论文标题
政策梯度库存gan用于金融市场中的现实离散订单数据生成
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets
论文作者
论文摘要
这项研究提出了一个新的生成对抗网络（GAN），用于在金融市场中产生现实的订单。在以前的一些作品中，金融市场的甘恩斯（Gans for Ginancial Markets）由于GAN Architectures的学习限制而在连续空间中产生了假订单。但是，实际上，订单是离散的，例如具有最低订单价格单位或订单类型的订单价格。因此，我们更改了将生成的假订单放入本研究中离散空间的生成方法。由于这一变化使普通的GAN学习算法削弱了，因此本研究采用了一种政策梯度，经常用于增强学习，用于学习算法。通过我们的实验，我们表明我们提出的模型在生成的顺序分布中优于先前的模型。作为引入政策梯度的另一个好处，生成的策略的熵可用于检查GAN的学习状态。将来，可以解决较高的性能，更好的评估方法或我们gan的应用。
This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete, such as order prices, which has minimum order price unit, or order types. Thus, we change the generation method to place the generated fake orders into discrete spaces in this study. Because this change disabled the ordinary GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN's learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed.
