Skip to content

MESA Stochastic

Ehlers DSP oscillator stochastic ehlers cycle adaptive

Standard Stochastic calculation applied to Roofing Filtered data, followed by SuperSmoothing.

Visual Example

MESA Stochastic — annotated preview mapping to core implementation

Synthetic ideal per library logic. Generated 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The MESA Stochastic indicator is a technical analysis tool that standard stochastic calculation applied to roofing filtered data, followed by supersmoothing.

This indicator is primarily used for identifying key market conditions. It provides a robust signal that can be easily integrated into both simple strategies and more complex machine learning feature pipelines. Compared to its alternatives, it offers a distinct balance of responsiveness and stability.

Traders often combine this with other metrics to confirm signals and avoid false positives during sideways market regimes. It remains a standard tool for systematic trading models.

Use as a cycle-synchronized stochastic that automatically scales its lookback to the measured dominant cycle period for consistent overbought/oversold signals.

The MESA Stochastic extends Ehlers adaptive stochastic concept by using the MESA-measured dominant cycle period as the lookback window. Unlike traditional stochastics with fixed periods, it adapts to the current market rhythm, keeping the oscillator calibrated to one full cycle at all times.

QuantWave implements this indicator via the universal Next<T> trait, guaranteeing bit-identical results between Rust streaming, Python streaming, and Polars batch (.ta() / map_batches) surfaces.

Formula / Specification

Implementation (quantwave-core/src/indicators/mesa_stochastic.rs):

[ Filt = \text{RoofingFilter}(Price, P_{hp}, P_{ss}) ] [ Stoc = \frac{Filt - \min(Filt, L)}{\max(Filt, L) - \min(Filt, L)} ] [ MESAStoch = \text{SuperSmoother}(Stoc \times 100, P_{ss}) ]

Gold-standard parity vectors: quantwave-core/tests/gold_standard/mesa_stochastic.json.

Parameters

Parameter Default Description
length 20 Stochastic lookback length
hp_period 48 HighPass critical period
ss_period 10 SuperSmoother critical period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::MESA_STOCHASTIC;
use quantwave_core::traits::Next;

let mut ind = MESA_STOCHASTIC::new(20);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import MESA_STOCHASTIC

ind = MESA_STOCHASTIC(20)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_mesa_stochastic(series: pl.Series) -> pl.Series:
    ind = qw.MESA_STOCHASTIC(20)
    return pl.Series([ind.next(float(v)) for v in series.to_list()])

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("close").map_batches(apply_mesa_stochastic, return_dtype=pl.Float64).alias("mesa_stochastic")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Recursive DSP filters require a warm-up period; first N bars may be unstable or raw-pass-through.
  • Designed for cyclic/mean-reverting regimes; trending markets can produce lag or drift.
  • Parameter period (or equivalent) controls cutoff — too small adds noise, too large adds lag.
  • Prefer chaining with other Ehlers tools (Roofing Filter, SuperSmoother) on noisy inputs.
  • Validated via proptests against gold-standard vectors where available.
  • No look-ahead bias; suitable for live streaming and batch feature pipelines.

Boundary Behavior

Condition Behavior
Warm-up Leading bars return NaN until warmup_bars is satisfied.
period > len When period exceeds series length, output is all NaN.
NaN inputs NaN in input propagates to output (NaN out).
Invalid params Non-positive period or missing required params raise ValueError.
Empty data Empty input returns an empty result series.

Sources & References

Primary Source: https://github.com/lavs9/quantwave/blob/main/references/Ehlers%20Papers/Anticipating%20Turning%20Points.pdf

Implementation: quantwave-core/src/indicators/mesa_stochastic.rs (MESA_STOCHASTIC / MESA_STOCHASTIC_METADATA). Parity: quantwave-core/tests/gold_standard/mesa_stochastic.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.