Stochastic Distance Oscillator

Momentum momentum stochastic oscillator apirine trend

A momentum indicator based on the classic stochastic oscillator applied to price distances.

Visual Example

Stochastic Distance Oscillator — 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 Stochastic Distance Oscillator indicator is a technical analysis tool that a momentum indicator based on the classic stochastic oscillator applied to price distances.

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.

Identify bull and bear trend changes through overbought (+40) and oversold (-40) levels. Suitable for both trending and ranging markets.

The Stochastic Distance Oscillator (SDO) by Vitali Apirine adapts the stochastic formula to measure the current price distance relative to its historical range. By smoothing this relative distance with an EMA, it provides a cleaner momentum signal that identifies potential trend reversals when crossing extreme thresholds.

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/sdo.rs):

[ Dist = |Price_t - Price_{t-n}| ] [ DVal = \frac{Dist - \min(Dist_{lookback})}{\max(Dist_{lookback}) - \min(Dist_{lookback})} ] [ DDVal = \begin{cases} DVal & \text{if } Price_t > Price_{t-n} \ -DVal & \text{if } Price_t < Price_{t-n} \ 0 & \text{otherwise} \end{cases} ] [ SDO = EMA(DDVal, smoothing) \times 100 ]

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

Parameters

Parameter	Default	Description
`lookback_period`	200	Range lookback for stochastic calculation
`period`	12	Distance calculation period
`ema_pds`	3	Smoothing EMA period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::SDO;
use quantwave_core::traits::Next;

let mut ind = SDO::new(200);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import SDO

ind = SDO(200)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_stochastic_distance_oscillator(series: pl.Series) -> pl.Series:
    ind = qw.SDO(200)
    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_stochastic_distance_oscillator, return_dtype=pl.Float64).alias("stochastic_distance_oscillator")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 200 bars may return NaN or partial state per implementation.
Parameter sensitivity: smaller periods increase noise; larger periods increase lag.
Sudden gaps or bad ticks can distort rolling windows — consider pre-filtering.
Single-series indicators ignore volume unless otherwise documented.
Validated via proptests against gold-standard vectors where available.
No look-ahead bias; streaming and Polars batch paths are bit-identical.

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://traders.com/Documentation/FEEDbk_docs/2023/06/TradersTips.html

Implementation: quantwave-core/src/indicators/sdo.rs (SDO / SDO_METADATA). Parity: quantwave-core/tests/gold_standard/sdo.json

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