Average Price (AVGPRICE)

Classic price-transform classic smoothing

The simple average of the Open, High, Low, and Close prices for a given period.

Visual Example

Average Price (AVGPRICE) — 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 Average Price (AVGPRICE) indicator is a technical analysis tool that the simple average of the open, high, low, and close prices for a given period.

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 smoothed price input for other indicators. It provides a more balanced view of the period's price action than the Close price alone.

Average Price is the arithmetic mean of the four key price points in a bar. In technical analysis, using Average Price instead of Close can help filter out erratic price spikes and provide a more stable foundation for trend-following algorithms. — TA-Lib Documentation

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/price_transform.rs):

\[ AVGPRICE = \frac{Open + High + Low + Close}{4} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::AVGPRICE;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import AVGPRICE

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

Polars Batch (Python)

import polars as pl

df = (
    pl.read_csv('ohlcv.csv')
    .lazy()
    .with_columns(
        pl.col("open").ta.avgprice("open", "high", "low", "close").alias("average_price_avgprice")
    )
    .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.tradingview.com/support/solutions/43000502588-average-price-avgprice/

Implementation: quantwave-core/src/indicators/price_transform.rs (AVGPRICE / AVGPRICE_METADATA). Parity: quantwave-core/tests/gold_standard/avgprice.json

Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.