Aleksey / Fil d'actualité
Возможно ты попал по адресу! ))
Объявление простое.
Есть наработки в области [парного] кросс-трейдинга.
Последняя реализация ТС на С# для крипты. Надёжна и проста как "Теория всего."
Не ясна пока доходность, т.к. тестера не имеется.
Много граблей посчитано, шишки получены, достигнута определённая ясность сознания.
Могу поделиться опытом со страждущими!)
А так же идеями...
Взамен - взаимность, участие, энтузиазм, интерес к предмету.
Так же, в листе ожидания зависла задачка для программиста среднего уровня. Просто нет на это времени.
Задачка не очень сложная, но ставящая точку в представлении о доходности существующей ТС (кросс-трейдинг).
и вносящая ясность по её адаптации для форекс. Что потребует сосредоточенности и понимания mql-специфики.
Кому интересно, кликните Алексея в тг-группе: https://t.me/kulibyatino
и пообщаемся.
#кросс_трейдинг #парный_трейдинг
![Roman Poshtar](https://c.mql5.com/avatar/2019/12/5DE4DBC6-D492.jpg)
GOLD CatBoost Angle MA 3 ST V1 EA works on the Catboost algorithm. The technology described in my articles is used, which you can read below: Experiments with neural networks (Part 1): Revisiting geometry https://www.mql5.com/en/articles/11077 Experiments with neural networks (Part 2): Smart neural network optimization https://www.mql5.com/en/articles/11186 Experiments with neural networks (Part 3): Practical application https://www.mql5.com/en/articles/11949
![Nikolay Moskalev Nikolay Moskalev](https://c.mql5.com/avatar/2014/8/53EA0245-C4F5.jpg)
![Roman Poshtar](https://c.mql5.com/avatar/2019/12/5DE4DBC6-D492.jpg)
GOLD CatBoost Distans MA 3 ST V1 EA works on the Catboost algorithm. The technology described in my articles is used, which you can read below: Experiments with neural networks (Part 1): Revisiting geometry https://www.mql5.com/en/articles/11077 Experiments with neural networks (Part 2): Smart neural network optimization https://www.mql5.com/en/articles/11186 Experiments with neural networks (Part 3): Practical application
![Реter Konow](https://c.mql5.com/avatar/avatar_na2.png)
![](https://c.mql5.com/6/952/splash-758425.png)
![Roman Poshtar](https://c.mql5.com/avatar/2019/12/5DE4DBC6-D492.jpg)
CatBoost Angle MA 3 ST V1 EA works on the Catboost algorithm. The technology described in my articles is used, which you can read below: Experiments with neural networks (Part 1): Revisiting geometry https://www.mql5.com/en/articles/11077 Experiments with neural networks (Part 2): Smart neural network optimization https://www.mql5.com/en/articles/11186 Experiments with neural networks (Part 3): Practical application https://www.mql5.com/en/articles/11949 Experiments with
![Roman Poshtar](https://c.mql5.com/avatar/2019/12/5DE4DBC6-D492.jpg)
CatBoost Distans MA 3 ST V1 EA works on the Catboost algorithm. The technology described in my articles is used, which you can read below: Experiments with neural networks (Part 1): Revisiting geometry https://www.mql5.com/en/articles/11077 Experiments with neural networks (Part 2): Smart neural network optimization https://www.mql5.com/en/articles/11186 Experiments with neural networks (Part 3): Practical application https://www.mql5.com/en/articles/11949
![Nikolay Moskalev Nikolay Moskalev](https://c.mql5.com/avatar/2014/8/53EA0245-C4F5.jpg)
![Реter Konow](https://c.mql5.com/avatar/avatar_na2.png)
![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
https://www.mql5.com/ru/channels/machinelearning
![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
![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.
![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
![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.
![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
![Roman Poshtar](https://c.mql5.com/avatar/2019/12/5DE4DBC6-D492.jpg)
![Оптимизация и тестирование торговых стратегий (Часть 1): Взгляд на "Red Dragon H4", "BOLT", "YinYang", и "Statistics SAR"](https://c.mql5.com/2/70/Optimizing_and_Testing_Trading_Strategies_Part_1____LOGO.png)
Так как я постоянно занимаюсь, разработкой разного рода торговых систем сегодня хочу поделиться с Вами несколькими из них по стратегиям "Red Dragon H4", "BOLT", "YinYang" и "Statistics SAR". Данные стратегии были найдены на просторах интернета.
![Roman Poshtar](https://c.mql5.com/avatar/2019/12/5DE4DBC6-D492.jpg)
![Maxim Dmitrievsky](https://c.mql5.com/avatar/2024/3/66034e17-bb79.jpg)
![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.
![Ireneusz Pacek](https://c.mql5.com/avatar/2021/6/60BD3BB0-EC59.png)