Heikin-Ashi

Classic trend candlestick smoothing classic visualization

Heikin-Ashi candles filter market noise to reveal the underlying trend.

Visual Example

Heikin-Ashi — 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

Heikin-Ashi candles filter market noise to reveal the underlying trend.

Use to smooth candlestick charts and reduce noise for trend identification. Two or more consecutive same-colored HA candles with no lower/upper wicks confirm a strong trend.

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

Heikin-Ashi candles, developed by Munehisa Homma in the 18th century, use averaged OHLC values to produce smoother candles that better represent the prevailing trend. Each HA bar open equals the midpoint of the previous HA bar, while close equals the OHLC average, creating continuity that raw candles lack. — StockCharts ChartSchool

Typical applications:

Trend filter or signal line for systematic entries
Default lookback N — tune per asset volatility
Cross with faster oscillator for entry timing
Streaming and Polars paths are bit-identical for production parity

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/heikin_ashi.rs):

\[ HA_{Close} = \frac{O + H + L + C}{4} \\ HA_{Open} = \frac{HA_{Open, t-1} + HA_{Close, t-1}}{2} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::HEIKIN_ASHI;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import HEIKIN_ASHI

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_heikin_ashi(series: pl.Series) -> pl.Series:
    ind = qw.HEIKIN_ASHI(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_heikin_ashi, return_dtype=pl.Float64).alias("heikin_ashi")
    )
    .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/trading/heikin-ashi-better-candlestick/

Implementation: quantwave-core/src/indicators/heikin_ashi.rs (HEIKIN_ASHI / HEIKIN_ASHI_METADATA). Parity: quantwave-core/tests/gold_standard/heikin_ashi.json

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