Files
pandas-ta/pandas_ta/volume/pvi.py
T
2019-05-20 16:20:47 -07:00

75 lines
2.2 KiB
Python

# -*- coding: utf-8 -*-
from ..momentum.roc import roc
from ..utils import get_offset, signed_series, verify_series
def pvi(close, volume, length=None, initial=None, offset=None, **kwargs):
"""Indicator: Positive Volume Index (PVI)"""
# Validate arguments
close = verify_series(close)
volume = verify_series(volume)
length = int(length) if length and length > 0 else 1
min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs['min_periods'] is not None else length
initial = int(initial) if initial and initial > 0 else 1000
offset = get_offset(offset)
# Calculate Result
roc_ = roc(close=close, length=length)
signed_volume = signed_series(volume, initial=1)
pvi = signed_volume[signed_volume > 0].abs() * roc_
pvi.fillna(0, inplace=True)
pvi.iloc[0]= initial
pvi = pvi.cumsum()
# Offset
if offset != 0:
pvi = pvi.shift(offset)
# Handle fills
if 'fillna' in kwargs:
pvi.fillna(kwargs['fillna'], inplace=True)
if 'fill_method' in kwargs:
pvi.fillna(method=kwargs['fill_method'], inplace=True)
# Name and Categorize it
pvi.name = f"PVI_{length}"
pvi.category = 'volume'
return pvi
pvi.__doc__ = \
"""Positive Volume Index (PVI)
The Positive Volume Index is a cumulative indicator that uses volume change in
an attempt to identify where smart money is active. Used in conjunction with NVI.
Sources:
https://www.investopedia.com/terms/p/pvi.asp
Calculation:
Default Inputs:
length=1, initial=1000
ROC = Rate of Change
roc = ROC(close, length)
signed_volume = signed_series(volume, initial=1)
pvi = signed_volume[signed_volume > 0].abs() * roc_
pvi.fillna(0, inplace=True)
pvi.iloc[0]= initial
pvi = pvi.cumsum()
Args:
close (pd.Series): Series of 'close's
volume (pd.Series): Series of 'volume's
length (int): The short period. Default: 13
initial (int): The short period. Default: 1000
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.
"""