- Introduced `FeatureScale.mqh` with `FeatSquash` function for stateless feature scaling.
- Added `RegimeMath.mqh` class for regime arithmetic, including efficiency and variance calculations.
- Documented the Mind trading logic in `MIND.md`, detailing the trading process and modes.
- Created `VOLNORM_PLAN.md` and `VOLNORM_RESULTS.md` for tick-volume normalization testing.
- Implemented `read_book.py` for analyzing trade book data and correlations.
- Developed `volnorm.py` for testing tick-volume normalization with new and old methods.
- Replaced standard library signal modules with custom implementations to allow for named patterns and improved voting.
- Added new input parameters for module weights, allowing for optimization of individual signal contributions.
- Enhanced the management of trades with new options for breakeven and management cut.
- Introduced a mechanism for dynamic ranking of signal weights based on historical performance.
- Improved initialization logic to ensure proper registration of filters and handling of trading conditions.
- Added detailed logging for trading permissions and account status during initialization.
