mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame, concat
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from pandas_ta.overlap import rma
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from pandas_ta.utils import get_drift, get_offset, verify_series, signals
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def er(close, length=None, drift=None, offset=None, **kwargs):
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"""Indicator: Efficiency Ratio (ER)"""
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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 10
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offset = get_offset(offset)
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drift = get_drift(drift)
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# Calculate Result
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abs_diff = close.diff(length).abs()
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abs_volatility = close.diff(drift).abs()
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er = abs_diff
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er /= abs_volatility.rolling(window=length).sum()
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# Offset
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if offset != 0:
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er = er.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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er.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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er.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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er.name = f"ER_{length}"
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er.category = "momentum"
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signal_indicators = kwargs.pop("signal_indicators", False)
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if signal_indicators:
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signalsdf = concat(
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[
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DataFrame({er.name: er}),
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signals(
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indicator=er,
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xa=kwargs.pop("xa", 80),
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xb=kwargs.pop("xb", 20),
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xserie=kwargs.pop("xserie", None),
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xserie_a=kwargs.pop("xserie_a", None),
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xserie_b=kwargs.pop("xserie_b", None),
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cross_values=kwargs.pop("cross_values", False),
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cross_series=kwargs.pop("cross_series", True),
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offset=offset,
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),
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],
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axis=1,
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)
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return signalsdf
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else:
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return er
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er.__doc__ = """Efficiency Ratio (ER)
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The Efficiency Ratio was invented by Perry J. Kaufman and presented in his book "New Trading Systems and Methods". It is designed to account for market noise or volatility.
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It is calculated by dividing the net change in price movement over N periods by the sum of the absolute net changes over the same N periods.
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Sources:
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https://help.tc2000.com/m/69404/l/749623-kaufman-efficiency-ratio
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Calculation:
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Default Inputs:
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length=10
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ABS = Absolute Value
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EMA = Exponential Moving Average
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abs_diff = ABS(close.diff(length))
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volatility = ABS(close.diff(1))
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ER = abs_diff / SUM(volatility, length)
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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: 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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