Top Days for Bitcoin Prices

Here’s a list of the top days for Bitcoin in terms of weighted averages: 1. 2011-06-09 Avg: 29.58 2. 2011-06-08 Avg: 27.25 3. 2011-06-10 Avg: 24.67 4. 2011-06-13 Avg: 20.11 5. 2011-06-07 Avg: 19.90 6. 2011-06-15 Avg: 19.68 7. 2011-06-14 Avg: 19.25 8. 2011-06-16 Avg: 18.86 9. 2011-06-06 Avg: 18.46 10. 2011-06-19 Avg: 17.77 11. 2011-06-11 Avg: 17.61 12. 2011-06-05 Avg: 17.32 13. 2013-01-22 Avg: 17.15 14. 2011-06-27 Avg: 17.01 15. 2011-06-28 Avg: 16.93 16. 2011-06-29 Avg: 16.88 17. 2011-06-30 Avg: 16.51 18. 2011-06-04 Avg: 16.41 19. 2013-01-21 Avg: 16.38 20. 2011-06-12 Avg: 16.21.

Digging deeper, there’s something called the king effect. Basically, when Bitcoin crosses $30, it’ll either keep going or drop hard. Like a fork in the road.

What kinda assumptions are we making with this model? I mean, why use the log of the rank?

That’s the king effect for ya. Most points fit the line, but the top ones are above it. This means we probably won’t see the top 10 days with similar prices, so a drop is likely after the peak.

True, but the line seems to overestimate prices around 20 and underestimate around 14. Kinda off.

Yeah, my line's distorted by those three kings. If I tilt it a bit, it'll look better.

Gotcha. So if we trust this analysis, we gotta fill in more data points around $16-$18 before pushing higher. It’s gotta be slow and steady to let sellers at lower prices offload.

Just got a new weighted avg: $17.19569. Looks solid.

Would love to see that on a log scale, starting from zero.

What do you mean by that? The day isn’t over yet.

Another thought: why stop at the top 100 days? If we plot all days, the log curve doesn’t fit. Just curious about assumptions behind the king effect.

The wiki has a cool graph showing population-ranking data acting similarly. The big data points are outliers, with the rest correlating really well.

But why assume price=log(rank) means stability? Stability's a long-term thing, and the graph doesn’t cover that.

Yesterday it was 17.22, still rank #13. It’s kinda wild.

The top 100 ain’t special. Top 200 shows the same thing, even more pronounced.

So if the price stays as is for the next 21 hours, we might hit a record at #10.

Here’s the weekly weighted averages for reference: 1. 2011-06-04 to 2011-06-10 Avg: 23.38 2. 2011-06-05 to 2011-06-11 Avg: 22.62 and so on.

To see the king effect, we need non-overlapping time spans. Otherwise, high prices dominate.

If we jump $8 in a day again, that’s definitely a bubble, right?

There’s only one main assumption in the model, which I bolded earlier: outlier data points don’t follow the log correlation. No need to claim that the log correlation means stability.