# -*- coding: utf-8 -*-
from pandas import DataFrame
from .rsi import rsi
from pandas_ta.overlap import ma
from pandas_ta.utils import get_offset, non_zero_range, verify_series
[docs]def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, offset=None, **kwargs):
"""Indicator: Stochastic RSI Oscillator (STOCHRSI)"""
# Validate arguments
length = length if length and length > 0 else 14
rsi_length = rsi_length if rsi_length and rsi_length > 0 else 14
k = k if k and k > 0 else 3
d = d if d and d > 0 else 3
close = verify_series(close, max(length, rsi_length, k, d))
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "sma"
if close is None: return
# Calculate Result
rsi_ = rsi(close, length=rsi_length)
lowest_rsi = rsi_.rolling(length).min()
highest_rsi = rsi_.rolling(length).max()
stoch = 100 * (rsi_ - lowest_rsi)
stoch /= non_zero_range(highest_rsi, lowest_rsi)
stochrsi_k = ma(mamode, stoch, length=k)
stochrsi_d = ma(mamode, stochrsi_k, length=d)
# Offset
if offset != 0:
stochrsi_k = stochrsi_k.shift(offset)
stochrsi_d = stochrsi_d.shift(offset)
# Handle fills
if "fillna" in kwargs:
stochrsi_k.fillna(kwargs["fillna"], inplace=True)
stochrsi_d.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
stochrsi_k.fillna(method=kwargs["fill_method"], inplace=True)
stochrsi_d.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_name = "STOCHRSI"
_props = f"_{length}_{rsi_length}_{k}_{d}"
stochrsi_k.name = f"{_name}k{_props}"
stochrsi_d.name = f"{_name}d{_props}"
stochrsi_k.category = stochrsi_d.category = "momentum"
# Prepare DataFrame to return
data = {stochrsi_k.name: stochrsi_k, stochrsi_d.name: stochrsi_d}
df = DataFrame(data)
df.name = f"{_name}{_props}"
df.category = stochrsi_k.category
return df
stochrsi.__doc__ = \
"""Stochastic (STOCHRSI)
"Stochastic RSI and Dynamic Momentum Index" was created by Tushar Chande and Stanley Kroll and published in Stock & Commodities V.11:5 (189-199)
It is a range-bound oscillator with two lines moving between 0 and 100.
The first line (%K) displays the current RSI in relation to the period's
high/low range. The second line (%D) is a Simple Moving Average of the %K line.
The most common choices are a 14 period %K and a 3 period SMA for %D.
Sources:
https://www.tradingview.com/wiki/Stochastic_(STOCH)
Calculation:
Default Inputs:
length=14, rsi_length=14, k=3, d=3
RSI = Relative Strength Index
SMA = Simple Moving Average
RSI = RSI(high, low, close, rsi_length)
LL = lowest RSI for last rsi_length periods
HH = highest RSI for last rsi_length periods
STOCHRSI = 100 * (RSI - LL) / (HH - LL)
STOCHRSIk = SMA(STOCHRSI, k)
STOCHRSId = SMA(STOCHRSIk, d)
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
length (int): The STOCHRSI period. Default: 14
rsi_length (int): RSI period. Default: 14
k (int): The Fast %K period. Default: 3
d (int): The Slow %K period. Default: 3
mamode (str): See ```help(ta.ma)```. Default: 'sma'
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.DataFrame: RSI %K, RSI %D columns.
"""