Vortex Indicator
The Vortex Indicator helps identify the start of a new trend or the continuation of an existing one.
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 Vortex Indicator indicator is a technical analysis tool that the vortex indicator helps identify the start of a new trend or the continuation of an existing one.
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 detect the start of new trends. A Vortex Indicator crossover (VI+ crossing above VI-) signals the beginning of an uptrend; the reverse signals a downtrend.
The Vortex Indicator, developed by Etienne Botes and Douglas Siepman (2010), is inspired by the vortex flow of water discovered by Viktor Schauberger. VI+ measures upward movement relative to the prior bar low; VI- measures downward movement relative to the prior bar high. Normalized by ATR, they produce two oscillating lines whose crossovers signal trend changes. — Technical Analysis of Stocks and Commodities, 2010
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/vortex.rs):
Gold-standard parity vectors: quantwave-core/tests/gold_standard/vortex.json.
Parameters
| Parameter | Default | Description |
|---|---|---|
period |
14 | Period |
Usage Examples
Streaming (Rust)
use quantwave_core::indicators::VORTEX;
use quantwave_core::traits::Next;
let mut ind = VORTEX::new(14);
for price in &prices {
let value = ind.next(price);
}
Streaming (Python)
Polars Batch (Python)
import polars as pl
import quantwave as qw
def apply_vortex_indicator(series: pl.Series) -> pl.Series:
ind = qw.VORTEX(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_vortex_indicator, return_dtype=pl.Float64).alias("vortex_indicator")
)
.collect()
)
All surfaces are bit-identical via the single Next<T> implementation and proptests.
Edge Cases & Limitations
- Warm-up: first
14bars 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.investopedia.com/terms/v/vortex-indicator-vi.asp
Implementation: quantwave-core/src/indicators/vortex.rs (VORTEX / VORTEX_METADATA).
Parity: quantwave-core/tests/gold_standard/vortex.json
Provenance: Standards bulk upgrade 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.