Alpha Decay Detection

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Detect and analyze alpha decay in trading strategies using statistical methods. Helps identify strategies with degrading performance over time.

Sby Skills Guide Bot
Data & AIIntermediate
506/2/2026
Claude Code
#alpha-decay#trading-strategies#risk-management#statistical-analysis

Recommended for

Our review

Detects and analyzes alpha decay in trading strategies using statistical indicators.

Strengths

  • Provides multiple indicators (Sharpe, IC, hit rate, capacity, regime) for comprehensive evaluation.
  • Allows customizable thresholds and periods to adapt to different strategies.
  • Generates actionable recommendations based on signal severity.

Limitations

  • Requires reliable historical return data at regular frequency.
  • Severity thresholds (0.3, 0.6) are arbitrary and may not suit all strategies.
  • Does not identify root causes of decay (e.g., market regime changes).
When to use it

Use this skill to regularly monitor the health of your trading strategies and identify performance degradation before it becomes critical.

When not to use it

Do not use it for strategies with too short a history (less than 30 days) or in contexts where benchmark data is unavailable.

Security analysis

Safe
Quality score95/100

The skill performs analytical computation on internal data using a Python class; it does not invoke destructive commands, external data exfiltration, or hidden payloads. No elevated permissions or dangerous tools are declared.

No concerns found

Examples

Check all active strategies
/alpha-decay
Analyze a specific strategy with custom threshold
/alpha-decay --strategy momentum-001 --threshold 0.25 --period 90
Get detailed decay metrics
/alpha-decay --strategy mean-reversion-002 --detailed

name: alpha-decay description: Detect and analyze strategy alpha decay signals argument-hint: "[--strategy id|--all|--threshold pct|--period days]"

Alpha Decay Detection

Detect and analyze alpha decay in trading strategies using statistical methods.

Usage

  • /alpha-decay - Check all active strategies
  • /alpha-decay --strategy momentum-001 - Analyze specific strategy
  • /alpha-decay --threshold 0.3 - Custom decay threshold
  • /alpha-decay --period 90 - Analysis period in days
  • /alpha-decay --detailed - Show detailed decay metrics

Decay Indicators

| Indicator | Description | Warning Level | |-----------|-------------|---------------| | Sharpe Decay | Rolling Sharpe ratio decline | > 30% decline | | IC Decay | Information coefficient drop | IC < 0.02 | | Hit Rate | Win rate degradation | < 45% | | Capacity | Returns vs AUM correlation | r < -0.3 | | Regime | Regime change detection | Confidence > 0.8 |

Related Files

  • scripts/risk_management/strategy_analytics.py - AlphaDecayDetector class
  • scripts/risk_management/alpha_research.py - Signal evaluation
  • services/risk/risk_manager.py - Strategy monitoring

Instructions

When this skill is invoked:

  1. Parse arguments:

    • No args: Scan all active strategies
    • --strategy <id>: Single strategy analysis
    • --threshold: Custom decay threshold (default 0.3)
    • --period: Lookback period in days (default 60)
  2. Load strategy data:

    • Fetch returns from database/API
    • Get strategy metadata and targets
    • Load benchmark/factor returns
  3. Run decay detection:

    from risk_management.strategy_analytics import AlphaDecayDetector
    
    detector = AlphaDecayDetector(
        decay_threshold=0.3,
        confidence_level=0.95,
        lookback_window=60
    )
    signals = detector.detect_decay(returns)
    
  4. Display decay report:

    Alpha Decay Analysis
    ═══════════════════════════════════════════════════════════
    
    Strategy: momentum-001
    Period: Last 60 days
    Status: ⚠️  WARNING - Decay signals detected
    
    DECAY SIGNALS
    ─────────────────────────────────────────────────────────
    Signal          Severity    Confidence    Description
    ─────────────────────────────────────────────────────────
    Sharpe Decay    0.65        87%          Sharpe dropped 42%
    IC Decay        0.45        72%          IC now 0.015 (was 0.04)
    Hit Rate        0.30        65%          Win rate 43% (target 52%)
    
    METRICS COMPARISON
    ─────────────────────────────────────────────────────────
    Metric          Current     Historical    Change
    ─────────────────────────────────────────────────────────
    Sharpe Ratio    0.85        1.45          -41%
    IC Mean         0.015       0.042         -64%
    Hit Rate        43%         52%           -17%
    Avg Return      0.02%       0.08%         -75%
    
    RECOMMENDATIONS
    ─────────────────────────────────────────────────────────
    1. Review regime indicators - potential regime change
    2. Check for crowding in signal factors
    3. Validate data inputs for drift
    4. Consider reducing position sizing by 50%
    
  5. For --detailed:

    • Rolling IC time series
    • Distribution shift analysis
    • Factor exposure changes
    • Correlation regime changes
  6. Severity thresholds:

    • ACTIVE: No decay (severity < 0.3)
    • WARNING: Moderate decay (0.3 <= severity < 0.6)
    • CRITICAL: Severe decay (severity >= 0.6)
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