Sequential Stock Trading with Continuous Deep Q Learning
首发时间:2017-12-07
Abstract:This paper proposes limit order is a more intelligent and profitable way to trade stock. When a bad market order is executed, trader will encounter certain loss since the bad decision makes trader stuck in bad price position. A Limit order is superior to market order in such way that it always give the trader a better price position. We use a customized deep continuous Q learning algorithm to pricing limit order and trade stocks in discrete time steps. Experiments on NSC market data show our strategy is better than market order strategy and our algorithm is more suitable for our problem.
keywords: Computer Science and Technology , deep reinforcement learning , stock trading strategy
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基于连续深度强化学习的股票序列交易模型
摘要:本文提出使用限价交易单相比其他股票交易形式能获得更多的利益。如果一个失败的市价订单被执行,交易者必然会承受其带来的损失。限价交易单的执行则总会为交易者带来一定的收益。本文提出使用一个DQN网络为限价单定价,在分钟级别进行股票的交易。我们在印度股票市场的历史数据上进行了模拟实验,结果显示我们的方法比其他的方法更优。
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