I liked when this thread was about Bitcoin analysis, not analyzing how to analyze whose analysis is best.
But why would you trust a bitcoin analysis that hasn't been analyzed? Etc, until first principles. Greeks dude, the greeks got that far. |
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I realized that the sum of all the guesses would give a fancy consensus prior on the price at a given date too...  |
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Predicting a range is clearly useful, but having the value fall into that range or hit the mean value of the range does not always mean the prediction is better.
Example:
Prediction 1: 90-100; Prediction 2: 50-150. Actual: 101
The first is clearly the better prediction, although the actual is outside the range and further from the mean than the second.
What you really want to do is 1) reward a predicted mean value (i.e. max+min/2) that is close to the actual; 2) reward a smaller range preferentially to a larger range.
Actually, it is not clear that the first prediction is better. It depends. Also, to the point 1), consider that the probability density function can be asymmetric within the range, i.e. the mean can be different from 0.5*(max+min). No. D is the deviation from the interval edge and is added to the weighted interval width. I think you misread it.
Sorry, I misread your AND as an OR in the conditional and I didn't look too closely at the inequalities. In that case, this proposal would give the same score to two forecasts with the same I, regardless of where within the interval the actual outcome falls. For example: a prediction of 90-110 would be ranked equal to a prediction of 99-119 with an outcome of 100. I don't find that intuitive either. Ok in that case people will have to guess distributions rather than intervals, then just compare the probability density at the price. If people want to do intervals, then they can use uniform distributions.  |
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What you want to do is reward for the prediction being within the interval and penalize for distance from the interval as well as for the width of the interval. For example: I= upper_limit -lower_limit
W=Weight of Interval width penalty
P= Actual
if( P>lower_limit AND P<upper_limit){
D=0
}else{
D=min [ abs ( P - upper_limit); abs ( P - lower_limit) ]
}
Score= D+W*I
W could even be 1, IDK. Lowest score wins. Sorry for coming off as very negative today, I really don't mean to smack down everything, but this wouldn't work either. My counterexample: I would choose upper_limit = lower_limit = 0 and win every time (assuming P is not zero)! I would get D=0 and I=0, which would give me Score=0, regardless of W. Furthermore 0 is the lowest possible score since D>=0 and I>=0, so I would win every time!  No. D is the deviation from the interval and is added to the weighted interval width. I think you misread it. |
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Eureka! It is this simple: - Every predictor gives two prices in log scale eg. "In 2014-5-16 the price is between 2.7 and 2.85 (roughly 500 and 700)" - When the actual price is known, you take min [ abs ( actual - upper_limit); abs ( actual - lower_limit) ]- Whoever has the lowest average error after a reasonable number of predictions (predictions can be renewed as often as you wish regardless of their maturity) is the best!  - Proof omitted I would be very grateful if you could explain this to a simpleton like myself. Whoever was the closest to the actual price with the narrowest range was the best. I think he's being a little facetious here, because this is, of course, obvious. Except that it doesn't actually work. Proof by counterexample: Imagine a forecast range of 50-100. If the outcome if 95, i.e. within the range, the formula produces a score of 5. However, if the outcome is 105, i.e. outside the range, the formula produces a score of 5. But clearly, the first situation should score better, but with this formula it does not! QED? Edit: I can think of more examples where it doesn't work too, can I leave those as an exercise to the reader? Edit 2: For those wondering how to do it properly, I suggest searching the meteorology literature - it's much more comprehensive on this issue than the financial/economic/econometric literature. Suppose rpietila's formula was amended to yield zero in the case that the prediction is within the range. Then your offered counterexample fails. Did you have others that would prove the amended scoring formula invalid? Note, to anyone interested, that the reduction of the price to log10 form allows the predictions to be compared on widely differing timescales, in which price values might be 10x larger or smaller. rpietila has been talking about deviation from the log10 trendline in terms of these log10 deltas. What you want to do is reward for the prediction being within the interval and penalize for distance from the interval as well as for the width of the interval. For example: I= upper_limit -lower_limit
W=Weight of Interval width penalty
P= Actual
if( P>lower_limit AND P<upper_limit){
D=0
}else{
D=min [ abs ( P - upper_limit); abs ( P - lower_limit) ]
}
Score= D+W*I
W could even be 1, IDK. Lowest score wins. |
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If it requires 1 HUC to create a team, isn't the total number of teams limited to the number of coins in existence? Has anyone given thought to this aspect?
In some sense, yes. But the cost will be recovered when the general dies, so it is only a limit on the number of players on the game at the same time. Also, if it ever becomes a problem, we could lower the fees for player creation. Perhaps the cost of a new team should be a decreasing function of number of generals playing. I'm just throwing the idea out there. My motivation is that ad hoc decisions about future rule changes are undesirable. |
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If it requires 1 HUC to create a team, isn't the total number of teams limited to the number of coins in existence? Has anyone given thought to this aspect?
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Hello,
Huntercoin is very interesting. To me what is needed is clear. You need to provide non-coder users with the ability to arrange their hunters into formations, and give them certain tasks such as
1) Landmine = blow up if someone not on a list comes near
2) Gatherer = Go gather nearest coin within x-spaces
I am sure by playing around with it I can think of more. Essentially there needs to be convenient ways for people to control large numbers of hunters.
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This whole thread appears to boil down to a multiplication table. Also there is no normality assumption, because the sum is over all the coins. The multiplication table still holds even if one person has all the coins.
Yes, if one person holds all the coins they determine the "price". It would be whatever they sell them for. I agree it is a simple multiplication table. It is easier to get intuitive estimations of number of users and average holdings than price, price is more mysterious at first glance. We could make the same chart for USD vs anything, cryptos are special because we do know the upper bound of how much is in existence. |
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Interesting. It is worth noticing that MtGox had a million users and losses were somewhere between half a billion and one billion dollars.
That means we should already be at a few million users since not all users had MtGox accounts. Those who did stored on average $500 there, mostly in Bitcoins. But how large part of their holdings did the average user put in? (And how average were MtGox's users, really?)
These numbers should at least put is in the right ballpark. I am a bit surprised that we are this far ahead really. That the valuation of Bitcoin is in its future promises, and not in today's economy, should not come as a surprise to anybody, but I think your heatmap shows that we are not really that overvalued. Especially not considering what dot-com companies are worth, and Bitcoin is not just a company but a whole potential economy, should it work out.
I do however think that your axis are skewed. Should Bitcoins become widely used, I think the average worth per user should be expected to drop (since its value as an investment lessens) but the number of users should be expected to be at least an order of magnitude larger than twelve million. Facebook alone has over a billion users. Two and a half billion use email. How many users wants the ability to pay someone effortlessly over the Internet?
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So, did anyone calculate the average fiat value of btc holdings from the mt gox leak? The data may not be representative (or even real) but if it is that would tell us the number of users supporting the current price.
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It works for Earth troposphere pressures (200-1000 mbar) which overlaps the cloud layer. It is slightly off within the cloud layer. Anyway you seem to be proposing an alternative reason there is no affect of CO2 on venus's temperature, and thus no evidence for runaway greenhouse effect.
Well, I always understood that the greenhouse effect was this: shorter wavelength radiation crosses the atmosphere in spite of any CO2, gets absorbed by the ground, is re-radiated as infrared, and this is prevented from escaping by the CO2. If so then Venus may not have a greenhouse effect because the shorter-wavelength radiation gets reflected by the clouds without being converted to infrared, and therefore does not get trapped by the CO2. But I am just, er, academizing about it. Thanks, first sensible response I have seen to this data. I will think about it and make a new thread possibly. It still does not explain why two planets with vastly different albedo's and atmosphere concentrations would have the same temperature (corrected for pressure and incident energy) if other factors were important, though. |
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Go find the original data from the Magellan Probe. From this you can get the temperature and pressure at various elevations on Venus. From that you can get the temperature at each pressure. Compare the temperatures on venus to the temperatures on earth at equivalent pressure. The relationship between the two is exactly proportional to the square root of planet's relative distances from the sun, exactly as predicted by the inverse square law and stefan-boltzman law. There is no room for greenhouse effect in explaining this data as far as I can tell.
Is that above or below the clouds? (Below the clouds there should be little greenhouse effect anyway, methinks, since little light gets down there. And above the clouds the shorter wavelengths should be refleted off to space without conversion to infrared, so I would expect no greenhouse effect either.) It works for Earth troposphere pressures (200-1000 mbar) which overlaps the cloud layer. It is slightly off within the cloud layer. Anyway you seem to be proposing an alternative reason there is no affect of CO2 on venus's temperature, and thus no evidence for runaway greenhouse effect. |
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A well written article, if it were written about something else. Global warming comes to mind.
That's an interesting article: there are no doubts there's gong to be a major financial storm pretty soon as the sins of the recent past catch up with the global market, however whether BT will 'save' us is pretty dubious. However, parallels with global warming are not accurate, it's a pity so many sceptics and deniers inhabit this forum but I imagine the US media has a lot to do with this (as well as your education system). I'm on a ferry on the Tasman strait and can't be bothered producing all the academic references. But even the 'skeptic' scientists who were employed by the corporate PR companies have jumped ship -- the evidence is now overwhelming. Hopefully, a few more super-storms will hit the east coast, wake you from your insular torpor. But loving the new, aggressive JorgeStolfi. As for the market....well, I can see some choppy water ahead: volume seems low, the bid order sum is reducing and I have no idea how we managed to breach 700 but I can't see that happening again until the constant flow of negative sentiment recedes. No. There have been now two separate events where climate scientists have been busted doctoring data. Their warming models have failed repeatedly. You have no way to prove that current storms have anything to do with any sort of warming event. We just had one of the coldest summers and winters on record. The recent expedition by the global warmers were stuck in record ice caps as they attempted to document the shrinking ice caps. Go find the original data from the Magellan Probe. From this you can get the temperature and pressure at various elevations on Venus. From that you can get the temperature at each pressure. Compare the temperatures on venus to the temperatures on earth at equivalent pressure. The relationship between the two is exactly proportional to the square root of planet's relative distances from the sun, exactly as predicted by the inverse square law and stefan-boltzman law. There is no room for greenhouse effect in explaining this data as far as I can tell. --END OT |
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Koryu's interpretation is correct. The price is calculated simply as (#users X AvgHoldings)/#Coins. I actually used 12 million coins because rounding and lost coins.
Oda, the 12 million number is the estimated number of coins (not number of users). This chart contains no data other than the number of coins in circulation, which is actually an upper limit. I do not assume a normal distribution in making the chart. I mentioned that just because interpreting a mean is somewhat non intuitive for the extremely skewed distribution that probably describes bitcoin holdings per user. However it is still valid to calculate the mean and use it as a summary statistic.
I found the chart interesting because it is an intuitive way of showing the dependance of price on the number of users, wealth of those users, and their sentiment towards bitcoin. Even though the chart shows the mean, and the mean can be unintuitive when describing a skewed distribution, we also know that the mean cannot be less than the minimum.
So the way I looked at that chart is to ask "Is it plausible that there are 2,000,000 people out there willing to store at least $4k of value in bitcoin?" If that many people are willing to store at least that amount of value, some will be willing to store more and thus the users will be able to support an even higher price. I think that this is plausible and so the current price of $600 is not insane or anything like that.
I have no data for this however and was hoping others had estimates of users and average btc wealth.
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 The x-axis shows the number of Bitcoin users and y axis the average (arithmetic mean) value of their bitcoin holdings in dollars. Taken together these allow us to know what the current price is. Of course the distribution of bitcoins held will not be normal, it probably follows some kind of power law. I still think that using the mean can be informative. The heatmap and contours show the USD/BTC ratio for the given possible combinations of users and average holdings. For example, given a price of $600 either there are 2 million users holding a little under $4k worth of bitcoin each, or 1 million users holding on average ~$7.5k worth. To predict the future price we would need to guess the distribution of how much each would be willing to hold as well as growth/loss of users. |
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Surely there are enough predictions on this forum that a few will seem to be right just through random chance.
The Professor Pigskin of BTC?
Agreed, good predictions involve exact timeframes and/or values. Ones that get close may be worth investigating further, but mean nothing more. |
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 https://en.wikipedia.org/wiki/File:Vostok_Petit_data.svgAll the peaks of temp and co2 happen at around the same level. What is the explanation for this, because I have never seen one. If there is no accepted explanation that means we do not understand the system very well. If that is the case, treating the patient may cause more problems than it solves. Medicine was in this state for pretty much every ailment until recently, and still is for many such as cancer. |
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 I just checked this and it looks like the pattern broke, theres been deviations in the past as well though. Would you please care to elaborate? The mystery indicator wasnt supposed to drop so low if the 7-8 month cycle was still on going. Another thing to keep in mind is it is based on gox only right now. I just checked this and it looks like the pattern broke, theres been deviations in the past as well though.
Interesting to see that some of the previous buy points were positioned on a downtrend. Index ~160 and ~480 Is it showing a buy signal now? When it is high it has indicated buy in the past. The way it is calculated doesn't make this need to be true. Right now it went high but then the price dropped alot, also it went low after the drops, so it almost seems reversed. Some kind of decomposition using orthogonal cyclic functions ... Chebyshev polynomials? Bessel functions, Legendre Polynomials ... how would anyone know, it could be your own special brew.? NB: Just a ridiculous question ask given the evidence posted. Ill-posed problem. It really isn't ridiculous. Extra info: I didn't come up with this indicator. |
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