Topic: Satoshi Dice - how to last longer - page 2. (Read 21196 times)

library(ggplot2)
library(gridExtra)
####plot functions

####plot strategy based results
strategy.plot.func	<- function(r, bets, max, p){

	m		<- 1 / (1 - 1 / r)
	n  		<- round(-log (bets * p + 1)/log(1 - p))
	init	<- (max - n * 0.0005)/(sum(m^(1:n))*(1 - sum(m^(1:(n-1)))/(sum(m^(1:n)))*0.005))

rg	<- bets + 1
while(round(sum(rg)/10,digits =1) != round(bets/10,digits=1)) {
	if(bets*p < 3){stop("increase number of bets or change to higher payout game"); break}
		rg	<- rgeom(bets*p, p) + 1}

cost.func	<- function(x){sum(init/(1-p)^(0:rg[x]))}
win.func	<- function(x){init/(1-p)^rg[x] * r}
cost	<- sapply(1:(length(rg)), cost.func)
win		<- sapply(1:(length(rg)), win.func)

x	<- cbind(seq(1,length(cost)*2,2),cost*-1)
y	<- cbind(seq(2,length(win)*2,2),win)
z	<- rbind(x,y)
z	<- z[order(z[,1]),]
a	<- cbind(seq(1,length(rg)*2,2),rg-1)
b	<- cbind(seq(2,length(rg)*2,2),1)
c	<- rbind(a,b)
c	<- c[order(c[,1]),]

plot.df	<- data.frame(bets = cumsum(c[,2]), btc = cumsum(z[,2]))
plot	<-	ggplot(plot.df) + geom_line(aes(bets, btc)) +
			opts(title=paste(bets,"bets, max loss =",max,"btc, p =", p*65536,"/ 65536"))
return(plot)			
}			

####plot naive martingale results
naive.plot.func	<- function(r, bets, p){

	m		<- 2
	init	<- 1

rg	<- bets + 1
while(round(sum(rg)/10,digits =1) != round(bets/10,digits=1)) {
	if(bets*p < 3){stop("increase number of bets or change to higher payout game"); break}
		rg	<- rgeom(bets*p, p) + 1}

cost.func	<- function(x){sum(init/(1-p)^(0:rg[x]))}
win.func	<- function(x){init/(1-p)^rg[x] * r}
cost	<- sapply(1:(length(rg)), cost.func)
win		<- sapply(1:(length(rg)), win.func)

x	<- cbind(seq(1,length(cost)*2,2),cost*-1)
y	<- cbind(seq(2,length(win)*2,2),win)
z	<- rbind(x,y)
z	<- z[order(z[,1]),]
a	<- cbind(seq(1,length(rg)*2,2),rg-1)
b	<- cbind(seq(2,length(rg)*2,2),1)
c	<- rbind(a,b)
c	<- c[order(c[,1]),]

plot.df	<- data.frame(bets = cumsum(c[,2]), btc = cumsum(z[,2]))
plot	<-	ggplot(plot.df) + geom_line(aes(bets, btc)) +
			opts(title=paste("Naive martingale,",bets,"bets, p =", p*65536,"/ 65536"))
return(plot)			
}			



#change these variables to suit
r		<- 8
p 		<- 8000/65536
bets	<- 100
max		<- 1


png(paste("sd", rounds,max,p*65536,".png", sep='.'), height = 500*2, width = 500*16/9)
   grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow = 4, ncol = 2,
    	        heights = unit(c(1,1), "null"),
         default.units = "null", respect = FALSE,
        just="centre")))
    vplayout <- function(x, y)
        viewport(layout.pos.row = x, layout.pos.col = y)

print(strategy.plot.func(r, bets, max, p), vp = vplayout(1,1))
print(strategy.plot.func(r, bets, max, p), vp = vplayout(2,1))
print(strategy.plot.func(r, bets, max, p), vp = vplayout(3,1))
print(strategy.plot.func(r, bets, max, p), vp = vplayout(4,1))

print(naive.plot.func(r, bets, p), vp = vplayout(1,2))
print(naive.plot.func(r, bets, p), vp = vplayout(2,2))
print(naive.plot.func(r, bets, p), vp = vplayout(3,2))
print(naive.plot.func(r, bets, p), vp = vplayout(4,2))


dev.off()

#reports strategy parameters
strategy.func <- function(r, bets, max, p){
	m	<- 1 / (1 - 1 / r)
	n  	<- round(-log (bets * p + 1)/log(1 - p))
	init	<- (max - n * 0.0005)/(sum(m^(1:n))*(1 - sum(m^(1:(n-1)))/(sum(m^(1:n)))*0.005))
	pc.loss	<- (1 - (init*m^n*r - 0.0005)/(init*m^n*r)) * 100
	return(noquote(paste("m =",m," |  init =",init," |  percentage loss to fees =",pc.loss,"%.")))
	}
	

#reports order of martingale betting
allbets.func <- function(r, bets, max, p){
	m	<- 1 / (1 - 1 / r)
	n  	<- round(-log (bets * p + 1)/log(1 - p))
	init	<- (max - n * 0.0005)/(sum(m^(1:n))*(1 - sum(m^(1:(n-1)))/(sum(m^(1:n)))*0.005))
	mat		<- matrix(c(1:bets,round(init*m^(1:bets), 6)),bets,2)
	colnames(mat)<-c("bet number", "bet amount" )
	return(mat)
	}

#change these variables to suit
r	<- 8
p 	<- 8000/65536
bets	<- 100
max	<- 1
	
strategy.func(r, bets, max, p)

allbets.func(r, bets, max, p)

> strategy.func(r, bets, max, p)
[1] m = 1.14285714285714  |  init = 0.10186450536666  |  percentage loss to fees = 0.027536291774799 %.
> 
> allbets.func(r, bets, max, p)

      bet number bet amount
 [1,]          1   0.116417
 [2,]          2   0.133048
 [3,]          3   0.152054
 [4,]          4   0.173776
 [5,]          5   0.198602
 [6,]          6   0.226973
 [7,]          7   0.259398
 [8,]          8   0.296455
 [9,]          9   0.338805
[10,]         10   0.387206