We use a deep neural network to generate controllers for optimal trading on high frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that in intraday trading, trader's actions influence price dynamics in closed loop via the market impact. The exploration--exploitation tradeoff generated by the efficient execution is addressed by tuning the trader's preferences to ensure long enough trajectories are produced during the learning phase. The issue of scarcity of financial data is solved by transfer learning: the neural network is first trained on trajectories generated thanks to a Monte-Carlo scheme, leading to a good initialization before training on historical trajectories. Moreover, to answer to genuine requests of financial regulators on the explainability of machine learning generated controls, we project the obtained "blackbox controls" on the space usually spanned by the closed-form solution of the stylized optimal trading problem, leading to a transparent structure. For more realistic loss functions that have no closed-form solution, we show that the average distance between the generated controls and their explainable version remains small. This opens the door to the acceptance of ML-generated controls by financial regulators.

中文翻译：

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学习高频金融的功能控制

我们使用深度神经网络生成控制器，以优化高频数据交易。神经网络第一次学习了交易者偏好（即风险规避参数）与最佳控制之间的映射。学习这种映射的一个重要挑战是，在日内交易中，交易者的行为通过市场影响在闭环中影响价格动态。通过调整交易者的偏好以确保在学习阶段产生足够长的轨迹，可以解决由有效执行产生的探索 - 开发权衡。通过迁移学习解决了金融数据稀缺的问题：神经网络首先在蒙特卡罗方案生成的轨迹上进行训练，从而在对历史轨迹进行训练之前进行良好的初始化。此外，为了满足金融监管机构对机器学习生成控制的可解释性的真正要求，我们将获得的“黑箱控制”投射到通常由程式化最优交易问题的封闭形式解决方案跨越的空间上，从而形成一个透明的结构. 对于没有封闭形式解决方案的更现实的损失函数，我们表明生成的控件与其可解释版本之间的平均距离仍然很小。这为金融监管机构接受 ML 生成的控制打开了大门。导致一个透明的结构。对于没有封闭形式解决方案的更现实的损失函数，我们表明生成的控件与其可解释版本之间的平均距离仍然很小。这为金融监管机构接受 ML 生成的控制打开了大门。导致一个透明的结构。对于没有封闭形式解决方案的更现实的损失函数，我们表明生成的控件与其可解释版本之间的平均距离仍然很小。这为金融监管机构接受 ML 生成的控制打开了大门。