Articles on data analysis and statistics in MQL5

In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.

From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.

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.

The article considers creation of classes of descendant objects of base abstract indicator. Such objects will provide access to features of creating indicator EAs, collecting and getting data value statistics of various indicators and prices. Also, create indicator object collection from which getting access to properties and data of each indicator created in the program will be possible.

In the current article, I will study and test the most powerful optimization algorithm - harmonic search (HS) inspired by the process of finding the perfect sound harmony. So what algorithm is now the leader in our rating?

In this article, we investigate how the Generalized Hurst Exponent and the Variance Ratio test can be utilized to analyze the behaviour of price series in MQL5.

Trading is, first of all, a calculus of probabilities. The proverb about idleness being an engine for progress reveals us the reason why all those indicators and trading systems have been developed. It comes that the major of newcomers in trading study "ready-made" trading theories. But, as luck would have it, there are some more undiscovered market secrets, and tools used in analyzing of price movements exist, basically, as those unrealized technical indicators or math and stat packages. Thanks awfully to Bill Williams for his contribution to the market movements theory. Though, perhaps, it's too early to rest on oars.

The article considers an example of creating multi-symbol multi-period standard indicators using a single indicator buffer for construction and working in the indicator subwindow. I am going to prepare the library classes for working with standard indicators working in the program main window and having more than one buffer for displaying their data.

This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.

Statistics has always been at the heart of financial analysis. By definition, statistics is the discipline that collects, analyzes, interprets, and presents data in meaningful ways. Now imagine applying that same framework to candlesticks—compressing raw price action into measurable insights. How helpful would it be to know, for a specific period of time, the central tendency, spread, and distribution of market behavior? In this article, we introduce exactly that approach, showing how statistical methods can transform candlestick data into clear, actionable signals.

In this article, we will create a random forest model in Python, train the model, and save it as an ONNX pipeline with data preprocessing. After that we will use the model in the MetaTrader 5 terminal.

In this article, inheritance will be introduced into our previous and new code. A new database design will be implemented to provide efficiency. Additionally, a risk management class will be created to tackle volume calculations.

Dive into the heart of neural networks as we demystify the optimization algorithms used inside the neural network. In this article, discover the key techniques that unlock the full potential of neural networks, propelling your models to new heights of accuracy and efficiency.

In this article, I will start developing the methods of working with standard indicators, which will ultimately allow creating multi-symbol multi-period standard indicators based on library classes. Besides, I will add the "Skipped bars" event to the timeseries classes and eliminate excessive load from the main program code by moving the library preparation functions to CEngine class.

Successfully operating for 1.5 years, MQL5 Market has become the largest traders' store of trading strategies and technical indicators. It offers around 800 trading applications provided by 350 developers from around the world. Over 100.000 trading programs have already been purchased and downloaded by traders to their MetaTrader 5 terminals.