{"repo":"nuglifeleoji/Factor-Research","free":true,"listed":false,"github":"https://github.com/nuglifeleoji/Factor-Research","clone":"git clone https://github.com/nuglifeleoji/Factor-Research.git","description":"Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).","language":"Jupyter Notebook","stars":417,"topics":["alpha-factors","backtesting","deep-learning","factor-modeling","feature-selection","lstm","machine-learning","pytorch","transformer"],"license":null,"category":"machine-learning","readme_excerpt":"Quantitative Factor Research Project A comprehensive quantitative finance research project implementing factor modeling , feature selection , and machine learning for stock return prediction. This project demonstrates advanced techniques in alpha factor generation, systematic feature selection, and deep learning model comparison. 🎯 Project Overview This project implements a complete quantitative research pipeline covering the entire workflow from raw market data to production-ready predictive models. The research achieved significant performance improvements with the best model (LSTM+ResNet) reaching an Information Coefficient of 0.06476 , representing a 72% improvement over traditional linear regression benchmarks. 📊 Key Achievements - 🏗️ Factor Library : Generated comprehensive alpha factor library using vectorized operations and technical operators - 🔍 Feature Selection : Systematically reduced features from 100+ candidates to 85 high-quality factors using statistical and ML methods - 🤖 Model Performance : Achieved IC of 0.06476 with LSTM+ResNet architecture, significantly outperforming baseline models - ⚡ Optimization : Implemented automated hyperparameter tuning using Optuna for all model architectures 🏗️ Project Structure 🔬 Research Methodology 1. Factor Modeling Objective : Develop a comprehensive factor library capturing various market dynamics and price-volume relationships. Implementation : - Technical Operators : Implemented time-series operators (ts max, ts","default_branch":null,"files":null,"tree":[],"storefront":"/r/nuglifeleoji","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/nuglifeleoji/Factor-Research/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}