Quant Researcher — Universal Iron Condor Strategy: A Quantitative and AI-Driven Approach
Developed an adaptive options trading strategy combining statistical modeling and AI/ML methods to generate trading signals. Used probabilistic modeling (e.g., Black-Scholes and Monte Carlo) and reinforcement learning ideas to adjust strategy behavior across market regimes. Focused on robust decision-making through Bayesian updates and risk controls for improved payoff consistency. • Built ML pipelines trained on historical options data to predict behavior around expiry. • Applied volatility modeling (e.g., GARCH/IV surface) to select strikes and expirations dynamically. • Used probability metrics to refine entry points and update success likelihood over time. • Implemented delta-neutral/gamma-scaled risk adjustments and VaR-based position sizing.