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.

深度学习概念的有趣应用和扩展应该在博士研究中开创，将其应用于高频订单簿数据，即使用机器学习工具推导高频交易机制。正如我在算法交易硕士期间的课程研究中所研究的那样，我评估了 NVWAP（名义交易量加权平均价格）曲线由四种统计数据控制，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簿的买盘和卖盘总量的变化来量化的。这个概念应该被机器学习工具作为输入并进一步开发，以得出自动交易系统在实践中是否可以盈利。为此，数据应该被分成不同的块，作为深度神经网络的输入。然后，其研究结果应应用于实时市场交易，并评估是否可以实现盈利。为了了解市场间关系，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，机器学习科学是相当新的。考虑到最近在记录的数据量和可访问性以及现代计算技术方面的发展，寻找两个主题之间的协同作用并扫描数据以寻找市场参与者以前不明显的模式是值得的。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参与者以及机构和专业投资者也致力于这样做。先进计算工具和金融相结合的最新重大进展进一步推动了这一点。机器学习技术并没有什么神奇之处；然而，它能够学习过去的数据模式，以便在未来应用其发现。基于机器学习的一般定义，即计算机程序从关于某类任务 T 和性能测量 P 的经验 E 中学习，如果其在 T 中任务的性能（按 P 测量）随着经验 E 的增加而提高，则该任务将被调查。机器学习的最新工具之一是深度神经网络（DNN）。深度神经网络 (DNN) 是一种人工神经网络，在输入层和输出层之间具有多个隐藏单元层。例如，人们可以使用超过 1.2 亿个优质和次级抵押贷款的数据集构建深度神经网络，用于对抵押贷款拖欠和提前还款风险进行建模，并模拟抵押贷款 17 投资组合以进行风险分析。甚至发现金融领域的一些经典理论，例如有效市场假说，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市场上尚未执行的交易策略。为此，历史数据被分为两个组成部分。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，即神经网络以前没有见过的数据。换句话说，80% 数据的学习成果现在被应用到剩下的 20% 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。