论文标题
通过深度学习级别2限制订单簿数据，自动创建高性能算法交易者
Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data
论文作者
论文摘要
我们提出结果表明，适当配置的深度学习神经网络（DLNN）可以自动学会成为一种高性能的算法交易系统，纯粹是由被动观察到现有成功交易者T的被动观察产生的培训数据输入。系统称为DeepTrader，采用了从2级市场数据中得出的输入，即市场的限制订单簿（LOB）或梯子，用于可交易的资产。不寻常的是，DeepTrader不明确预测未来价格。取而代之的是，我们纯粹是在输入配对上训练它的，其中在每对中，输入是2级LOB数据的快照，当时T向市场发出报价Q（即出价或询问订单）时获取； DeepTrader所需的输出是在显示S时产生Q。也就是说，我们通过显示t发出的报价Q时看到的LOB数据来训练我们的DLNN，并且在这样做时，我们的系统表现得像t一样，用作算法交易者，以对特定的LOB条件发出特定的报价。我们对大量S/Q快照/报价对进行培训，然后在各种市场场景中对其进行测试，对公共域文献中其他算法交易系统进行评估，其中包括两种反复证明以胜过人类贸易商。我们的结果表明，DeepTrader学会了匹配或胜过此类现有算法交易系统。我们分析了成功的DeepTrader网络，以确定其依赖于哪些功能，并且可以忽略哪些功能。我们建议我们的方法原则上可以通过“黑框”深度学习方法创建一个可解释的副本。
We present results demonstrating that an appropriately configured deep learning neural network (DLNN) can automatically learn to be a high-performing algorithmic trading system, operating purely from training-data inputs generated by passive observation of an existing successful trader T. That is, we can point our black-box DLNN system at trader T and successfully have it learn from T's trading activity, such that it trades at least as well as T. Our system, called DeepTrader, takes inputs derived from Level-2 market data, i.e. the market's Limit Order Book (LOB) or Ladder for a tradeable asset. Unusually, DeepTrader makes no explicit prediction of future prices. Instead, we train it purely on input-output pairs where in each pair the input is a snapshot S of Level-2 LOB data taken at the time when T issued a quote Q (i.e. a bid or an ask order) to the market; and DeepTrader's desired output is to produce Q when it is shown S. That is, we train our DLNN by showing it the LOB data S that T saw at the time when T issued quote Q, and in doing so our system comes to behave like T, acting as an algorithmic trader issuing specific quotes in response to specific LOB conditions. We train DeepTrader on large numbers of these S/Q snapshot/quote pairs, and then test it in a variety of market scenarios, evaluating it against other algorithmic trading systems in the public-domain literature, including two that have repeatedly been shown to outperform human traders. Our results demonstrate that DeepTrader learns to match or outperform such existing algorithmic trading systems. We analyse the successful DeepTrader network to identify what features it is relying on, and which features can be ignored. We propose that our methods can in principle create an explainable copy of an arbitrary trader T via "black-box" deep learning methods.
