From 230453b4b8d389f6531756596ea139bc791ae6ea Mon Sep 17 00:00:00 2001
From: GSlinger <24567123+gslinger@users.noreply.github.com>
Date: Wed, 5 May 2021 01:19:20 +0100
Subject: [PATCH] Delete kvo.py

---
 pandas_ta/volume/kvo.py | 108 ----------------------------------------
 1 file changed, 108 deletions(-)
 delete mode 100644 pandas_ta/volume/kvo.py

diff --git a/pandas_ta/volume/kvo.py b/pandas_ta/volume/kvo.py
deleted file mode 100644
index 13f7582..0000000
--- a/pandas_ta/volume/kvo.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# -*- coding: utf-8 -*-
-from numpy import where as npWhere
-from pandas import DataFrame
-from pandas_ta.overlap import ma
-from pandas_ta.utils import get_offset, verify_series
-
-
-def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=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
-    length_sig = int(length_sig) if length_sig and length_sig > 0 else 13
-    mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema"
-    high = verify_series(high, max(fast, slow) + length_sig)
-    low = verify_series(low, max(fast, slow) + length_sig)
-    close = verify_series(close, max(fast, slow) + length_sig)
-    volume = verify_series(volume, max(fast, slow) + length_sig)
-    offset = get_offset(offset)
-
-    if high is None or low is None or close is None or volume is None: return
-
-    # Calculate Result
-    mom = (high + low + close).diff(1)
-    trend = npWhere(mom > 0, 1, 0) + npWhere(mom < 0, -1, 0)
-    dm = high - low
-
-    cm = [0.0] * len(high)
-    for i in range(1, len(high)):
-        cm[i] = (cm[i - 1] + dm[i]) if trend[i] == trend[i - 1] else (dm[i - 1] + dm[i])
-
-    vf = volume * trend * abs(dm / cm * 2 - 1) * 100
-
-    kvo = ma(mamode, vf, length=fast) - ma(mamode, vf, length=slow)
-    kvo_signal = ma(mamode, kvo, length=length_sig)
-
-    # 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
-    kvo.name = f"KVO_{fast}_{slow}"
-    kvo_signal.name = f"KVOSig_{length_sig}"
-    kvo.category = kvo_signal.category = "volume"
-
-    # Prepare DataFrame to return
-    data = {kvo.name: kvo, kvo_signal.name: kvo_signal}
-    kvoandsig = DataFrame(data)
-    kvoandsig.name = f"KVO_{fast}_{slow}_{length_sig}"
-    kvoandsig.category = kvo.category
-
-    return kvoandsig
-
-
-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.tradingview.com/script/Qnn7ymRK-Klinger-Volume-Oscillator/
-    https://www.daytrading.com/klinger-volume-oscillator
-
-Calculation:
-    Default Inputs:
-        fast = 34, slow = 55, length_sig = 13. 
-    HLC3 = (h + l + c) / 3
-    MOM = HLC3t - HLC3t-1
-    TREND = { 1   if MOM > 0  \
-             -1   if MOM < 0  \
-              0   otherwise
-    DM = h - l
-    CM = { CMt-1 + DMt  if TRENDt == TRENDt-1   \
-           DMt-1 + DMt  otherwise
-
-    vf = 100 * v * TREND * abs(2 * dm / cm - 1) 
-    kvo = ema(vf, fast) - ema(vf, slow)
-    kvo_signal = ema(kvo, length_sig)
-    
-
-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): "sma", "ema", "wma" or "rma". 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 kvo_signal columns. 
-"""