Overview

This repository contains a Long Short-Term Memory (LSTM) model implemented using Keras for stock analysis. The model is designed to predict stock prices based on historical data.

Introduction

This project focuses on the analysis and prediction of stock data for major tech companies, including Apple (AAPL), Google (GOOG), Microsoft (MSFT), Amazon (AMZN), and Tesla (TSLA). By leveraging historical stock information, various metrics, and advanced machine learning techniques, the project aims to provide valuable insights for investors.

Project Structure

The project is structured into several key sections, each addressing specific aspects of stock analysis:

1. Data Collection:

   • Utilize the Yahoo Finance API to fetch historical stock data for the selected companies.

2. Data Analysis and Visualization:

   • Conduct exploratory data analysis (EDA) to understand stock characteristics.
   • Visualize key metrics, trends, and relationships using matplotlib and seaborn.

3. Moving Averages and Technical Indicators:

   • Calculate and visualize moving averages (10, 20, and 50 days) to identify trends.
   • Compute the Relative Strength Index (RSI) to gauge overbought or oversold conditions.

4. Risk Assessment:

   • Evaluate the risk associated with each stock using historical volatility metrics.
   • Generate scatter plots to visualize the risk-return trade-off.

5. Stock Price Prediction using LSTM:

   • Implement an LSTM neural network for predicting the future closing price of Apple Inc. (AAPL).
   • Fine-tune hyperparameters and assess the model's performance.

Running the Code

To replicate the analysis and run the LSTM model, follow these steps:

1. Install required dependencies: pip install -r requirements.txt
2. Execute the Jupyter notebooks in sequential order.

Key Questions Answered

The project addresses the following questions:

1. What was the change in price of the stock over time?
2. What was the daily return of the stock on average?
3. What was the moving average of the various stocks?
4. What was the correlation between different stocks?
5. How much value do we put at risk by investing in a particular stock?
6. How can we attempt to predict future stock behavior?

Conclusion

The Stock Analysis and Prediction project aims to empower users with a comprehensive understanding of historical stock trends, risk assessment strategies, and the potential for future price prediction using advanced deep learning techniques. By leveraging this information, investors can make more informed decisions in the dynamic world of financial markets.

Feel free to explore the Jupyter notebooks for detailed code implementations and analyses.

License

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

Resources

• Yahoo Finance API
• Keras Documentation
• Matplotlib Documentation
• Seaborn Documentation
• NumPy Documentation
• Pandas Documentation
• Scikit-Learn Documentation