Topic: Monthly average USD/bitcoin price & trend - page 20. (Read 118254 times)
Watch out for emunie, it may just take the crypto crown.
yes
Anyone who understands what the man is talking? 
(I have been called "the greatest linear thinker alive", but even that is too much I think...)
(I have been called "the greatest linear thinker alive", but even that is too much I think...)
Armstrong has coincidentally blogged today on what we are discussing here. Interesting read. You won't be disappointed.
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I personally believe the order and laws behind all natural phenomena can be successfully extrapolated to macro environments like collective valuation of financial instruments
Well said. At least to a usefull approximation if your resolutions requirements are not to high.
And in the end your resolution requirements can never be high enough because of the disproportionate role of tail events that are difficult to predict and the psychological bias implicit in reduction to theory that favors certainty and denies ambiguity (because of death anxiety). In other words:
Quote
I personally believe the order and laws behind all natural phenomena can be successfully extrapolated to macro environments like collective valuation of financial instruments
Well said. At least to a usefull approximation if your resolutions requirements are not to high.
I'm a R n00b, is this a correct way to get the trendline in R? (just for fun)
https://i.imgur.com/DUYs1k8.png
Code:
prices <- c(
0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.005,
0.005, 0.005, 0.005, 0.005, 0.005, 0.008,
0.06, 0.065, 0.062, 0.106, 0.27, 0.24,
0.39, 0.90, 0.85, 1.50, 6.38, 18.55,
14.10, 9.75, 5.76, 3.30, 2.60, 3.51,
6.11, 5.06, 4.88, 4.98, 5.06, 6.03,
8.04, 10.88, 11.46, 11.58, 11.51, 13.33,
16.31, 25.93, 58.14, 115.09, 112.81, 108.39,
83.80, 105.80, 124.33, 156.50
)
months <- 1:length(prices)
result <- lm(log10(prices) ~ months)
plot(months, log10(prices))
abline(result, col="red")
a <- coef(result)[1]
b <- coef(result)[2]
r2 <- summary(result)$r.squared
https://i.imgur.com/DUYs1k8.png
OMG, the dopamine spikes from the get-rich-quick-fever are running so high that the new religion is finding information in aliasing error[1].
Nice catch, but I carefully picked the image from dozens to not refer to aliasing error.Generally speaking only if the alignment of the overlapping circles is not randomly chosen.
See the fundamental point of the Shannon-Nyquist theorem (and yes I had to argue with the Wikipedia editors to get this language in their summary) is that a signal can not be both band-limited and time-limited (e.g. if time-limited you have infinite frequencies at the start and stop points).
In short, we can't know what is random and what is not. We can only base our expectations of those fixed points which are only fixed relative to the world we perceive (shared amongst those who share the common perception). I get deeper into the math in my blog article about what the universe really is made of:
http://unheresy.com/The%20Universe.html
So therefor you make your assumptions then you place your bets on those assumptions holding for the duration of your bets.
You won't be able to rule out serendipity ever. Long-tail distributions lurk, which is the point of Taleb's Antifragility taken with the inertia of concentration of mass.
Cheers
Nice to talk with someone who can (I assume) understand what I wrote above.@gcinc
I enjoyed your analysis. Group behavior is definately measurable. Applied to financial instruments esp. Speculation on potential adoption can obv render the spec line inflated for the time, until adoption ensues...find rptiela's work interesting, and slippery slope's log model convincing.
Obv it's a gamble, obv it's risky, and obv it's about making money, so what...that's how markets work and innovations come to life.
I enjoyed your analysis. Group behavior is definately measurable. Applied to financial instruments esp. Speculation on potential adoption can obv render the spec line inflated for the time, until adoption ensues...find rptiela's work interesting, and slippery slope's log model convincing.
Obv it's a gamble, obv it's risky, and obv it's about making money, so what...that's how markets work and innovations come to life.
Indeed, serendipity for example an altcoin comes along which allows you to exit BTC without it being a ponzi scheme, by gradually sucking value out while increasing distribution to the masses.
But in this case it isn't random relative to our shared perception, because I just told you what will happen
That is unless it disappears from your consciousness, which is quite likely I presume because you are not privy to everything I can see at the moment.
I came to my own thread and.. wtf have I just read. Nice work, guys You keep on surprising me.
What I am waiting with interest is:
a) How the trendline will change after we add a point above previous trend for November.
b) How the bitcoin exchange rate will hold. We have already visited 3.3x the trend (in China, 24 hours ago). This equals to 6-7 months of trendy appreciation. I am giving 33% chance that we already saw the interim top in all exchanges. 67% that we already saw the bottom
You keep on surprising me.What I am waiting with interest is:
a) How the trendline will change after we add a point above previous trend for November.
b) How the bitcoin exchange rate will hold. We have already visited 3.3x the trend (in China, 24 hours ago). This equals to 6-7 months of trendy appreciation. I am giving 33% chance that we already saw the interim top in all exchanges. 67% that we already saw the bottom
@gcinc
I enjoyed your analysis. Group behavior is definately measurable. Applied to financial instruments esp. Speculation on potential adoption can obv render the spec line inflated for the time, until adoption ensues...find rptiela's work interesting, and slippery slope's log model convincing.
Obv it's a gamble, obv it's risky, and obv it's about making money, so what...that's how markets work and innovations come to life.
I enjoyed your analysis. Group behavior is definately measurable. Applied to financial instruments esp. Speculation on potential adoption can obv render the spec line inflated for the time, until adoption ensues...find rptiela's work interesting, and slippery slope's log model convincing.
Obv it's a gamble, obv it's risky, and obv it's about making money, so what...that's how markets work and innovations come to life.
OMG, the dopamine spikes from the get-rich-quick-fever are running so high that the new religion is finding information in aliasing error[1].
Nice catch, but I carefully picked the image from dozens to not refer to aliasing error.https://en.wikipedia.org/wiki/Shape_moir%C3%A9
More complex line moiré patterns are created if the lines are curved or not exactly parallel. Moiré patterns revealing complex shapes, or sequences of symbols embedded in one of the layers (in form of periodically repeated compressed shapes) are created with shape moiré
The example of Moiré was a metaphor - one for finding hidden, ethereal meaning between the lines. Even in the aliasing error the sophisticated patterns convey information, although this information on the surface (pun!) seems to carry no relationship to the subject at hand. Mathematical analysis of the pattern may or may not reveal connection to the subject whatsoever. Although by definition there always is connection between things connected to each other. Quantitave measures may not be enough - you may need consciousness to interpret that on a whole other operative level.
Anyhow now that you preferred to take it to the concrete domain, it serves as a reinforcement to the more discernible meaning of the trendline. Opposite of what you wanted to demonstrate, there is information to be found when you apply the chaotic looking layers over each other appropriately.
Thanks for the challenge, I really enjoy this deviation to the occult
If a guy has found such a pattern in Bitcoin price and made it visible from all the noise, and if that even somehow matches a long history up to this point, I'm going to listen to him very closely.
OMG, the dopamine spikes from the get-rich-quick-fever are running so high that the new religion is finding information in aliasing error[1].
[1] Shannon-Nyquist Sampling Theorem
Noting than Shannon defined information content for us with his work on Shannon entropy.
Sorry just had to reply to that one
It is too funny (for a math and physics nerd like me). Fitting to a trendline has no relationship to these fundamentals
I disagree. Risto specifically developed the trendline & formula to support the fundamentals in mind, ie. $300k USD/BTC for a fair price in the position of Bitcoin being a major facilitator of trade worldwide. Thus, the fitting has been rigged to match these perceived fundamentals. Given other projections, the trendline could be adjusted with an altered or altogether different formula, still seeming to "match perfectly" with the secular trend with constant deviations from the trendline.Thus, it all is to a large degree sleight of mind. The only aspect that is obvious for everyone is the exponential uphill. How steep in the big picture is anyone's guess. I consider Risto's version rather solid compromise, with a beautiful connection and parables to the laws of nature that govern this world (can't remember how or through whose arguments I came to this conclusion).
On a more esoteric angle, I personally believe the order and laws behind all natural phenomena can be successfully extrapolated to macro environments like collective valuation of financial instruments. I'm no specialist in TA but am inclined to think that's the particular point why Technical Analysis works.
Bitcoin price is a result of natural phenomena consisting of numerous layers of invisible laws of large numbers governing the average distribution of stuff. As such, the laws add up to a sort of very complex interference pattern that reveals a virtual image to an outside observer. A bit like Moire pattern:
See the whiteish - convex cylinder that comes up in the middle of the two large rings. That's a Moire pattern. If you examine it, it's not drawn anywhere and you will have to conclude it appears spontaneously, more likely unintended. Yet it's a very specific form arising out of the interaction of the properties of the two large red-blue rings.
If a guy has found such a pattern in Bitcoin price and made it visible from all the noise, and if that even somehow matches a long history up to this point, I'm going to listen to him very closely.
Remember; As above, so below.
There is a very compelling argument right now that the recent price movement is driven by fundamentals because of China's recent en masse entry to the buyers market.
Fitting to a trendline has no relationship to these fundamentals, and if we are to believe that there is something technologically-transformative or revolutionary about Bitcoin, then the fundamentals are the dominant factor in driving long-term pricing. This is not pork bellies, barrels of oil, or frozen concentrated orange juice.
Fitting to a trendline has no relationship to these fundamentals, and if we are to believe that there is something technologically-transformative or revolutionary about Bitcoin, then the fundamentals are the dominant factor in driving long-term pricing. This is not pork bellies, barrels of oil, or frozen concentrated orange juice.
I understand bitcoin speculation fundamentals as per your example. Clearly the price is driven upwards at this point by exponentially increasing numbers of speculators. See my logistic model of speculator adoption of bitcoin - https://bitcointalksearch.org/topic/m.3549092
And I distinguish the fundamentals of the underlying bitcoin economy as very well presented by http://coinometrics.com/bitcoin/btix .
Regression fitting and other sorts of technical analysis abstract out the underlying phenomena and simply model the behavior of informed and uninformed traders. Technical analysis is good at predicting bitcoin price bubbles and for giving us the best possible predictions about future bitcoin valuations.
Please share these ideas here for noobs
https://bitcointalksearch.org/topic/when-will-bitcoin-reach-1000-328203
(some of which are probably future power players, since Bitcoin is booming right now and big people are probably coming in)
&
Don't forget Bitcoin day:
https://bitcointalksearch.org/topic/bitcoin-day-329422
https://bitcointalksearch.org/topic/when-will-bitcoin-reach-1000-328203
(some of which are probably future power players, since Bitcoin is booming right now and big people are probably coming in)
&
Don't forget Bitcoin day:
https://bitcointalksearch.org/topic/bitcoin-day-329422
Just to inject some sanity into this discussion and take things back to basics, the notion of producing a simple trendline projecting future prices is 100% grounded in the technicals (and this analysis while interesting is basically the most primitive technical analysis possible). It's important to also consider the fact that technical analysis is not a crystal ball for any kind of long-term predictions, it's a tool for the trader to react to the graph and hopefully execute the correct trade.
The moment it becomes apparent that the Bitcoin price is driven more by fundamentals, this entire mode of analysis goes out the window. There is a very compelling argument right now that the recent price movement is driven by fundamentals because of China's recent en masse entry to the buyers market.
Fitting to a trendline has no relationship to these fundamentals, and if we are to believe that there is something technologically-transformative or revolutionary about Bitcoin, then the fundamentals are the dominant factor in driving long-term pricing. This is not pork bellies, barrels of oil, or frozen concentrated orange juice.
The moment it becomes apparent that the Bitcoin price is driven more by fundamentals, this entire mode of analysis goes out the window. There is a very compelling argument right now that the recent price movement is driven by fundamentals because of China's recent en masse entry to the buyers market.
Fitting to a trendline has no relationship to these fundamentals, and if we are to believe that there is something technologically-transformative or revolutionary about Bitcoin, then the fundamentals are the dominant factor in driving long-term pricing. This is not pork bellies, barrels of oil, or frozen concentrated orange juice.
This script calulates rpietila's trendline for any given date >= 2009-01. I hope it works as expected 
I made a minor tweak to make this compatible with python 2.6 and earlier. (printf needs a positional argument specified explicitly ('{0}' rather than '{}'))
Code:
import argparse
from calendar import monthrange
from datetime import date, datetime
a = 0.092
b = -2.9124
def get_x(year, month=None, day=None):
assert(2009 <= year)
if month is None:
assert(day is None)
month = 6.5
numof_months = (year - 2009) * 12 + month
if day is None:
return numof_months
numof_days = monthrange(year, month)[1]
return numof_months + (day - 0.5) / numof_days - 0.5
def price(dt):
x = get_x(*dt)
return 10 ** (a * x + b)
def date_tuple(str):
month, day = None, None
try:
dt = datetime.strptime(str, '%Y-%m-%d')
year, month, day = dt.year, dt.month, dt.day
except ValueError:
try:
dt = datetime.strptime(str, '%Y-%m')
year, month = dt.year, dt.month
except ValueError:
dt = datetime.strptime(str, '%Y')
year = dt.year
return year, month, day
def main():
parser = argparse.ArgumentParser()
parser.add_argument('date', type=date_tuple, nargs='?',
help='YYYY-MM-DD or YYYY-MM or YYYY')
args = parser.parse_args()
if args.date is None:
dt = date.today()
args.date = (dt.year, dt.month, dt.day)
print '{0:.2f}'.format(price(args.date))
if __name__ == '__main__':
main()
the chart suggests this is still more often a better time to buy than waiting for prices to fall below trend.
That's true. So you should buy in any case at this point in time and in the near future, no matter where the spot is compared to the trendline. With the discretion of course that if the trendline has been exceeded greatly the odds are there will be a drawback. The data on how much "exceeding greatly" means, is sparse, but the 80x breakpoint figure is better than nothing to have handy when making decisions. However deferring your buying in a bull market situation like now you risk never getting in, or getting in at a very high price at the wrong time, making you pretty frustrated for the 6-12 month stagnant breathing periods that are likely to occur.This script calulates rpietila's trendline for any given date >= 2009-01. I hope it works as expected
Nice, thanks! I'm not using it now, because the trendline values look so lame compared to the current spot. To avoid the depressing feelings arising from the fact that we must wait 3 to 4 months before the trendline reaches the current spot prices, I'm inclined to start drooling around with your script early next year 
The only valid trading advice, like rpietila and others have said many times is BUY BELOW TRENDLINE and only add to your coins before hitting your final target (should be in the $10k's or $100k's).
There is clearly a higher risk of short term loss, but even when the price is above the trendline, the chart suggests this is still more often a better time to buy than waiting for prices to fall below trend.
This script calulates rpietila's trendline for any given date >= 2009-01. I hope it works as expected
Price for today:
$ python -m btctrend
336.00
Price for Christmas:
$ python -m btctrend 2013-12-25
430.83
Typical price 2014:
$ python -m btctrend 2014
1605.46
Price for today:
$ python -m btctrend
336.00
Price for Christmas:
$ python -m btctrend 2013-12-25
430.83
Typical price 2014:
$ python -m btctrend 2014
1605.46
Code:
import argparse
from calendar import monthrange
from datetime import date, datetime
a = 0.092
b = -2.9124
def get_x(year, month=None, day=None):
assert(2009 <= year)
if month is None:
assert(day is None)
month = 6.5
numof_months = (year - 2009) * 12 + month
if day is None:
return numof_months
numof_days = monthrange(year, month)[1]
return numof_months + (day - 0.5) / numof_days - 0.5
def price(dt):
x = get_x(*dt)
return 10 ** (a * x + b)
def date_tuple(str):
month, day = None, None
try:
dt = datetime.strptime(str, '%Y-%m-%d')
year, month, day = dt.year, dt.month, dt.day
except ValueError:
try:
dt = datetime.strptime(str, '%Y-%m')
year, month = dt.year, dt.month
except ValueError:
dt = datetime.strptime(str, '%Y')
year = dt.year
return year, month, day
def main():
parser = argparse.ArgumentParser()
parser.add_argument('date', type=date_tuple, nargs='?',
help='YYYY-MM-DD or YYYY-MM or YYYY')
args = parser.parse_args()
if args.date is None:
dt = date.today()
args.date = (dt.year, dt.month, dt.day)
print '{:.2f}'.format(price(args.date))
if __name__ == '__main__':
main()
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