A robust, regime-adaptive QQQ trading strategy utilizing ensemble machine learning and options market microstructure signals (GEX, VRP, Skew).

End-to-end algorithmic trading system using PyTorch LSTM. Features a custom Cross-Sectional Alpha ranking engine, dynamic portfolio compounding, and event-driven backtesting (+483% return vs SPY).

An open-source sell-side analyst that never sleeps. Screens stocks, runs DCF + reverse DCF, extracts earnings call signals, and ships an institutional-grade research note ;automatically.

Cross-sectional Transformer and FFN for stock return prediction and alpha generation. Implements GKX (2020) NN5 replication and MSRR loss (Kelly et al. 2025) for direct portfolio Sharpe optimization. Avg SDF Sharpe 2.05, significant alpha (t=5.34) unexplained by FF5+Momentum.

Quantitative Research framework for cross-sectional equity factor ranking. Evaluates Value (EY) and Quality (ROC) factors with point-in-time integrity and sector-neutrality.

A quantitative framework modeling recursive interactions between technology, capital, and delayed information absorption.

An open-source framework for autonomous digital entities, focused on decentralized threat & alpha intelligence.

A Topological Data Analysis (TDA) based trading framework that models financial markets as geometric manifolds to detect persistent structural trends using Betti-0 connectivity, enabling robust alpha generation under real-world execution constraints.

A Python trading bot that combines momentum, mean reversion, and volatility signals to run a long-short strategy. Includes a custom walk-forward optimizer to automatically tune the model as market conditions change.

Strat-ML: S&P 500 Alpha Generation Framework:- This repository contains a complete quantitative pipeline designed to outperform the S&P 500 Index using Machine Learning. The project focuses on out-of-sample signal generation using constituent-level OHLCV data, rigorous Blocking Time Series Cross-Validation, and a Long/Short Stock Picking strategy.

A comprehensive, production-grade implementation of quantitative trading strategies across 18 asset classes. Features modular Python architecture, institutional risk management, and deep-dive research papers for every strategy.