Explaining the Short and Long Term Holders Threshold:

In addition to the traditional 155-day threshold, we provide several other period alternatives.

Usually, the threshold between Short Term Holders (STH) and Long Term Holders (LTH) is defined as 155 days (approximately 5 months). This is based on a 2020 study by Glassnode, which found that after 155 days, the likelihood of someone selling their bitcoins drops significantly.

However, I always found this threshold quite arbitrary. So here at Bitcoin Is Data, I decided to include various different thresholds.

We classify as STH all the coins which moved within less than:

1, 2, 3, 4, 5, 6 months and 1, 2 and 4 years.

And we classify as LTH all the coins which were last traded for more time than the same thresholds above.

That's why the buttons show, for example, "STH<5m." This is the classic 5-month STH that you will find on other on-chain data websites. In other words, all the coins that were moved within less than five months. But if you want, you can change the threshold that defines what is STH/LTH to another period.

Similarly, "LTH>1y," for example, includes all the coins that have not been moved for more than a year.

We can see, for example, that right now, on average, all bitcoins purchased in the last 365 days are at break-even, considering their price in dollars (the actual bitcoin price is equal to the realized price of STH<1y):

In other words, the average price of all coins moved within less than a year is equal to the current price of bitcoin today. So, on average, all the coins that were bought in the last year are breaking even.

Finally, you also have the option to use the “total” button. In this case, you display the Total Realized Price, which is the average price at which coins were bought when they last moved for all the holders, regardless of the period of time that they have been holding them (currently USD 31,240.00).

And that’s all for today. Have a great one!

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Been having some pretty lucid dreams lately. Really cool lol

It depends,

if a dataset needs to be parsed from the chain then yes but then the speed varies a lot depending on what you need to parse.

if I only need to parse let's say the block's weight it gonna be super fast because I can skip a lot of things that I already parsed and don't need for that and so, keep the local databases that I have from the previous iteration.

Whereas if I want to parse (or reparse for whatever reason) the amount of sats per address then I need to recompute all the wallet's history and the databases linked to them which is very expensive.

There is also the much more common case when a dataset can be computed from other datasets which very very fast

You might notice that the datasets doesn't seem to be live anymore, it's because I'm currently working on the backend (parser) and need to recompute a whole lot of stuff. Sadly, one of the cons of having the same machine for development and production

Netflix top 10 movies/shows is a good data point to know what normies are currently watching / what’s influencing them atm

had a blast at bitdevs yesterday. next month we're going to start doing workshops as well 👀

Learning more about running my own LLM models at home. Apparently, the quantization method impacts performance differently on different kinds of hardware.

This is why, if you’re browsing models on Hugging Face, you’ll see files with suffixes like “Q3_K_S” and “IQ2_XXS”. The number after the “Q” tells you which quantization method the model uses. Some will be much slower than others depending on the capabilities of the CPU and GPU in the machine. #llm