Srry, I missed half of your post. But again, I need the presented equations.


Kind regards,--  
Dominykas Grigonis


On Saturday, 29 December 2012 at 15:12, Dominykas Grigonis wrote:

> Well the simplest but most inefficient approach is a "for" loop. However the best ant kinda sophisticated approach is to use "Reduce".
>  
> I did not analyse your problem as it would take some time for dubious knowledge, but here is a simple random walk simulation presented in both ways.
>  
> Reduce(function(x,eps) {x+eps},rnorm(100,sd=2.5),accumulate=TRUE)
>  
> a=numeric();a[1]=1
> for (i in 2:100){a[i]=a[i-1]+rnorm(1,sd=2.5)}
>  
> If you can present me with the paper I might be able to simulate the ones you need. As I am not going to analyse something what I doubt it will have any value to me.
>  
> Also for efficiency:
> Unit: microseconds
>     expr      min       lq   median        uq      max
> 1  sim() 1074.634 1100.702 1120.667 1172.0290 4291.588
> 2 sim2()  499.730   507.990   527.797  582.7205 1516.850
>  
>  
> where sim2 is Reduce function.
>  
>  
>  
> Kind regards,--  
> Dominykas Grigonis
>  
>  
> On Saturday, 29 December 2012 at 13:33, Simone Gogna wrote:
>  
> > Dear R users,
> > suppose we have a random walk such as:
> >  
> > v_t+1 = v_t + e_t+1
> >  
> > where e_t is a normal IID noise pocess with mean = m and standard deviation = sd and v_t is the fundamental value of a stock.
> >  
> > Now suppose I want a trading strategy to be:
> >  
> > x_t+1 = c(v_t – p_t)
> >  
> > where c is a costant.
> > I know, from the paper where this equations come from (Farmer and Joshi, The price dynamics of common trading strategies, 2001) that the induced price dynamics is:
> >  
> > r_t+1 = –a*r_t + a*e_t + theta_t+1
> >  
> > and
> >  
> > p_t+1 = p_t +r_t+1
> >  
> > where r_t = p_t – p_t-1 , e_t = v_t – v_t-1 and a = c/lambda (lambda is another constant).
> >  
> > How can I simulate the equations I have just presented?
> > I have good confidence with R for statistical analysis, but not for simulation therefore I apologize for my ignorance.
> > What I came up with is the following:
> >  
> > ##general settings
> > c<-0.5
> > lambda<-0.3
> > a<-c/lambda
> > n<-500  
> >  
> > ## Eq.12 (the v_t random walk)
> > V_init_cond<-0
> > Et<-ts(rnorm(n+100,mean=0,sd=1))
> > Vt<-Et*0
> > Vt[1]<-V_init_cond+Et[1]
> > for(i in 2:(n+100)) {
> > Vt[i]<-Vt[i-1]+Et[i]
> > }
> > Vt<-ts(Vt[(length(Vt)-n+1):length(Vt)])
> > plot(Vt)
> >  
> > ## Eq.13 (the strategy)
> > Xt_init_cond<-0
> > Xt<-Xt_init_cond*0
> > Xt[2]<-c(Vt[1]-Pt[1])
> > for(i in 2:(n)){
> > Xt[i]<-c(Vt[i-1]-Pt[i-1])
> > }
> > Xt<-ts(Xt[(length(Xt)-n+1):length(Xt)])
> > plot(Xt)
> >  
> > ## Eq. 14 (pice dynamics)
> > P_init_cond<-0
> > Pt<-Rt*0
> > Pt[1]<-P_init_cond+Rt[1]
> > for(i in 2:(n+100)) {
> > Pt[i]<-Pt[i-1]+Rt[i]
> > }
> > Pt<-ts(Pt[(length(Pt)-n+1):length(Pt)])
> > plot(Pt)
> > Rt_init_cond<-0
> > Rt<-Rt_init_cond*0
> > Rt[2]<- -a*Rt[1]+a*Et[1]+e[2]
> > for(i in 2:(n)){
> > Rt[i]<- -a*Rt[i-1]+a*Et[i-1]+e[i]
> > }
> > Rt<-ts(Rt[(length(Rt)-n+1):length(Rt)])
> > plot(Rt)
> >  
> > I don’t think the code above is correct, and I don’t even know if this is the approach I have to take.
> > Any suggestion is warmly appreciated.
> >  
> > thanks,
> > Simone Gogna  
> > [[alternative HTML version deleted]]
> >  
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> >  
> >  
> >  
>  
>  


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