correlation-regime-switcher
Automatically switches strategy sets when correlation regimes change. Use this skill whenever the user asks about "correlation regime change", "adaptive strategy switching", "when correlations break", "regime-based strategy selection", "correlation breakdown trading", "dynamic strategy switching", "auto-switch strategy", or any question about adapting to changing inter-market relationships. Works with pair-correlation-engine and market-regime-classifier.
Informações da origem
- Repositório
- mahmoud20138/Tradecraft
- Última atividade na origem
- 23 de abril de 2026 às 08:40
- Idioma detectado do SKILL.md
- inglês
- Estrelas
- 15
- Forks
- 4
Opções de instalação
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SKILL.md
Instruções da origem · Visualização somente leitura- name
- correlation-regime-switcher
- description
- Automatically switches strategy sets when correlation regimes change. Use this skill whenever the user asks about "correlation regime change", "adaptive strategy switching", "when correlations break", "regime-based strategy selection", "correlation breakdown trading", "dynamic strategy switching", "auto-switch strategy", or any question about adapting to changing inter-market relationships. Works with pair-correlation-engine and market-regime-classifier.
- kind
- reference
- category
- trading/market-context
- status
- active
- tags
- ["correlation","market-context","regime","switcher","trading"]
- related_skills
- ["cross-asset-relationships","correlation-crisis","market-regime-classifier","multi-pair-basket-trader","pair-scanner-screener"]
# Correlation Regime Switcher
```python
import pandas as pd
import numpy as np
class CorrelationRegimeSwitcher:
REGIME_STRATEGIES = {
"normal_correlation": {
"description": "Correlations at historical norms",
"strategies": ["trend_following", "carry_trade", "mean_reversion_pairs"],
"risk_level": "NORMAL",
},
"correlation_breakdown": {
"description": "Historical correlations breaking down",
"strategies": ["single_pair_momentum", "volatility_selling"],
"risk_level": "ELEVATED — reduce correlated positions",
},
"correlation_spike": {
"description": "All assets moving together (crisis mode)",
"strategies": ["safe_haven_only", "volatility_buying", "cash"],
"risk_level": "HIGH — correlation=1 means no diversification benefit",
},
"decorrelation": {
"description": "Assets becoming uncorrelated — dispersion rising",
"strategies": ["pairs_trading", "relative_value", "basket_trades"],
"risk_level": "OPPORTUNITY — dispersion creates relative value trades",
},
}
@staticmethod
def detect_regime(correlation_matrix: pd.DataFrame, historical_avg_corr: float) -> dict:
"""Classify current correlation regime."""
upper_tri = correlation_matrix.values[np.triu_indices_from(correlation_matrix.values, k=1)]
current_avg = np.mean(np.abs(upper_tri))
deviation = current_avg - abs(historical_avg_corr)
if current_avg > 0.8:
regime = "correlation_spike"
elif deviation > 0.15:
regime = "correlation_spike"
elif deviation < -0.15:
regime = "decorrelation"
elif abs(deviation) < 0.05:
regime = "normal_correlation"
else:
regime = "correlation_breakdown"
strategies = CorrelationRegimeSwitcher.REGIME_STRATEGIES[regime]
return {
"regime": regime,
"current_avg_correlation": round(current_avg, 4),
"historical_avg": round(abs(historical_avg_corr), 4),
"deviation": round(deviation, 4),
**strategies,
}
@staticmethod
def transition_detector(rolling_corr: pd.Series, window: int = 20) -> dict:
"""Detect regime transitions from rolling correlation data."""
recent = rolling_corr.tail(window)
prior = rolling_corr.iloc[-(window*2):-window]
change = recent.mean() - prior.mean()
return {
"transition_detected": abs(change) > 0.2,
"direction": "CONVERGING" if change > 0.2 else "DIVERGING" if change < -0.2 else "STABLE",
"magnitude": round(abs(change), 4),
"action": "Switch strategy set — correlation regime changing" if abs(change) > 0.2 else "Hold current strategies",
}
```
Ver no GitHub