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Percent Rank

Statistics statistics rank percentile normalization

Where the current value sits inside its own trailing window, as the fraction of window values less than or equal to it.

Visual Example

Window 5 on [1, 2, 3, 4, 3]. The first four bars are NaN. The fifth bar's window is 1, 2, 3, 4, 3; four of those five values are <= 3, so the rank is 0.8. A constant window ranks 1.

Description

Where the current value sits inside its own trailing window, as the fraction of window values less than or equal to it.

Rank a series against its own history before comparing years. A daily chain gextotal column uses period 252. The rank is not a signal by itself; compare it with a threshold in the caller.

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

Not an Ehlers filter. Empirical percent rank of a trailing window, including the current observation.

Typical applications:

  • See Parameters — default period/length 252
  • 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 (series_norm):

\[\mathrm{percent\_rank}_t = \frac{\#\{x_i \le x_t : i \in [t-n+1, t]\}}{n}\]

Parameters

Parameter Default Description
period 252 Trailing window length, in bars. 252 is one trading year of daily data.

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::PercentRank;
use quantwave_core::traits::Next;

let mut ind = PercentRank::new(252);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import PercentRank

ind = PercentRank(252)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_percent_rank(series: pl.Series) -> pl.Series:
    ind = qw.PercentRank(252)
    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_percent_rank, return_dtype=pl.Float64).alias("percent_rank")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first 252 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: Standard trailing percent rank. Window and 0.90 / 0.10 thresholds follow the gamma-exposure normalization in David Bergstrom, Build Alpha, Gamma Exposure (Sep 2026).

Implementation: quantwave-core/src/indicators/series_norm (PercentRank / _METADATA).

Provenance: Standards bulk upgrade 2026-10-02 IST — see docs/DOCUMENTATION_STANDARDS.md.