A Deribit options trading framework that finds mispriced crypto options by comparing market-implied probabilities against historical statistical forecasts. Automatically flags overvalued tail risk and skew to structure delta-neutral option spreads.
Building a cointegration pairs-trading strategy, then systematically dismantling every bias until only the honest result remains.
Volatility-aware ML system for statistical arbitrage with the purpose of learning when a pair spread is actually tradeable after costs, risk, and regime effects.
Quantitative research framework for cross-sectional equity alpha: random matrix theory covariance cleaning, purged combinatorial cross-validation, and Deflated Sharpe Ratio — measuring how much of a backtest survives leakage, multiple testing and market impact. Python + C++.
A comprehensive quantitative analysis of the Decentralized Finance (DeFi) ecosystem, featuring AMM microstructure modeling, high-frequency statistical arbitrage, Monte Carlo stochastic risk simulation, and Black-Scholes options pricing.
Pairs trading framework that models spreads as Ornstein-Uhlenbeck processes and derives optimal entry thresholds by solving the Kolmogorov Backward Equation — replacing conventional fixed z-score heuristics with mathematically grounded, dynamically calibrated signals.
Statistical arbitrage research framework — equity pairs, crypto pairs, and funding rate carry strategy
Production-grade framework for prediction market signal discovery using Bayesian inference, regime detection, and rigorous walk-forward backtesting. 122 unit tests, 85% code coverage.
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