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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
"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.
FinRL®: Financial Reinforcement Learning. 🔥
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
Self-hosted Python strategy research and Alpaca paper trading with broker reconciliation, evidence gates, and live execution disabled.
From Idea to Execution - Manage your trading operation across a distributed cluster
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.
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.
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.