Paper: arXiv 2304.06037

Authors: Soumyadip Sarkar

Abstract

Reinforcement learning (RL) is a subfield of machine learning that has been used in many fields, such as robotics, gaming, and autonomous systems. There has been growing interest in using RL for quantitative trading, where the goal is to make trades that generate profits in financial markets. This paper presents the use of RL for quantitative trading and reports a case study based on an RL-based trading algorithm. The results show that RL can be a useful tool for quantitative trading and can perform better than traditional trading algorithms. The use of reinforcement learning for quantitative trading is a promising area of research that can help develop more sophisticated and efficient trading systems. Future research can explore the use of other reinforcement learning techniques, the use of other data sources, and the testing of the system on a range of asset classes. Together, our work shows the potential in the use of reinforcement learning for quantitative trading and the need for further research and development in this area. By developing the sophistication and efficiency of trading systems, it may be possible to make financial markets more efficient and generate higher returns for investors.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper provides a standard high-level implementation of Deep Q-Learning applied to trading without introducing novel theoretical extensions or deep mathematical proofs. Empirically, the work remains quite basic, lacking rigorous multi-asset backtesting, transaction cost modeling, and robust statistical validation against modern benchmarks.

Research Flowchart

  flowchart TD
    A[Research Goal: Apply Reinforcement Learning to Quantitative Trading] --> B[Input Data: Financial Market Data & Multi-Asset Sources]
    B --> C[Methodology: Develop Deep Q-Learning Trading Algorithm]
    C --> D[Computational Process: Train RL Agent to Execute Optimal Trades]
    D --> E[Outcome: Outperforms Traditional Trading Algorithms]
    E --> F[Future Work: Test New RL Techniques & Asset Classes]