Can be achieved by chance alone.
If not chance, I can give you a strategy that would be highly likely to achieve such a result: it would take a lot of risk though!
I love it when people post this stuff to HN. Naive people try it, loose a bundle to market makers, then go back to their day job.
If you're going to make a claim like that, you should actually follow up with the calculations. When you do that, you'll realize that the issue is quite a bit more complex than this shallow dismissal.
He has very low correlation to the index, which means he's not just levering beta and getting lucky on a trending market. His standard deviation is smaller than the index, which means he didn't just make one large and lucky bet. The evidence that he has real alpha is certainly not incontrovertible, but the numbers look quite good.
It also doesn't appear that he cherry picked his reporting/aggregation cadence, because he sent out a weekly newsletter ex ante, and all his stats are reported weekly. He could still just be lucky, but his numbers are much better than this sort of dismissal would imply.
One real risk is that, in some implicit way, he's pursing a negatively-skewed strategy. That is, one that has a latent large downside risk. Strategies like this can produce very good looking numbers for longish periods, but still have ultimately negative alpha. Judging whether or not that is the case here is hard without more detail, but nothing he says in the writeup indicates to me that that is the case here.
However, his write up is completely devoid of talk of risk (beta is not risk), bankroll, Kelly sizing, etc. This is integral to understanding the trade.
For example, he could have a successful strategy that works in small lots. However, absent from nearly every ML model is the impact on sizing up. As soon as you post a sizable bid, the market will lean against you, and the edge evaporates. Same if you cross bid-ask, plus you're now giving up edge. ML cannot take this into account, at least not very easily and with the usual models.
Most programmers with models like this fall into this last category.