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https://github.com/wassname/pandas-ta.git
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James Sawyer
76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
# -*- coding: utf-8 -*-
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from ..overlap.hlc3 import hlc3
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from ..overlap.sma import sma
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from ..statistics.mad import mad
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from ..utils import get_offset, verify_series
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def cci(high, low, close, length=None, c=None, offset=None, **kwargs):
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"""Indicator: Commodity Channel Index (CCI)"""
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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 14
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c = float(c) if c and c > 0 else 0.015
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offset = get_offset(offset)
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# Calculate Result
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typical_price = hlc3(high=high, low=low, close=close)
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mean_typical_price = sma(typical_price, length=length)
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mad_typical_price = mad(typical_price, length=length)
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cci = typical_price - mean_typical_price
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cci /= c * mad_typical_price
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# Offset
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if offset != 0:
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cci = cci.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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cci.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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cci.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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cci.name = f"CCI_{length}_{c}"
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cci.category = "momentum"
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return cci
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cci.__doc__ = """Commodity Channel Index (CCI)
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Commodity Channel Index is a momentum oscillator used to primarily identify
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overbought and oversold levels relative to a mean.
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Sources:
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https://www.tradingview.com/wiki/Commodity_Channel_Index_(CCI)
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Calculation:
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Default Inputs:
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length=14, c=0.015
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SMA = Simple Moving Average
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MAD = Mean Absolute Deviation
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tp = typical_price = hlc3 = (high + low + close) / 3
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mean_tp = SMA(tp, length)
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mad_tp = MAD(tp, length)
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CCI = (tp - mean_tp) / (c * mad_tp)
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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): It's period. Default: 14
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c (float): Scaling Constant. Default: 0.015
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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.Series: New feature generated.
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"""
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