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Not Another Bitcoin Chart

Today I’m going to show you some charts you may have never seen before. Some of them are mine and some are not. Each presents a new way of thinking that can enrich your depth of understanding in the Bitcoin ecosystem.

Tamas Blummer is a software developer and author who writes over on Medium. Recently, he came out with this article called Liveliness of Bitcoin. He explains:

Liveliness is new quantitative measure that gives insight to shifts in HODLing behavior.

Liveliness would temporarily reach 1 if all coins would move within a single block and remain at 0 for a blockchain that have not yet had a transaction other than issuance. Liveliness will trend down for a blockchain with dormant units and up for a vivid ecosystem. A reading between 0 and 1 hence may be understood as a measure of Liveliness.

bitcoin liveliness

Source: Tamas Blummer via Medium

Based on this research, Bitcoin has become more lively over time, which suggests very positive things in the future if we buy into the fact that the value comes from the network (which I suggest you do).

Realized Cap

The Realized Cap of Bitcoin is the value of the network based on the last time the coins were moved. For example, if I mined some Bitcoin and then just sat on it, the value would be taken at the time the coins were mined, not the price of the coins under the market conditions today.

Things start to get interesting when you compare the Market Cap to the Realized Cap.

This chart suggests that we may have bottomed out in the Bitcoin price already. However, there are many other things to consider.

Plan B

You might think “Plan B” is a form of birth control, but in this case, I’m talking about the Bitcoin chart master who is popular on Twitter. Recently, he published this beauty, which demonstrates the price on top of the 200-week moving average.

I’m going to be keeping an eye on this metric for sure. The next few months may validate this observation which appears to have marked the bottom of the Bitcoin price three times now.

Standardized NVT

Willy Woo is well known for coming up with NVT. But as I have pointed out, this metric can be somewhat unreliable. My take on this model was to standardize the NVT the same way I standardize most of my other charts, by using a z-score.

standardized nvt

Source: charts – data from blockchain.com

That wild swing near the end of 2018 makes very little sense from an investing or trading standpoint. To illustrate this point, look at the chart below. Notice the red boxes.

nvt round 2

Source: charts – data from blockchain.com

If you had purchased in the lower red box, you would have bought Bitcoin at $4,300. If you sold in the next red box, you would have sold at $3,800. I don’t know about you but that’s not a trade that looks very attractive to me.

To sum up, NVT should be used with caution.

Kalitchkin’s Bitcoin Bands

Dmitry Kalitchkin posted a very powerful article on Medium last year. In fact, I wrote about this article and his models here in one of my pricing model reviews. There are many topics covered in this article, but my two biggest takeaways were the network value bands and NVM.

kalitchkin bands

Source: cryptolab.capital

The upper band is represented by Metcalfe’s Law. The lower band is represented by Odlyzyko’s Law (with some modifications).

But there is one annoying thing about those network bands. That’s the fact that they tend to get wider over time. Fortunately, I found a solution.

By applying the same type of modifications applied to Odlyzyko’s Law (shifting the band into a position of best fit without changing its shape) and using them on Metcalfe’s Law, and then compressing time, we end up with a much more consistent band structure. See the result below.

time compressed bands

Source: charts – data from blockchain.com and coinmetrics.io

I’m really happy about how this chart turned out, but you may have noticed that it does have some implications to consider. That is the fact that we’re still somewhat close to the upper band.

The upper and lower bands are tied to the Daily Active Addresses figure, which you can see below.

bitcoin daily active addresses

Source: charts – data from coinmetrics.io

Daily Active Addresses And Unique Addresses In Use

Daily Active Addresses and Unique Addresses in use are essentially two approaches to the same problem. The source data all comes from the blockchain, but aggregating, de-duping, and filtering out the noise is a tricky process that involves some proprietary calculations.

Unique Addresses in Use comes from blockchain.com, which you can see below.

bitcoin unique addresses in use

Source: blockchain.com

This chart looks very similar to the daily active addresses, which comes from coinmetrics.io.

active addresses

Source: coinmetrics.io

The Daily Active Addresses, or DAA is more commonly used to calculate network value.

I was curious about the difference between these two figures, so I wrote a program that pulled in the data from both sources and fed it into my database. Then I put them side by side for comparison. See below:

daa vs ua

Source: chart, data from coinmetrics.io and blockchain.com

As you can see, the DAA almost always come in a bit higher, while the Unique Addresses, UA, are more conservative most of the time. I honestly don’t know which one is more accurate. I just wanted to bring this up for clarity.

Bonus Material

This is the first time that I’ve shared this next chart. I call it the Bitcoin Decision Oracle. It compares the Bitcoin price to four different pricing models. The Bitcoin price is the thicker black line, and each color is a different model.

bitcoin decision oracle

Source: charts, data from blockchain.com

As you can see from this model, the Bitcoin price is now smack dab in between two upper predictions and two lower predictions. Let’s compress time and modify that curve.

time compressed decision oracle

Source: charts, data from blockchain.com

I like the way this chart looks. The purple line forms something of a sine wave while the yellow line basically turns into a trend line. Cool stuff, eh?

Conclusion

I hope you guys liked these charts. Feel free to drop a comment below and ask any questions you may have.

What do you think?

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Written by Ravi Gupat

Ravi grew up in India and graduated in Economics. He is a serial entrepreneur who has founded and exited several companies in tech and media over the past 15 years. He is also an early stage investor and advisor in various blockchain-based companies.

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