Stock Price Analyzer with Prediction

This project is a single-page web application built with Dash (Python framework) that displays company information, stock plots, and predicted stock prices.

Project Goal

• Enter a stock code.
• View company logo, registered name, and description.
• Visualize historical stock price data using a plot.
• Input a future date and get a predicted stock price based on a machine learning model.

Getting Started

This project requires the following Python libraries:

• dash

• dash-html-components

• dash-core-components

• yfinance (for retrieving stock data)

• plotly (for generating visualizations)

• A machine learning library of your choice (e.g., scikit-learn, tensorflow) for prediction

  1. Install Libraries: Use pip install to install the required libraries.
  2. Run the App: Navigate to the project directory in your terminal and run python app.py.
  3. Access the App: Open http://127.0.0.1:8050/ in your web browser.

Project Structure

The project consists of a single Python file (app.py) that defines the Dash app layout, functionalities, and callbacks.

• Layout: The layout is built using components from dash_html_components and dash_core_components. It includes elements for user input, company information display, and a container for the stock plot and prediction output.
• Data Fetching: A function utilizes yfinance to retrieve stock data based on the user-provided code. It handles potential errors and returns company information and historical prices.
• Stock Plots: A function generates a plotly chart for the retrieved historical stock prices.
• Stock Price Prediction: A machine learning model predicts the stock price for the user-specified date.
• Callbacks: Dash callbacks connect user interactions with data updates and plot generation. Entering a stock code and date triggers a callback that fetches data, generates plots, and updates the prediction based on the model.

Customization

• Machine Learning Model: We have used the Support Vector Regression (SVR) module from the sklearn library to fetch stock prices for the last 60 days and spliting the dataset into 9:1 ratio for training and testing respectively.
• Used the rbf kernel in GridSearchCV for tuning the hyperparameters.
• Styling: Include a separate CSS file or use inline styles with Dash components to enhance the app's visual appearance.

Further Enhancements

• Implement functionalities for searching companies by name.
• Integrate technical indicators for stock analysis.
• Allow users to visualize different timeframes for stock data.