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    <abstract>The 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.</abstract>
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%0 Conference Proceedings
%T Sam‘s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on FinMem Framework
%A Wang, You
%A Wei, Jingyi
%A Ye, Mingsong
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-sams
%X The 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.
%U https://aclanthology.org/2025.finnlp-1.47/
%P 407-413

Markdown (Informal)

[Sam’s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on FinMem Framework](https://aclanthology.org/2025.finnlp-1.47/) (Wang et al., FinNLP 2025)

• Sam’s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on FinMem Framework (Wang et al., FinNLP 2025)

ACL

• You Wang, Jingyi Wei, and Mingsong Ye. 2025. Sam’s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on FinMem Framework. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 407–413, Abu Dhabi, UAE. Association for Computational Linguistics.