Achieving a 67% Directional Accuracy: The Complete Record of Building a Nikkei 225 Stock Price Prediction AI

This is a thrilling project that brilliantly bridges the gap between institutional quantitative analysis and individual investors by democratizing access to advanced stock prediction tools. By employing a robust stacking ensemble of LightGBM, XGBoost, and Ridge models, the developer achieved an impressive directional accuracy of up to 70% on specific timeframes. Sharing the complete journey, including six failed improvement challenges, provides incredibly valuable insights for the machine learning community!