深度确定性投资组合优化

Ayman Chaouki , Stephen Hardiman , Christian Schmidt , Emmanuel Sérié , Joachim de Lataillade
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引用次数: 12

摘要

深度强化学习算法可以作为最优交易策略的求解器吗?这项工作的目的是在概念上简单,但数学上不平凡的交易环境中测试强化学习算法。环境的选择使得最优或接近最优的交易策略是已知的。我们研究了深度确定性策略梯度算法,并证明了这种强化学习智能体可以成功地恢复最优交易策略的本质特征并获得接近最优的奖励。
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Deep deterministic portfolio optimization

Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.

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来源期刊
Journal of Finance and Data Science
Journal of Finance and Data Science Mathematics-Statistics and Probability
CiteScore
3.90
自引率
0.00%
发文量
15
审稿时长
30 days
期刊最新文献
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