Grid trading strategy: Put several buy order at each $100 step down and sell at each $100 bounce. Did once in December and now will do it again

Speka for yourself. I knwo how to tpye.

^^^^^^       [& thanks for not using that over used Picard WTF meme]

Well too each his own.   For myself,  I started with X bitcoins.  I added 1.25X bitcoins through mining, and then about 1.25X bitcoins through trading.  And than I lost about 75% of my bitcoins through scams, failed group buys, and mostly one really bad investment (riding asicminer stock down from 4 btc to .8 btc).   So that's why I AM HODLING.  To each his own.

I think for us bitcoin-heads here in this forum, hodling isn't such a bad idea -- we are the minority (I presume ?).   I think the majority of new money (that drives the btc economy) is coming from new people entering who are not hodling, so there is still enough new-bucks money to keep the bitcoin economy improving, which is the most important thing.

I can't say I'm sure though. These are my current guesses. The more people holding the more stable the price is, so I don't see that as a negative. It is only negative when so many folks are hodling that the economy doesn't have enough gas in the tank to run, but that is not happening IMHO.

Smartcoin.
Brand new coin! Only 1 week old.

but seriously - this apple stuff is fuckin bad news

Well seeing as the destabilization is coming in the form of currency crises, they will move out of their local currencies and into the US Dollar.

Once they've done that, they could take their time with reinvestment, but they'll almost certainly go into US investments.

I think the Fed will make US markets attractive by making emerging markets look bad in comparison.

I'm starting to think Yellen will continue with the taper; I don't think the Fed will make such a public u-turn. It might continue through some other means which is not as obvious. But I think for now, the plan is to continue with the taper to destabilize emerging markets and force capital into US equities and bonds which are 'safe' in comparison. If they manage to pull that off, they can keep the stock market, real estate and other assets inflated and taper at the same time.

Thoughts?

Gox chart https://www.tradingview.com/v/4hKqJpci/

I am sure that there exist better prediction methods (say, that provide "X% confidence" intervals that are narrower than those of the Brownian model but still  contain the predicted value at least X% of the time in the long run).  I am a complete amateur in this field.

Indeed, as you say, a fat tail is evident in the distribution of the one-step increments Z(i+1) - Z(i).  However, both theory and data tell me that a Gaussian distribution, with linearly increasing variance,  works well for larger steps Z(i+n) - Z(i) when n is ~20 hours or more.  See this plot:



This is a series of crude histograms of the quantity R(i,n) = (Z(i+n) - Z(i))/sqrt(n), sampled from the reference datafile (Bitstamp 2013-09-01 to 2014-01-30, 1 hour intervals) with various values of i,  tallied separately for each stride n.  The "n" axis runs across the middle of the plot, with n=1 at the upper left edge and increasing down to the right. The "R" axis is perpendicular to it, with R=0 in the middle.  The histogram for each n is normalized to unit sum.

The long fat tails and narrower peak of the distribution for small n is quite evident.  However, as n increases, the tails and sharp peak seem to disappear, as one would expect from the Law of Large Numbers.

Actually, I am not really sure about the tails because the number of samples in each histogram also gets smaller when n increases.  Perhaps the following plot is more convincing:



In this plot, the horizontal axis is the stride n (hours), and the vertical axis is the log increment D(i,n) = Z(i+n) - Z(i)  (not divided by sqrt(n)).

The light brown crosses on each vertical line are a sample of differences D(i,n) with same value of n and various values of i.  Each green dot is the mean of the sample increments D(i,n) with same n.  Each red dot is the standard deviation of those increments, computed assuming that their expected value is 0 (rather than the empirical mean shown by the green dot).  The histogram-like lines are the 2.5% and 97.5% percentiles of those samples.  

The orange curve is the deviation sigma(n) = C*sqrt(n) of D(i,n) predicted by the log-Brownian model.  The purple curves are the ±2*sigma(n) bounds above and below D=0.

According to this plot, for n ~15 hours or more, the empirical deviation (red dots) is very close to the model C*sqrt(n) (orange line).  For n ~30 hours or more, the curves ± 2*C*sqrt(n) folow the empirical 2.5%-97.5% percentiles as accurately as one could expect.  

The model clearly fails for smaller n; in particular, over the span of 5 hours, large swings occur more often than would be expected in a log-Brownian model.   Therefore, I  was too confident in my prediction for tomorrow; but with a bit of luck, I should be safe for the rest of the month.  

The slightly ascending green line means that there is a consistent increasing trend in that sample.  That trend also manifests itself in the growing gap between the empirical 2.5% percentile and the -2*sigma(n) curve.  However, if I had used only the last 2 months of data for the analysis, instead of the last 5, the trend would have been decreasing - and stronger.  That is one of the reasons why I did not include a trend term.

I have looked for correlations between successive increments (Z(i) - Z(i-1)  and Z(i+1)-Z(i)), but did not see anything clearly significant.  If there is such a correlation, it must be very subtle, and should be quickly "forgotten" after a few time steps.

Note that real stock prices are influenced by "real world" factors such as demand for the product, raw material prices, etc.  Those factors vary according to their own nature in various time scales, that range from decades to hours.  Maybe it is those factors that provide the long-term correlations characteristic of fractal signals?

In contrast, Bitcoin's price is almost entirely set by speculation; while external news may trigger changes, they do not directly determine the magnitude of those changes. ("How many billion dollars were subtracted from Bitcoin's future usage in e-commerce payments because of Shrem's arrest?")

So perhaps Bitcoin's price is indeed better described by a log-Brownian model than by a fractal process...

Most bitcoin ATMs are one way.  They sell bitcoins but don't buy them. Imagine having to stand in front of an ATM for even three confirmations when the miners take 70 minutes to find a block (happens all the time).

Which incidentally is a serious time delay problem for bitcoin generally don't know how that is going to be resolved unless you take the risk of zero confirmation attacks.  

Here is my prediction for the coming month:



The plot shows the Bitstamp prices from Nov/2013 to Jan/2014 (purple) and estimated lower and upper bounds for Feb/2014 (green).  I claim that, in any 1-hour interval in the coming month, the Bitstamp price will be within the two green lines above, with 95% probability.

For example, I claim that on Feb/22 the price will be between 300 USD and 2200 USD with 95% probability.

(Note that this is not the same thing as saying that the entire price plot will stay in that region for the whole month with 95% probability.)

This prediction is based on the simple "log Brownian" model that I described a while ago.  Namely, it assumes that the price change from one 1-hour interval to the next, in log scale, is a normal random variable that is independent of the previous history.  

More precisely let P(i) be the weighted mean transaction price in the 1-hourinterval number i.  Let Z(i) be log_10(P(i)). The model assumes that

  Z(i+1) = Z(i) + C*RAND(i)

where the factors RAND(i) are independent random variabls with zero mean, unit deviation, and Gaussian distribution.  Therefore, for any n > 0,

  Z(i+n) = Z(i) + C*sqrt(n)*RAND(i,n)

where factors RAND(i,n) are again random (but not independent) variables with zero mean, unit deviation, and Gaussian distribution.

Analysis of the Bitstamp prices from Sep/2013 to Jan/2014 yields 0.00964 as the best fit for the parameter C.

Since 95% of the probability of the Gaussian distribution is within 2 deviations of the mean, the model implies that, n steps in the future,
the value Z(i+n) will be within the values

  Zmin(i,n) = Z(i) - 2*C*sqrt(n) and
  Zmax(i,n) = Z(i) + 2*C*sqrt(n)

with 95% probability, where Z(i) is the current (last known) price.  The green lines are these two bounds, converted back to linear scale (Pmin(i,n) = 10**Zmin(i,n), etc.)

More specifically, according to the model, at any specific 1-hour interval i+n in the future, the log price Z(i+n) will be

   below Zmin(i,n) with 2.5% probability;
   between Zmin(i,n) and the current price Z(i) with 47.5% probability;
   between the current price Z(i) and Zmax(i,n) with 47.5% probability;
   above Zmax(i,n) with 2.5% probability.

Note that, by this model, the future prices P(i+n) may be on either side of the current price (red line) with equal probability, even though the inverse log mapping yields a much wider range above than below.
 
Of course this model is quite rudimentary and basically useless for traders.  There may be more sophisticated models, but I doubt that they can yield better predictions.  

(The Brownian model is visibly inadequate for small n, since the log price increments from one interval to the next do not have a Gaussian distribution.  As a consequence, the 2*sigma interval Zmin(i,n) to Zmax(i,n) contains somewhat less than 95% of the probability.  However, by the Law of Large Numbers the distribution becomes almost Gaussian after a few hours. )

(before people ask: no, a model with a linear "historical trend" term Z(i+n) = Z(i) + T*n + C*sqrt(n)*RAND(i,n) does not seem appropriate, and its predictions for the next month would not be significantly different.)

BTC fees don't go to any charity.
They support large mining business mostly,
said businesses are pretty well supported without fees.

This is stupid charge unless they have explicit proof that he was part of some organized crime group.

Hopefully, he didn't left his coins on laptop too though, who would listen all those trolls and their crap about FBI coins sales

The Nobel Laureate pronounced that bitcoin was a return to the dark ages because at present there is very little clarity surrounding the crypto currency.

Using fiatleak as an estimator, about $6mm flowing in daily at present.   Now 10k starts to seem pretty achievable.  I think the psychology of the historical spike pattern and the characteristic viral distribution curve militate for 3-5k in 2014, however.  10k in 2015.

There are now 3 camps here:

1. Hodling
2. Day tarding
3. Fuckign bubble

Miners are mostly hodlers.. even worse than the bubble noobs. If things keep up like this the market might get a major miner hoarding crash like in 2011.