Regression Channel on the S&P 500 Just Got Interesting
The relentless selling pressure on stocks over the last few months has presented an interesting situation on the S&P 500. If we look at a Raff Regression channel, the index has just ventured further away from the regression line than at any point since the low in 2009.
A Raff Regression channel starts with the regression line over the set period. After the regression line is drawn, there are outer rails that are equidistant from it. The distance of the rails from the regression line is determined by the point that is farthest from the line itself.
In the current case for the S&P the furthest point from the regression line occurred in early 2016, but the huge drop in the last few weeks has the index sitting right on that lower rail.
We also see that the weekly stochastic readings are the lowest they have been in the last 10 years and the 10-week RSI is oversold territory for the first time since August ’11.
To give you an idea of how harsh the selling has been in the last few weeks, I ran a scan of the S&P 500 members to see how many were in oversold territory. Based on a daily stochastic reading below 20, there are 479 members of the S&P that are oversold. If we look at a 10-day RSI reading below 30, there are 444 members that are oversold at this time.
I don’t recall ever seeing numbers that high in my 20 years of writing about the market.
Contributor
Harry Richardson — Algorithmic Trader & Strategy Developer Harry is an algorithmic trader specializing in impulse and breakout trading strategies across cryptocurrency and equity markets. With more than 10 years of experience in developing automated trading systems, he focuses on building structured algorithms designed to capture momentum while maintaining strict risk control. His approach combines quantitative analysis, real-market execution, and continuous performance monitoring. Vitalii prioritizes risk management, drawdown control, and strategy stability over short-term optimization, ensuring algorithms are adaptable to changing market conditions. He has developed and tested hundreds of automated strategies, working extensively with live trading environments, forward testing, and portfolio-level algorithm management. His work centers on transforming trading ideas into fully operational, scalable automated systems.
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