zeonta.roc() — Percentage price change over n bars — the normalised sibling of momentum.
What it measures
The normalised sibling of momentum: the same n-bars-back comparison, expressed as a percentage instead of a raw price difference. That one change makes it comparable across symbols and across price levels of the same symbol over time.
Formula
ROC = [(Close - Close n periods ago) / (Close n periods ago)] x 100
Parameters
Required inputs: close
| Parameter | Default |
|---|---|
length |
12 |
Returns
| Column |
|---|
ROC_12 |
Usage
Examples run against the 300-bar OHLCV fixture in tests/data/ohlcv.csv, loaded as df. The output shown is the real output.
import pandas as pd
import zeonta
df = pd.read_csv('tests/data/ohlcv.csv', parse_dates=['date']).set_index('date')
zeonta.roc(df['close'], length=12).tail(3)
date
2024-10-25 -1.119992
2024-10-26 -2.012009
2024-10-27 -4.132452
Name: ROC_12, dtype: float64
Accessor form: df.zta.roc(...)
How to read it
ROC oscillates around zero the same way Momentum does, but a reading of “+5” always means the same thing — a 5% rise over the window — whether the symbol trades at $10 or $10,000. Sharp spikes away from zero mark unusually fast moves relative to the instrument’s own recent pace.
Pitfalls
ROC divides by the price n bars ago, so it is undefined (returned as NaN) on any bar whose reference close happened to be exactly zero — a real possibility on instruments quoted as a spread or a rate rather than a price. It also inherits Momentum’s whipsaw behaviour in a range: a fast oscillation with no persistent trend behind it.
Reference
Formula source: https://chartschool.stockcharts.com/table-of-contents/technical-indicators-and-overlays/technical-indicators/rate-of-change-roc