文章
Applying Localized Feature Selection in Python and MQL5 MetaTrader 5
This article explores a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard et al. The algorithm is implemented in Python to build binary classifier models that can be integrated with MetaTrader 5 applications for inference
Pattern Recognition Using Dynamic Time Warping in MQL5 MetaTrader 5
In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5
Causal analysis of time series using transfer entropy MetaTrader 5
In this article, we discuss how statistical causality can be applied to identify predictive variables. We will explore the link between causality and transfer entropy, as well as present MQL5 code for detecting directional transfers of information between two variables
Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5 MetaTrader 5
In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data
Integrating Hidden Markov Models in MetaTrader 5 MetaTrader 5
In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A
A feature selection algorithm using energy based learning in pure MQL5 MetaTrader 5
In this article we present the implementation of a feature selection algorithm described in an academic paper titled,"FREL: A stable feature selection algorithm", called Feature weighting as regularized energy based learning
The Group Method of Data Handling: Implementing the Combinatorial Algorithm in MQL5 MetaTrader 5
In this article we continue our exploration of the Group Method of Data Handling family of algorithms, with the implementation of the Combinatorial Algorithm along with its refined incarnation, the Combinatorial Selective Algorithm in MQL5
The Group Method of Data Handling: Implementing the Multilayered Iterative Algorithm in MQL5 MetaTrader 5
In this article we describe the implementation of the Multilayered Iterative Algorithm of the Group Method of Data Handling in MQL5
在 MQL5 中实现广义赫斯特指数和方差比检验 MetaTrader 5
在本文中，我们将研究如何利用广义赫斯特指数（Generalized Hurst Exponent）和方差比检验（Variance Ratio Test）来分析 MQL5 中价格序列的行为。
在 MQL5 中实现增广迪基–富勒检验 MetaTrader 5
在本文中，我们演示了增广迪基–富勒（Augmented Dickey-Fuller，ADF）检验的实现，并将其应用于使用 Engle-Granger 方法进行协整检验。