DEPLOYED LINK - https://exchange-ruby-iota.vercel.app
A high-performance, real-time trading system built to handle low-latency order execution at scale. The platform combines modular, event-driven architecture with efficient queuing, real-time communication, and scalable infrastructure.
This project leverages a robust and modern tech stack to ensure high performance, scalability, and real-time capabilities:
| Category | Technologies | Description |
|---|---|---|
| Frontend | Next.js, React, Tailwind CSS | Dynamic, server-rendered UI with responsive styling |
| Backend | Node.js, Express, PostgreSQL, Time Series DB | High-performance API services and data storage |
| Real-Time Communication | WebSockets (Socket.io) | Live trade and market updates |
| Queue & Event System | Redis | Order queuing, Pub/Sub for real-time communication |
| Matching Engine | Custom-built in Node.js | Fast order matching with event triggers |
| Deployment | Railway | Cloud deployment platform |
The platform's architecture is designed for efficiency and reliability, ensuring seamless order processing and real-time data distribution.
- User Input: Traders place buy/sell orders via the intuitive frontend interface.
- API Request: Order data is securely transmitted to the
POST /api/v1/orderendpoint. - Redis Queue: The API validates incoming orders and enqueues them in Redis for asynchronous processing, decoupling the order intake from the matching engine.
- Engine Polling: A custom-built, high-performance matching engine continuously polls the Redis queue for new orders.
- Execution: Orders are matched against existing orders in the order book, and trades are executed with millisecond-level precision.
- Event Trigger: Upon trade execution, a
trade_createdevent is emitted via Redis Pub/Sub, notifying all relevant services and clients.
- WebSockets: Real-time updates, including trade confirmations and market data, are broadcast instantly to connected clients via WebSockets, ensuring users have the latest information.
- PostgreSQL: All trade records and critical transactional data are stored reliably in PostgreSQL, ensuring data integrity and persistence.
- Time Series DB: High-frequency price and volume data are logged in a dedicated Time Series Database for advanced analytics, historical charting, and visualization.
- ⚡ Real-Time Execution: Achieve millisecond-level trade processing, crucial for high-frequency trading environments.
- 📈 Live Market Updates: Instantaneous market data and trade updates delivered via WebSockets, keeping users informed.
- 🔁 Scalable Design: Horizontally scalable architecture with decoupled services, capable of handling increasing loads and traffic.
- 🧮 Efficient Storage: A dual-database approach (PostgreSQL for core trades, Time Series DB for analytics) optimizes both transactional integrity and analytical performance.
- Problem: Managing concurrent order spikes and maintaining system responsiveness under heavy load.
- Solution: Implementing a Redis queue to decouple order intake from processing, allowing the system to absorb and process orders efficiently without bottlenecks.
- Problem: Minimizing trade delays to provide a competitive trading environment.
- Solution: Utilizing an optimized matching engine combined with Redis Pub/Sub and WebSockets for rapid event propagation and instant trade execution.
- Problem: Storing and retrieving high-frequency trading data without impacting overall system performance.
- Solution: Employing PostgreSQL for reliable storage of core trade data and a specialized Time Series Database for efficient logging and analysis of market data.
To get the Exchange Platform running locally, follow these steps:
# 1. Clone the repository
git clone https://github.com/yuvrajnode/exchange.git
cd exchange
# 2. Install dependencies
npm install
# 3. Set up environment variables
cp .env.example .env
# Edit the .env file with your database credentials and other configurations
# 4. Start the development server
npm run devThis will start the Next.js development server, and you can access the application in your browser at http://localhost:3000 (or the port specified in your .env file).