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Learning the Market: Sentiment-Based Ensemble Trading Agents

Author

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

Abstract

We propose the integration of sentiment analysis and deep-reinforcement learning ensemble algorithms for stock trading, and design a strategy capable of dynamically altering its employed agent given concurrent market sentiment. In particular, we create a simple-yet-effective method for extracting news sentiment and combine this with general improvements upon existing works, resulting in automated trading agents that effectively consider 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 determine that the conventional practice of switching ensemble agents every fixed-number of months is sub-optimal, and that a dynamic sentiment-based framework greatly unlocks additional performance within these agents. 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 should be relatively simple.

Suggested Citation

  • Andrew Ye & James Xu & Yi Wang & Yifan Yu & Daniel Yan & Ryan Chen & Bosheng Dong & Vipin Chaudhary & Shuai Xu, 2024. "Learning the Market: Sentiment-Based Ensemble Trading Agents," Papers 2402.01441, arXiv.org.
    • 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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