mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-22 12:38:06 +00:00
feat(dynamics): add PlusDI, MinusDI, PlusDM, MinusDM indicators
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:
@@ -1,5 +1,7 @@
|
||||
# Gauss: Gaussian Filter
|
||||
|
||||
> *SMA smears data like cheap paint. Gaussian filtering respects the signal's soul.*
|
||||
|
||||
| Property | Value |
|
||||
| ---------------- | -------------------------------- |
|
||||
| **Category** | Filter |
|
||||
@@ -18,8 +20,6 @@
|
||||
- Requires `2⌈3σ⌉+1` bars of warmup before first valid output (IsHot = true). Default: **7 bars** (σ=1.0).
|
||||
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
|
||||
|
||||
> "SMA smears data like cheap paint. Gaussian filtering respects the signal's soul."
|
||||
|
||||
Gauss (Gaussian Filter) is a smoothing filter that applies a Gaussian kernel to time series data. Unlike Simple Moving Average (SMA), which weights all points in the window equally (boxcar function), the Gaussian filter applies weights that follow a bell curve distribution. This minimizes lag while providing superior noise reduction and significantly better preservation of signal edges.
|
||||
|
||||
## Historical Context
|
||||
|
||||
Reference in New Issue
Block a user