This study proposes a novel approach to financial time series classification by transforming numerical stock market data into candlestick chart images and analyzing them using deep convolutional neural networks (CNNs). Unlike traditional methods that rely on raw numeric sequences, our technique leverages image-based representations enriched with technical indicators (e.g., RSI, MACD, trend channels) to detect visual patterns associated with future price movements. The method is applied to daily price data from ten major publicly traded companies. A custom CNN architecture is trained to classify short-term trends (uptrend vs. downtrend) based on 30-day image windows. The model achieves a test accuracy of 92.83%, with F1-scores exceeding 92% for both classes. These results suggest that visual representations can effectively encode temporal and structural information in price data. While promising, the method’s performance may be sensitive to image resolution and labeling heuristics, which are discussed as potential limitations. Overall, this research demonstrates the feasibility and effectiveness of image-based deep learning in financial market forecasting.