- Enhanced Alma class by simplifying the CalculateWeightedSum method and removing unnecessary comments.
- Removed SIMD-related methods from Conv class, replacing them with optimized DotProduct calls.
- Updated Sma and Wma classes to use source.ContainsNonFinite() for non-finite value checks, improving readability and performance.
- 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.
- Implemented unit tests for the following indicators:
- KAMA (Kaufman Adaptive Moving Average)
- SMA (Simple Moving Average)
- T3 (Tillson T3 Moving Average)
- TEMA (Triple Exponential Moving Average)
- TRIMA (Triangular Moving Average)
- WMA (Weighted Moving Average)
- Each test class includes tests for constructor defaults, history depth, short name, initialization, processing updates, and source type handling.
- Updated the Quantower.Tests.csproj to include all new test files in the lib directory.
- 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.
- Deleted DebugTulip.Tests.cs as it was no longer needed.
- Refactored T3.cs to encapsulate parameters in a struct for better organization and readability.
- Updated methods in T3.cs to use the new Parameters struct, improving clarity and reducing redundancy.
- Enhanced T3.md documentation to provide clearer explanations of the T3 moving average and its parameters.
- Removed Wma.Coverage.Tests.cs as it was obsolete.
- Added new tests in IndicatorExtensions.Tests.cs to validate logic methods and ensure correct calculations.
- Updated IndicatorExtensions.cs to improve method organization and add new functionality for handling chart coordinates.
- Refactored mocks in TradingPlatformMocks.cs to align with new chart interface definitions.
- 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.
- Updated SMA validation tests to include separate methods for Skender and TA-Lib with batch, streaming, and span calculations.
- Enhanced WMA validation tests similarly, ensuring comprehensive coverage for Skender and TA-Lib.
- Improved verification helper methods for better clarity and maintainability.
- 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 Wma class for calculating the Weighted Moving Average with detailed documentation and optimized performance using dual running sums.
- Introduced WmaVector class to handle multiple WMAs simultaneously, supporting batch calculations and real-time updates.
- Implemented last-value substitution for handling invalid inputs (NaN/Infinity) in both classes.
- Created comprehensive unit tests for Wma and WmaVector to ensure accuracy and reliability of calculations.
- Updated documentation to include usage examples, mathematical foundations, and performance characteristics.
- 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.
- Introduced type definitions for various classes in the QuanTAlib library, including Ema, EmaVector, EmaState, TSeries, CsvFeed, GBM, TBarSeries, TBar, and TValue.
- Added methods and properties for each class to enhance functionality and maintainability.
- Created a lock file to manage dependencies and ensure consistent builds.
- Enhanced TBarSeriesTests with additional constructors, methods, and assertions for better coverage.
- Improved TSeriesTests to include new constructors, methods, and edge cases.
- Expanded TValueTests to cover constructors, implicit conversions, equality checks, and hash codes.
- Updated project file to target .NET 10.0 and include internal visibility for tests.
- Added Codacy configuration for code quality checks.
- Introduced TBar struct for efficient OHLCV data representation.
- Implemented TBarSeries class for high-performance collection of TBar instances using Structure of Arrays (SoA) layout.
- Added TSeries class for time-series data management with zero-copy access.
- Created TValue struct for time-value pairs with implicit conversions.
- Defined IFeed interface for consistent data feed implementations.
- Developed CsvFeed class for loading historical OHLCV data from CSV files.
- Implemented GBM class for generating synthetic financial data using Geometric Brownian Motion.
- Added Quantower project files for Averages indicator with necessary dependencies and configurations.
- Included extensive usage examples and notebooks for TBar, TBarSeries, TSeries, TValue, and feed implementations.
- 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