This document contains the R code syntax and computational workflow for an undergraduate thesis focusing on the risk analysis of a stock portfolio within the Indonesian banking sub-sector (BBCA, BBNI, BBRI, BMRI, and BRIS). This analysis specifically estimates market risk—rather than seeking an optimal portfolio—using the Extreme Value Theory (EVT) approach to capture fat-tail phenomena and extreme events in the capital market. A crucial step in this computation is data transformation, where the log returns are multiplied by -1. This transformation is mandatory to invert the distribution direction so that the loss metric can be accurately modeled using extreme value theory. Broadly, the workflow in this document encompasses: Data Acquisition & Preparation: Retrieval of daily stock price data via Yahoo Finance (2019–2025) and calculation of log returns. Loss Transformation & Weighting: Determination of individual asset weights and the transformation of portfolio returns into losses. Extreme Value Identification: Data extraction using two EVT methods: Block Maxima and Peak Over Threshold (POT). GEV & GPD Distributions: Fitting extreme data into the Generalized Extreme Value (GEV) and Generalized Pareto Distribution (GPD) models, followed by Anderson-Darling goodness-of-fit tests. Value at Risk (VaR) Estimation: Calculation of VaR at a 95% confidence level for both models. Backtesting: Validation of the VaR models' accuracy using the Likelihood Ratio test (Kupiec Test) to ensure the actual violation rate aligns with the expected target. This document serves as a computational reference for students and quantitative practitioners looking to implement EVT-based VaR measurements using R packages such as quantmod, extRemes, evmix, and eva.