.

[SheHadMANHands]

Out of curiousity, is it Gox or Bitstamp value projection?

Gox

[1714948169]

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[1714948169]

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[1714948169]

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[1714948169]

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[Tzupy]

On Bitstamp, during these last 2 push-ups all you had to do was to panic buy after the move on Gox, there was plenty on the ask side ( no smart whale buy as on Gox ).
And then sell sometime at the upper price level. Then just wait for another push-up, if it happened just repeat panic buy, if it didn't you had nothing to lose.

[Nemo1024]

Thanks for the info. Being in Europe, I am fully on Bitstamp, so I always have to mentally adjust the prices when people talk about them on the forums, as the majority is still referencing Gox by default.

[keystroke]

Yup, big whales eat slightly smaller whales, they don't care that much about the minnows. This is what the game of "The Biggest Fish" is all about. They are after the biggest trade, on the biggest move, because that is the best they can do to keep their positions from being overrun. 

How many BTC do you think these whales have? How are they distributed at the top?

[Tzupy]

And whale strikes today as predicted, but I doubt it's the smart whale, doesn't fit the MO, should have waited just a bit longer.

[rampantparanoia]

And whale strikes today as predicted, but I doubt it's the smart whale, doesn't fit the MO, should have waited just a bit longer.

Are you saying he already struck?

[Tzupy]

No, IMO this was a preemptive buy, by a whale who expects an upcoming larger buy. He thought 'why buy above 150$ when I can buy below 149$'.
And if the expected larger buy smashes through the 150$ resistance level, that was a good move, for a whale who still had $ to spend.
I suspect most whales are full coins now, and $ may be running out faster than coins. Anyway, small players' $ are running out, and about
hypothetical billionaires' $, those are unknown and unlikely to run out anytime soon ( if they are present ).

[Miz4r]

Couldn't it reverse right where we are now? Selling pressure seems to building up and buying pressure doesn't seem that strong except for a whale push once in a while. If a few whales start dumping it could start an avalanche right now imo. Anyway it looks harder to push up than down atm, I think we may see a pullback to 110-120 first before going up further.

[saddambitcoin]

chodpaba how do i learn more about this stuff?

[saddambitcoin]

chodpaba how do i learn more about this stuff?

The thing you want to learn is Systems Theory: http://www.amazon.com/b?ie=UTF8&node=14596

ordering now, thank you!

[DrJoeGrine]

Paying close attention to this thread.

[adpinbr]

chodpaba how do i learn more about this stuff?

The thing you want to learn is Systems Theory: http://www.amazon.com/b?ie=UTF8&node=14596

ordering now, thank you!

Chodpaba which book on that list do you reccomend the most?

[rampantparanoia]

Paying close attention to this thread.

+1, the sole purpose I come to bitcointalk speculation

[powpow]

Paying close attention to this thread.

+1, the sole purpose I come to bitcointalk speculation

Try not to make your financial investments in a vacuum.  Human nature can put our own interests above others.  Use this information how you will, but I encourage you to do your own research and analysis at the same time.

[bb113]

I've recently discovered chaos theory and have come to think it is probably relevant to most types of research although it is going largely ignored. Definitely more people should be made familiar with chaos theory and fractals.

The logistic map example helped me understand what they mean by "chaos".
https://en.wikipedia.org/wiki/Logistic_map

This R script will run the logistic function with different parameters (default A from 1 to 4 by .1) and different starting points. You can see that for A between 1 and 3 the function always converges on a certain value, then as A is made larger x will oscillate between two values, then bifurcate a second time to oscillate between 4 values, then 8, then it eventually becomes chaotic (no repeating pattern no matter how many iterations).

R code:

Code:

#### x.new<-A*x.old*(1-x.old)	# Logistic Function

# Settings
iters<-200  # Number of iterations
step<-.1	 # Increase A by this step size
burn.in.steps<-150 # Throw Away first n steps when plotting A vs x.new
sleep.time<-.0000001 # Control speed (in seconds)

x.old.init<-seq(0.1,.9,by=.9)	# Initial x values
A.max<-4		# Max A value
A<- 1		# Initial value for A parameter



#Initialize Variables and Chart
x.old<-x.old.init[1]	# Set initial x value to first value determined above
out=cbind(A,x.old.init[1],x.old)	# Initialize output matrix

dev.new(); plot(0,0,type="n", ylim=c(0,1), xlim=c(1,iters))

#Run function for increasing A values until equal to A max
while(A<=A.max){
  
  # Set initial x value to next one in vector determined above
  for(j in x.old.init){
    x.old<-j
    
    # Run for number of iterations set by iters
    for(i in 1:iters){
      
      x.new<-A*x.old*(1-x.old)	# Logistic Function (update x.new)
      
      out<-rbind(out,cbind(A,j,x.new))	# Update output
      
      # Plot current run
      plot(c(j,out[which(out[,1]==A & out[,2]==j),3]), type="n", lwd=2,
           col="White", 
           ylim=c(0,1), xlim=c(0,iters), pch=16,
           xlab="Iteration", ylab="X.new",
           main=c("X.new = A*X.old*( 1 - X.old )",
                  paste("A=",A,  "   X.initial=",j),
                  paste("X.new=", round(x.new,3))))
      for(k in x.old.init){
        color<-rainbow(length(x.old.init))[which(x.old.init==k)]
        points(c(j,out[which(out[,1]==A & out[,2]==k),3]), type="b", lwd=2,
               col=color, 
               ylim=c(0,1), xlim=c(0,iters), pch=16,
               xlab="Iteration", ylab="X.new",
               main=c("X.new = A*X.old*( 1 - X.old )",
                      paste("A=",A,  "   X.initial=",j),
                      paste("X.new=", round(x.new,3))))
      }
      ##
      
      
      x.old<-x.new	# x.old becomes x.new
    }
    Sys.sleep(sleep.time)
    
  }
  A<-round(A+step,2)	# update A parameter
  
}

#Plot "A" parameter vs Results
dev.new()
plot(out[,1],out[,3], type="n", ylab="X.new", xlab="A",
     main="X.new = X.old*( 1 - X.old )"
)
for(i in 1:length(unique(out[,1]))){
  points(out[which(out[,1]==unique(out[,1])[i])[-(1:burn.in.steps)],1],
         out[which(out[,1]==unique(out[,1])[i])[-(1:burn.in.steps)],3])
}

The lorenz strange attractor is also pretty cool to mess around with:
https://en.wikipedia.org/wiki/Lorenz_system

This script will let you watch the system evolve over time from 3 different starting points. Even a tiny difference in initial state means you will not know what state the system will be in at any given point, but regardless of initial state you can know a probability distribution. You can also rotate the image as it runs and add stochastic noise to see what happens.

R code:

Code:

############################
##LORENZ strange attractor##
############################
#Modified From:
#http://fractalswithr.blogspot.com/2007/04/lorenz-attractor.html


# install.packages("rgl") # Install if needed.
library(rgl)

#####Settings
add.noise=F  #Gaussian +/- noise.sd
noise.sd=.01
live.plot=T
##

####Parameters
a=10; r=28; b=8/3; dt=0.01

n=5000   #Iterations
##


####Initial Conditions
Xa=0.01; Ya=0.01; Za=0.01    #Initial Condition Blue
Xb=0.01+.0001; Yb=0.01; Zb=0.01    #Initial Condition Red (Slight Difference)
Xc=20; Yc=20; Zc=.01    #Initial Condition Black (Large Difference)
##


####Misc
XYZa=array(0,dim=c(n,3))
XYZb=array(0,dim=c(n,3))
XYZc=array(0,dim=c(n,3))

par3d(font=2, family="serif", 
      bg3d(color=c("darkslategray3","Black"), 
           fogtype="exp2", sphere=TRUE, back="fill")
)
##


####Run
for(i in 1:n)
{
X1a=Xa; Y1a=Ya; Z1a=Za

Xa=X1a+(-a*X1a+a*Y1a)*dt
Ya=Y1a+(-X1a*Z1a+r*X1a-Y1a)*dt
Za=Z1a+(X1a*Y1a-b*Z1a)*dt


X1b=Xb; Y1b=Yb; Z1b=Zb

Xb=X1b+(-a*X1b+a*Y1b)*dt
Yb=Y1b+(-X1b*Z1b+r*X1b-Y1b)*dt
Zb=Z1b+(X1b*Y1b-b*Z1b)*dt


X1c=Xc; Y1c=Yc; Z1c=Zc

Xc=X1c+(-a*X1c+a*Y1c)*dt
Yc=Y1c+(-X1c*Z1c+r*X1c-Y1c)*dt
Zc=Z1c+(X1c*Y1c-b*Z1c)*dt


if(add.noise==T){
Xa<-rnorm(1,Xa,noise.sd)
Xb<-rnorm(1,Xb,noise.sd)
Xc<-rnorm(1,Xc,noise.sd)

Ya<-rnorm(1,Ya,noise.sd)
Yb<-rnorm(1,Yb,noise.sd)
Yc<-rnorm(1,Yc,noise.sd)

Za<-rnorm(1,Za,noise.sd)
Zb<-rnorm(1,Zb,noise.sd)
Zc<-rnorm(1,Zc,noise.sd)
}

XYZa[i,]=c(Xa,Ya,Za)
XYZb[i,]=c(Xb,Yb,Zb)
XYZc[i,]=c(Xc,Yc,Zc)

if(live.plot==T){
points3d(XYZa[i,1],XYZa[i,2],XYZa[i,3], col="Blue", alpha=.7, add=T)
points3d(XYZb[i,1],XYZb[i,2],XYZb[i,3], col="Red", alpha=.7, add=T)
points3d(XYZc[i,1],XYZc[i,2],XYZc[i,3], col="Black", alpha=.7, add=T)

points3d(XYZa[i,1],XYZa[i,2],XYZa[i,3], col="Blue", alpha=1, size=10, add=T)
points3d(XYZb[i,1],XYZb[i,2],XYZb[i,3], col="Red", alpha=1, size=10, add=T)
points3d(XYZc[i,1],XYZc[i,2],XYZc[i,3], col="Black", alpha=1, size=10, add=T)

rgl.pop()
rgl.pop()
rgl.pop()
}

}
##

if(!live.plot==T){
points3d(XYZa[,1],XYZa[,2],XYZa[,3], col="Blue", alpha=.5, add=T)
points3d(XYZb[,1],XYZb[,2],XYZb[,3], col="Red", alpha=.5, add=T)
points3d(XYZc[,1],XYZc[,2],XYZc[,3], col="Black", alpha=.5, add=T)
}

[bb113]

Bonus: Albert Barabasi is also a name to know for network theory. He has been pushing it for understanding social and biological systems for the last 10 years or so. There hasn't been much adoption but he does get his stuff published in nature rather regularly. I'm not sure whats out there about applying it to markets, but preferential attachment seems like it should be related to wealth distribution, adoption of novel technologies, etc.

Here is a nice documentary introduction:
https://www.youtube.com/watch?v=RcCpEf6_Ofg

[endlessdark]

I've recently discovered chaos theory and have come to think it is probably relevant to most types of research although it is going largely ignored. Definitely more people should be made familiar with chaos theory and fractals.

The logistic map example helped me understand what they mean by "chaos".
https://en.wikipedia.org/wiki/Logistic_map

This R script will run the logistic function with different parameters (default A from 1 to 4 by .1) and different starting points. You can see that for A between 1 and 3 the function always converges on a certain value, then as A is made larger x will oscillate between two values, then bifurcate a second time to oscillate between 4 values, then 8, then it eventually becomes chaotic (no repeating pattern no matter how many iterations).

R code:

Code:

#### x.new<-A*x.old*(1-x.old)	# Logistic Function

# Settings
iters<-200  # Number of iterations
step<-.1	 # Increase A by this step size
burn.in.steps<-150 # Throw Away first n steps when plotting A vs x.new
sleep.time<-.0000001 # Control speed (in seconds)

x.old.init<-seq(0.1,.9,by=.9)	# Initial x values
A.max<-4		# Max A value
A<- 1		# Initial value for A parameter



#Initialize Variables and Chart
x.old<-x.old.init[1]	# Set initial x value to first value determined above
out=cbind(A,x.old.init[1],x.old)	# Initialize output matrix

dev.new(); plot(0,0,type="n", ylim=c(0,1), xlim=c(1,iters))

#Run function for increasing A values until equal to A max
while(A<=A.max){
  
  # Set initial x value to next one in vector determined above
  for(j in x.old.init){
    x.old<-j
    
    # Run for number of iterations set by iters
    for(i in 1:iters){
      
      x.new<-A*x.old*(1-x.old)	# Logistic Function (update x.new)
      
      out<-rbind(out,cbind(A,j,x.new))	# Update output
      
      # Plot current run
      plot(c(j,out[which(out[,1]==A & out[,2]==j),3]), type="n", lwd=2,
           col="White", 
           ylim=c(0,1), xlim=c(0,iters), pch=16,
           xlab="Iteration", ylab="X.new",
           main=c("X.new = A*X.old*( 1 - X.old )",
                  paste("A=",A,  "   X.initial=",j),
                  paste("X.new=", round(x.new,3))))
      for(k in x.old.init){
        color<-rainbow(length(x.old.init))[which(x.old.init==k)]
        points(c(j,out[which(out[,1]==A & out[,2]==k),3]), type="b", lwd=2,
               col=color, 
               ylim=c(0,1), xlim=c(0,iters), pch=16,
               xlab="Iteration", ylab="X.new",
               main=c("X.new = A*X.old*( 1 - X.old )",
                      paste("A=",A,  "   X.initial=",j),
                      paste("X.new=", round(x.new,3))))
      }
      ##
      
      
      x.old<-x.new	# x.old becomes x.new
    }
    Sys.sleep(sleep.time)
    
  }
  A<-round(A+step,2)	# update A parameter
  
}

#Plot "A" parameter vs Results
dev.new()
plot(out[,1],out[,3], type="n", ylab="X.new", xlab="A",
     main="X.new = X.old*( 1 - X.old )"
)
for(i in 1:length(unique(out[,1]))){
  points(out[which(out[,1]==unique(out[,1])[i])[-(1:burn.in.steps)],1],
         out[which(out[,1]==unique(out[,1])[i])[-(1:burn.in.steps)],3])
}

The lorenz strange attractor is also pretty cool to mess around with:
https://en.wikipedia.org/wiki/Lorenz_system

This script will let you watch the system evolve over time from 3 different starting points. Even a tiny difference in initial state means you will not know what state the system will be in at any given point, but regardless of initial state you can know a probability distribution. You can also rotate the image as it runs and add stochastic noise to see what happens.

R code:

Code:

############################
##LORENZ strange attractor##
############################
#Modified From:
#http://fractalswithr.blogspot.com/2007/04/lorenz-attractor.html


# install.packages("rgl") # Install if needed.
library(rgl)

#####Settings
add.noise=F  #Gaussian +/- noise.sd
noise.sd=.01
live.plot=T
##

####Parameters
a=10; r=28; b=8/3; dt=0.01

n=5000   #Iterations
##


####Initial Conditions
Xa=0.01; Ya=0.01; Za=0.01    #Initial Condition Blue
Xb=0.01+.0001; Yb=0.01; Zb=0.01    #Initial Condition Red (Slight Difference)
Xc=20; Yc=20; Zc=.01    #Initial Condition Black (Large Difference)
##


####Misc
XYZa=array(0,dim=c(n,3))
XYZb=array(0,dim=c(n,3))
XYZc=array(0,dim=c(n,3))

par3d(font=2, family="serif", 
      bg3d(color=c("darkslategray3","Black"), 
           fogtype="exp2", sphere=TRUE, back="fill")
)
##


####Run
for(i in 1:n)
{
X1a=Xa; Y1a=Ya; Z1a=Za

Xa=X1a+(-a*X1a+a*Y1a)*dt
Ya=Y1a+(-X1a*Z1a+r*X1a-Y1a)*dt
Za=Z1a+(X1a*Y1a-b*Z1a)*dt


X1b=Xb; Y1b=Yb; Z1b=Zb

Xb=X1b+(-a*X1b+a*Y1b)*dt
Yb=Y1b+(-X1b*Z1b+r*X1b-Y1b)*dt
Zb=Z1b+(X1b*Y1b-b*Z1b)*dt


X1c=Xc; Y1c=Yc; Z1c=Zc

Xc=X1c+(-a*X1c+a*Y1c)*dt
Yc=Y1c+(-X1c*Z1c+r*X1c-Y1c)*dt
Zc=Z1c+(X1c*Y1c-b*Z1c)*dt


if(add.noise==T){
Xa<-rnorm(1,Xa,noise.sd)
Xb<-rnorm(1,Xb,noise.sd)
Xc<-rnorm(1,Xc,noise.sd)

Ya<-rnorm(1,Ya,noise.sd)
Yb<-rnorm(1,Yb,noise.sd)
Yc<-rnorm(1,Yc,noise.sd)

Za<-rnorm(1,Za,noise.sd)
Zb<-rnorm(1,Zb,noise.sd)
Zc<-rnorm(1,Zc,noise.sd)
}

XYZa[i,]=c(Xa,Ya,Za)
XYZb[i,]=c(Xb,Yb,Zb)
XYZc[i,]=c(Xc,Yc,Zc)

if(live.plot==T){
points3d(XYZa[i,1],XYZa[i,2],XYZa[i,3], col="Blue", alpha=.7, add=T)
points3d(XYZb[i,1],XYZb[i,2],XYZb[i,3], col="Red", alpha=.7, add=T)
points3d(XYZc[i,1],XYZc[i,2],XYZc[i,3], col="Black", alpha=.7, add=T)

points3d(XYZa[i,1],XYZa[i,2],XYZa[i,3], col="Blue", alpha=1, size=10, add=T)
points3d(XYZb[i,1],XYZb[i,2],XYZb[i,3], col="Red", alpha=1, size=10, add=T)
points3d(XYZc[i,1],XYZc[i,2],XYZc[i,3], col="Black", alpha=1, size=10, add=T)

rgl.pop()
rgl.pop()
rgl.pop()
}

}
##

if(!live.plot==T){
points3d(XYZa[,1],XYZa[,2],XYZa[,3], col="Blue", alpha=.5, add=T)
points3d(XYZb[,1],XYZb[,2],XYZb[,3], col="Red", alpha=.5, add=T)
points3d(XYZc[,1],XYZc[,2],XYZc[,3], col="Black", alpha=.5, add=T)
}

[Tzupy]

The smart whale is still missing in action. But I think I spotted a fragile trend reversal in the bid sum / ask sum ratio ( which until now was dropping ),
and in the money flow index. The market may be slowly moving up on its own. If Chodpaba is right meaning that the whale just wants to accumulate coins,
and he has still $ to spend, he might decide to buy soon. That's not how I would buy lots of coins if I were a $ whale, so I'm still suspicious on his intentions.

[ElectricMucus]

chaos theory ... fractals ... R script ... logistic function ... oscillate then bifurcate ... lorenz strange attractor ... probability distribution ... stochastic noise

Sounds bullish.

get out.

[Joerii]

Very interesting, chodpa. thanks for sharing.

So If I follow your theory ; a sustained price development has to be supported with volume. I guess your indicators warn of a point where buying pressure no longer can maintain the current price level and a ( perhaps temporary ) drop in price has to occur. Is this correct ?