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MetaTrader 5 Python package

This is a discussion on MetaTrader 5 Python package within the HowToBasic forums, part of the Announcements category; New version of MetaTrader5-Python-5.0.34 available for MetaTrader 5 build 2765 read more here ....

      
   
  1. #21
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    New version of MetaTrader5-Python-5.0.34 available for MetaTrader 5 build 2765
    read more here.
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  2. #22
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    Understand and efficiently use OpenCL API by recreating built-in support as DLL on Linux (Part 1): Motivation and validation

    MetaTrader 5 Python package-opencl0203.png


    OpenCL (Open Computing Language) is framework that allows users to write programs to execute across CPU (Central Processing Unit), GPU (Graphics Processing Unit), or dedicated accelerator device with benefit that it can speed up heavy computation required as per problem domain.


    MetaTrader 5 supports OpenCL version 1.2. It has several built-in functions that users can take benefit in using of out-of-box.
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  3. #23
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    Everything you need to learn about the MQL5 program structure

    Every software in any programming language has a structure, after understanding this structure we can create or develop our software smoothly. MQL5 language's programs are the same as any other programming language has their structure and it is supposed to be understood by the developer to achieve the objectives of his project smoothly and effectively. In this article, we will provide information in this context to try to deliver its content easily as possible.
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  4. #24
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    Algorithmic Trading With MetaTrader 5 And R For Beginners

    For community members transitioning from R, irrespective of their background in Academia or Scientific Computation, the MetaQuotes community welcomes you with open arms. Despite the advancements in Python, and the exclusive integration of Python as the only other fully supported language within the MetaTrader terminal, individuals proficient in R need not perceive their programming skills as obsolete. This article challenges any notion suggesting obsolescence by illustrating that, with the application of creativity and a little ingenuity, it remains entirely feasible to construct a comprehensive algorithmic trading advisor using R and MetaTrader 5.
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  5. #25
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    Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file

    Deep learning is a subfield of machine learning that focuses on artificial neural networks, inspired by the structure and function of the human brain. It involves training models to perform tasks without explicit programming but by learning patterns and representations from data. Deep learning has gained significant attention due to its ability to automatically learn hierarchical features and representations, making it effective in various domains such as image and speech recognition, natural language processing, and more.
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  6. #26
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    Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models

    This is the continuation of Deep Learning Forecast and Order Placement using Python, the MetaTrader5 Python package and an ONNX model file, but you continue this one without the previous one. All will be explained. Everything we will use is included in this article. In this section, we will guide you through the entire process, culminating in the creation of an Expert Advisor (EA) for trading and subsequent testing.

    Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to perform tasks without being explicitly programmed. The primary goal of machine learning is to enable computers to learn from data and improve their performance over time.
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  7. #27
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    Cross-validation and basics of causal inference in CatBoost models, export to ONNX format

    In the previous articles, I have described various ways to use machine learning algorithms to create trading systems. Some turned out to be quite successful, others (mostly from early publications) were greatly overtrained. Thus, the sequence of my articles reflects the evolution of understanding: what machine learning is actually capable of. We are, of course, talking about the classification of time series.

    The current article is a development of the previous topic and the next step towards creating a self-training algorithm that is able to look for patterns in data while minimizing overfitting. After all, we want to get a real effect from the use of machine learning, so that it not only generalizes training examples, but also determines the presence of cause-and-effect relationships in them.
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  8. #28
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    Seasonality Filtering and time period for Deep Learning ONNX models with python for EA

    When I read the article: Benefiting from Forex market seasonality, I thought of this to make this what I think is an interesting article. I could start comparing an EA with and without seasonality's and with to see if it can benefit.

    I all ready new that markets depend on seasonality since I read that the Mark Zuckerberg got the money for Facebook, from an investor that got his money investing the one that they gave him in the Bar Mitzvah in stocks of petrol when the climate in the Caribbean there were going to have hurricanes. He first studied the climate and forecasted that there would be a bad weather during a period of time.
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  9. #29
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    Python, ONNX and MetaTrader 5: Creating a RandomForest model with RobustScaler and PolynomialFeatures data preprocessing

    Random Forest is widely used in a variety of fields, and its flexibility makes it suitable for both classification and regression problems. In a classification task, the model decides which of the predefined classes the current state belongs to. For example, in the financial market, this could mean a decision to buy (class 1) or sell (class 0) an asset based on a variety of indicators.

    However, in this article, we will focus on regression problems. Regression in machine learning is an attempt to predict the future numerical values of a time series based on its past values. Instead of classification, where we assign objects to certain classes, in regression we aim to predict specific numbers. This could be, for example, forecasting stock prices, predicting temperature or any other numerical variable.
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