Source code for pandas_ta.volume.cmf

# -*- 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 = 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: 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. """