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Gravitational Search Algorithm (GSA) was proposed by E. Rashedi to solve the optimization problem, especially non-linear problems, following the principles of Newton's law of gravitation. In the proposed algorithm, particles are considered as objects and their performance is estimated taking into account their masses. more...
Originally Posted by mql5 The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm that has been used to solve numerous complex problems over the past decade. The Harmony Search algorithm (HS) was first proposed in 2001 by Z. W. Geem. The HS method is inspired by the founding principles of musical improvisation and the search for musical harmony. The combinations of perfect harmony of sounds are matched with the global extremum in the multidimensional ...
For stable trading, it is usually recommended to diversify both the traded instruments and the trading strategies. The same refers to machine learning models: it is easier to create several simpler models that one complex one. But it can be difficult to assemble these models into one ONNX model. In this article, we will consider one of the ensembles called the voting classifier. We will show you how easy it is to implement such an ensemble. ...
By using special data types 'matrix' and 'vector', it is possible to create the code which is very close to mathematical notation while avoiding the need to create nested loops or to mind correct indexing of arrays in calculations. In this article, we will see how to create, initialize, and use matrix and vector objects in MQL5. more...
Candlesticks are a very helpful technical tool if we use them correctly, as we can find a potential movement based on their patterns. Candlesticks can form specific patterns on the chart and these patterns can be divided into two types single-candle patterns and blended candle patterns (more than one candle). In this article, we will learn how we can use MQL5 to detect some of these patterns automatically in the MetaTrader 5 trading terminal, we ...
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