# -*- coding: utf-8 -*-
from numpy import nan as npNaN
from pandas import concat
from pandas_ta import Imports
from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
[docs]def true_range(high, low, close, talib=None, drift=None, offset=None, **kwargs):
"""Indicator: True Range"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
drift = get_drift(drift)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import TRANGE
true_range = TRANGE(high, low, close)
else:
high_low_range = non_zero_range(high, low)
prev_close = close.shift(drift)
ranges = [high_low_range, high - prev_close, prev_close - low]
true_range = concat(ranges, axis=1)
true_range = true_range.abs().max(axis=1)
true_range.iloc[:drift] = npNaN
# Offset
if offset != 0:
true_range = true_range.shift(offset)
# Handle fills
if "fillna" in kwargs:
true_range.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
true_range.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
true_range.name = f"TRUERANGE_{drift}"
true_range.category = "volatility"
return true_range
true_range.__doc__ = \
"""True Range
An method to expand a classical range (high minus low) to include
possible gap scenarios.
Sources:
https://www.macroption.com/true-range/
Calculation:
Default Inputs:
drift=1
ABS = Absolute Value
prev_close = close.shift(drift)
TRUE_RANGE = ABS([high - low, high - prev_close, low - prev_close])
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
drift (int): The shift period. Default: 1
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
"""