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81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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from ..utils import get_offset, verify_series
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def cmf(high, low, close, volume, open_=None, length=None, offset=None, **kwargs):
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"""Indicator: Chaikin Money Flow (CMF)"""
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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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volume = verify_series(volume)
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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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offset = get_offset(offset)
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# Calculate Result
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if open_ is not None:
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open_ = verify_series(open_)
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ad = close - open_ # AD with Open
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else:
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ad = 2 * close - high - low # AD with High, Low, Close
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hl_range = high - low
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ad *= volume / hl_range
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cmf = ad.rolling(length, min_periods=min_periods).sum() / volume.rolling(length, min_periods=min_periods).sum()
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# Offset
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if offset != 0:
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cmf = cmf.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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cmf.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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cmf.fillna(method=kwargs['fill_method'], inplace=True)
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# Name and Categorize it
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cmf.name = f"CMF_{length}"
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cmf.category = 'volume'
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return cmf
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cmf.__doc__ = \
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"""Chaikin Money Flow (CMF)
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Chailin Money Flow measures the amount of money flow volume over a specific
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period in conjunction with Accumulation/Distribution.
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Sources:
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https://www.tradingview.com/wiki/Chaikin_Money_Flow_(CMF)
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https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
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Calculation:
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Default Inputs:
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length=20
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if 'open':
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ad = close - open
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else:
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ad = 2 * close - high - low
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hl_range = high - low
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ad = ad * volume / hl_range
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CMF = SUM(ad, length) / SUM(volume, length)
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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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open (pd.Series): Series of 'open's
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volume (pd.Series): Series of 'volume's
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length (int): The short period. Default: 20
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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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""" |