mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from pandas_ta.overlap import ema
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from pandas_ta.utils import get_offset, verify_series
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def pvo(volume,
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fast=None,
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slow=None,
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signal=None,
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scalar=None,
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offset=None,
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**kwargs):
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"""Indicator: Percentage Volume Oscillator (PVO)"""
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# Validate Arguments
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volume = verify_series(volume)
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fast = int(fast) if fast and fast > 0 else 12
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slow = int(slow) if slow and slow > 0 else 26
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signal = int(signal) if signal and signal > 0 else 9
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scalar = float(scalar) if scalar else 100
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if slow < fast:
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fast, slow = slow, fast
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offset = get_offset(offset)
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# Calculate Result
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fastma = ema(volume, length=fast)
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slowma = ema(volume, length=slow)
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pvo = scalar * (fastma - slowma)
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pvo /= slowma
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signalma = ema(pvo, length=signal)
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histogram = pvo - signalma
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# Offset
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if offset != 0:
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pvo = pvo.shift(offset)
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histogram = histogram.shift(offset)
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signalma = signalma.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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pvo.fillna(kwargs["fillna"], inplace=True)
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histogram.fillna(kwargs["fillna"], inplace=True)
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signalma.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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pvo.fillna(method=kwargs["fill_method"], inplace=True)
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histogram.fillna(method=kwargs["fill_method"], inplace=True)
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signalma.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_props = f"_{fast}_{slow}_{signal}"
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pvo.name = f"PVO{_props}"
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histogram.name = f"PVOh{_props}"
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signalma.name = f"PVOs{_props}"
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pvo.category = histogram.category = signalma.category = "momentum"
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#
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data = {pvo.name: pvo, histogram.name: histogram, signalma.name: signalma}
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df = DataFrame(data)
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df.name = pvo.name
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df.category = pvo.category
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return df
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pvo.__doc__ = """Percentage Volume Oscillator (PVO)
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Percentage Volume Oscillator is a Momentum Oscillator for Volume.
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Sources:
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https://www.fmlabs.com/reference/default.htm?url=PVO.htm
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Calculation:
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Default Inputs:
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fast=12, slow=26, signal=9
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EMA = Exponential Moving Average
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PVO = (EMA(volume, fast) - EMA(volume, slow)) / EMA(volume, slow)
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Signal = EMA(PVO, signal)
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Histogram = PVO - Signal
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Args:
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volume (pd.Series): Series of 'volume's
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fast (int): The short period. Default: 12
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slow (int): The long period. Default: 26
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signal (int): The signal period. Default: 9
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scalar (float): How much to magnify. Default: 100
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.DataFrame: pvo, histogram, signal columns.
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"""
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