{"repo":"SMalaekeh/qqq-options-alpha-research","free":true,"listed":false,"github":"https://github.com/SMalaekeh/qqq-options-alpha-research","clone":"git clone https://github.com/SMalaekeh/qqq-options-alpha-research.git","description":"A robust, regime-adaptive QQQ trading strategy utilizing ensemble machine learning and options market microstructure signals (GEX, VRP, Skew).","language":"Jupyter Notebook","stars":11,"topics":["algorithmic-trading","alpha-generation","backtesting","options-trading","quantitative-finance"],"license":null,"category":"trading","readme_excerpt":"QQQ Options Alpha Research Forecasting QQQ with its Own Options Data: A Ensemble Machine Learning Approach This repository contains a robust, production-ready trading strategy that uses end-of-day QQQ options data to forecast next-day directional movement and generate daily trading signals with leverage between -1.0x and +1.5x. 🎯 Objective Design a model that systematically deciphers sentiment, risk appetite, and positioning embedded within the QQQ options market to gain an edge on future price action. Key Performance Target: - Calmar Ratio 2.0 (Risk-adjusted returns) - Robustness: Strategy stable to ±10% parameter variations - Leverage Range: -1.0x (full short) to +1.5x (leveraged long) 📊 Results Summary Test Set Performance (2024-07-26 to 2025-09-17) Metric Value -------- ------- Calmar Ratio 2.14 ✅ Sharpe Ratio 1.92 Total Return 23.5% Max Drawdown -13.7% Win Rate 55% Robustness Check: Strategy maintains Calmar 1.5 across all parameter variations (±10%). 🏗️ Repository Structure 🚀 Quick Start 1. Setup Environment 2. Run Feature Engineering This will: - Load raw options data - Generate 100+ features including: - Volatility surface (IV by moneyness × tenor) - Greeks & GEX (Gamma Exposure) - Variance Risk Premium (VRP) - Put/Call ratios & flow metrics - Regime detection (High/Low volatility) - Save features to data/daily features.parquet 3. Train & Evaluate Model This will: - Train ensemble model (LightGBM + XGBoost + RF + Ridge) - Generate trading signals with volatility t","default_branch":null,"files":null,"tree":[],"storefront":"/r/SMalaekeh","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/SMalaekeh/qqq-options-alpha-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."}