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Weighted Close Price (WCLPRICE)

Classic price-transform classic weighted

An average of the High, Low, and Close prices, with double weight given to the Close price.

Visual Example

Weighted Close Price (WCLPRICE) — 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, with double weight given to the Close price.

Use to emphasize the importance of the closing price while still accounting for the total range of the bar.

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

Weighted Close Price gives additional significance to the Close, reflecting the widely held belief that the closing price is the most important data point in a trading session. It provides a more nuanced input for smoothing algorithms. — 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):

\[ WCLPRICE = \frac{High + Low + 2 \times Close}{4} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::WCLPRICE;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import WCLPRICE

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_weighted_close_price_wclprice(series: pl.Series) -> pl.Series:
    ind = qw.WCLPRICE(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_weighted_close_price_wclprice, return_dtype=pl.Float64).alias("weighted_close_price_wclprice")
    )
    .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/43000502590-weighted-close-wclprice/

Implementation: quantwave-core/src/indicators/price_transform.rs (WCLPRICE / WCLPRICE_METADATA). Parity: quantwave-core/tests/gold_standard/wclprice.json

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