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

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

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

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

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。

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.

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.

In this study, I empirically and statistically investigate the credibility of common asset pricing beliefs using data from S&P 500® constituents from January 2010–December 2020.