The paper conducted a comprehensive analysis of the time series of stock prices of three leading energy companies – Shell, BP and ExxonMobil – for the period from January 2021 to January 2025. At the initial stage, data quality was checked: dates were set as indices, the absence of duplicates and missing values was confirmed, and descriptive statistics (mean, variance, skewness and kurtosis) were calculated. Next, the trends of adjusted closing prices (AdjClose) were analysed using moving averages (SMA14, SMA50), exponential smoothing, moving volatility (30-day standard deviation) and cumulative returns. It was found that еhe price dynamics growth has accelerated since 2022 against the background of the energy crisis caused by the war in Ukraine: ExxonMobil’s cumulative return reached ≈250% by mid-2022 and ≈350% at the beginning of 2025, Shell and BP, respectively ≈220% and ≈200% by 2024. Correlation analysis showed that BP and Shell have the most significant interdependence (r = 0.87, R² = 0.75). The autocorrelation method established high non-stationarity of the time series (ACF about one at low lags). K-Means clustering (k = 2) allowed us to distinguish periods of active growth and relative price consolidation, although the feature selection behind this clustering requires further clarification. The initially reported financial metrics (Sharpe, Sortino, and Calmar ratios) were significantly overstated due to unit errors, specifically, using percentage values as absolute figures. After applying appropriate annualization and decimal scaling performance indicators were obtained for ExxonMobil – CAGR = 36.84%, Sharpe ≈ 1.24, Sortino ≈ 1.9–2.5, Max Drawdown = 20.51%, Calmar ≈ 1.80; Shell: CAGR = 21.29%, Sharpe ≈ 0.76, Sortino ≈ 1.2–1.5, Max Drawdown = 25.04%, Calmar ≈ 0.85; BP: CAGR = 14.54%, Sharpe ≈ 0.53, Sortino ≈ 0.9–1.2, Max Drawdown = 26.23%, Calmar ≈ 0.55. The study confirms that ExxonMobil showed the most stable and substantial growth during the examined period, while BP exhibited the highest volatility. Shell demonstrated an intermediate performance level. The close correlation between Shell and BP is attributed to the similarity in their geographical market activity and stock behaviour. The choice of these methods of analysis is due to the desire to assess the behaviour of stocks during the period of increased market volatility caused by the energy crisis, geopolitical risks and changes in investor priorities. Technical analysis allows you to identify short- and medium-term patterns, clustering allows you to automatically separate market phases without the need for subjective hypotheses, and statistical metrics will enable you to compare the performance of assets within the industry. This research contributes to the broader field of financial analysis by demonstrating how machine learning and technical analytics tools can be applied to assess the resilience and relationships of assets during periods of market turmoil. The results can be helpful for institutional investors, financial analysts, and portfolio managers looking to adapt strategies to dynamic energy market conditions.