Topic: Top 20 days for Bitcoin - page 131. (Read 105501 times)

I meant periods where daily average price doubled

like period starting from somewhere in November 2011 where price was 2 usd , and somewhere around new year when price passed 4

there were a few periods of doubling in first half of 2011, when price started at 0.3 usd and finished at 30 usd

those kind of periods, where daily average price is doubled at the end of some time interval,

so, list should have starting date and average price of that day and ending date and average price for the day

That doesn't matter though, given that no two-day doubles didn't include a one-day double in them.

two day doubles culd be a one day double if the aforementioned doubling happened within 24hrs

I don't think it is very useful.

Price doubled in one day:
1. 2010-09-14 Low: 0.06, High: 0.18
1. 2010-10-08 Low: 0.01, High: 0.09
1. 2010-11-06 Low: 0.24, High: 0.50
1. 2011-01-31 Low: 0.47, High: 0.95
1. 2011-06-12 Low: 10.25, High: 24.99

Interestingly, the only two-day doubles start the day before the aforementioned one-day spikes. Only 3 three-day doubles do not include the spikes above.

can we have a list of 5

shortest periods for price doubling

there is only one assumption in the model and it was included, bolded, in my post: that since these data points are outliers, they must not adhere to whatever mechanism is constraining the rest of the trading days to the log correlation.

there is no need to assert that the log correlation implies stability, but rather that the outliers are outliers because they occurred when trading was behaving abnormally in comparison to the rest of the data. one possible explanation for this is the extreme price instability that accompanied these outlying price events.

in other words, we're focussing on the set of outliers, and asking: what makes them different? rather than asserting anything about the rest of the data which is unified only by the observed log rule.

wow if we jump $8 in one day again that's for sure da big bubble

In order to see the King effect, you have to make the time spans non-overlapping, or it will be dominated by a short period of very high price (i.e. June 2011)

For those who are curious, here are weekly weighted averages (weeks do not start on any particular day):

1. 2011-06-04 through 2011-06-10 W. Avg: 23.38
2. 2011-06-05 through 2011-06-11 W. Avg: 22.62
3. 2011-06-08 through 2011-06-14 W. Avg: 21.42
4. 2011-06-07 through 2011-06-13 W. Avg: 21.41
5. 2011-06-06 through 2011-06-12 W. Avg: 21.31
6. 2011-06-03 through 2011-06-09 W. Avg: 21.30
7. 2011-06-09 through 2011-06-15 W. Avg: 20.25
8. 2011-06-10 through 2011-06-16 W. Avg: 19.06
9. 2011-06-02 through 2011-06-08 W. Avg: 18.83
10. 2011-06-13 through 2011-06-19 W. Avg: 17.84
11. 2011-06-11 through 2011-06-17 W. Avg: 17.47
12. 2011-06-12 through 2011-06-18 W. Avg: 17.33
13. 2011-06-26 through 2011-07-02 W. Avg: 16.40
14. 2011-06-27 through 2011-07-03 W. Avg: 16.38
15. 2013-01-17 through 2013-01-23 W. Avg: 16.16
16. 2011-06-28 through 2011-07-04 W. Avg: 15.79
17. 2013-01-16 through 2013-01-22 W. Avg: 15.79
18. 2013-01-15 through 2013-01-21 W. Avg: 15.31
19. 2013-01-14 through 2013-01-20 W. Avg: 15.01
20. 2011-06-01 through 2011-06-07 W. Avg: 14.94

Note that June 2011 is underrepresented because of the Mt. Gox hack.

Unsurprisingly, 2012 has dominated the top 365-day period lists (note that the leap year may cause confusion):

1. 2012-01-25 through 2013-01-23 W. Avg: 8.07
2. 2012-01-24 through 2013-01-22 W. Avg: 8.04
3. 2012-01-23 through 2013-01-21 W. Avg: 8.01
4. 2012-01-22 through 2013-01-20 W. Avg: 7.98
5. 2012-01-21 through 2013-01-19 W. Avg: 7.96
6. 2012-01-20 through 2013-01-18 W. Avg: 7.94
7. 2012-01-19 through 2013-01-17 W. Avg: 7.91
8. 2012-01-18 through 2013-01-16 W. Avg: 7.86
9. 2012-01-17 through 2013-01-15 W. Avg: 7.82
10. 2012-01-16 through 2013-01-14 W. Avg: 7.80
11. 2012-01-15 through 2013-01-13 W. Avg: 7.79
12. 2012-01-14 through 2013-01-12 W. Avg: 7.77
13. 2012-01-13 through 2013-01-11 W. Avg: 7.75
14. 2012-01-12 through 2013-01-10 W. Avg: 7.73
15. 2012-01-11 through 2013-01-09 W. Avg: 7.71
16. 2012-01-10 through 2013-01-08 W. Avg: 7.69
17. 2012-01-09 through 2013-01-07 W. Avg: 7.67
18. 2012-01-07 through 2013-01-05 W. Avg: 7.66
19. 2012-01-08 through 2013-01-06 W. Avg: 7.66
20. 2012-01-06 through 2013-01-04 W. Avg: 7.64

The past 17 days have consistently set all time highs for the 365-day weighted average. January 6, 2012 was the last day that did not accomplish this, ending up below January 5, 2012. In 52nd place is 2010-10-11 through 2011-10-16 (which spans longer than 365 days because of the hack) with a weighted average of 7.03 USD, the highest 365-day weighted average that does not include a single day from 2012. All periods that include at least one day from 2013 (23 of them) rank in the top 23.

Edit: Then again, the exclusion of crucial dates from June isn't necessarily fair. Here are the top thirty weekly, allowing the hack dates as zero-trade days:

1. 2011-06-04 through 2011-06-10 W. Avg: 23.38
2. 2011-06-05 through 2011-06-11 W. Avg: 22.62
3. 2011-06-08 through 2011-06-14 W. Avg: 21.42
4. 2011-06-07 through 2011-06-13 W. Avg: 21.41
5. 2011-06-06 through 2011-06-12 W. Avg: 21.31
6. 2011-06-03 through 2011-06-09 W. Avg: 21.30
7. 2011-06-09 through 2011-06-15 W. Avg: 20.25
8. 2011-06-10 through 2011-06-16 W. Avg: 19.06
9. 2011-06-02 through 2011-06-08 W. Avg: 18.83
10. 2011-06-13 through 2011-06-19 W. Avg: 17.84
11. 2011-06-19 through 2011-06-25 W. Avg: 17.77
12. 2011-06-11 through 2011-06-17 W. Avg: 17.47
13. 2011-06-12 through 2011-06-18 W. Avg: 17.33
14. 2011-06-14 through 2011-06-20 W. Avg: 17.26
15. 2011-06-15 through 2011-06-21 W. Avg: 16.97
16. 2011-06-18 through 2011-06-24 W. Avg: 16.83
17. 2011-06-23 through 2011-06-29 W. Avg: 16.73
18. 2011-06-22 through 2011-06-28 W. Avg: 16.68
19. 2011-06-24 through 2011-06-30 W. Avg: 16.67
20. 2011-06-16 through 2011-06-22 W. Avg: 16.63
21. 2011-06-21 through 2011-06-27 W. Avg: 16.55
22. 2011-06-25 through 2011-07-01 W. Avg: 16.51
23. 2011-06-26 through 2011-07-02 W. Avg: 16.40
24. 2011-06-27 through 2011-07-03 W. Avg: 16.38
25. 2013-01-17 through 2013-01-23 W. Avg: 16.16
26. 2011-06-17 through 2011-06-23 W. Avg: 16.01
27. 2011-06-28 through 2011-07-04 W. Avg: 15.79
28. 2013-01-16 through 2013-01-22 W. Avg: 15.79
29. 2011-06-20 through 2011-06-26 W. Avg: 15.59
30. 2013-01-15 through 2013-01-21 W. Avg: 15.31

And the yearly now has 2010-10-15 through 2011-10-14 ranking 47th.

If the price in the following 21 hours remains the current level, we will have a #10 record

The top 100 isn't special. The top 200 demonstrates the same effect, even more pronounced if I might say:


BTW, today's weighted average ranks #13, making the third 2013 day that ranked in the top 20 (the remainder are from June 2011):

1. 2011-06-09 W. Avg: 29.58
2. 2011-06-08 W. Avg: 27.25
3. 2011-06-10 W. Avg: 24.67
4. 2011-06-13 W. Avg: 20.11
5. 2011-06-07 W. Avg: 19.9
6. 2011-06-15 W. Avg: 19.68
7. 2011-06-14 W. Avg: 19.25
8. 2011-06-16 W. Avg: 18.86
9. 2011-06-06 W. Avg: 18.46
10. 2011-06-19 W. Avg: 17.77
11. 2011-06-11 W. Avg: 17.61
12. 2011-06-05 W. Avg: 17.32
13. 2013-01-23 W. Avg: 17.22
14. 2013-01-22 W. Avg: 17.15
15. 2011-06-27 W. Avg: 17.01
16. 2011-06-28 W. Avg: 16.93
17. 2011-06-29 W. Avg: 16.88
18. 2011-06-30 W. Avg: 16.51
19. 2011-06-04 W. Avg: 16.41
20. 2013-01-21 W. Avg: 16.38

It was 17.22 yesterday, still rank #13

Ok, but why does the price=log(rank) (of top 100 prices) imply stability? What is the mechanism behind this? Stability is something that is assessed over time, a factor which the above graph ignores. I'm not trying to say its wrong, just that I don't know if I follow the assumptions that need to be made to draw inferences from it.

the wikipedia page has a good graph of population-ranking data behaving in a similar way. the idea is that the few very large data points represent statistical aberrations, as evidenced by that the rest of the price data correlate (with an extremely good r- value) on that scale.

from this, one assumes: The price events at the top of the list represent behavior that is abnormal in relation to the rest of the [top 100]* trading days.

*disclaimer: this data only regards the top 100 trading days and makes no prediction about the behavior of any other data set

from this assumption, we can reason from history that these outlier price events all relate to an event generally regarded as a bubble (i.e. high prices, low stability). the full number of predictions extendable from this assumption was made clear in the OP:

[/list]

Another possible assumption is that the top 100 days is somehow different than the top 101,102, etc days. Why stop at 100? If you plot all the days then the log curve definitely does not fit. This is originally why I asked what assumptions were usually made with the king effect model. The outliers are only outliers in that they don't fit the proposed model (price=log(rank)). Its not clear from that wikipedia page why we should expect such a relationship between rank and price.

What do you mean by this?

UTC day isn't over yet.

would love to see this on a log scale, starting at zero.

i believe we have a new one
Weighted Avg:$17.19569

This make sense to me.  If you buy into this analysis, it basically says we need to fill in some more data points around $16 - $18 before moving higher with any stability.  For the price to move higher with stability, it has to happen at a measured pace...basically to give the people that want or need to sell at these lower prices a chance to sell.  If you quickly move to much higher prices (say $50), then you'll still have a lot of people that may have been willing to sell at lower prices that are still holding.  As the price bounces around at $50, they may start to sell...and continue selling as the price experiences a substantial decline.

That's cause my line is slightly distorted by the three kings. Slightly tilting it towards the horizontal axis would illustrate it better.