performance-review

name: performance-review shortcut: review description: This skill should be used when the user asks to review trading performance, analyze wins/losses, or understand why their strategy is underperforming. Trigger phrases include "analyze my trades", "how is my strategy doing", "why am I losing", "review performance", "trading results", "what went wrong". description_zh: 当用户要求回顾交易表现、分析盈亏或了解策略表现不佳的原因时使用此技能。

Performance Review

Analyze trading performance and provide actionable optimization suggestions. Combines Attribution AI analysis with memory-based historical context.

Pre-requisites (MUST confirm before proceeding)

1. Confirm which trader or strategy to analyze
2. Confirm the time period (last 7 days, 30 days, specific range)
3. Confirm the exchange and environment

Workflow

Phase 1: Performance Data Collection

• list_traders(trader_id) → get trader details and current status
• get_wallet_status(wallet_address) → current balance and positions

Delegate deep analysis to Attribution AI:

• call_attribution_ai(task="Analyze trading performance for trader X over the last N days. Identify patterns in winning and losing trades.")

→ [CHECKPOINT] Present performance summary in plain language:

• Overall P&L
• Win rate and profit factor
• Best and worst trades
• Common patterns in losses Wait for user to ask questions or request optimization.

Phase 2: Insight Extraction

From the analysis, identify:

• Strategy strengths (what's working)
• Strategy weaknesses (what's not)
• Market conditions where strategy underperforms
• Risk management observations

These insights will be automatically saved to user memory by the context compression system for future reference.

→ [CHECKPOINT] Present key insights and ask if user wants optimization suggestions.

Phase 3: Optimization Suggestions (if requested)

Based on findings, suggest specific improvements:

Signal Pool Adjustments:

• Trigger frequency too high/low
• Missing market regime filters
• Thresholds need recalibration

Strategy Logic Adjustments:

• Risk parameters (leverage, position size, stop loss)
• Entry/exit conditions
• Market regime awareness

If user agrees to optimize:

• Delegate to appropriate sub-agent with specific improvement instructions
• Use existing resource IDs (edit, not create new)
• Follow resource-management patterns

→ [CHECKPOINT] Show proposed changes before applying. Wait for user confirmation.

Key Rules

• Always delegate analysis to Attribution AI — don't guess performance data
• Present numbers in user-friendly format (percentages, not raw decimals)
• Be honest about poor performance — don't sugarcoat
• Always frame suggestions as options, not directives
• Remind users that past performance doesn't guarantee future results