Schaff Trend Cycle

get_stc(quotes, cycle_periods=10, fast_periods=23, slow_periods=50)


name type notes
quotes Iterable[Quote] Iterable of the Quote class or its sub-class.
See here for usage with pandas.DataFrame
cycle_periods int, default 10 Number of periods (C) for the Trend Cycle. Must be greater than or equal to 0.
fast_periods int, default 23 Number of periods (F) for the faster moving average. Must be greater than 0.
slow_periods int, default 50 Number of periods (S) for the slower moving average. Must be greater than fast_periods.

Historical quotes requirements

You must have at least 2×(S+C) or S+C+100 worth of quotes, whichever is more, to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least S+C+250 data points prior to the intended usage date for better precision.

quotes is an Iterable[Quote] collection of historical price quotes. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.



Convergence warning: The first S+C+250 periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.


name type notes
date datetime Date
stc float, Optional Schaff Trend Cycle


See Utilities and Helpers for more information.


from stock_indicators import indicators

# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")

# Calculate STC(12,26,9)
results = indicators.get_stc(quotes, 10, 23, 50)

About Schaff Trend Cycle

Created by Doug Schaff, Schaff Trend Cycle is a stochastic oscillator view of two converging/diverging exponential moving averages (a.k.a MACD). [Discuss] 💬