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Practice Drills: Coding a Deep Learning Model for Price Prediction

A hands-on drill course for quant researcher aspirants, prop trading applicants, and traders scaling a systematic book. You code a next-day return forecaster for the Nifty 50 in Python and PyTorch from a blank notebook: framing the target correctly, windowing sequences without leaking the future, building a dense baseline and an LSTM, writing the training loop with early stopping, and then attacking your own results with naive baselines, walk-forward retraining, seed variance tests, and real Indian trading costs. The capstone runs the full pipeline end to end on Bank Nifty.

Deep LearningLSTM NetworksTime Series ForecastingWalk-Forward ValidationPython for Quant Finance

MODULES

5

DURATION

~3.5 hrs

TRACK

Quantitative Finance

What You'll Master

Frame price prediction as a return forecasting problem and set a naive baseline the network must beat

Scale features and slice sliding windows without leaking test data into training

Build a dense network and an LSTM in PyTorch and write a training loop with early stopping

Regularise with dropout and weight decay and read train versus validation loss curves

Detect the lagged-copy illusion and score forecasts with MAE, RMSE, and directional accuracy

Convert forecasts into positions, net out STT, brokerage and slippage, and test stability across random seeds

Access Level

LEARNER

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Everything included

Full Text Playbooks

Actionable Exercises

Mobile Reading Mode

Lifetime Updates

Curriculum Breakdown

Chapter 1: Framing the Prediction Problem

3 Lessons

Drill 1: Why You Predict Returns, Not Prices10 min read

Drill 2: Building the Naive Baselines Your Network Must Beat11 min read

Drill 3: Loading and Aligning Nifty 50 OHLCV Data for Sequence Models12 min read

Chapter 2: Sequence Data Drills

3 Lessons

Drill 4: Scaling Features Without Leaking the Test Set10 min read

Drill 5: Slicing a Time Series into Sliding Windows12 min read

Drill 6: Chronological Train, Validation and Test Splits for Windowed Data10 min read

Chapter 3: Building and Training the Network

4 Lessons

Drill 7: Coding a Dense Network Baseline in PyTorch12 min read

Drill 8: Coding an LSTM Forecaster Layer by Layer14 min read

Drill 9: Writing the Training Loop with Early Stopping13 min read

Drill 10: Regularising with Dropout and Weight Decay11 min read

Chapter 4: Evaluating the Forecaster Honestly

3 Lessons

Drill 11: Scoring Forecasts with MAE, RMSE and Directional Accuracy11 min read

Drill 12: Spotting the Lagged-Copy Illusion in Prediction Plots10 min read

Drill 13: Walk-Forward Retraining for a Neural Network13 min read

Chapter 5: From Forecast to Trading Rule

3 Lessons

Drill 14: Turning Forecasts into Positions and Netting Out Costs12 min read

Drill 15: Testing Stability Across Random Seeds10 min read

Drill 16: Capstone LSTM Pipeline on Bank Nifty16 min read