Volume Positive Negative
Detects high-volume breakouts by comparing volume on up days vs down days, normalized between -100 and 100.
Visual Example

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 Volume Positive Negative indicator is a technical analysis tool that detects high-volume breakouts by comparing volume on up days vs down days, normalized between -100 and 100.
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 to confirm breakouts. A VPN value crossing above a critical threshold (e.g., 10) signals a high-volume positive breakout.
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.
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/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)
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
30bars 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. |
Related Indicators & See Also
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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.