Real Time Trade Sim

A high-performance cryptocurrency trade simulator that leverages real-time Level 2 orderbook data from OKX to estimate transaction costs including slippage, fees, and market impact.

Features

• Connects to OKX's real-time L2 orderbook via WebSocket.
• Calculates expected slippage using regression models.
• Estimates trading fees based on exchange fee tiers.
• Implements Almgren-Chriss market impact model.
• Predicts maker/taker trade proportions.
• Displays detailed metrics and latency in a Tkinter GUI.
• Modular architecture with logging and error handling.

Installation

1. Clone the repository:

git clone https://github.com/arnvjshi/real-time-trade-sim.git
cd real-time-trade-sim

2. Install required packages:

pip install -r requirements.txt

Usage

Run the main application:

python main.py

Adjust input parameters on the left panel and monitor output metrics on the right panel.

Project Structure

• main.py — Entry point to start the WebSocket client and data processing loop.
• simulator_ui.py — Tkinter-based user interface.
• websocket/ — WebSocket client handling real-time data streaming.
• models/ — Implementation of slippage, fee, and market impact models.
• utils/ — Logger and utility functions.

Dependencies

• Python 3.9+
• websockets
• numpy
• scikit-learn
• tkinter (usually included with Python)

License

MIT License

Acknowledgements

• OKX for providing public API endpoints.
• Almgren and Chriss for market impact modeling.

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