momentum.py
"""
.. module:: momentum
:synopsis: Momentum Indicators.
.. moduleauthor:: Dario Lopez Padial (Bukosabino)
"""
import numpy as np
import pandas as pd
from ta.utils import IndicatorMixin, ema
class RSIIndicator(IndicatorMixin):
"""Relative Strength Index (RSI)
Compares the magnitude of recent gains and losses over a specified time
period to measure speed and change of price movements of a security. It is
primarily used to attempt to identify overbought or oversold conditions in
the trading of an asset.
https://www.investopedia.com/terms/r/rsi.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, n: int = 14, fillna: bool = False):
self._close = close
self._n = n
self._fillna = fillna
self._run()
def _run(self):
diff = self._close.diff(1)
up = diff.where(diff > 0, 0.0)
dn = -diff.where(diff < 0, 0.0)
min_periods = 0 if self._fillna else self._n
emaup = up.ewm(alpha=1/self._n, min_periods=min_periods, adjust=False).mean()
emadn = dn.ewm(alpha=1/self._n, min_periods=min_periods, adjust=False).mean()
rs = emaup / emadn
self._rsi = pd.Series(np.where(emadn == 0, 100, 100-(100/(1+rs))), index=self._close.index)
def rsi(self) -> pd.Series:
"""Relative Strength Index (RSI)
Returns:
pandas.Series: New feature generated.
"""
rsi = self._check_fillna(self._rsi, value=50)
return pd.Series(rsi, name='rsi')
class TSIIndicator(IndicatorMixin):
"""True strength index (TSI)
Shows both trend direction and overbought/oversold conditions.
https://school.stockcharts.com/doku.php?id=technical_indicators:true_strength_index
Args:
close(pandas.Series): dataset 'Close' column.
r(int): high period.
s(int): low period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, r: int = 25, s: int = 13, fillna: bool = False):
self._close = close
self._r = r
self._s = s
self._fillna = fillna
self._run()
def _run(self):
m = self._close - self._close.shift(1)
min_periods_r = 0 if self._fillna else self._r
min_periods_s = 0 if self._fillna else self._s
m1 = m.ewm(span=self._r, min_periods=min_periods_r, adjust=False).mean().ewm(
span=self._s, min_periods=min_periods_s, adjust=False).mean()
m2 = abs(m).ewm(span=self._r, min_periods=min_periods_r, adjust=False).mean().ewm(
span=self._s, min_periods=min_periods_s, adjust=False).mean()
self._tsi = m1 / m2
self._tsi *= 100
def tsi(self) -> pd.Series:
"""True strength index (TSI)
Returns:
pandas.Series: New feature generated.
"""
tsi = self._check_fillna(self._tsi, value=0)
return pd.Series(tsi, name='tsi')
class UltimateOscillator(IndicatorMixin):
"""Ultimate Oscillator
Larry Williams' (1976) signal, a momentum oscillator designed to capture
momentum across three different timeframes.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator
BP = Close - Minimum(Low or Prior Close).
TR = Maximum(High or Prior Close) - Minimum(Low or Prior Close)
Average7 = (7-period BP Sum) / (7-period TR Sum)
Average14 = (14-period BP Sum) / (14-period TR Sum)
Average28 = (28-period BP Sum) / (28-period TR Sum)
UO = 100 x [(4 x Average7)+(2 x Average14)+Average28]/(4+2+1)
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
s(int): short period.
m(int): medium period.
len(int): long period.
ws(float): weight of short BP average for UO.
wm(float): weight of medium BP average for UO.
wl(float): weight of long BP average for UO.
fillna(bool): if True, fill nan values with 50.
"""
def __init__(self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
s: int = 7,
m: int = 14,
len: int = 28,
ws: float = 4.0,
wm: float = 2.0,
wl: float = 1.0,
fillna: bool = False):
self._high = high
self._low = low
self._close = close
self._s = s
self._m = m
self._len = len
self._ws = ws
self._wm = wm
self._wl = wl
self._fillna = fillna
self._run()
def _run(self):
cs = self._close.shift(1)
tr = self._true_range(self._high, self._low, cs)
bp = self._close - pd.DataFrame({'low': self._low, 'close': cs}).min(axis=1, skipna=False)
min_periods_s = 0 if self._fillna else self._s
min_periods_m = 0 if self._fillna else self._m
min_periods_len = 0 if self._fillna else self._len
avg_s = bp.rolling(
self._s, min_periods=min_periods_s).sum() / tr.rolling(self._s, min_periods=min_periods_s).sum()
avg_m = bp.rolling(
self._m, min_periods=min_periods_m).sum() / tr.rolling(self._m, min_periods=min_periods_m).sum()
avg_l = bp.rolling(
self._len, min_periods=min_periods_len).sum() / tr.rolling(self._len, min_periods=min_periods_len).sum()
self._uo = (100.0 * ((self._ws * avg_s) + (self._wm * avg_m) + (self._wl * avg_l))
/ (self._ws + self._wm + self._wl))
def uo(self) -> pd.Series:
"""Ultimate Oscillator
Returns:
pandas.Series: New feature generated.
"""
uo = self._check_fillna(self._uo, value=50)
return pd.Series(uo, name='uo')
class StochasticOscillator(IndicatorMixin):
"""Stochastic Oscillator
Developed in the late 1950s by George Lane. The stochastic
oscillator presents the location of the closing price of a
stock in relation to the high and low range of the price
of a stock over a period of time, typically a 14-day period.
https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full
Args:
close(pandas.Series): dataset 'Close' column.
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
n(int): n period.
d_n(int): sma period over stoch_k.
fillna(bool): if True, fill nan values.
"""
def __init__(self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
n: int = 14,
d_n: int = 3,
fillna: bool = False):
self._close = close
self._high = high
self._low = low
self._n = n
self._d_n = d_n
self._fillna = fillna
self._run()
def _run(self):
min_periods = 0 if self._fillna else self._n
smin = self._low.rolling(self._n, min_periods=min_periods).min()
smax = self._high.rolling(self._n, min_periods=min_periods).max()
self._stoch_k = 100 * (self._close - smin) / (smax - smin)
def stoch(self) -> pd.Series:
"""Stochastic Oscillator
Returns:
pandas.Series: New feature generated.
"""
stoch_k = self._check_fillna(self._stoch_k, value=50)
return pd.Series(stoch_k, name='stoch_k')
def stoch_signal(self) -> pd.Series:
"""Signal Stochastic Oscillator
Returns:
pandas.Series: New feature generated.
"""
min_periods = 0 if self._fillna else self._d_n
stoch_d = self._stoch_k.rolling(self._d_n, min_periods=min_periods).mean()
stoch_d = self._check_fillna(stoch_d, value=50)
return pd.Series(stoch_d, name='stoch_k_signal')
class KAMAIndicator(IndicatorMixin):
"""Kaufman's Adaptive Moving Average (KAMA)
Moving average designed to account for market noise or volatility. KAMA
will closely follow prices when the price swings are relatively small and
the noise is low. KAMA will adjust when the price swings widen and follow
prices from a greater distance. This trend-following indicator can be
used to identify the overall trend, time turning points and filter price
movements.
https://www.tradingview.com/ideas/kama/
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
pow1(int): number of periods for the fastest EMA constant.
pow2(int): number of periods for the slowest EMA constant.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, n: int = 10, pow1: int = 2, pow2: int = 30, fillna: bool = False):
self._close = close
self._n = n
self._pow1 = pow1
self._pow2 = pow2
self._fillna = fillna
self._run()
def _run(self):
close_values = self._close.values
vol = pd.Series(abs(self._close - np.roll(self._close, 1)))
min_periods = 0 if self._fillna else self._n
ER_num = abs(close_values - np.roll(close_values, self._n))
ER_den = vol.rolling(self._n, min_periods=min_periods).sum()
ER = ER_num / ER_den
sc = ((ER*(2.0/(self._pow1+1)-2.0/(self._pow2+1.0))+2/(self._pow2+1.0)) ** 2.0).values
self._kama = np.zeros(sc.size)
n = len(self._kama)
first_value = True
for i in range(n):
if np.isnan(sc[i]):
self._kama[i] = np.nan
else:
if first_value:
self._kama[i] = close_values[i]
first_value = False
else:
self._kama[i] = self._kama[i-1] + sc[i] * (close_values[i] - self._kama[i-1])
def kama(self) -> pd.Series:
"""Kaufman's Adaptive Moving Average (KAMA)
Returns:
pandas.Series: New feature generated.
"""
kama = pd.Series(self._kama, index=self._close.index)
kama = self._check_fillna(kama, value=self._close)
return pd.Series(kama, name='kama')
class ROCIndicator(IndicatorMixin):
"""Rate of Change (ROC)
The Rate-of-Change (ROC) indicator, which is also referred to as simply
Momentum, is a pure momentum oscillator that measures the percent change in
price from one period to the next. The ROC calculation compares the current
price with the price “n” periods ago. The plot forms an oscillator that
fluctuates above and below the zero line as the Rate-of-Change moves from
positive to negative. As a momentum oscillator, ROC signals include
centerline crossovers, divergences and overbought-oversold readings.
Divergences fail to foreshadow reversals more often than not, so this
article will forgo a detailed discussion on them. Even though centerline
crossovers are prone to whipsaw, especially short-term, these crossovers
can be used to identify the overall trend. Identifying overbought or
oversold extremes comes naturally to the Rate-of-Change oscillator.
https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, n: int = 12, fillna: bool = False):
self._close = close
self._n = n
self._fillna = fillna
self._run()
def _run(self):
self._roc = ((self._close - self._close.shift(self._n)) / self._close.shift(self._n)) * 100
def roc(self) -> pd.Series:
"""Rate of Change (ROC)
Returns:
pandas.Series: New feature generated.
"""
roc = self._check_fillna(self._roc)
return pd.Series(roc, name='roc')
class AwesomeOscillatorIndicator(IndicatorMixin):
"""Awesome Oscillator
From: https://www.tradingview.com/wiki/Awesome_Oscillator_(AO)
The Awesome Oscillator is an indicator used to measure market momentum. AO
calculates the difference of a 34 Period and 5 Period Simple Moving
Averages. The Simple Moving Averages that are used are not calculated
using closing price but rather each bar's midpoints. AO is generally used
to affirm trends or to anticipate possible reversals.
From: https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator
Awesome Oscillator is a 34-period simple moving average, plotted through
the central points of the bars (H+L)/2, and subtracted from the 5-period
simple moving average, graphed across the central points of the bars
(H+L)/2.
MEDIAN PRICE = (HIGH+LOW)/2
AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34)
where
SMA — Simple Moving Average.
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
s(int): short period.
len(int): long period.
fillna(bool): if True, fill nan values with -50.
"""
def __init__(self, high: pd.Series, low: pd.Series, s: int = 5, len: int = 34, fillna: bool = False):
self._high = high
self._low = low
self._s = s
self._len = len
self._fillna = fillna
self._run()
def _run(self):
mp = 0.5 * (self._high + self._low)
min_periods_s = 0 if self._fillna else self._s
min_periods_len = 0 if self._fillna else self._len
self._ao = mp.rolling(
self._s, min_periods=min_periods_s).mean() - mp.rolling(self._len, min_periods=min_periods_len).mean()
def ao(self) -> pd.Series:
"""Awesome Oscillator
Returns:
pandas.Series: New feature generated.
"""
ao = self._check_fillna(self._ao, value=0)
return pd.Series(ao, name='ao')
class WilliamsRIndicator(IndicatorMixin):
"""Williams %R
Developed by Larry Williams, Williams %R is a momentum indicator that is
the inverse of the Fast Stochastic Oscillator. Also referred to as %R,
Williams %R reflects the level of the close relative to the highest high
for the look-back period. In contrast, the Stochastic Oscillator reflects
the level of the close relative to the lowest low. %R corrects for the
inversion by multiplying the raw value by -100. As a result, the Fast
Stochastic Oscillator and Williams %R produce the exact same lines, only
the scaling is different. Williams %R oscillates from 0 to -100.
Readings from 0 to -20 are considered overbought. Readings from -80 to -100
are considered oversold.
Unsurprisingly, signals derived from the Stochastic Oscillator are also
applicable to Williams %R.
%R = (Highest High - Close)/(Highest High - Lowest Low) * -100
Lowest Low = lowest low for the look-back period
Highest High = highest high for the look-back period
%R is multiplied by -100 correct the inversion and move the decimal.
https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r
The Williams %R oscillates from 0 to -100. When the indicator produces
readings from 0 to -20, this indicates overbought market conditions. When
readings are -80 to -100, it indicates oversold market conditions.
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
lbp(int): lookback period.
fillna(bool): if True, fill nan values with -50.
"""
def __init__(self, high: pd.Series, low: pd.Series, close: pd.Series, lbp: int = 14, fillna: bool = False):
self._high = high
self._low = low
self._close = close
self._lbp = lbp
self._fillna = fillna
self._run()
def _run(self):
min_periods = 0 if self._fillna else self._lbp
hh = self._high.rolling(self._lbp, min_periods=min_periods).max() # highest high over lookback period lbp
ll = self._low.rolling(self._lbp, min_periods=min_periods).min() # lowest low over lookback period lbp
self._wr = -100 * (hh - self._close) / (hh - ll)
def wr(self) -> pd.Series:
"""Williams %R
Returns:
pandas.Series: New feature generated.
"""
wr = self._check_fillna(self._wr, value=-50)
return pd.Series(wr, name='wr')
class StochRSIIndicator(IndicatorMixin):
"""Stochastic RSI
The StochRSI oscillator was developed to take advantage of both momentum
indicators in order to create a more sensitive indicator that is attuned to
a specific security's historical performance rather than a generalized analysis
of price change.
https://school.stockcharts.com/doku.php?id=technical_indicators:stochrsi
https://www.investopedia.com/terms/s/stochrsi.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period
d1(int): moving average of Stochastic RSI
d2(int): moving average of %K
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, n: int = 14, d1: int = 3, d2: int = 3, fillna: bool = False):
self._close = close
self._n = n
self._d1 = d1
self._d2 = d2
self._fillna = fillna
self._run()
def _run(self):
self._rsi = RSIIndicator(close=self._close, n=self._n, fillna=self._fillna).rsi()
lowest_low_rsi = self._rsi.rolling(self._n).min()
self._stochrsi = (self._rsi - lowest_low_rsi) / (self._rsi.rolling(self._n).max() - lowest_low_rsi)
def stochrsi(self):
"""Stochastic RSI
Returns:
pandas.Series: New feature generated.
"""
stochrsi = self._check_fillna(self._stochrsi)
return pd.Series(stochrsi, name='stochrsi')
def stochrsi_k(self):
"""Stochastic RSI %k
Returns:
pandas.Series: New feature generated.
"""
self.stochrsi_k = self._stochrsi.rolling(self._d1).mean()
stochrsi_K = self._check_fillna(self.stochrsi_k)
return pd.Series(stochrsi_K, name='stochrsi_k')
def stochrsi_d(self):
"""Stochastic RSI %d
Returns:
pandas.Series: New feature generated.
"""
stochrsi_D = self.stochrsi_k.rolling(self._d2).mean()
stochrsi_D = self._check_fillna(stochrsi_D)
return pd.Series(stochrsi_D, name='stochrsi_d')
class PercentagePriceOscillator(IndicatorMixin):
"""
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
the difference between two moving averages as a percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo
Args:
price(pandas.Series): dataset 'Price' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
"""
def __init__(self,
close: pd.Series,
n_slow: int = 26,
n_fast: int = 12,
n_sign: int = 9,
fillna: bool = False):
self._close = close
self._n_slow = n_slow
self._n_fast = n_fast
self._n_sign = n_sign
self._fillna = fillna
self._run()
def _run(self):
_emafast = ema(self._close, self._n_fast, self._fillna)
_emaslow = ema(self._close, self._n_slow, self._fillna)
self._ppo = ((_emafast - _emaslow)/_emaslow) * 100
self._ppo_signal = ema(self._ppo, self._n_sign, self._fillna)
self._ppo_hist = self._ppo - self._ppo_signal
def ppo(self):
"""Percentage Price Oscillator Line
Returns:
pandas.Series: New feature generated.
"""
ppo = self._check_fillna(self._ppo, value=0)
return pd.Series(ppo, name=f'PPO_{self._n_fast}_{self._n_slow}')
def ppo_signal(self):
"""Percentage Price Oscillator Signal Line
Returns:
pandas.Series: New feature generated.
"""
ppo_signal = self._check_fillna(self._ppo_signal, value=0)
return pd.Series(ppo_signal, name=f'PPO_sign_{self._n_fast}_{self._n_slow}')
def ppo_hist(self):
"""Percentage Price Oscillator Histogram
Returns:
pandas.Series: New feature generated.
"""
ppo_hist = self._check_fillna(self._ppo_hist, value=0)
return pd.Series(ppo_hist, name=f'PPO_hist_{self._n_fast}_{self._n_slow}')
class PercentageVolumeOscillator(IndicatorMixin):
"""
The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
The PVO measures the difference between two volume-based moving averages as a
percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo
Args:
volume(pandas.Series): dataset 'Volume' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
"""
def __init__(self,
volume: pd.Series,
n_slow: int = 26,
n_fast: int = 12,
n_sign: int = 9,
fillna: bool = False):
self._volume = volume
self._n_slow = n_slow
self._n_fast = n_fast
self._n_sign = n_sign
self._fillna = fillna
self._run()
def _run(self):
_emafast = ema(self._volume, self._n_fast, self._fillna)
_emaslow = ema(self._volume, self._n_slow, self._fillna)
self._pvo = ((_emafast - _emaslow)/_emaslow) * 100
self._pvo_signal = ema(self._pvo, self._n_sign, self._fillna)
self._pvo_hist = self._pvo - self._pvo_signal
def pvo(self):
"""PVO Line
Returns:
pandas.Series: New feature generated.
"""
pvo = self._check_fillna(self._pvo, value=0)
return pd.Series(pvo, name=f'PVO_{self._n_fast}_{self._n_slow}')
def pvo_signal(self):
"""Signal Line
Returns:
pandas.Series: New feature generated.
"""
pvo_signal = self._check_fillna(self._pvo_signal, value=0)
return pd.Series(pvo_signal, name=f'PVO_sign_{self._n_fast}_{self._n_slow}')
def pvo_hist(self):
"""Histgram
Returns:
pandas.Series: New feature generated.
"""
pvo_hist = self._check_fillna(self._pvo_hist, value=0)
return pd.Series(pvo_hist, name=f'PVO_hist_{self._n_fast}_{self._n_slow}')
def rsi(close, n=14, fillna=False):
"""Relative Strength Index (RSI)
Compares the magnitude of recent gains and losses over a specified time
period to measure speed and change of price movements of a security. It is
primarily used to attempt to identify overbought or oversold conditions in
the trading of an asset.
https://www.investopedia.com/terms/r/rsi.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return RSIIndicator(close=close, n=n, fillna=fillna).rsi()
def tsi(close, r=25, s=13, fillna=False):
"""True strength index (TSI)
Shows both trend direction and overbought/oversold conditions.
https://en.wikipedia.org/wiki/True_strength_index
Args:
close(pandas.Series): dataset 'Close' column.
r(int): high period.
s(int): low period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return TSIIndicator(close=close, r=r, s=s, fillna=fillna).tsi()
def uo(high, low, close, s=7, m=14, len=28, ws=4.0, wm=2.0, wl=1.0, fillna=False):
"""Ultimate Oscillator
Larry Williams' (1976) signal, a momentum oscillator designed to capture
momentum across three different timeframes.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator
BP = Close - Minimum(Low or Prior Close).
TR = Maximum(High or Prior Close) - Minimum(Low or Prior Close)
Average7 = (7-period BP Sum) / (7-period TR Sum)
Average14 = (14-period BP Sum) / (14-period TR Sum)
Average28 = (28-period BP Sum) / (28-period TR Sum)
UO = 100 x [(4 x Average7)+(2 x Average14)+Average28]/(4+2+1)
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
s(int): short period.
m(int): medium period.
len(int): long period.
ws(float): weight of short BP average for UO.
wm(float): weight of medium BP average for UO.
wl(float): weight of long BP average for UO.
fillna(bool): if True, fill nan values with 50.
Returns:
pandas.Series: New feature generated.
"""
return UltimateOscillator(
high=high, low=low, close=close, s=s, m=m, len=len, ws=ws, wm=wm, wl=wl, fillna=fillna).uo()
def stoch(high, low, close, n=14, d_n=3, fillna=False):
"""Stochastic Oscillator
Developed in the late 1950s by George Lane. The stochastic
oscillator presents the location of the closing price of a
stock in relation to the high and low range of the price
of a stock over a period of time, typically a 14-day period.
https://www.investopedia.com/terms/s/stochasticoscillator.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
d_n(int): sma period over stoch_k
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return StochasticOscillator(high=high, low=low, close=close, n=n, d_n=d_n, fillna=fillna).stoch()
def stoch_signal(high, low, close, n=14, d_n=3, fillna=False):
"""Stochastic Oscillator Signal
Shows SMA of Stochastic Oscillator. Typically a 3 day SMA.
https://www.investopedia.com/terms/s/stochasticoscillator.asp
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
d_n(int): sma period over stoch_k
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return StochasticOscillator(high=high, low=low, close=close, n=n, d_n=d_n, fillna=fillna).stoch_signal()
def wr(high, low, close, lbp=14, fillna=False):
"""Williams %R
From: http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:williams_r
Developed by Larry Williams, Williams %R is a momentum indicator that is
the inverse of the Fast Stochastic Oscillator. Also referred to as %R,
Williams %R reflects the level of the close relative to the highest high
for the look-back period. In contrast, the Stochastic Oscillator reflects
the level of the close relative to the lowest low. %R corrects for the
inversion by multiplying the raw value by -100. As a result, the Fast
Stochastic Oscillator and Williams %R produce the exact same lines, only
the scaling is different. Williams %R oscillates from 0 to -100.
Readings from 0 to -20 are considered overbought. Readings from -80 to -100
are considered oversold.
Unsurprisingly, signals derived from the Stochastic Oscillator are also
applicable to Williams %R.
%R = (Highest High - Close)/(Highest High - Lowest Low) * -100
Lowest Low = lowest low for the look-back period
Highest High = highest high for the look-back period
%R is multiplied by -100 correct the inversion and move the decimal.
From: https://www.investopedia.com/terms/w/williamsr.asp
The Williams %R oscillates from 0 to -100. When the indicator produces
readings from 0 to -20, this indicates overbought market conditions. When
readings are -80 to -100, it indicates oversold market conditions.
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
lbp(int): lookback period.
fillna(bool): if True, fill nan values with -50.
Returns:
pandas.Series: New feature generated.
"""
return WilliamsRIndicator(high=high, low=low, close=close, lbp=lbp, fillna=fillna).wr()
def ao(high, low, s=5, len=34, fillna=False):
"""Awesome Oscillator
From: https://www.tradingview.com/wiki/Awesome_Oscillator_(AO)
The Awesome Oscillator is an indicator used to measure market momentum. AO
calculates the difference of a 34 Period and 5 Period Simple Moving
Averages. The Simple Moving Averages that are used are not calculated
using closing price but rather each bar's midpoints. AO is generally used
to affirm trends or to anticipate possible reversals.
From: https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator
Awesome Oscillator is a 34-period simple moving average, plotted through
the central points of the bars (H+L)/2, and subtracted from the 5-period
simple moving average, graphed across the central points of the bars
(H+L)/2.
MEDIAN PRICE = (HIGH+LOW)/2
AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34)
where
SMA — Simple Moving Average.
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
s(int): short period.
len(int): long period.
fillna(bool): if True, fill nan values with -50.
Returns:
pandas.Series: New feature generated.
"""
return AwesomeOscillatorIndicator(high=high, low=low, s=s, len=len, fillna=fillna).ao()
def kama(close, n=10, pow1=2, pow2=30, fillna=False):
"""Kaufman's Adaptive Moving Average (KAMA)
Moving average designed to account for market noise or volatility. KAMA
will closely follow prices when the price swings are relatively small and
the noise is low. KAMA will adjust when the price swings widen and follow
prices from a greater distance. This trend-following indicator can be
used to identify the overall trend, time turning points and filter price
movements.
https://www.tradingview.com/ideas/kama/
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n number of periods for the efficiency ratio.
pow1(int): number of periods for the fastest EMA constant.
pow2(int): number of periods for the slowest EMA constant.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return KAMAIndicator(close=close, n=n, pow1=pow1, pow2=pow2, fillna=fillna).kama()
def roc(close, n=12, fillna=False):
"""Rate of Change (ROC)
The Rate-of-Change (ROC) indicator, which is also referred to as simply
Momentum, is a pure momentum oscillator that measures the percent change in
price from one period to the next. The ROC calculation compares the current
price with the price “n” periods ago. The plot forms an oscillator that
fluctuates above and below the zero line as the Rate-of-Change moves from
positive to negative. As a momentum oscillator, ROC signals include
centerline crossovers, divergences and overbought-oversold readings.
Divergences fail to foreshadow reversals more often than not, so this
article will forgo a detailed discussion on them. Even though centerline
crossovers are prone to whipsaw, especially short-term, these crossovers
can be used to identify the overall trend. Identifying overbought or
oversold extremes comes naturally to the Rate-of-Change oscillator.
https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n periods.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return ROCIndicator(close=close, n=n, fillna=fillna).roc()
def stochrsi(close, n=14, d1=3, d2=3, fillna=False):
"""Stochastic RSI
The StochRSI oscillator was developed to take advantage of both momentum
indicators in order to create a more sensitive indicator that is attuned to
a specific security's historical performance rather than a generalized analysis
of price change.
https://www.investopedia.com/terms/s/stochrsi.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period
d1(int): moving average of Stochastic RSI
d2(int): moving average of %K
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return StochRSIIndicator(close=close, n=n, d1=d1, d2=d2, fillna=fillna).stochrsi()
def stochrsi_k(close, n=14, d1=3, d2=3, fillna=False):
"""Stochastic RSI %k
The StochRSI oscillator was developed to take advantage of both momentum
indicators in order to create a more sensitive indicator that is attuned to
a specific security's historical performance rather than a generalized analysis
of price change.
https://www.investopedia.com/terms/s/stochrsi.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period
d1(int): moving average of Stochastic RSI
d2(int): moving average of %K
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return StochRSIIndicator(close=close, n=n, d1=d1, d2=d2, fillna=fillna).stochrsi_k()
def stochrsi_d(close, n=14, d1=3, d2=3, fillna=False):
"""Stochastic RSI %d
The StochRSI oscillator was developed to take advantage of both momentum
indicators in order to create a more sensitive indicator that is attuned to
a specific security's historical performance rather than a generalized analysis
of price change.
https://www.investopedia.com/terms/s/stochrsi.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period
d1(int): moving average of Stochastic RSI
d2(int): moving average of %K
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return StochRSIIndicator(close=close, n=n, d1=d1, d2=d2, fillna=fillna).stochrsi_d()
def ppo(close, n_slow=26, n_fast=12, n_sign=9, fillna=False):
"""
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
the difference between two moving averages as a percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo
Args:
price(pandas.Series): dataset 'Price' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return PercentagePriceOscillator(close=close, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna).ppo()
def ppo_signal(close, n_slow=26, n_fast=12, n_sign=9, fillna=False):
"""
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
the difference between two moving averages as a percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo
Args:
price(pandas.Series): dataset 'Price' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return PercentagePriceOscillator(
close=close, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna).ppo_signal()
def ppo_hist(close, n_slow=26, n_fast=12, n_sign=9, fillna=False):
"""
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures
the difference between two moving averages as a percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo
Args:
price(pandas.Series): dataset 'Price' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return PercentagePriceOscillator(
close=close, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna).ppo_hist()
def pvo(volume: pd.Series, n_slow: int = 26, n_fast: int = 12, n_sign: int = 9, fillna: bool = False):
"""
The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
The PVO measures the difference between two volume-based moving averages as a
percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo
Args:
volume(pandas.Series): dataset 'Volume' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PercentageVolumeOscillator(volume=volume, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna)
return indicator.pvo()
def pvo_signal(volume: pd.Series, n_slow: int = 26, n_fast: int = 12, n_sign: int = 9, fillna: bool = False):
"""
The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
The PVO measures the difference between two volume-based moving averages as a
percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo
Args:
volume(pandas.Series): dataset 'Volume' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PercentageVolumeOscillator(volume=volume, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna)
return indicator.pvo_signal()
def pvo_hist(volume: pd.Series, n_slow: int = 26, n_fast: int = 12, n_sign: int = 9, fillna: bool = False):
"""
The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume.
The PVO measures the difference between two volume-based moving averages as a
percentage of the larger moving average.
https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo
Args:
volume(pandas.Series): dataset 'Volume' column.
n_slow(int): n period long-term.
n_fast(int): n period short-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PercentageVolumeOscillator(volume=volume, n_slow=n_slow, n_fast=n_fast, n_sign=n_sign, fillna=fillna)
return indicator.pvo_hist()
