QuanTAlib

523 Commits 5 Branches 13 Tags

2 Commits

Include renames

Author	SHA1	Message	Date
Miha Kralj	967096d4f5	Refactor and optimize various components of QuanTAlib - 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.	2025-12-04 13:49:05 -08:00
Miha Kralj	3ed35322a5	Add TRIMA implementation and benchmarks; optimize WMA with SIMD - 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.	2025-12-04 13:05:56 -08:00

Author

SHA1

Message

Date

Miha Kralj

967096d4f5

Refactor and optimize various components of QuanTAlib

- 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.

2025-12-04 13:49:05 -08:00

Miha Kralj

3ed35322a5

Add TRIMA implementation and benchmarks; optimize WMA with SIMD

- 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.

2025-12-04 13:05:56 -08:00