- 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