★ 20 Ways to Generate Stock Investment Profits with AI - 11. Deep Learning for Price Prediction

 

11. Deep Learning for Price Prediction

Introduction
Deep learning models—LSTM, Temporal CNNs, Transformers—capture complex, non‐linear relationships in price series for short‐term forecasting.

Modeling Steps

1. Data Pipeline: Normalize OHLC data; augment with technical indicators (RSI, MACD).

2. Architecture Choice:

   • LSTM for capturing long‐term dependencies.

   • Temporal CNN for local pattern detection.

   • Transformers for attention‐based feature weighting.

3. Training Regime: Chronological split (2010–2018 train, 2019–2023 test); early stopping and dropout for regularization.

4. Evaluation: MSE for price error; directional accuracy for classification tasks.

Example Results
An LSTM on FX pairs achieved 56% directional accuracy at t+1, outperforming a random classifier (50%) and a basic ARIMA benchmark.

Conclusion
While deep models require more data and care to avoid overfitting, they can uncover subtle temporal patterns that boost predictive edge.