- Enhanced WMA indicator with event-driven capabilities using ITValuePublisher interface.
- Created a new TODO file listing various indicators and their corresponding libraries.
- Added unit tests for DEMA, HMA, TEMA, and WMA indicators to ensure proper functionality.
- Implemented tests for handling new bars, ticks, and historical data updates across indicators.
- Verified that indicators correctly compute values and handle different source types.
- Implemented TEMA calculation in QuanTAlib with O(1) update complexity.
- Added validation tests for TEMA against Skender, TA-Lib, and Tulip indicators.
- Updated documentation for TEMA, including its mathematical foundation and usage examples.
- Enhanced existing tests for other indicators (TRIMA, WMA) to generate more records.
- Adjusted benchmark tests to include DEMA and TEMA comparisons.
- Refactored code for better readability and performance, including zero-allocation Span API.
- Removed WmaVector class to streamline weighted moving average calculations.
- Simplified RingBuffer implementation by removing unnecessary comments and improving clarity.
- Enhanced SIMD extensions for better performance and readability.
- Updated TBar and TBarSeries classes to improve property calculations and reduce overhead.
- Cleaned up TValue struct by removing redundant comments.
- Added comprehensive unit tests for IndicatorExtensions and TrimaIndicator to ensure functionality and correctness.
- Introduced `TrimaVector` class for multi-period Triangular Moving Average (TRIMA) calculations, optimized for SIMD.
- Implemented last-value substitution for invalid inputs in TRIMA.
- Added methods for calculating TRIMA for entire series and individual updates.
- Enhanced `Wma` class with periodic resync to prevent floating-point drift and introduced SIMD optimizations for performance.
- Updated benchmark suite to include TRIMA calculations alongside existing SMA, EMA, and WMA benchmarks.
- Implemented zero-allocation methods for SMA, EMA, and WMA calculations using ReadOnlySpan and Span.
- Added unit tests for Span API to validate input, match TSeries calculations, handle NaN values, and ensure zero allocation.
- Enhanced documentation to include usage examples for the new Span API.
- Introduced performance benchmarks comparing the new Span API against existing TSeries implementations and other libraries.
- Added SmaVector class for calculating multiple SMAs in parallel using SIMD.
- Introduced RingBuffer class for efficient circular buffer management with running sum.
- Implemented unit tests for RingBuffer to ensure correctness and performance.
- Enhanced Add method in RingBuffer to support bar correction semantics.
- Added methods for calculating Min and Max using SIMD acceleration.
- Improved performance with pinned memory and direct span access for SIMD compatibility.
Miha Kralj