Schaff Trend Cycle

Modern trend momentum cycle oscillator classic

A hybrid indicator that applies a double-smoothed stochastic to MACD for faster trend identification.

Visual Example

Schaff Trend Cycle — 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

A hybrid indicator that applies a double-smoothed stochastic to MACD for faster trend identification.

Use as a faster trend-cycle momentum indicator. STC typically reaches overbought/oversold levels sooner than MACD while generating fewer false signals than a raw stochastic.

Native Rust implementation with gold-standard or TA-Lib parity tests where applicable.

The Schaff Trend Cycle, developed by Doug Schaff, applies the stochastic oscillator formula twice to MACD values rather than to price. This double stochastic smoothing produces faster, more defined overbought and oversold levels than MACD alone, while the cycle component reduces the lag of a conventional stochastic. — investopedia.com

Typical applications:

Fade extremes in ranges; trade with trend on recoveries from oversold/overbought
Use divergences as early warning — confirm with structure or volume
Parameter default 10 — shorten for sensitivity, lengthen for stability
Drop into build_feature_matrix() for ML research

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/stc.rs):

[ MACD = EMA(23) - EMA(50) ] [ STC = EMA(Stochastic(EMA(Stochastic(MACD, 10), 3), 10), 3) ]

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

Parameters

Parameter	Default	Description
`cycle_period`	10	Stochastic lookback period
`fast_period`	23	Fast EMA period for MACD
`slow_period`	50	Slow EMA period for MACD

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::STC;
use quantwave_core::traits::Next;

let mut ind = STC::new(10);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import STC

ind = STC(10)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_schaff_trend_cycle(series: pl.Series) -> pl.Series:
    ind = qw.STC(10)
    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_schaff_trend_cycle, return_dtype=pl.Float64).alias("schaff_trend_cycle")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 10 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://www.investopedia.com/articles/forex/10/schaff-trend-cycle-indicator.asp

Implementation: quantwave-core/src/indicators/stc.rs (STC / STC_METADATA). Parity: quantwave-core/tests/gold_standard/stc.json

Provenance: Standards bulk upgrade 2026-07-01 IST — see docs/DOCUMENTATION_STANDARDS.md.