Source code for pandas_ta.momentum.apo

# -*- 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 = 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: 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(```. 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. """