Quantitative finance researcher specializing in market regime detection and systematic trading systems.

Hybrid Regime Detection in Semiconductor Equities: A Bayesian HMM-LSTM Framework
SSRN Working Paper | Paper | Code

• Combined HMM and LSTM outputs using entropy-weighted Bayesian model averaging
• Achieved >50% volatility reduction and 15-17pt drawdown improvement vs static benchmarks
• Production-ready framework with comprehensive backtesting and Monte Carlo simulation

HybridQuantRegimes - Market regime detection and risk management framework

• Hybrid HMM-LSTM architecture with real-time regime classification
• Dynamic risk overlays, walk-forward backtesting, Monte Carlo stress testing
• Modular design for research and production deployment

Narrative-Alpha-Detector - Prediction market mispricing scanner

• Ensemble forecasting with Perplexity, GPT, Claude, Grok
• Real-time Polymarket scanning and confidence filtering
• Adaptive weights, risk-managed portfolio simulation
• Regime tagging, audit trails, and Streamlit dashboards

Crypto Trading & Arbitrage Bot - Advanced automated trading and arbitrage bot (work in progress)

• Multi-exchange: Binance, Kraken, and more
• Statistical arbitrage: cointegration, Z-score signals, mean reversion
• LLM-powered sentiment scoring and market signal fusion
• Real-time monitoring web dashboard, Prometheus, Grafana
• Advanced risk management, backtesting, and cloud deployment

Languages: Python, R, MATLAB, SQL
ML/AI: PyTorch, TensorFlow, scikit-learn, hmmlearn
Quant: statsmodels, pandas, numpy, QuantLib
Tools: Git, Jupyter, Docker, Streamlit