qqq-options-alpha-research
Unverified ML strategy on Options by SMalaekeh. BotFinder score 37 out of 100.
A robust, regime-adaptive QQQ trading strategy utilizing ensemble machine learning and options market microstructure signals (GEX, VRP, Skew).
Source: github
BotFinder analysis pending.
qqq-options-alpha-research
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
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