- Full template: property table, tagline, key takeaways, historical context,
architecture & physics with code references, mathematical foundation,
interpretation & signal zones, related indicators, validation table,
performance profile, common pitfalls, FAQ, references
- Title changed from "Rocket RSI" to "Ehlers Rocket RSI" across all indexes
- Updated _sidebar.md, lib/_index.md, lib/oscillators/_index.md
Comprehensive refactor across all indicators replacing the periodic
ResyncInterval-based drift correction (every 1000 ticks recalculate
from scratch) with Kahan compensated summation for running sums.
Key changes:
- Remove ResyncInterval constants and TickCount fields from all State records
- Add Kahan compensation fields (SumComp, SumSqComp, etc.) to State records
- Replace naive sum += val - removed with Kahan delta pattern
- Remove Resync()/RecalculateSum() methods that did O(N) recalculation
- Update batch/SIMD paths to use Kahan compensation instead of resync loops
- IIR filters (EMA, REMA, RGMA) simplified: inherently self-correcting
- Version bump to 0.8.7
- Build system: README version stamping via Directory.Build.props
- Minor doc/test tolerance adjustments for new numerical characteristics
Affected modules: channels, core, cycles, dynamics, errors, momentum,
oscillators, statistics, trends_FIR, trends_IIR, volatility, volume
- Publish.yml: Change workflow permissions from {} to contents:read
to fix GITHUB_TOKEN having no scopes for actions/checkout@v4
- Wrmse.cs: Use DateTime.UtcNow instead of DateTime.MinValue in
Update(double,double,double) to match Beta/Covariance pattern
- Bump version to 0.8.5
Complete thin Dx-composition wrapper indicators with full test coverage:
- PlusDi/MinusDi: Directional Indicator wrappers (DiPlus/DiMinus from Dx)
- PlusDm/MinusDm: Directional Movement wrappers (DmPlus/DmMinus from Dx)
- Individual validation tests per indicator directory (TALib, Skender, bounds)
- Combined unit tests (DiDm.Tests.cs) and validation tests (DiDm.Validation.Tests.cs)
- Quantower wrappers + tests for all 4 indicators
- PineScript v6 implementations with compensated RMA
- Normalized .md documentation for all indicators and categories
- 182 tests passing, 0 failures
Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
- 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.
Miha Kralj