All about MQL5 Wizard : create robots without programming. - page 3

[Fernando Carreiro]

I have moved this topic to the Expert Advisors and Automated Trading section, given that the MQL5 Wizard is primarily a tool for generating code for Expert Advisors (EAs).

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 07): Dendrograms

This article which is part of a series on using the MQL5 wizard looks at dendrograms. We have considered already a few ideas that can be useful to traders via the MQL5 wizard like: Linear discriminant analysis, Markov chains, Fourier transform, and a few others, and this article aims to take this endeavor further of looking at ways of capitalizing on the extensive ALGLIB code as translated by MetaQuotes together with the use of the inbuilt MQL5 wizard, to proficiently test and develop new ideas.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 08): Perceptrons

The MQL5 wizard Expert-Signal class comes with a lot of example instances under the folder “Include\Expert\Signal” and each one of them can be used independently or combined with each other in putting together an Expert Advisor in the Wizard. For this article we will aim to create and use one such file in an expert adviser. This approach besides minimizing preliminary coding efforts, it allows testing more than one signal in a single expert advisor by attributing weighting to each used signal.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 09). Pairing K-Means Clustering with Fractal Waves

This article continues the look at possible simple ideas that can be implemented and tested thanks to the MQL5 wizard, by delving into k-means clustering. This like AHC which we looked at in this prior article, is an unsupervised approach to classifying data.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM

Restrictive Boltzmann Machines (RBMs) are a form of neural network that are quite simple in their structure but are none the less revered, in certain circles, for what they can accomplish when it comes to revealing hidden properties and features in data-sets.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 11): Number Walls

For a few time-series, it is possible to devise a formula for the next value in the sequences basing off of previous values that appeared within it. Number walls allow this to be accomplished by preliminarily generating a ‘wall of numbers’, in the form of a matrix via what is referred to as the cross-rule. In generating this matrix, the primary goal is to establish if the sequence in question is convergent and the number wall cross rule algorithm gladly answers this question, if after a few rows of application, the subsequent rows in the matrix are only zeroes.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial

Time series analysis plays an important role not just in supporting fundamental analysis but in very liquid markets like forex, it can be the main driver for decisions on how one is positioned in the markets. Traditional technical indicators have tended to lag the market a lot which has brought them out of favor for most traders, leading to the rise of alternatives perhaps the most predominant of which, at the moment is neural networks. But what about polynomial interpolation?

[Sergey Golubev]

MQL5 Wizard Techniques you should know (Part 13). DBSCAN for Expert Signal Class

These series of articles, on the MQL5 Wizard, are a segue on how often abstract ideas in Mathematics of other fields of life can be enlivened as trading systems and tested or validated before any serious commitments is made on their premise. This ability to take simple and not fully implemented or envisaged ideas and explore their potential as trading systems is one of the gems presented by the MQL5 wizard assembly for expert advisers. The expert classes of the wizard furnish a lot of the mundane features required by any expert adviser especially as it relates to opening and closing trades but also in overlooked aspects like executing decisions only on a new bar formation.

So, in keeping this library of processes as a separate aspect of an expert adviser, with the MQL5 Wizard any idea can not only be tested independently, but also compared on a somewhat equal footing to any other ideas (or methods) that could be under consideration. In these series we have looked at alternative clustering methods like the agglomerative clustering as well as the k-means clustering.

[Sergey Golubev]

MQL5 Wizard Techniques you should know (14): Multi Objective Timeseries Forecasting with STF

This paper on Spatial Temporal Fusion (STF) piqued my interest on the subject thanks to its two-sided approach to forecasting. For a refresher, the paper is inspired by solving a probability-based forecasting problem that is collaborative for both supply and demand in two-sided ride-hailing platforms, such as Uber and Didi. Collaborative supply and demand relationships are common in various two-sided markets, such as Amazon, Airbnb, and eBay where in essence the company not only serves the traditional ‘customer’ or purchaser, but also caters to suppliers of the customer.

So, two-sided forecasting in a case where supply is partly dependent on demand can be important to these companies on a frequent basis. This dual projection though, of demand and supply, was certainly a break from the conventional approach of forecasting a specific value to a timeseries or data set. The paper also introduced what it called a causaltrans framework where the causal ‘collaborative’ relationship between supply and demand was captured by a matrix G and all forecasts were made via transformer network and its results were noteworthy.

[Sergey Golubev]

MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial

Support Vector Machines (SVM) is a machine learning classification algorithm. Classification is different from clustering which we have considered in previous articles here and here with the primary difference between the two being that classification separates data into predefined sets, with supervision, while clustering seeks to determine what and how many of these sets there are, without supervision.

In a nutshell, SVM classifies data by considering the relationship each data point will have with all the others, if a dimension were to be added to the data. Classification is achieved if a hyperplane, can be defined that cleanly dissects the predefined data sets.

For this article we will consider a very basic SVM case that handles 2-dimensional data (also known as linear-SVM), since complete implementation source code is to be shared without reference to any 3rd party libraries. Usually, the separating hyperplane is derived from either one of two methods: a polynomial kernel or a radial kernel. The latter is more complex and will not be discussed here as we will be dealing only with the former, the polynomial kernel.