# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.overlap import ma
from pandas_ta.utils import get_offset, non_zero_range, verify_series
[docs]def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=None, **kwargs):
"""Indicator: Stochastic Oscillator (STOCH)"""
# Validate arguments
k = k if k and k > 0 else 14
d = d if d and d > 0 else 3
smooth_k = smooth_k if smooth_k and smooth_k > 0 else 3
_length = max(k, d, smooth_k)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "sma"
if high is None or low is None or close is None: return
# Calculate Result
lowest_low = low.rolling(k).min()
highest_high = high.rolling(k).max()
stoch = 100 * (close - lowest_low)
stoch /= non_zero_range(highest_high, lowest_low)
stoch_k = ma(mamode, stoch.loc[stoch.first_valid_index():,], length=smooth_k)
stoch_d = ma(mamode, stoch_k.loc[stoch_k.first_valid_index():,], length=d)
# Offset
if offset != 0:
stoch_k = stoch_k.shift(offset)
stoch_d = stoch_d.shift(offset)
# Handle fills
if "fillna" in kwargs:
stoch_k.fillna(kwargs["fillna"], inplace=True)
stoch_d.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
stoch_k.fillna(method=kwargs["fill_method"], inplace=True)
stoch_d.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_name = "STOCH"
_props = f"_{k}_{d}_{smooth_k}"
stoch_k.name = f"{_name}k{_props}"
stoch_d.name = f"{_name}d{_props}"
stoch_k.category = stoch_d.category = "momentum"
# Prepare DataFrame to return
data = {stoch_k.name: stoch_k, stoch_d.name: stoch_d}
df = DataFrame(data)
df.name = f"{_name}{_props}"
df.category = stoch_k.category
return df
stoch.__doc__ = \
"""Stochastic (STOCH)
The Stochastic Oscillator (STOCH) was developed by George Lane in the 1950's.
He believed this indicator was a good way to measure momentum because changes in
momentum precede changes in price.
It is a range-bound oscillator with two lines moving between 0 and 100.
The first line (%K) displays the current close 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)
https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=332&Name=KD_-_Slow
Calculation:
Default Inputs:
k=14, d=3, smooth_k=3
SMA = Simple Moving Average
LL = low for last k periods
HH = high for last k periods
STOCH = 100 * (close - LL) / (HH - LL)
STOCHk = SMA(STOCH, smooth_k)
STOCHd = SMA(FASTK, d)
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
k (int): The Fast %K period. Default: 14
d (int): The Slow %K period. Default: 3
smooth_k (int): The Slow %D 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: %K, %D columns.
"""