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Bill Williams Fractals

Classic pattern support-resistance classic williams

Fractals are indicators on candlestick charts that identify reversal points in the market.

Visual Example

Bill Williams Fractals — 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

Fractals are indicators on candlestick charts that identify reversal points in the market.

Use to mark potential support and resistance levels at local price extremes. Williams Fractals are commonly combined with Alligator lines to filter valid fractal signals.

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

Bill Williams introduced Fractals in Trading Chaos (1995) as a pattern-recognition tool identifying local price extremes. A bullish fractal is a bar whose low is lower than the two bars on either side; a bearish fractal is a bar whose high is higher than the two bars on either side. Combined with the Alligator indicator, fractals provide entry triggers. — StockCharts ChartSchool

Typical applications:

  • See Parameters — default period/length N
  • Validated via proptests and gold-standard vectors where available
  • Use Polars .ta plugins for batch; streaming_class() for live

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/fractals.rs):

\[ \text{Up Fractal} = \text{High} > \text{High}_{t-1, t-2, t+1, t+2} \]

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

Parameters

Parameter Default Description
(none) — No tunable parameters for this detector.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::FRACTALS;
use quantwave_core::traits::Next;

let mut ind = FRACTALS::new(14);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import FRACTALS

ind = FRACTALS(14)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_bill_williams_fractals(series: pl.Series) -> pl.Series:
    ind = qw.FRACTALS(14)
    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_bill_williams_fractals, return_dtype=pl.Float64).alias("bill_williams_fractals")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first N 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/terms/f/fractal.asp

Implementation: quantwave-core/src/indicators/fractals.rs (FRACTALS / FRACTALS_METADATA). Parity: quantwave-core/tests/gold_standard/fractals.json

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