Quant Predictor

Quantitative analysis of historical data plays a financial decision-making processes by utilizing advanced techniques to detect patterns, predict trends, and guide trading strategies. Conventional statistical methods often struggle to effectively capture the intricate and ever-changing nature of financial markets. challenge by introducing novel methodologies for forecasting stock market movements, short-term price predictions. The suggested framework integrates historical stock data with customized, and MACD to enrich the feature set. Various models, Forest, and LSTM neural network, are trained and assessed using RMSE and R² metrics. The findings indicate that although regression models provide interpretability, LSTM stands out in capturing temporal relationships and market volatility. This research underscores the opportunity to combine machine learning with financial analysis promptness of investment decision-making