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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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The Schaff Trend Cycle indicator is a technical analysis tool that a hybrid indicator that applies a double-smoothed stochastic to macd for faster trend identification.

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

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

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/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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.