Discussing the article: "Exporting Symbol Tick Data to Binary Files in MQL5 for Offline Analysis"
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Check out the new article: Exporting Symbol Tick Data to Binary Files in MQL5 for Offline Analysis.
The article delivers a complete, verifiable tick export path from MQL5 to a binary file and into Python. It defines a 64‑byte header, 48‑byte records with millisecond time and flags, an export pipeline using CopyTicksRange(), and a single‑call NumPy loader. Users obtain compact, precision‑preserving files and a reproducible workflow for vectorized analysis.
MetaTrader 5 stores complete tick history for every symbol in the terminal's data cache. Every bid change, ask change, and trade print that arrived during the terminal's connected lifetime is accessible through MQL5's tick history API. That data is valuable for offline analysis: spread distribution studies, microstructure research, feed quality audits, input generation for machine learning models, and backtesting at tick resolution with external tools. The problem is that extracting it in a form that external tools can read efficiently is not straightforward.
The obvious approach is to export ticks to CSV. A CSV file is human-readable and universally supported, but it has serious practical limitations for tick data. A single active trading day for a major forex pair can produce several million ticks. At around 60 bytes per tick in CSV format, one trading day fills roughly 180 MB. Reading and parsing that file in Python or R requires string-buffer allocations, delimiter splits, and per-field text-to-float conversion. Only then do you get usable numeric data. Floating-point values printed as text lose precision at the last decimal place. The parse step dominates the load time for any substantial tick history.
This article builds an MQL5 script that exports tick data to a structured binary file. The binary format stores each tick in a fixed-width 48-byte record with no text conversion, no delimiter parsing, and no precision loss. The same million ticks that occupy 180 MB in CSV occupy roughly 46 MB in this format. A Python reader using NumPy loads and fully decodes that data in under one second using a single array read. The implementation separates the tick record layout, the file header, and the export pipeline into distinct modules with clearly bounded responsibilities.
Author: Ushana Kevin Iorkumbul