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CBITS: Crypto BERT Incorporated Trading System
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DOI：10.1109/ACCESS.2023.3236032.png)
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摘要
En 中文
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.
Keyword:
Predictive models
Investment
Data models
Cryptocurrency
Fluctuations
Bit error rate
Annotations
Sentiment analysis
Bitcoin
Korean pre-trained language model
sentiment analysis
bitcoin trading models
agglutinative language
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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