Everything worth keeping on anomaly detection in bot-driven trading: core mechanics, parameter choices, failure record.
How It Works
anomaly detection operates as a pipeline: ingest, compute, decide, route. Each stage has its own ways to break.
Configuration
Configuring anomaly detection comes down to a handful of parameters. Vendor defaults suit demos; production deserves its own tuning pass.
Failure Modes
The failure record for anomaly detection includes regime changes, missed heartbeats, and positions that could not be closed gracefully.
Evaluation
Assess anomaly detection with fixed look-ahead-free tests, then stress the operational side before calling it production-ready.
Configuration Reference
# Anomaly Detection tested configuration mode: production timeframe: auto risk_limits: max_drawdown: 15 percent max_position: 3 percent monitoring: alert_threshold: 0.5 report_interval: hourly
Parameter Impact
| Parameter | Effect | Trade-off |
|---|---|---|
| timeframe | Signal frequency | Higher means fewer but more reliable signals |
| risk_limit | Maximum exposure | Lower means safer but slower returns |
| lookback | Indicator sensitivity | Longer means smoother but lagging |