https://github.com/bukosabino/ta
Tip revision: 8edcd3a27e5b7285cc9870d6c074247b7d777d6d authored by Bukosabino on 09 January 2022, 20:20:41 UTC
Fixing bug on name columns (PVO - PPO)
Fixing bug on name columns (PVO - PPO)
Tip revision: 8edcd3a
trend.py
"""
.. module:: trend
:synopsis: Trend Indicators.
.. moduleauthor:: Dario Lopez Padial (Bukosabino)
"""
import numpy as np
import pandas as pd
from ta.utils import IndicatorMixin, _ema, _get_min_max, _sma
class AroonIndicator(IndicatorMixin):
"""Aroon Indicator
Identify when trends are likely to change direction.
Aroon Up = ((N - Days Since N-day High) / N) x 100
Aroon Down = ((N - Days Since N-day Low) / N) x 100
Aroon Indicator = Aroon Up - Aroon Down
https://www.investopedia.com/terms/a/aroon.asp
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int = 25, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
min_periods = 0 if self._fillna else self._window
rolling_close = self._close.rolling(self._window, min_periods=min_periods)
self._aroon_up = rolling_close.apply(
lambda x: float(np.argmax(x) + 1) / self._window * 100, raw=True
)
self._aroon_down = rolling_close.apply(
lambda x: float(np.argmin(x) + 1) / self._window * 100, raw=True
)
def aroon_up(self) -> pd.Series:
"""Aroon Up Channel
Returns:
pandas.Series: New feature generated.
"""
aroon_up_series = self._check_fillna(self._aroon_up, value=0)
return pd.Series(aroon_up_series, name=f"aroon_up_{self._window}")
def aroon_down(self) -> pd.Series:
"""Aroon Down Channel
Returns:
pandas.Series: New feature generated.
"""
aroon_down_series = self._check_fillna(self._aroon_down, value=0)
return pd.Series(aroon_down_series, name=f"aroon_down_{self._window}")
def aroon_indicator(self) -> pd.Series:
"""Aroon Indicator
Returns:
pandas.Series: New feature generated.
"""
aroon_diff = self._aroon_up - self._aroon_down
aroon_diff = self._check_fillna(aroon_diff, value=0)
return pd.Series(aroon_diff, name=f"aroon_ind_{self._window}")
class MACD(IndicatorMixin):
"""Moving Average Convergence Divergence (MACD)
Is a trend-following momentum indicator that shows the relationship between
two moving averages of prices.
https://school.stockcharts.com/doku.php?id=technical_indicators:moving_average_convergence_divergence_macd
Args:
close(pandas.Series): dataset 'Close' column.
window_fast(int): n period short-term.
window_slow(int): n period long-term.
window_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
close: pd.Series,
window_slow: int = 26,
window_fast: int = 12,
window_sign: int = 9,
fillna: bool = False,
):
self._close = close
self._window_slow = window_slow
self._window_fast = window_fast
self._window_sign = window_sign
self._fillna = fillna
self._run()
def _run(self):
self._emafast = _ema(self._close, self._window_fast, self._fillna)
self._emaslow = _ema(self._close, self._window_slow, self._fillna)
self._macd = self._emafast - self._emaslow
self._macd_signal = _ema(self._macd, self._window_sign, self._fillna)
self._macd_diff = self._macd - self._macd_signal
def macd(self) -> pd.Series:
"""MACD Line
Returns:
pandas.Series: New feature generated.
"""
macd_series = self._check_fillna(self._macd, value=0)
return pd.Series(
macd_series, name=f"MACD_{self._window_fast}_{self._window_slow}"
)
def macd_signal(self) -> pd.Series:
"""Signal Line
Returns:
pandas.Series: New feature generated.
"""
macd_signal_series = self._check_fillna(self._macd_signal, value=0)
return pd.Series(
macd_signal_series,
name=f"MACD_sign_{self._window_fast}_{self._window_slow}",
)
def macd_diff(self) -> pd.Series:
"""MACD Histogram
Returns:
pandas.Series: New feature generated.
"""
macd_diff_series = self._check_fillna(self._macd_diff, value=0)
return pd.Series(
macd_diff_series, name=f"MACD_diff_{self._window_fast}_{self._window_slow}"
)
class EMAIndicator(IndicatorMixin):
"""EMA - Exponential Moving Average
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int = 14, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
def ema_indicator(self) -> pd.Series:
"""Exponential Moving Average (EMA)
Returns:
pandas.Series: New feature generated.
"""
ema_ = _ema(self._close, self._window, self._fillna)
return pd.Series(ema_, name=f"ema_{self._window}")
class SMAIndicator(IndicatorMixin):
"""SMA - Simple Moving Average
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
def sma_indicator(self) -> pd.Series:
"""Simple Moving Average (SMA)
Returns:
pandas.Series: New feature generated.
"""
sma_ = _sma(self._close, self._window, self._fillna)
return pd.Series(sma_, name=f"sma_{self._window}")
class WMAIndicator(IndicatorMixin):
"""WMA - Weighted Moving Average
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int = 9, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
_weight = pd.Series(
[
i * 2 / (self._window * (self._window + 1))
for i in range(1, self._window + 1)
]
)
def weighted_average(weight):
def _weighted_average(x):
return (weight * x).sum()
return _weighted_average
self._wma = self._close.rolling(self._window).apply(
weighted_average(_weight), raw=True
)
def wma(self) -> pd.Series:
"""Weighted Moving Average (WMA)
Returns:
pandas.Series: New feature generated.
"""
wma = self._check_fillna(self._wma, value=0)
return pd.Series(wma, name=f"wma_{self._window}")
class TRIXIndicator(IndicatorMixin):
"""Trix (TRIX)
Shows the percent rate of change of a triple exponentially smoothed moving
average.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int = 15, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
ema1 = _ema(self._close, self._window, self._fillna)
ema2 = _ema(ema1, self._window, self._fillna)
ema3 = _ema(ema2, self._window, self._fillna)
self._trix = (ema3 - ema3.shift(1, fill_value=ema3.mean())) / ema3.shift(
1, fill_value=ema3.mean()
)
self._trix *= 100
def trix(self) -> pd.Series:
"""Trix (TRIX)
Returns:
pandas.Series: New feature generated.
"""
trix_series = self._check_fillna(self._trix, value=0)
return pd.Series(trix_series, name=f"trix_{self._window}")
class MassIndex(IndicatorMixin):
"""Mass Index (MI)
It uses the high-low range to identify trend reversals based on range
expansions. It identifies range bulges that can foreshadow a reversal of
the current trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window_fast(int): fast period value.
window_slow(int): slow period value.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
window_fast: int = 9,
window_slow: int = 25,
fillna: bool = False,
):
self._high = high
self._low = low
self._window_fast = window_fast
self._window_slow = window_slow
self._fillna = fillna
self._run()
def _run(self):
min_periods = 0 if self._fillna else self._window_slow
amplitude = self._high - self._low
ema1 = _ema(amplitude, self._window_fast, self._fillna)
ema2 = _ema(ema1, self._window_fast, self._fillna)
mass = ema1 / ema2
self._mass = mass.rolling(self._window_slow, min_periods=min_periods).sum()
def mass_index(self) -> pd.Series:
"""Mass Index (MI)
Returns:
pandas.Series: New feature generated.
"""
mass = self._check_fillna(self._mass, value=0)
return pd.Series(
mass, name=f"mass_index_{self._window_fast}_{self._window_slow}"
)
class IchimokuIndicator(IndicatorMixin):
"""Ichimoku Kinkō Hyō (Ichimoku)
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window1(int): n1 low period.
window2(int): n2 medium period.
window3(int): n3 high period.
visual(bool): if True, shift n2 values.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
window1: int = 9,
window2: int = 26,
window3: int = 52,
visual: bool = False,
fillna: bool = False,
):
self._high = high
self._low = low
self._window1 = window1
self._window2 = window2
self._window3 = window3
self._visual = visual
self._fillna = fillna
self._run()
def _run(self):
min_periods_n1 = 0 if self._fillna else self._window1
min_periods_n2 = 0 if self._fillna else self._window2
self._conv = 0.5 * (
self._high.rolling(self._window1, min_periods=min_periods_n1).max()
+ self._low.rolling(self._window1, min_periods=min_periods_n1).min()
)
self._base = 0.5 * (
self._high.rolling(self._window2, min_periods=min_periods_n2).max()
+ self._low.rolling(self._window2, min_periods=min_periods_n2).min()
)
def ichimoku_conversion_line(self) -> pd.Series:
"""Tenkan-sen (Conversion Line)
Returns:
pandas.Series: New feature generated.
"""
conversion = self._check_fillna(self._conv, value=-1)
return pd.Series(
conversion, name=f"ichimoku_conv_{self._window1}_{self._window2}"
)
def ichimoku_base_line(self) -> pd.Series:
"""Kijun-sen (Base Line)
Returns:
pandas.Series: New feature generated.
"""
base = self._check_fillna(self._base, value=-1)
return pd.Series(base, name=f"ichimoku_base_{self._window1}_{self._window2}")
def ichimoku_a(self) -> pd.Series:
"""Senkou Span A (Leading Span A)
Returns:
pandas.Series: New feature generated.
"""
spana = 0.5 * (self._conv + self._base)
spana = (
spana.shift(self._window2, fill_value=spana.mean())
if self._visual
else spana
)
spana = self._check_fillna(spana, value=-1)
return pd.Series(spana, name=f"ichimoku_a_{self._window1}_{self._window2}")
def ichimoku_b(self) -> pd.Series:
"""Senkou Span B (Leading Span B)
Returns:
pandas.Series: New feature generated.
"""
spanb = 0.5 * (
self._high.rolling(self._window3, min_periods=0).max()
+ self._low.rolling(self._window3, min_periods=0).min()
)
spanb = (
spanb.shift(self._window2, fill_value=spanb.mean())
if self._visual
else spanb
)
spanb = self._check_fillna(spanb, value=-1)
return pd.Series(spanb, name=f"ichimoku_b_{self._window1}_{self._window2}")
class KSTIndicator(IndicatorMixin):
"""KST Oscillator (KST Signal)
It is useful to identify major stock market cycle junctures because its
formula is weighed to be more greatly influenced by the longer and more
dominant time spans, in order to better reflect the primary swings of stock
market cycle.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:know_sure_thing_kst
Args:
close(pandas.Series): dataset 'Close' column.
roc1(int): roc1 period.
roc2(int): roc2 period.
roc3(int): roc3 period.
roc4(int): roc4 period.
window1(int): n1 smoothed period.
window2(int): n2 smoothed period.
window3(int): n3 smoothed period.
window4(int): n4 smoothed period.
nsig(int): n period to signal.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
close: pd.Series,
roc1: int = 10,
roc2: int = 15,
roc3: int = 20,
roc4: int = 30,
window1: int = 10,
window2: int = 10,
window3: int = 10,
window4: int = 15,
nsig: int = 9,
fillna: bool = False,
):
self._close = close
self._r1 = roc1
self._r2 = roc2
self._r3 = roc3
self._r4 = roc4
self._window1 = window1
self._window2 = window2
self._window3 = window3
self._window4 = window4
self._nsig = nsig
self._fillna = fillna
self._run()
def _run(self):
min_periods_n1 = 0 if self._fillna else self._window1
min_periods_n2 = 0 if self._fillna else self._window2
min_periods_n3 = 0 if self._fillna else self._window3
min_periods_n4 = 0 if self._fillna else self._window4
rocma1 = (
(
(
self._close
- self._close.shift(self._r1, fill_value=self._close.mean())
)
/ self._close.shift(self._r1, fill_value=self._close.mean())
)
.rolling(self._window1, min_periods=min_periods_n1)
.mean()
)
rocma2 = (
(
(
self._close
- self._close.shift(self._r2, fill_value=self._close.mean())
)
/ self._close.shift(self._r2, fill_value=self._close.mean())
)
.rolling(self._window2, min_periods=min_periods_n2)
.mean()
)
rocma3 = (
(
(
self._close
- self._close.shift(self._r3, fill_value=self._close.mean())
)
/ self._close.shift(self._r3, fill_value=self._close.mean())
)
.rolling(self._window3, min_periods=min_periods_n3)
.mean()
)
rocma4 = (
(
(
self._close
- self._close.shift(self._r4, fill_value=self._close.mean())
)
/ self._close.shift(self._r4, fill_value=self._close.mean())
)
.rolling(self._window4, min_periods=min_periods_n4)
.mean()
)
self._kst = 100 * (rocma1 + 2 * rocma2 + 3 * rocma3 + 4 * rocma4)
self._kst_sig = self._kst.rolling(self._nsig, min_periods=0).mean()
def kst(self) -> pd.Series:
"""Know Sure Thing (KST)
Returns:
pandas.Series: New feature generated.
"""
kst_series = self._check_fillna(self._kst, value=0)
return pd.Series(kst_series, name="kst")
def kst_sig(self) -> pd.Series:
"""Signal Line Know Sure Thing (KST)
nsig-period SMA of KST
Returns:
pandas.Series: New feature generated.
"""
kst_sig_series = self._check_fillna(self._kst_sig, value=0)
return pd.Series(kst_sig_series, name="kst_sig")
def kst_diff(self) -> pd.Series:
"""Diff Know Sure Thing (KST)
KST - Signal_KST
Returns:
pandas.Series: New feature generated.
"""
kst_diff = self._kst - self._kst_sig
kst_diff = self._check_fillna(kst_diff, value=0)
return pd.Series(kst_diff, name="kst_diff")
class DPOIndicator(IndicatorMixin):
"""Detrended Price Oscillator (DPO)
Is an indicator designed to remove trend from price and make it easier to
identify cycles.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(self, close: pd.Series, window: int = 20, fillna: bool = False):
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
min_periods = 0 if self._fillna else self._window
self._dpo = (
self._close.shift(
int((0.5 * self._window) + 1), fill_value=self._close.mean()
)
- self._close.rolling(self._window, min_periods=min_periods).mean()
)
def dpo(self) -> pd.Series:
"""Detrended Price Oscillator (DPO)
Returns:
pandas.Series: New feature generated.
"""
dpo_series = self._check_fillna(self._dpo, value=0)
return pd.Series(dpo_series, name="dpo_" + str(self._window))
class CCIIndicator(IndicatorMixin):
"""Commodity Channel Index (CCI)
CCI measures the difference between a security's price change and its
average price change. High positive readings indicate that prices are well
above their average, which is a show of strength. Low negative readings
indicate that prices are well below their average, which is a show of
weakness.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
constant(int): constant.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
window: int = 20,
constant: float = 0.015,
fillna: bool = False,
):
self._high = high
self._low = low
self._close = close
self._window = window
self._constant = constant
self._fillna = fillna
self._run()
def _run(self):
def _mad(x):
return np.mean(np.abs(x - np.mean(x)))
min_periods = 0 if self._fillna else self._window
typical_price = (self._high + self._low + self._close) / 3.0
self._cci = (
typical_price
- typical_price.rolling(self._window, min_periods=min_periods).mean()
) / (
self._constant
* typical_price.rolling(self._window, min_periods=min_periods).apply(
_mad, True
)
)
def cci(self) -> pd.Series:
"""Commodity Channel Index (CCI)
Returns:
pandas.Series: New feature generated.
"""
cci_series = self._check_fillna(self._cci, value=0)
return pd.Series(cci_series, name="cci")
class ADXIndicator(IndicatorMixin):
"""Average Directional Movement Index (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
window: int = 14,
fillna: bool = False,
):
self._high = high
self._low = low
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
if self._window == 0:
raise ValueError("window may not be 0")
close_shift = self._close.shift(1)
pdm = _get_min_max(self._high, close_shift, "max")
pdn = _get_min_max(self._low, close_shift, "min")
diff_directional_movement = pdm - pdn
self._trs_initial = np.zeros(self._window - 1)
self._trs = np.zeros(len(self._close) - (self._window - 1))
self._trs[0] = diff_directional_movement.dropna()[0 : self._window].sum()
diff_directional_movement = diff_directional_movement.reset_index(drop=True)
for i in range(1, len(self._trs) - 1):
self._trs[i] = (
self._trs[i - 1]
- (self._trs[i - 1] / float(self._window))
+ diff_directional_movement[self._window + i]
)
diff_up = self._high - self._high.shift(1)
diff_down = self._low.shift(1) - self._low
pos = abs(((diff_up > diff_down) & (diff_up > 0)) * diff_up)
neg = abs(((diff_down > diff_up) & (diff_down > 0)) * diff_down)
self._dip = np.zeros(len(self._close) - (self._window - 1))
self._dip[0] = pos.dropna()[0 : self._window].sum()
pos = pos.reset_index(drop=True)
for i in range(1, len(self._dip) - 1):
self._dip[i] = (
self._dip[i - 1]
- (self._dip[i - 1] / float(self._window))
+ pos[self._window + i]
)
self._din = np.zeros(len(self._close) - (self._window - 1))
self._din[0] = neg.dropna()[0 : self._window].sum()
neg = neg.reset_index(drop=True)
for i in range(1, len(self._din) - 1):
self._din[i] = (
self._din[i - 1]
- (self._din[i - 1] / float(self._window))
+ neg[self._window + i]
)
def adx(self) -> pd.Series:
"""Average Directional Index (ADX)
Returns:
pandas.Series: New feature generated.tr
"""
dip = np.zeros(len(self._trs))
for idx, value in enumerate(self._trs):
dip[idx] = 100 * (self._dip[idx] / value)
din = np.zeros(len(self._trs))
for idx, value in enumerate(self._trs):
din[idx] = 100 * (self._din[idx] / value)
directional_index = 100 * np.abs((dip - din) / (dip + din))
adx_series = np.zeros(len(self._trs))
adx_series[self._window] = directional_index[0 : self._window].mean()
for i in range(self._window + 1, len(adx_series)):
adx_series[i] = (
(adx_series[i - 1] * (self._window - 1)) + directional_index[i - 1]
) / float(self._window)
adx_series = np.concatenate((self._trs_initial, adx_series), axis=0)
adx_series = pd.Series(data=adx_series, index=self._close.index)
adx_series = self._check_fillna(adx_series, value=20)
return pd.Series(adx_series, name="adx")
def adx_pos(self) -> pd.Series:
"""Plus Directional Indicator (+DI)
Returns:
pandas.Series: New feature generated.
"""
dip = np.zeros(len(self._close))
for i in range(1, len(self._trs) - 1):
dip[i + self._window] = 100 * (self._dip[i] / self._trs[i])
adx_pos_series = self._check_fillna(
pd.Series(dip, index=self._close.index), value=20
)
return pd.Series(adx_pos_series, name="adx_pos")
def adx_neg(self) -> pd.Series:
"""Minus Directional Indicator (-DI)
Returns:
pandas.Series: New feature generated.
"""
din = np.zeros(len(self._close))
for i in range(1, len(self._trs) - 1):
din[i + self._window] = 100 * (self._din[i] / self._trs[i])
adx_neg_series = self._check_fillna(
pd.Series(din, index=self._close.index), value=20
)
return pd.Series(adx_neg_series, name="adx_neg")
class VortexIndicator(IndicatorMixin):
"""Vortex Indicator (VI)
It consists of two oscillators that capture positive and negative trend
movement. A bullish signal triggers when the positive trend indicator
crosses above the negative trend indicator or a key level.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
window: int = 14,
fillna: bool = False,
):
self._high = high
self._low = low
self._close = close
self._window = window
self._fillna = fillna
self._run()
def _run(self):
close_shift = self._close.shift(1, fill_value=self._close.mean())
true_range = self._true_range(self._high, self._low, close_shift)
min_periods = 0 if self._fillna else self._window
trn = true_range.rolling(self._window, min_periods=min_periods).sum()
vmp = np.abs(self._high - self._low.shift(1))
vmm = np.abs(self._low - self._high.shift(1))
self._vip = vmp.rolling(self._window, min_periods=min_periods).sum() / trn
self._vin = vmm.rolling(self._window, min_periods=min_periods).sum() / trn
def vortex_indicator_pos(self):
"""+VI
Returns:
pandas.Series: New feature generated.
"""
vip = self._check_fillna(self._vip, value=1)
return pd.Series(vip, name="vip")
def vortex_indicator_neg(self):
"""-VI
Returns:
pandas.Series: New feature generated.
"""
vin = self._check_fillna(self._vin, value=1)
return pd.Series(vin, name="vin")
def vortex_indicator_diff(self):
"""Diff VI
Returns:
pandas.Series: New feature generated.
"""
vid = self._vip - self._vin
vid = self._check_fillna(vid, value=0)
return pd.Series(vid, name="vid")
class PSARIndicator(IndicatorMixin):
"""Parabolic Stop and Reverse (Parabolic SAR)
The Parabolic Stop and Reverse, more commonly known as the
Parabolic SAR,is a trend-following indicator developed by
J. Welles Wilder. The Parabolic SAR is displayed as a single
parabolic line (or dots) underneath the price bars in an uptrend,
and above the price bars in a downtrend.
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
step(float): the Acceleration Factor used to compute the SAR.
max_step(float): the maximum value allowed for the Acceleration Factor.
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
high: pd.Series,
low: pd.Series,
close: pd.Series,
step: float = 0.02,
max_step: float = 0.20,
fillna: bool = False,
):
self._high = high
self._low = low
self._close = close
self._step = step
self._max_step = max_step
self._fillna = fillna
self._run()
def _run(self): # noqa
up_trend = True
acceleration_factor = self._step
up_trend_high = self._high.iloc[0]
down_trend_low = self._low.iloc[0]
self._psar = self._close.copy()
self._psar_up = pd.Series(index=self._psar.index)
self._psar_down = pd.Series(index=self._psar.index)
for i in range(2, len(self._close)):
reversal = False
max_high = self._high.iloc[i]
min_low = self._low.iloc[i]
if up_trend:
self._psar.iloc[i] = self._psar.iloc[i - 1] + (
acceleration_factor * (up_trend_high - self._psar.iloc[i - 1])
)
if min_low < self._psar.iloc[i]:
reversal = True
self._psar.iloc[i] = up_trend_high
down_trend_low = min_low
acceleration_factor = self._step
else:
if max_high > up_trend_high:
up_trend_high = max_high
acceleration_factor = min(
acceleration_factor + self._step, self._max_step
)
low1 = self._low.iloc[i - 1]
low2 = self._low.iloc[i - 2]
if low2 < self._psar.iloc[i]:
self._psar.iloc[i] = low2
elif low1 < self._psar.iloc[i]:
self._psar.iloc[i] = low1
else:
self._psar.iloc[i] = self._psar.iloc[i - 1] - (
acceleration_factor * (self._psar.iloc[i - 1] - down_trend_low)
)
if max_high > self._psar.iloc[i]:
reversal = True
self._psar.iloc[i] = down_trend_low
up_trend_high = max_high
acceleration_factor = self._step
else:
if min_low < down_trend_low:
down_trend_low = min_low
acceleration_factor = min(
acceleration_factor + self._step, self._max_step
)
high1 = self._high.iloc[i - 1]
high2 = self._high.iloc[i - 2]
if high2 > self._psar.iloc[i]:
self._psar[i] = high2
elif high1 > self._psar.iloc[i]:
self._psar.iloc[i] = high1
up_trend = up_trend != reversal # XOR
if up_trend:
self._psar_up.iloc[i] = self._psar.iloc[i]
else:
self._psar_down.iloc[i] = self._psar.iloc[i]
def psar(self) -> pd.Series:
"""PSAR value
Returns:
pandas.Series: New feature generated.
"""
psar_series = self._check_fillna(self._psar, value=-1)
return pd.Series(psar_series, name="psar")
def psar_up(self) -> pd.Series:
"""PSAR up trend value
Returns:
pandas.Series: New feature generated.
"""
psar_up_series = self._check_fillna(self._psar_up, value=-1)
return pd.Series(psar_up_series, name="psarup")
def psar_down(self) -> pd.Series:
"""PSAR down trend value
Returns:
pandas.Series: New feature generated.
"""
psar_down_series = self._check_fillna(self._psar_down, value=-1)
return pd.Series(psar_down_series, name="psardown")
def psar_up_indicator(self) -> pd.Series:
"""PSAR up trend value indicator
Returns:
pandas.Series: New feature generated.
"""
indicator = self._psar_up.where(
self._psar_up.notnull() & self._psar_up.shift(1).isnull(), 0
)
indicator = indicator.where(indicator == 0, 1)
return pd.Series(indicator, index=self._close.index, name="psariup")
def psar_down_indicator(self) -> pd.Series:
"""PSAR down trend value indicator
Returns:
pandas.Series: New feature generated.
"""
indicator = self._psar_up.where(
self._psar_down.notnull() & self._psar_down.shift(1).isnull(), 0
)
indicator = indicator.where(indicator == 0, 1)
return pd.Series(indicator, index=self._close.index, name="psaridown")
class STCIndicator(IndicatorMixin):
"""Schaff Trend Cycle (STC)
The Schaff Trend Cycle (STC) is a charting indicator that
is commonly used to identify market trends and provide buy
and sell signals to traders. Developed in 1999 by noted currency
trader Doug Schaff, STC is a type of oscillator and is based on
the assumption that, regardless of time frame, currency trends
accelerate and decelerate in cyclical patterns.
https://www.investopedia.com/articles/forex/10/schaff-trend-cycle-indicator.asp
Args:
close(pandas.Series): dataset 'Close' column.
window_fast(int): n period short-term.
window_slow(int): n period long-term.
cycle(int): cycle size
smooth1(int): ema period over stoch_k
smooth2(int): ema period over stoch_kd
fillna(bool): if True, fill nan values.
"""
def __init__(
self,
close: pd.Series,
window_slow: int = 50,
window_fast: int = 23,
cycle: int = 10,
smooth1: int = 3,
smooth2: int = 3,
fillna: bool = False,
):
self._close = close
self._window_slow = window_slow
self._window_fast = window_fast
self._cycle = cycle
self._smooth1 = smooth1
self._smooth2 = smooth2
self._fillna = fillna
self._run()
def _run(self):
_emafast = _ema(self._close, self._window_fast, self._fillna)
_emaslow = _ema(self._close, self._window_slow, self._fillna)
_macd = _emafast - _emaslow
_macdmin = _macd.rolling(window=self._cycle).min()
_macdmax = _macd.rolling(window=self._cycle).max()
_stoch_k = 100 * (_macd - _macdmin) / (_macdmax - _macdmin)
_stoch_d = _ema(_stoch_k, self._smooth1, self._fillna)
_stoch_d_min = _stoch_d.rolling(window=self._cycle).min()
_stoch_d_max = _stoch_d.rolling(window=self._cycle).max()
_stoch_kd = 100 * (_stoch_d - _stoch_d_min) / (_stoch_d_max - _stoch_d_min)
self._stc = _ema(_stoch_kd, self._smooth2, self._fillna)
def stc(self):
"""Schaff Trend Cycle
Returns:
pandas.Series: New feature generated.
"""
stc_series = self._check_fillna(self._stc)
return pd.Series(stc_series, name="stc")
def ema_indicator(close, window=12, fillna=False):
"""Exponential Moving Average (EMA)
Returns:
pandas.Series: New feature generated.
"""
return EMAIndicator(close=close, window=window, fillna=fillna).ema_indicator()
def sma_indicator(close, window=12, fillna=False):
"""Simple Moving Average (SMA)
Returns:
pandas.Series: New feature generated.
"""
return SMAIndicator(close=close, window=window, fillna=fillna).sma_indicator()
def wma_indicator(close, window=9, fillna=False):
"""Weighted Moving Average (WMA)
Returns:
pandas.Series: New feature generated.
"""
return WMAIndicator(close=close, window=window, fillna=fillna).wma()
def macd(close, window_slow=26, window_fast=12, fillna=False):
"""Moving Average Convergence Divergence (MACD)
Is a trend-following momentum indicator that shows the relationship between
two moving averages of prices.
https://en.wikipedia.org/wiki/MACD
Args:
close(pandas.Series): dataset 'Close' column.
window_fast(int): n period short-term.
window_slow(int): n period long-term.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return MACD(
close=close,
window_slow=window_slow,
window_fast=window_fast,
window_sign=9,
fillna=fillna,
).macd()
def macd_signal(close, window_slow=26, window_fast=12, window_sign=9, fillna=False):
"""Moving Average Convergence Divergence (MACD Signal)
Shows EMA of MACD.
https://en.wikipedia.org/wiki/MACD
Args:
close(pandas.Series): dataset 'Close' column.
window_fast(int): n period short-term.
window_slow(int): n period long-term.
window_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return MACD(
close=close,
window_slow=window_slow,
window_fast=window_fast,
window_sign=window_sign,
fillna=fillna,
).macd_signal()
def macd_diff(close, window_slow=26, window_fast=12, window_sign=9, fillna=False):
"""Moving Average Convergence Divergence (MACD Diff)
Shows the relationship between MACD and MACD Signal.
https://en.wikipedia.org/wiki/MACD
Args:
close(pandas.Series): dataset 'Close' column.
window_fast(int): n period short-term.
window_slow(int): n period long-term.
window_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return MACD(
close=close,
window_slow=window_slow,
window_fast=window_fast,
window_sign=window_sign,
fillna=fillna,
).macd_diff()
def adx(high, low, close, window=14, fillna=False):
"""Average Directional Movement Index (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return ADXIndicator(
high=high, low=low, close=close, window=window, fillna=fillna
).adx()
def adx_pos(high, low, close, window=14, fillna=False):
"""Average Directional Movement Index Positive (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return ADXIndicator(
high=high, low=low, close=close, window=window, fillna=fillna
).adx_pos()
def adx_neg(high, low, close, window=14, fillna=False):
"""Average Directional Movement Index Negative (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return ADXIndicator(
high=high, low=low, close=close, window=window, fillna=fillna
).adx_neg()
def vortex_indicator_pos(high, low, close, window=14, fillna=False):
"""Vortex Indicator (VI)
It consists of two oscillators that capture positive and negative trend
movement. A bullish signal triggers when the positive trend indicator
crosses above the negative trend indicator or a key level.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return VortexIndicator(
high=high, low=low, close=close, window=window, fillna=fillna
).vortex_indicator_pos()
def vortex_indicator_neg(high, low, close, window=14, fillna=False):
"""Vortex Indicator (VI)
It consists of two oscillators that capture positive and negative trend
movement. A bearish signal triggers when the negative trend indicator
crosses above the positive trend indicator or a key level.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return VortexIndicator(
high=high, low=low, close=close, window=window, fillna=fillna
).vortex_indicator_neg()
def trix(close, window=15, fillna=False):
"""Trix (TRIX)
Shows the percent rate of change of a triple exponentially smoothed moving
average.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return TRIXIndicator(close=close, window=window, fillna=fillna).trix()
def mass_index(high, low, window_fast=9, window_slow=25, fillna=False):
"""Mass Index (MI)
It uses the high-low range to identify trend reversals based on range
expansions. It identifies range bulges that can foreshadow a reversal of
the current trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window_fast(int): fast window value.
window_slow(int): slow window value.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return MassIndex(
high=high,
low=low,
window_fast=window_fast,
window_slow=window_slow,
fillna=fillna,
).mass_index()
def cci(high, low, close, window=20, constant=0.015, fillna=False):
"""Commodity Channel Index (CCI)
CCI measures the difference between a security's price change and its
average price change. High positive readings indicate that prices are well
above their average, which is a show of strength. Low negative readings
indicate that prices are well below their average, which is a show of
weakness.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
window(int): n periods.
constant(int): constant.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return CCIIndicator(
high=high, low=low, close=close, window=window, constant=constant, fillna=fillna
).cci()
def dpo(close, window=20, fillna=False):
"""Detrended Price Oscillator (DPO)
Is an indicator designed to remove trend from price and make it easier to
identify cycles.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return DPOIndicator(close=close, window=window, fillna=fillna).dpo()
def kst(
close,
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
fillna=False,
):
"""KST Oscillator (KST)
It is useful to identify major stock market cycle junctures because its
formula is weighed to be more greatly influenced by the longer and more
dominant time spans, in order to better reflect the primary swings of stock
market cycle.
https://en.wikipedia.org/wiki/KST_oscillator
Args:
close(pandas.Series): dataset 'Close' column.
roc1(int): r1 period.
roc2(int): r2 period.
roc3(int): r3 period.
roc4(int): r4 period.
window1(int): n1 smoothed period.
window2(int): n2 smoothed period.
window3(int): n3 smoothed period.
window4(int): n4 smoothed period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return KSTIndicator(
close=close,
roc1=roc1,
roc2=roc2,
roc3=roc3,
roc4=roc4,
window1=window1,
window2=window2,
window3=window3,
window4=window4,
nsig=9,
fillna=fillna,
).kst()
def stc(
close, window_slow=50, window_fast=23, cycle=10, smooth1=3, smooth2=3, fillna=False
):
"""Schaff Trend Cycle (STC)
The Schaff Trend Cycle (STC) is a charting indicator that
is commonly used to identify market trends and provide buy
and sell signals to traders. Developed in 1999 by noted currency
trader Doug Schaff, STC is a type of oscillator and is based on
the assumption that, regardless of time frame, currency trends
accelerate and decelerate in cyclical patterns.
https://www.investopedia.com/articles/forex/10/schaff-trend-cycle-indicator.asp
Args:
close(pandas.Series): dataset 'Close' column.
window_fast(int): n period short-term.
window_slow(int): n period long-term.
cycle(int): n period
smooth1(int): ema period over stoch_k
smooth2(int): ema period over stoch_kd
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return STCIndicator(
close=close,
window_slow=window_slow,
window_fast=window_fast,
cycle=cycle,
smooth1=smooth1,
smooth2=smooth2,
fillna=fillna,
).stc()
def kst_sig(
close,
roc1=10,
roc2=15,
roc3=20,
roc4=30,
window1=10,
window2=10,
window3=10,
window4=15,
nsig=9,
fillna=False,
):
"""KST Oscillator (KST Signal)
It is useful to identify major stock market cycle junctures because its
formula is weighed to be more greatly influenced by the longer and more
dominant time spans, in order to better reflect the primary swings of stock
market cycle.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:know_sure_thing_kst
Args:
close(pandas.Series): dataset 'Close' column.
roc1(int): roc1 period.
roc2(int): roc2 period.
roc3(int): roc3 period.
roc4(int): roc4 period.
window1(int): n1 smoothed period.
window2(int): n2 smoothed period.
window3(int): n3 smoothed period.
window4(int): n4 smoothed period.
nsig(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return KSTIndicator(
close=close,
roc1=roc1,
roc2=roc2,
roc3=roc3,
roc4=roc4,
window1=window1,
window2=window2,
window3=window3,
window4=window4,
nsig=nsig,
fillna=fillna,
).kst_sig()
def ichimoku_conversion_line(
high, low, window1=9, window2=26, visual=False, fillna=False
) -> pd.Series:
"""Tenkan-sen (Conversion Line)
It identifies the trend and look for potential signals within that trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window1(int): n1 low period.
window2(int): n2 medium period.
visual(bool): if True, shift n2 values.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return IchimokuIndicator(
high=high,
low=low,
window1=window1,
window2=window2,
window3=52,
visual=visual,
fillna=fillna,
).ichimoku_conversion_line()
def ichimoku_base_line(
high, low, window1=9, window2=26, visual=False, fillna=False
) -> pd.Series:
"""Kijun-sen (Base Line)
It identifies the trend and look for potential signals within that trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window1(int): n1 low period.
window2(int): n2 medium period.
visual(bool): if True, shift n2 values.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return IchimokuIndicator(
high=high,
low=low,
window1=window1,
window2=window2,
window3=52,
visual=visual,
fillna=fillna,
).ichimoku_base_line()
def ichimoku_a(high, low, window1=9, window2=26, visual=False, fillna=False):
"""Ichimoku Kinkō Hyō (Ichimoku)
It identifies the trend and look for potential signals within that trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window1(int): n1 low period.
window2(int): n2 medium period.
visual(bool): if True, shift n2 values.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return IchimokuIndicator(
high=high,
low=low,
window1=window1,
window2=window2,
window3=52,
visual=visual,
fillna=fillna,
).ichimoku_a()
def ichimoku_b(high, low, window2=26, window3=52, visual=False, fillna=False):
"""Ichimoku Kinkō Hyō (Ichimoku)
It identifies the trend and look for potential signals within that trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
window2(int): n2 medium period.
window3(int): n3 high period.
visual(bool): if True, shift n2 values.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return IchimokuIndicator(
high=high,
low=low,
window1=9,
window2=window2,
window3=window3,
visual=visual,
fillna=fillna,
).ichimoku_b()
def aroon_up(close, window=25, fillna=False):
"""Aroon Indicator (AI)
Identify when trends are likely to change direction (uptrend).
Aroon Up - ((N - Days Since N-day High) / N) x 100
https://www.investopedia.com/terms/a/aroon.asp
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return AroonIndicator(close=close, window=window, fillna=fillna).aroon_up()
def aroon_down(close, window=25, fillna=False):
"""Aroon Indicator (AI)
Identify when trends are likely to change direction (downtrend).
Aroon Down - ((N - Days Since N-day Low) / N) x 100
https://www.investopedia.com/terms/a/aroon.asp
Args:
close(pandas.Series): dataset 'Close' column.
window(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
return AroonIndicator(close=close, window=window, fillna=fillna).aroon_down()
def psar_up(high, low, close, step=0.02, max_step=0.20, fillna=False):
"""Parabolic Stop and Reverse (Parabolic SAR)
Returns the PSAR series with non-N/A values for upward trends
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
step(float): the Acceleration Factor used to compute the SAR.
max_step(float): the maximum value allowed for the Acceleration Factor.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PSARIndicator(
high=high, low=low, close=close, step=step, max_step=max_step, fillna=fillna
)
return indicator.psar_up()
def psar_down(high, low, close, step=0.02, max_step=0.20, fillna=False):
"""Parabolic Stop and Reverse (Parabolic SAR)
Returns the PSAR series with non-N/A values for downward trends
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
step(float): the Acceleration Factor used to compute the SAR.
max_step(float): the maximum value allowed for the Acceleration Factor.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PSARIndicator(
high=high, low=low, close=close, step=step, max_step=max_step, fillna=fillna
)
return indicator.psar_down()
def psar_up_indicator(high, low, close, step=0.02, max_step=0.20, fillna=False):
"""Parabolic Stop and Reverse (Parabolic SAR) Upward Trend Indicator
Returns 1, if there is a reversal towards an upward trend. Else, returns 0.
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
step(float): the Acceleration Factor used to compute the SAR.
max_step(float): the maximum value allowed for the Acceleration Factor.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PSARIndicator(
high=high, low=low, close=close, step=step, max_step=max_step, fillna=fillna
)
return indicator.psar_up_indicator()
def psar_down_indicator(high, low, close, step=0.02, max_step=0.20, fillna=False):
"""Parabolic Stop and Reverse (Parabolic SAR) Downward Trend Indicator
Returns 1, if there is a reversal towards an downward trend. Else, returns 0.
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
step(float): the Acceleration Factor used to compute the SAR.
max_step(float): the maximum value allowed for the Acceleration Factor.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
indicator = PSARIndicator(
high=high, low=low, close=close, step=step, max_step=max_step, fillna=fillna
)
return indicator.psar_down_indicator()
