gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-24 21:48:03 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'dev'

Browse Source
This commit is contained in:
Miha Kralj
2026-03-13 13:47:10 -07:00
parent a8946cfab7 c75135ab14
commit 86a1db286c
404 changed files with 2754 additions and 1763 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/trends_FIR/rwma/Rwma.md
+2 -4
View File
@@ -13,9 +13,7 @@
| **PineScript** | [rwma.pine](rwma.pine) |
- RWMA weights each bar's contribution to the average by its price range (high minus low), giving greater influence to volatile bars and less to narr...
- Parameterized by `period` (default 14).
- Output range: Tracks input.
- Requires `> period` bars of warmup before first valid output (IsHot = true).
- **Similar:** [WMA](../wma/wma.md), [EMA](../../trends_IIR/ema/ema.md) | **Trading note:** Right-weighted MA; concentrates weight on recent data while maintaining FIR structure.
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
RWMA weights each bar's contribution to the average by its price range (high minus low), giving greater influence to volatile bars and less to narrow-range, indecisive bars. The logic: a bar with a large range represents stronger price discovery and carries more informational content than a low-range doji. This produces a moving average that gravitates toward prices established during high-activity periods, naturally incorporating volatility as a relevance signal without requiring a separate volatility indicator.
@@ -120,4 +118,4 @@ O(1) per bar. The division is the dominant cost. Resync every 1000 bars prevents
| Prefix sum of range | Partial | Same as above |
| Final division | Yes | `VDIVPD` after prefix sums built; zero-guard via `VCMPPD` + blend |
Both prefix sums can be built with AVX2 prefix-scan kernels. Once built, all N sliding-window divisions can be computed in parallel. Batch speedup: approximately 4× over scalar for large series.
Both prefix sums can be built with AVX2 prefix-scan kernels. Once built, all N sliding-window divisions can be computed in parallel. Batch speedup: approximately 4× over scalar for large series.