Our custom panel will allow us to add any required amount of the necessary data to it, sign it, as well as display and update readings from the program code. It should be possible to move the panel around the chart with the mouse, dock it in the desired chart position, as well as collapse/expand it. For the convenience of placing the data on the panel, it will be possible to display a table with the specified number of rows and columns. The data from this table can be displayed in ...
Welcome back to our MQL5 journey! In Part One, we embarked on the adventure of algorithmic trading, breaking down the complexities of MQL5 for beginners without prior programming experience. As we step into Part Two, the excitement continues as we delve even deeper into the essential building blocks of MQL5. Our goal is simple yet profound: to ensure everyone, regardless of their programming background, feels the embrace of understanding. Feel free to ask any questions, and let's unravel ...
We continue our look at category theory with one more take on functors. So far, we have seen applications of category theory in implementing custom instances of the Expert trailing class, and the Expert Signal class so we will consider applications in using the Expert Money class for this article. All these classes come with the Meta Editor IDE and are used with the MQL5 wizard in assembling expert advisors with minimal coding. In this article as we sum up our look at functors ...
In our previous article, we delved into monoid groups, by exploring concept of symmetry within typical monoids. In introducing an additional axiom that all members of a monoid group must possess an inverse and restricting binary operations between mirror elements to yield the identity element, we extended applicability of monoids at crucial trade decision points. Building upon this, we now continue our study of category theory and its practical applications in trade system development by examining ...
We continue to explore reinforcement learning methods. As you know, all algorithms for training models in this area of machine learning are based on the paradigm of maximizing rewards from the environment. The reward function plays a key role in the model training process. Its signals are usually pretty ambiguous. more...
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