Market Rhythm Analysis Model: The Market Rhythm Model is an advanced predictive tool for financial market analysis, aimed at detecting rhythmic patterns and market trends based on stock price and volume data. This model leverages various signal processing techniques, including Fast Fourier Transform (FFT) and Wavelet Transform, to identify underlying patterns in stock data.

Key Features: Data Integration: Utilizes yfinance to pull real-time stock data for customizable analysis. Feature Engineering: Computes essential features like price rhythm, volume rhythm, price acceleration, and trend strength. Modeling and Evaluation: Employs machine learning techniques, specifically Random Forest, for prediction. The model includes cross-validation and extensive metrics like ROC-AUC, precision, recall, and F1 scores. Signal Processing: Integrates FFT and wavelet transformations to extract frequency components and detect rhythmic behaviors in stock movements. This project is ideal for finance researchers, quantitative analysts, or enthusiasts interested in blending machine learning with financial market analysis. The repository includes model training, evaluation, and visualization steps to assist users in understanding stock market rhythms.