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  1. Neural networks made easy (Part 36): Relational Reinforcement Learning

    by , 06-04-2024 at 02:45 AM
    The main advantage of relational models is the ability to build dependencies between objects. That enables the structuring of the source data. The relational model can be represented in the form of graphs, in which objects and events are represented as nodes, while relationships show dependencies between objects and events.
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  2. Experiments with neural networks (Part 4): Templates

    by , 05-21-2024 at 02:45 AM
    In the previous articles (Part 1, Part 2, Part 3), we experimented with shapes and angles whose values were passed to the perceptron and the neural network built on the basis of the DeepNeuralNetwork.mqh library. We also conducted experiments on optimization methods in the strategy tester.
    An important task in the current experiments was to track the influence of the amount of transmitted data and the depth of history we take this data from. In addition, we needed to reveal patterns,
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  3. Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction

    by , 05-12-2024 at 04:41 AM
    In the previous article, we completed the implementation of a CSV file management class for storing and retrieving data related to financial markets. Having created the infrastructure, we are now ready to use this data to build and train a machine learning model.


    Our task in this article is to implement a regression model that can predict the closing price of a financial asset within a week. This forecast will allow us to analyze market behavior and make informed decisions
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  4. Alternative risk return metrics in MQL5

    by , 05-07-2024 at 02:45 AM
    All traders hope to maximize the percentage return on their investment by as much as possible, however higher returns usually come at a higher risk. This is the reason why risk adjusted returns are the main measure of performance in the investment industry. There are many different measures of risk adjusted return, each one with its own set of advantages and disadvantages. The Sharpe ratio is a popular risk return measure famous for imposing unrealistic preconditions on the distribution of returns
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  5. Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

    by , 04-10-2024 at 08:13 AM
    In the article Calculating the Hurst Exponent , we were introduced to the concept of fractal analysis and how it can be applied to financial markets. In that article the author described the rescaled range method (R/S) of estimating the Hurst Exponent. In this article we take a different approach by demonstrating the implementation of the Generalized Hurst Exponet (GHE) to classify the nature of a series. We will focus on using the GHE to identify forex symbols that exhibit a tendency to
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