Deep Reinforcement Learning for Quantitative Trading

ArXiv ID: 2312.15730 “View on arXiv”

Authors: Unknown

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.

Keywords: deep reinforcement learning, POMDP, imitative learning, high-frequency trading, QTNet, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: 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.

  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"]