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, ...
MQL5 wizard can be a test bed for a wide variety of ideas, as we have covered so far in these series. And every once in a while, one is presented with a custom signal that has more than one way of being implemented. We looked at this scenario in the 2 articles about learning rates, as well as the last article on batch normalization. Each of those aspects to machine learning presented more than one potential custom signal, as was discussed. The loss , also by virtue of having multiple formats, ...
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. more...
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 first article of the series, we loaded the dataset, placed labels, enriched the dataset and also performed dataset labeling. The second article was devoted to the creation and training of the model, as well as implementation of cross-validation and bagging. Now that our model is trained and tested, it is time to start real trading using the MetaTrader 5 library for Python. This powerful library allows us to automate trading directly through Python using the functions ...
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