Neural networks made easy (Part 18): Association rules
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The growth in the analyzed data volumes leads to a growth of interest in unsupervised learning methods. In the last few articles, we have already seen clustering and dimensionality reduction algorithms which belong to unsupervised learning methods. In this article, we continue studying unsupervised learning methods. This time, we will consider another type of problems which can be addressed within these methods: association rule mining. This problem type has originated from supermarket shopping marketing, where it was used to analyze market baskets with the purpose of finding the most frequently encountered product sets. Nowadays, algorithms solving these problems are widely used in various fields. We will look into how such algorithms can be used in trading.
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Developing a trading Expert Advisor from scratch (Part 21): New order system (IV)
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In the previous article,
Developing a trading Expert Advisor from scratch (Part 20), we considered the main changes that need to be made to get the visual system of orders. However, further steps required more explanation, so I decided to split the article into several parts. Here we will finish making the main changes. There will be quite a few of them, but they are all necessary. Well, the whole work will be quite interesting. However, I will not complete the work here, because there is still something left to do to really finish the system. Anyway, by the end of this article, the system will have almost all the necessary functionality.
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MQL5 Wizard techniques you should know (Part 03): Shannon's Entropy
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Claude Shannon in 1948 introduced his paper “
A mathematical theory of communication” that had the novel ideal of information entropy. Entropy is a concept from physics. It is a measure of the extent to which particles within an object are active. If we consider the 3 states of water namely ice, liquid and vapor for example; we can see that the particle kinetic energy is highest in vapor and least in ice. This same concept is applied in mathematics via probability.
Let’s see how this can be coded as an expert signal for the MQL5 wizard.
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Matrix and Vector operations in MQL5
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Special data types — matrices and vectors — have been added to the MQL5 language to solve a large class of mathematical problems. The new types offer built-in methods for creating concise and understandable code that is close to mathematical notation. In this article, we provide a brief description of built-in methods from the
Matrix and vector methods help section.
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Experiments with neural networks (Part 2): Smart neural network optimization
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The main objective of this article is the development of a full-fledged trading robot based on a neural network and using MetaTrader 5 without third-party software.
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Market math: profit, loss and costs
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After reading this article, you will receive a complete and working mathematical model, as well as learn to understand and correctly calculate everything related to orders.
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How to deal with lines using MQL5
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We all as traders draw many lines while trading to help us to observe some important levels to take a suitable trading decision based on them. So, these lines are very important for us as traders and we may wonder if we have a method that can be used to draw these lines or take a suitable decision based on them automatically because I think that it will help us a lot. The answer is yes, we have a method to do that by MQL5 (MetaQuotes Language 5).
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Population optimization algorithms: Particle swarm (PSO)
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In this article, I will try to find out if the algorithm is actually good for solving complex problems. In the classical version of the algorithm and in many of its modifications, there are significant limitations associated with the fact that the optimized function must be smooth and continuous, which means it is completely unsuitable for discrete functions. However, in line with the series of articles, all the algorithms under consideration will be changed in such a way (if there are any restrictions) in order to eliminate the shortcomings, at least to make the algorithms work at least purely technically. In other words, all algorithms must be indifferent to the smoothness of functions (such as in traders' problems) and have no restrictions on the argument step.
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Developing an Expert Advisor from scratch (Part 30): CHART TRADE as an indicator?
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In a previous article Development of a trading EA from scratch (Part 29), we have removed Chart Trade from the EA. Previously, we did the same to some other thinks, like Volume At Price and Times & Trade, to improve the EA performance and reliability. By removing Chart Trade from the EA, we are left with only the basic order system. Although this may seem insufficient to some users, the EA can actually do all the work. But there are people who like to enter and exit trades in the market, but they don't like to place them as pending and they to wait for the price to reach a certain level in order to enter or exit the trade.
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MQL5 Wizard techniques you should know (Part 04): Linear Discriminant Analysis
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Linear discriminant analysis (LDA) is a very common dimensionality reduction technique for classification problems. Like kohonen maps in prior article if you have high-dimensional data (i.e. with a large number of attributes or
variables) from which you wish to
classify observations, LDA will help you transform your data so as to make the classes as distinct as possible.
LDA is very much like the techniques PCA, QDA, & ANOVA; and the fact that they are all usually abbreviated is not very helpful. This article isn’t going to introduce or explain these various techniques, but simply highlight their differences.
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