Volatility Forecasting Web App

Description

The Volatility Forecasting Web App is an interactive platform that allows users to analyze and predict the volatility of financial assets. Leveraging advanced techniques like GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and LSTM (Long Short-Term Memory) neural networks, the app enables users to import historical financial data directly from Yahoo Finance using yfinance, making it easier to generate accurate volatility forecasts.

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README

Overview

The Volatility Forecasting Web App is designed for traders and analysts seeking to make informed decisions based on robust volatility predictions. By combining statistical and machine learning methods, users can interactively explore their data and visualize forecasting results.

Key Features

• User-Friendly Interface: Intuitive design for easy data upload and visualization.
• Data Import: Users can fetch historical financial data directly from Yahoo Finance.
• ACF and PACF Analysis: Automatically generates plots for time series analysis.
• GARCH Model Fitting: Estimates conditional volatility using GARCH models.
• LSTM Model Training: Trains LSTM networks for future volatility forecasting.
• Performance Metrics: Visualizes training and validation metrics for both models.
• Interactive Visualization: Compare predicted and actual volatility in an engaging way.

Technologies Used

• Frontend: Streamlit, Plotly for interactive visualizations.
• Backend: Python with Streamlit framework.
• Data Processing: Pandas, NumPy.
• Modeling: GARCH from the arch library, LSTM from keras.
• Data Import: yfinance for fetching historical data.
• Visualization: Plotly for interactive graphs.

Installation

To set up the Volatility Forecasting Web App locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/wass1m-k/volatility-forecasting-dashboard.git
   cd volatility-forecasting-web-app

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py

5. Open your web browser and go to http://localhost:8501 to access the app.

Usage

1. Import Data: Use the app interface to fetch historical financial data directly from Yahoo Finance using a stock ticker (e.g., AAPL for Apple).

2. Run Analysis: Choose GARCH or LSTM modeling options and initiate the analysis. The app will process the data, fit the selected models, and generate plots.

3. View Results:

   • ACF and PACF plots.
   • Training and validation metrics visualization.
   • Interactive comparison of forecasted values with actual volatility.

Example Code Summary

• Data Import: Fetches financial data from Yahoo Finance using yfinance.
• Data Preprocessing: Processes financial data for volatility calculation.
• ACF and PACF Plots: Generates autocorrelation plots for time series data.
• GARCH Model: Fits a GARCH model to forecast future volatility.
• LSTM Model: Constructs and trains an LSTM model for predictions.
• Forecasting: Predicts future volatility and visualizes results interactively.

Contributing

Contributions are welcome! If you encounter issues or have suggestions, please create an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Utilizes the arch library for GARCH modeling.
• Implements LSTM with Keras and TensorFlow.
• Uses yfinance for data import.
• Thanks to all contributors and libraries that made this project possible.

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