About

This canvas implements an end-to-end machine learning pipeline to predict IBM stock's next-day returns and develop a systematic trading strategy. The workflow flows from financial data collection (fetching 10+ years of OHLCV, technical indicators, and macro features) → EDA and visualization → feature engineering with standardization → model training (comparing Gradient Boosting, Random Forest, and Ridge Regression using time-series-safe cross-validation) → out-of-sample evaluation on a held-out 2024+ test period comparing strategy returns vs buy-and-hold, with comprehensive performance metrics (MAE, RMSE, R², directional accuracy, Sharpe ratio) and diagnostic charts, supplemented by an API error analysis block detailing deployment requirements and potential integration issues.