Author here. Let's assume you cannot got short (which you can't in most crypto markets) and can only make money when the market goes up. The best you can do is avoid losing money when the market goes down.

But there are no pure market downturns. On a daily scale the market may be down, but that does not mean that on a millisecond or second-scale you will only see downward movement. There is just an overall downtrend, but there is still almost the same upward movement to make money. For HFT systems, it really doesn't matter if the market, on a daily scale, goes up or down. There is no difference.

In fact, on many markets the system does better in downward trends. Probably because there is more liquidity on that side of the book, a bias that may come from certain market participants.

How do you maintain a market neutral portfolio without the ability to go short? Also I would be interested in hearing about your Sharpe ratio. Thanks! Great article!

You can't go short but you can still hedge your exposure by going on short on something very correlated - like Bitcoin futures. I do this manually depending on how much capital I'm trading.

I actually have no idea what my Sharpe ratio is, sorry. With the system constantly changing, data formats changing, exchange balance apis changing, and accurate monitoring already being a challenge in itself, it's very difficult to keep track of exact returns and historical data. I could probably calculate it if I spent several days (weeks?) trying to process all my historical trade data, but that would be a waste of time for me personally.

If you don’t know your Sharpe, how do you know you are actually making alpha? Without knowing your return and volatility, you can’t really evaluate the performance of your algo or know if changes are helping.

Ever try trading futures basis and spreads? I was killing it in mid-2018 in that space, although I got cocky and used my winnings to make foolish leveraged bets that did not end well.

Are you not calculating your risk-adjusted return at all?

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.

How many monkey bots are flipping the coin daily? Selection bias just needs one.

I very much look forward to the author's follow up post at the end of 2020 to see if another 5k turns into 200.

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.

Wouldn't it be better to look at how much this deviates from average market returns during the time frame?

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.

IANAquant, but he said in the intro that he used a market neutral strategy, so he _should_ make money both when the broader market is going up or down. It would be interesting to know though!

I wish he'd keep it running, then write on lessons learned turning $200K into something much less, should such a loss be manifest.