Articles on data analysis and statistics in MQL5

Trading account monitoring provides a detailed report on all completed deals. All trading statistics are collected automatically and provided to you as easy-to-understand diagrams and graphs.

The article deals with evaluating the performance of Signals Providers. We offer several additional parameters highlighting signal trading results from a slightly different angle than in traditional approaches. The concepts of the proper management and perfect deal are described. We also dwell on the optimal selection using the obtained results and compiling the portfolio of multiple signal sources.

In this article, we will develop a tool for CFTC report analysis. We will solve the following problem: to develop an indicator, that allows using the CFTC report data directly from the data files provided by Commission without an intermediate processing and conversion. Further, it can be used for the different purposes: to plot the data as an indicator, to proceed with the data in the other indicators, in the scripts for the automated analysis, in the Expert Advisors for the use in the trading strategies.

Tutorial to build a Binary Options strategy an test it in Strategy-Tester of MetaTrader 4 with Binary-Options-Strategy-Tester utility from marketplace.

One of the most interesting aspects of Self-Organizing Feature Maps (Kohonen maps) is that they learn to classify data without supervision. In its basic form it produces a similarity map of input data (clustering). The SOM maps can be used for classification and visualizing of high-dimensional data. In this article we will consider several simple applications of Kohonen maps.

Comprehensive data processing requires extensive tools and is often beyond the sandbox of one single application. Specialized programming languages are used for processing and analyzing data, statistics and machine learning. One of the leading programming languages for data processing is Python. The article provides a description of how to connect MetaTrader 5 and Python using sockets, as well as how to receive quotes via the terminal API.

Construction of Japanese candlestick chart and analysis of candlestick patterns constitute an amazing area of technical analysis. The advantage of candlesticks is that they represent data in such a manner that you can track the dynamics inside the data. In this article we analyze candlestick types, classification of candlestick patterns and present an indicator that can determine candlestick patterns.

The article considers the ideology and methodology of building a recommendatory system for time-efficient trading by combining the capabilities of forecasting with the singular spectrum analysis (SSA) and important machine learning method on the basis of Bayes' Theorem.

The article reveals the portfolio trading principles and their application to Forex market. A few simple mathematical portfolio arrangement models are considered. The article contains examples of practical implementation of the portfolio trading in MetaTrader 4: portfolio indicator and Expert Advisor for semi-automated trading. The elements of trading strategies, as well as their advantages and pitfalls are described.

This article is dedicated to a new and perspective direction in machine learning - deep learning or, to be precise, deep neural networks. This is a brief review of second generation neural networks, the architecture of their connections and main types, methods and rules of learning and their main disadvantages followed by the history of the third generation neural network development, their main types, peculiarities and training methods. Conducted are practical experiments on building and training a deep neural network initiated by the weights of a stacked autoencoder with real data. All the stages from selecting input data to metric derivation are discussed in detail. The last part of the article contains a software implementation of a deep neural network in an Expert Advisor with a built-in indicator based on MQL4/R.

Article explains how to read testing reports and to interpret the obtained results properly.

The Market Profile was developed by trully brilliant thinker Peter Steidlmayer. He suggested to use the alternative representation of information about "horizontal" and "vertical" market movements that leads to completely different set of models. He assumed that there is an underlying pulse of the market or a fundamental pattern called the cycle of equilibrium and disequilibrium. In this article I will consider Price Histogram — a simplified model of Market Profile, and will describe its implementation in MQL5.

Trading in financial markets is associated with a whole range of risks that should be taken into account in the algorithms of trading systems. Reducing such risks is the most important task to make a profit when trading.

This article serves to familiarize the reader with the empirical mode decomposition (EMD) method. It is the fundamental part of the Hilbert–Huang transform and is intended for analyzing data from nonstationary and nonlinear processes. This article also features a possible software implementation of this method along with a brief consideration of its peculiarities and gives some simple examples of its use.

The article considers the classic method for divergence construction and provides an additional divergence interpretation method. A trading strategy was developed based on this new interpretation method. This strategy is also described in the article.

This article shows you how to easily use Neural Networks in your MQL4 code taking advantage of best freely available artificial neural network library (FANN) employing multiple neural networks in your code.

Fuzzy logic expands our boundaries of mathematical logic and set theory. This article reveals the basic principles of fuzzy logic as well as describes two fuzzy inference systems using Mamdani-type and Sugeno-type models. The examples provided will describe implementation of fuzzy models based on these two systems using the FuzzyNet library for MQL5.

This article covers the main principles set fourth in evolutionary algorithms, their variety and features. We will conduct an experiment with a simple Expert Advisor used as an example to show how our trading system benefits from optimization. We will consider software programs that implement genetic, evolutionary and other types of optimization, and provide examples of application when optimizing a predictor set and parameters of the trading system.

This article deals with seven types of moving averages (MA) and a trading strategy to work with them. We also test and compare various MAs at a single trading strategy and evaluate the efficiency of each moving average compared to others.

The article deals with the concept of night trading, as well as trading strategies and their implementation in MQL5. We perform tests and make appropriate conclusions.

The article dwells on the development of an application for selecting the best optimization passes using several possible options. The application is able to sort out the optimization results by a variety of factors. Optimization passes are always written to a database, therefore you can always select new robot parameters without re-optimization. Besides, you are able to see all optimization passes on a single chart, calculate parametric VaR ratios and build the graph of the normal distribution of passes and trading results of a certain ratio set. Besides, the graphs of some calculated ratios are built dynamically beginning with the optimization start (or from a selected date to another selected date).

Subscribers often search for an appropriate signal by analyzing the total growth on the signal provider's account, which is not a bad idea. However, it is also important to analyze potential risks of a particular trading strategy. In this article we will show a simple and efficient way to evaluate a Trading Signal based on its performance values.

In this article, I would like to give an example of what a trader's program can be like as well as what results can be achieved in 9 months, having started to learn MQL5 from scratch. This example will also show how multi-functional and informative such a program can be for a trader while taking minimum space on the price chart. And we will be able to see just how colorful, bright and intuitively clear to the user trade information panels can get. As well as many other features...

This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package.

The article is based on 'The Mathematics of Money Management' by Ralph Vince. It provides the description of empirical and parametric methods used for finding the optimal size of a trading lot. Also the article features implementation of trading modules for the MQL5 Wizard based on these methods.

The article introduces a structure for an Expert Advisor that trades multiple symbols and uses several trading systems simultaneously. If you already identified the optimal input parameters for all your EAs and got good backtesting results for each of them separately, ask yourself what results you would get if testing all EAs simultaneously, with all your strategies put together.

In this article, we will start the development of the library base trading class and add the initial verification of permissions to conduct trading operations to its first version. Besides, we will slightly expand the features and content of the base trading class.

In the previous article, we have created the classes of pending request objects corresponding to the general concept of library objects. This time, we are going to deal with the class allowing the management of pending request objects.

The article considers the development of indicator buffer object classes as descendants of the abstract buffer object simplifying declaration and working with indicator buffers, while creating custom indicator programs based on DoEasy library.

We now know that probability density function (PDF) of a market cycle does not remind a Gaussian but rather a PDF of a sine wave and most of the indicators assume that the market cycle PDF is Gaussian we need a way to "correct" that. The solution is to use Fisher Transform. The Fisher transform changes PDF of any waveform to approximately Gaussian. This article describes the mathematics behind the Fisher Transform and the Inverse Fisher Transform and their application to trading. A proprietary trading signal module based on the Inverse Fisher Transform is presented and evaluated.

In this article, we test the statistical properties of the Labouchere money management system. It is considered to be a less aggressive kind of Martingale, since bets are not doubled, but are raised by a certain amount instead.

The article deals with examples of applying fuzzy set theory in trading by means of MQL4. The use of FuzzyNet library for MQL4 in the development of an indicator and an Expert Advisor is described as well.

Many programs of technical analysis allow to test trading strategies on history data. In the most cases, the testing is conducted on already completed data without any attempts to model the trends within a price bar. It was made quickly, but not precisely

The article familiarizes the reader with exponential smoothing models used for short-term forecasting of time series. In addition, it touches upon the issues related to optimization and estimation of the forecast results and provides a few examples of scripts and indicators. This article will be useful as a first acquaintance with principles of forecasting on the basis of exponential smoothing models.

There are a lot of measures that allow determining the effectiveness and profitability of a trade system. However, traders are always ready to put any system to a new crash test. The article tells how the statistics based on measures of effectiveness can be used for the MetaTrader 5 platform. It includes the class for transformation of the interpretation of statistics by deals to the one that doesn't contradict the description given in the "Statistika dlya traderov" ("Statistics for Traders") book by S.V. Bulashev. It also includes an example of custom function for optimization.