High-Frequency Trading (HFT) Market Maker Component

Requirements Overview

1. Low Latency: The system must execute trades and update orders with minimal delay. Order Placement: Rapidly place buy and sell orders based on market conditions. Order Cancellation: Quickly cancel or modify existing orders.
2. Reliability: Ensure continuous operation and accurate order processing.
3. Scalability: Handle increasing trade volumes and market data efficiently.
4. Maintainability: Easy to monitor, manage, and update.

Functional Requirements

1. Order Management

   • Order Placement: Rapidly place buy and sell orders based on market conditions.
   • Order Cancellation: Quickly cancel or modify existing orders.

2. Market Data Handling

   • Real-time Data Processing: Consume and process real-time market data feeds with minimal delay.
   • Data Storage: Store historical market data for analysis and backtesting.

3. Risk Management

   • Position Limits: Enforce limits on positions to manage risk.
   • Exposure Monitoring: Continuously monitor market exposure and adjust positions.

4. Strategy Execution

   • Algorithmic Trading: Implement and execute trading algorithms based on predefined strategies.
   • Parameter Adjustments: Dynamically adjust strategy parameters based on market conditions.

Non-Functional Requirements

1. Performance

   • Sub-millisecond latency for order execution.
   • High throughput to handle large volumes of orders and market data.

2. Scalability

   • Horizontal scaling to handle more trades and data.
   • Vertical scaling for enhanced performance on powerful hardware.

3. Reliability

   • High availability with minimal downtime.
   • Data integrity and consistency in order processing.

4. Maintainability

   • Clear and concise logging for troubleshooting.
   • Automated deployment and configuration management.

Limitations

1. Network Latency

   • The speed of order execution is limited by network latency between the trading system and exchanges.

2. Algorithm Complexity

   • Highly complex algorithms might introduce processing delays, impacting overall latency.

3. Data Volume

   • Storing and processing large volumes of historical market data can be challenging and resource-intensive.

Back-of-the-Envelope Calculations

1. Latency Estimation

   • Order Execution Latency: Assume an execution latency of 200 microseconds for placing or canceling an order.

2. Throughput Estimation

   • Assume a single instance can handle 10,000 orders per second.

3. Market Data Processing

   • Data Feed Rate: Assume receiving 1,000 market updates per second per feed.
   • Processing Latency: Each update processed within 50 microseconds.

4. Memory Usage

   • Order Book Storage: Each order entry is 100 bytes. For 1 million active orders, memory usage is:
     • 1,000,000 orders * 100 bytes = 100 MB

5. Scalability

   • Horizontal Scaling: Adding more instances to handle additional orders and market data. If each instance can handle 10,000 orders/sec, a cluster of 10 instances can handle 100,000 orders/sec.

6. Risk Management Overhead

   • Position Monitoring: Assume monitoring 1,000 positions per second with 10 microseconds per position.
   • Total Monitoring Time: 1,000 positions * 10 microseconds = 10 milliseconds

7. Network Overhead

   • Bandwidth per Order: Assume each order involves 1 KB of data transfer.
   • Bandwidth per Node: For 10,000 orders/sec, the bandwidth requirement is:
     • 10,000 orders/sec * 1 KB = 10 MB/sec

Aggregate the Total Requirements

Given these factors, let's estimate the number of nodes and their RAM:

1. Order Processing Nodes:
   • To handle 100,000 orders per second, we need 10 nodes (as each node handles 10,000 orders/sec).
   • Latency Requirements: Assume that each order must be processed within 200 microseconds.

Estimating CPU Requirements

1. Order Processing Load

   • 10,000 orders per second
   • Assume each order processing takes approximately 200 microseconds.
   • CPU cycles needed per order: ( 200 \text{ microseconds} = 0.2 \text{ milliseconds} ).
   • Total CPU time per second: ( 10,000 \text{ orders} \times 0.2 \text{ milliseconds} = 2,000 \text{ milliseconds} = 2 \text{ seconds} ).
   • Since this is per second, a single core running at full capacity can handle this.

CPU Cores and Frequency Calculation

To handle 2,000 milliseconds of CPU time per second, we need 3 CPU cores (2+1 for smooth operation).

CPU Frequency

1. Latency Requirements:

   • Order execution latency is 200 microseconds.
   • Modern high-performance CPUs (e.g., Intel Xeon or AMD EPYC) can achieve clock speeds around 3 GHz to 4 GHz.
   • At 3 GHz, each clock cycle is approximately ( \frac{1}{3,000} ) microseconds, or about 0.333 nanoseconds.

2. Memory per Node:

   • Each node requires at least 100 MB for order book storage.
   • Additional memory for processing and overhead, let's assume a buffer of 400 MB.
   • Total Memory per Node: ( 100 \text{ MB} + 400 \text{ MB} = 500 \text{ MB} ).

3. Network Bandwidth per Node:

   • Each node requires 10 MB/sec bandwidth.

Final Node Specifications

• CPU: 3 cores per node, 3+ GHz frequency.
• Number of Nodes: 10
• RAM per Node: 500 MB
• Network Bandwidth per Node: 10 MB/sec

Summary

• Latency: Sub-millisecond latency for order execution, around 200 microseconds.
• Throughput: A single instance can handle 10,000 orders/sec, scaling to 100,000 orders/sec with a 10-instance cluster.
• Memory: Order book storage requires around 100 MB for 1 million active orders.
• Market Data: Processing 1,000 market updates per second with 50 microseconds latency per update.
• Network: Bandwidth requirement per node is around 10 MB/sec.

Please check system_diagram.md file and simple png diagram for it.