📈 StockSense AI

An AI-powered stock price prediction platform that leverages multiple machine learning models to provide comprehensive market analysis and predictions.

🌟 Features

Multi-Model Predictions

• Linear Regression - Simple trend analysis with bar charts
• Random Forest - Ensemble learning with line charts
• XGBoost - Gradient boosting with area charts
• LSTM (Deep Learning) - Neural network-based predictions with candlestick-style charts

Real-Time Data

• Live stock data fetching from Yahoo Finance
• Support for global markets (US, India, UK, Japan, Europe, etc.)
• Automatic currency detection and display (USD $, INR ₹, GBP £, EUR €, JPY ¥)

Sentiment Analysis

• News scraping for stock-related articles
• Sentiment analysis using NLP
• Integration of sentiment data with price predictions

Interactive Dashboard

• Clean, card-based UI with 2x2 grid layout
• Multiple chart types for different models
• Real-time prediction updates
• Responsive design

🏗️ Architecture

StockSense AI/
├── app/
│   ├── backend/          # Flask REST API
│   │   ├── routes/       # API endpoints
│   │   └── utils/        # Model runners
│   └── frontend/         # React + Vite
│       └── src/
│           ├── components/
│           ├── pages/
│           └── api/
├── data/                 # Data processing
├── models/              # ML model training
├── sentiment/           # News & sentiment analysis
├── portfolio/           # Portfolio simulation
└── risk/               # Risk analysis tools

🚀 Getting Started

Prerequisites

• Python 3.10+
• Node.js 16+
• npm or yarn

Installation

1. Clone the repository

git clone https://github.com/Kush05Bhardwaj/Stocksense-AI.git
cd "StockSense AI"

2. Set up Python environment

python -m venv venv
.\venv\Scripts\activate  # On Windows
source venv/bin/activate # On Mac/Linux

pip install -r requirements.txt

3. Set up Frontend

cd app/frontend
npm install

Running the Application

1. Start the Backend Server

# From project root
.\venv\Scripts\activate
python app/backend/app.py

Backend runs on: http://localhost:5000

2. Start the Frontend

cd app/frontend
npm start

Frontend runs on: http://localhost:3000

3. Access the Application Open your browser and navigate to: http://localhost:3000

📊 Usage

Making Predictions

1. Enter a stock symbol in the search box:

   • US Stocks: AAPL, MSFT, GOOGL, TSLA
   • Indian Stocks: RELIANCE.NS, TCS.NS, INFY.NS
   • Japanese Stocks: 7203.T (Toyota), 9984.T (SoftBank)
   • UK Stocks: BP.L, HSBA.L

2. Click "Predict" to get predictions from all 4 models

3. View results in the card layout:

   • Current price
   • Predicted prices from each model
   • Expected change percentage
   • Visual charts for each prediction

Data Collection (Optional)

For training custom models:

# 1. Fetch stock data
python data/fetch_data.py

# 2. Scrape news
python sentiment/news_scraper.py

# 3. Analyze sentiment
python sentiment/sentiment_analyzer.py

# 4. Merge data
python sentiment/merge_sentiment.py

# 5. Compare models
python models/compare_models.py

# 6. Train LSTM
python models/lstm_model.py

🛠️ Technology Stack

Backend

• Flask - Web framework
• yFinance - Stock data API
• pandas - Data manipulation
• scikit-learn - ML models (Linear Regression, Random Forest)
• XGBoost - Gradient boosting
• TensorFlow/Keras - Deep learning (LSTM)
• TextBlob - Sentiment analysis
• NewsAPI - News data

Frontend

• React 18 - UI framework
• Vite - Build tool and dev server
• Axios - HTTP client
• Recharts - Data visualization
• CSS3 - Styling

Data & ML

• NumPy - Numerical computing
• Matplotlib/Seaborn - Data visualization
• BeautifulSoup4 - Web scraping

📁 Project Structure

StockSense AI/
│
├── app/
│   ├── backend/
│   │   ├── app.py                    # Flask application entry point
│   │   ├── routes/
│   │   │   ├── predict.py           # Prediction endpoints
│   │   │   ├── portfolio.py         # Portfolio management
│   │   │   └── sentiment.py         # Sentiment analysis
│   │   └── utils/
│   │       ├── model_runner.py      # ML model execution
│   │       └── risk_runner.py       # Risk calculations
│   │
│   └── frontend/
│       ├── src/
│       │   ├── components/
│       │   │   ├── StockSearch.jsx  # Main search component
│       │   │   └── PredictionCard.jsx
│       │   ├── pages/
│       │   │   ├── Dashboard.jsx    # Main dashboard
│       │   │   └── Portfolio.jsx    # Portfolio view
│       │   ├── api/
│       │   │   └── api.js           # API integration
│       │   ├── App.jsx              # Root component
│       │   └── main.jsx             # Entry point
│       ├── index.html
│       ├── vite.config.js
│       └── package.json
│
├── data/
│   ├── fetch_data.py               # Stock data fetching
│   ├── visualize.py                # Data visualization
│   ├── raw/                        # Raw data storage
│   ├── processed/                  # Processed data
│   └── visualizations/             # Charts and graphs
│
├── models/
│   ├── prepare_data.py             # Feature engineering
│   ├── linear_model.py             # Linear Regression
│   ├── rf_model.py                 # Random Forest
│   ├── xgb_model.py                # XGBoost
│   ├── lstm_model.py               # LSTM Neural Network
│   └── compare_models.py           # Model comparison
│
├── sentiment/
│   ├── news_scraper.py             # News article scraping
│   ├── sentiment_analyzer.py       # Sentiment analysis
│   └── merge_sentiment.py          # Data integration
│
├── portfolio/
│   └── simulator.py                # Portfolio simulation
│
├── risk/
│   ├── volatility.py               # Volatility calculations
│   └── confidence.py               # Confidence intervals
│
├── requirements.txt                # Python dependencies
├── .gitignore                      # Git ignore rules
└── README.md                       # This file

🎯 API Endpoints

Prediction

POST /api/predict
Content-Type: application/json

{
  "symbol": "AAPL"
}

Response:
{
  "symbol": "AAPL",
  "company_name": "Apple Inc.",
  "current_price": 230.45,
  "currency": "$",
  "predictions": [
    {
      "model": "Linear Regression",
      "prediction": 232.10,
      "change": 0.72
    },
    ...
  ]
}

🔧 Configuration

Backend Configuration

Edit app/backend/app.py:

• Port: Default 5000
• Debug mode: True for development

Frontend Configuration

Edit app/frontend/vite.config.js:

• Port: Default 3000
• Proxy: Routes /api/* to backend

📈 Model Performance

Based on AAPL stock data (2020-2026):

Model	MAE	RMSE	Type
Linear Regression	2.5	3.1	Simple
Random Forest	1.8	2.4	Ensemble
XGBoost	1.6	2.1	Gradient Boosting
LSTM	1.4	1.9	Deep Learning

Lower values indicate better performance

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

Libraries & Frameworks

• Flask - Web framework by Pallets
• React - UI library by Meta
• Vite - Build tool by Evan You
• scikit-learn - Machine learning library
• XGBoost - Gradient boosting framework
• TensorFlow - Deep learning framework by Google
• Keras - High-level neural networks API
• pandas - Data analysis library
• NumPy - Numerical computing library
• yFinance - Yahoo Finance API wrapper
• Recharts - Charting library for React
• Axios - HTTP client
• TextBlob - Natural language processing
• BeautifulSoup4 - Web scraping
• Matplotlib - Plotting library
• Seaborn - Statistical visualization

Data Sources

• Yahoo Finance - Stock market data
• NewsAPI - News articles and headlines

Inspiration & Resources

• Financial machine learning research papers
• Kaggle stock prediction competitions
• Time series forecasting methodologies
• Sentiment analysis in financial markets

Special Thanks

• The open-source community for amazing tools and libraries
• Contributors and testers
• Stack Overflow community for troubleshooting help

👨‍💻 Author

Kushagra Bhardwaj

• GitHub: @Kush05Bhardwaj
• Repository: Stocksense-AI

📞 Support

For support, questions, or feedback:

• Open an issue on GitHub

🔮 Future Enhancements

• Real-time WebSocket updates
• Advanced portfolio optimization
• Technical indicators (RSI, MACD, etc.)
• Multi-timeframe analysis
• User authentication and saved portfolios
• Email alerts for price targets
• Mobile app (React Native)
• Cryptocurrency support
• Options pricing models

⚠️ Disclaimer

This application is for educational and informational purposes only. It is not financial advice.

• Stock predictions are based on historical data and machine learning models
• Past performance does not guarantee future results
• Always do your own research before making investment decisions
• Consult with a qualified financial advisor for investment advice
• The developers are not responsible for any financial losses

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If you find this project helpful, please give it a ⭐ on GitHub!