Genetic Algorithm Portfolio Optimization: Minimizing Risk and Maximizing Returns on Indonesia

Overview This repository contains the dataset and Python source code used in the research paper titled "Portfolio Optimization Using Genetic Algorithm to Minimize Financial Risk and Maximize Returns on the Indonesia Stock Exchange." The data comprises the historical daily closing prices of 45 highly liquid stocks listed in the LQ45 index on the Indonesia Stock Exchange (IDX) for the period of 2023 to 2025.

The primary objective of this dataset and the accompanying computational model is to solve large-scale combinatorial asset optimization problems, overcoming the limitations of classical mathematical models (such as the Markowitz Mean-Variance approach).

Methodology The optimization was conducted using a Genetic Algorithm (GA) modeled in Python with the following operators and parameters:

Asset Pool: 45 Stocks (LQ45 Index)

Time Horizon: 2023 – 2025

Fitness Function: Sharpe Ratio (Risk-adjusted return)

Selection Method: Tournament Selection

Crossover Rate: 80% (Arithmetic Crossover)

Mutation Rate: 10% (Gaussian Mutation)

Constraint: 100% capital distribution (No short-selling)