# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.volatility import atr
from pandas_ta.utils import get_offset, verify_series
[docs]def cksp(high, low, close, p=None, x=None, q=None, tvmode=None, offset=None, **kwargs):
"""Indicator: Chande Kroll Stop (CKSP)"""
# Validate Arguments
# TV defaults=(10,1,9), book defaults = (10,3,20)
p = int(p) if p and p > 0 else 10
x = float(x) if x and x > 0 else 1 if tvmode is True else 3
q = int(q) if q and q > 0 else 9 if tvmode is True else 20
_length = max(p, q, x)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
if high is None or low is None or close is None: return
offset = get_offset(offset)
tvmode = tvmode if isinstance(tvmode, bool) else True
mamode = "rma" if tvmode is True else "sma"
# Calculate Result
atr_ = atr(high=high, low=low, close=close, length=p, mamode=mamode)
long_stop_ = high.rolling(p).max() - x * atr_
long_stop = long_stop_.rolling(q).max()
short_stop_ = low.rolling(p).min() + x * atr_
short_stop = short_stop_.rolling(q).min()
# Offset
if offset != 0:
long_stop = long_stop.shift(offset)
short_stop = short_stop.shift(offset)
# Handle fills
if "fillna" in kwargs:
long_stop.fillna(kwargs["fillna"], inplace=True)
short_stop.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
long_stop.fillna(method=kwargs["fill_method"], inplace=True)
short_stop.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{p}_{x}_{q}"
long_stop.name = f"CKSPl{_props}"
short_stop.name = f"CKSPs{_props}"
long_stop.category = short_stop.category = "trend"
# Prepare DataFrame to return
ckspdf = DataFrame({long_stop.name: long_stop, short_stop.name: short_stop})
ckspdf.name = f"CKSP{_props}"
ckspdf.category = long_stop.category
return ckspdf
cksp.__doc__ = \
"""Chande Kroll Stop (CKSP)
The Tushar Chande and Stanley Kroll in their book
“The New Technical Trader”. It is a trend-following indicator,
identifying your stop by calculating the average true range of
the recent market volatility. The indicator defaults to the implementation
found on tradingview but it provides the original book implementation as well,
which differs by the default periods and moving average mode. While the trading
view implementation uses the Welles Wilder moving average, the book uses a
simple moving average.
Sources:
https://www.multicharts.com/discussion/viewtopic.php?t=48914
"The New Technical Trader", Wikey 1st ed. ISBN 9780471597803, page 95
Calculation:
Default Inputs:
p=10, x=1, q=9, tvmode=True
ATR = Average True Range
LS0 = high.rolling(p).max() - x * ATR(length=p)
LS = LS0.rolling(q).max()
SS0 = high.rolling(p).min() + x * ATR(length=p)
SS = SS0.rolling(q).min()
Args:
close (pd.Series): Series of 'close's
p (int): ATR and first stop period. Default: 10 in both modes
x (float): ATR scalar. Default: 1 in TV mode, 3 otherwise
q (int): Second stop period. Default: 9 in TV mode, 20 otherwise
tvmode (bool): Trading View or book implementation mode. 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.DataFrame: long and short columns.
"""