Anchored VWAP

Classic trend volume classic support-resistance

Volume Weighted Average Price anchored to a specific starting point.

Visual Example

Anchored VWAP — 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 Anchored VWAP indicator is a technical analysis tool that volume weighted average price anchored to a specific starting point.

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 an intraday fair value benchmark. Institutional traders buy below VWAP and sell above it; breakouts above VWAP on heavy volume signal bullish institutional interest.

Volume Weighted Average Price calculates the average price weighted by volume transacted at each level throughout the trading session. It serves as the primary execution benchmark for institutional orders — TWAP and VWAP algorithms are the two most common order execution strategies in equity markets. — Investopedia

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/vwap.rs):

\[ VWAP = \frac{\sum (Price \times Volume)}{\sum Volume} \]

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

Parameters

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

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::VWAP;
use quantwave_core::traits::Next;

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

Streaming (Python)

from quantwave import VWAP

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

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_anchored_vwap(series: pl.Series) -> pl.Series:
    ind = qw.VWAP(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_anchored_vwap, return_dtype=pl.Float64).alias("anchored_vwap")
    )
    .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	Output starts from bar 1; warmup_bars marks period-stability, not NaN.
period > len	Cumulative sum continues; period only affects smoothed variants.
NaN inputs	NaN inputs may produce NaN or skip depending on indicator.
Invalid params	Invalid params raise ValueError.
Empty data	Empty input returns an empty result series.

Sources & References

Primary Source: https://www.investopedia.com/terms/v/vwap.asp

Implementation: quantwave-core/src/indicators/vwap.rs (VWAP / VWAP_METADATA). Parity: quantwave-core/tests/gold_standard/vwap.json

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