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Volume Positive Negative

Volume volume breakout katsanos vpn momentum

Uses the EMA-smoothed Atr, not Wilder's ATR

The VPN volume threshold is built on QuantWave's Atr, which smooths true range with an EMA (alpha = 2/(period+1)) rather than Wilder's RMA (alpha = 1/period, SMA-seeded) used by TA-Lib and TradingView Pine's ta.atr. Threshold crossings can differ from a Wilder-ATR VPN.

No source has been recorded for the EMA smoothing — the formula_source recorded for this indicator describes the Wilder-based construction. See Average True Range for the full surface-by-surface breakdown, and quantwave.conventions("vpn") to read the divergence programmatically.

Detects high-volume breakouts by comparing volume on up days vs down days, normalized between -100 and 100.

Visual Example

Volume Positive Negative — 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

Detects high-volume breakouts by comparing volume on up days vs down days, normalized between -100 and 100.

Use to confirm breakouts. A VPN value crossing above a critical threshold (e.g., 10) signals a high-volume positive breakout.

Volume-flow indicator for confirming price moves and detecting accumulation/distribution.

While originally using EMA for smoothing, this implementation employs the UltimateSmoother to further reduce lag in detecting volume-driven trend shifts, aligning with modern DSP standards for technical indicators.

Typical applications:

  • Fade extremes in ranges; trade with trend on recoveries from oversold/overbought
  • Use divergences as early warning — confirm with structure or volume
  • Parameter default 30 — shorten for sensitivity, lengthen for stability
  • Drop into build_feature_matrix() for ML research

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/vpn.rs):

[ TP = \frac{High + Low + Close}{3} ] [ MF = TP - TP_{t-1} ] [ MC = 0.1 \times ATR(Period) ] [ VP = \sum_{i=0}^{Period-1} (\text{if } MF_{t-i} > MC_{t-i} \text{ then } Volume_{t-i} \text{ else } 0) ] [ VN = \sum_{i=0}^{Period-1} (\text{if } MF_{t-i} < -MC_{t-i} \text{ then } Volume_{t-i} \text{ else } 0) ] [ MAV = \text{Average}(Volume, Period) ] [ VPN = \frac{VP - VN}{MAV \times Period} \times 100 ]

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

Parameters

Parameter Default Description
period 30 Calculation period for volume sums and ATR
smooth_period 3 Smoothing period for the final VPN value

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::VPN;
use quantwave_core::traits::Next;

let mut ind = VPN::new(30);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import VPN

ind = VPN(30)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_volume_positive_negative(series: pl.Series) -> pl.Series:
    ind = qw.VPN(30)
    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_volume_positive_negative, return_dtype=pl.Float64).alias("volume_positive_negative")
    )
    .collect()
)

All surfaces are bit-identical via the single Next<T> implementation and proptests.

Edge Cases & Limitations

  • Warm-up: first 30 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.traders.com/Documentation/FEEDbk_docs/2021/04/TradersTips.html

Implementation: quantwave-core/src/indicators/vpn.rs (VPN / VPN_METADATA). Parity: quantwave-core/tests/gold_standard/vpn.json

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