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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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Forecasting plays an important role in time series analysis. Deep models have brought significant improvement in this area. In addition to successfully predicting future values, they also extract abstract representations that can be applied to other tasks such as classification and anomaly detection.
The Transformer architecture, which originated in the field of natural language processing (NLP), demonstrated its advantages in computer vision (CV) and is successfully applied in time

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