Through hypothesis testing you can estimate the probability that this was due to luck is very low. Assuming that a monkey would have a 50% chance of profiting on a day, the chance of going a month without a losing day is less than 1 in a billion.
You have to make some basic assumptions to do any statistical inference, because if you don't then literally anything can be explained by luck. For example, even if the author did a follow-up post (which I'd love to see as well!) every year for 30 years and made money every time, it could still be "selection bias".
The number of monkeys required to match the author's results over a 12-month period is well over the number of atoms in the universe.
Depends on which null hypothesis you're testing. He told us that he rarely had a down day, and based on Bitcoin's volatility I'd say it's reasonable to assume the probability of it going up or down on any given day is a coin toss (regardless of the longer-term trend and independent of other days). Thus the probability that he's a day-trading monkey is 0.5^n, where n is the number of up days he had in a row. (since he had a few down days it's actually a binomial coefficient but the value is < 1e-80 anyway, far less than a conventional p-value of 0.05)
On the other hand, if we wanted to test his 3900% yearly return, we might assume that monkey returns are equal in distribution to Bitcoin's price and then test the hypothesis that he's a monkey via something like a paired t-test. The problem here is that we only have one data point so p-value is undefined, and due to high variance it would probably take about n=10 points to get something significant. The upside of this approach is that you can get a confidence interval for how much better he is than a monkey, instead of just a yes/no answer.
In any case, since the author has at least 365 data points, he probably has an extremely good idea of both a) whether he's a monkey, and b) how much better he is than a monkey.
