performance-review: Анализ торговой стратегии и результатов

--- 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