Maxim Dmitrievsky / Profil
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10+ années
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14379
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![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
https://www.mql5.com/ru/channels/machinelearning
![Кластеризация временных рядов в причинно-следственном выводе](https://c.mql5.com/2/74/Time_series_clustering_in_causal_inference___LOGO.png)
Алгоритмы кластеризации в машинном обучении — это важные алгоритмы обучения без учителя, которые позволяют разделять исходные данные на группы с похожими наблюдениями. Используя эти группы, можно проводить анализ рынка для конкретного кластера, искать наиболее устойчивые кластеры на новых данных, а также делать причинно-следственный вывод. В статье предложен авторский метод кластеризации временных рядов на языке 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.
![Cross-validation and basics of causal inference in CatBoost models, export to ONNX format](https://c.mql5.com/2/60/CatBoost_export_to_ONNX_format_LOGO.png)
The article proposes the method of creating bots using machine learning.
![Machine learning in Grid and Martingale trading systems. Would you bet on it?](https://c.mql5.com/2/42/yandex_catboost__3.png)
This article describes the machine learning technique applied to grid and martingale trading. Surprisingly, this approach has little to no coverage in the global network. After reading the article, you will be able to create your own trading bots.
![Finding seasonal patterns in the forex market using the CatBoost algorithm](https://c.mql5.com/2/41/yandex_catboost__3.png)
The article considers the creation of machine learning models with time filters and discusses the effectiveness of this approach. The human factor can be eliminated now by simply instructing the model to trade at a certain hour of a certain day of the week. Pattern search can be provided by a separate algorithm.
![Gradient boosting in transductive and active machine learning](https://c.mql5.com/2/41/yandex_catboost__2.png)
In this article, we will consider active machine learning methods utilizing real data, as well discuss their pros and cons. Perhaps you will find these methods useful and will include them in your arsenal of machine learning models. Transduction was introduced by Vladimir Vapnik, who is the co-inventor of the Support-Vector Machine (SVM).
![Advanced resampling and selection of CatBoost models by brute-force method](https://c.mql5.com/2/41/yandex_catboost__1.png)
This article describes one of the possible approaches to data transformation aimed at improving the generalizability of the model, and also discusses sampling and selection of CatBoost models.
![Gradient Boosting (CatBoost) in the development of trading systems. A naive approach](https://c.mql5.com/2/41/yandex_catboost.png)
Training the CatBoost classifier in Python and exporting the model to mql5, as well as parsing the model parameters and a custom strategy tester. The Python language and the MetaTrader 5 library are used for preparing the data and for training the model.
![Econometric approach to finding market patterns: Autocorrelation, Heat Maps and Scatter Plots](https://c.mql5.com/2/37/jlp_0d3zw11j.png)
The article presents an extended study of seasonal characteristics: autocorrelation heat maps and scatter plots. The purpose of the article is to show that "market memory" is of seasonal nature, which is expressed through maximized correlation of increments of arbitrary order.