Price Forecasting with Neural Networks
This program walks you through building, training, and deploying deep learning models for financial time series. You'll work with real market data, configure recurrent architectures, handle sequence preprocessing, and validate predictions against historical patterns. Each module combines theoretical grounding with hands-on implementation so you understand both what the model does and why it behaves that way.
Start by sourcing price data from public APIs and CSV files. You'll clean irregular timestamps, handle missing values, and normalize features across different scaling ranges. Then build windowed sequences suitable for supervised learning, splitting data chronologically to prevent look-ahead bias. The module covers feature engineering for technical indicators, volatility measures, and lag variables.
Construct LSTM and GRU networks from scratch using TensorFlow and PyTorch. You'll configure layer stacking, dropout regularization, and bidirectional processing. Compare vanilla RNNs against gated variants to understand gradient flow and memory retention. The sessions include exercises on weight initialization, activation functions, and output layer configuration for regression tasks.
Train models using adaptive learning rate schedules and early stopping criteria. You'll monitor loss curves, detect overfitting through validation metrics, and adjust hyperparameters systematically. The module covers gradient clipping to prevent exploding gradients, batch size tuning for memory constraints, and checkpoint management for long training runs. You'll also implement custom loss functions tailored to directional accuracy.
Assess model performance using mean absolute error, directional accuracy, and profit-based metrics. You'll construct backtests on held-out test data, compare predictions against baseline strategies, and analyze error distributions across different market regimes. The sessions include visualization techniques for prediction intervals, feature importance extraction, and failure case analysis to identify when the model breaks down.
Package trained models for inference in live environments. You'll export weights, configure REST API endpoints using Flask or FastAPI, and implement data preprocessing pipelines that mirror training steps. The module covers latency optimization, model versioning, and monitoring systems to detect distribution drift. You'll also set up logging for prediction audits and error tracking in production.