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