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lib/oscillators/mstoch/Mstoch.md
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# MSTOCH: Ehlers MESA Stochastic
# MSTOCH: Ehlers MESA Stochastic
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Oscillator |
| **Inputs** | Source (close) |
| **Parameters** | `stochLength` (default 20), `hpLength` (default 48), `ssLength` (default 10) |
| **Outputs** | Single series (Mstoch) |
| **Output range** | Varies (see docs) |
| **Warmup** | 1 bar |
### TL;DR
- The MESA Stochastic applies John Ehlers' Roofing Filter as a preprocessing stage before computing a stochastic oscillator, then smooths the stochas...
- Parameterized by `stochlength` (default 20), `hplength` (default 48), `sslength` (default 10).
- Output range: Varies (see docs).
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
The MESA Stochastic applies John Ehlers' Roofing Filter as a preprocessing stage before computing a stochastic oscillator, then smooths the stochastic output with a Super Smoother. The Roofing Filter removes both low-frequency trend components (via highpass) and high-frequency noise (via Super Smoother), isolating the dominant cycle. The stochastic calculation on this filtered data produces a clean 0-to-1 oscillator that responds to cycle turning points rather than trend or noise, with substantially reduced whipsaw compared to conventional stochastic indicators.