Predicting Crypto Prices by Month: Is it Possible?

You’ve probably heard the joke before: crypto traders and speculators are no different from 19-year-old dorm room astrologers, looking for some type of discernible pattern in a world that is likely mostly random. The only difference? Instead of romantic analysis (on love interests and college boyfriends), traders perform technical analysis (on internet tokens).

In fact, the two disciplines have even crossed paths: Maren Altman is a popular Twitter influencer who used to largely base herself around producing trading content directly related to astrology:

At a deeper level, the broad definition of technical analysis is based around discerning knowledge from secondary information: primarily price. A news event that serves as a price catalyst? That’s fundamental analysis, as the non-price information moves price. Price moving price, on the other hand? That’s technical analysis. As far as astrology moving price? That’s neither.

So what is my opinion on all of this?

Frankly, I think a large majority of people lose money trading on long time frames. You might be able to win over a short time frame; a few might be able to win on long time frames, but most lose on long time frames.

But that doesn’t stop most people (including me) from speculating, and today, I’m curious to chat about trading based on the month: if you’ve ever heard about Uptober or Rektember, you know what I’m talking about.

If you’d like to look at raw data, here we go:

The price data seems to back it up: September is usually pretty crappy for Bitcoin, while October is pretty fantastic. Also notable is November, with the highest mean percentage of price action. But four of the last five years have been negative in November; although when the getting is good, the getting is good.

With less accuracy: January isn’t typically great and December is volatile. February tends to be green as well. Other months seem to be minimally predictable.

There are different theories for this. Months have also had significance in traditional equity markets: famously, January’s performance often predicts the rest of the year. If January is a green month for the S&P 500, the rest of the year tends to be green as well (about 70% of the time).

In recent years this has applied for Bitcoin as well, as evidenced by the diagram below, but pre-2017 Bitcoin tended to perform spectacularly (often gaining over 100%) despite Januarys that ended in the red.

Looking at some of this data is fantastically interesting, and it seems possible to pick patterns out of the noise. Ultimately, however, incredibly difficult to create actionable insights from patterns like these, which may be no more different than a coin flip.

If we link this back towards the concept of technical analysis, however, I think there’s something worth examining: why does it exist and work in the first place?

Ultimately, it comes down to crowd behavior: it’s why Bitcoin has tapped, but not broken through $30k in the last few weeks, it’s why everyone has $100k BTC and $10k ETH targets, it’s why we often see resistance and support and round numbers. Participants consider the market to have varying degrees of efficiency, but most will agree that the market is, at the very least, irrational.

And if we remember that irrationality and consider our own behavioral quirks throughout this process, perhaps we can do a better job of observing irrational markets and combating our own tendencies to be foolish as well.

Now that I think about it, I’ve got some portfolio rebalancing to do.