mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
103 lines
3.1 KiB
Python
103 lines
3.1 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 rsi(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
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"""Indicator: Relative Strength Index (RSI)"""
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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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scalar = float(scalar) if scalar 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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negative = close.diff(drift)
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positive = negative.copy()
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positive[positive < 0] = 0 # Make negatives 0 for the postive series
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negative[negative > 0] = 0 # Make postives 0 for the negative series
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positive_avg = rma(positive, length=length)
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negative_avg = rma(negative, length=length)
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rsi = scalar * positive_avg / (positive_avg + negative_avg.abs())
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# Offset
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if offset != 0:
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rsi = rsi.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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rsi.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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rsi.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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rsi.name = f"RSI_{length}"
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rsi.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({rsi.name: rsi}),
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signals(
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indicator=rsi,
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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 rsi
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rsi.__doc__ = """Relative Strength Index (RSI)
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The Relative Strength Index is popular momentum oscillator used to measure the
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velocity as well as the magnitude of directional price movements.
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Sources:
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https://www.tradingview.com/wiki/Relative_Strength_Index_(RSI)
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Calculation:
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Default Inputs:
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length=14, scalar=100, drift=1
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ABS = Absolute Value
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EMA = Exponential Moving Average
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positive = close if close.diff(drift) > 0 else 0
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negative = close if close.diff(drift) < 0 else 0
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pos_avg = EMA(positive, length)
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neg_avg = ABS(EMA(negative, length))
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RSI = scalar * pos_avg / (pos_avg + neg_avg)
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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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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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