The paper proposes a deep reinforcement learning (DRL) framework for portfolio management. The framework consists of an environment and an agent that interact to devise an optimal trading algorithm.
The environment provides the agent with the current state of the portfolio, which includes preprocessed asset prices, moving averages, and a correlation matrix. The agent's task is to assign weights to the assets in the portfolio to maximize the cumulative reward, which is measured by the daily returns.
The agent uses a combination of exploration (random weight assignment) and exploitation (using a neural network to predict optimal weights) to learn the optimal policy. A replay buffer is used to store past experiences, which are then used to train the neural network and update the Q-tables.
The performance of the DRL model is compared to conventional portfolio management strategies, such as minimum variance and maximum returns. The DRL model outperforms these strategies in terms of risk-adjusted returns, as measured by the Sharpe ratio.
The key insights from the paper are:
Overall, the paper demonstrates the potential of DRL in portfolio management and provides a framework for further research in this area.
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by Ashish Anil ... at arxiv.org 05-06-2024
https://arxiv.org/pdf/2405.01604.pdfDeeper Inquiries