Example of using an ON N X model to recognize handwritten numbers
This professional-grade solution for MetaTrader 5 helps traders achieve greater efficiency in their daily workflow. This Expert Advisor serves as automated trading software. It is utilized to monitor financial markets and execute trades based on predefined algorithmic rules, enabling precise position management without the need for constant manual oversight.
How to Setup and Use Example of using an ON N X model to recognize handwritten numbers
1. Installation: Open the "File" menu, select "Open Data Folder," navigate to MQL/Experts, paste your file, and restart the terminal.
2. Activation: Drag the EA from the Navigator onto a chart, ensure "Allow live trading" is checked in the Common tab, and verify the AutoTrading button is green.
3. Optimization: Right-click your chart, choose "Expert List," click "Properties" to adjust inputs, and save your preferred setup as a set file for future use.
4. Maintenance: Regularly check the "Experts" tab in the terminal window to monitor trade logs and potential execution errors.
Frequently Asked Questions
Q: Why is my EA not opening trades? A: Check the "AutoTrading" button, ensure "Allow live trading" is enabled, and verify your broker allows automated trading on your account type.
Q: Can I run multiple EAs on one chart? A: No, each chart can only host one active EA; however, you can open multiple charts for different currency pairs to run several EAs.
Q: What does the "smiley face" icon mean? A: A smiley face in the top-right corner of the chart indicates the EA is successfully running; a frowny face means it is disabled.
Description & Settings
The database consists of 60,000 images for training and 10,000 images for testing. These images were created by "re-mixing" an original NIST set of 20x20 pixel black-and-white samples, which in turn were obtained from the US Census Bureau and supplemented with testing samples taken from American high school students. The samples were normalized to 28x28 pixel size and anti-aliased, which introduced grayscale levels.
The trained handwritten digit recognition model mnist.onnx was downloaded from Github from (opset 8). Those interested can download and try other models, excluding models with opset 1, which is no longer supported by the latest ONNX runtime. Surprisingly, the output vector was not processed with the activation function, as is common in classification models. Well, this is not a problem as we can easily implement this ourselves.
Draw digits in a special grid using the mouse, holding down the left mouse button. To recognize the drawn digit, press the CLASSIFY button.
If the obtained probability for the recognized digit is less than 0.8, the resulting vector with probabilities for each class is printed to the log. For example, try classifying an empty unfilled input field.
For some reason, the recognition accuracy is notably lower for the number nine (9). Left-slanted digits are recognized more accurately.
