Source code for pandas_ta.momentum.pvo

# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series


[docs]def pvo(volume, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs): """Indicator: Percentage Volume Oscillator (PVO)""" # Validate Arguments fast = int(fast) if fast and fast > 0 else 12 slow = int(slow) if slow and slow > 0 else 26 signal = int(signal) if signal and signal > 0 else 9 scalar = float(scalar) if scalar else 100 if slow < fast: fast, slow = slow, fast volume = verify_series(volume, max(fast, slow, signal)) offset = get_offset(offset) if volume is None: return # Calculate Result fastma = ema(volume, length=fast) slowma = ema(volume, length=slow) pvo = scalar * (fastma - slowma) pvo /= slowma signalma = ema(pvo, length=signal) histogram = pvo - signalma # Offset if offset != 0: pvo = pvo.shift(offset) histogram = histogram.shift(offset) signalma = signalma.shift(offset) # Handle fills if "fillna" in kwargs: pvo.fillna(kwargs["fillna"], inplace=True) histogram.fillna(kwargs["fillna"], inplace=True) signalma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: pvo.fillna(method=kwargs["fill_method"], inplace=True) histogram.fillna(method=kwargs["fill_method"], inplace=True) signalma.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _props = f"_{fast}_{slow}_{signal}" pvo.name = f"PVO{_props}" histogram.name = f"PVOh{_props}" signalma.name = f"PVOs{_props}" pvo.category = histogram.category = signalma.category = "momentum" # data = {pvo.name: pvo, histogram.name: histogram, signalma.name: signalma} df = DataFrame(data) df.name = pvo.name df.category = pvo.category return df
pvo.__doc__ = \ """Percentage Volume Oscillator (PVO) Percentage Volume Oscillator is a Momentum Oscillator for Volume. Sources: https://www.fmlabs.com/reference/default.htm?url=PVO.htm Calculation: Default Inputs: fast=12, slow=26, signal=9 EMA = Exponential Moving Average PVO = (EMA(volume, fast) - EMA(volume, slow)) / EMA(volume, slow) Signal = EMA(PVO, signal) Histogram = PVO - Signal Args: volume (pd.Series): Series of 'volume's fast (int): The short period. Default: 12 slow (int): The long period. Default: 26 signal (int): The signal period. Default: 9 scalar (float): How much to magnify. Default: 100 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.DataFrame: pvo, histogram, signal columns. """