Applied AI for Trading Systems
Why trading AI fails in production
Latency, regime shifts, and leakage create silent failure modes. Many systems look profitable in backtests but fail when exposed to live market dynamics — see Steve — Trading Bot for a real-world example. The difference is not the model alone; it is the full pipeline, data integrity, and operational risk controls.
Typical constraints we handle
Data integrity, compliance, low-latency pipelines, and robust backtesting. Real constraints include:
- Strict latency budgets and execution timing
- Regime shifts that invalidate historical patterns
- Data source reliability and normalization
- Compliance and audit requirements
- Risk controls embedded into every step
Typical production targets we design around:
- 10–50 ms decision latency for time‑sensitive signals
- Deterministic, reproducible backtests
- Hard risk limits with automated circuit breakers
Our approach
Architecture-first, reproducible research, and production-grade MLOps. We build a system that:
- Separates research from production decisions
- Makes data lineage and reproducibility explicit
- Includes automated checks for leakage and overfitting
- Provides clear operational monitoring and alerting
Architecture examples
Signal pipelines, execution services, and monitoring stacks. Typical components:
- Data ingestion and normalization services
- Feature pipelines with strict versioning
- Backtesting infrastructure with realistic simulation
- Execution services with risk controls and kill switches
- Monitoring for latency, slippage, and drift
Case studies
Trading systems and platform modernization. Example:
Engagement models
Short technical audit → controlled rollout → ongoing optimization.
What we build
We deliver complete trading systems rather than isolated ML models:
- Reproducible research environments
- Production-grade data pipelines
- Execution services with safety controls
- Monitoring and alerting for operational risk
Failure modes we mitigate
Common trading AI failure patterns include:
- Leakage and look-ahead bias that inflate offline results
- Overfitting to historical regimes
- Execution mismatch between simulated and live market conditions
- Latency spikes that turn a profitable strategy into a loss
We build explicit tests and safeguards to detect these early.
Evaluation approach
We evaluate beyond backtest metrics:
- Slippage-adjusted performance
- Risk-adjusted returns
- Robustness across multiple market regimes
- Sensitivity analysis on latency and cost
Governance and compliance
We design systems with auditability in mind:
- Clear decision logs
- Versioned models and data
- Rollback paths and incident response
Risk controls and safety
Trading systems must fail safely. We implement:
- Hard risk limits and circuit breakers
- Position and exposure monitoring
- Automated alerts for anomalies and latency spikes
- Controlled rollout with gradual capital allocation
Typical delivery timeline
Most engagements follow a clear path:
- Audit and data validation (1–2 weeks)
- Pipeline and backtesting build-out (2–4 weeks)
- Controlled live rollout and monitoring (4–8 weeks)
Typical range outcomes
For mature teams, common outcomes include:
- Faster iteration cycles without sacrificing risk controls
- Reduced slippage through better monitoring and execution timing
When to engage
Engage us if:
- You need a production-grade system, not just a model
- Your current strategy does not survive live deployment
- You require robust backtesting and reproducibility
FAQs
How do you prevent leakage and look‑ahead bias?
We enforce data lineage, time‑aware feature pipelines, and automated leakage checks in the backtesting stack.
Do you handle low‑latency execution?
Yes. We design for strict latency budgets, controlled execution paths, and circuit‑breaker safeguards.
What is a safe rollout in trading AI?
Gradual capital allocation, monitored performance, and explicit kill switches.