University of Twente Student Theses
Sentiment analysis trading indicators
Singpurwala, K. (2021) Sentiment analysis trading indicators.
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| Abstract: | Trading with strategies based on market indicators has become commonplace, especially in the domain of automated trading. A wide variety of different indicators and heuristics are available. In this research, we use deep learning via natural language processing to create sentiment driven trading indicators. We figure out if there is a correlation between market sentiment and bitcoin price movements using Pearson’s correlation coefficient and how these sentiment driven indicators can be used to create trading strategies. The aforementioned sentiment driven trading strategies are then compared to other trading strategies. Surprisingly, it was found that market sentiment does not correlate with bitcoin price movements. On the contrary, bitcoin related tweet volume (how trending bitcoin is a topic) did correlate with bitcoin price movements. The sentiment driven trading strategy implemented based on tweet volume was the most profitable strategy created. It outperformed simply buying and holding bitcoin by 72.37%. Thus it can be inferred that sentiment driven trading indicators can manifest profitable trading strategies |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/87003 |
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