MetaTrader 5 Python User Group - the summary - page 28

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Vladimir Perervenko #:

Only Windows :(


Some python pip installs allow the setup to compile to the native platform, for instance if one was to perform a pip install psycopg2. Would it be possible to get a version that allows one to do so in the case of pip install MetaTrader5? It would help in this case.

I for one would like to ask if it is possible, so that it would help. Also where can one ask to get this?

 

Classification models in the Scikit-Learn library and their export to ONNX

Classification models in the Scikit-Learn library and their export to ONNX

The ONNX-ML profile is a part of ONNX designed specifically for machine learning (ML) models. It is intended for describing and representing various types of ML models, such as classification, regression, clustering, and others, in a convenient format that can be used on various platforms and environments that support ONNX. The ONNX-ML profile simplifies the transmission, deployment, and execution of machine learning models, making them more accessible and portable.

In this article, we will explore the application of all classification models in the Scikit-learn package for solving the Fisher's Iris classification task. We will also attempt to convert these models into the ONNX format and use the resulting models in MQL5 programs.

Furthermore, we will compare the accuracy of the original models with their ONNX versions on the complete Iris dataset.

Classification models in the Scikit-Learn library and their export to ONNX
Classification models in the Scikit-Learn library and their export to ONNX
  • www.mql5.com
In this article, we will explore the application of all classification models available in the Scikit-Learn library to solve the classification task of Fisher's Iris dataset. We will attempt to convert these models into ONNX format and utilize the resulting models in MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions on the full Iris dataset.
 

Mastering ONNX: The Game-Changer for MQL5 Traders

Mastering ONNX: The Game-Changer for MQL5 Traders

It is undeniable that we are in the age of AI and machine learning, on every single day, there is a new AI-based technology deployed in finance, arts and gaming, education, and many more aspects of life.

To us traders learning to harness the power of Artificial Intelligence could give us an edge over the market, letting us detect patterns and relationships that we couldn't see with the human eye.

Mastering ONNX: The Game-Changer for MQL5 Traders
Mastering ONNX: The Game-Changer for MQL5 Traders
  • www.mql5.com
Dive into the world of ONNX, the powerful open-standard format for exchanging machine learning models. Discover how leveraging ONNX can revolutionize algorithmic trading in MQL5, allowing traders to seamlessly integrate cutting-edge AI models and elevate their strategies to new heights. Uncover the secrets to cross-platform compatibility and learn how to unlock the full potential of ONNX in your MQL5 trading endeavors. Elevate your trading game with this comprehensive guide to Mastering ONNX
 

Regression models of the Scikit-learn Library and their export to ONNX 

Regression models of the Scikit-learn Library and their export to ONNX

In this article, we will explore the application of regression models in the Scikit-learn package, compute their parameters with double precision for the test dataset, attempt to convert them to the ONNX format for float and double precision, and use the obtained models in programs on MQL5. Additionally, we will compare the accuracy of the original models and their ONNX versions for float and double precision. Furthermore, we will examine the ONNX representation of regression models, which will provide a better understanding of their internal structure and operation.
Regression models of the Scikit-learn Library and their export to ONNX
Regression models of the Scikit-learn Library and their export to ONNX
  • www.mql5.com
In this article, we will explore the application of regression models from the Scikit-learn package, attempt to convert them into ONNX format, and use the resultant models within MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions for both float and double precision. Furthermore, we will examine the ONNX representation of regression models, aiming to provide a better understanding of their internal structure and operational principles.
 

Building Your First Glass-box Model Using Python And MQL5

Building Your First Glass-box Model Using Python And MQL5

Glass-box algorithms are machine learning algorithms that are fully transparent and inherently intelligible. They defy conventional wisdom that there is a tradeoff between prediction accuracy and interpretability in Machine Learning because they offer an unparalleled level of accuracy and transparency. This means they are exponentially easier to debug, maintain, and improve upon iteration when compared to their black-box alternatives that we are more familiar with. Black-box models are all machine learning models whose inner workings are complex and not easily interpretable. These models can represent high dimensional and non-linear relationships which aren't easily understood by us as humans.
Building Your First Glass-box Model Using Python And MQL5
Building Your First Glass-box Model Using Python And MQL5
  • www.mql5.com
Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.
 

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.
Algorithmic Trading With MetaTrader 5 And R For Beginners
Algorithmic Trading With MetaTrader 5 And R For Beginners
  • www.mql5.com
Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.
 

Data label for time series mining (Part 6)：Apply and Test in EA Using ONNX

Data label for time series mining (Part 6)：Apply and Test in EA Using ONNX

We discussed in the previous article how to use socket (websocket) to communicate between EA and python server to solve the backtesting problem, and also discussed why we adopted this technique. In this article, we will discuss how to use onnx, which is natively supported by mql5, to perform inference with our model, but this method has some limitations. If your model uses operators that are not supported by onnx, it may end in failure, so this method is not suitable for all models (of course, you can also add operators to support your model, but it requires a lot of time and effort). This is why I spent a lot of space in the previous article to introduce the socket method and recommend it to you. Of course, converting a general model to onnx format is very convenient, and it provides us with effective support for cross-platform operations. This article mainly involves some basic operations of operating ONNX models in mql5, including how to match the input and output of torch models and ONNX models, and how to convert suitable data formats for ONNX models. Of course, it also includes EA order management. I will explain it in detail for you. Now let’s start the main topic of this article!
Data label for time series mining (Part 6)：Apply and Test in EA Using ONNX
Data label for time series mining (Part 6)：Apply and Test in EA Using ONNX
  • www.mql5.com
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
 

Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file

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.
Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file
Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file
  • www.mql5.com
The project involves using Python for deep learning-based forecasting in financial markets. We will explore the intricacies of testing the model's performance using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) and we will learn how to wrap everything into an executable. We will also make a ONNX model file with its EA.
 

Forum on trading, automated trading systems and testing trading strategies

Python 3.12

MetaQuotes , 2024.02.02 18:55

MetaTrader 5 Python 5.0.50 version available for Python 3.12

You can update like this: 

python -m pip install --upgrade metatrader5

 

Working with ONNX models in float16 and float8 formats

With the advancement of machine learning and artificial intelligence technologies, there is a growing need to optimize processes for working with models. The efficiency of model operation directly depends on the data formats used to represent them. In recent years, several new data types have emerged, specifically designed for working with deep learning models.

In this article, we will focus on two such new data formats - float16 and float8, which are beginning to be actively used in modern ONNX models. These formats represent alternative options to more precise but resource-intensive floating-point data formats. They provide an optimal balance between performance and accuracy, making them particularly attractive for various machine learning tasks. We will explore the key characteristics and advantages of float16 and float8 formats, as well as introduce functions for converting them to standard float and double formats.

This will help developers and researchers better understand how to effectively use these formats in their projects and models. As an example, we will examine the operation of the ESRGAN ONNX model, which is used for image quality enhancement.
Working with ONNX models in float16 and float8 formats
Working with ONNX models in float16 and float8 formats
  • www.mql5.com
Data formats used to represent machine learning models play a crucial role in their effectiveness. In recent years, several new types of data have emerged, specifically designed for working with deep learning models. In this article, we will focus on two new data formats that have become widely adopted in modern models.
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