mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import sign as npSign
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from ..utils import get_drift, get_offset, verify_series
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def psl(close,
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open_=None,
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length=None,
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scalar=None,
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drift=None,
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offset=None,
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**kwargs):
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"""Indicator: Psychological Line (PSL)"""
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# Validate Arguments
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close = verify_series(close)
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length = int(length) if length and length > 0 else 12
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scalar = float(scalar) if scalar and scalar > 0 else 100
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drift = get_drift(drift)
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offset = get_offset(offset)
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# Calculate Result
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if open_ is not None:
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open_ = verify_series(open_)
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diff = npSign(close - open_)
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else:
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diff = npSign(close.diff(drift))
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diff.fillna(0, inplace=True)
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diff[diff <= 0] = 0 # Zero negative values
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psl = scalar * diff.rolling(length).sum()
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psl /= length
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# Offset
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if offset != 0:
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psl = psl.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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psl.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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psl.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_props = f"_{length}"
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psl.name = f"PSL{_props}"
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psl.category = "momentum"
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return psl
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psl.__doc__ = """Psychological Line (PSL)
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The Psychological Line is an oscillator-type indicator that compares the
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number of the rising periods to the total number of periods. In other
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words, it is the percentage of bars that close above the previous
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bar over a given period.
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Sources:
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https://www.quantshare.com/item-851-psychological-line
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Calculation:
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Default Inputs:
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length=12, scalar=100, drift=1
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IF NOT open:
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DIFF = SIGN(close - close[drift])
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ELSE:
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DIFF = SIGN(close - open)
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DIFF.fillna(0)
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DIFF[DIFF <= 0] = 0
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PSL = scalar * SUM(DIFF, length) / length
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Args:
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close (pd.Series): Series of 'close's
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open_ (pd.Series, optional): Series of 'open's
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length (int): It's period. Default: 12
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scalar (float): How much to magnify. Default: 100
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drift (int): The difference period. Default: 1
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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.Series: New feature generated.
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"""
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