Alpha Signal Decomposition

Regime-Conditional Sparse Bayesian Learning for Financial Markets

A sophisticated quantitative finance system that decomposes alpha signals into regime-dependent components using advanced Bayesian machine learning techniques.

System Overview

This institutional-grade platform combines cutting-edge machine learning with robust financial engineering to create a comprehensive alpha generation and portfolio optimization system.

Key Innovations

  • Regime-Conditional Modeling: Separate sparse Bayesian models for different market regimes
  • Dynamic Feature Selection: Adaptive feature importance based on regime transitions
  • Advanced Risk Management: Multi-database support with comprehensive factor models
  • Real-Time Processing: Production-ready data streaming with technical indicators

Architecture

Core Components

  • Sparse Bayesian Learning Engine: 1,300+ lines of advanced mathematical implementation
  • Regime Detection: Multiple methods (HMM, MS-VAR, GMM) with ensemble weighting
  • Portfolio Optimization: Mean-Variance, Black-Litterman, Risk Parity, and robust methods
  • Risk Management: Comprehensive factor models with regime-aware adjustments

Technical Features

  • 100+ Engineered Features: Technical, fundamental, macro, and cross-sectional signals
  • Multi-Database Support: SQLite, PostgreSQL, MySQL compatibility
  • Container-Ready: Docker and Kubernetes deployment configurations
  • Monitoring & Analytics: Prometheus metrics with bootstrap confidence intervals

Performance Analytics

Advanced Testing Framework

  • Walk-forward analysis with regime-aware attribution
  • Monte Carlo stress testing capabilities
  • Bootstrap confidence intervals for performance metrics
  • Statistical significance testing for alpha generation

Risk Analytics

  • Risk attribution analysis by regime and factor
  • Value-at-Risk and Conditional Value-at-Risk calculations
  • Maximum drawdown and recovery analysis
  • Factor exposure drift monitoring

Research Methodology

Mathematical Framework

  • Variational Bayesian inference with automatic relevance determination
  • Hierarchical parameter structure linking regime-specific models
  • Cross-regime regularization for feature selection consistency
  • Uncertainty quantification through Bayesian posteriors

Regime Detection

  • Hidden Markov Models for volatility regime identification
  • Markov-Switching Vector Autoregression for multi-factor regimes
  • Threshold Vector Autoregression for non-linear switching
  • Ensemble approach with weighted regime probabilities

Research Contributions

  • Novel regime-conditional sparse feature selection methodology
  • Theoretical framework for cross-regime parameter sharing
  • Empirical validation across multiple market cycles

Results & Performance

Key Metrics

  • Regime-specific Sharpe ratios and risk-adjusted returns
  • Feature importance evolution across market cycles
  • Regime transition prediction accuracy
  • Portfolio turnover and transaction cost analysis

Contact & Collaboration

For academic collaboration, institutional deployment, or technical inquiries:

  • Author: Abhishek Tiwari
  • Institution: Independent Researcher
  • Email: abhishekt282001@gmail.com
  • Repository: GitHub

Quick Start

from src.regime_conditional_sbl import RegimeConditionalSBL
from src.data.collectors import MarketDataCollector
from src.regime_detection import RegimeDetector

# Initialize system
regime_detector = RegimeDetector()
sbl_model = RegimeConditionalSBL(n_regimes=3)
data_collector = MarketDataCollector()

# Load and process data
market_data = data_collector.get_market_data(['AAPL', 'GOOGL', 'MSFT'])
regimes = regime_detector.detect_regimes(market_data)

# Train regime-conditional model
sbl_model.fit(market_data, regimes)

# Generate predictions
alpha_signals = sbl_model.predict(new_data)

This project represents cutting-edge research in quantitative finance, combining advanced machine learning techniques with practical portfolio management applications.