In the last chapter, we learned how to design trading strategies, create trading signals, and implement advanced concepts, such as seasonality in trading instruments. Understanding those concepts in greater detail is a vast field comprising stochastic processes, random walks, martingales, and time series analysis, which we leave to you to explore at your own pace.
So what's next? Let's look at an even more advanced method of prediction and forecasting: statistical inference and prediction. This is known as machine learning, the fundamentals of which were developed in the 1800s and early 1900s and have been worked on ever since. Recently, there has been a resurgence in interest in machine learning algorithms and applications owing to the av

本文介绍了如何使用机器学习预测金融市场,特别是通过线性回归和分类方法。讨论了监督学习中的术语和概念,如回归和分类问题,以及R方等评估指标。还探讨了K近邻、支持向量机和逻辑回归在预测交易信号中的应用,并展示了使用Python的scikit-learn库实现这些模型的例子。
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