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 = 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. """