I use engineering methods to analysis stocks such as $GME because the overall behavior is so well controlled that it behaves just like a systemic process.
Manufacturing engineering often uses the below chart to monitor live data. The upper and lower control limit (UCL /LCL) are used for quality controls. These values can be defined by a static value defined by requirement or general distribution of the population. 
As a very general statement, $GME's"high-low" values appear to be constant despite how the average values ranged from ~$120-$300. If ~$120-$300 is considered the average, the UCL and LCL are static and not relative ratio to the values suggesting a user defined variable.
When there is enough output data, one can create a distribution to aid in identifying outliers. When all the data is grouped together but a few are off, something is fucky with those fucked few. Sometimes, it's just a "glitch" but when it happens a few times, it's suss AF.
Outliers are improbabilities so when they correlate with outliers, something is really fucked. This thought process is how I discovered how $GME and $VIX correlated. They each had improbable events and then these improbable events correlated with the other improbable event.
"The #Algorithm. The Ouroboros: Characterizing #GameStop share price and reverse engineering #HFT #algos - Part 3" is going to be lit.
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