This project develops a high-performance trade simulator leveraging real-time Level 2 orderbook data from the OKX cryptocurrency exchange. The simulator processes streaming market data, estimates transaction costs including slippage, fees, and market impact, and displays these through an interactive user interface optimized for low latency and accuracy.

Problem Statement Cryptocurrency trading incurs costs and risks due to slippage, fees, and market impact, which traders must estimate to make informed decisions. This assignment requires building a system that consumes OKX’s real-time L2 orderbook WebSocket feed and applies quantitative models to dynamically estimate these costs while maintaining efficient processing and UI responsiveness.

Tools and Technologies • Programming Language: Python • WebSocket: websockets with asyncio for asynchronous data streaming • UI Framework: Streamlit • Data Processing: Pandas, NumPy • Modelling: scikit-learn, statsmodels • Logging: Python logging module • Data Formats: JSON parsing • Visualization: Matplotlib, Plotly