Chicago Quant Alley Trading Simulator and Optimizer
Designed and implemented a modular Python-based framework for developing, simulating, and optimizing algorithmic trading strategies in cryptocurrency markets. This end-to-end system comprises three core modules: Historical Data Acquisition: Integrated with Delta Exchange API to fetch granular tick-level and order book data for futures and options. Event-Driven Simulation Engine: Built a high-fidelity backtester that models slippage, transaction costs, and execution mechanics. Supports both directional and complex derivatives strategies. Strategy Optimization: Leveraged Multi-Armed Bandit (MAB) algorithms to efficiently navigate the parameter search space, improving convergence and robustness of strategies.