# Source code for pandas_ta.momentum.roc

```# -*- coding: utf-8 -*-
from .mom import mom
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series

[docs]def roc(close, length=None, scalar=None, talib=None, offset=None, **kwargs):
"""Indicator: Rate of Change (ROC)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
scalar = float(scalar) if scalar and scalar > 0 else 100
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True

if close is None: return

# Calculate Result
if Imports["talib"] and mode_tal:
from talib import ROC
roc = ROC(close, length)
else:
roc = scalar * mom(close=close, length=length) / close.shift(length)

# Offset
if offset != 0:
roc = roc.shift(offset)

# Handle fills
if "fillna" in kwargs:
roc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
roc.fillna(method=kwargs["fill_method"], inplace=True)

# Name and Categorize it
roc.name = f"ROC_{length}"
roc.category = "momentum"

return roc

roc.__doc__ = \
"""Rate of Change (ROC)

Rate of Change is an indicator is also referred to as Momentum (yeah, confusingly).
It is a pure momentum oscillator that measures the percent change in price with the
previous price 'n' (or length) periods ago.

Sources:

Calculation:
Default Inputs:
length=1
MOM = Momentum
ROC = 100 * MOM(close, length) / close.shift(length)

Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 1
scalar (float): How much to magnify. Default: 100
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0

Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method

Returns:
pd.Series: New feature generated.
"""
```