Quant Trade Simulator

A high-performance trade simulator leveraging real-time L2 orderbook data from OKX via WebSocket, with transaction cost and market impact estimation. Built with Python and Dash.

Features

• Real-time L2 orderbook data processing with multi-endpoint fallback
• Slippage, fee, and market impact estimation
• Professional Dash/Plotly UI with Bootstrap styling
• Performance and latency metrics
• AI-powered market analysis using Google's Gemini
• Comprehensive documentation and performance analysis

Setup

1. Clone the repository

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Create a .env file in the project root
   • Add GEMINI_API_KEY=your_key_here for AI market analysis
   • Or set this environment variable in your system

4. Run the app:

   python app.py

Main Components

• app.py: Dash application entry point
• websocket_client.py: WebSocket client for L2 orderbook
• models.py: Slippage, market impact, and regression models
• fee_model.py: Fee calculation logic
• utils.py: Helper functions (latency, logging, etc.)
• gemini_integration.py: AI-powered market analysis

Documentation

The project includes comprehensive documentation:

• DOCUMENTATION.md: Detailed explanation of models, algorithms, and implementation
• PERFORMANCE_ANALYSIS.md: Performance benchmarks and optimization techniques

Models Implemented

1. Linear Regression for Slippage Estimation

   • Predicts expected slippage based on order size and market volatility

2. Almgren-Chriss Model for Market Impact

   • Estimates both permanent and temporary price impacts
   • Accounts for order size, execution time, and market liquidity

3. Logistic Regression for Maker/Taker Proportion

   • Predicts probability of order executing as maker vs. taker
   • Uses order size and current market spread as features

4. Rule-based Fee Model

   • Calculates expected fees based on exchange fee tiers

5. AI Market Analysis

   • Uses Google's Gemini AI to analyze orderbook data
   • Provides market sentiment and trading strategy recommendations

Future Enhancements

• Implement real-time model training based on market data
• Add more sophisticated market impact models
• Integrate with exchange APIs for actual order placement
• Develop advanced trade strategy backtesting