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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 2026-07-01 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

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

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

Part of QuantWave's Ehlers digital signal processing suite. Designed for low-lag cycle and trend work — pair with Roofing Filter or SuperSmoother on noisy inputs.

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.

Typical applications:

  • Use for cycle timing in mean-reverting regimes
  • Gate with Hurst exponent or ADX before taking cycle signals
  • Allow 20+ bars warm-up for filter state to stabilise
  • Chain with Roofing Filter when input is noisy

QuantWave implements this via the universal Next<T> trait — bit-identical across Rust streaming, Python streaming, and Polars .ta() batch plugins.

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 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.