Learning the Market: Sentiment-Based Ensemble Trading Agents

ArXiv ID: 2402.01441 “View on arXiv”

Authors: Unknown

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.

Keywords: Sentiment Analysis, Deep Reinforcement Learning, Stock Trading, Ensemble Algorithms, Dynamic Agent Selection

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs deep reinforcement learning algorithms like DDPG and A2C, which involve advanced mathematical concepts and neural network architectures, but lacks visible code, detailed datasets, or extensive statistical metrics in the provided excerpt, indicating a focus on theoretical modeling over implementation-heavy validation.

  flowchart TD
    A["Research Goal:<br/>Integrate Sentiment & DRL for Profitable/Robust Trading"] --> B["Data Acquisition & Preprocessing<br/>Historical Stock Data + Financial News Text"]
    B --> C["Methodology - Key Components<br/>Sentiment Analysis Module<br/>DRL Trading Agents"]
    C --> D["Computational Process:<br/>Dynamic Ensemble Framework<br/>- Extract Real-Time Sentiment<br/>- Select Best Agent Based on Sentiment"]
    D --> E["Backtesting & Evaluation<br/>vs. Fixed-Ensemble, Single Agents, & Market Metrics"]
    E --> F["Key Outcomes & Findings<br/>1. Higher Profitability & Lower Risk<br/>2. Dynamic Selection beats Fixed Scheduling<br/>3. Framework is Simplicity-Efficient for Live Trading"]