Machine Learning Methods In Algorithmic Trading

在交易业务的新趋势的背景下，本论文的目的是有助于开发自动交易算法，用于获取明确的交易信号;在具有流动性基础资产SP 500，DAX 30和德国国债的期货产品中的多头或空头（分别为买入或卖出）头寸。该算法是基于两个当代的分类机器学习技术：SVM（支持向量机）前馈多层感知器（MLP）神经网络（NN）上述方法的参数优化使用启发式方法称为差分进化（DE），这是一种有前途的进化算法，能够产生一个优化的分类方法更快，更准确比经典的网格搜索。原始高频数据，也称为日内数据，用于生成规范化的技术指标，将被用作我们的算法中的数据集。定义的数据集包括使用Python作为适当的编程语言来训练和优化（通过DE）SVM和NN分类方法。使用样本数据来计算利润和损失结果，从而评估算法输出的准确性，比较和对比每种方法的优点和缺点。

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.