ASSESSING THE PERFORMANCE OF AN INTER-MARKET TRADING STRATEGY IN THE LOW AND HIGH FREQUENCY DOMAIN BASED ON HISTORIC DATA USING MACHINE LEARNING

深度学习概念的一个有趣的应用和扩展应该在博士研究中率先应用于高频订单数据，即使用机器学习工具来推导高频交易机制。根据我在算法交易硕士课程中的研究，我评估了NVWAP（名义成交量加权平均价格）曲线由四个统计量控制，即陡峭/平坦和收缩/扩张。NVWAP曲线的收缩/扩张是通过计算限价订单的买盘和卖盘两侧的总成交量变化来量化的。这个概念应该作为输入，并通过机器学习工具进一步发展，以得出自动化交易系统在实践中是否可以盈利。为了做到这一点，数据应该被分成不同的块，作为深度神经网络的输入。然后将其发现应用于实时市场交易，并评估是否可以实现盈利操作。为了了解市场间的关系，可以同时研究两个或多个相关资产，以得出交易决策。与金融和投资的概念相比，机器学习的科学是相当新的。鉴于记录和可访问的数据量以及现代计算技术的最新发展，有必要寻找这两个主题之间的协同作用，并扫描数据以寻找以前对市场参与者来说并不明显的模式。显然，这是一项具有挑战性的任务，因为大型投资银行等强大的参与者，以及机构和专业投资者都希望做到这一点。将先进计算工具与金融相结合的最新重大进展进一步推动了这一进程。机器学习技术没有魔法；然而，它能够从过去的数据中学习模式，以便在未来应用它的发现。根据机器学习的一般定义，机器学习是一个计算机程序从经验E中学习一些任务T和性能指标P，如果它在T中的任务中的表现，如P所衡量的，随着经验E的提高，该任务将被调查。机器学习中最新的工具之一是深度神经网络（DNN）。深度神经网络（deep neural network， DNN）是一种人工神经网络，在输入和输出层之间有多个隐藏层的单元。例如，可以使用超过1.2亿次优质和次级抵押贷款的数据集构建深度神经网络来建模抵押贷款拖欠和提前还款风险，并模拟抵押贷款组合以进行风险分析。甚至有人发现，一些经典的金融理论，如有效市场假说，可能会受到深度学习的挑战。另一个动机是应用深度学习来发现尚未在市场上执行的交易策略。为此，历史数据被分成两个组成部分。首先，80%的历史数据被定义为测试数据，即调查市场结构的数据。剩下的20%是训练数据，即神经网络以前从未见过的数据。换句话说，80%的数据的学习成果现在被应用到剩下的20%的数据中，结果是一些金融工具确实可以进行有利可图的交易。

An interesting application and expansion of the deep learning concepts should be pioneered in PhD research by applying them to high frequency order book data, namely to use machine learning tools to derive a high frequency trading mechanism. As researched in coursework during my MSc in Algorithmic Trading, I evaluated that NVWAP (Notional Volume Weighted Average Price) curves are governed by four statistics, namely steepening/flattening and contraction/expansion. Contraction/expansion of NVWAP curves is quantified by computing the change in total volume on both, the bid and ask-side of the limit order book. This concept should be taken as input and further developed by machine learning tools to derive whether an automated trading system could operate profitably in practice. To do this, the data is supposed to be separated in various chunks serving as input for the deep neural net. Its findings should then be applied on trading real time markets and evaluated whether a profitable operation is achievable. To appreciate inter-market relations, two or more correlated assets can be investigated simultaneously to derive trading decisions. The science of machine learning is quite new compared to the concepts of finance and investments. Given recent developments in terms of the amount of data recorded and being accessible, and modern computing technology, it is worthwhile to search for synergy between the two subjects and scan the data for patterns that have not been obvious for market participants before. Clearly, this is a challenging task since powerful players such as large investment banks, but also institutional and professional investors aim at doing the same. Recent and major advances in combining advanced computational tools and finance further motivate to do so. There is no magic in machine learning technique; however, it is capable of learning patterns of data from the past to apply its findings in future. Grounded on the general definition of machine learning to be a computer program learning from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E, the task will be investigated. One of the most recent tools in machine learning are deep neural nets (DNN). A deep neural network (DNN) is an artificial neural network with multiple hidden layers of units between the input and output layers. For example, one can built deep neural networks for modeling mortgage delinquency and prepayment risk using a dataset of over 120 million prime and subprime mortgages and simulate mortgage 17 portfolios for risk analysis purposes. It was even found that some of the classical theory in finance such as the efficient market hypothesis might be challenged by deep learning. Yet another motivation is to apply deep learning to discover trading strategies not yet executed in the markets. To do so, the historic data was split up into two constituent parts. Firstly, 80% of the historic data were defined to be test data, i.e. data on which the market structure was investigated. The remaining 20% were training data, i.e. data the neural net has not seen before. In other words, the learning outcomes of 80% of the data were now applied to the remaining 20% of the data with the result that several financial instruments could indeed be profitably traded.