Data Science and Machine Learning(Part 21): Unlocking Neural Networks, Optimization algorithms demystified
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It seems like everybody nowadays is interested in Artificial Intelligence, it's everywhere, and the big guys in the tech industry such as Google and Microsoft behind openAI are pushing for AI adaptation in different aspects and industries such as entertainment, healthcare industry, arts, creativity, etc.
I see this trend also in the MQL5 community
why not, with the introduction of
matrices and vectors and
ONNX to Metatrader5, It is now possible to make Artificial intelligence trading models of any complexity, you don't even need to be an expert in Linear algebra or a nerd enough to understand everything that goes into the system.
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Using optimization algorithms to configure EA parameters on the fly
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For novice traders, understanding the basic principles of optimization algorithms can be a powerful tool in finding profitable trades and minimizing risks. For seasoned professionals, deep knowledge in this area can open up new horizons and help create sophisticated trading strategies that exceed expectations.
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Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)
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Recurrent Neural Networks (RNNs) are artificial neural networks designed to recognize patterns in sequences of data, such as time series, language, or video. Unlike traditional
neural networks, which assume that inputs are independent of each other, RNNs can detect and understand patterns from a sequence of data (information).
A basic understanding of
Python,
ONNX in MQL5, and
Python machine learning is required to understand the contents of this article fully.
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Feature Engineering With Python And MQL5 (Part I): Forecasting Moving Averages For Long-Range AI Models
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When applying AI to any task, we must try our best to give the model as much useful information about the real world as we can. To describe different properties of the market to our AI models, we must manipulate and transform the input data, this process is referred to as feature engineering. This series of articles will teach you how to transform your market data, to reduce the error levels of your models. Today, I will focus on how to use moving averages to increase the forecasting range of your AI models in a fashion that gives complete control and a reasonable understanding of the global effectiveness of the strategy.
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Deconstructing examples of trading strategies in the client terminal
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In this article, we explore a feature selection algorithm introduced in the paper
'Local Feature Selection for Data Classification' by Narges Armanfard, James P. Reilly, and Majid Komeili. This method aims to identify predictive features that are often overlooked by traditional selection techniques due to their limited global utility. We will begin with a general overview of the algorithm, followed by its implementation in Python to create classifier models suitable for export to MetaTrader 5.
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Data Science and ML (Part 38): AI Transfer Learning in Forex Markets
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Transfer learning is a machine learning technique where a model trained on one task is repurposed as the foundation for a second task.
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