Part 5 of our series will introduce you to the fascinating world of MQL5, designed especially for complete novices looking for a gentle introduction to the intricacies of array functions. This section aims to dismantle the misconceptions that are frequently associated with array functions, guaranteeing that each line of code is not only understood but comprehended thoroughly. Regardless of prior coding experience, I sincerely believe that everyone should have the opportunity to learn about the MQL5 ...
This algorithm is based on a self-learning method, where the agent uses information obtained during interaction with the environment to generate "intrinsic" rewards and update its strategy. The algorithm is based on the use of several agent models that interact with the environment and generate various predictions. If the models disagree, it is considered an "interesting" event and the agent is incentivized to explore that space of the environment. In this way, the algorithm incentivizes the agent ...
It’s been 13 years since Maroon 5 and Christina Aguilera topped charts with “Moves Like Jagger,” and still nobody loves it more than Mick Jagger himself. On Wednesday, he posted an Instagram Reel of himself dancing to the song as played by a covers group in a bar with a decent crowd on the dance floor. But the band performs the song as if it’s ...
In the first article I introduced programming paradigms and focused on how to implement procedural programming in MQL5. I also explored functional programming. After gaining a deeper understanding of how procedural programming works, we created a basic price action expert advisor using the exponential moving average indicator (EMA) and candlestick price data. This article will take a deeper dive into the object-oriented programming paradigm. We'll then apply this knowledge ...
Behavior cloning methods, largely based on the principles of supervised learning, show fairly good results. But their main problem remains the search for ideal role models, which are sometimes very difficult to collect. In turn, reinforcement learning methods are able to work with non-optimal raw data. At the same time, they can find suboptimal policies to achieve the goal. However, when searching for an optimal policy, we often encounter an optimization problem that is more relevant in high-dimensional ...
Recent Comments