Financial Data Analysis and Machine Learning

This Streamlit application provides tools for financial data analysis and machine learning-based stock price prediction.

Features

• Technical Indicators:

  •  Retrieves historical stock data from Yahoo Finance.
    

  •  Calculates and visualizes common technical indicators, including:
    

    •  50-day Moving Average (MA50)
      

    •  Relative Strength Index (RSI)
      

    •  Stochastic Oscillator (%K and %D)
      

    •  Volume
      

  •  Allows users to select a stock ticker and date range.
    

• Machine Learning Models:

  •  Implements machine learning models for stock price prediction:
    

    •  Long Short-Term Memory (LSTM) neural network
      

    •  Support Vector Machine (SVM)
      

    • Light Gradient Boosting Machine (LightGBM)

  •  Preprocesses data using MinMaxScaler.
    

  •  Splits data into training and testing sets.
    

  •  Trains and evaluates the models using Root Mean Squared Error (RMSE).
    

  • Displays the prediction for the next days closing price.

  •  Visualizes the predicted vs. actual stock prices.
    

  • Allows users to select a stock ticker and date range.

Dependencies

• streamlit
• yfinance
• pandas
• matplotlib
• scikit-learn (sklearn)
• numpy
• tensorflow (keras)
• lightgbm

How to Run

1. Install dependencies:

   pip install -r requirements.txt

2. Save the Python script:

   Save the provided Python code as a .py file (e.g., financial_analysis.py).

3. Run the Streamlit app:

   streamlit run financial_analysis.py

4. Usage:

   • The application will open in your web browser.
   • Use the sidebar to select between "Indicators" and "Machine Learning" tabs.
   • Enter the desired stock ticker and date range in the sidebar.
   • The selected indicators or machine learning predictions will be displayed.

Code Explanation

• yfinance: Used to download historical stock data.

• pandas: Used for data manipulation and analysis.

• matplotlib: Used for data visualization.

• scikit-learn: Used for data preprocessing (MinMaxScaler) and machine learning models (SVR).

• numpy: Used for numerical operations.

• tensorflow (keras): Used for building and training the LSTM neural network.

• lightgbm: Used for the LightGBM regressor model.

• streamlit: Used to create the interactive web application.

• The application is divided into two main sections: "Indicators" and "Machine Learning," accessible through tabs in the sidebar.

• The "Indicators" section calculates and displays common technical indicators.

• The "Machine Learning" section trains and evaluates LSTM, SVM, and LightGBM models for stock price prediction.

• Error handling is implemented to catch and display any exceptions that may occur during data retrieval or model training.