- Implemented Aroon Indicator with constructor, initialization, and update methods.
- Added unit tests for AroonIndicator to verify default settings, historical depth, short name, source code link, and processing of historical bars.
- Created Aroon class for core calculations, including methods for updating with TBar and TBarSeries.
- Added validation tests to ensure Aroon calculations match results from Skender and TA-Lib.
- Updated documentation for Aroon Indicator with calculation methods and usage examples.
- Refactored Dema and Wma classes to use Batch methods for calculations.
- Enhanced performance benchmarks by increasing bar count and integrating OoplesFinance indicators.
- Updated project dependencies to include OoplesFinance.StockIndicators.
- Use curl with -fSL flags for fail-fast behavior and proper redirects
- Check curl exit code immediately after download
- Replace piped echo with temporary file for sha256sum verification
- Use sha256sum -c --status for cleaner verification
- Add detailed error logging with expected checksum
- Clean up temp file and binary on any failure
- Exit 1 immediately on download or verification failure
- Updated .coderabbit.yaml to exclude additional file types from reviews, improving the focus on relevant code changes.
- Modified scanner.sh to handle test failures more gracefully, ensuring that analysis stops on test failures and improving logging.
- Improved sonarscanner.sh to ensure build and test failures are properly reported, enhancing CI reliability.
- Refined SimdExtensions.cs documentation for clarity on variance calculation methods.
- Cleaned up TSeries.Tests.cs by simplifying the test structure and ensuring proper namespace usage.
- Fixed potential issues in tseries.cs by ensuring correct handling of DateTime values.
- Enhanced CsvFeed.cs to improve error handling during CSV parsing, ensuring robustness against malformed data.
- Updated GBM.cs to correctly calculate volume in the current bar, ensuring accurate simulation.
- Adjusted index.html to use globalThis for better compatibility across environments.
- Refined quantalib.csproj to exclude unnecessary files from compilation, streamlining the build process.
- Added comprehensive tests for the Mama class to ensure correct behavior during updates and state management.
- Improved error handling in various trend classes (Kama, Dema, Ema, T3, Tema, Wma) to ensure NaN values are managed correctly.
- Removed redundant Mama.Repro.Tests.cs file and consolidated tests into Mama.Tests.cs for better organization.
- Enhanced T3 and Tema classes to maintain state integrity during updates, particularly with NaN values.
- 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