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Using Reddit Sentiment Agents for High-Frequency Crypto Trading

Article arXiv papers · Author: Qiuhan Han et al.
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Summary

PulseReddit aligns large-scale Reddit discussions with high-frequency cryptocurrency market statistics to study whether social information can support short-term trading. The paper evaluates LLM-based multi-agent systems that incorporate this data and compares their performance with traditional baselines.

The reported experiments find better trading outcomes for sentiment-augmented systems, especially in bull markets, and describe adaptation across market regimes. The study also examines trade-offs between model performance and efficiency to inform model choice. The supplied description does not specify the dataset construction details, assets, evaluation periods, costs, or numerical results, so the claims cannot be assessed more precisely from this text alone.

Key ideas

  • The dataset pairs Reddit discussion data with high-frequency cryptocurrency market statistics.
  • LLM-based multi-agent systems use the social data to study short-term trading performance.
  • The reported advantage is strongest in bull markets, with adaptability also examined across regimes.
  • Model selection involves a trade-off between trading performance and computational efficiency.

Tags

CryptoHigh-frequency tradingSentimentMachine learning
Full text
# PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading


# PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading









High-Frequency Trading (HFT) is pivotal in cryptocurrency markets, demanding rapid decision-making. Social media platforms like Reddit offer valuable, yet underexplored, information for such high-frequency, short-term trading. This paper introduces \textbf{PulseReddit}, a novel dataset that is the first to align large-scale Reddit discussion data with high-frequency cryptocurrency market statistics for short-term trading analysis. We conduct an extensive empirical study using Large Language Model (LLM)-based Multi-Agent Systems (MAS) to investigate the impact of social sentiment from PulseReddit on trading performance. Our experiments conclude that MAS augmented with PulseReddit data achieve superior trading outcomes compared to traditional baselines, particularly in bull markets, and demonstrate robust adaptability across different market regimes. Furthermore, our research provides conclusive insights into the performance-efficiency trade-offs of different LLMs, detailing significant considerations for practical model selection in HFT applications. PulseReddit and our findings establish a foundation for advanced MAS research in HFT, demonstrating the tangible benefits of integrating social media.

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.