Although the vast majority of fundamental analysts believe that technical analysts’ estimates and technical indicators used in these analyses are unresponsive, recent research has revealed that both professionals and individual traders are using technical indicators. A correct estimate of the direction of the financial market is a very challenging activity, primarily due to the nonlinear nature of the financial time series. Deep learning and machine learning methods on the other hand have achieved very successful results in many different areas where human beings are challenged. In this study, technical indicators were integrated into the methods of deep learning and machine learning, and the behavior of the traders was modeled in order to increase the accuracy of forecasting of the financial market direction. A set of technical indicators has been examined based on their application in technical analysis as input features to predict the oncoming (one-period-ahead) direction of Istanbul Stock Exchange (BIST100) national index. To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. The performance of these models is evaluated on the basis of various performance metrics such as confusion matrix, compound return, and max drawdown.

中文翻译：

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使用深度学习和机器学习方法对交易者的行为建模：来自BIST 100指数的证据

尽管绝大多数基础分析师认为技术分析师的估计和这些分析中使用的技术指标没有反应，但最近的研究表明，专业人士和个体交易者都在使用技术指标。对金融市场方向的正确估计是一项非常具有挑战性的活动，这主要是由于金融时间序列的非线性性质。另一方面，深度学习和机器学习方法在许多挑战人类的不同领域都取得了非常成功的成果。在这项研究中，将技术指标整合到深度学习和机器学习的方法中，并对交易者的行为进行建模，以提高预测金融市场方向的准确性。根据一组技术指标在技术分析中的应用作为基础进行了检验，以此作为预测伊斯坦布尔证券交易所（BIST100）国家指数即将来临（一个周期）方向的输入特征。为了预测索引的方向，使用了深度神经网络（DNN），支持向量机（SVM），随机森林（RF）和逻辑回归（LR）分类技术。这些模型的性能是根据各种性能指标（如混淆矩阵，复合物收益率和最大跌幅）进行评估的。使用Logistic回归（LR）分类技术。这些模型的性能是根据各种性能指标（如混淆矩阵，复合物收益率和最大跌幅）进行评估的。使用Logistic回归（LR）分类技术。这些模型的性能是根据各种性能指标（例如，混淆矩阵，化合物收益率和最大跌幅）进行评估的。