Maxim Dmitrievsky / 个人资料
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![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
https://www.mql5.com/ru/channels/machinelearning
![Time series clustering in causal inference](https://c.mql5.com/2/74/Time_series_clustering_in_causal_inference___LOGO.png)
Clustering algorithms in machine learning are important unsupervised learning algorithms that can divide the original data into groups with similar observations. By using these groups, you can analyze the market for a specific cluster, search for the most stable clusters using new data, and make causal inferences. The article proposes an original method for time series clustering in Python.
![Propensity score in causal inference](https://c.mql5.com/2/72/Propensity_score_in_causal_inference____LOGO.png)
The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
![Causal inference in time series classification problems](https://c.mql5.com/2/66/Causal_inference_in_time_series_classification_problems___LOGO.png)
In this article, we will look at the theory of causal inference using machine learning, as well as the custom approach implementation in Python. Causal inference and causal thinking have their roots in philosophy and psychology and play an important role in our understanding of reality.
![网格和马丁格尔交易系统中的机器学习。 您敢为其打赌吗?](https://c.mql5.com/2/42/yandex_catboost__3.png)
本文介绍了应用于网格和马丁格尔交易的机器学习技术。 令人惊讶的是,这种方法在全球网络中难觅踪迹。 阅读过本文之后,您将能够创建自己的交易机器人。
![利用 CatBoost 算法寻找外汇市场的季节性模式](https://c.mql5.com/2/41/yandex_catboost__3.png)
本文探索了用时间过滤器建立机器学习模型,并讨论了这种方法的有效性。现在,只要简单地指示模型在一周中某一天的某个时间进行交易,就可以消除人为因素。模式搜索可以由单独的算法提供。
![直推和主动机器学习中的梯度提升](https://c.mql5.com/2/41/yandex_catboost__2.png)
在本文中,我们将探讨利用真实数据的主动机器学习方法,并讨论它们的优缺点。也许你会发现这些方法很有用,并将它们包含在你的机器学习模型库中。直推是由支持向量机(SVM)的共同发明者弗拉基米尔·瓦普尼克(Vladimir Vapnik)提出的。
![基于暴力算法的 CatBoost 模型高级重采样与选择](https://c.mql5.com/2/41/yandex_catboost__1.png)
本文描述了一种可能的数据转换方法,旨在提高模型的通用性,并讨论了 CatBoost 模型的采样和选择。
![梯度提升(CatBoost)在交易系统开发中的应用. 初级的方法](https://c.mql5.com/2/41/yandex_catboost.png)
在 Python 中训练 CatBoost 分类器,并将模型导出到mql5,以及解析模型参数和自定义策略测试程序。Python 语言和 MetaTrader 5 库用于准备数据和训练模型。
![寻找市场形态的计量经济学方法:自相关,热点图和散点图](https://c.mql5.com/2/37/jlp_0d3zw11j.png)
本文研讨季节性特征的扩展研究:自相关热点图和散点图。 本文之目的是展示“市场记忆”的季节性,它通过任意顺序增量的最大相关性来表达。