Topic: Non-spreadsheet long-term predictions - page 2. (Read 8915 times)
Bump for awesomeness
if BitCoin will be 1000 USD in 2016, is really good time to buy.
i believe that, if you are looking at 1-2 year terms, its a no-brainer that you will atleast double your earnings.
problem is that alot of daytraders invaded bitcoin trading, keeping their profits in fiat, and reducing the money inflow.
if BitCoin will be 1000 USD in 2016, is really good time to buy.
tl;dr. Doing well so far. "Interesting times" possible in late January.
So far. How is this very simplistic model doing so far? Here is the projection of the model using the data up to 2014-05-08, which is when I posted the original projection.
You will note that this is slightly different than the original projection. The reason is that the price that I originally used on 2014-05-08 was not the correct price for that day, but the price at the time of the writing, which was slightly different.
To make things more interesting, I am showing more percentiles.
Under the original prediction, today, a full 47 days later, the median predicted price is 604. The 50% CI is 445 to 821. The actual price as I am writing this is 568. Well within the 50% CI, and pretty close to the median prediction. Not bad, if I do say so myself. ;-)
Updated predictions. Same very simplistic model using the most current data.
mu, sigma, and cv are all down. Meaning, both expected return and volatility have adjusted down. These are very small changes, which is what you would expect since we only added a few new data points.
Using the median outlook (p_50), we should finally break through 1k by late October. By late January, we should be at 2,270. According to my Peak/trough model (PTM), once we reach that price level, "the moon" (the next peak) should be less than a month away. According to the PTM prediction, which I have not updated recently, the peak should happen in mid-January. If I did update the PTM prediction, that would push the peak back -- how far I don't know, but at least to late January.
In other words, the two models are making a similar prediction. Come late January, watch out, we might be blasting off (this model) or already on the moon (PTM).
This assumes that the median outlook holds. Under a "poor" outlook (p_25), come late January, we will "only" be breaking above 1k. Not the moon, not the launchpad, but still a lot of excitement.
About intervals. Look at the predictions again. Yes, these intervals are huge. I like to think of these as "honest intervals" (conditional on the model being a decent approximation of reality, which is a stretch). The intervals reflect the amount of uncertainty. There is a lot of uncertainty, especially the further out you go. These intervals help highlight that fact.
So far. How is this very simplistic model doing so far? Here is the projection of the model using the data up to 2014-05-08, which is when I posted the original projection.
Code:
n.data from to mu sigma cv
1 1,380 2010-07-17 2014-05-08 0.00656 0.0652 9.93
n.fut date p_5 p_25 p_50 p_75 p_95
1 0 2014-05-08 NA NA 444 NA NA
2 1 2014-05-09 401 428 447 467 497
3 7 2014-05-15 350 414 465 522 617
4 30 2014-06-07 299 424 540 689 978
5 47 2014-06-24 286 445 604 821 1,280
6 61 2014-07-08 282 466 662 940 1,560
You will note that this is slightly different than the original projection. The reason is that the price that I originally used on 2014-05-08 was not the correct price for that day, but the price at the time of the writing, which was slightly different.
To make things more interesting, I am showing more percentiles.
Under the original prediction, today, a full 47 days later, the median predicted price is 604. The 50% CI is 445 to 821. The actual price as I am writing this is 568. Well within the 50% CI, and pretty close to the median prediction. Not bad, if I do say so myself. ;-)
Updated predictions. Same very simplistic model using the most current data.
Code:
n.data from to mu sigma cv
1 1,430 2010-07-17 2014-06-26 0.00651 0.0642 9.87
n.fut date p_5 p_25 p_50 p_75 p_95
1 0 2014-06-26 NA NA 568 NA NA
2 1 2014-06-27 514 547 571 597 635
3 7 2014-07-03 449 530 594 666 786
4 30 2014-07-26 384 543 690 877 1,240
5 61 2014-08-26 363 597 844 1,190 1,960
6 91 2014-09-25 363 670 1,030 1,570 2,900
7 122 2014-10-26 372 763 1,260 2,070 4,240
8 152 2014-11-25 388 870 1,530 2,680 6,010
9 183 2014-12-26 409 1,000 1,870 3,480 8,520
10 188 2014-12-31 413 1,030 1,930 3,630 9,010
11 213 2015-01-25 435 1,150 2,270 4,470 11,900
12 244 2015-02-25 466 1,340 2,780 5,780 16,600
13 274 2015-03-27 500 1,540 3,380 7,380 22,800
14 304 2015-04-26 540 1,790 4,100 9,430 31,200
15 335 2015-05-27 586 2,080 5,020 12,100 43,000
16 365 2015-06-26 636 2,410 6,100 15,400 58,600
17 731 2016-06-26 1,970 15,600 66,000 279,000 2,210,000
mu, sigma, and cv are all down. Meaning, both expected return and volatility have adjusted down. These are very small changes, which is what you would expect since we only added a few new data points.
Using the median outlook (p_50), we should finally break through 1k by late October. By late January, we should be at 2,270. According to my Peak/trough model (PTM), once we reach that price level, "the moon" (the next peak) should be less than a month away. According to the PTM prediction, which I have not updated recently, the peak should happen in mid-January. If I did update the PTM prediction, that would push the peak back -- how far I don't know, but at least to late January.
In other words, the two models are making a similar prediction. Come late January, watch out, we might be blasting off (this model) or already on the moon (PTM).
This assumes that the median outlook holds. Under a "poor" outlook (p_25), come late January, we will "only" be breaking above 1k. Not the moon, not the launchpad, but still a lot of excitement.
About intervals. Look at the predictions again. Yes, these intervals are huge. I like to think of these as "honest intervals" (conditional on the model being a decent approximation of reality, which is a stretch). The intervals reflect the amount of uncertainty. There is a lot of uncertainty, especially the further out you go. These intervals help highlight that fact.
That P_50 has me hot and bothered.. let me take you out OP hehehe.
How about basing the predictions on real world money supply? Curiously, your answers are not that different!See https://bitcointalksearch.org/topic/if-bitcoin-became-the-world-reserve-currency-646186
Hum.. what?
Are you trying to get me all excited as well?
That P_50 has me hot and bothered.. let me take you out OP hehehe.
How about basing the predictions on real world money supply? Curiously, your answers are not that different!See https://bitcointalksearch.org/topic/if-bitcoin-became-the-world-reserve-currency-646186
That P_50 has me hot and bothered.. let me take you out OP hehehe.
Of all the dubious log linear extrapolation models, the least dubious one.
(yes, I mean that as a compliment.)
(yes, I mean that as a compliment.)
For sure.
However I say chart-based extrapolation still doesn't work longterm. Extrapolation can't predict major mood changes of market participants or sudden events. Whatever type of extrapolation you use, it is always just projecting the main trend into the future.
Such methods may work quite a long time, but suddenly they will fail. For better predictions you would have to monitor constantly the fundamentals that drive market mechanics (for example adoption rate, adoption pool (people likely to become adopters), transaction volume growth, businesses accepting btc).
Which is what gbianchi is doing in this thread...
https://bitcointalksearch.org/topic/18-mar-data-a-bitcoin-price-theory-441336
... modeling network growth (which is then related to price) based on zero balance addresses.
However, Joe200's model is mathematically more mature. How about a collaboration?
Of all the dubious log linear extrapolation models, the least dubious one.
(yes, I mean that as a compliment.)
(yes, I mean that as a compliment.)
For sure.
However I say chart-based extrapolation still doesn't work longterm. Extrapolation can't predict major mood changes of market participants or sudden events. Whatever type of extrapolation you use, it is always just projecting the main trend into the future.
Such methods may work quite a long time, but suddenly they will fail. For better predictions you would have to monitor constantly the fundamentals that drive market mechanics (for example adoption rate, adoption pool (people likely to become adopters), transaction volume growth, businesses accepting btc).
Of all the dubious log linear extrapolation models, the least dubious one.
(yes, I mean that as a compliment.)
(yes, I mean that as a compliment.)
This post deserves more visibility.
Good and realistic predictions and if your predictions are correct then I'll be one happy man.
2016-05-08 p_95 me want 
I was reading a few old threads (2012 ish i think) where price was 10-30. People said 1k would happen in 2016. No one mentioned 100 (which came shortly after). 1k happened 2013. Many people say 1-8k 2015. Not sure, lets see what happens 2015-2016.
We don't even know what surprises are in store for last half 2014 even!
Interesting. Going to look into this when I have time.
2016-05-08 p_95 me want
I was reading a few old threads (2012 ish i think) where price was 10-30. People said 1k would happen in 2016. No one mentioned 100 (which came shortly after). 1k happened 2013. Many people say 1-8k 2015. Not sure, lets see what happens 2015-2016.
100 and 1K were both in 2013, yes?
2016-05-08 p_95 me want
I was reading a few old threads (2012 ish i think) where price was 10-30. People said 1k would happen in 2016. No one mentioned 100 (which came shortly after). 1k happened 2013. Many people say 1-8k 2015. Not sure, lets see what happens 2015-2016.
2016-05-08 p_95 me want
Great post and good math. The only thing I want to add to get this more "visibility" is that you should actually add some visual charts (graphs) to support your predictions
Cheers for the prediction and explanations. Thanks again for post - Good effort!
This post deserves more visibility
Outstanding charting and mathematics. Thanks!
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