This research explores the integration of Symbolic Aggregate Approximation (SAX) and the Vector Space Model (VSM) to classify stock trading signals into actionable categories: buy, hold, or sell. SAX reduces the dimensionality of financial time series data while preserving essential patterns, and VSM leverages Term Frequency-Inverse Document Frequency (TF-IDF) and cosine similarity for precise classification. The study employs historical data from 25 Indonesian stocks, analyzing trends to address financial markets’ volatile and noisy nature. Key steps include data preprocessing, symbolic transformation, and vector-based classification. Results validate the efficacy of the SAX-VSM framework in identifying significant market patterns and its scalability for broader financial applications. This work highlights the framework's potential as a computationally efficient tool for informed investment decisions in dynamic market environments.