gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-20 19:48:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

doc headers

Browse Source
This commit is contained in:
Miha Kralj
2026-02-27 07:48:12 -08:00
parent 8a1ba95173
commit 4ab3a7fb53
389 changed files with 6682 additions and 468 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/trends_IIR/hwma/Hwma.md
+18 -1
View File
@@ -1,5 +1,22 @@
# HWMA: Holt-Winters Moving Average
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Trend (IIR MA) |
| **Inputs** | Source (close) |
| **Parameters** | `period` (default 10) |
| **Outputs** | Single series (Hwma) |
| **Output range** | Tracks input |
| **Warmup** | `period` bars |
### TL;DR
- HWMA is an Infinite Impulse Response (IIR) filter that applies triple exponential smoothing with level (F), velocity (V), and acceleration (A) comp...
- Parameterized by `period` (default 10).
- Output range: Tracks input.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Triple exponential smoothing: because sometimes tracking level, velocity, and acceleration is exactly what a price series needs—and sometimes it's overkill. Holt and Winters figured this out for inventory forecasting in the 1950s. Traders rediscovered it decades later."
HWMA is an Infinite Impulse Response (IIR) filter that applies triple exponential smoothing with level (F), velocity (V), and acceleration (A) components. Unlike simple exponential smoothing which only tracks the current level, HWMA anticipates future values by extrapolating trend and trend changes.
@@ -236,4 +253,4 @@ HWMA has constant memory regardless of period—approximately **142 bytes** per
5. **Seasonal Confusion**: "Holt-Winters" often implies seasonal decomposition. This implementation is the non-seasonal variant focusing on level-trend-acceleration only.
6. **Parameter Sensitivity**: Small changes in β and γ significantly affect behavior. Start with the default period-based derivation before experimenting with custom values.
6. **Parameter Sensitivity**: Small changes in β and γ significantly affect behavior. Start with the default period-based derivation before experimenting with custom values.