Source code for pandas_ta.trend.cksp

# -*- 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}" = f"CKSPl{_props}" = f"CKSPs{_props}" long_stop.category = short_stop.category = "trend" # Prepare DataFrame to return ckspdf = DataFrame({ long_stop, short_stop}) = 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: "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. """