in Rust

AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.

Self-tuning multi-agent AI trading system. 8-source signal fusion, Bull/Bear/Judge debate on Claude Opus 4.7, Kelly + ATR position sizing. Python · Kalshi + Polymarket adapters.

A Python SDK for FinancialData.Net API - Real-time & Historical Stock Market Data