Chicago Quant Alley: Crypto Trading Simulator & Optimizer

Designed and deployed a modular, high-throughput Python framework for developing, simulating, and systematically optimizing quantitative trading strategies in cryptocurrency derivative markets.

Architecture & System Modules

• Asynchronous Market Data Pipeline: Built an event-driven data ingestion layer integrating with the Delta Exchange API and ccxt async routines to capture granular tick-level trades, order book depth, and implied volatility surfaces with minimal latency.
• Realistic Execution Simulation: Formulated a simulation engine that faithfully replicates market microstructure frictions:
  • Models non-linear market impact, dynamic slippage, and tiered taker/maker fee schedules.
  • Implements realistic order queue priority and partial fill mechanics for limit orders.
• Dynamic Options Lifecycle & Greeks Management: Engineered dynamic strike selection and continuous risk monitoring covering real-time portfolio Greeks (Delta, Gamma, Vega, Theta), margin utilization, automated roll strategies, and drawdown controls.
• Multi-Armed Bandit (MAB) Strategy Tuning: Deployed Upper Confidence Bound (UCB) and Thompson Sampling algorithms to optimize strategy hyperparameters, balancing exploration of parameter regimes with exploitation of top-performing parameter sets for rapid convergence.
• Repository: GitHub: Chicago-Quant-Alley