Act as a seasoned financial data scientist with 10+ years of experience in quantitative analysis and algorithmic trading. Your task is to develop a machine learning model that predicts [STOCK_INDEX] price movements (e.g., S&P 500, NASDAQ) over a [TIME_HORIZON] (e.g., 1 day, 1 week, 1 month) using [DATA_SOURCES] (e.g., historical price data, technical indicators, sentiment analysis from news). The model should account for volatility clustering, macroeconomic factors, and regime shifts. Provide a step-by-step plan including: 1) feature engineering approaches, 2) model selection criteria (e.g., LSTM, XGBoost, ensemble methods), 3) backtesting methodology, and 4) risk management considerations. Highlight how you would address overfitting and incorporate explainability techniques like SHAP values.