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Statistical mean reversion oscillator designed to identify regions where price deviates significantly from its average.
Built with a quantitative approach, the indicator highlights periods in which the market exhibits abnormal deviations from its typical behavior, providing an objective assessment of price overextension conditions.
The tool can be used to support mean reversion studies, market context evaluation, and the identification of statistical extremes across multiple asset classes and timeframes.
Developed by Frequência do Mercado.
Updated 2026-06-12 02:36:17 +00:00
Codes for the article Regression models in the Scikit-learn library and their export to ONNX
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MT5 prop firm risk manager: daily loss, drawdown, trade limits, lock state, and dashboard.
Updated 2026-06-11 17:17:41 +00:00
Sanitized SourceBank for IchiGridEA ONNX/MQL5/Python audit evidence, deep review notes, contracts and non-executable knowledge base. Not a trading EA. Not production code.
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Codes for the article Regression models in the Scikit-learn library and their export to ONNX
Updated 2026-06-09 11:12:42 +00:00
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This reference project is based on an MQL5 article describing a native Sequential Least Squares Programming (SLSQP) optimizer for constrained nonlinear optimization and its integration into an existing conditional volatility library intended to mirror Python's `arch` module behavior.
Updated 2026-06-05 13:01:39 +00:00
This project documents an MQL5 implementation approach for asymmetric volatility modeling, extending a prior ARCH/GARCH library with support for:
- GJR-GARCH
- TARCH
The article explains why asymmetric volatility matters in financial time series, especially under the leverage effect, and shows how these models can be integrated into an MQL5 volatility framework for fitting, forecasting, and comparative analysis across asset classes.
Updated 2026-06-05 12:44:15 +00:00
This project documents and organizes the concepts presented in an MQL5 article about a native library for univariate volatility modeling and forecasting. The article describes a modular framework inspired by Python's `arch` package.
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