- Updated Bessel, Bilateral, Blma, Butter, Conv, Ema, Kama, LSMA, MAMA, MGDI, SSF, USF, ATR, ADL, and ADOSC documentation to use bullet points for key concepts and features.
- Added a new Qodana configuration file for code analysis.
- Removed coverage configuration from Quantower.Tests.csproj to streamline testing setup.
- Introduced the Ultimate Oscillator (UltOsc) indicator with detailed mathematical foundation and performance profile.
- Added historical context and common pitfalls for better user understanding.
- Implemented Bilateral filter with enhanced update methods and batch calculations.
- Updated Blackman Moving Average (BLMA) with improved handling of NaN values and batch processing capabilities.
- Created unit tests for AmatIndicator to ensure proper functionality and signal generation.
- Integrated AmatIndicator into the Quantower platform with appropriate line series for trend and strength visualization.
- Updated project file to include new indicator implementations.
- Implemented AFIRMA (Autoregressive Finite Impulse Response Moving Average) class with support for various window types and batch processing.
- Created unit tests for AFIRMA to validate internal consistency, streaming, and batch processing.
- Added comprehensive documentation for AFIRMA, including usage examples, performance profile, and parameter selection guide.
- Removed obsolete omnisharp.json configuration file.
- Introduced a new `Sum` class implementing the Kahan-Babuška algorithm for high-precision rolling summation.
- Added comprehensive documentation for the `Sum` class, detailing its mathematical foundation, performance profile, and use cases.
- Refactored `VarianceIndicator` tests to improve clarity and coverage, including checks for different source types and the ability to change properties.
- Enhanced `UsfIndicator` tests to validate initialization, processing of updates, and property changes.
- Updated `UsfIndicator` implementation to simplify source handling and improve short name generation.
- Modified Qodana configuration to exclude unused auto property accessor warnings.
- Introduced Cma class for calculating the Cumulative Moving Average using Welford's algorithm with FMA for precision.
- Added methods for batch processing and streaming updates.
- Implemented a comprehensive markdown documentation for CMA, covering its mathematical foundation, performance profile, and use cases.
- Enhanced existing trend indicators (Bessel, Butter, Htit, Jma, Mama, Ssf, Vidya) with FMA for improved numerical stability and precision.
- Updated Adosc to utilize a single-pass algorithm for performance optimization.
- Fixed date initialization in benchmarks to ensure UTC consistency.
Miha Kralj