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Quantitative Researcher | Algorithmic Strategy & Risk Modeling
Building systematic trading strategies with rigorous statistical methodology and robust risk management.
Featured Projects
Quantitative strategies with rigorous backtesting
Mean-Reversion Strategy for S&P 500 Futures
Developed a statistical arbitrage strategy exploiting mean-reverting behavior in S&P 500 E-mini futures using Kalman Filter for dynamic hedge ratio estimation and Ornstein-Uhlenbeck process for spread modeling.
Hybrid-Adaptive Quant Trading System
A production-ready multi-agent trading brain combining Gramian Angular Field (GAF) pattern recognition, ConvNeXt-Tiny neural network for regime/direction/volatility prediction, and LLM-as-a-judge confidence calibration with pgvector memory.
Volatility Surface Modeling & Options Pricing
Implemented SABR and SVI volatility surface calibration for options pricing, with application to exotic derivatives valuation and volatility arbitrage strategies.