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https://github.com/wassname/pandas-ta.git
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Kevin Johnson
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import Series
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from pandas_ta._typing import DictLike, Int, IntFloat
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from pandas_ta.ma import ma
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from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
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# - Standard definition of your custom indicator function (including docs)-
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def ni(
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close: Series, length: Int = None,
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centered: bool = False, mamode: str = None,
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offset: Int = None, **kwargs: DictLike
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):
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"""Example indicator (NI)
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Is an indicator provided solely as an example
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Sources:
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https://github.com/twopirllc/pandas-ta/issues/264
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Calculation:
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Default Inputs:
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length=20, centered=False
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SMA = Simple Moving Average
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t = int(0.5 * length) + 1
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ni = close.shift(t) - SMA(close, length)
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if centered:
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ni = ni.shift(-t)
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Args:
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close (pd.Series): Series of 'close's
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length (int): It's period. Default: 20
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mamode (str): Chosen Moving Average. Default: "sma"
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centered (bool): Shift the ni back by int(0.5 * length) + 1. Default: False
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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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""" # Validate Arguments
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length = v_pos_default(length, 20)
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close = v_series(close, length)
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if close is None:
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return
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mamode = v_mamode(mamode, "sma")
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offset = v_offset(offset)
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# Calculate Result
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ma = ma(mamode, close, length=length, **kwargs)
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t = int(0.5 * length) + 1
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ni = close - ma.shift(t)
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if centered:
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ni = (close.shift(t) - ma).shift(-t)
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# Offset
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if offset != 0:
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ni = ni.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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ni.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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ni.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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ni.name = f"ni_{length}"
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ni.category = "trend"
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return ni
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# - Define a matching class method --------------------------------------------
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def ni_method(self, length=None, offset=None, **kwargs):
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close = self._get_column(kwargs.pop("close", "close"))
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result = ni(close=close, length=length, offset=offset, **kwargs)
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return self._post_process(result, **kwargs) |