Deployment of a Stock Volatility Forecasting System using GARCH Model, FastAPI, and Web Interface

This project demonstrates the application of machine learning and API development techniques to create a practical tool for financial analysis and decision-making. In this project, we will utilize a GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model for stock volatility prediction, then we will develope a stock volatility prediction API using the model. This API will allow users to:

1. Train an ML model for a specific stock ticker, using either the latest data from Alpha Vantage or historical data stored in a SQLite database.
2. Obtain volatility predictions for a trained model, specifying the desired prediction horizon (number of days).

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Technical Details:

• Data Acquisition and Storage: The project utilizes the Alpha Vantage API to retrieve daily stock data and stores it in a SQLite database for efficient management.
• Model Training: The GARCH model is employed to capture the volatility patterns in the stock data.
• API Development: FastAPI is used to create a RESTful API that exposes endpoints for model training and prediction. Pydantic is used for data validation and ensuring the correctness of request parameters.
• Model Persistence: Trained models are saved to disk using joblib, allowing for reuse without retraining.
• Deployment with ngrok: The API is deployed using ngrok, which creates a public URL for accessing the application hosted on your Google Colab instance.
• Interactive Documentation: Swagger UI or ReDoc is automatically generated by FastAPI, providing users with interactive documentation to understand and interact with the API.
• Web Application Development: A web app is developed using Plotly Dash to provide an interactive and user-friendly interface that enables real-time use of the prediction system without requiring users to understand the underlying code.

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Benefits and Use Cases:

• Volatility Forecasting: The API provides a tool for predicting the volatility of a stock, which is valuable information for investors and traders in making informed decisions.
• Risk Management: Volatility predictions can be used to assess the risk associated with a particular stock and to develop risk mitigation strategies.
• Automated Trading: The API can be integrated into automated trading systems to trigger actions based on volatility forecasts.
• Research and Analysis: Researchers and analysts can utilize the API to study stock market volatility and develop trading algorithms.

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Watch the demo video!

Access the Colab Notebook