mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
108 lines
3.4 KiB
Python
108 lines
3.4 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from .rsi import rsi
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from pandas_ta.overlap import sma
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from pandas_ta.utils import get_offset, non_zero_range, verify_series
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def stochrsi(close,
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length=None,
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rsi_length=None,
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k=None,
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d=None,
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offset=None,
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**kwargs):
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"""Indicator: Stochastic RSI Oscillator (STOCHRSI)"""
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# Validate arguments
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close = verify_series(close)
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length = length if length and length > 0 else 14
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rsi_length = rsi_length if rsi_length and rsi_length > 0 else 14
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k = k if k and k > 0 else 3
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d = d if d and d > 0 else 3
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offset = get_offset(offset)
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# Calculate Result
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rsi_ = rsi(close, length=rsi_length)
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lowest_rsi = rsi_.rolling(length).min()
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highest_rsi = rsi_.rolling(length).max()
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stoch = 100 * (rsi_ - lowest_rsi)
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stoch /= non_zero_range(highest_rsi, lowest_rsi)
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stochrsi_k = sma(stoch, length=k)
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stochrsi_d = sma(stochrsi_k, length=d)
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# Offset
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if offset != 0:
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stochrsi_k = stochrsi_k.shift(offset)
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stochrsi_d = stochrsi_d.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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stochrsi_k.fillna(kwargs["fillna"], inplace=True)
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stochrsi_d.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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stochrsi_k.fillna(method=kwargs["fill_method"], inplace=True)
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stochrsi_d.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_name = "STOCHRSI"
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_props = f"_{length}_{rsi_length}_{k}_{d}"
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stochrsi_k.name = f"{_name}k{_props}"
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stochrsi_d.name = f"{_name}d{_props}"
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stochrsi_k.category = stochrsi_d.category = "momentum"
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# Prepare DataFrame to return
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data = {stochrsi_k.name: stochrsi_k, stochrsi_d.name: stochrsi_d}
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df = DataFrame(data)
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df.name = f"{_name}{_props}"
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df.category = stochrsi_k.category
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return df
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stochrsi.__doc__ = """Stochastic (STOCHRSI)
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"Stochastic RSI and Dynamic Momentum Index" was created by Tushar Chande and Stanley Kroll and published in Stock & Commodities V.11:5 (189-199)
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It is a range-bound oscillator with two lines moving between 0 and 100.
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The first line (%K) displays the current RSI in relation to the period's
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high/low range. The second line (%D) is a Simple Moving Average of the %K line.
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The most common choices are a 14 period %K and a 3 period SMA for %D.
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Sources:
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https://www.tradingview.com/wiki/Stochastic_(STOCH)
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Calculation:
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Default Inputs:
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length=14, rsi_length=14, k=3, d=3
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RSI = Relative Strength Index
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SMA = Simple Moving Average
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RSI = RSI(high, low, close, rsi_length)
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LL = lowest RSI for last rsi_length periods
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HH = highest RSI for last rsi_length periods
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STOCHRSI = 100 * (RSI - LL) / (HH - LL)
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STOCHRSIk = SMA(STOCHRSI, k)
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STOCHRSId = SMA(STOCHRSIk, d)
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Args:
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series): Series of 'close's
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length (int): The STOCHRSI period. Default: 14
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rsi_length (int): RSI period. Default: 14
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k (int): The Fast %K period. Default: 3
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d (int): The Slow %K period. Default: 3
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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.DataFrame: RSI %K, RSI %D columns.
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"""
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