Volatility Alchemist: Quantitative Options Trading
Published:
An end-to-end solution for quantitative options trading featuring real-time market data integration, ML-based volatility surface modeling, interactive visualization dashboards, and automated trading signal generation.
Key work: Implied volatility surface construction from options chain data · ML models (LSTM, gradient boosting) for realized volatility forecasting · Options Greeks computation and portfolio risk decomposition · Backtesting framework with realistic options pricing (Black-Scholes, Heston) · Interactive Plotly dashboards for strategy monitoring
Stack: Python, PyTorch, quantitative finance, Black-Scholes, options pricing, Plotly