Paper: arXiv 2508.20467

Authors: Xiangdong Liu, Jiahao Chen

Abstract

In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empirical rules often fail to adapt to dynamic market changes and black swan events due to rigid assumptions and limited generalization. To address these issues, this paper proposes QTMRL (Quantitative Trading Multi-Indicator Reinforcement Learning), an intelligent trading agent combining multi-dimensional technical indicators with reinforcement learning (RL) for adaptive and stable portfolio management. We first construct a comprehensive multi-indicator dataset using 23 years of S&P 500 daily OHLCV data (2000-2022) for 16 representative stocks across 5 sectors, enriching raw data with trend, volatility, and momentum indicators to capture holistic market dynamics. Then we design a lightweight RL framework based on the Advantage Actor-Critic (A2C) algorithm, including data processing, A2C algorithm, and trading agent modules to support policy learning and actionable trading decisions. Extensive experiments compare QTMRL with 9 baselines (e.g., ARIMA, LSTM, moving average strategies) across diverse market regimes, verifying its superiority in profitability, risk adjustment, and downside risk control. The code of QTMRL is publicly available at https://github.com/ChenJiahaoJNU/QTMRL.git

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs only partially modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics including Markov Decision Processes (MDPs), policy gradient methods, and loss functions (Ltotal = Lpolicy + cv·Lvalue + ce·Lentropy), while demonstrating high empirical rigor through a public codebase, backtesting on 23 years of S&P 500 data across 16 stocks, and comparative analysis against 9 baselines across market regimes.

Research Flowchart

  flowchart TD
  A["Research Goal: Adaptive Trading Agent<br>for Volatile Markets"] --> B["Methodology: QTMRL Framework"]
  B --> C["Data: 23 Yrs S&P 500 OHLCV +<br>Multi-Indicators Trend/Vol/Momentum"]
  C --> D["Process: A2C Reinforcement Learning<br>Actor-Critic Architecture"]
  D --> E["Outcome: Superior Performance vs 9 Baselines<br>Higher Profit & Better Risk Control"]

Cited References (22)

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  8. From deep learning to LLMs: a survey of AI in quantitative investment — → 2503.21422
and 14 more
  1. Quantformer: from attention to profit with a quantitative transformer trading strategy — → 2404.00424
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  3. Reinforcement Learning for MDPs with Constraints (2006)
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  6. Sample Efficient Reinforcement Learning with REINFORCE (2021)
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  8. Asynchronous Methods for Deep Reinforcement Learning (2016)
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4 of 22 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.