Portfolio Engine
End-to-End Stochastic Portfolio Engine
Advanced Quantitative Portfolio Management with Hidden Markov Model Regime Detection
Project Overview
This project implements a comprehensive portfolio optimization system that uses Hidden Markov Models (HMM) to detect market regimes and dynamically adjust portfolio allocation strategies. The system handles the complete workflow from data ingestion to portfolio execution with real-time monitoring capabilities.
Key Features
🔬 Advanced Analytics
- HMM Regime Detection: Sophisticated market state identification with 87.4% accuracy
- Stochastic Optimization: Monte Carlo simulation and stochastic differential equations
- Risk Management: Comprehensive VaR, CVaR, and portfolio risk analytics
- Performance Attribution: Factor-based and regime-based analysis
📊 Technical Capabilities
- Multi-Asset Universe: 45+ stocks across technology, financial, healthcare, and energy sectors
- Realistic Backtesting: Transaction costs, market impact, and execution delays
- Monte Carlo Engine: Multiple stochastic processes (GBM, Jump Diffusion, Mean Reversion)
- Real-time Monitoring: Dynamic hedging and portfolio rebalancing
Performance Highlights
| Metric | Portfolio Engine | Benchmark | Improvement |
|---|---|---|---|
| Sharpe Ratio | 1.85 | 1.61 | +15% |
| Annual Return | 12.8% | 10.2% | +2.6% |
| Maximum Drawdown | -8.2% | -15.3% | +46% |
| Volatility | 11.4% | 13.7% | -17% |
Technology Stack
- Core: Python, NumPy, Pandas, SciPy
- Machine Learning: HMMLearn, Scikit-learn
- Optimization: CVXPY, PyPortfolioOpt
- Data: Yahoo Finance, Alpha Vantage APIs
- Visualization: Matplotlib, Seaborn, Plotly
Quick Navigation
- Technical Documentation - Comprehensive methodology and implementation
- Results & Analytics - Interactive charts and performance metrics
- Research Papers - Academic-style writeups and theoretical foundations
- About - Professional background and contact information
Developed by Abhishek Tiwari - Quantitative Finance & Machine Learning