📈 Stock Price Prediction Using LSTM

A powerful stock price prediction system built with LSTM neural networks, featuring real-time data from yfinance, interactive model training, and comprehensive performance metrics through a user-friendly Streamlit interface.

✨ Features

• 🔍 Real-time Data: Fetches real-time stock data using yfinance
• 📈 Interactive Training: Fine-tune model parameters through an intuitive interface
• 🤖 Advanced LSTM Architecture: Multi-layer LSTM with dropout for robust predictions
• 📊 Comprehensive Metrics: Track MSE, RMSE, MAE, and R² scores
• 🎯 Future Predictions: Generate price predictions with confidence intervals
• 📉 Performance Tracking: Monitor model performance over time
• 📱 Responsive UI: User-friendly interface built with Streamlit

🚀 Tech Stack

Technology	Purpose
Python	Core language
TensorFlow	Deep learning framework
Streamlit	Web interface
yfinance	Stock data source
Pandas	Data manipulation
Matplotlib	Data visualization
Scikit-learn	Model evaluation
Docker	Containerization
Render	Cloud hosting

🏗️ Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Web Interface  │────▶│  Data Fetching  │────▶│  Preprocessing  │
│   (Streamlit)   │     │   (yfinance)    │     │                 │
│                 │     │                 │     │                 │
└─────────────────┘     └────────┬────────┘     └────────┬────────┘
                                │                        │
                                │                        ▼
                                │             ┌─────────────────────┐
                                │             │                     │
                                │             │   LSTM Training     │
                                │             │                     │
                                │             └──────────┬──────────┘
                                │                        │
                                │                        ▼
                                │             ┌─────────────────────┐
                                │             │                     │
                                │             │  Model Evaluation   │
                                │             │                     │
                                │             └──────────┬──────────┘
                                ▼                        ▼
                      ┌─────────────────────────────────────────────┐
                      │                                             │
                      │              Price Prediction               │
                      │                                             │
                      └─────────────────────────────────────────────┘

🔧 Installation

Prerequisites

• Python 3.8 or higher
• No API key required (yfinance is used)

Option 1: Using Setup Scripts (Recommended) 🚀

1. Clone the repository:

git clone https://github.com/VisionExpo/Stock_price_prediction.git
cd Stock_price_prediction

2. Run the setup script:

For Windows:

setup_env.bat

For macOS/Linux:

chmod +x setup_env.sh
./setup_env.sh

This script will:

• 🔨 Create a virtual environment
• ⚡ Activate the virtual environment
• 📦 Install dependencies
• 🔑 Create a .env file from the example if it doesn't exist

3. No API keys are required for this project

Option 2: Manual Setup 🛠️

1. Clone the repository:

git clone https://github.com/VisionExpo/Stock_price_prediction.git
cd Stock_price_prediction

2. Create and activate a virtual environment:

For Windows:

python -m venv venv
venv\Scripts\activate

For macOS/Linux:

python -m venv venv
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up your environment variables:

cp .env.example .env

No API keys are required for this project.

🚀 Usage

Running the Application

streamlit run app.py

Then open http://localhost:8501 in your web browser.

📊 Data Visualization Mode

View historical stock data with interactive charts:

• Price trends
• Volume analysis
• Moving averages
• Technical indicators

🧠 Model Training Mode

Train custom LSTM models with:

• Adjustable look-back periods
• Customizable layer architecture
• Hyperparameter tuning
• Early stopping options

🔮 Prediction Mode

Generate and visualize predictions:

• Short-term forecasts
• Long-term trends
• Confidence intervals
• Downloadable prediction data

📈 Performance Analysis

Evaluate model performance with:

• Error metrics (MSE, RMSE, MAE)
• R² scores
• Prediction vs. actual comparisons
• Model version tracking

🌐 Deployment

This application can be deployed on Render. You can access it at: https://stock-price-prediction.onrender.com/

For detailed deployment instructions, see DEPLOYMENT.md.

🧪 Testing

To run tests:

python -m pytest tests/

📝 License

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

🙏 Acknowledgements

• TensorFlow team for the deep learning framework
• Streamlit team for the web app framework
• yfinance for providing stock data access
• The open-source community for various libraries used in this project

📞 Contact

For questions or feedback, please open an issue on GitHub or contact the maintainer at [email protected].

Made with ❤️ by Vishal Gorule