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Gap Momentum

Momentum momentum gap kaufman oscillator

Accumulates positive and negative opening gaps to derive a cumulative gap ratio, smoothed by a signal line.

Visual Example

Gap Momentum — 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

Accumulates positive and negative opening gaps to derive a cumulative gap ratio, smoothed by a signal line.

Used to identify momentum shifts based on price gaps. Buy when the signal line is rising and sell when it is falling.

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

Perry J. Kaufman introduced Gap Momentum as a way to quantify price gaps relative to their cumulative volatility, similar to an On-Balance Volume (OBV) logic applied to opening gaps. It helps traders identify if gap-driven momentum is increasing or decreasing by comparing the sum of upward gaps against downward gaps over a rolling window. — Perry Kaufman, S&C 2024

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 40 — 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/gap_momentum.rs):

[ Gap = Open_t - Close_{t-1} ] [ UpGaps = \sum_{i=0}^{Period-1} \max(0, Gap_{t-i}) ] [ DnGaps = \sum_{i=0}^{Period-1} \max(0, -Gap_{t-i}) ] [ GapRatio = \begin{cases} 1 & \text{if } DnGaps = 0 \ 100 \times \frac{UpGaps}{DnGaps} & \text{otherwise} \end{cases} ] [ Signal = SMA(GapRatio, SignalPeriod) ]

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

Parameters

Parameter Default Description
period 40 Rolling window for gap accumulation
signal_period 20 Smoothing period for the gap ratio

Usage Examples

Streaming (Rust)

use quantwave_core::indicators::GAP_MOMENTUM;
use quantwave_core::traits::Next;

let mut ind = GAP_MOMENTUM::new(40);
for price in &prices {
    let value = ind.next(price);
}

Streaming (Python)

from quantwave import GAP_MOMENTUM

ind = GAP_MOMENTUM(40)
for price in prices:
    value = ind.next(price)

Polars Batch (Python)

import polars as pl
import quantwave as qw

def apply_gap_momentum(series: pl.Series) -> pl.Series:
    ind = qw.GAP_MOMENTUM(40)
    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_gap_momentum, return_dtype=pl.Float64).alias("gap_momentum")
    )
    .collect()
)

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

Edge Cases & Limitations

  • Warm-up: first 40 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://github.com/lavs9/quantwave/blob/main/references/traderstipsreference/TRADERS%E2%80%99%20TIPS%20-%20JANUARY%202024.html

Implementation: quantwave-core/src/indicators/gap_momentum.rs (GAP_MOMENTUM / GAP_MOMENTUM_METADATA). Parity: quantwave-core/tests/gold_standard/gap_momentum.json

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