AI Stock Analyzer

The Stock Analyzer project is a complete stock market analysis tool utilizing ML models. It allows users to input stock symbols, select date ranges, view historical stock trends, and see future stock price predictions via interactive charts.

Live Demo

View Deployed App on Render

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Overview

Home Page

Stock Analysis View

Stock Predictions

Technologies Used

• React: User Interface
• Plotly.js: Interactive visualizations
• Axios: API calls
• Flask: Backend Framework
• yfinance: Stock data extraction
• Pandas: Data manipulation
• Scikit-learn: to load and use the model

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Directory Structure

Directory structure:
└── srigadaakshaykumar-stock/
    ├── README.md
    ├── SETUP.md
    ├── CONTRIBUTION.md
    ├── package.json
    ├── static.json
    ├── backend/
    │   ├── app.py
    │   ├── requirements.txt
    │   ├── stock-prediction.ipynb
    │   └── tf.keras
    ├── public/
    │   ├── index.html
    │   ├── manifest.json
    │   └── robots.txt
    └── src/
        ├── App.css
        ├── App.js
        ├── App.test.js
        ├── index.css
        ├── index.js
        ├── reportWebVitals.js
        ├── setupTests.js
        └── components/
            ├── AuthContext.js
            ├── firebase.js
            ├── Footer.jsx
            ├── Header.jsx
            ├── Login.js
            ├── Prediction.jsx
            ├── Signup.js
            ├── Stockdata.jsx
            ├── StockList.jsx
            └── data/
                └── stockData.json

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API Endpoints

Endpoint	Method	Description
/api/stocks/<ticker>	GET	Fetch historical stock data
/api/predict/<ticker>	GET	Predict future stock prices

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Data Pipeline Architecture

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Project Status

Stock Analyzer is currently in the development stage and hosted on a free hosting service for testing purposes.

Future Enhancements

We have a clear roadmap for improvements:

• Allow more API calls per day
• Reduce response time for end users
• Add International stock exchanges
• Enhance the user interface for better experience
• Improve machine learning model accuracy
• Provide more insightful and interactive visualizations
• Migrate deployment from Render to Google Cloud Platform (GCP)

Contributions

We welcome all forms of open-source contributions — whether it's a:

• Bug fix
• New feature
• Enhancement or optimization

Please make sure to:

• Review our Contribution Guidelines
• Follow the Setup Instructions to run the project locally
• Join the Discord

License

This project is licensed under the MIT License.
You’re free to use, modify, and share this software under the license terms.