Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage Made Easier

One of the critical aspects of working with machine learning models is maintaining consistent data structures across various phases – training, validation, and testing. The integration of Open Neural Network Exchange (ONNX) models into MQL5 and MetaTrader 5 provides the opportunity to use models trained externally within the MQL5 environment for trading. This opens up a new realm for data handling demands, highlighting the need for structures like Python’s Pandas in MQL5.

Python remains the predominant language for training AI models that are later deployed in MetaTrader 5 using MQL5 code. However, discrepancies in data organization and value representation often arise between these technologies. Our aim in this article is to emulate the Pandas library from Python within the MQL5 environment, which is crucial for handling and manipulating large datasets.

Essentials of the Pandas Library

Pandas offers two primary classes for data manipulation: Series and Dataframe. While a Pandas Series is akin to a one-dimensional array or vector, our focus is on the more complex two-dimensional “Dataframe”. A Dataframe is similar to a table with rows and columns, representing data stored in a manner that is both human-readable and practical for data scientists.

Understanding that the core of a Pandas Dataframe is a two-dimensional array, we can recreate this concept in MQL5 using matrices. Let’s delve into how this can be implemented.

File: pandas.mqh

Central to our implementation is an array, m_columns, designed to hold column names. Unlike other data libraries like Numpy, Pandas maintains human-friendly data by associating it with these names. Therefore, implementing both the data matrix and column tracking is essential.

MQL5’s syntax constraints prevent a direct replica of Python’s Dataframe creation. Therefore, we provide an Insert method to allow adding information to our Dataframe class:

Implementation and Usage

The class constructor can receive a matrix and its corresponding column names, adding versatility to how data can be ingested into the Dataframe. For most scenarios, using the Insert method is recommended for populating your Dataframe.

Once your dataset is prepared, it’s often necessary to load existing datasets. A common task is reading a CSV file, a functionality readily available in Python’s Pandas. In MQL5, reading from a CSV and assigning it directly to a Dataframe can be mirrored:

It is crucial to quickly inspect your Dataframe to ensure data integrity and structure. The Pandas head method allows viewing the leading n rows of your Dataframe. By default, the first five rows are displayed, providing a convenient snapshot of your data’s composition.

Exporting and Indexing Data

After data collection into your Dataframe, exporting it for further ML procedures is necessary. A CSV file serves as an ideal format for this task, ensuring seamless integration back into Python for advanced analytics and model training.

Indexing and selecting specific Dataframe components is vital. Whether for making predictions by accessing recent data points or leveraging initial training rows, efficient slicing and selection are mandatory:

Accessing Specific Columns

A string index operator enables column access by name, while the Iloc method can select specific integers ranges akin to Python’s iloc, facilitating both column and row access by position.

Dropping Unnecessary Data

Using the drop method, remove unwanted columns, allowing optimization of data for training processes.

Data Exploration and Analysis

Following indexing, functions aiding in data exploration and inspection provide foundational support. Viewing the last few rows using methods akin to Pandas, along with grasping data structure, types, memory usage, and ensuring absence of null values, are instrumental tasks.

Descriptive Statistics and Data Insights

For numeric data columns, methods inspired by Pandas provide descriptive statistics, including means, standard deviations, and percentiles. Such functionalities are crucial for deriving insights into dataset characteristics.

The MQL5 adaptations of Pandas features culminate in comprehensive dataset handling, enabling seamless transitions from data collection to exploration and application in machine learning workflows.

Conclusion

Creating, managing, and utilizing Dataframes within MQL5 bridges the gap between Python’s robust data manipulation capabilities and MetaTrader 5’s trading environment. By mirroring Pandas’ functionalities, we provide a powerful toolset for traders and data scientists alike, enhancing ML model integrations across platforms.

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