This project provides data analysis, forecasting, and backtesting for the GMF investment portfolio, focusing on time series modeling, portfolio optimization, and performance evaluation.

• data/ - Contains processed data files used for analysis.
• models/ - Directory for saving trained models and related artifacts. -- notebooks/ - Jupyter notebooks for data exploration, model development, forecasting, portfolio optimization, and backtesting.
  • data_exploration.ipynb - Initial exploration and visualization of the dataset.
  • model_development.ipynb - Time series modeling and forecasting (e.g., ARIMA for TSLA stock).
  • forcasting.ipynb - Additional forecasting analysis and experiments.
  • portfolio_optimization.ipynb - Portfolio optimization techniques and results.
  • backtesting.ipynb - Backtesting of portfolio strategies and performance analysis.
• src/ - Source code for custom scripts and utilities.
• requirements.txt - List of required Python packages.

1. Clone the repository
2. Install dependencies

   pip install -r requirements.txt

3. Run the notebooks
   • Open notebooks/data_exploration.ipynb for initial data analysis.
   • Open notebooks/model_development.ipynb for time series modeling and forecasting.
   • Open notebooks/forcasting.ipynb for additional forecasting analysis and experiments.
   • Open notebooks/portfolio_optimization.ipynb for portfolio optimization techniques and results.
   • Open notebooks/backtesting.ipynb for portfolio backtesting and performance evaluation.

• Data loading and preprocessing from CSV files
• Exploratory data analysis and visualization
• Time series forecasting using ARIMA models
• Portfolio optimization and strategy definition
• Backtesting of portfolio strategies against benchmarks
• Performance evaluation with metrics such as total return and Sharpe ratio

• Python 3.8+
• See requirements.txt for all dependencies

This project is for educational and research purposes.