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.
• Showcased strategies included a dynamic trend-following system and a multi-leg options volatility strategy. Delivered performance analytics including Sharpe, Sortino, max drawdown and trade logs for each backtest iteration.
• Project GitHub: github.com/VatshVan/Chicago-Quant-Alley-Crypto-Trading-Simulator-Strategy-Optimizer