trade-data-generator

Market Depth Generator is a lightweight library for simulating market depth, trade data, and OHLC candlesticks. Designed for trading platforms and financial applications, it generates realistic trading data with customizable configurations.

🌟 Features

• Simulate Market Depth with dynamic buy and sell orders.
• Generate OHLC Candlesticks (Open, High, Low, Close) with customizable intervals.
• Track Executed Trades in real-time.
• Redis support for caching candlestick data.
• Multi-symbol support for crypto.
• WebSocket integration for real-time updates.
• Fully customizable and developer-friendly.

🚀 Installation

Install via npm:

npm install market-depth-generator

🛠️ Usage

1. Basic Example

Create a simple WebSocket server that generates and serves market depth and trade data:

const MarketDepthGenerator = require("market-depth-generator");
const express = require("express");
const WebSocket = require("ws");

const PORT = 8080;
const app = express();

// Initialize the MarketDepthGenerator
const generator = new MarketDepthGenerator({
  redisHost: "127.0.0.1",
  redisPort: 6379,
  symbols: ["BTCUSD", "ETHUSD"], // Add multiple symbols
  middlePrice: 305.12, // Base price for simulation
});

(async () => {
  await generator.init(); // Initialize the generator
  setInterval(() => {
    generator.simulateTrade("BTCUSDT");
  }, 1000);
})();

// HTTP API for fetching market depth for a specific symbol
app.get("/api/market-depth/:symbol", (req, res) => {
  const symbol = req.params.symbol;
  try {
    const depth = generator.getMarketDepth(symbol);
    res.json(depth);
  } catch (err) {
    res.status(400).json({ error: err.message });
  }
});

// HTTP API for fetching market stats for a specific symbol
app.get("/api/market-stats/:symbol", (req, res) => {
  const symbol = req.params.symbol;
  try {
    const stats = generator.getMarketStats(symbol);
    res.json(stats);
  } catch (err) {
    res.status(400).json({ error: err.message });
  }
});

const server = app.listen(PORT, () => {
  console.log(`Server running on http://localhost:${PORT}`);
});

// WebSocket server for real-time updates
const wss = new WebSocket.Server({ server });

wss.on("connection", (ws) => {
  console.log("Client connected");

  const sendMarketUpdates = setInterval(() => {
    const updates = {};

    Object.keys(generator.symbols).forEach((symbol) => {
      updates[symbol] = {
        depth: generator.getMarketDepth(symbol),
        stats: generator.getMarketStats(symbol),
      };
    });

    ws.send(JSON.stringify(updates));
  }, 2500);

  ws.on("close", () => {
    clearInterval(sendMarketUpdates);
    console.log("Client disconnected");
  });
});

Run the script and connect a WebSocket client to view the live simulation data.

📡 Output

The WebSocket server broadcasts the following JSON structure:

{
  "BTCUSD": {
    "marketDepth": {
      "buyOrders": [
        { "price": "1.23456", "quantity": "10.1234" },
        { "price": "1.23450", "quantity": "5.4321" }
      ],
      "sellOrders": [
        { "price": "1.23470", "quantity": "8.5678" },
        { "price": "1.23480", "quantity": "3.2100" }
      ]
    },
    "lastPrice": "1.23456",
    "priceChange": "0.00010",
    "percentageChange": "0.81",
    "highPrice": "1.23480",
    "lowPrice": "1.23450",
    "volume": "123.45",
    "candlestickData": [
      {
        "open": "1.23000",
        "high": "1.24000",
        "low": "1.23000",
        "close": "1.23456",
        "volume": "100.50",
        "timestamp": "2025-01-10T12:00:00Z"
      }
    ]
  }
}

📖 Documentation

Configuration Options

| Option | Type | Default | Description | | -------------------- | ------ | ------------- | --------------------------------------- | | symbols | Array | ['BTCUSDT'] | List of symbols for simulation. | | middlePrice | Number | 305.12 | Base price for market simulation. | | simulationInterval | Number | 1000 | Interval (ms) for simulating trades. | | updateInterval | Number | 2500 | Interval (ms) for broadcasting updates. |

🧪 Testing

Run tests using Jest:

npm test

📌 Important Notes

• Redis Connectivity:
  The library no longer handles Redis connectivity internally. Users are responsible for initializing and managing their own Redis connections. This allows greater flexibility and customization for your specific use cases.

• Candlestick Data:
  Candlestick (OHLC) data is now directly accessible via getCandlestickData(symbol). You can save this data to Redis or any other database based on your preferences.

• Custom Configuration:
  The library supports a wide range of configurations to simulate realistic market behavior. Customize parameters like middlePrice, highPriceLimit, lowPriceLimit, and symbols to suit your requirements.

• WebSocket/REST APIs:
  Use WebSocket or REST API endpoints to serve real-time data to clients. The library provides tools to generate depth, trade stats, and candlestick data seamlessly.

• Data Persistence:
  Persist generated data (like market depth, stats, and candlesticks) in your own storage solutions (e.g., Redis, SQL, or NoSQL databases) for long-term usage and analysis.

• Interval Management:
  Ensure that the simulation and update intervals are optimized for your use case to avoid excessive resource consumption.

📌 TODOs

• [x] Add support for multiple symbols.
• [ ] Extend candlestick intervals beyond 1 minute.
• [ ] Add more realistic trade simulations.
• [ ] Implement better test coverage for edge cases.
• [ ] Add support for WebSocket authentication.

🌟 What's New

• Multi-symbol support: Simulate multiple symbols like BTCUSD and ETHUSD.
• Real-time WebSocket updates: Get live updates for depth and stats.
• 24-hour stats tracking: Includes price change, percentage change, and volume.

👤 Author

• Love Pareek
• GitHub: monupareeklg

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🛠️ Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add a new feature'.
4. Push to the branch: git push origin feature-name.
5. Submit a pull request.

Make sure your code is clean and well-documented before submitting. We appreciate your contributions!

🌟 Support

If you like this project, please give it a ⭐️ on GitHub!

For issues, feel free to open a ticket on the GitHub issues page.

Need help or have questions? Contact me at [email protected].