"1.Trades ETFs such as UVXY. Volatility ETFs do not follow mean reversion"
Sure they do.
"2.This seems to get lucky on some stocks and unlucky on others"
That is the entire purpose, else it would overfit the data.
"3.As the leverage indicates, this is having unfilled orders, when in real life they would probably fill at much greater slippage"
Now you are talkin'. This is due to the technical limitations of the platform. However, in real life you can enter real life order types like "fill or kill" or something like "fill whatever you can today then cancel"
"This is high risk, and the fact it appears to make so much is due to luck on some high volatility events"
Luck on some high vol events? This algo FOCUSES ON HIGH VOL events, by definition.
"Does not make money from 2013 to present"
It does not matter. You can pick a bad run in any backtest. The interesting side note is that mean reversion as a market force is mean reverting itself, so this period is one when momentum rules and not mean reversion. It all depends on how much total money is dedicated to each strategy at a particular time.
"Leverage goes above 1"
This is a good question for Quantopian - if you set the leverage explicitly in the code, as the sample code does, then why is leverage going above 1 at certain points in time? I will rerun it after setting slippage to zero, and see if that fixes it i.e. if the theoretical slippage model is causing this issue.