S am’s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on F in M em Framework (2025)

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.

## introduction cryptocurrency trading markets are among the most complex and fast-paced environments in the financial world. these markets exhibit extreme volatility and are influenced by a broad range of data sources, including real-time price changes, breaking news, regulatory updates, social media sentiment, and macroeconomic indicators @xcite . successfully extracting actionable insights from this diverse and time-sensitive information requires sophisticated systems that can process multi-temporal data while addressing uncertainty and rapid market shifts. large language models (llms) @xcite have emerged as powerful tools for processing unstructured data, offering advanced capabilities in reasoning, natural language understanding, and decision-making. models like qwen2.5-7b (hugging face, 2024) and llama-3.1-8b @xcite have been proven effective in various financial applications, such as sentiment analysis, market text summarization, and asset price prediction @xcite . furthermore, the fine-tuning @xcite techniques can enhance llms' ability to handle data and tasks in specific domains. in this study, we proposed a fine-tuning strategy to enhance llms' performance in automating the currency trading, combined with finmem framework. we first curated the data from diverse areas and implemented lora fine-tuning techniques to enhance llms' understanding of the complex cryptocurrency trading environment. and then supervised llms for the desired actions also through lora. we tested our approach with two standard llms, llama-3.1-8b and qwen2.5-7b, the results show the potential of llms' advance in the automated trading tasks. our solution ranks as the top trading agent in the cryptocurrency trading challenge competition (finnlp workshop@coling25, 2024). our extensive experiments demonstrate that our approach is effective and partially meets the objectives underscored by this competition. first, llms shows different behaviors after being finetuned with knowledge base, suggesting a potential that llms understand cryptocurrency trading's unique complexity. second, our final solution agents achieve a robust higher return in btc trading than baseline agent. however, those final fine-tuned agents did not demonstrate significant improvement in eth trading. we believe that this is caused by the naivety of the strategy we implemented to supervise llms' trading actions. ## methodology in this section, we propose a fine-tuning strategy to enhance llms for cryptocurrency trading tasks. our approach includes two steps which are shown in figure 1 : the first step is to enhance the llms' understanding of cryptocurrency trading environments through a knowledge dataset consisting of domain-specific questions and answers; the second step is to supervise the llms' trading actions. ## conclusion in this study, we fine-tuned the llama-3.1-8b and qwen2.5-7b models, combined with the finmem framework to address the challenges of automated cryptocurrency trading with bitcoin and ethereum data. our approach integrated domain-specific knowledge and implemented a threshold-based decision-making framework to handle the volatility and complexity of cryptocurrency markets. despite these efforts, the models did not outperform the baseline "buy and hold" strategy in the eth market, highlighting areas for improvement in our methodology. several factors could explain these results. first, the relatively small size of the 8b and 7b models may limit their inference capabilities, suggesting that larger models with more parameters could better capture complex market patterns. second, the threshold-based decision framework may require further tuning to adapt to specific market characteristics, such as ethereum's unique trading behavior. lastly, the static knowledge dataset itself may lack sufficient granularity or timeliness to align well with real-time market fluctuations. cr ↑ llama-3.1-8b -btc qwen2.5-7b -btc llama-3.1-8b -eth qwen2.5-7b -eth b_h bk final b_h bk final b_h bk final b_h bk final 0.298 0.259 0.298 0.298 0.259 0.319 0.200 -0.011 -0.063 0.200 -0.194 -0.155 sr ↑ 4.633 4.071 4.069 4.633 4.071 5.165 3.336 -0.180 -1.117 3.336 -3.532 -2.655 'cr': cumulative return, 'sr': sharpe ratio. 'b_h': 'buy and hold'. 'bk': model with base knowledge, 'final': finalized model with base knowledge trained on threshold-based decisions. ' ↑ ′ indicates the higher the better. future work should focus on addressing these limitations by exploring larger models, implementing more sophisticated decision strategies, and combining static knowledge with real-time inputs in a more seamless and adaptive way. these refinements could help bridge the gap between static knowledge and dynamic market conditions, enhancing the models' overall performance.