get_super_trend(quotes, lookback_periods=10, multiplier=3)


name type notes
quotes Iterable[Quote] Iterable of the Quote class or its sub-class.
See here for usage with pandas.DataFrame
lookback_periods int, default 10 Number of periods (N) for the ATR evaluation. Must be greater than 1 and is usually set between 7 and 14.
multiplier float, default 3 Multiplier sets the ATR band width. Must be greater than 0 and is usually set around 2 to 3.

Historical quotes requirements

You must have at least N+100 periods of quotes to cover the warmup periods. Since this uses a smoothing technique, we recommend you use at least N+250 periods prior to the intended usage date for optimal 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 line segment before the first reversal and the first N+100 periods are unreliable due to an initial guess of trend direction and precision convergence for the underlying ATR values.


name type notes
date datetime Date
super_trend Decimal, Optional SuperTrend line contains both Upper and Lower segments
upper_band Decimal, Optional Upper band only (bearish/red)
lower_band Decimal, Optional Lower band only (bullish/green)

upper_band and lower_band values are provided to differentiate bullish vs bearish trends and to clearly demark trend reversal. super_trend is the contiguous combination of both upper and lower line data.


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 SuperTrend(14,3)
results = indicators.get_super_trend(quotes, 14, 3)

About SuperTrend

Created by Oliver Seban, the SuperTrend indicator attempts to determine the primary trend of Close prices by using Average True Range (ATR) band thresholds. It can indicate a buy/sell signal or a trailing stop when the trend changes. [Discuss] 💬