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Combining Bitcoin Market Data and Social Signals for Trading

Article arXiv papers · Author: David Garcia et al.
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Summary

This study combines Bitcoin market measures with social and behavioral signals to investigate algorithmic trading. Its inputs include exchange price and volume, technology adoption, and Bitcoin transaction volume, alongside search activity, word-of-mouth volume, tweet sentiment, and opinion polarization. The analysis covers more than three years of Bitcoin-related data and examines how these signals relate to price movements.

The authors report that rising opinion polarization and exchange volume precede higher Bitcoin prices, while emotional valence precedes changes in polarization and rising exchange volume. They use these relationships to construct trading strategies and report high profits over a period of less than a year. They also say statistical checks account for risk and trading costs. The document does not provide the precise strategy rules, performance figures, or validation details, so the findings are evidence for the approach in the studied sample rather than a guarantee that social signals will remain predictive.

Key ideas

  • The analysis combines Bitcoin market activity, adoption, transactions, and social signals.
  • Opinion polarization and exchange volume are reported to precede rising Bitcoin prices.
  • Emotional valence is reported to precede polarization and increasing exchange volume.
  • The authors use these relationships to build strategies and report profitable results.
  • The description lacks enough detail to independently assess the strategy rules or their durability.

Tags

CryptoSentimentMachine learningBacktesting
Full text
# Social signals and algorithmic trading of Bitcoin


# Social signals and algorithmic trading of Bitcoin









The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behavior offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology, and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence, and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading based social media sentiment has the potential to yield positive returns on investment.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.