Machine Learning Methods In Algorithmic Trading

在交易业务新趋势的背景下，本论文的目的是帮助开发自动交易算法，以获取明确的交易信号；具有流动性标的资产SP500、DAX 30和德国国债的期货产品的多头或空头头寸(分别为买入或卖出)。这些算法是基于两种当前的分类机器学习技术：支持向量机(支持向量机)前馈多层感知器(MLP)神经网络(NN)。上述方法的参数使用一种称为差异进化(DE)的启发式方法进行优化，这是一种很有前途的进化算法，能够产生比经典网格搜索更快和更准确的优化分类方法。定义的数据集包括训练和优化(通过DE)支持向量机和神经网络分类方法，使用Python语言作为适当的编程语言。使用输出的样本数据来计算损益结果，从而评估算法输出的准确性，比较和对比每种方法的优缺点。

In the context of the new trends in trading business, the aim of this thesis is to contribute in developing automated trading algorithms for acquiring explicit trading signals; Long or Short (Buy or Sell respectively) positions in Futures products with liquid underlying assets SP500, DAX 30 and German Bunds. The algorithms are based on two contemporary classification Machine Learning techniques:SVM (Support Vector Machines)Feed Forward Multilayer Perceptron (MLP) Neural Network (NN) The parameters of the above methods are optimized using a heuristic method known as Differential Evolution (DE), which is a promising evolutionary algorithm able to produce an optimized classification method faster and more accurate than a classical grid search.Raw high frequency data, also known as intraday data, are employed with the aim to generate normalised technical indicators which will be used as data set in our algorithms. The defined data set comprises to train and optimise (via DE) the SVM and NN classification methods, using Python as the appropriate programme language.Out of sample data are used in order to calculate the profit and loss outcome and hence to evaluate the accuracy of the algorithmic outputs, comparing and contrasting the advantages and disadvantages of each method.