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111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import sqrt as npsqrt
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from pandas import DataFrame
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from .atr import atr
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from ..overlap.hlc3 import hlc3
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from ..statistics.variance import variance
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from ..utils import get_offset, verify_series
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def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **kwargs):
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"""Indicator: Keltner Channels (KC)"""
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# Validate arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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length = int(length) if length and length > 0 else 20
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min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
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scalar = float(scalar) if scalar and scalar > 0 else 2
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mamode = mamode.lower() if mamode else None
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offset = get_offset(offset)
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# Calculate Result
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std = variance(close=close, length=length).apply(npsqrt)
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if mamode == 'ema':
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basis = close.ewm(span=length, min_periods=min_periods).mean()
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band = atr(high=high, low=low, close=close)
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else:
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hl_range = high - low
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typical_price = hlc3(high=high, low=low, close=close)
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basis = typical_price.rolling(length, min_periods=min_periods).mean()
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band = hl_range.rolling(length, min_periods=min_periods).mean()
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lower = basis - scalar * band
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upper = basis + scalar * band
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# Offset
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if offset != 0:
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lower = lower.shift(offset)
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basis = basis.shift(offset)
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upper = upper.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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lower.fillna(kwargs['fillna'], inplace=True)
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basis.fillna(kwargs['fillna'], inplace=True)
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upper.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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lower.fillna(method=kwargs['fill_method'], inplace=True)
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basis.fillna(method=kwargs['fill_method'], inplace=True)
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upper.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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lower.name = f"KCL_{length}"
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basis.name = f"KCB_{length}"
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upper.name = f"KCU_{length}"
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basis.category = upper.category = lower.category = 'volatility'
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# Prepare DataFrame to return
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data = {lower.name: lower, basis.name: basis, upper.name: upper}
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kcdf = DataFrame(data)
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kcdf.name = f"KC_{length}"
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kcdf.category = 'volatility'
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return kcdf
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kc.__doc__ = \
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"""Keltner Channels (KC)
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A popular volatility indicator similar to Bollinger Bands and
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Donchian Channels.
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Sources:
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https://www.tradingview.com/wiki/Keltner_Channels_(KC)
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Calculation:
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Default Inputs:
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length=20, scalar=2
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ATR = Average True Range
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EMA = Exponential Moving Average
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SMA = Simple Moving Average
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if 'ema':
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BASIS = EMA(close, length)
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BAND = ATR(high, low, close)
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else:
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hl_range = high - low
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tp = typical_price = hlc3(high, low, close)
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BASIS = SMA(tp, length)
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BAND = SMA(hl_range, length)
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LOWER = BASIS - scalar * BAND
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UPPER = BASIS + scalar * BAND
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Args:
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series): Series of 'close's
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length (int): The short period. Default: 20
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scalar (float): A positive float to scale the bands. Default: 2
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mamode (str): Two options: None or 'ema'. Default: 'ema'
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.DataFrame: lower, basis, upper columns.
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""" |