# -*- coding: utf-8 -*-
from pandas import DataFrame
from .true_range import true_range
from pandas_ta.overlap import ma
from pandas_ta.utils import get_offset, high_low_range, verify_series
[docs]def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **kwargs):
"""Indicator: Keltner Channels (KC)"""
# Validate arguments
length = int(length) if length and length > 0 else 20
scalar = float(scalar) if scalar and scalar > 0 else 2
mamode = mamode if isinstance(mamode, str) else "ema"
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
if high is None or low is None or close is None: return
# Calculate Result
use_tr = kwargs.pop("tr", True)
if use_tr:
range_ = true_range(high, low, close)
else:
range_ = high_low_range(high, low)
basis = ma(mamode, close, length=length)
band = ma(mamode, range_, length=length)
lower = basis - scalar * band
upper = basis + scalar * band
# Offset
if offset != 0:
lower = lower.shift(offset)
basis = basis.shift(offset)
upper = upper.shift(offset)
# Handle fills
if "fillna" in kwargs:
lower.fillna(kwargs["fillna"], inplace=True)
basis.fillna(kwargs["fillna"], inplace=True)
upper.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
lower.fillna(method=kwargs["fill_method"], inplace=True)
basis.fillna(method=kwargs["fill_method"], inplace=True)
upper.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"{mamode.lower()[0] if len(mamode) else ''}_{length}_{scalar}"
lower.name = f"KCL{_props}"
basis.name = f"KCB{_props}"
upper.name = f"KCU{_props}"
basis.category = upper.category = lower.category = "volatility"
# Prepare DataFrame to return
data = {lower.name: lower, basis.name: basis, upper.name: upper}
kcdf = DataFrame(data)
kcdf.name = f"KC{_props}"
kcdf.category = basis.category
return kcdf
kc.__doc__ = \
"""Keltner Channels (KC)
A popular volatility indicator similar to Bollinger Bands and
Donchian Channels.
Sources:
https://www.tradingview.com/wiki/Keltner_Channels_(KC)
Calculation:
Default Inputs:
length=20, scalar=2, mamode=None, tr=True
TR = True Range
SMA = Simple Moving Average
EMA = Exponential Moving Average
if tr:
RANGE = TR(high, low, close)
else:
RANGE = high - low
if mamode == "ema":
BASIS = sma(close, length)
BAND = sma(RANGE, length)
elif mamode == "sma":
BASIS = sma(close, length)
BAND = sma(RANGE, length)
LOWER = BASIS - scalar * BAND
UPPER = BASIS + scalar * BAND
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
length (int): The short period. Default: 20
scalar (float): A positive float to scale the bands. Default: 2
mamode (str): See ```help(ta.ma)```. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
tr (bool): When True, it uses True Range for calculation. When False, use a
high - low as it's range calculation. Default: True
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.DataFrame: lower, basis, upper columns.
"""