Source code for pandas_ta.momentum.cci

# -*- 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 = 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: 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. """