Paper: arXiv 2312.15730

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting through extensive financial datasets to pinpoint lucrative investment openings. AI-driven models, particularly those employing ML techniques such as deep learning and reinforcement learning, have shown great prowess in predicting market trends and executing trades at a speed and accuracy that far surpass human capabilities. Its capacity to automate critical tasks, such as discerning market conditions and executing trading strategies, has been pivotal. However, persistent challenges exist in current QT methods, especially in effectively handling noisy and high-frequency financial data. Striking a balance between exploration and exploitation poses another challenge for AI-driven trading agents. To surmount these hurdles, our proposed solution, QTNet, introduces an adaptive trading model that autonomously formulates QT strategies through an intelligent trading agent. Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model. To tackle the challenges posed by volatile financial datasets, we conceptualize the QT mechanism within the framework of a Partially Observable Markov Decision Process (POMDP). Moreover, by embedding imitative learning, the model can capitalize on traditional trading tactics, nurturing a balanced synergy between discovery and utilization. For a more realistic simulation, our trading agent undergoes training using minute-frequency data sourced from the live financial market. Experimental findings underscore the model’s proficiency in extracting robust market features and its adaptability to diverse market conditions.

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 uses advanced mathematical concepts like Partially Observable Markov Decision Processes (POMDP) and deep reinforcement learning algorithms (RDPG), indicating high mathematical complexity. It is empirically rigorous as it trains on live minute-frequency financial data, details experimental findings on profitability and generalization, and addresses real-world challenges like noise and data handling.

Research Flowchart

  flowchart TD
  Start(["Research Goal: Develop robust AI model for Quantitative Trading"]) -->|Methodology| Method["QTNet: DRL + POMDP + Imitative Learning"]
  Method -->|Input Data| Data["Minute-frequency Live Market Data"]
  Data -->|Process| POMDP["Model Market as POMDP<br/>to handle noisy/high-freq data"]
  POMDP -->|Agent Training| Train["Deep Reinforcement Learning<br/>+ Imitative Learning"]
  Train -->|Outcomes| Results["Key Findings: <br/>1. Robust Feature Extraction<br/>2. Adaptive to Market Conditions<br/>3. Balanced Exploration/Exploitation"]

Cited References (34)

  1. AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching (2023)
  2. Sub-gmn: The subgraph matching network model (2021) — link
  3. More interpretable graph similarity computation via maximum common subgraph inference (2022) — link
  4. Global-aware beam search for neural abstractive summarization (2021)
  5. RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning (2023) — link
  6. Use neural networks to recognize students’ handwritten letters and incorrect symbols (2023) — link
  7. Gated feedback recurrent neural networks (2015)
  8. Efficient reductions for imitation learning (2010)
and 26 more
  1. Deep direct reinforcement learning for financial signal representation and trading (2016)
  2. Temporal relational ranking for stock prediction (2019)
  3. Overcoming exploration in reinforcement learning with demonstrations (2018)
  4. An introduction to deep reinforcement learning (2018)
  5. Memory-based control with recurrent neural networks (2015) — link
  6. Deep q-learning from demonstrations (2018)
  7. Prioritized experience replay (2015) — link
  8. Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards (2017) — link
  9. A deep reinforcement learning framework for the financial portfolio management problem (2017) — link
  10. Estimating the days to success of campaigns in crowdfunding: A deep survival perspective (2019)
  11. Planning and acting in partially observable stochastic domains (1998)
  12. Reinforcement learning: A survey, journal of artificial intelligence research 4 (1996)
  13. A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets (2007)
  14. Tensor-based learning for predicting stock movements (2015)
  15. Model-based deep reinforcement learning for dynamic portfolio optimization (2019) — link
  16. Technical analysis of the financial markets: A comprehensive guide to trading methods and applications (1999)
  17. Continuous control with deep reinforcement learning (2015) — link
  18. Maximum drawdown (2004)
  19. Efficient capital markets: A review of theory and empirical work (1970)
  20. Human-level control through deep reinforcement learning (2015)
  21. Reinforcement learning for trading (1998)
  22. Reinforcement learning: An introduction (2018)
  23. Policy gradient methods for reinforcement learning with function approximation (1999)
  24. A sequential approach to market state modeling and analysis in online p2p lending (2017)
  25. Tracking the dynamics in crowdfunding (2017)
  26. Optimal asset allocation using adaptive dynamic programming (1995)

0 of 34 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.