Quantitative Trading Infrastructure
Building production-ready algorithmic trading systems with institutional-grade reliability, performance, and compliance.
- Signal Aggregation - Multi-agent, multi-horizon signal processing with Thompson Sampling
- Risk Management - Real-time budget enforcement and position sizing
- Conflict Resolution - Deterministic resolution of contradictory trading signals
- Audit & Compliance - Tamper-proof audit trails with 2-year retention
- Operational Monitoring - Real-time metrics via Prometheus, Grafana, and StatsD
- Deterministic Replay - Every aggregation is reproducible from seed
- Type Safety - 100% type-hinted Python with mypy validation
- Test Coverage - ≥80% coverage with unit, integration, and property-based tests
- Performance - Sub-100ms p99 latency for full aggregation pipeline
- Constitutional Compliance - Hard invariants enforced at runtime
Multi-Horizon Signal Aggregator - Production system for combining trading signals from multiple agents across 1d/20d/60d horizons.
Status: Production Ready Tests: 516 passing (100%) Coverage: 80.67% Performance: 95ms p99 latency
- Adaptive agent weighting via Thompson Sampling
- Multi-horizon portfolio construction
- Conflict resolution in multi-agent systems
- Real-time risk constraint enforcement
Languages: Python 3.11+ Storage: DuckDB, Parquet, Redis Monitoring: Prometheus, Grafana, StatsD Testing: pytest, hypothesis (property-based) CI/CD: GitHub Actions
For collaboration or inquiries, please open an issue in the relevant repository.
Built with precision. Deployed with confidence.