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96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import arange, array, polyfit, std
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from pandas import DataFrame, DatetimeIndex, Series
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from pandas_ta._typing import DictLike, Int, List
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from pandas_ta.utils import v_list, v_lowerbound, v_offset, v_series
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def tos_stdevall(
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close: Series, length: Int = None,
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stds: List = None, ddof: Int = None,
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offset: Int = None, **kwargs: DictLike
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) -> DataFrame:
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"""TD Ameritrade's Think or Swim Standard Deviation All (TOS_STDEV)
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A port of TD Ameritrade's Think or Swim Standard Deviation All indicator
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which returns the standard deviation of data for the entire plot or for
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the interval of the last bars defined by the length parameter.
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Sources:
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https://tlc.thinkorswim.com/center/reference/thinkScript/Functions/Statistical/StDevAll
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Args:
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close (pd.Series): Series of 'close's
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length (int): Bars from current bar. Default: None
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stds (list): List of Standard Deviations in increasing order from the
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central Linear Regression line. Default: [1,2,3]
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ddof (int): Delta Degrees of Freedom.
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The divisor used in calculations is N - ddof,
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where N represents the number of elements. 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.DataFrame: Central LR, Pairs of Lower and Upper LR Lines based on
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multiples of the standard deviation. Default: returns 7 columns.
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"""
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# Validate
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_props = f"TOS_STDEVALL"
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if length is None:
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length = close.size
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else:
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length = v_lowerbound(length, 2, 30)
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close = close.iloc[-length:]
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_props = f"{_props}_{length}"
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close = v_series(close, 2)
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if close is None:
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return
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stds = v_list(stds, [1, 2, 3])
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if min(stds) <= 0:
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return
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if not all(i < j for i, j in zip(stds, stds[1:])):
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stds = stds[::-1]
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ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1
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offset = v_offset(offset)
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# Calculate
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X = src_index = close.index
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if isinstance(close.index, DatetimeIndex):
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X = arange(length)
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close = array(close)
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m, b = polyfit(X, close, 1)
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lr = Series(m * X + b, index=src_index)
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stdev = std(close, ddof=ddof)
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# Name and Category
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df = DataFrame({f"{_props}_LR": lr}, index=src_index)
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for i in stds:
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df[f"{_props}_L_{i}"] = lr - i * stdev
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df[f"{_props}_U_{i}"] = lr + i * stdev
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df[f"{_props}_L_{i}"].name = df[f"{_props}_U_{i}"].name = f"{_props}"
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df[f"{_props}_L_{i}"].category = df[f"{_props}_U_{i}"].category = "statistics"
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# Offset
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if offset != 0:
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df = df.shift(offset)
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# Fill
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if "fillna" in kwargs:
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df.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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df.fillna(method=kwargs["fill_method"], inplace=True)
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df.name = f"{_props}"
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df.category = "statistics"
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return df
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