Predicting Goldman Sachs Stock Returns with Advanced Deep Learning Techniques in Keras

This comprehensive tutorial delves into building a robust Deep Learning model to predict the 5-day price movement (positive or negative return) of Goldman Sachs stock. We’ll use the power of Keras, a user-friendly Deep Learning library, to construct our model. By incorporating technical indicators from TA-Lib, creating lagged OHLCV features and integrating correlated asset data, we aim to capture intricate market patterns and enhance the model’s predictive accuracy. This tutorial goes beyond basic model building; we will rigorously evaluate its performance through backtesting on historical data, employing the Sharpe Ratio as a key metric to assess risk-adjusted returns. Whether you’re a seasoned trader or new to the world of quantitative finance, this guide will provide you with valuable insights and practical techniques for applying Deep Learning in the financial markets.

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Table of Contents

• Data Acquisition and Preparation: Gathering historical Goldman Sachs stock data and cleaning it for analysis.
• Feature Engineering: Constructing powerful predictive features using technical indicators from TA-Lib, lagged OHLCV data and correlated asset prices.
• Train-Test Split and Feature Scaling: Splitting the data into training and…