/performance

description: Generate a strategy performance report with key metrics
argument-hint: "[strategy name or 'all']"

Performance Report

Generate aggregate performance statistics per strategy or across all strategies. Shows win rate, profit factor, P&L distribution, best/worst trades, and behavioral metrics.

Workflow

Step 1: Determine Scope

If a strategy name is provided, focus on that strategy. If "all" or nothing is provided, report across all strategies.

Optional filters:

• **Date range**: Last week, last month, custom range
• **Symbol**: Filter by trading instrument
• **Session**: Filter by trading session

Step 2: Fetch Performance Data

Use the `get_strategy_performance` MCP tool:

get_strategy_performance({
  strategy: "VolBreakout",  // or null for all
  date_from: "2026-01-01",
  date_to: "2026-03-16"
})

Step 3: Fetch Behavioral Analysis

Use the `get_behavioral_analysis` MCP tool for deeper insights:

get_behavioral_analysis({
  strategy: "VolBreakout"
})

This returns:

• **Disposition ratio**: Are you cutting winners short / holding losers long?
• **Hold time asymmetry**: Winners vs losers average hold time
• **Lot sizing variance**: Consistency vs Kelly criterion optimal
• **Streak analysis**: Current and historical win/loss streaks

Step 4: Present Report

Structure the report as:

| Metric | Value | |--------|-------| | Total Trades | N | | Win Rate | X% | | Profit Factor | X.XX | | Total P&L | $X,XXX | | Avg Win | $XXX | | Avg Loss | -$XXX | | Best Trade | $XXX (date, context) | | Worst Trade | -$XXX (date, context) | | Max Drawdown | X% | | Sharpe Ratio | X.XX |

Plus behavioral insights:

• Disposition ratio (target: < 1.0)
• Hold time asymmetry (target: winners held longer)
• Confidence calibration (are high-confidence trades actually better?)

Step 5: Actionable Takeaways

End with 2-3 specific, data-backed recommendations. No vague advice.

Example

User: /performance VolBreakout