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  1. Color buffers in multi-symbol multi-period indicators

    by , 07-05-2024 at 03:55 PM
    We continue the development of multi-symbol, multi-period indicators which we started in the previous article.

    A single-color indicator buffer is a regular double array, which is filled with data when calculating the indicator. We can obtain data from this array and display it on a chart using the CopyBuffer() function provided that the receiving array will be a double array set as an indicator's plotting buffer (SetIndexBuffer()). When copying data from the buffer of the calculated
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  2. Indicator of historical positions on the chart as their profit/loss diagram

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

    by , 06-04-2024 at 01:45 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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  4. Experiments with neural networks (Part 4): Templates

    by , 05-21-2024 at 01:45 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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  5. Integrating ML models with the Strategy Tester (Conclusion): Implementing a regression model for price prediction

    by , 05-12-2024 at 03:41 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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