beadboard/.agents/skills/tradelab-backtest-interpretation/SKILL.md

name	description
tradelab-backtest-interpretation	Use when reviewing TradeLab backtest output so recommendations are grounded in getBacktestResults data and mapped to concrete strategy changes.

TradeLab Backtest Interpretation

Overview

Use getBacktestResults output as the source of truth for strategy feedback. Interpret risk and return metrics first, then propose specific code-level changes through the strategy workflow.

When to Use

• User asks why strategy performance is good or bad.
• User asks how to improve a strategy after a backtest.
• A new currentBacktestId is available in context.

Non-Optional Rules

1. Retrieve metrics via getBacktestResults before proposing optimizations.
2. Anchor every recommendation to returned fields, not guesses.
3. Separate analysis into: performance, risk, and action plan.
4. If code changes are needed, route them through full-class saveStrategy flow.

Metric Interpretation Baselines

• sharpeRatio: < 1.0 weak, 1.5-2.0 good, > 2.0 elite.
• profitFactor: < 1.2 fragile, 1.5-2.5 robust, > 3.0 possible overfit.
• maxDrawdown: flag if > 15%; suggest volatility controls or regime filters.
• winRate < 40%: verify payoff ratio; add entry-quality filters.

Tool Usage Pattern

{
  "backtestId": "optional-explicit-id",
  "strategyId": "optional-fallback-id",
  "includeTrades": false,
  "metricFilter": ["performance", "risk", "ratios"]
}

Output Pattern

1. State key facts from tool output.
2. Explain likely failure mode (entries, exits, regime mismatch, risk sizing).
3. Propose 2-3 prioritized improvements tied to those facts.
4. If user approves changes, produce full updated class through saveStrategy.

Common Mistakes

• Recommending changes without calling getBacktestResults.
• Giving generic advice not tied to metric values.
• Treating high profitFactor without checking overfit risk.