基于演化图表模式敏感神经网络的外汇交易代理
Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents
摘要：
虽然机器学习已经应用于外汇市场的算法交易已经有一段时间了，并且神经网络（NN）已经显示出产生积极的结果，但在大多数现代方法中，NN系统通过传统方法进行优化，例如反向传播算法，其输入信号是价格列表和由其他技术指标元素组成的列表。本文的目的是双重的：拓扑和权重演化人工神经网络（TWEANN）系统的应用程序的演示和测试自动货币交易，并演示使用外汇图表图像作为几何规律感知间接编码神经网络系统的输入时的性能，使他们能够在交易时使用其中的模式和趋势。本文提出了基于神经网络的自动货币交易系统的基准测试结果，使用TWEANN，并比较这些直接编码的NN的性能和泛化能力，使用标准的基于滑动窗口的价格向量输入，和间接（基板）编码的NN使用图表作为输入。我将在本文中使用的TWEANN算法来进化这些货币交易代理是基于模因算法的TWEANN系统，称为Deus Ex神经网络（DXNN）平台。
英文摘要：
Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical indicator elements. The aim of this paper is twofold: the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and to demonstrate the performance when using Forex chart images as input to geometrical regularity aware indirectly encoded neural network systems, enabling them to use the patterns & trends within, when trading. This paper presents the benchmark results of NN based automated currency trading systems evolved using TWEANNs, and compares the performance and generalization capabilities of these direct encoded NNs which use the standard sliding-window based price vector inputs, and the indirect (substrate) encoded NNs which use charts as input. The TWEANN algorithm I will use in this paper to evolve these currency trading agents is the memetic algorithm based TWEANN system called Deus Ex Neural Network (DXNN) platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
