Paper: arXiv 2411.08637

Abstract

The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. However, the low signal-to-noise ratio in financial markets results in noisy estimates of environment components, including the reward function, which hinders effective policy learning by RL agents. Given the critical importance of reward function design in RL problems, this paper introduces a novel and more robust reward function by leveraging imitation learning, where a trend labeling algorithm acts as an expert. We integrate imitation (expert’s) feedback with reinforcement (agent’s) feedback in a model-free RL algorithm, effectively embedding the imitation learning problem within the RL paradigm to handle the stochasticity of reward signals. Empirical results demonstrate that this novel approach improves financial performance metrics compared to traditional benchmarks and RL agents trained solely using reinforcement feedback.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper integrates advanced RL/IL concepts with formal MDP definitions and algorithmic modifications, reflecting high math complexity. Empirical results on intraday futures data and risk-adjusted metrics show backtest-ready implementation and data-heavy validation.

Research Flowchart

  flowchart TD
  A["Research Goal: <br>Robust RL for <br>Noisy Financial Markets"] --> B["Methodology: <br>Imitation-Enhanced Reward <br>(Expert + RL Feedback)"]
  B --> C["Input: <br>Asset Trading Data <br>(Equities)"]
  C --> D["Computational Process: <br>Model-Free RL Algorithm <br>using Hybrid Reward Signal"]
  D --> E["Outcome: <br>Improved Financial Metrics <br>vs. Benchmarks"]

Cited References (41)

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and 33 more
  1. Adaptive quantitative trading: An imitative deep reinforcement learning approach (2020)
  2. Imitation Learning for Financial Applications (2023)
  3. Proximal policy optimization algorithms (2017)
  4. Deep reinforcement learning for trading (2020) — link
  5. Deep reinforcement learning for automated stock trading: An ensemble strategy (2020)
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  31. Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision (2023)
  32. Goal-conditioned imitation learning (2019)
  33. Deep reinforcement learning with positional context for intraday trading — → 2406.08013

1 of 41 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.