# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.overlap import hlc3, ma
from pandas_ta.utils import get_drift, get_offset, signed_series, verify_series
[docs]def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None, drift=None, offset=None, **kwargs):
"""Indicator: Klinger Volume Oscillator (KVO)"""
# Validate arguments
fast = int(fast) if fast and fast > 0 else 34
slow = int(slow) if slow and slow > 0 else 55
signal = int(signal) if signal and signal > 0 else 13
mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema"
_length = max(fast, slow, signal)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
volume = verify_series(volume, _length)
drift = get_drift(drift)
offset = get_offset(offset)
if high is None or low is None or close is None or volume is None: return
# Calculate Result
signed_volume = volume * signed_series(hlc3(high, low, close), 1)
sv = signed_volume.loc[signed_volume.first_valid_index():,]
kvo = ma(mamode, sv, length=fast) - ma(mamode, sv, length=slow)
kvo_signal = ma(mamode, kvo.loc[kvo.first_valid_index():,], length=signal)
# Offset
if offset != 0:
kvo = kvo.shift(offset)
kvo_signal = kvo_signal.shift(offset)
# Handle fills
if "fillna" in kwargs:
kvo.fillna(kwargs["fillna"], inplace=True)
kvo_signal.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
kvo.fillna(method=kwargs["fill_method"], inplace=True)
kvo_signal.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{fast}_{slow}_{signal}"
kvo.name = f"KVO{_props}"
kvo_signal.name = f"KVOs{_props}"
kvo.category = kvo_signal.category = "volume"
# Prepare DataFrame to return
data = {kvo.name: kvo, kvo_signal.name: kvo_signal}
df = DataFrame(data)
df.name = f"KVO{_props}"
df.category = kvo.category
return df
kvo.__doc__ = \
"""Klinger Volume Oscillator (KVO)
This indicator was developed by Stephen J. Klinger. It is designed to predict
price reversals in a market by comparing volume to price.
Sources:
https://www.investopedia.com/terms/k/klingeroscillator.asp
https://www.daytrading.com/klinger-volume-oscillator
Calculation:
Default Inputs:
fast=34, slow=55, signal=13, drift=1
EMA = Exponential Moving Average
SV = volume * signed_series(HLC3, 1)
KVO = EMA(SV, fast) - EMA(SV, slow)
Signal = EMA(KVO, signal)
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
volume (pd.Series): Series of 'volume's
fast (int): The fast period. Default: 34
long (int): The long period. Default: 55
length_sig (int): The signal period. Default: 13
mamode (str): See ```help(ta.ma)```. Default: 'ema'
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: KVO and Signal columns.
"""