Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Guides systematic backtesting of quantitative trading strategies using a methodology that prioritizes robustness over optimistic results. Covers parameter sensitivity testing, slippage modeling, bias prevention, and out-of-sample validation. Designed for rule-based strategies where every decision is codified in advance.
It enforces a stress-first framework that deliberately adds friction and punishes strategies, which filters out curve-fitted results that would fail in live trading.
When to use it
- Testing whether a gap-up earnings strategy survives realistic slippage
- Checking if stop loss parameters are fragile or stable across a range
- Running walk-forward analysis on a mean-reversion system
- Diagnosing why a backtest shows suspiciously high win rates
- Evaluating year-by-year consistency before deploying a strategy live
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: I have a gap-up earnings strategy with 87% win rate in backtesting — is it ready to trade?
- 1Ask user to state the edge hypothesis in one sentence and confirm all rules are fully codified with no discretion
- 2Check sample size — flag if fewer than 100 trades and warn about statistical confidence
- 3Walk through parameter sensitivity: vary stop loss and profit target at 50–150% of baseline values to identify plateaus vs narrow spikes
- 4Apply pessimistic slippage (1.5–2x estimates) and worst-case fill assumptions, then compare performance degradation
- 5Flag the 87% win rate as a red flag and audit for look-ahead bias or data alignment issues
Assessment of whether the strategy survives stress tests, with specific failure patterns identified and a deploy/refine/abandon recommendation