# -*- 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.
"""