Analysis of full .slnx (800+ files across 5 projects) still times out
even with --no-build. Target lib/quantalib.csproj directly to scope
analysis to the main library (~400 files). Consistent with proven
local inspectcode approach.
- Pre-build solution before inspectcode to avoid redundant 4-min internal build
- Add --no-build flag to jb inspectcode
- Add --exclude patterns for bin/obj/TestResults and binary artifacts
- Bump timeout-minutes from 15 to 20 as safety margin
- 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
PyPI trusted publisher OIDC token exchange failed because the workflow
job did not declare a GitHub environment. PyPI's trusted publisher config
requires environment claim to match. Added environment: pypi to the
Publish_Package job in Release.yml.
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.
.sonarlint/ is gitignored so CI and fresh clones lack these files. Unconditional CodeAnalysisRuleSet reference caused MSB3884 error which with /warnaserror broke Release builds and froze JB InspectCode.
- 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