QuantMind is an open source agent-native knowledge extraction and retrieval framework for quantitative finance.
AI+金融（量化）：1.多因子股票量化框架开源教程 2.学界和业界的经典资料收录 3.AI + 金融的相关工作，包括LLM, Agent, benchmark(evaluation), etc.
Cookiy AI Skill for AI agents (Claude, Codex, Cursor, OpenClaw) — end-to-end user research: AI interviews, synthetic users, quant surveys, participant recruitment.
Python-based quant trading research project for short-term reversal option setups, universe selection, staged-entry backtesting, and live paper trading.
FTShare financial data MCP for AI Agents｜通过标准 MCP 接入基础金融数据与 FTShare 特色因子，返回结构化、可核对的结果。
Finathink — Financial Research Optimizer for auditable, point-in-time research, mathematical learning, and constrained portfolio analysis.
A failure-aware harness for reproducible quantitative-strategy research, with typed agent workflows and inspectable experiment memory.
Open-source quantitative decision lab: test market assumptions, preserve negative results, and reproduce evidence.
Vibe Trading — agentic AI trading platform for Indian markets. 7 LLM analyst agents analyze stocks in parallel, debate bull vs bear, and deliver trade plans. Open-source agentic quantitative researcher for NSE/BSE/NFO.
An open-source, lightweight, and blazing-fast financial machine learning library built with Numba. Process raw trades, generate advanced bars, features, and labels for quantitative research.
FTShare financial data Python SDK｜通过 Python 和 pandas 接入基础金融数据与 FTShare 特色因子。
Research experiments exploring uncommon quant techniques.
A community-curated vault of openly available resources that replicates the rigorous syllabus of top MFE / Quant Finance programs
Rust-powered backtesting engine for quantitative research
Self-hosted portfolio journaling and agent-driven stock research: reproducible experiments, backtesting, durable jobs, API/CLI/MCP extensions, and multi-currency tracking.
Holdings exposure, what-if scenarios, cost and capacity, backtest checks, and published research.
Prediction-market research with ensemble models, calibration, expected value, and risk-aware sizing.
End-to-end RL trading framework with PPO agent, self-attention neural network, custom Gym environment, and advanced backtesting.
Options-flow features, unusual activity, dealer positioning, and short-horizon forecasting.
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