Summary of Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning, by Arthur Emanuel De Oliveira Carosia
Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning
by Arthur Emanuel de Oliveira Carosia
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for predicting Bitcoin’s price movements by combining the Fear & Greedy Index (a measure of market sentiment), Technical Analysis indicators, and Machine Learning algorithms. By leveraging sentiment metrics, this study aims to improve the accuracy of cryptocurrency forecasting. The authors demonstrate promising results, surpassing the Buy & Hold baseline and offering valuable insights about the combination of sentiment and market indicators in a prediction model. This work contributes to the existing literature on Bitcoin’s price fluctuations, which are influenced by its decentralized nature and potential for financial innovation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to guess what will happen to the value of Bitcoin, a popular digital currency. Some people have found that how people feel about Bitcoin (like being scared or excited) can affect its value. This paper shows a new way to predict Bitcoin’s price by combining this “sentiment” with some technical indicators and special computer algorithms. It looks like this approach is more successful than just buying and holding onto Bitcoin, which is the usual strategy. The study provides useful insights for people who want to invest in or trade digital currencies. |
Keywords
* Artificial intelligence * Machine learning
