Alpha Decay Detection

VerifiedSafe

Detects and analyzes alpha decay signals in trading strategies using statistical methods. Evaluates Sharpe ratio, information coefficient, and win rate degradation.

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

Recommended for

Our review

Detects and analyzes alpha decay in trading strategies using statistical indicators like Sharpe ratio decline, information coefficient drop, and hit rate degradation, providing severity warnings and recommendations.

Strengths

  • Comprehensive decay indicators (Sharpe, IC, hit rate, capacity, regime).
  • Customizable thresholds and analysis periods.
  • Generates actionable recommendations such as reducing position sizing.
  • Detailed mode for deeper analysis of time series and distribution shifts.

Limitations

  • Relies on historical data and may not perfectly predict future decay.
  • Requires integration with data sources for returns and strategy metadata.
  • Interpretation of severity thresholds may vary by strategy context.
When to use it

When monitoring active trading strategies for performance degradation and deciding whether to adjust or halt them.

When not to use it

When only qualitative assessment is needed or when historical data is insufficient or unreliable.

Security analysis

Safe
Quality score85/100

The skill performs analytical computations on strategy returns data within a Python environment. No destructive, exfiltrating, or system-modifying actions are described. No shell commands, network calls to external services without user intent, or obfuscated code are present.

No concerns found

Examples

Check all strategies for alpha decay
/alpha-decay
Detailed analysis of a specific strategy
/alpha-decay --strategy momentum-001 --detailed
Custom threshold and period
/alpha-decay --threshold 0.5 --period 90

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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