mirror of
https://github.com/wassname/pandas-ta.git
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ENH kvo indicator #280 MAINT refactoring
This commit is contained in:
Kevin Johnson
@@ -42,13 +42,13 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
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* [Candles](#candles-64)
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* [Cycles](#cycles-1)
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* [Momentum](#momentum-39)
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* [Overlap](#overlap-31)
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* [Overlap](#overlap-32)
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* [Performance](#performance-3)
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* [Statistics](#statistics-9)
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* [Trend](#trend-16)
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* [Utility](#utility-5)
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* [Volatility](#volatility-13)
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* [Volume](#volume-14)
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* [Volatility](#volatility-14)
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* [Volume](#volume-15)
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* [Performance Metrics](#performance-metrics)
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* [Changes](#changes)
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* [General](#general)
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@@ -97,7 +97,7 @@ $ pip install pandas_ta
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Latest Version
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--------------
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Best choice! Version: *0.2.76b*
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Best choice! Version: *0.2.77b*
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```sh
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$ pip install -U git+https://github.com/twopirllc/pandas-ta
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```
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@@ -195,7 +195,7 @@ Thanks for using **Pandas TA**!
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_Thank you for your contributions!_
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[alexonab](https://github.com/alexonab) | [allahyarzadeh](https://github.com/allahyarzadeh) | [codesutras](https://github.com/codesutras) | [DrPaprikaa](https://github.com/DrPaprikaa) | [daikts](https://github.com/daikts) | [dorren](https://github.com/dorren) | [edwardwang1](https://github.com/edwardwang1) | [ffhirata](https://github.com/ffhirata) | [FGU1](https://github.com/FGU1) | [JoeSchr](https://github.com/JoeSchr) | [lluissalord](https://github.com/lluissalord) | [luisbarrancos](https://github.com/luisbarrancos) |[M6stafa](https://github.com/M6stafa) | [maxdignan](https://github.com/maxdignan) | [mchant](https://github.com/mchant) | [moritzgun](https://github.com/moritzgun) | [NkosenhleDuma](https://github.com/NkosenhleDuma) | [pbrumblay](https://github.com/pbrumblay) | [RajeshDhalange](https://github.com/RajeshDhalange) | [rengel8](https://github.com/rengel8) | [rluong003](https://github.com/rluong003) | [SoftDevDanial](https://github.com/SoftDevDanial) | [tg12](https://github.com/tg12) | [twrobel](https://github.com/twrobel) | [whubsch](https://github.com/whubsch) | [witokondoria](https://github.com/witokondoria) | [wouldayajustlookatit](https://github.com/wouldayajustlookatit) | [YuvalWein](https://github.com/YuvalWein)
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[alexonab](https://github.com/alexonab) | [allahyarzadeh](https://github.com/allahyarzadeh) | [codesutras](https://github.com/codesutras) | [DrPaprikaa](https://github.com/DrPaprikaa) | [daikts](https://github.com/daikts) | [dorren](https://github.com/dorren) | [edwardwang1](https://github.com/edwardwang1) | [ffhirata](https://github.com/ffhirata) | [FGU1](https://github.com/FGU1) | [GSlinger](https://github.com/gslinger) | [JoeSchr](https://github.com/JoeSchr) | [lluissalord](https://github.com/lluissalord) | [luisbarrancos](https://github.com/luisbarrancos) |[M6stafa](https://github.com/M6stafa) | [maxdignan](https://github.com/maxdignan) | [mchant](https://github.com/mchant) | [moritzgun](https://github.com/moritzgun) | [NkosenhleDuma](https://github.com/NkosenhleDuma) | [pbrumblay](https://github.com/pbrumblay) | [RajeshDhalange](https://github.com/RajeshDhalange) | [rengel8](https://github.com/rengel8) | [rluong003](https://github.com/rluong003) | [SoftDevDanial](https://github.com/SoftDevDanial) | [tg12](https://github.com/tg12) | [twrobel](https://github.com/twrobel) | [whubsch](https://github.com/whubsch) | [witokondoria](https://github.com/witokondoria) | [wouldayajustlookatit](https://github.com/wouldayajustlookatit) | [YuvalWein](https://github.com/YuvalWein)
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<br/>
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@@ -566,7 +566,7 @@ help(ta.yf)
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<br/><br/>
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# **Indicators** (_by Category_)
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### **Candles** (63)
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### **Candles** (64)
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Patterns that are **not bold**, require TA-Lib to be installed: ```pip install TA-Lib```
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* 2crows
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@@ -651,7 +651,7 @@ df = df.ta.cdl_pattern(name=["doji", "inside"])
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<br/>
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### **Momentum** (38)
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### **Momentum** (39)
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* _Awesome Oscillator_: **ao**
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* _Absolute Price Oscillator_: **apo**
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* _Bias_: **bias**
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@@ -702,7 +702,7 @@ df = df.ta.cdl_pattern(name=["doji", "inside"])
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<br/>
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### **Overlap** (31)
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### **Overlap** (32)
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* _Arnaud Legoux Moving Average_: **alma**
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* _Double Exponential Moving Average_: **dema**
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@@ -815,13 +815,14 @@ Use parameter: cumulative=**True** for cumulative results.
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<br/>
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### **Volatility** (13)
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### **Volatility** (14)
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* _Aberration_: **aberration**
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* _Acceleration Bands_: **accbands**
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* _Average True Range_: **atr**
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* _Bollinger Bands_: **bbands**
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* _Donchian Channel_: **donchian**
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* _Holt-Winter Channel_: **hwc**
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* _Keltner Channel_: **kc**
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* _Mass Index_: **massi**
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* _Normalized Average True Range_: **natr**
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@@ -837,7 +838,7 @@ Use parameter: cumulative=**True** for cumulative results.
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<br/>
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### **Volume** (14)
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### **Volume** (15)
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* _Accumulation/Distribution Index_: **ad**
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* _Accumulation/Distribution Oscillator_: **adosc**
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@@ -845,6 +846,7 @@ Use parameter: cumulative=**True** for cumulative results.
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* _Chaikin Money Flow_: **cmf**
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* _Elder's Force Index_: **efi**
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* _Ease of Movement_: **eom**
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* _Klinger Volume Oscillator_: **kvo**
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* _Money Flow Index_: **mfi**
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* _Negative Volume Index_: **nvi**
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* _On-Balance Volume_: **obv**
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@@ -907,6 +909,7 @@ trading account, or fund. See: ```help(ta.drawdown)```
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* _Candle Z Score_ (**cdl_z**) normalizes OHLC Candles with a rolling Z Score. See: ```help(ta.cdl_z)```
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* _Correlation Trend Indicator_ (**cti**) is an oscillator created by John Ehler in 2020. See: ```help(ta.cti)```
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* _Even Better Sinewave_ (**ebsw**) measures market cycles and uses a low pass filter to remove noise. See: ```help(ta.ebsw)```
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* _Klinger Volume Oscillator_ (**kvo**) was developed by Stephen J. Klinger. It is designed to predict price reversals in a market by comparing volume to price.. See: ```help(ta.kvo)```
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* _Schaff Trend Cycle_ (**stc**) is an evolution of the popular MACD incorportating two
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cascaded stochastic calculations with additional smoothing. See: ```help(ta.stc)```
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* _Tom DeMark's Sequential_ (**td_seq**) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from ```df.ta.strategy()``` for performance reasons. See: ```help(ta.td_seq)```
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+145
-145
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
+57
-57
File diff suppressed because one or more lines are too long
@@ -81,8 +81,8 @@ Category = {
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# Volume, "vp" or "Volume Profile" is unique
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"volume": [
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"ad", "adosc", "aobv", "cmf", "efi", "eom", "mfi", "nvi", "obv", "pvi",
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"pvol", "pvr", "pvt"
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"ad", "adosc", "aobv", "cmf", "efi", "eom", "kvo", "mfi", "nvi", "obv",
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"pvi", "pvol", "pvr", "pvt"
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],
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}
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@@ -1647,6 +1647,14 @@ class AnalysisIndicators(BasePandasObject):
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result = eom(high=high, low=low, close=close, volume=volume, length=length, divisor=divisor, offset=offset, drift=drift, **kwargs)
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return self._post_process(result, **kwargs)
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def kvo(self, fast=None, slow=None, length_sig=None, mamode=None, offset=None, drift=None, **kwargs):
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high = self._get_column(kwargs.pop("high", "high"))
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low = self._get_column(kwargs.pop("low", "low"))
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close = self._get_column(kwargs.pop("close", "close"))
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volume = self._get_column(kwargs.pop("volume", "volume"))
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result = kvo(high=high, low=low, close=close, volume=volume, fast=fast, slow=slow, length_sig=length_sig, mamode=mamode, offset=offset, drift=drift, **kwargs)
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return self._post_process(result, **kwargs)
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def mfi(self, length=None, drift=None, offset=None, **kwargs):
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high = self._get_column(kwargs.pop("high", "high"))
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low = self._get_column(kwargs.pop("low", "low"))
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@@ -8,8 +8,8 @@ from .hl2 import hl2
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from .hlc3 import hlc3
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from .hma import hma
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from .hwma import hwma
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from .kama import kama
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from .ichimoku import ichimoku
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from .kama import kama
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from .linreg import linreg
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from .ma import ma
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from .mcgd import mcgd
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@@ -87,7 +87,7 @@ def unsigned_differences(series: Series, amount: int = None, **kwargs) -> Series
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"""Unsigned Differences
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Returns two Series, an unsigned positive and unsigned negative series based
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on the differences of the original series. The positive series are only the
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increases and the negative series is only the decreases.
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increases and the negative series are only the decreases.
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Default Example:
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series = Series([3, 2, 2, 1, 1, 5, 6, 6, 7, 5, 3]) and returns
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@@ -66,7 +66,7 @@ def get_time(exchange: str = "NYSE", full:bool = True, to_string:bool = False) -
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s = f"{date}, {exchange_}, {local_}, {doy}"
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else:
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s = f"{exchange}: {exchange_time}"
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s = f"{date}, {exchange}: {exchange_time}"
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return s if to_string else print(s)
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@@ -7,8 +7,8 @@ from .donchian import donchian
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from .hwc import hwc
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from .kc import kc
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from .massi import massi
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from .pdist import pdist
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from .natr import natr
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from .pdist import pdist
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from .rvi import rvi
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from .thermo import thermo
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from .true_range import true_range
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+27
-26
@@ -1,35 +1,38 @@
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# -*- coding: utf-8 -*-
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from numpy import where as npWhere
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from pandas import DataFrame
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from pandas_ta.overlap import ma
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from pandas_ta.utils import get_offset, verify_series
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from pandas_ta.overlap import hlc3, ma
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from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
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def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=None, offset=None, **kwargs):
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def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=None, drift=None, offset=None, **kwargs):
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"""Indicator: Klinger Volume Oscillator (KVO)"""
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# Validate arguments
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fast = int(fast) if fast and fast > 0 else 34
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slow = int(slow) if slow and slow > 0 else 55
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length_sig = int(length_sig) if length_sig and length_sig > 0 else 13
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mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema"
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high = verify_series(high, max(fast, slow) + length_sig)
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low = verify_series(low, max(fast, slow) + length_sig)
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close = verify_series(close, max(fast, slow) + length_sig)
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volume = verify_series(volume, max(fast, slow) + length_sig)
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_length = max(fast, slow, length_sig)
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high = verify_series(high, _length)
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low = verify_series(low, _length)
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close = verify_series(close, _length)
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volume = verify_series(volume, _length)
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drift = get_drift(drift)
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offset = get_offset(offset)
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if high is None or low is None or close is None or volume is None: return
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# Calculate Result
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mom = (high + low + close).diff(1)
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mom = hlc3(high, low, close).diff(drift)
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trend = npWhere(mom > 0, 1, 0) + npWhere(mom < 0, -1, 0)
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dm = high - low
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dm = non_zero_range(high, low)
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cm = [0.0] * len(high)
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for i in range(1, len(high)):
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m = high.size
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cm = [0] * m
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for i in range(1, m):
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cm[i] = (cm[i - 1] + dm[i]) if trend[i] == trend[i - 1] else (dm[i - 1] + dm[i])
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vf = volume * trend * abs(dm / cm * 2 - 1) * 100
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vf = 100 * volume * trend * abs(2 * dm / cm - 1)
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kvo = ma(mamode, vf, length=fast) - ma(mamode, vf, length=slow)
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kvo_signal = ma(mamode, kvo, length=length_sig)
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@@ -64,8 +67,8 @@ def kvo(high, low, close, volume, fast=None, slow=None, length_sig=None, mamode=
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kvo.__doc__ = \
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"""Klinger Volume Oscillator (KVO)
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This indicator was developed by Stephen J. Klinger. It is designed to predict price reversals in a market
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by comparing volume to price.
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This indicator was developed by Stephen J. Klinger. It is designed to predict
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price reversals in a market by comparing volume to price.
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Sources:
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https://www.tradingview.com/script/Qnn7ymRK-Klinger-Volume-Oscillator/
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@@ -73,20 +76,18 @@ Sources:
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Calculation:
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Default Inputs:
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fast = 34, slow = 55, length_sig = 13.
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HLC3 = (h + l + c) / 3
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MOM = HLC3t - HLC3t-1
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TREND = { 1 if MOM > 0 \
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-1 if MOM < 0 \
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0 otherwise
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DM = h - l
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CM = { CMt-1 + DMt if TRENDt == TRENDt-1 \
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DMt-1 + DMt otherwise
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fast=34, slow=55, length_sig=13, drift=1
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MOM = HLC3.diff(drift)
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NEG_TREND = -1 if MOM < 0 else 0
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POS_TREND = 1 if MOM > 0 else 0
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TREND = POS_TREND + NEG_TREND
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DM = high - low
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CM = [CMt-1 + DMt if TRENDt == TRENDt-1 else DMt-1 + DMt]
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vf = 100 * v * TREND * abs(2 * dm / cm - 1)
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vf = 100 * volume * TREND * abs(2 * dm / cm - 1)
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kvo = ema(vf, fast) - ema(vf, slow)
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kvo_signal = ema(kvo, length_sig)
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Args:
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high (pd.Series): Series of 'high's
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@@ -104,5 +105,5 @@ Kwargs:
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fill_method (value, optional): Type of fill method
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Returns:
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pd.DataFrame: kvo and kvo_signal columns.
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pd.DataFrame: kvo and kvo_signal columns.
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"""
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@@ -18,7 +18,7 @@ setup(
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"pandas_ta.volatility",
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"pandas_ta.volume"
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],
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version=".".join(("0", "2", "76b")),
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version=".".join(("0", "2", "77b")),
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description=long_description,
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long_description=long_description,
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author="Kevin Johnson",
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@@ -60,6 +60,11 @@ class TestVolumeExtension(TestCase):
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self.assertIsInstance(self.data, DataFrame)
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self.assertEqual(self.data.columns[-1], "EOM_14_100000000")
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def test_kvo_ext(self):
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self.data.ta.kvo(append=True)
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self.assertIsInstance(self.data, DataFrame)
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self.assertEqual(self.data.columns[-1], "KVOSig_13")
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def test_mfi_ext(self):
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self.data.ta.mfi(append=True)
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self.assertIsInstance(self.data, DataFrame)
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@@ -89,6 +89,11 @@ class TestVolume(TestCase):
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self.assertIsInstance(result, Series)
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self.assertEqual(result.name, "EOM_14_100000000")
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def test_kvo(self):
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result = pandas_ta.kvo(self.high, self.low, self.close, self.volume_)
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self.assertIsInstance(result, DataFrame)
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self.assertEqual(result.name, "KVO_34_55_13")
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def test_mfi(self):
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result = pandas_ta.mfi(self.high, self.low, self.close, self.volume_)
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self.assertIsInstance(result, Series)
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