In this paper, we address the Agent-Based Sin- gle Cryptocurrency Trading Challenge, focus- ing on decision-making for trading Bitcoin and Etherium. Our approach utilizes fine- tuning a Mistral AI model on a dataset com- prising summarized cryptocurrency news, en- abling it to make informed “buy,” “sell,” or “hold” decisions and articulate its reasoning. The model integrates textual sentiment analysis and contextual reasoning with real-time mar- ket trends, demonstrating the potential of Large Language Models (LLMs) in high-stakes finan- cial decision-making. The model achieved a notable accuracy, highlighting its capacity to manage risk while optimizing returns. This work contributes to advancing AI-driven so- lutions for cryptocurrency markets and offers insights into the practical deployment of LLMs in real-time trading environments. We made our model publicly available.