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

🦊 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.

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

Quantitative factor research skills for AI coding assistants

生产级 A 股 ETF 轮动策略平台 | 三层验证引擎 (WFO→VEC→BT) | 实盘验证胜率 83.3%

Your personal multi-asset quant research team, powered by LLMs. Crypto + A-share + HK + US, with backtest engine, factor lab and event-driven news intake.

众人的因子回测框架 stock factor test

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

Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction

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

Web dashboard to visualize equity factor dynamics using solely publicly available data.

🦞 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

基于 CrewAI 的 WorldQuant Alpha 因子自动研究与回测系统 6个专业 AI 智能体协作 从研报到可验证公式的全自动流水线 支持网格搜索和智能优化

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.

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

Cognitive Factor Intelligence Platform — AI-powered portfolio optimization with 80+ factors, episodic learning (CVRF), Connect Alpaca, paper trading, server-side factor history, and explainable recommendations. Two-tier (Vercel + Railway). Metaventions AI.

Dynamic Factor Investing with Transformer-Based Return Prediction for China's A-Share Market (CSI 500). Combines rolling PCA dimension reduction with attention-based sequence models to forecast cross-sectional returns. 基于 Transformer 的采用滚动 PCA 降维 + 注意力机制的混合架构动态因子投资研究 — 针对中国 A 股市场的跨期收益预测。

Three ML strategies compete head-to-head on S&P 500 stocks. Runs autonomously with daily GitHub Actions execution and live dashboard. Which model wins? Check the dashboard.

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