The Stock Market Predictor is an advanced tool designed to predict stock prices using Long Short-Term Memory (LSTM) models and provide an intuitive user interface for real-time stock market analysis. The application integrates historical stock data, moving averages, Fibonacci retracement levels, and real-time news updates to offer a comprehensive solution for investors and traders.

• Historical Stock Data Retrieval: Fetches historical stock price data from Yahoo Finance.
• Data Preprocessing: Scales and preprocesses the data for LSTM model training.
• LSTM Model Training: Utilizes LSTM neural networks to predict future stock prices.
• Interactive User Interface: Built using Streamlit, allowing users to input stock symbols and visualize data.
• Moving Averages: Displays moving averages (50-day, 100-day, and 200-day) for trend analysis.
• Fibonacci Retracement Levels: Calculates and visualizes Fibonacci retracement levels.
• Real-Time News Updates: Integrates real-time news related to the selected stock.

To set up the Stock Market Predictor, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/stock-market-predictor.git
   cd stock-market-predictor

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py

1. Input Stock Symbol: Enter the stock symbol (e.g., GOOG for Google) in the input field.
2. View Stock Information: The app fetches and displays essential stock information and visualizes historical data.
3. Analyze Moving Averages: Select from various moving average graphs to analyze stock trends.
4. Check Fibonacci Retracement Levels: View calculated Fibonacci levels for potential support and resistance areas.
5. Read Recent News: Access real-time news updates related to the selected stock to stay informed about market events.

• app.py: Main application file containing the Streamlit app code.
• models/: Directory containing the LSTM model and other machine learning scripts.
• data/: Folder for storing fetched and preprocessed data.
• requirements.txt: List of dependencies required to run the project.
• README.md: Project documentation file.

• Python 3.7 or higher
• pip (Python package installer)
• Internet connection (to fetch real-time data from Yahoo Finance)

We welcome contributions to enhance the Stock Market Predictor. To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes and push them to your forked repository.
4. Submit a pull request with a detailed description of your changes.

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

We extend our gratitude to the contributors and maintainers of the following libraries and tools:

• Yahoo Finance
• Streamlit
• Keras
• TensorFlow

For any questions or feedback, please contact me at [[email protected]].

By combining machine learning techniques with a user-friendly interface, the Stock Market Predictor aims to empower investors with valuable insights and tools for making informed decisions in the complex world of stock markets.