Market Regime Detection with Hidden Markov Models

Comprehensive documentation for AlphaPulse - an AI-powered algorithmic trading system designed to operate as an automated hedge fund.

Market Regime Detection with Hidden Markov Models

Overview

The AlphaPulse Market Regime Detection system uses Hidden Markov Models (HMMs) to identify and classify market conditions into distinct regimes. This sophisticated approach enables the trading system to adapt strategies based on current market states, improving risk management and performance.

IMPORTANT UPDATE (v1.16.0.0): While the regime detection system is fully implemented and sophisticated, it is currently operating at approximately 10% of its potential due to integration gaps. The RegimeDetectionService exists but is not started in the API, and only 1 of 6 agents uses a simplified version of regime detection. See the Integration Status section for details.

Key Features

1. Multi-Factor Regime Detection

2. Advanced HMM Variants

3. Real-Time Classification

Market Regime Types

1. Bull Market

2. Bear Market

3. Sideways Market

4. Crisis Market

5. Recovery Market

Architecture

Feature Engineering Pipeline

# Feature extraction example
from alpha_pulse.ml.regime.regime_features import RegimeFeatureEngineer

engineer = RegimeFeatureEngineer()
features = engineer.extract_features(market_data, additional_data={
    'vix': vix_data,
    'sentiment': sentiment_data
})

HMM Training

# Train regime detection model
from alpha_pulse.models.market_regime_hmm import MarketRegimeHMM

regime_model = MarketRegimeHMM()
regime_model.fit(historical_data)

Real-Time Detection

# Detect current regime
regime_info = regime_model.predict_regime(current_data)
print(f"Current regime: {regime_info.regime_type.value}")
print(f"Confidence: {regime_info.confidence:.2%}")

Integration with Trading System

1. Dynamic Risk Management

2. Strategy Selection

3. Portfolio Optimization

Model Optimization

Hyperparameter Tuning

from alpha_pulse.utils.hmm_optimization import HMMOptimizer

optimizer = HMMOptimizer()
best_params = optimizer.optimize_hyperparameters(
    features,
    n_trials=100,
    use_optuna=True
)

Model Selection

Monitoring and Alerts

Prometheus Metrics

Alert Conditions

Performance Analysis

Regime Statistics

Backtesting Integration

# Backtest with regime awareness
results = backtest_with_regimes(
    strategy,
    historical_data,
    regime_model
)

Configuration

Basic Configuration

regime_detection:
  n_states: 5
  update_interval: 60  # minutes
  min_confidence: 0.6
  features:
    volatility_windows: [5, 10, 20, 60]
    return_windows: [1, 5, 20, 60]
    use_vix: true
    use_sentiment: true

Advanced Settings

hmm_config:
  covariance_type: "full"
  init_method: "kmeans"
  n_iter: 100
  transition_penalty: 0.01
  
optimization:
  use_optuna: true
  n_trials: 100
  cv_splits: 5

Best Practices

  1. Data Requirements
    • Minimum 2 years of historical data for training
    • Include multiple market cycles
    • Ensure data quality and consistency
  2. Feature Selection
    • Use domain knowledge for feature engineering
    • Validate feature importance regularly
    • Consider regime-specific features
  3. Model Validation
    • Out-of-sample testing crucial
    • Monitor regime stability
    • Track false transition rates
  4. Production Deployment
    • Regular model retraining (monthly)
    • A/B testing for strategy changes
    • Gradual position adjustments on transitions

Troubleshooting

Common Issues

  1. Too Many Regime Changes
    • Increase transition penalty
    • Use longer confirmation windows
    • Check feature normalization
  2. Low Confidence Classifications
    • Review feature quality
    • Consider fewer regime states
    • Check for data anomalies
  3. Poor Out-of-Sample Performance
    • Avoid overfitting with regularization
    • Use proper cross-validation
    • Consider simpler models

Integration Status

Current State (10% Integration)

Required Integration Steps

  1. Start the Service (Critical): Add RegimeDetectionService initialization in src/alpha_pulse/api/main.py
  2. Create Integration Hub: Use RegimeIntegrationHub from src/alpha_pulse/integration/regime_integration.py
  3. Update All Agents: Implement RegimeAwareAgent pattern for all 6 agents
  4. Risk Management: Replace with RegimeIntegratedRiskManager
  5. Portfolio Optimization: Use RegimeIntegratedPortfolioOptimizer
  6. Add API Endpoints: Create /regime/* endpoints for monitoring
  7. Update Backtesting: Add regime tracking to backtest engine

Integration Files Created

Future Enhancements

  1. Immediate Priority - Complete Integration
    • Start the RegimeDetectionService
    • Integrate all trading agents
    • Full risk management integration
    • Portfolio optimization with regime constraints
  2. Deep Learning Integration
    • LSTM for sequence modeling
    • Attention mechanisms for feature selection
    • Neural HMMs for complex patterns
  3. Multi-Asset Regimes
    • Cross-asset regime correlation
    • Sector-specific regime detection
    • Global macro regime integration
  4. Adaptive Learning
    • Online HMM parameter updates
    • Regime definition evolution
    • Transfer learning across markets