Adaptive Portfolio Allocation Using Market Regime Detection and Momentum Factors

📊 Building an Adaptive AI Investment Strategy with Machine Learning Financial markets are constantly evolving—what works today may fail tomorrow. In this project, I developed a simple adaptive investment strategy that adjusts to changing market conditions by combining: • Machine Learning • Quantitative Finance • Factor Investing 💡 Core Idea Instead of relying on a static portfolio, this strategy dynamically adapts to different market regimes. 🔎 Framework Overview 1️⃣ Market Regime Detection A Hidden Markov Model is used to identify different market environments. 2️⃣ Momentum Factor Signals Assets are ranked based on their recent performance. 3️⃣ Adaptive Portfolio Allocation Portfolio weights adjust depending on both the detected regime and the strength of factor signals. 📊 Assets Used in the Backtest • SPY (U.S. equities) • QQQ (technology stocks) • TLT (long-term U.S. Treasuries) • GLD (gold) 📈 Key Results • Market-like returns • Lower volatility • Higher Sharpe ratio • Better drawdown control 🚀 While this is a simplified model, it highlights a powerful concept in quantitative finance: Adaptive strategies can deliver more stable, risk-adjusted performance than static allocations. 📎 Full notebook and code available here: 👉 GitHub link: https://github.com/jcc2023/financial-ml-blueprints/blob/main/Adaptive_Portfolio_Allocation_Using_Market_Regime_Detection_and_Momentum_Factors.ipynb #MachineLearning #DataScience #QuantFinance #AlgorithmicTrading #AI #PortfolioManagement