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lib/trends_IIR/dsma/Dsma.md
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# DSMA: Deviation-Scaled Moving Average
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Trend (IIR MA) |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `scaleFactor` (default 0.5) |
| **Outputs** | Single series (Dsma) |
| **Output range** | Tracks input |
| **Warmup** | `period` bars |
### TL;DR
- DSMA (Deviation-Scaled Moving Average) is a volatility-adaptive trend filter that combines a Super Smoother (2-pole Butterworth IIR filter) with RM...
- Parameterized by `period`, `scalefactor` (default 0.5).
- 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.
> "When the market screams, DSMA sprints. When it whispers, DSMA crawls. An adaptive moving average that lets volatility dictate the pace."
DSMA (Deviation-Scaled Moving Average) is a volatility-adaptive trend filter that combines a Super Smoother (2-pole Butterworth IIR filter) with RMS-based deviation scaling. Unlike fixed-period moving averages that treat all market conditions identically, DSMA adjusts its responsiveness based on measured volatility—accelerating when trends are strong and decelerating when prices consolidate.
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6. **Bar Correction**: Like all QuanTAlib indicators, DSMA supports bar correction via the `isNew` parameter. When `isNew = false`, it rolls back to the previous state before recalculating. Ensure your data feed correctly signals bar updates versus corrections.
7. **SIMD Limitation**: The recursive nature of the Super Smoother filter and adaptive alpha calculation precludes efficient SIMD vectorization. The `Calculate(Span)` method uses a scalar loop. For bulk backtesting, consider parallelizing across multiple series rather than within a single series.
7. **SIMD Limitation**: The recursive nature of the Super Smoother filter and adaptive alpha calculation precludes efficient SIMD vectorization. The `Calculate(Span)` method uses a scalar loop. For bulk backtesting, consider parallelizing across multiple series rather than within a single series.