Abstract
Most textual analysis-based trading approaches in cryptocurrency (crypto) involve lexical, rule-based methods for extracting news sentiments. Furthermore, language models (LMs) are not always suitable for the crypto domain due to jargon that is not covered in general-purpose texts. This study answers the question of “Is it possible that the LMs can profit by effectively applying the sentiment score of the natural language processing task with chart score in the BTC trading system?” by focusing on the effectiveness of both scores, which significantly affect the profit of the trading system. We introduce CBITS: Cryptocurrency BERT Incorporated Trading System based on pre-trained LMs for Korean crypto sentiment analysis to aid Bitcoin (BTC) trading models. We pre-trained crypto-specific LMs, which are transformer encoder-based architectures. Along with our pre-trained LMs, we also present our custom fine-tuning dataset used to train our LMs on the BTC sentiment classifier and show that using sentiment scores along with BTC chart data boosts the performance of BTC trading models and also allows us to create a market-neutral trading strategy.
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