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Awesome List
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
GitHub stars and default-branch commits for wilsonfreitas/awesome-quant.
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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.
基于Python的开源量化交易平台开发框架
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.
FinRL®: Financial Reinforcement Learning. 🔥
"Vibe-Trading: Your Personal Trading Agent"
Open Source Wealth Management Software. Angular + NestJS + Prisma + Nx + TypeScript 🤍
modular quant framework.
Common financial technical indicators implemented in Pandas.
quant framework for stock
An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎
Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]
A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine
From Idea to Execution - Manage your trading operation across a distributed cluster
Self-hosted AI trading strategy lab — paper trading, overnight strategy tournaments, 15+ technical indicators
An open-source toolkit for quantitative analysis of crypto & stock markets, featuring an advanced market screener, portfolio backtester, and companion tools for the Gunbot trading bot.
Self-tuning multi-agent AI trading system. 8-source signal fusion (Polymarket + Kalshi + 10 ML models incl. Kronos foundation model), Bull/Bear/Judge debate on Claude Opus 4.7, Portfolio Manager gate.
AI-powered trading research platform. Test any idea on stocks, futures, and crypto with event studies, backtesting, and statistical validation. MCP server with 8 tools. pip install varrd.
Manipulates Stock / ETF Data
Cent-accurate mortgage amortization schedules for Python — validated against CFPB, Fannie Mae, textbooks, and real-world published examples.