Building real-time FX trading systems

At Pangea, we're building real-time FX trading systems that need to handle market data updates, execute trades, and maintain position state. Python's async capabilities combined with proper architecture make this possible.

The Challenge

FX trading requires:

• Low latency (milliseconds matter)
• High throughput (thousands of updates per second)
• Reliability (no data loss)
• Consistency (accurate position tracking)

Architecture

We use:

• asyncio for concurrent processing
• WebSockets for real-time market data
• Redis for position state
• PostgreSQL for persistence
• Message queues for order execution

Market Data Handler

import asyncio
import websockets
import json

class FXMarketDataHandler:
    def __init__(self):
        self.subscribers = set()
        self.positions = {}
    
    async def handle_market_data(self, websocket, path):
        async for message in websocket:
            data = json.loads(message)
            await self.process_update(data)
            await self.notify_subscribers(data)
    
    async def process_update(self, data):
        # Update positions in Redis
        currency_pair = data['pair']
        rate = data['rate']
        # Process update...

Performance Considerations

• Use connection pooling
• Batch database writes
• Cache frequently accessed data
• Monitor latency metrics
• Optimize hot paths

Lessons Learned

1. Async is essential for real-time systems
2. State management is critical
3. Monitoring and alerting are non-negotiable
4. Test with production-like load
5. Plan for failure scenarios

"In trading systems, milliseconds matter. But correctness matters more."