Everything worth keeping on normality testing in bot-driven trading: core mechanics, parameter choices, failure record.
How It Works
At the core of normality testing sits a chain of transformations that turns raw market data into decisions an engine can act on.
Configuration
Parameter choices in normality testing shape both signal quality and operational load. Record every change and the reason for it.
Failure Modes
The failure record for normality testing includes regime changes, missed heartbeats, and positions that could not be closed gracefully.
Evaluation
An honest evaluation of normality testing weighs quantitative metrics alongside code quality and how much attention it demands in production.
Configuration Reference
# Normality Testing 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 |