factor-investing

Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn

Agent-driven alpha factory — LLM autonomously designs, backtests, and submits factors to WorldQuant BRAIN

Quantitative factor research skills for AI coding assistants

Agentic quant research workbench with CSV data inspection, local Codex and compatible APIs, predictive models, independent reviews, and reproducible backtests.

A-share ETF rotation research and backtesting platform with walk-forward, vectorized, and event-driven validation.

PyTorch ML quant research: 213 factors, bias correction, portfolio optimization, backtesting, and System-One/Jev-style decisions.

Rewriting the code in "Machine Learning for Factor Investing" in Python

QuantBot（AI 量化机器人）的全量源码。基于 **Go+React + TypeScript + Vite** 构建，通过 **Wails** 与 Go 后端通信，提供 A 股量化交易的图形界面：六维判势、多因子选股、AI 智能体决策、策略回测、实时活动、审计日志等。

RL stock selection for China A-share — bundled polars-native factor library (105 Alpha101 + 191 GTJA Alpha191 = 296 factors), board-aware price limits, GPU train + ONNX CPU infer, MIT-licensed.

🦞 AI 量化交易系统 — 37 因子选股 · 8 层风控 · 同花顺数据大屏 · 全自动盯盘

"🐳 Dsh-Quant: The Everything-Plugin Ai native Quant OS "

🦊 Open-source professional quant agent framework. Agents pick the factors working now to time entries, write full strategies, and evolve them in a sandbox — every order through machine approval, the LLM never on the order path. Multi-market, audit-grade.

FSQ K-line foundation model for China A-share stock selection, with a containerised multi-factor research platform: data governance, factor research and observability.

End-to-End Factor-Driven Quant System for Crypto Perpetuals — Factor Mining, Statistical Evaluation, Combo Search, Walk-Forward Backtest

道·量化研究：83篇A股市量化分析研究文章，基于双引擎四层融合模型 | Dao Quant Research: 83 A-share quantitative analysis articles with dual-engine fusion model

众人的因子回测框架 stock factor test

Multi-market stock analysis, screening & backtesting plugin for AI agents — 39 tools + 20 strategy skills across A/HK/US/JP/KR/TW markets, works with Pi Agent, Hermes, OpenClaw & Deepseek Harness

Data Science Project: Replication of "Forest Through the Trees: Building Cross-Sections of Stock Returns" - creation of assets to test validity of factor models with Python

Calculate technical factors for stocks in an efficient, maintainable and correct way

Multi-factor scoring and research tool for long-term stocks & ETFs — US, European, Asian and superinvestor-consensus (SEC 13F) universes from public market data. Decision support, not investment advice.