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

使用机器学习根据历史数据评估低频和高频领域间市场交易策略的表现

• 批准号: 2118751
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2118751
• 关键词: ASSESSING; PERFORMANCE; INTER; MARKET; TRADING

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.

在博士研究中，应将其应用于高频订单数据数据，即使用机器学习工具来得出高频交易机制，将它们应用于高频订单数据数据中，应在博士学位研究中率先提出一个有趣的应用和扩展。正如我在算法交易硕士学位期间的课程中所研究的那样，我评估了NVWAP（概念量加权平均价格）曲线受四个统计数据的控制，即陡峭/扁平/平坦和收缩/扩张。 NVWAP曲线的收缩/扩展是通过计算限制顺序簿的出价和问答侧的总量的变化来量化的。该概念应作为输入，并由机器学习工具进一步开发，以得出自动交易系统是否可以在实践中有利可图。为此，数据应该在各种块中分开，以作为深神经网的输入。然后，它的发现应应用于实时市场交易，并评估是否可以实现盈利运营。为了欣赏市场间关系，可以同时研究两个或更多相关的资产以提出交易决策。与金融和投资的概念相比，机器学习的科学非常新。鉴于在记录和可访问的数据量以及现代计算技术方面的最新发展，值得在这两个主题之间搜索协同作用，并扫描数据以了解以前对市场参与者而言并不明显的模式。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，但机构和专业投资者的目标是这样做。结合高级计算工具和资金的最新和重大进展进一步促使这样做。机器学习技术没有魔术。但是，它能够学习过去的数据模式，以便将来应用其发现。基于机器学习的一般定义是从经验E中学习的计算机程序，相对于某些类别的任务T和绩效测量P，如果按照P的衡量，则可以通过经验E来改进其在T中的任务的性能，将研究该任务。机器学习的最新工具之一是深神经网（DNN）。深度神经网络（DNN）是一个人工神经网络，在输入层和输出层之间具有多个隐藏的单元。例如，人们可以使用超过1.2亿主要和次级抵押贷款的数据集建立深层神经网络来对抵押贷款犯罪和预付风险进行建模，并模拟抵押贷款17投资组合以进行风险分析。甚至还发现，财务中的某些经典理论，例如有效的市场假设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市场尚未执行的交易策略。为此，历史数据分为两个组成部分。首先，将80％的历史数据定义为测试数据，即研究市场结构的数据。其余的20％是培训数据，即神经网以前从未见过的数据。换句话说，现在80％的数据的学习成果已应用于其余20％的数据，结果是几种金融工具确实可以得到盈利。