# -*- coding: utf-8 -*-
from pandas import DataFrame, concat
from pandas_ta import Imports
from pandas_ta.overlap import rma
from pandas_ta.utils import get_drift, get_offset, verify_series, signals
[docs]def rsi(close, length=None, scalar=None, talib=None, drift=None, offset=None, **kwargs):
"""Indicator: Relative Strength Index (RSI)"""
# Validate arguments
length = int(length) if length and length > 0 else 14
scalar = float(scalar) if scalar else 100
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import RSI
rsi = RSI(close, length)
else:
negative = close.diff(drift)
positive = negative.copy()
positive[positive < 0] = 0 # Make negatives 0 for the postive series
negative[negative > 0] = 0 # Make postives 0 for the negative series
positive_avg = rma(positive, length=length)
negative_avg = rma(negative, length=length)
rsi = scalar * positive_avg / (positive_avg + negative_avg.abs())
# Offset
if offset != 0:
rsi = rsi.shift(offset)
# Handle fills
if "fillna" in kwargs:
rsi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
rsi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
rsi.name = f"RSI_{length}"
rsi.category = "momentum"
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
DataFrame({rsi.name: rsi}),
signals(
indicator=rsi,
xa=kwargs.pop("xa", 80),
xb=kwargs.pop("xb", 20),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return rsi
rsi.__doc__ = \
"""Relative Strength Index (RSI)
The Relative Strength Index is popular momentum oscillator used to measure the
velocity as well as the magnitude of directional price movements.
Sources:
https://www.tradingview.com/wiki/Relative_Strength_Index_(RSI)
Calculation:
Default Inputs:
length=14, scalar=100, drift=1
ABS = Absolute Value
RMA = Rolling Moving Average
diff = close.diff(drift)
positive = diff if diff > 0 else 0
negative = diff if diff < 0 else 0
pos_avg = RMA(positive, length)
neg_avg = ABS(RMA(negative, length))
RSI = scalar * pos_avg / (pos_avg + neg_avg)
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 14
scalar (float): How much to magnify. Default: 100
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
drift (int): The difference period. Default: 1
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""