Reinforcement Learning for Quantitative Trading

Sun, Shuo; Wang, Rundong; An, Bo

doi:10.48550/arxiv.2109.13851

Cited by 2 publications

(2 citation statements)

References 91 publications

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“…• Time in the market. Time in the market means the times when the trader has the asset (Sun et al, 2021).…”

Section: • Maximum Drawdown(mdd)mentioning

confidence: 99%

Algorithmic trading on financial time series using Deep Reinforcement Learning

Asghari,

Mozayani

2024

Preprint

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The use of technology in financial markets has led to extensive changes in conventional trading structures.Today, most orders that reach exchanges are created by algorithmic trading agents. Today, machine learning-based methods play an important role in building automated trading systems. The increasing complexity and dynamism of financial markets are among the key challenges of these methods. The most widely used machine learning approach is supervised learning, but in interactive environments, the use of supervised learning alone has limitations such as difficulty in defining appropriate labels and lack of modeling of the dynamic nature of the market. Due to the good performance of deep reinforcement learning-based approaches, we will use these approaches to solve the mentioned problems. In this paper, we presented a deep reinforcement learning framework for trading in the financial market, a set of input features and indicators selected and tailored to the purpose of the problem, reward function, appropriate models based on fully connected, convolutional and hybrid networks. The proposed top models traded under real market conditions such as transaction costs and then were evaluated. In addition to outperforming the buy and hold strategy, these models achieved excellent cumulative returns while having appropriate risk metrics.

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“…• Time in the market. Time in the market means the times when the trader has the asset (Sun et al, 2021).…”

Section: • Maximum Drawdown(mdd)mentioning

confidence: 99%

Algorithmic trading on financial time series using Deep Reinforcement Learning

Asghari,

Mozayani

2024

Preprint

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“…Reinforcement Learning in Portfolio Management. Compared to the supervised learning methods, RL provides a seamless and flexible framework for portfolio management [43]. With different risk appetite, previous RL-based portfolio management algorithms adopt various reward functions including the Sharpe ratio [49], the maximum drawdown [1,51], and the total profits [21,50,54].…”

Section: Related Workmentioning

confidence: 99%

MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

Niu,

Li,

2022

Preprint

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Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequential decision making. However, due to the non-stationary nature of financial markets, applying RL techniques to portfolio optimization remains a challenging problem. Extracting trading knowledge from various expert strategies could be helpful for agents to accommodate the changing markets. In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. In the first stage, MetaTrader incorporates an imitation learning objective into the reinforcement learning framework. Through imitating different expert demonstrations, MetaTrader acquires a set of trading policies with great diversity. In the second stage, MetaTrader learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow. We evaluate the proposed approach on three real-world index datasets and compare it to state-of-the-art baselines. The empirical results demonstrate that MetaTrader significantly outperforms those baselines in balancing profits and risks. Furthermore, thorough ablation studies validate the effectiveness of the components in the proposed approach. CCS CONCEPTS• Computing methodologies → Reinforcement learning; • Applied computing → Economics.

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Reinforcement Learning for Quantitative Trading

Cited by 2 publications

References 91 publications

Algorithmic trading on financial time series using Deep Reinforcement Learning

Algorithmic trading on financial time series using Deep Reinforcement Learning

MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

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