Cadenza design note
# Cadenza
Creating a large-scale trading engine requires careful architectural planning to ensure high performance, low latency, and scalability. Below is a high-level architecture design for your trading engine that leverages HTTP, WebSocket, and FIX protocol.
**High-Level Architecture:**
**1. Client Layer**
• **HTTP API**: For standard request-response interactions.
• **WebSocket API**: For real-time data delivery and bi-directional communication.
• **FIX API**: For institutional clients and high-frequency trading.
**2. Gateway Layer**
• **API Gateway**: Handles and routes HTTP and WebSocket requests. This layer ensures security (authentication and authorization), rate limiting, and request validation.
**3. Load Balancer**
• **Load Balancer**: Distributes incoming connections from clients to multiple instances of trading servers to ensure even load distribution and high availability.
**4. Trading Core**
• **Trading Engine**: High-performance, low-latency engine that handles order matching, order book management, and execution.
• **Quote Aggregator**: Aggregates quotes from liquidity providers in real-time.
• **Order Router**: Routes orders to the appropriate liquidity provider based on the best available price and execution policies.
**5. Market Data Layer**
• **Market Data Feeds**: Consumes and processes real-time market data from multiple liquidity providers.
• **Normalization Layer**: Normalizes data from various formats to a unified format.
• **Market Data Distributor**: Distributes processed market data to clients via WebSocket or HTTP endpoints.
**6. Liquidity Provider Layer**
• **FIX Connectors**: Connects to liquidity providers using FIX protocol.
• **WebSocket/HTTP Connectors**: Connects to liquidity providers using WebSocket or HTTP APIs.
**7. Data Storage Layer**
• **High-Performance Databases**: For storing order books, trade history, and market data.
• Use in-memory databases (e.g., Redis) for real-time data.
• Use relational databases (e.g., PostgreSQL) for transactional data.
• Use NoSQL databases (e.g., Cassandra) for scalable market data storage.
**8. Monitoring and Logging**
• **Monitoring Tools**: Tools like Prometheus and Grafana to monitor system health, performance metrics, and alerting.
• **Centralized Logging**: Utilizing ELK Stack (Elasticsearch, Logstash, Kibana) for log aggregation and analysis.
**9. Security Layer**
• **DDoS Protection**: To protect against distributed denial-of-service attacks.
• **WAF (Web Application Firewall)**: To filter and monitor HTTP/HTTPS requests.
• **Encryption**: SSL/TLS for data-in-transit and encryption for data-at-rest.
**Detailed Components:**
**Client Layer**
• **Clients**: Various clients such as web platforms, mobile apps, and institutional traders use the HTTP, WebSocket, and FIX APIs to interact with the trading engine.
**Gateway Layer**
• **API Gateway**: Manages all incoming API requests. Examples: Kong, NGINX, AWS API Gateway.
• **Authentication & Authorization**: JWT tokens, OAuth.
• **Rate Limiting**: To prevent abuse and ensure fair usage.
**Load Balancer**
• **Load Balancer**: Examples: HAProxy, NGINX, AWS Elastic Load Balancing, Google Cloud Load Balancing.
**Trading Core**
• **Trading Engine**: Developed with languages known for high performance (e.g., C++, Java).
• **Order Matching Algorithm**: FIFO, Pro-Rata, etc.
• **Quote Aggregator**: Receives and aggregates quotes from liquidity providers continuously.
• **Order Router**: Implements smart order routing (SOR) to achieve optimal execution.
**Market Data Layer**
• **Market Data Feeds**: Interfaces with market data providers.
• **Normalization Layer**: Unifies data formats across different providers.
• **Market Data Distributor**: Distributes real-time data to subscribed clients.
**Liquidity Provider Layer**
• **FIX Connectors**: Handles connections to liquidity providers via the FIX protocol.
• **WebSocket/HTTP Connectors**: Manages connections using WebSocket or HTTP APIs.
**Data Storage Layer**
• **In-memory Databases**: For high-speed data access (e.g., Redis).
• **Relational Databases**: For reliable transactional data storage (e.g., PostgreSQL).
• **NoSQL Databases**: Handles large volumes of market data (e.g., Cassandra).
**Monitoring and Logging**
• **Prometheus/Grafana**: Metric collection and visualization.
• **ELK Stack**: Centralized logging and log analysis.
**Security Layer**
• **DDoS Protection**: Services like Cloudflare.
• **WAF**: Protection against web attacks.
• **Encryption**: Ensuring secure data transfer and storage.
**Scalability Considerations:**
• **Horizontal Scalability**: Adding more servers to handle increased loads.
• **Microservices Architecture**: Decoupling services to independently scale and manage.
• **Auto-Scaling**: Leveraging cloud auto-scaling features to dynamically manage resources based on load.
**Example Technology Stack:**
• **Languages**: C++, Java, Node.js, Python.
• **APIs**: REST, GraphQL, FIX protocol.
• **Databases**: PostgreSQL, Redis, Cassandra.
• **Servers**: NGINX, HAProxy.
• **Hosting**: AWS, Google Cloud, Azure.
