Latency Optimization in High-Frequency Trading

Why Latency Matters

In algorithmic trading, latency isn't just a performance metric—it's a competitive advantage. The difference between 50ms and 150ms can mean:

• Missing arbitrage opportunities that exist for 100-500ms
• Worse execution prices due to market movement
• Reduced ability to react to flash crashes
• Lower signal-to-noise ratio in microstructure analysis

Our target: Sub-150ms p95 latency end-to-end (market data → signal → order execution)

Our Latency Budget

Breaking down our 150ms target:

We maintain 30ms+ headroom for spikes and degradation under load.

WebSocket Streams: Sub-Millisecond Updates

Why WebSockets Over Polling

HTTP Polling (traditional approach):

• Poll exchange every 100-500ms
• Miss trades between polls
• Server overhead from constant requests
• Typical latency: 200-1000ms

WebSocket Streams (our approach):

• Push-based: exchange sends updates instantly
• No missed data
• Persistent connection, lower overhead
• Typical latency: <10ms p95

Multi-Exchange Aggregation

We maintain WebSocket connections to 10+ exchanges simultaneously, consolidating order books in real-time for arbitrage detection and best execution routing.

Event-Driven Architecture

Asynchronous Processing

Instead of blocking sequential execution, we use async/await patterns:

```python async def trading_pipeline(): # Fetch data and run risk prechecks in parallel data_task = asyncio.create_task(fetch_market_data()) risk_task = asyncio.create_task(precheck_limits())

data, risk_status = await asyncio.gather(data_task, risk_task)

if risk_status.ok:
    signals = await generate_signals(data)
    await execute_orders(signals)

Total: ~80ms with parallelization vs 100ms sequential

```

Signal Generation Optimization

Columnar Storage for Speed

Using Apache Arrow + Polars for lightning-fast time-series operations:

• 10-100x faster than Pandas
• SIMD vectorization for calculations
• Zero-copy data sharing
• Lazy evaluation for efficiency

```python import polars as pl

Fast VWAP calculation

df = pl.scan_parquet("trades/*.parquet") vwap = ( df.filter(pl.col("timestamp") > cutoff) .select([ (pl.col("price") * pl.col("volume")).sum() / pl.col("volume").sum() ]) .collect() # Execute in parallel )

Typical time: <10ms for 1M rows

```

Cached Computations

Pre-computing expensive calculations and caching with 60s TTL:

• Cache hit: <1ms
• Cache miss: 20-30ms
• Hit rate: 85-90% in production

Order Execution Speed

Direct WebSocket Order Placement

Instead of REST API calls, we use WebSocket connections for order placement:

• REST API: 50-100ms latency
• WebSocket: 30-80ms latency
• Reduction: 20-40ms per order

Smart Order Routing

We route to the fastest available exchange with sufficient liquidity:

```python def select_exchange(symbol, liquidity_needed): candidates = [] for exchange in active_exchanges: if exchange.get_liquidity(symbol) >= liquidity_needed: candidates.append({ 'exchange': exchange, 'latency': exchange.avg_latency_p95, 'fee': exchange.taker_fee })

# Sort by latency, then fee
return sorted(candidates, key=lambda x: (x['latency'], x['fee']))[0]

```

Production Performance Metrics

Our live system achieves:

• ✅ Order book update latency: <10ms p95
• ✅ Signal generation: <35ms end-to-end
• ✅ Order execution: <100ms to exchange
• ✅ Total round-trip: 90-120ms p95

Comparison to Industry

We're dramatically faster than retail/pro platforms without requiring expensive co-location.

Real-World Impact

Arbitrage Capture

With 90-120ms latency:

• ✅ Capture 60-70% of arbitrage opportunities (exist for 100-500ms)
• ✅ Execute before prices converge
• ✅ Sufficient speed for cross-exchange strategies

Flash Crash Response

During flash crashes:

• Market drops 5-10% in seconds
• Our system detects and responds in <500ms
• Cancels orders, flattens positions, waits for stability
• Re-enters at discounted prices opportunistically

Microstructure Signal Quality

Faster latency = fresher data:

• Order flow imbalance (OFI) calculations use <50ms old data
• VPIN calculated on near-real-time volume buckets
• Microprice reflects current market conditions

Continuous Monitoring

We track latency metrics 24/7 with alerting:

• Warning: >100ms p95 (any component)
• Critical: >150ms p95 (triggers investigation)
• Automatic degradation: Reduce trading frequency if latency spikes

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For more technical details, see our system architecture page.