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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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This article explores the application of quantum-inspired concepts in trading systems, bridging theoretical quantum computing with practical implementation in MQL5. We’ll introduce essential quantum principles and guide you from Python prototyping to MQL5 integration, with real-world performance data.

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Introducing two innovative portfolio optimization programs designed to revolutionize trading strategies and maximize returns while minimizing risk The first a Python-based solution leverages the power of MetaTrader 5 integration alongside advanced libraries such as pandas Numpy and cvxpy to analyze historical data optimize asset allocation and visualize results with Matplotlib. The second a similar implementation crafted in MQL5 harnesses the native capabilities of the MetaTrader 5 platform offering

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Machine learning models are very sensitive instruments. In this series of articles, we will pay significantly more attention to how the transformations we apply to our data, affects our model's performance. Likewise, our models are also sensitive to how the relationship between the input and the target is conveyed. This means, we may need to create new features from the data we have at hand, in order for our model to effectively learn.

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