Field notes on statistical arbitrage in automated trading: mechanics, configuration, and the pitfalls that surface in production.
How It Works
At the core of statistical arbitrage sits a chain of transformations that turns raw market data into decisions an engine can act on.
Configuration
Configuring statistical arbitrage comes down to a handful of parameters. Vendor defaults suit demos; production deserves its own tuning pass.
Failure Modes
Documenting how statistical arbitrage fails teaches more than documenting how it succeeds. Regime shifts, infrastructure faults, and drift top the list.
Evaluation
An honest evaluation of statistical arbitrage weighs quantitative metrics alongside code quality and how much attention it demands in production.
Configuration Reference
# Statistical Arbitrage 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 |