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Typical Price (TYPPRICE)

Classic price-transform classic

An average of the High, Low, and Close prices.

Visual Example

Typical Price (TYPPRICE) — 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

An average of the High, Low, and Close prices.

Use as the primary price input for the Money Flow Index (MFI) and Commodity Channel Index (CCI). It provides a representative price level for the entire bar.

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

Typical Price is a simple average of the High, Low, and Close. It is widely used in indicators that measure the relationship between price and volume, as it offers a more comprehensive view of the day's activity than the Close price alone. — 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/price_transform.rs):

\[ TYPPRICE = \frac{High + Low + Close}{3} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::TYPPRICE;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import TYPPRICE

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_typical_price_typprice(series: pl.Series) -> pl.Series:
    ind = qw.TYPPRICE(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_typical_price_typprice, return_dtype=pl.Float64).alias("typical_price_typprice")
    )
    .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/t/typicalprice.asp

Implementation: quantwave-core/src/indicators/price_transform.rs (TYPPRICE / TYPPRICE_METADATA). Parity: quantwave-core/tests/gold_standard/typprice.json

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