Sam’s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on FinMem Framework

AbstractThe advancements of large language models (LLMs) demonstrate the value of pre-training on diverse datasets, enabling these models to excel across a wide range of tasks while adapting effectively to specialized applications. This study presents an approach to enhance LLMs’ ability to process and trade based on cryptocurrency data across different time horizons. We fine-tuned two established language models, Llama-3.1-8b and Qwen2.5-7b, to effectively interpret and utilize temporal market data provided by the FinMem framework. Our methodology enables these models to analyze multi-period market data from FinMem, including price movements and momentum indicators, to execute effective cryptocurrency trading decisions. Results show that this fine-tuning approach improves the models’ capacity to analyze market conditions and inform trading decisions based on multi-period market dynamics.