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  1. Self Optimizing Expert Advisor With MQL5 And Python (Part VI): Taking Advantage of Deep Double Descent

    by , 12-31-2024 at 05:53 PM
    Overfitting in machine learning can take on many different forms. Most commonly, it happens when an AI model learns too much of the noise in the data, and fails to make any useful generalizations. This leads to dismal performance when we assess the model on data it has not seen before. There are many techniques that have been developed to mitigate overfitting, but such methods can often prove challenging to implement, especially when you are just getting started on your journey. However,
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  2. Trading with the MQL5 Economic Calendar (Part 3): Adding Currency, Importance, and Time Filters

    by , 12-31-2024 at 03:46 PM
    In this article, we build upon our previous work on the MetaQuotes Language 5 (MQL5) Economic Calendar, where we developed a News dashboard panel for displaying economic events in real time. Now, we will enhance this dashboard by implementing specific filters for currency, importance, and time, allowing traders to focus only on the news events most relevant to their strategies. These filters will provide a targeted view of market-moving events, helping to streamline decision-making and improve
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  3. Price Action Analysis Toolkit Development (Part 3): Analytics Master — EA

    by , 12-30-2024 at 03:46 PM
    Having developed the analytic comment script in the previous episode, I have identified its limitations regarding script properties, which I will outline below. While I acknowledge the considerable impact that the analytical comment script has had within the trading community, I have developed an Expert Advisor (EA) called Analytics Master. This EA offers the same analysis while also providing extra metrics for better trading insights. This new tool offers several advantages, including the ability
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  4. MQL5 Wizard Techniques you should know (Part 28): GANs Revisited with a Primer on Learning Rates

    by , 12-27-2024 at 11:47 AM
    We revisit a form of neural network we had considered in an earlier article by dwelling on one specific hyperparameter. The learning-rate. The Generative Adversarial Network is a neural network that operates in pairs, where one network is trained traditionally to discern the truth, while another is trained to discern the former’s projections from real occurrences. This duality does imply that the traditionally trained network (the former) is trying to fool the latter and this is true, however
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  5. Automating Trading Strategies in MQL5 (Part 2): The Kumo Breakout System with Ichimoku and Awesome Oscillator

    by , 12-26-2024 at 07:20 AM
    In the previous article (Part 1 of the series) we demonstrated how to automate the Profitunity System (Trading Chaos by Will Williams). In this article (Part 2), we demonstrate how to transform the Kumo Breakout Strategy into a fully functional Expert Advisor (EA) in MetaQuotes Language 5 (MQL5). The Kumo Breakout Strategy uses the Ichimoku Kinko Hyo indicator to identify potential market reversals and trend continuations, with price movements relative to the Kumo (cloud)—a dynamic support
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