Everything worth keeping on exception tracking in bot-driven trading: core mechanics, parameter choices, failure record.
How It Works
exception tracking operates as a pipeline: ingest, compute, decide, route. Each stage has its own ways to break.
Configuration
Configuring exception tracking comes down to a handful of parameters. Vendor defaults suit demos; production deserves its own tuning pass.
Failure Modes
Documenting how exception tracking fails teaches more than documenting how it succeeds. Regime shifts, infrastructure faults, and drift top the list.
Evaluation
Judge exception tracking on measured outcomes — distribution of returns, drawdown depth, operational burden — not on a single lucky stretch.
Configuration Reference
# Exception Tracking 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 |