Our review
Detects and analyzes alpha decay signals in trading strategies using statistical methods.
Strengths
- Provides multiple indicators (Sharpe, IC, hit rate, capacity) for comprehensive assessment.
- Early warning via configurable severity thresholds.
- Generates actionable recommendations to adjust strategy.
Limitations
- Requires sufficient data history to be reliable.
- Signals may lag market changes.
- Does not replace qualitative human analysis of underlying causes.
Use this skill when you suspect a drop in performance of an algorithmic strategy and want to confirm alpha decay.
Avoid using it on strategies with less than 60 days of data or during atypical extreme volatility periods.
Security analysis
SafeThe skill only imports and executes a Python class for statistical analysis; no external commands, network access, or destructive actions are involved.
No concerns found
Examples
/alpha-decay/alpha-decay --strategy momentum-001 --threshold 0.25/alpha-decay --strategy momentum-001 --period 90 --detailedname: 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 classscripts/risk_management/alpha_research.py- Signal evaluationservices/risk/risk_manager.py- Strategy monitoring
Instructions
When this skill is invoked:
-
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)
-
Load strategy data:
- Fetch returns from database/API
- Get strategy metadata and targets
- Load benchmark/factor returns
-
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) -
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% -
For
--detailed:- Rolling IC time series
- Distribution shift analysis
- Factor exposure changes
- Correlation regime changes
-
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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