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There are two hard problems in computer science: 1) computers and 2) science.
Maxim Dmitrievsky
Published article Показатель склонности (Propensity score) в причинно-следственном выводе
Показатель склонности (Propensity score) в причинно-следственном выводе

В статье рассматривается тема матчинга в причинно-следственном выводе. Матчинг используется для сопоставления похожих наблюдений в наборе данных. Это необходимо для правильного определения каузальных эффектов, избавления от предвзятости. Автор рассказывает, как это помогает в построении торговых систем на машинном обучении, которые становятся более устойчивыми на новых данных, на которых не обучались. Центральная роль отводится показателю склонности, который широко используется в причинно-следственном выводе.

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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
Maxim Dmitrievsky
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Maxim Dmitrievsky
Published article Причинно-следственный вывод в задачах классификации временных рядов
Причинно-следственный вывод в задачах классификации временных рядов

В этой статье мы рассмотрим теорию причинно-следственного вывода с применением машинного обучения, а также реализацию авторского подхода на языке Python. Причинно-следственный вывод и причинно-следственное мышление берут свои корни в философии и психологии, это важная часть нашего способа мыслить эту реальность.

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Maxim Dmitrievsky
Published article Кросс-валидация и основы причинно-следственного вывода в моделях CatBoost, экспорт в ONNX формат
Кросс-валидация и основы причинно-следственного вывода в моделях CatBoost, экспорт в ONNX формат

В данной статье предложен авторский способ создания ботов с использованием машинного обучения.

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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.