Deep Reinforcement Learning for Adaptive Stock Trading

Summary

The study explores deep reinforcement learning as a way to develop an adaptive trading strategy for a portfolio of 30 stocks. It uses daily prices as the training data and trading environment, then compares the agent with the Dow Jones Industrial Average and a traditional minimum-variance portfolio allocation strategy. The stated objectives are to optimize trading decisions and investment returns.

The authors report that their agent outperforms both comparison approaches on Sharpe ratio and cumulative returns. The excerpt does not specify the training and evaluation dates, transaction costs, risk constraints, reward design, or whether the evaluation avoids look-ahead bias. It also provides no numerical results, so the reported advantage cannot be assessed for size or robustness from this description alone. The findings are evidence for the proposed experiment, not proof that the approach will generalize to other stocks or market conditions.

Key ideas

• A deep reinforcement learning agent is trained to trade a basket of 30 stocks using daily prices.
• The learned strategy is intended to adapt to a complex and changing stock market.
• The study compares the agent with the Dow Jones Industrial Average and minimum-variance allocation.
• The authors report higher Sharpe ratio and cumulative returns for the agent than for both baselines.
• The excerpt omits evaluation details such as costs, dates, reward design, and numerical performance.

Tags

# Practical Deep Reinforcement Learning Approach for Stock Trading


# Practical Deep Reinforcement Learning Approach for Stock Trading









Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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