Tools and Methods:

The project employs a variety of machine learning techniques and data manipulation tools:

• Python for Data Analysis and Modeling: Used libraries such as Pandas for data manipulation, Scikit-learn for machine learning, and Keras for neural networks.

• Data Preprocessing and Feature Engineering: Stock data, including the S&P 500, was preprocessed to include calculated moving averages, returns, volatility, and additional features such as historical trends and market indicators.

• Machine Learning Models:

  • Linear Regression for baseline predictions.
  • Random Forest for capturing non-linear relationships.
  • Gradient Boosting for advanced predictive power.
  • Neural Networks (LSTM) for modeling sequential time-series data, particularly useful for predicting stock trends based on previous performance.

• S&P 500 Data Integration: The S&P 500 data was used to account for broader market conditions, providing context for individual FAANG stock movements and improving model accuracy.

• Hyperparameter Tuning: Models were optimized using GridSearchCV to enhance performance and minimize overfitting.

• BigQuery Integration: Data was processed and stored in BigQuery, allowing automated storage and streamlined analysis. Python notebooks were used to connect and update results efficiently.

Predictions and Performance:

• FAANG Stocks: The project achieved 57% accuracy in predicting the directionality (up or down) for FAANG stock movements, providing valuable insights into stock price trends.

• S&P 500: Developed multiple models that performed near 90% accuracy in predicting win rates (profitable predictions) for the S&P 500 index, showcasing the robustness of the machine learning approaches used in the broader market context.

Visualization and Reporting:

• Interactive Dashboards: Created using Python (Matplotlib and Plotly), these dashboards allow users to visualize stock price predictions, trends, and key indicators across FAANG stocks and the S&P 500.

• Automated Data Pipelines: SQL scripts combined with Python to ensure regular updates to the stock data and predictions, creating a seamless process for continuous analysis without manual intervention.

This study demonstrates the application of machine learning for stock market prediction, highlighting the integration of both FAANG stocks and the S&P 500 index for more accurate and context-aware predictions. Achieving 57% directionality accuracy for FAANG stocks and near 90% success in S&P 500 models, the use of advanced techniques like LSTM neural networks and gradient boosting, along with automated pipelines, showcases a sophisticated approach to financial forecasting.