What does the mean (instead of median) result look like for it compared to these other models?

Also, how extreme is the variance between the runs (for the same model vs itself over the 5 runs)? Like is there some way of expressing the severity of the volatility experienced over the course of the 30 days like standard deviation of volatility per 1-day segment or per 5-day segment across the runs to give a sense of the volatility severity? Like how big are the jumps and dips on the graph as it goes along? Are they severe enough that it would need a lot more than just 5 runs/30 days for it to mean much, or are the size of the wobbles small enough relative to the overall run that it ends up being ultra-meaningful even when taking variance/volatility into account? (I assume it's at least somewhat volatile given that some of the overall profitable models are going broke on some runs, meaning its enough volatility that they are dying in the early phase some significant percentage of the time).

Also, since the models all start with a starting amount of just $2,000 (which is a proportionally fairly small starting amount relative to the ending amounts of money over the course of the month), if the volatility/variance going on during these runs is fairly high, and even models that on avg are doing fairly well overall sometimes just go broke in the early portion of their runs if the volatility dips below 0 during the dangerous early part of the run when they start with such a small amount of money relative to the volatility size, I was wondering if you have considered doing an additional version of this test where you basically "allow the models to go broke" (and basically receive a bailout if they do, like, let's say if one of them goes broke on day 10 or something, you just reboost it back to $2k and let it continue its run but still note the run as a "it went broke" run with an asterisk next to it, but this way you can get more data on the runs if like half the time these models go broke in their early portion of their runs or something, you could get like ~1.1x-1.5x as much data if you did it that way, for example. (The idea of why this is different than just having it do a few extra runs from scratch and noting how many extra runs they did being that you also get to keep the data of what it was doing before it went broke on the runs where it went broke in the analyses of the runs afterwards. Although maybe you are still including data from all aspects of the runs where they go broke anyway, in which case maybe it wouldn't make much difference to any of this?). Also, if you did this, it should go without saying that the models would still need to think that going broke meant going broke, though (obviously don't tell the models that they get to receive a bailout and continue their run if they go broke, otherwise it would affect their strategy since they would try much riskier strategies if they knew they'd get a bailout if they went broke).