300k/ns team at the Crypto Trading Challenge Task: Enhancing the justification of accurate trading decisions through parameter-efficient fine-tuning of reasoning models (2025)

Abstract: 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.

## introduction cryptocurrency trading has emerged as one of the most dynamic and volatile sectors in the global financial landscape, attracting considerable attention from investors, researchers, and traders alike. the market is characterized by its sensitivity to a vast array of real-time information-from news and social media trends to regulatory updates and technological advancements @xcite . these data streams vary not only in type but also in their timeliness and impact, creating a complex environment that demands quick and accurate decisionmaking. various studies @xcite @xcite show that news data has a significant impact on cryptocurrency prices, so it should be taken into account when making trading decisions. the rapid fluctuations in cryptocurrency prices, driven by both short-term market sentiment and longer-term economic trends, make the need for sophisticated, automated systems even more urgent. in recent years, advancements in natural language processing (nlp) and, more specifically, large language models (llms), have significantly improved the ability of machines to understand and process complex data. llms like @xcite , llama @xcite , fingpt @xcite and bloomberggpt @xcite have shown considerable promise in various financial applications, particularly in making informed decisions based on diverse and multi-timely data. however, cryptocurrency trading, with its unique set of challenges (lopez-lira and tang, 2023), requires further advancements @xcite in model capabilities. some studies @xcite show that llm algorithms can effectively extract textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. we think that price correlations and various trends are more related to financial news. this paper addresses the need for specialized llms tailored to the cryptocurrency market, capable of interpreting both immediate and long-term market signals, while making reasoned decisions over sustained trading periods. this research aims to evaluate the performance of llm-based agents in the context of automated cryptocurrency trading, utilizing the finmem framework @xcite . finmem is an integrated agent system designed for financial decision-making, leveraging llms to support complex trading strategies. ## dataset the datasets provided for this study included historical price data and daily news related to two prominent cryptocurrencies, bitcoin and ethereum. specifically, over a two-month period, the dataset contained daily price information for each cryptocurrency alongside a collection of news articles relevant to these assets on each given day. while this data offered a foundational starting point, it was insufficient to train a model capable of capturing the intricate dynamics of cryptocurrency trading. to address this limitation, we extended and enriched the dataset through additional data collection and preprocessing efforts. news from the world of cryptocurrencies was scraped from such sites as the block, cnbc, coindesk, fortune, dlnews, bloomberg, significantly broadening the dataset coverage. by expanding the dataset through these processes, the study ensured that the model had access to a broader context, improving its ability to analyze, reason, and make informed trading decisions in the cryptocurrency market. we compare the suggested and our custom datasets in figure 2 . ## approach the rapid advancements in large language models (llms), such as gpt-4 @xcite and mistral (albert q. jiang et al., 2023), highlight the power of integrating diverse data sources during pre-training and fine-tuning. by leveraging multi-faceted datasets, these models achieve remarkable generalization capabilities and domainspecific precision. using the same approach, we tried to diversify our data as much as possible so that our model knew as much historical information about cryptocurrencies and major players as possible. our pipeline can be seen in figure 1 . ## experimental setup this section provides a detailed explanation of the implementation of the proposed pipeline and the model training process. ## results the results are presented in we also present the cumulative return graphs for btc and eth on the evaluation dataset, covering ## conclusion this paper presented a novel approach to cryptocurrency trading using fine-tuned large language models (llms). by incorporating real-time news sentiment, historical price data, and reasoning capabilities, the cryptotrader-lm model was able to make informed and reasonable trading decisions for bitcoin and ethereum. the use of parameter-efficient fine-tuning techniques, such as lora, allowed the model to achieve high accuracy with a relatively small computational footprint. our experimental results demonstrated that the model achieved an overall sharpe ratio of -0.14. the low sharpe ratios suggest that model's forecasting ability underperformed relative to the risk taken. a likely reason for this is that the model may have struggled to capture a dominant trend or macroeconomic event driving the market. for instance, in november 2024, cryptocurrency growth was largely influenced by macro-political events, such as the election of donald trump as president. the model might not have been equipped to fully contextualize or prioritize such long-term news narratives in its decision-making process. to improve performance in the future, we would like to adjust the model to better interpret and emphasize long-term news trends. by incorporating and analyzing broader contextual information, particularly those tied to significant geopolitical, economic, or technological developments, the model could better align its predictions with prevailing market drivers and enhance its strategic positioning.