# -*- coding: utf-8 -*-
from numpy import nan as npNaN
from pandas import DataFrame, Series
from pandas_ta.utils import get_offset, verify_series, zero
[docs]def psar(high, low, close=None, af0=None, af=None, max_af=None, offset=None, **kwargs):
"""Indicator: Parabolic Stop and Reverse (PSAR)"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
af = float(af) if af and af > 0 else 0.02
af0 = float(af0) if af0 and af0 > 0 else af
max_af = float(max_af) if max_af and max_af > 0 else 0.2
offset = get_offset(offset)
def _falling(high, low, drift:int=1):
"""Returns the last -DM value"""
# Not to be confused with ta.falling()
up = high - high.shift(drift)
dn = low.shift(drift) - low
_dmn = (((dn > up) & (dn > 0)) * dn).apply(zero).iloc[-1]
return _dmn > 0
# Falling if the first nan -DM is positive
falling = _falling(high.iloc[:2], low.iloc[:2])
if falling:
sar = high.iloc[0]
ep = low.iloc[0]
else:
sar = low.iloc[0]
ep = high.iloc[0]
if close is not None:
close = verify_series(close)
sar = close.iloc[0]
long = Series(npNaN, index=high.index)
short = long.copy()
reversal = Series(0, index=high.index)
_af = long.copy()
_af.iloc[0:2] = af0
# Calculate Result
m = high.shape[0]
for row in range(1, m):
high_ = high.iloc[row]
low_ = low.iloc[row]
if falling:
_sar = sar + af * (ep - sar)
reverse = high_ > _sar
if low_ < ep:
ep = low_
af = min(af + af0, max_af)
_sar = max(high.iloc[row - 1], high.iloc[row - 2], _sar)
else:
_sar = sar + af * (ep - sar)
reverse = low_ < _sar
if high_ > ep:
ep = high_
af = min(af + af0, max_af)
_sar = min(low.iloc[row - 1], low.iloc[row - 2], _sar)
if reverse:
_sar = ep
af = af0
falling = not falling # Must come before next line
ep = low_ if falling else high_
sar = _sar # Update SAR
# Seperate long/short sar based on falling
if falling:
short.iloc[row] = sar
else:
long.iloc[row] = sar
_af.iloc[row] = af
reversal.iloc[row] = int(reverse)
# Offset
if offset != 0:
_af = _af.shift(offset)
long = long.shift(offset)
short = short.shift(offset)
reversal = reversal.shift(offset)
# Handle fills
if "fillna" in kwargs:
_af.fillna(kwargs["fillna"], inplace=True)
long.fillna(kwargs["fillna"], inplace=True)
short.fillna(kwargs["fillna"], inplace=True)
reversal.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
_af.fillna(method=kwargs["fill_method"], inplace=True)
long.fillna(method=kwargs["fill_method"], inplace=True)
short.fillna(method=kwargs["fill_method"], inplace=True)
reversal.fillna(method=kwargs["fill_method"], inplace=True)
# Prepare DataFrame to return
_params = f"_{af0}_{max_af}"
data = {
f"PSARl{_params}": long,
f"PSARs{_params}": short,
f"PSARaf{_params}": _af,
f"PSARr{_params}": reversal,
}
psardf = DataFrame(data)
psardf.name = f"PSAR{_params}"
psardf.category = long.category = short.category = "trend"
return psardf
psar.__doc__ = \
"""Parabolic Stop and Reverse (psar)
Parabolic Stop and Reverse (PSAR) was developed by J. Wells Wilder, that is used
to determine trend direction and it's potential reversals in price. PSAR uses a
trailing stop and reverse method called "SAR," or stop and reverse, to identify
possible entries and exits. It is also known as SAR.
PSAR indicator typically appears on a chart as a series of dots, either above or
below an asset's price, depending on the direction the price is moving. A dot is
placed below the price when it is trending upward, and above the price when it
is trending downward.
Sources:
https://www.tradingview.com/pine-script-reference/#fun_sar
https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=66&Name=Parabolic
Calculation:
Default Inputs:
af0=0.02, af=0.02, max_af=0.2
See Source links
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series, optional): Series of 'close's. Optional
af0 (float): Initial Acceleration Factor. Default: 0.02
af (float): Acceleration Factor. Default: 0.02
max_af (float): Maximum Acceleration Factor. Default: 0.2
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.DataFrame: long, short, af, and reversal columns.
"""