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There are two hard problems in computer science: 1) computers and 2) science.
Maxim Dmitrievsky
Maxim Dmitrievsky
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Maxim Dmitrievsky
Published article Кластеризация временных рядов в причинно-следственном выводе
Кластеризация временных рядов в причинно-следственном выводе

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.

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Maxim Dmitrievsky
Published article Propensity score in causal inference
Propensity score in causal inference

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.

Maxim Dmitrievsky
Published code ONNX Trader
Пример бота со встроенной моделью машинного обучения, которая обучена на питоне и сохранена в формат ONNX.
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Maxim Dmitrievsky
Published article Causal inference in time series classification problems
Causal inference in time series classification problems

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.

Maxim Dmitrievsky
Published article Cross-validation and basics of causal inference in CatBoost models, export to ONNX format
Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

The article proposes the method of creating bots using machine learning.

Maxim Dmitrievsky
Published article Machine learning in Grid and Martingale trading systems. Would you bet on it?
Machine learning in Grid and Martingale trading systems. Would you bet on it?

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.

Maxim Dmitrievsky
Published article Finding seasonal patterns in the forex market using the CatBoost algorithm
Finding seasonal patterns in the forex market using the CatBoost algorithm

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.

Maxim Dmitrievsky
Published article Gradient boosting in transductive and active machine learning
Gradient boosting in transductive and active machine learning

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).

Maxim Dmitrievsky
Published article Advanced resampling and selection of CatBoost models by brute-force method
Advanced resampling and selection of CatBoost models by brute-force method

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.

Maxim Dmitrievsky
Published article Gradient Boosting (CatBoost) in the development of trading systems. A naive approach
Gradient Boosting (CatBoost) in the development of trading systems. A naive approach

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.

Maxim Dmitrievsky
Published article Econometric approach to finding market patterns: Autocorrelation, Heat Maps and Scatter Plots
Econometric approach to finding market patterns: Autocorrelation, Heat Maps and Scatter Plots

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.

Maxim Dmitrievsky
Published article Exploring Seasonal Patterns of Financial Time Series with Boxplot
Exploring Seasonal Patterns of Financial Time Series with Boxplot

In this article we will view seasonal characteristics of financial time series using Boxplot diagrams. Each separate boxplot (or box-and-whiskey diagram) provides a good visualization of how values are distributed along the dataset. Boxplots should not be confused with the candlestick charts, although they can be visually similar.

Maxim Dmitrievsky
Published article Grokking market "memory" through differentiation and entropy analysis
Grokking market "memory" through differentiation and entropy analysis

The scope of use of fractional differentiation is wide enough. For example, a differentiated series is usually input into machine learning algorithms. The problem is that it is necessary to display new data in accordance with the available history, which the machine learning model can recognize. In this article we will consider an original approach to time series differentiation. The article additionally contains an example of a self optimizing trading system based on a received differentiated series.