A written reference on wash trading detection — how it works, how to configure it, and where it breaks under load.
How It Works
At the core of wash trading detection sits a chain of transformations that turns raw market data into decisions an engine can act on.
Configuration
Configuring wash trading detection comes down to a handful of parameters. Vendor defaults suit demos; production deserves its own tuning pass.
Failure Modes
wash trading detection breaks in recognizable ways: silent data gaps, parameters that stopped matching the market, and latency spikes at the worst moment.
Evaluation
An honest evaluation of wash trading detection weighs quantitative metrics alongside code quality and how much attention it demands in production.
Configuration Reference
# Wash Trading 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 |