Portfolio Benchmark Analysis Project

This project aims to analyze and benchmark the performance of a financial portfolio against major market indices like the S&P 500 and TSX300. It involves fetching historical market data, calculating performance metrics, visualizing data, and predicting future portfolio values.

Project Structure

The project consists of Python scripts for data manipulation and analysis, Docker configurations for containerization, and a Jupyter Notebook for interactive data visualization.

Key Features

• Data fetching from Yahoo Finance using yfinance.
• Performance comparison with S&P 500 and TSX300 indices.
• Calculation of financial metrics such as Sharpe Ratio, Alpha, and Beta.
• Interactive visualizations with plotly.
• Predictive analysis for future portfolio performance.
• Containerized Jupyter environment for analysis.

Requirements

• Python 3.11
• Docker
• Docker Compose

Installation

1. Clone the repository

   git clone https://github.com/TeomanEgeSelcuk/Portfolio-Benchmarks.git
   

2. Build the Docker container

   Navigate to the project directory and run:

   docker-compose up --build
   

   This command builds and runs the container defined in the Dockerfile and docker-compose.yml, setting up the Jupyter Notebook environment.

Usage

After building and running the container, access the Jupyter Notebook via the link displayed in the console. The link contains a token for authentication.

The notebook directory within the container is mapped to the notebooks directory in your project folder, allowing you to persist your work.

Data Analysis Workflow

1. Importing Libraries

   Essential libraries for data manipulation, visualization, and financial data fetching are used, including pandas, numpy, plotly, and yfinance.

2. Data Fetching and Preparation

   Historical data for the S&P 500 and TSX300 indices is fetched for a specified period. Closing prices and daily percentage changes are processed and prepared for analysis.

3. Portfolio Performance Analysis

   The portfolio's historical values are loaded, and various performance metrics are calculated and compared with the market indices.

4. Visualizing Data

   Interactive visualizations are created to compare portfolio performance metrics against the market indices.

5. Performance Metrics Calculation

   Key financial metrics including Sharpe Ratio, Alpha, and Beta are calculated to evaluate the portfolio's performance.

6. Predictive Analytics

   A predictive model is built to forecast future portfolio values, utilizing polynomial features and linear regression.

Docker Configuration

• Dockerfile: Defines the Python environment, installs dependencies, and sets up the Jupyter Notebook server.
• docker-compose.yml: Configures the service, including port mapping and volume setup for the Jupyter environment.

Contributing

Contributions to the project are welcome. Please follow the standard fork and pull request workflow.

License

Specify your project's license here.