- stc(close, tclength=None, fast=None, slow=None, factor=None, offset=None, **kwargs)[source]#
Schaff Trend Cycle (STC)
The Schaff Trend Cycle is an evolution of the popular MACD incorportating two cascaded stochastic calculations with additional smoothing.
The STC returns also the beginning MACD result as well as the result after the first stochastic including its smoothing. This implementation has been extended for Pandas TA to also allow for separatly feeding any other two moving Averages (as ma1 and ma2) or to skip this to feed an oscillator (osc), based on which the Schaff Trend Cycle should be calculated.
Feed external moving averages: Internally calculation..
stc = ta.stc(close=df[“close”], tclen=stc_tclen, fast=ma1_interval, slow=ma2_interval, factor=stc_factor)
extMa1 = df.ta.zlma(close=df[“close”], length=ma1_interval, append=True) extMa2 = df.ta.ema(close=df[“close”], length=ma2_interval, append=True) stc = ta.stc(close=df[“close”], tclen=stc_tclen, ma1=extMa1, ma2=extMa2, factor=stc_factor)
The same goes for osc=, which allows the input of an externally calculated oscillator, overriding ma1 & ma2.
Implemented by rengel8 based on work found here: https://www.prorealcode.com/prorealtime-indicators/schaff-trend-cycle2/
STCmacd = Moving Average Convergance/Divergance or Oscillator STCstoch = Intermediate Stochastic of MACD/Osc. 2nd Stochastic including filtering with results in the STC = Schaff Trend Cycle
close (pd.Series): Series of ‘close’s, used for indexing Series, mandatory tclen (int): SchaffTC Signal-Line length. Default: 10 (adjust to the half of cycle) fast (int): The short period. Default: 12 slow (int): The long period. Default: 26 factor (float): smoothing factor for last stoch. calculation. Default: 0.5 offset (int): How many periods to offset the result. Default: 0
ma1: 1st moving average provided externally (mandatory in conjuction with ma2) ma2: 2nd moving average provided externally (mandatory in conjuction with ma1) osc: an externally feeded osillator fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method
pd.DataFrame: stc, macd, stoch