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Awesome List
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
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Financial data platform for analysts, quants and AI agents.
Free, open source crypto trading bot
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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.
A cryptocurrency trading API with more than 100 exchanges in JavaScript / TypeScript / Python / C# / PHP / Go / Java
基于Python的开源量化交易平台开发框架
Extremely fast Query Engine for DataFrames, written in Rust
The fundamental package for scientific computing with Python.
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.
Download market data from Yahoo! Finance's API
Production-grade Rust-native trading engine with deterministic event-driven architecture
Python Backtesting library for trading strategies
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Lean Algorithmic Trading Engine by QuantConnect (Python, C#)
Zipline, a Pythonic Algorithmic Trading Library
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
FinRL®: Financial Reinforcement Learning. 🔥
SciPy library main repository
A computer algebra system written in pure Python
Python training for business analysts and traders
"Vibe-Trading: Your Personal Trading Agent"
Python wrapper for TA-Lib (http://ta-lib.org/).
Statsmodels: statistical modeling and econometrics in Python
Python toolkit for quantitative finance
QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
Bayesian Modeling and Probabilistic Programming in Python
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
Automatic extraction of relevant features from time series:
Open Source Wealth Management Software. Angular + NestJS + Prisma + Nx + TypeScript 🤍
An advanced crypto trading bot written in Python
The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
Portfolio analytics for quants, written in Python
The QuantLib C++ library
A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
Portfolio and risk analytics in Python
Free open source crypto trading bot to automate AI, Grid, DCA and TradingView strategies on Binance, Hyperliquid and 15+ exchanges, with a simple interface.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
High-performance TensorFlow library for quantitative finance.
Probabilistic time series modeling in Python
Visualizer for pandas data structures
Technical Analysis Library using Pandas and Numpy
A python wrapper for Alpha Vantage API for financial data.
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Python Algorithmic Trading Library
Financial Markets Data Visualization using Matplotlib
Performance analysis of predictive (alpha) stock factors
Portfolio Optimization in Python