Absolute Price Oscillator (APO)

Classic trend momentum moving-average classic

Shows the absolute difference between two moving averages of different periods.

Visual Example

Absolute Price Oscillator (APO) — 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

Shows the absolute difference between two moving averages of different periods.

Use to identify trend crossovers and momentum. It is essentially a MACD without the signal line, showing the raw distance between fast and slow averages.

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

The Absolute Price Oscillator (APO) is based on the difference between two exponential moving averages. It is a trend-following indicator that signals a change in direction when the fast EMA crosses the slow EMA, providing a clear visual of trend development. — TA-Lib Documentation

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 12 — 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/momentum.rs):

\[ APO = EMA(fast) - EMA(slow) \]

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

Parameters

Parameter	Default	Description
`fastperiod`	12	Fast period
`slowperiod`	26	Slow period

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::APO;
use quantwave_core::traits::Next;

let mut ind = APO::new(12);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import APO

ind = APO(12)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_absolute_price_oscillator_apo(series: pl.Series) -> pl.Series:
    ind = qw.APO(12)
    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_absolute_price_oscillator_apo, return_dtype=pl.Float64).alias("absolute_price_oscillator_apo")
    )
    .collect()
)

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

Edge Cases & Limitations

Warm-up: first 12 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.tradingview.com/support/solutions/43000501826-absolute-price-oscillator-apo/

Implementation: quantwave-core/src/indicators/momentum.rs (APO / APO_METADATA). Parity: quantwave-core/tests/gold_standard/apo.json

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