This article examines a stock price forecasting method based on Euclidean distance. It discusses the widespread use of Euclidean distance in science, specifically exploring its applications in geographic information systems, physics, and machine learning. Fluctuations in the share price of Sberbank (traded on the Moscow Exchange) are proposed as the data source for the study. The article notes that fundamental and technical analyses are commonly used for forecasting in stock markets, highlighting the fundamental tenet of technical analysis: that the price and its movement history encapsulate all market information. The study concludes that Euclidean distance can be used for the univariate forecasting of stock prices. Daily closing prices over a five-year period are proposed as the data source. A forecasting algorithm is formulated based on the well-known concept that "history repeats itself." Data preprocessing involves removing data from weekends and holidays, converting values to percentage price changes, trimming anomalous spikes, and normalization. Euclidean distances are calculated between the price-change segment under analysis and 976 other segments. An analysis of the obtained results is presented. Forecasting based on the single closest segment yielded an accuracy of 63.56%, whereas using the ten closest segments resulted in 75.5% accuracy. Limitations of this approach, such as the non-uniform distribution of Euclidean distances, are noted, as are potential improvements – for instance, the additional application of the Dynamic Time Warping algorithm. Nevertheless, the conclusion is drawn that the forecasting result obtained is acceptable, particularly for trading applications.