High-Probability Mean Reversion Indicator (ETFs Heading into the Week of October 18)

Below is my list of ETFs with the most liquid options for the week of October 18.

Here are the various levels I use to determine if an ETF is in an oversold or overbought state.

Very Overbought – an RSI reading greater than or equal to 90.0

Overbought – greater than or equal to 80.0

Neutral – between 20.0 and 80.0

Oversold – less than or equal to 20.0

Very Oversold – less than or equal to 10.0

I will provide this information for the following ETFs on a weekly basis going forward. Stay tuned!


Some may average together various RSI readings, like the 2-day, 3-day and 5-day, but that makes absolutely no sense. We look at various levels of overbought and oversold over different timeframes and use these readings, again, as a way to help decide when to enter and exit a trade.

I like to use three different RSI readings: the 2-day, 5-day and the standard 14-day. Each timeframe gives me a different idea as to how a security has performed over the short, intermediate and long term.

Once I have this information and see that an extreme is hit, I can then apply a high-probability options strategy around the extreme reading. I like to use various credit spreads like bear call spreads, bull put spreads, iron condors and several others. The reason I like to use these strategies is because they give me even more room for error just in case the security I’m trading continues to trend in one direction.

4 comments on “High-Probability Mean Reversion Indicator (ETFs Heading into the Week of October 18)

  1. Jerrod Mason on

    It would be more helpful if the table were sorted by HPMR rather than Ticker. Even more helpful would be a button to export to csv.

    (Yes, I know I have no right to carp about free stuff. Bad bad Jerry.)

  2. Farhan on

    Hi Andy,
    Can you make a post about this with an example using any stock/ETF or point me to one if it already exists? You are the first person I’ve come across that is looking at RSI readings from different time scales so I’m very interested in educating myself further on how one can apply this.


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