Thanks to the transparency that is brought to bitcoin by blockchain technology, it has become possible for everybody to follow the money. Even though there is the advantage of anonymity for an entity that is using bitcoin, a lot of insights can be found about them, especially if that entity is moving large volume. With just the right amount of data science and a single bitcoin node, you have all the data that you want to see what those entities do. When do they buy their bitcoin? and when do they spend it? You can even calculate how much profit they made.
Going back to the main question, I can tell you that the crash in December 20th, 2017 was triggered by not too large Short-term traders. I will leave it at this. Going further down to the address level of each entity would fall into the realms of Forensic Analysis that I am not intending to do at the post.
Let’s look at this through the lens of on chain data that is provided to us by Glassnode. A quick reminder; The hyper bullish rally of bitcoin in 2017 was upended close to Dec 20th that year. A rally that had started almost 30 days before. It took 7 days for the brutal crash to shave more than 20% of the price.
Looking at a combination of on-chain metrics the above conclusion becomes evident.
First, Coin Days Destroyed (CDD), which is used extensively in bitcoin fundamental analysis. Basically, it shows us what type of bitcoins are sold every day; old bitcoins or young ones? By age we mean the time it stayed in the custody of the owner before sale. When its value is high for a given day, the bitcoins sold on that day were old, and vice versa. The chart below features the CDD before and after the peak in price.
Looking at the crash date, it appears that CDD was lower on the day of the crash than the few days before. This could mean that the bitcoins sold on that day and even the few days after, were younger bitcoins compared to the ones that were sold a week before. The low CDD is also partly due to lower transaction volume on the day of the peak, compared to before, as seen on the chart below.
The next metric to look at is the Spent Output Profit Ratio (SOPR), in the chart below. On the day of the crash, bitcoins got sold at a lower profit rate than a week before the crash. This shows that the entities who sold on the day of the crash weren’t long term holders, because if they were, the SOPR would have been higher than 7 days before, not lower! In this case it was lower.
And finally, the Active entities data in the following chart reveals to us that at the day of the crash, not many entities were actively buying and selling. The number was lower than 7 days before.
Putting SOPR and CDD together, we realize that it was short term players that sold on the crash date. Looking at transaction volume makes us believe that the crash did not take place with large volume. It didn’t take too many bitcoins to dump for the price to crash 22% in 7 days. This leads us to believe that there were small players behind this.
In a future post we will analyze the current market dynamics in 2021, with the price at 36000 USD at the time of writing. One thing we all know by now. Big whales are out there in numbers incomparable to 2017. Stay tuned for more.
Credit goes to Bitprobe.