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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 2026-06-25 IST via docs/generate_all_previews.py (reproducible; maps to core Next<T> implementation).

Description

The Gap Momentum indicator is a technical analysis tool that accumulates positive and negative opening gaps to derive a cumulative gap ratio, smoothed by a signal line.

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.

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

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

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/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 2026-06-25 IST — see docs/DOCUMENTATION_STANDARDS.md.