Trade Sim

A high-performance simulator for analyzing execution costs of large crypto orders using real-time L2 orderbook data

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Live Demo Source

Crypto Trade Simulator#

A high-performance crypto trade simulator for analyzing execution costs of large orders using real-time orderbook data.

Features#

  • Real-time L2 Orderbook Data: Streams market depth data via WebSocket connection
  • Advanced Market Models: Implementations of slippage calculation, fee structure, market impact (Almgren-Chriss model), and maker/taker probability
  • Interactive UI: Adjust order parameters and see results in real-time
  • Visualizations: View the orderbook, cost breakdowns, and maker/taker probabilities

Getting Started#

Prerequisites#

  • Node.js 16.8.0 or later

Installation#

  1. Clone the repository
  2. Install dependencies:
    bash
    npm install
  3. Create a .env.local file in the root directory with the following content:
    text
    NEXT_PUBLIC_WS_URL=wss://ws.okx.com:8443/ws/v5/public

Development#

Start the development server:

bash
npm run dev

Building for Production#

Build the application:

bash
npm run build

Model Descriptions#

Slippage Model#

Slippage is calculated by walking the orderbook and determining the effective average execution price compared to the best available price.

Fee Model#

Fees are calculated based on the exchange’s tier structure, with different rates for maker and taker orders.

Market Impact Model#

The market impact model is based on a simplified version of the Almgren-Chriss model:

text
Impact = σ * √(Q/V) * C

Where:

  • σ (sigma): Asset volatility
  • Q: Order size in base currency
  • V: Market daily volume in base currency
  • C: Constant factor (typically 0.5-1.0)

Maker/Taker Probability Model#

The probability of an order being executed as a maker order (vs. taker) is modeled using a logistic function based on order size:

text
P(maker) = 1 / (1 + e^((x-midpoint)/steepness))

Where:

  • x: Order size
  • midpoint: Order size where probability = 0.5
  • steepness: How quickly the probability changes with order size

Limitations#

  • The current implementation uses mock data for development purposes
  • For production use, replace the mock data with an actual WebSocket connection
  • The market impact model is a simplified version and may need calibration for specific markets

License#

MIT