COSC 442 - Assignment 1: Portfolio Optimization with Hill Climbing and Genetic Algorithms

Authors:

• Amna Bubshait (A00723).
• Zahra Alshehabi (A00755).

Problem:

This assignment explores portfolio optimization using Hill Climbing and Genetic Algorithms. The goal is to find the best combination of investments (from the S&P 500) to maximize returns or minimize risk. We compare the performance of both algorithms based on solution quality, convergence rate, time complexity, and sensitivity to parameters.

Dataset:

The primary dataset used is the "S&P 500 Stocks" dataset from Kaggle: Dataset URL

Methodology:

1. Load and Explore the Dataset: Import necessary libraries, load the dataset, and perform initial data exploration.
2. Identify and Handle Missing Values: Identify and handle missing values using imputation techniques.
3. Standardize Data: Standardize numerical features to ensure fair comparison between algorithms.
4. Portfolio Optimization: Implement Hill Climbing and Genetic Algorithms to optimize portfolio selection based on a fitness function (Sharpe ratio approximation).
5. Performance Metrics: Evaluate and compare the performance of both algorithms using metrics such as solution quality, convergence rate, time complexity, and space complexity.
6. Comparison and Results: Analyze the results and draw conclusions about the effectiveness of each algorithm for portfolio optimization.

Results:

• The Hill Climbing algorithm generally outperforms the Genetic Algorithm in terms of solution quality.
• Hill Climbing takes slightly longer to converge but uses less memory.
• Sensitivity analysis shows that the Genetic Algorithm's performance can be improved by adjusting parameters like mutation rate and population size.

Conclusion:

For this specific portfolio optimization problem, Hill Climbing appears to be the more effective algorithm.

Running the Notebook:

1. Make sure you have the necessary libraries installed (kagglehub, numpy, pandas, matplotlib, seaborn, sklearn).
2. Download the dataset from the provided Kaggle link.
3. Open and run the notebook in Google Colab.