mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
141 lines
4.0 KiB
Python
141 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import NaN as npNaN
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from pandas import DataFrame, Series
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from pandas_ta.utils import get_offset, verify_series
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def psar(high, low, close=None, af=None, max_af=None, offset=None, **kwargs):
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"""Indicator: Parabolic Stop and Reverse (PSAR)"""
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# Validate Arguments
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high = verify_series(high)
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low = verify_series(low)
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af = float(af) if af and af > 0 else 0.02
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max_af = float(max_af) if max_af and max_af > 0 else 0.2
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offset = get_offset(offset)
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# Initialize
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m = high.shape[0]
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af0 = af
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bullish = True
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high_point = high.iloc[0]
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low_point = low.iloc[0]
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if close is not None:
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close = verify_series(close)
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sar = close.copy()
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else:
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sar = low.copy()
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long = Series(npNaN, index=sar.index)
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short = long.copy()
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reversal = Series(False, index=sar.index)
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_af = long.copy()
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_af.iloc[0:2] = af0
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# Calculate Result
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for i in range(2, m):
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reverse = False
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_af[i] = af
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if bullish:
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sar[i] = sar[i - 1] + af * (high_point - sar[i - 1])
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if low[i] < sar[i]:
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bullish, reverse, af = False, True, af0
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sar[i] = high_point
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low_point = low[i]
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else:
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sar[i] = sar[i - 1] + af * (low_point - sar[i - 1])
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if high[i] > sar[i]:
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bullish, reverse, af = True, True, af0
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sar[i] = low_point
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high_point = high[i]
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reversal[i] = reverse
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if not reverse:
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if bullish:
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if high[i] > high_point:
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high_point = high[i]
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af = min(af + af0, max_af)
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if low[i - 1] < sar[i]:
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sar[i] = low[i - 1]
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if low[i - 2] < sar[i]:
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sar[i] = low[i - 2]
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else:
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if low[i] < low_point:
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low_point = low[i]
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af = min(af + af0, max_af)
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if high[i - 1] > sar[i]:
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sar[i] = high[i - 1]
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if high[i - 2] > sar[i]:
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sar[i] = high[i - 2]
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if bullish:
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long[i] = sar[i]
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else:
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short[i] = sar[i]
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# Offset
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if offset != 0:
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_af = _af.shift(offset)
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long = long.shift(offset)
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short = short.shift(offset)
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reversal = reversal.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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_af.fillna(kwargs["fillna"], inplace=True)
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long.fillna(kwargs["fillna"], inplace=True)
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short.fillna(kwargs["fillna"], inplace=True)
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reversal.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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_af.fillna(method=kwargs["fill_method"], inplace=True)
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long.fillna(method=kwargs["fill_method"], inplace=True)
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short.fillna(method=kwargs["fill_method"], inplace=True)
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reversal.fillna(method=kwargs["fill_method"], inplace=True)
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# Prepare DataFrame to return
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_params = f"_{af0}_{max_af}"
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data = {
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f"PSARl{_params}": long,
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f"PSARs{_params}": short,
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f"PSARaf{_params}": _af,
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f"PSARr{_params}": reversal,
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}
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psardf = DataFrame(data)
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psardf.name = f"PSAR{_params}"
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psardf.category = long.category = short.category = "trend"
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return psardf
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psar.__doc__ = """Parabolic Stop and Reverse (psar)
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Parabolic Stop and Reverse
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Source:
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https://github.com/virtualizedfrog/blog_code/blob/master/PSAR/psar.py
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Calculation:
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Default Inputs:
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af=0.02
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max_af=0.2
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Args:
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series, optional): Series of 'close's. Optional
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af (float): Acceleration Factor. Default: 0.02
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max_af (float): Maximum Acceleration Factor. Default: 0.2
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.DataFrame: long, short, af, and reversal columns.
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"""
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