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feat(dynamics): add PlusDI, MinusDI, PlusDM, MinusDM indicators

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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
This commit is contained in:
Miha Kralj
2026-03-11 20:21:52 -07:00
parent 56b86bebfb
commit 33d20f2a18
437 changed files with 4589 additions and 2792 deletions
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lib/forecasts/afirma/Afirma.md
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# AFIRMA: Autoregressive FIR Moving Average
> *Standard Moving Averages assume linear or exponential weights. AFIRMA asks: what if we used signal processing window functions instead?*
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Forecast |
@@ -16,8 +18,6 @@
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Standard Moving Averages assume linear or exponential weights. AFIRMA asks: what if we used signal processing window functions instead?"
AFIRMA is a Windowed Weighted Moving Average that replaces standard linear weighting with weights derived from signal processing window functions (Hanning, Hamming, Blackman, Blackman-Harris). This approach achieves specific frequency response characteristics tailored to noise reduction.
The optional Least Squares mode fits a linear regression to recent bars and blends the fitted values with original data, producing a hybrid smoothed-predicted output.