- Implement tests for HMA (Hull Moving Average) indicator to verify default settings, history depth calculations, and value computations during updates.
- Create tests for KAMA (Kaufman Adaptive Moving Average) indicator, ensuring correct defaults, history depth, and value calculations.
- Add tests for SMA (Simple Moving Average) indicator, checking default values, history depth, and value computations.
- Develop tests for T3 (Tillson T3 Moving Average) indicator, validating defaults, history depth, and value calculations.
- Implement tests for TEMA (Triple Exponential Moving Average) indicator, ensuring correct defaults and value computations.
- Create tests for TRIMA (Triangular Moving Average) indicator, verifying defaults, history depth, and value calculations.
- Add tests for WMA (Weighted Moving Average) indicator, checking default values, history depth, and value computations.
- 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.
- Changed from O(n) CircularBuffer.Average() to O(1) running sum
- Maintains _sum and _p_sum for state management
- Tracks _lastValue and _p_lastValue for isNew=false updates
- Provides ~15-20x speedup for large periods
- Pattern verified against Pine Script reference implementation
- All tests pass including update test for isNew handling
Also added .github/copilot-instructions.md with comprehensive
AI agent guidance for QuanTAlib development patterns
Miha Kralj