mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
126 lines
4.0 KiB
Python
126 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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import math
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from pandas_ta.utils import get_offset, verify_series
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def linreg(close, length=None, offset=None, **kwargs):
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"""Indicator: Linear Regression"""
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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 14
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min_periods = (int(kwargs["min_periods"]) if "min_periods" in kwargs and
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kwargs["min_periods"] is not None else length)
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offset = get_offset(offset)
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angle = kwargs.pop("angle", False)
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intercept = kwargs.pop("intercept", False)
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degrees = kwargs.pop("degrees", False)
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r = kwargs.pop("r", False)
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slope = kwargs.pop("slope", False)
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tsf = kwargs.pop("tsf", False)
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# Calculate Result
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x = range(1, length + 1) # [1, 2, ..., n] from 1 to n keeps Sum(xy) low
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x_sum = 0.5 * length * (length + 1)
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x2_sum = x_sum * (2 * length + 1) / 3
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divisor = length * x2_sum - x_sum * x_sum
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def linear_regression(series):
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y_sum = series.sum()
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xy_sum = (x * series).sum()
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m = (length * xy_sum - x_sum * y_sum) / divisor
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if slope:
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return m
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b = (y_sum * x2_sum - x_sum * xy_sum) / divisor
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if intercept:
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return b
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if angle:
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theta = math.atan(m)
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if degrees:
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theta *= 180 / math.pi
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return theta
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if r:
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y2_sum = (series * series).sum()
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rn = length * xy_sum - x_sum * y_sum
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rd = math.sqrt(divisor * (length * y2_sum - y_sum * y_sum))
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return rn / rd
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return m * length + b if tsf else m * (length - 1) + b
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linreg = close.rolling(length, min_periods=length).apply(linear_regression,
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raw=False)
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# Offset
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if offset != 0:
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linreg = linreg.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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linreg.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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linreg.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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linreg.name = f"LR"
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if slope:
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linreg.name += "m"
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if intercept:
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linreg.name += "b"
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if angle:
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linreg.name += "a"
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if r:
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linreg.name += "r"
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linreg.name += f"_{length}"
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linreg.category = "overlap"
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return linreg
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linreg.__doc__ = """Linear Regression Moving Average (linreg)
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Linear Regression Moving Average (LINREG). This is a simplified version of a
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Standard Linear Regression. LINREG is a rolling regression of one variable. A
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Standard Linear Regression is between two or more variables.
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Source: TA Lib
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Calculation:
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Default Inputs:
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length=14
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x = [1, 2, ..., n]
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x_sum = 0.5 * length * (length + 1)
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x2_sum = length * (length + 1) * (2 * length + 1) / 6
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divisor = length * x2_sum - x_sum * x_sum
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lr(series):
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y_sum = series.sum()
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y2_sum = (series* series).sum()
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xy_sum = (x * series).sum()
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m = (length * xy_sum - x_sum * y_sum) / divisor
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b = (y_sum * x2_sum - x_sum * xy_sum) / divisor
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return m * (length - 1) + b
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linreg = close.rolling(length).apply(lr)
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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: 10
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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angle (bool, optional): Default: False. If True, returns the angle of the slope in radians
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degrees (bool, optional): Default: False. If True, returns the angle of the slope in degrees
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intercept (bool, optional): Default: False. If True, returns the angle of the slope in radians
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r (bool, optional): Default: False. If True, returns it's correlation 'r'
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slope (bool, optional): Default: False. If True, returns the slope
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tsf (bool, optional): Default: False. If True, returns the Time Series Forecast value.
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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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