Case Study: Reconstructing a Simple Machine Learning Model for Stock Direction Prediction

A hands-on case study for quant analyst aspirants, prop trading applicants and traders who want to systematise their process. We take a typical claim you will see on social media, a simple machine learning model that 'predicts next-day Nifty direction with high accuracy', and reconstruct it step by step in Python on real NSE data. Along the way you will frame direction prediction as a classification problem, build labels and features correctly, catch the look-ahead leakage that inflates most published results, train logistic regression and random forest models with walk-forward validation, and judge them with the right metrics instead of raw accuracy. The course ends by turning predicted probabilities into a trading rule, subtracting real Indian costs on Nifty futures, testing the edge across regimes like 2020 and 2022, and delivering an honest verdict on what a simple ML model can and cannot do.