Steve — Trading Bot
At a glance
- Industry: FinTech / Algorithmic Trading
- Focus: Low-latency execution, MLOps, risk management
- Goal: Controlled live rollout with strict risk limits and full reproducibility
- Duration: 12 months from research to production trading
Context
Steve started as a promising research project with strong backtesting results. The models showed consistent alpha in historical simulations. The challenge was making this work in live markets — where latency matters, risk is real, and “it worked in backtesting” is not good enough.
Moving from backtests to production trading requires more than model deployment. It requires a complete trading systems infrastructure: reproducible research, risk controls, execution monitoring, and audit trails. Without this foundation, even a good model will fail in production.
Every trading system looks profitable in backtests. The question is whether it survives contact with live markets.
Challenge
Primary objective: Deploy a trading system with strict risk controls, reproducible research, and production-grade operational observability.
Key constraints:
- Decision latency under 50ms for time-sensitive signals
- Complete reproducibility — any backtest result must be replicable
- Hard risk limits with automatic position controls
- Full audit trail for regulatory compliance
- Graceful degradation during market stress
Technical Approach
Signal Pipeline
The signal pipeline was designed for speed and traceability:
- Data ingestion: Low-latency market data feeds with timestamp validation
- Feature computation: Pre-computed feature stores for common indicators
- Signal generation: Model inference with confidence scores
- Data lineage: Every signal traceable to its source data and model version
We separated signal generation from execution decisions. A signal is an observation; an execution is a commitment. This separation allowed us to tune risk controls independently of model changes.
Reproducible Backtesting
Backtesting infrastructure was built for determinism:
- Versioned data: Historical data snapshots with point-in-time correctness
- Versioned models: Model artifacts stored with full training metadata
- Versioned code: Every backtest run tied to a specific code commit
- Execution simulation: Realistic slippage, partial fills, and market impact modeling
Any backtest result could be reproduced months later with identical inputs and outputs. This was essential for debugging production discrepancies and regulatory audits.
Execution Services
The execution layer enforced risk controls before any trade:
- Position limits: Hard caps on position size by instrument
- Drawdown limits: Automatic position reduction on daily P&L thresholds
- Circuit breakers: Immediate halt on abnormal market conditions
- Rate limits: Maximum order frequency to prevent runaway behavior
- Audit logging: Every order decision logged with full context
Risk controls were implemented as a separate service layer, not embedded in trading logic. This made them easier to audit, test, and update independently.
Monitoring & Alerting
Production observability included:
- Real-time P&L tracking: Live position and exposure monitoring
- Latency metrics: End-to-end signal-to-execution timing
- Model drift detection: Statistical monitoring of signal distributions
- Anomaly detection: Alerts on unusual trading patterns
We invested heavily in alerting thresholds. Too many alerts cause alert fatigue; too few cause missed incidents. Tuning these thresholds was an ongoing process based on production experience.
Trade-offs
| Decision | Trade-off |
|---|---|
| Hard risk limits | Caps potential upside but prevents catastrophic losses |
| Reproducibility | Higher infrastructure cost for full determinism |
| Separate risk layer | Additional latency for risk checks |
| Conservative execution | Reduced fill rate for better slippage control |
- Risk control over peak returns. We accepted lower potential returns in exchange for controlled drawdowns. A system that occasionally loses big is worse than a system that consistently wins small.
- Determinism over speed. Reproducible research enabled safer iteration. We could confidently deploy model updates because we could verify their behavior.
- Observability as a feature. Monitoring wasn’t an afterthought — it was a core system capability.
Results
| Metric | Outcome |
|---|---|
| Decision latency | 10–50 ms for time-sensitive signals |
| Backtest reproducibility | 100% deterministic replay |
| Risk incidents | Zero uncontrolled drawdowns |
| Slippage | Reduced 30% via execution monitoring |
| Audit compliance | Full trade lineage for regulatory review |
Stack
- Signal Pipeline: Low-latency data feeds, feature stores, model inference
- Backtesting: Versioned data/code/models, realistic execution simulation
- Execution: Risk-controlled order routing with circuit breakers
- Monitoring: Real-time P&L, latency dashboards, drift detection, alerting
Key Learnings
- Trading AI fails without explicit risk controls. A model with no position limits will eventually blow up. Hard limits are not optional.
- Reproducibility is a feature, not a luxury. When production behavior differs from backtests, you need to know why. Without reproducibility, you’re guessing.
- The system is only as good as its execution and monitoring pipeline. A brilliant signal is worthless if execution is sloppy or monitoring is blind.
- Invest in operational confidence before scaling. Scale after you trust the system under stress, not before.
