论文标题
AI用于交易策略
AI for trading strategies
论文作者
论文摘要
在本学士学位论文中，我们展示了与已经成功应用的交易策略（例如交叉信号交易和常规统计时间序列型号ARMA-GARCH）相比，如何进行四种不同的机器学习方法（长期短期记忆，随机森林，支持向量机回归和K-Nearest邻居）。目的是表明，正确使用时，机器学习方法在原油市场中的表现要好于传统方法。进行了更详细的性能分析，显示了不同市场阶段不同模型的性能，因此可以更仔细地研究高波动率阶段中各个模型的鲁棒性。为了进一步调查，这些模型还必须在其他市场中进行分析。
In this bachelor thesis, we show how four different machine learning methods (Long Short-Term Memory, Random Forest, Support Vector Machine Regression, and k-Nearest Neighbor) perform compared to already successfully applied trading strategies such as Cross Signal Trading and a conventional statistical time series model ARMA-GARCH. The aim is to show that machine learning methods perform better than conventional methods in the crude oil market when used correctly. A more detailed performance analysis was made, showing the performance of the different models in different market phases so that the robustness of individual models in high and low volatility phases could be examined more closely. For further investigation, these models would also have to be analyzed in other markets.
