It took me a couple of days just to write the below Maplet code which loads a CSV file (time series organized in

columns where the first column is date/year etc and first row contain the names of the stocks)

and displays the stock names in a dropdownbox. Now I want to plot my selection from my dropdownbox

but now I have a new problem. When I include the "onchange = AC"

command in the drop1 command line in order to update my selection from the dropdownBox

I get an error message :

The usual optimization constraint in Maple looks like: con := [ w[1] <= 1,  w[2] <= 1,  w[1] >= -1,  w[2] >= -1, w[1] + w[2] = 1 ]

 I was wondering if it is possible to have a constraint where the

different weights w[1] , w[2]  can only take the values   -1 , 0 , 1   

I have always wanted to know why the lognormal distribution in the Black & Scholes (BS) option pricing model

is assumed to have a mean of  T*(r-0.5*sigma^2 ) . I have seen various explanation but non that make any sense.

I know that the mean in a lognormal distribution is exp(u+0.5*sigma^2) but why does it suddenly become

T*(r-0.5*sigma^2 ) in the BS model ?   I can understand the T = mean proportional to the time increament but why does

I have this code (see below) which calculates the probability of finding 4 aces in a 52 card deck through simulation.

My problem is however that the code is very slow and the probability is very low which means that I have to run the

simulation maybe one milion times to get an good approximation. The code can not handle this.

Is there any way to make the code faster so it can handle one million simulations ?

I was wondering why the permute function in Maple removes [1,1]  [2,2]  [3,3]  [4,4]  [5,5]  [6,6]

for example: