get_connors_rsi(quotes, rsi_periods=3, streak_periods=2, rank_periods=100)


name type notes
quotes Iterable[Quote] Iterable of the Quote class or its sub-class.
See here for usage with pandas.DataFrame
rsi_periods int, default 3 Lookback period (R) for the close price RSI. Must be greater than 1.
streak_periods int, default 2 Lookback period (S) for the streak RSI. Must be greater than 1.
rank_periods int, default 100 Lookback period (P) for the Percentile Rank. Must be greater than 1.

Historical quotes requirements

N is the greater of R+100, S, and P+2. You must have at least N periods of quotes to cover the convergence periods. Since this uses a smoothing technique, we recommend you use at least N+150 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 N 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
rsi_close float, Optional RSI(R) of the Close price.
rsi_streak float, Optional RSI(S) of the Streak.
percent_rank float, Optional Percentile rank of the period gain value.
connors_rsi float, Optional ConnorsRSI


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 ConnorsRsi(3,2.100)
results = indicators.get_connors_rsi(quotes, 3, 2, 100)

About ConnorsRSI

Created by Laurence Connors, the ConnorsRSI is a composite oscillator that incorporates RSI, winning/losing streaks, and percentile gain metrics on scale of 0 to 100. See analysis. [Discuss] 💬