Normalizing MetaTrader Price Data for CGI Visualization

Summary

This article describes a small data preparation workflow for turning MetaTrader 5 price history into inputs for a computer-generated character whose size changes with price. It outlines linear and exponential mappings from price changes to object scale, then focuses on exporting bar data and using Python with Pandas to select open and close prices and normalize them to a common zero-to-one range. The normalized output is intended for later use in Blender animation and other visual presentations.

The document is primarily a visualization and data handling project rather than a trading strategy. It explains manual export and scripted processing, and reports that the script handled more than a thousand values, but gives no market analysis or evidence that the character conveys sentiment accurately. Because normalization uses the minimum and maximum across the dataset, its output depends on the selected sample and may change when the range changes. The proposed real-time MetaTrader panel and animation workflow are future development goals, not completed or evaluated results.

Key ideas

• The project maps price changes to the scale of a CGI object as a visual representation of market movement.
• Linear and exponential functions are proposed for controlling the relationship between price and object size.
• A Pandas workflow selects open and close prices from exported MetaTrader data and normalizes them to a zero-to-one range.
• The normalized series are intended for visualization and animation rather than signal generation.
• The article reports data processing progress but does not evaluate whether the visualization measures market sentiment.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.