Antique
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MQL5 wizard can be a test bed for a wide variety of ideas, as we have covered so far in these series. And every once in a while, one is presented with a custom signal that has more than one way of being implemented. We looked at this scenario in the 2 articles about learning rates, as well as the last article on batch normalization. Each of those aspects to machine learning presented more than one potential custom signal, as was discussed. The loss , also by virtue of having multiple formats,

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The moving average cross-over is probably one of the oldest existing trading strategies. The Moving Average Convergence Divergence (MACD) is a very popular indicator is built on top of the notion of moving average cross-overs. There are many new members of our community who may be curious to know the predictive power of the MACD indicator, in their search to build the best trading strategy possible. Additionally, there are seasoned technical analysts who utilize the MACD in their strategies

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Security is paramount in any field of expertise, and we cannot afford to overlook its importance. With the persistent threat of unauthorized access, it’s crucial to safeguard our Admin Panel from potential intruders. If unauthorized individuals gain access, they could easily manipulate the panel and jeopardize our communication efforts with the broadcast community. The primary purpose of this system is to facilitate reliable communication, and while we can enhance functionality at the Expert

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In this article, we will develop a script from scratch to visualize deals during retrospective analysis of trading decisions. Simply put, if we trade manually and analyze our past market entries in order to improve our trading performance, we want to spend as little time as possible on manual history analysis and the purely technical work associated with it: opening charts, searching for deals in history, manually saving print screens in the terminal, independently drawing Stop Loss and Take

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Reinforcement Learning (RL) allows trading systems to learn from their environment or market data and thus improve their ability to trade over time. RL enables adaptation to changing market conditions, making it suitable for certain dynamic financial markets and securities. Financial markets are unpredictable, as often they feature a high degree of uncertainty. RL excels at making decisions under uncertainty by continuously adjusting its actions based on received feedback (rewards), thus

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