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Learning the Market: Sentiment-Based Ensemble Trading Agents

Author

Listed:
  • Andrew Ye
  • James Xu
  • Vidyut Veedgav
  • Yi Wang
  • Yifan Yu
  • Daniel Yan
  • Ryan Chen
  • Vipin Chaudhary
  • Shuai Xu

Abstract

We propose and study the integration of sentiment analysis and deep reinforcement learning ensemble algorithms for stock trading by evaluating strategies capable of dynamically altering their active agent given the concurrent market environment. In particular, we design a simple-yet-effective method for extracting financial sentiment and combine this with improvements on existing trading agents, resulting in a strategy that effectively considers both qualitative market factors and quantitative stock data. We show that our approach results in a strategy that is profitable, robust, and risk-minimal - outperforming the traditional ensemble strategy as well as single agent algorithms and market metrics. Our findings suggest that the conventional practice of switching and reevaluating agents in ensemble every fixed-number of months is sub-optimal, and that a dynamic sentiment-based framework greatly unlocks additional performance. Furthermore, as we have designed our algorithm with simplicity and efficiency in mind, we hypothesize that the transition of our method from historical evaluation towards real-time trading with live data to be relatively simple.

Suggested Citation

  • Andrew Ye & James Xu & Vidyut Veedgav & Yi Wang & Yifan Yu & Daniel Yan & Ryan Chen & Vipin Chaudhary & Shuai Xu, 2024. "Learning the Market: Sentiment-Based Ensemble Trading Agents," Papers 2402.01441, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2402.01441
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    References listed on IDEAS

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    1. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
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