mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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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 stoch(high,
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low,
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close,
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k=None,
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d=None,
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smooth_k=None,
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offset=None,
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**kwargs):
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"""Indicator: Stochastic Oscillator (STOCH)"""
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# Validate arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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k = k if k and k > 0 else 14
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d = d if d and d > 0 else 3
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smooth_k = smooth_k if smooth_k and smooth_k > 0 else 3
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offset = get_offset(offset)
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# Calculate Result
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lowest_low = low.rolling(k).min()
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highest_high = high.rolling(k).max()
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stoch = 100 * (close - lowest_low)
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stoch /= non_zero_range(highest_high, lowest_low)
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stoch_k = sma(stoch, length=smooth_k)
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stoch_d = sma(stoch_k, length=d)
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# Offset
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if offset != 0:
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stoch_k = stoch_k.shift(offset)
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stoch_d = stoch_d.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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stoch_k.fillna(kwargs["fillna"], inplace=True)
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stoch_d.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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stoch_k.fillna(method=kwargs["fill_method"], inplace=True)
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stoch_d.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_name = "STOCH"
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_props = f"_{k}_{d}_{smooth_k}"
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stoch_k.name = f"{_name}k{_props}"
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stoch_d.name = f"{_name}d{_props}"
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stoch_k.category = stoch_d.category = "momentum"
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# Prepare DataFrame to return
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data = {stoch_k.name: stoch_k, stoch_d.name: stoch_d}
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df = DataFrame(data)
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df.name = f"{_name}{_props}"
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df.category = stoch_k.category
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return df
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stoch.__doc__ = """Stochastic (STOCH)
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The Stochastic Oscillator (STOCH) was developed by George Lane in the 1950's.
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He believed this indicator was a good way to measure momentum because changes in
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momentum precede changes in price.
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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 close 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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https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=332&Name=KD_-_Slow
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Calculation:
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Default Inputs:
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k=14, d=3, smooth_k=3
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SMA = Simple Moving Average
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LL = low for last k periods
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HH = high for last k periods
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STOCH = 100 * (close - LL) / (HH - LL)
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STOCHk = SMA(STOCH, smooth_k)
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STOCHd = SMA(FASTK, 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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k (int): The Fast %K period. Default: 14
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d (int): The Slow %K period. Default: 3
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smooth_k (int): The Slow %D 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: %K, %D columns.
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"""
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