Yes, this is minute data, which comes from the Schawb StreetSmart trading application. It allows download of six days of data, which I have been doing every Saturday for the past 14 months, and updating the data base. The data seems to be of very good quality. Unfortunately I didn't start before the COVID crash. If you have academic connections you may know somebody who has access to WRDS. Unfortunately they won't share with mere mortals. The problem with the minute data is that trading causes high volumes in the morning and at the close, so sampling over represents the lightest trading with lowest volatility. I have some transaction by transaction data which I plan to post in a few days, when I get it ready, which will show a power tail exponent of about 3.0 which is more in line with the final daily output of the SPY. The tick data are very difficult to work with because most of the sequential prices show no change, nevertheless it is the tick data that we really need in the Wolfram Data Repository for any serious research..

I’m starting to work on understanding “Scaling in the Norwegian Stock Market” by Skjeltorp Physica A283 (2000) 496-582. This paper applies chaos theory and fractal analysis to a stock market.

Are you familiar with this paper? If so, can you describe how your algorithm might apply in an analysis such as this?

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I practiced cardiology for forty years and would expect failure of medical devices to be a memory-less process with numbers of events over fixed intervals to have exponential tails. If you can show that this is not true with a quantile plot of your tail data versus the ExponentialDistribution[1], and if there is a good linear relationship in the quantile plot the Log[tail data] to the ExponentialDistribution[1], that would be evidence for power tail scaling which might indicate a fractal process.

If you are interested in long cycles and crashes, then it is perfectly adequate to use readily available daily data in the same way. In general, the North American markets are the least volatile, followed by European markets, with larger countries being less volatile than smaller countries. Asian markets tend to be the most volatile.