PyTorch research stack for ML multi-factor trading: 213 factors, bias correction, portfolio optimization, and vectorized backtesting.
用 Python 实现A股历史日线数据获取与清洗,复现 WorldQuant Alpha101 经典因子,实现因子评估、合成、回测全流程 Pipeline
基于LLM的量化因子开发Agent
Deep Reinforcement Learning trading strategies: Double DQN with Transformer Attention + Multi-Factor Model (Fama-French inspired). Features adaptive risk management and volatility targeting.
A modular Python framework for researching and backtesting multi-factor equity strategies using classical factors (Value, Momentum, Size), Fama–MacBeth regressions, IC/IR analysis, and long–short portfolio evaluation.
ICIR-weighted multi-factor stock selection and CTA trend enhancement for China A-share lithium battery industry research(A 股锂电池 ICIR多因子选股与CTA趋势增强策略)
Reproducible multi-factor equity research with simulated panels, Fama-French evidence, and an experimental A-share Tushare data adapter.
A multi-factor stock analysis bot & Material Design 3 web application supporting US and international tickers. Synthesizes financial metrics, valuation, RSI/MACD technicals, news & Reddit sentiment, analyst price targets, and macro/moat modeling into a 6-pillar composite score and BUY/SELL/HOLD signal.
This project was developed as the final project for the Quantitative Investment course at Zhejiang University. It implements a multi-factor framework in quantitative investing, incorporating 15 financial factors and generating a simple backtesting report to evaluate strategy performance.
An AI-driven quantitative stock analysis system for A-Shares. Integrates multi-factor models, fundamental scoring, and enhanced technical analysis (MACD/RSI) to generate interactive HTML investment reports with price predictions.
Reproducible A-share multi-factor stock-selection research with Transformer ranking, transaction costs, risk controls, and robustness analysis.
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