- Add docs/license.md with Apache 2.0 rationale and patent protection analysis
- Add docs/python.md and docs/pinescript.md platform guides
- Expand README license section with disclosure and link to rationale
- Update docs/api.md and docs/architecture.md
- Update Python bindings: helpers, all indicator modules, pyproject.toml
- Add Python tests for Arrow and Polars integration
- Update TValue core type and documentation
- Add fix_length_to_period tooling script
The cycle zone t parameter must use (i-phase+1) per original MQL4 NonLagMA v7.1.
Using (i-phase) eliminated the intentional discontinuity at the phase/cycle boundary, producing incorrect kernel weights visible in the signature SVG.
- Implemented the TRAMA (Trend Regularity Adaptive Moving Average) class with adaptive EMA logic.
- Added unit tests for TRAMA functionality, including constructor validation, basic calculations, state management, and robustness checks.
- Created validation tests to ensure consistency across different modes of operation (streaming, batch, and static calculations).
- Enhanced documentation for TRAMA, including performance profiles and quality metrics.
- Updated workspace configuration by removing unnecessary folder references.
- LTMA (Linear Trend Moving Average): Introduces a predictive moving average using dual cascaded EMAs for trend estimation.
- MCNMA (McNicholl EMA): Implements a zero-lag TEMA using a cascaded EMA structure for enhanced responsiveness.
- NLMA (Non-Lag Moving Average): Utilizes a damped cosine kernel to achieve reduced lag in moving averages.
- NMA (Natural Moving Average): Adapts smoothing based on volatility profiles using a square-root kernel.
- NYQMA (Nyquist Moving Average): Applies the Nyquist-Shannon theorem to prevent aliasing in cascaded moving averages.
- RAIN (Rainbow Moving Average): Combines multiple SMA layers with weighted averages for multi-scale smoothing.
- TRAMA (Trend Regularity Adaptive Moving Average): Adapts smoothing based on the frequency of new highs and lows in price data.
- Updated the name and description of the Hilbert Trendline (HTIT) to "Ehlers Hilbert Transform Instantaneous Trend (HTIT)".
- Changed the name and description of the MESA Adaptive Moving Average (MAMA) to "Ehlers MESA Adaptive Moving Average".
- Modified the Center of Gravity (CG) indicator to "Ehlers Center of Gravity (CG)".
- Renamed the Detrended Synthetic Price (DSP) to "Ehlers Detrended Synthetic Price (DSP)".
- Updated the Autocorrelation Periodogram (EACP) to "Ehlers Autocorrelation Periodogram (EACP)".
- Changed the Homodyne Discriminator (HOMOD) to "Ehlers Homodyne Discriminator (HOMOD)".
- Updated the Hilbert Transform Dominant Cycle Period and Phase indicators to include "Ehlers" in their names.
- Renamed the Hilbert Transform Phasor Components to "Ehlers Hilbert Transform Phasor Components (HT_PHASOR)".
- Updated the SineWave indicator to "Ehlers Hilbert Transform SineWave (HT_SINE)".
- Changed the Phasor Analysis indicator to "Ehlers Hilbert Transform Phasor Components (HT_PHASOR)".
- Updated the SSF-Based Detrended Synthetic Price to "Ehlers SSF Detrended Synthetic Price (SSFDSP)".
- Renamed the Ultimate Channel to "Ehlers Ultimate Channel (UCHANNEL)".
- Added new indicators: Moving Average Variable Period (MAVP), Ehlers Predictive Moving Average (PMA), Ehlers Reverse EMA (REVERSEEMA), and Ehlers Trendflex Indicator (TRENDFLEX).
- Updated various SVG badges to reflect changes in classes, comments, source files, lines of code, methods, and public types.
- Added Ztest class to compute the one-sample t-statistic using sample standard deviation with Bessel correction.
- Implemented validation tests for Ztest to ensure accuracy against manual calculations and PineScript.
- Updated documentation for Ztest, detailing its mathematical foundation, performance profile, and common pitfalls.
- Adjusted NDepend badges to reflect changes in code metrics after implementation.
- Updated missing indicators report to reflect the completion of statistical indicators, including ZTEST.
- Updated class count in classes.svg from 938 to 1078.
- Adjusted comments percentage in comments.svg from 33.06 to 33.02.
- Revised average cyclomatic complexity in complexity.svg from 2.19 to 2.12.
- Increased source files count in files.svg from 1099 to 1275.
- Updated lines of code in loc.svg from 114549 to 129859.
- Increased methods count in methods.svg from 12035 to 14066.
- Updated public types count in public-api.svg from 1086 to 1225.
- Revised missing indicators report with updated counts and categories, reflecting recent implementations and planned additions.
- Implemented SgmaIndicator class in C# with properties for Period, Degree, and Source.
- Added unit tests for SgmaIndicator covering constructor defaults, initialization, and various update scenarios.
- Created a new Quantower adapter for the SGMA indicator, including input parameters and line series setup.
- Removed legacy SGMA implementation and tests to streamline the codebase.
- Updated project files to include new indicator and tests in the build process.
- Generated a missing indicators report and outlined a plan for oscillator documentation rewrite.
- Introduced Massi validation tests to ensure mathematical properties hold for the Mass Index indicator.
- Added Va validation tests for Volume Accumulation, checking for finite outputs and correct accumulation behavior.
- Implemented Vf validation tests for Volume Force, verifying outputs for rising and falling prices, and ensuring batch and streaming results match.
- Created Vo validation tests for Volume Oscillator, confirming behavior with constant, increasing, and decreasing volumes.
- Developed Vroc validation tests for Volume Rate of Change, validating outputs for constant volume and changes in volume.
- Updated project file to include new momentum indicators (MACD and RSI) in the compilation.
- Implemented Stochastic Oscillator (%K and %D) in Stoch.cs with streaming and batch processing capabilities.
- Added validation tests for the Stochastic Oscillator in Stoch.Validation.Tests.cs, ensuring consistency with Skender.Stock.Indicators.
- Created documentation for the Stochastic Oscillator in Stoch.md, detailing its mathematical formula, architecture, parameters, and common pitfalls.
- Updated project file to include necessary numeric libraries for highest and lowest calculations.
- Implemented Prime method in Vel, Ao, Apo, Frama, Adl, Adosc, Aobv, Cmf, Efi, Eom, Iii, Kvo, Mfi, Nvi, Obv, Pvd, Pvi, Pvo, Pvr, Pvt, Tvi, Twap, Va, Vf, Vo, Vroc, Vwad, Vwap, and Vwma classes.
- The Prime method resets the indicator state and processes the provided historical bar data to initialize the indicator.
- Added warmup period property to Adl and Wad classes to define the minimum number of data points required for validity.
- Updated benchmark tests to use Batch methods for performance evaluation.
- Added detailed comments explaining the validation limitations for MMA and ZLEMA due to differences in algorithm implementations.
- Implemented validation tests for True Range against TALib and Tulip, ensuring directional agreement.
- Updated Ulcer Index validation to clarify differences in algorithmic approaches between QuanTAlib and Skender.
- Enhanced Ease of Movement tests to verify directional agreement with Tulip's EMV, noting differences in volume scaling.
- Expanded Klinger Volume Oscillator tests to validate against Skender and Tulip, focusing on directional agreement across multiple period configurations.
- Improved Negative Volume Index tests to compare percentage changes with Tulip, addressing differences in starting values.
- Updated Positive Volume Index tests to validate against Tulip, emphasizing percentage change comparisons.
- Enhanced Williams Accumulation/Distribution tests to verify directional agreement with Tulip, highlighting formula differences.
- Implemented the Standardize class for calculating Z-Score normalization over a specified lookback period.
- Updated NDepend badge SVG files to reflect new metrics.
- Modified NDepend project files to reference the updated solution file name.
- Removed outdated documentation files related to indicator proposals and channel documentation remediation.
- Updated workspace configuration to point to the new solution file.
- Implemented Vortex Indicator in Vortex.cs, including calculation logic and event handling.
- Added detailed documentation for Vortex Indicator in Vortex.md, covering historical context, algorithm, outputs, and trading interpretation.
- Updated oscillators index to include TTM Wave indicator.
- Added TTM Wave documentation with algorithm and trading interpretation.
- Updated reversals index to include TTM Scalper Alert indicator.
- Added TTM Scalper Alert documentation with algorithm and trading strategy.
- Updated NDepend badges to reflect increased code metrics (classes, methods, lines of code, public types, comments, and complexity).
- Implemented ChopIndicator for Quantower with configurable period and cold value display.
- Created Chop class for calculating the Choppiness Index with detailed documentation.
- Added comprehensive unit tests for Chop functionality, covering various market conditions and edge cases.
- Developed markdown documentation for CHOP, detailing its historical context, mathematical foundation, and usage examples.
- Established a remediation plan for channel indicators documentation, identifying gaps and prioritizing updates.
- Implemented the SSF-DSP (Super Smooth Filter Detrended Synthetic Price) indicator using dual Super Smooth Filters.
- Added validation tests to ensure correctness against PineScript implementation and mathematical properties.
- Created comprehensive documentation outlining the architecture, mathematical foundation, performance profile, and common pitfalls.
- Included batch processing capabilities for efficient calculations on time series data.
- Introduced YZV class for calculating Yang-Zhang Volatility, a comprehensive volatility measure that incorporates overnight, open-to-close, and high-low components.
- Implemented calculation methods, including batch processing for TBarSeries and spans.
- Added documentation for YZV, detailing its mathematical foundation, performance profile, and trading applications.
- Updated volume index documentation to reflect changes in file paths.
- Refactored VWMA calculation method to use a more generic source parameter instead of price.
- Implemented CCV class for calculating annualized log return volatility using SMA, EMA, and WMA smoothing methods.
- Added comprehensive unit tests for CCV to validate mathematical correctness, consistency across methods, and edge cases.
- Created documentation for CCV detailing its mathematical foundation, smoothing methods, and performance metrics.
Miha Kralj