A machine learning project for predicting stock prices using LSTM neural networks. The project includes data collection, feature engineering, model training, and real-time predictions.

• Historical stock data collection using yfinance
• Technical indicator calculation (RSI, MACD, Bollinger Bands, etc.)
• LSTM model for time series prediction
• Feature normalization and preprocessing
• Model training with validation
• Real-time predictions

stock_prediction/
├── src/
│   ├── data/
│   │   ├── __init__.py
│   │   └── collect_data.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── base_model.py
│   │   └── lstm_model.py
│   ├── utils/
│   │   ├── __init__.py
│   │   └── features.py
│   └── api/
│       ├── __init__.py
│       ├── main.py
│       └── dashboard.py
├── data/
├── models/
├── results/
├── requirements.txt
└── README.md

1. Clone the repository:

git clone https://github.com/yourusername/stock_prediction.git
cd stock_prediction

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

1. Collect historical stock data:

python src/data/collect_data.py --symbol AAPL --start 2020-01-01

2. Train the model:

python src/models/train.py --model lstm --symbol AAPL --start 2020-01-01

3. Make predictions:

python test_model.py

4. Start the API server:

uvicorn src.api.main:app --reload

5. Launch the dashboard:

streamlit run src.api.dashboard

Feel free to submit issues, fork the repository, and create pull requests for any improvements.

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