# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.overlap import hlc3, sma
from pandas_ta.statistics.mad import mad
from pandas_ta.utils import get_offset, verify_series
[docs]def cci(high, low, close, length=None, c=None, talib=None, offset=None, **kwargs):
"""Indicator: Commodity Channel Index (CCI)"""
# Validate Arguments
length = int(length) if length and length > 0 else 14
c = float(c) if c and c > 0 else 0.015
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if high is None or low is None or close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import CCI
cci = CCI(high, low, close, length)
else:
typical_price = hlc3(high=high, low=low, close=close)
mean_typical_price = sma(typical_price, length=length)
mad_typical_price = mad(typical_price, length=length)
cci = typical_price - mean_typical_price
cci /= c * mad_typical_price
# Offset
if offset != 0:
cci = cci.shift(offset)
# Handle fills
if "fillna" in kwargs:
cci.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
cci.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
cci.name = f"CCI_{length}_{c}"
cci.category = "momentum"
return cci
cci.__doc__ = \
"""Commodity Channel Index (CCI)
Commodity Channel Index is a momentum oscillator used to primarily identify
overbought and oversold levels relative to a mean.
Sources:
https://www.tradingview.com/wiki/Commodity_Channel_Index_(CCI)
Calculation:
Default Inputs:
length=14, c=0.015
SMA = Simple Moving Average
MAD = Mean Absolute Deviation
tp = typical_price = hlc3 = (high + low + close) / 3
mean_tp = SMA(tp, length)
mad_tp = MAD(tp, length)
CCI = (tp - mean_tp) / (c * mad_tp)
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
length (int): It's period. Default: 14
c (float): Scaling Constant. Default: 0.015
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. 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.Series: New feature generated.
"""