In the previous article "Neural network in practice: Straight Line Function", we were talking about how algebraic equations can be used to determine part of the information we are looking for. This is necessary in order to formulate an equation, which in our particular case is the equation of a straight line, since our small set of data can actually be expressed as a straight line. All the material related to explaining how neural networks work is not easy to present without understanding
Welcome back to our MQL5 series! We'll discuss a fascinating subject in this chapter that I believe you'll find very useful and fascinating. We looked at how to use MQL5's built-in indicators in the last chapter, paying particular attention to the RSI. Not only that, but we even worked on a practical project that showed you how to incorporate the RSI into your trading approach with ease. We're going one step further this time by including not one but three potent indications in our project (RSI,
more...In this article we will make improvements to the storage database, new views will be added to present data such as displaying dates for the last news event or the next news event for each unique event in the MQL5 Economic calendar this will improve the user's experience when using the program as it will bring awareness to future or past events. In addition, the expert input menu will be expanded upon to accommodate news filtration and the stop order entry methods.
In the previous article, we have implemented the ability to choose the strategy option - with a constant position size and with a variable position size. This allowed us to introduce normalization of the results of the strategies' work according to the maximum drawdown and provided the possibility of combining them into groups, for which the maximum drawdown was also within the specified limits. For the sake of demonstration, we manually selected several of the most attractive combinations
In the previous article, we got acquainted with the ATFNet algorithm, which is an ensemble of 2 time series forecasting models. One of them works in the time domain and constructs predictive values of the studied time series based on the analysis of signal amplitudes. The second model works with the frequency characteristics of the analyzed time series and records its global dependencies, their periodicity and spectrum. Adaptive merging of two independent forecasts, according to the author
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