newdigital

In this article, we build upon the previous work in Part 4 of the MetaQuotes Language 5 (MQL5) series where we added real-time updates to the MQL5 Economic Calendar dashboard. Here, our focus is on making the dashboard more interactive by adding buttons that allow us to directly control the currency pair filters, importance levels, and time range filters, all from the panel itself—without needing to change the settings in the code. We will also include a "Cancel" button that clears the selected

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In the previous part, we dded the ability to restore the EA's state after a restart. It does not matter what the reason was - rebooting the terminal, changing the timeframe on the chart with the EA, launching a more recent version of the EA - in all cases, restoring the state allowed the EA not to start working from scratch and not to lose already open positions, but to continue handling them.

However, the size of the opened positions remained the same for each instance of

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In the previous article "Neural Network in Practice: Least Squares", we looked at how, in very simple cases, we can find an equation that best describes the data set we are using. The equation that was formed in this system was very simple, it used only one variable. We've already shown how to do the calculation, so we'll get straight to the point here. This is because the mathematics used to create an equation based on the values available in the database requires significant knowledge of

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In the previous article "Neural Network in Practice: Least Squares", we looked at how, in very simple cases, we can find an equation that best describes the data set we are using. The equation that was formed in this system was very simple, it used only one variable. We've already shown how to do the calculation, so we'll get straight to the point here. This is because the mathematics used to create an equation based on the values available in the database requires significant knowledge of

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We continue our series on the MQL5 wizard, where lately we are alternating between simple patterns from common indicators and reinforcement learning algorithms. Having considered indicator patterns (Bill Williams’ Alligator) in the last article, we now return to reinforcement learning, where this time the algorithm we are looking at is Proximal Policy Optimization (PPO). It is reported that this algorithm, that was first published 7 years ago, is the reinforcement-learning algorithm of choice

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