📈 Portfolio Optimization and efficient frontier - Lab 3

Jan 2025, Python Labs from CQF by Kannan Singaravelu

This project implements Portfolio Optimization using Python, extracting financial data from a SQLite database and applying optimization techniques to build an efficient portfolio.

Notebook

🚀 Features

• Data Extraction: Fetches historical stock prices from a SQLite database.

• Data Preprocessing: Cleans and structures price data for analysis.

• Portfolio Optimization: Computes optimal asset allocation using risk-return analysis.

• Visualization: Generates charts for portfolio weights and performance.

📈 Efficient Frontier

🎯 Monte Carlo Simulation

📂 File Structure

• Data Extraction: Fetches historical stock prices from a SQLite database.
• Data Preprocessing: Cleans and structures price data for analysis.
• Portfolio Optimization: Computes optimal asset allocation using risk-return analysis.
• Visualization: Generates charts for portfolio weights and performance.

Steps to set up the environment and install the required libraries

1. Installing uv
   pip install uv

2. Create a virtual environment with Python 3.10 Note: Use a virtual environment with Python 3.10 for this program. uv venv --python 3.10

3. Activate the virtual environment source .venv/bin/activate .venv\Scripts\activate for Windows

4. Specify required python libraries in requirements.txt

5. Install required python libraries uv pip install -r requirements.txt

6. Download & Install Visual Studio Code https://code.visualstudio.com/download
   Note: you will need to install appropriate microsfot extensions for python, jupyter including pylance, etc.

7. Open the notebooks, use any of the following methods: a. Use the command code . to launch Visual Studio Code from the terminal. b. Use the command jupyter lab to launch Jupyter Lab from the terminal.

8. When you are done with the lab, close the Jupyter Lab and exit the virtual environment. deactivate from the terminal.

Important: Please note that the virtual environment is only active for the duration of the session. If you wish to use the virtual environment for other purposes, you will need to activate it again. Python Labs are designed to be run in a virtual environment, and you should not install any libraries outside of it. The Python Labs content is shared as HTML files for you to replicate the labs in your own environment. It will not be shared as .ipynb files, as this aligns with good software engineering practices.