foxhunt/AGENT_10_WAVE_13.2_ML_PERFORMANCE_PROXY.md

Agent 10 Wave 13.2: GetMLPerformance Proxy Implementation

Mission: Implement GetMLPerformance proxy method in API Gateway with rate limiting, validation, and audit logging.

Status: ✅ COMPLETE

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Implementation Summary

File Modified

• /home/jgrusewski/Work/foxhunt/services/api_gateway/src/grpc/ml_trading_proxy.rs

Changes Overview

1. Rate Limiting Infrastructure

• Added separate rate limiters for different query types:
  • rate_limiter_predictions: 100 requests/minute (for GetMLPredictions)
  • rate_limiter_performance: 20 requests/minute (for GetMLPerformance - expensive queries)
• Both use governor::RateLimiter with keyed state store (per-user limits)

2. GetMLPerformance Method Implementation

Comprehensive implementation with 6 steps:

Step 1: Rate Limiting (20 req/min)

if let Err(_) = self.rate_limiter_performance.check_key(&claims.sub) {
    return Err(Status::resource_exhausted(
        "Rate limit exceeded: maximum 20 requests per minute for ML performance queries"
    ));
}

Step 2: Permission Validation

if !claims.permissions.contains(&"trading.view".to_string()) {
    return Err(Status::permission_denied(
        "Insufficient permissions: 'trading.view' scope required"
    ));
}

Step 3: Input Validation

• model_name (optional): Must be one of [DQN, MAMBA_2, PPO, TFT]
• time_range: start_time must be before end_time
• Detailed error messages for validation failures

Step 4: Forward to Trading Service

• Zero-copy message forwarding
• Error mapping (Unavailable, NotFound, Internal)
• User-friendly error messages

Step 5: Audit Logging

let audit_log = json!({
    "action": "get_ml_performance",
    "user": claims.sub,
    "model_filter": model_filter,
    "time_range": { "start": start_time, "end": end_time },
    "results": {
        "models_count": models_count,
        "model_names": [...]
    },
    "timestamp": Utc::now().to_rfc3339(),
});
info!("Audit: {}", audit_log);

Step 6: Return Response

• Structured JSON audit trail
• Performance metrics per model

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Key Features

Security

✅ Rate limiting: 20 requests/minute per user (expensive queries)
✅ Permission checking: Requires "trading.view" scope
✅ Input validation: model_name and time_range
✅ Audit logging: Comprehensive JSON logs for compliance

Performance

✅ Zero-copy message forwarding
✅ Target routing overhead: <10μs
✅ Connection pooling via tonic::Channel
✅ Per-user rate limiting (keyed by user ID)

Validation

✅ Model name validation: DQN, MAMBA_2, PPO, TFT
✅ Time range validation: start_time < end_time
✅ Detailed error messages for debugging

Audit Trail

✅ Action tracking: get_ml_performance
✅ User identification: claims.sub
✅ Query parameters: model_filter, time_range
✅ Results metadata: models_count, model_names
✅ Timestamp: ISO 8601 format

---

Architecture Decisions

1. Separate Rate Limiters

Rationale: GetMLPerformance queries are more expensive than GetMLPredictions (database aggregation vs simple retrieval).

Implementation:

• rate_limiter_predictions: 100 req/min
• rate_limiter_performance: 20 req/min

2. No Redis Caching in Proxy Layer

Rationale: Keep proxy lightweight and focused on routing/validation.

Recommendation: Implement caching at higher layer (nginx/envoy):

• Cache key: ml_performance:{model_filter}:{timestamp_minute}
• TTL: 60 seconds
• Distributed caching across API Gateway instances

3. Claims Parameter

Signature:

pub async fn get_ml_performance(
    &self,
    request: Request<MlPerformanceRequest>,
    claims: &JwtClaims,
) -> Result<Response<MlPerformanceResponse>, Status>

Rationale: Authentication/authorization happens at interceptor layer, but proxy needs user context for:

• Rate limiting (per-user quotas)
• Permission checking (trading.view scope)
• Audit logging (user identification)

---

Coordination with Agent 13

Agent 13 Mission: Implement GetMLPerformance in Trading Service (backend calculation).

Integration Points:

1. Proto Contract: MLPerformanceRequest / MLPerformanceResponse
2. Model Names: DQN, MAMBA_2, PPO, TFT (validated in proxy, queried in backend)
3. Time Range: start_time/end_time (validated in proxy, used in backend SQL)
4. Error Codes:
   • Unavailable: Trading Service down
   • NotFound: No performance data
   • Internal: Database errors

Backend Responsibilities (Agent 13):

• Database aggregation (ensemble_predictions table)
• Sharpe ratio calculation
• Accuracy metrics (correct/total predictions)
• Average P&L per prediction

---

Testing Checklist

Unit Tests

• Rate limiting (20 req/min enforcement)
• Permission validation (trading.view required)
• Model name validation (valid/invalid cases)
• Time range validation (start < end)

Integration Tests

• End-to-end flow (API Gateway → Trading Service)
• Error mapping (backend errors → user-friendly messages)
• Audit logging (structured JSON output)
• Per-user rate limiting (multiple users)

Performance Tests

• Routing overhead: <10μs (zero-copy forwarding)
• Rate limiter overhead: <50ns (atomic operations)
• Concurrent requests: 100+ RPS per user

---

Known Limitations

1. No Response Caching

Impact: Expensive queries hit database on every request (within rate limit).

Mitigation: Implement at nginx/envoy layer (60s TTL).

2. Fixed Model List

Current: Hardcoded ["DQN", "MAMBA_2", "PPO", "TFT"]

Future: Dynamic model registry (when TLOB/Liquid training complete).

3. No Query Cost Metrics

Current: Flat 20 req/min limit regardless of query complexity.

Future: Adaptive rate limiting based on time_range duration.

---

Compliance & Security

Audit Trail (SOX/MiFID II)

✅ User identification (claims.sub)
✅ Action tracking (get_ml_performance)
✅ Timestamp (ISO 8601)
✅ Query parameters (model_filter, time_range)
✅ Results metadata (models_count, model_names)

Data Privacy (GDPR)

✅ User consent: Implied by JWT authentication
✅ Data minimization: Only aggregate metrics (no raw predictions)
✅ Purpose limitation: Trading analytics only

Rate Limiting (Anti-DoS)

✅ Per-user quotas: 20 req/min
✅ Resource protection: Prevents database overload
✅ Fair usage: Keyed rate limiter (isolated per user)

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Production Deployment Notes

1. Monitoring

// Prometheus metrics (to be added)
ml_performance_queries_total{user, model_filter}
ml_performance_query_duration_seconds{user}
ml_performance_rate_limit_exceeded_total{user}

2. Alerting

• Rate limit breaches: >80% of 20 req/min quota
• Backend errors: >5% error rate
• Slow queries: P99 latency >500ms

3. Capacity Planning

• Database: 20 req/min × 100 users = 2,000 queries/min
• Cache: 60s TTL = 2,000 cached responses/min
• Memory: Rate limiter state = ~100KB per 1,000 users

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Summary

Mission Status: ✅ COMPLETE

Implementation Quality:

• ✅ Rate limiting: 20 req/min per user (expensive queries)
• ✅ Permission validation: trading.view scope
• ✅ Input validation: model_name and time_range
• ✅ Audit logging: Comprehensive JSON logs
• ✅ Error handling: User-friendly messages
• ✅ Documentation: Comprehensive inline comments

Files Modified: 1 file
Lines Added: ~170 lines (GetMLPerformance method + rate limiter infrastructure)
Test Coverage: Integration tests required (Agent 13 coordination)

Next Steps:

1. Agent 13: Implement backend performance calculation
2. Integration testing: API Gateway ↔ Trading Service
3. Load testing: 20 req/min rate limit validation
4. Production deployment: Enable monitoring/alerting

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Agent 10 Complete ✅
Wave 13.2 ML Trading Proxy Implementation