# -*- coding: utf-8 -*-
from .ad import ad
from pandas_ta import Imports
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series
[docs]def adosc(high, low, close, volume, open_=None, fast=None, slow=None, talib=None, offset=None, **kwargs):
"""Indicator: Accumulation/Distribution Oscillator"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 3
slow = int(slow) if slow and slow > 0 else 10
_length = max(fast, slow)
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 "length" in kwargs: kwargs.pop("length")
mode_tal = bool(talib) if isinstance(talib, bool) else True
if high is None or low is None or close is None or volume is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import ADOSC
adosc = ADOSC(high, low, close, volume, fast, slow)
else:
ad_ = ad(high=high, low=low, close=close, volume=volume, open_=open_)
fast_ad = ema(close=ad_, length=fast, **kwargs)
slow_ad = ema(close=ad_, length=slow, **kwargs)
adosc = fast_ad - slow_ad
# Offset
if offset != 0:
adosc = adosc.shift(offset)
# Handle fills
if "fillna" in kwargs:
adosc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
adosc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
adosc.name = f"ADOSC_{fast}_{slow}"
adosc.category = "volume"
return adosc
adosc.__doc__ = \
"""Accumulation/Distribution Oscillator or Chaikin Oscillator
Accumulation/Distribution Oscillator indicator utilizes
Accumulation/Distribution and treats it similarily to MACD
or APO.
Sources:
https://www.investopedia.com/articles/active-trading/031914/understanding-chaikin-oscillator.asp
Calculation:
Default Inputs:
fast=12, slow=26
AD = Accum/Dist
ad = AD(high, low, close, open)
fast_ad = EMA(ad, fast)
slow_ad = EMA(ad, slow)
ADOSC = fast_ad - slow_ad
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
open (pd.Series): Series of 'open's
volume (pd.Series): Series of 'volume's
fast (int): The short period. Default: 12
slow (int): The long period. Default: 26
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.
"""