Source code for pandas_ta.volume.kvo

# -*- 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}" = f"KVO{_props}" = f"KVOs{_props}" kvo.category = kvo_signal.category = "volume" # Prepare DataFrame to return data = { kvo, kvo_signal} df = DataFrame(data) = 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: 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(```. 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. """