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Median Price (MEDPRICE)

Classic price-transform classic midpoint

The midpoint between the High and Low prices for a given period.

Visual Example

Median Price (MEDPRICE) — 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

The midpoint between the High and Low prices for a given period.

Use to identify the central tendency of a bar's range. It is the basis for many oscillators and trend-following indicators like the Bill Williams Alligator.

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

Median Price represents the 50% retracement level of the current period's range. By focusing on the High-Low midpoint, it removes the 'bias' of the closing price, which can often be manipulated by end-of-day positioning. — TA-Lib Documentation

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):

\[ MEDPRICE = \frac{High + Low}{2} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::MEDPRICE;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import MEDPRICE

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_median_price_medprice(series: pl.Series) -> pl.Series:
    ind = qw.MEDPRICE(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_median_price_medprice, return_dtype=pl.Float64).alias("median_price_medprice")
    )
    .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/43000502589-median-price-medprice/

Implementation: quantwave-core/src/indicators/price_transform.rs (MEDPRICE / MEDPRICE_METADATA). Parity: quantwave-core/tests/gold_standard/medprice.json

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