Financial AI performance benchmark

WebSocket optimization

By improving the WebSocket architecture, we reduced startup time from ~46 seconds to immediate startup.

Problems

• Early design: Subscribing to every coin over WebSocket when selecting a coin caused a ~46s delay
• Wasted resources: Unnecessary subscriptions consumed bandwidth and CPU
• Slow startup: Poor first-run experience

Mitigations

• API-first analysis: Coin selection uses API-driven analysis instead of subscribing to all symbols up front
• Focused WebSockets: WebSockets are used primarily for position monitoring
• Subscription policy: Keep major symbols always on; add subscriptions only for open positions / active signals
• Batch cleanup: Release unused subscriptions in batches

Results

• Startup time: ~46s → immediate (~99% improvement)
• Resource use: ~70% fewer WebSocket subscriptions
• Memory: Fewer idle connections reduce memory pressure

API call optimization

Layered caching

To minimize API calls, we implemented a multi-layer caching system.

• AI caching: Cache repeated judgments under identical inputs
• Market data caching: Cache prices, volumes, and related market fields
• Coin selection caching: Cache selection results to shorten initial load
• Invalidation: Automatically invalidate when market conditions change materially

API reduction outcomes

• Initial load: ~4 minutes → immediate (~99% improvement)
• API dependency: 100% → ~30% (~70% reduction)
• Cost: Fewer calls reduce rate-limit pressure and operational costs
• Stability: Lower risk of hitting exchange rate limits

Memory efficiency

Database optimization

• Indexes: Add indexes for hot query paths
• Retention: Automatically prune old logs
• Compression: Store large blobs in compressed form where appropriate

Memory management

• WebSocket hygiene: Remove unused subscriptions to save memory
• Cache size caps: Bound caches to predictable memory usage
• GC-friendly patterns: Avoid retaining large graphs longer than necessary

Multi-exchange performance

Concurrent operation

We operate six exchanges concurrently while keeping stable performance without proportional slowdown.

• Isolation: Per-exchange monitoring paths reduce cross-talk
• Resource separation: Contain failures and resource spikes per venue
• Prioritization: Prioritize critical venues when needed
• Load spreading: Spread API calls across time windows to avoid bursts

Public R&D and investor lens

Technical quality

These benchmarks demonstrate engineering quality in a real financial AI stack.

• Startup: ~46s → immediate (~99% improvement)
• API efficiency: ~70% fewer calls
• Resource efficiency: Better memory and network utilization
• Scalability: Six concurrent exchanges without degradation

Operational efficiency

Performance work translates into lower operating cost and higher reliability.

• Cost: Fewer calls reduce rate-limit incidents and wasted cycles
• Stability: Lower chance of hitting exchange limits during volatility
• Scalability: Room to add venues without linear cost explosion
• UX: Fast startup and predictable runtime behavior

Conclusion

The benchmark shows that WebSocket optimization, API minimization, and memory efficiency can improve both operational efficiency and stability.

For public R&D and investors, these metrics support claims of technical quality, operational efficiency, and scalability.