# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.overlap import ma
from pandas_ta.utils import get_offset, tal_ma, verify_series
[docs]def apo(close, fast=None, slow=None, mamode=None, talib=None, offset=None, **kwargs):
"""Indicator: Absolute Price Oscillator (APO)"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, max(fast, slow))
mamode = mamode if isinstance(mamode, str) else "sma"
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import APO
apo = APO(close, fast, slow, tal_ma(mamode))
else:
fastma = ma(mamode, close, length=fast)
slowma = ma(mamode, close, length=slow)
apo = fastma - slowma
# Offset
if offset != 0:
apo = apo.shift(offset)
# Handle fills
if "fillna" in kwargs:
apo.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
apo.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
apo.name = f"APO_{fast}_{slow}"
apo.category = "momentum"
return apo
apo.__doc__ = \
"""Absolute Price Oscillator (APO)
The Absolute Price Oscillator is an indicator used to measure a security's
momentum. It is simply the difference of two Exponential Moving Averages
(EMA) of two different periods. Note: APO and MACD lines are equivalent.
Sources:
https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/
Calculation:
Default Inputs:
fast=12, slow=26
SMA = Simple Moving Average
APO = SMA(close, fast) - SMA(close, slow)
Args:
close (pd.Series): Series of 'close's
fast (int): The short period. Default: 12
slow (int): The long period. Default: 26
mamode (str): See ```help(ta.ma)```. Default: 'sma'
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.
"""