Paper: arXiv 2402.03338

Abstract

In prior methods, it was observed that the application of Convolutional Neural Networks agent in Deep Reinforcement Learning to financial data resulted in an enhanced reward. In this study, a specific permutation was applied to the feature vector, thereby generating a CNN matrix that strategically positions more pertinent features in close proximity. Our comprehensive experimental evaluations unequivocally demonstrate a substantial enhancement in reward attainment.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard DRL and CNN concepts without heavy derivations or novel math, keeping complexity moderate, while it employs a concrete financial environment, specific features, and backtest-ready metrics like Sharpe ratio and reward tracking, indicating solid empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance DRL Reward in Financial Markets"] --> B["Data Input: Financial Time Series Data"]
  B --> C["Feature Engineering: Apply Strategic Permutation to Features"]
  C --> D["CNN-DRL Processing: Convolutional Neural Network on Permuted Data"]
  D --> E["Computational Process: Deep Reinforcement Learning Agent"]
  E --> F["Outcome: Substantial Enhancement in Reward Attainment"]

Cited References (11)

  1. FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning (2022)
  2. ABIDES-gym: gym environments for multi-agent discrete event simulation and application to financial markets (2021)
  3. Towards a fully RL-based Market Simulator (2021)
  4. Towards Realistic Market Simulations: a Generative Adversarial Networks Approach. arXiv e-prints, page (2021) — link
  5. Practical deep reinforcement learning approach for stock trading (2018) — link
  6. Proximal Policy Optimization Algorithms (2017) — link
  7. Deep reinforcement learning for automated stock trading: An ensemble strategy (2020)
  8. Deep reinforcement learning for trading (2019) — link
and 3 more
  1. FinRL-Podracer: High performance and scalable deep reinforcement learning for quantitative finance (2021)
  2. Wharton — link
  3. Scalable Actions in CNN-based Deep Reinforcement Learning in Finance (2023)

0 of 11 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.