Intermediate
Practice Drills: Building a Simple Classification Model for Price Direction
A hands-on drill course for aspiring quant analysts, prop trading applicants, and discretionary traders who want to systematise. You build a next-day direction classifier for the Nifty 50 from scratch in Python: labelling the data, engineering features without leaking the future, training logistic regression and a decision tree with chronological splits, and scoring the output with confusion matrices, precision, recall, and calibration. The final chapter turns predictions into positions, subtracts real Indian trading costs, and runs the full pipeline end to end on Bank Nifty.
Classification ModelsFeature EngineeringWalk-Forward ValidationModel EvaluationPython for Quant Finance
MODULES
5
DURATION
~3 hrs
TRACK
Quantitative Finance
What You'll Master
Turn a raw Nifty 50 price series into a clean up-or-down label and measure the base rate you must beat
Build lagged return, RSI, moving average and volatility features without look-ahead leakage
Split time series data chronologically and retrain a model walk-forward
Fit and interpret logistic regression and a depth-limited decision tree in scikit-learn
Score a classifier with a confusion matrix, precision, recall, and a calibration check
Convert predicted probabilities into positions and net out STT, brokerage, and slippage
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates