How does the skill validate indicator accuracy?

It uses a correlation-driven validation gate, ensuring that the Python implementation matches the MQL5 reference data with a correlation coefficient of 0.999 or higher.

What indicators are supported for automated export?

The skill can automatically export OHLCV data and built-in indicators like RSI and SMA. Custom indicator buffers are limited by the MT5 API but can be validated once translated.

Can I use this on macOS?

Yes, it is specifically designed to work within CrossOver or Wine environments on macOS, targeting the standard MetaTrader 5 bottle paths.

Does this skill require the MetaTrader 5 GUI to be open?

No, the skill supports headless execution via Wine and the MetaTrader5 Python API, allowing for automated data exports and validation without GUI initialization.

MQL5 Python Workspace

Name: MQL5 Python Workspace
Author: terrylica

byterrylica

•

Ciencia de Datos y ML

Automates the translation of MetaTrader 5 (MQL5) indicators into validated Python implementations for algorithmic trading.

Acerca de

The MQL5 Python Workspace skill bridges the gap between MetaTrader 5 and Python environments, enabling developers to seamlessly port trading strategies and indicators. It provides a robust, autonomous workflow for exporting high-fidelity market data via headless Wine execution and features a validation-driven iteration process that ensures Python implementations achieve a 0.999+ correlation with original MQL5 references. This skill is essential for quantitative traders looking to leverage Python's data science ecosystem while maintaining the integrity of their existing MQL5 logic.

Características Principales

2 GitHub stars
High-precision validation gating with >=0.999 correlation requirements
Headless market data and indicator value export via MetaTrader 5 API
Wine and CrossOver environment management for macOS compatibility
Automated MQL5-to-Python logic translation and implementation
Self-correcting workflows based on documented error patterns and failure logs

Casos de Uso

Validating the accuracy of Python-based trading bots against MT5 benchmarks
Automating market data exports for external backtesting engines
Translating legacy MT5 indicators into Python for machine learning research

Acerca de

Características Principales

2 GitHub stars
High-precision validation gating with >=0.999 correlation requirements
Headless market data and indicator value export via MetaTrader 5 API
Wine and CrossOver environment management for macOS compatibility
Automated MQL5-to-Python logic translation and implementation
Self-correcting workflows based on documented error patterns and failure logs

Casos de Uso

Validating the accuracy of Python-based trading bots against MT5 benchmarks
Automating market data exports for external backtesting engines
Translating legacy MT5 indicators into Python for machine learning research