# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.utils import get_offset, non_zero_range, verify_series
[docs]def ad(high, low, close, volume, open_=None, talib=None, offset=None, **kwargs):
"""Indicator: Accumulation/Distribution (AD)"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
volume = verify_series(volume)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import AD
ad = AD(high, low, close, volume)
else:
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
high_low_range = non_zero_range(high, low)
ad *= volume / high_low_range
ad = ad.cumsum()
# Offset
if offset != 0:
ad = ad.shift(offset)
# Handle fills
if "fillna" in kwargs:
ad.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
ad.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
ad.name = "AD" if open_ is None else "ADo"
ad.category = "volume"
return ad
ad.__doc__ = \
"""Accumulation/Distribution (AD)
Accumulation/Distribution indicator utilizes the relative position
of the close to it's High-Low range with volume. Then it is cumulated.
Sources:
https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/accumulationdistribution-ad/
Calculation:
CUM = Cumulative Sum
if 'open':
AD = close - open
else:
AD = 2 * close - high - low
hl_range = high - low
AD = AD * volume / hl_range
AD = CUM(AD)
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
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.
"""