股票价格模拟R代码-慢-蒙特卡洛 [英] Stock Price Simulation R code - Slow - Monte Carlo

问题描述

我需要使用R代码执行股价模拟.问题是代码有点慢. 基本上，我需要模拟每个时间步(每天)的股价并将其存储在矩阵中.

I need to perform a stock price simulation using R code. The problem is that the code is a little bit slow. Basically I need to simulate the stock price for each time step (daily) and store it in a matrix.

假设库存过程为几何布朗运动"的示例

An example assuming the stock process is Geometric Brownian Motion

for(j in 1:100000){
    for(i in 1:252){
        S[i] <- S[i-1]*exp((r-v^2/2)*dt+v*sqrt(dt)*rnorm(1))
    }
    U[j,] <- S
}

是否有任何改进和加快代码速度的建议?

Any suggestion to improve and speed up the code?

推荐答案

假设S[0] = 1，您可以按照以下步骤构建U:

Assuming S[0] = 1, you can build U as a follows:

Ncols <- 252

Nrows <- 100000

U <- matrix(exp((r-v^2/2)*dt+v*sqrt(dt)*rnorm(Ncols*Nrows)), ncol=Ncols, nrow=Nrows)

U <- do.call(rbind, lapply(1:Nrows, function(j)cumprod(U[j,])))

使用约书亚和本的建议:

using Joshua's and Ben's suggestions:

产品版本:

U <- matrix(exp((r-v^2/2)*dt+v*sqrt(dt)*rnorm(Ncols*Nrows)), ncol=Ncols, nrow=Nrows)

U <- t(apply(U, 1, cumprod))

总和版本:

V <- matrix((r-v^2/2)*dt+v*sqrt(dt)*rnorm(Ncols*Nrows), ncol=Ncols, nrow=Nrows)

V <- exp( t(apply(V, 1, cumsum)) )

由@Paul建议:

每个提案的执行时间(使用10000行而不是10 ^ 5):

Execution time for each proposal (using 10000 rows instead of 10^5):

使用apply + cumprod

 user  system elapsed 
0.61    0.01    0.62 

使用apply + cumsum

 user  system elapsed 
0.61    0.02    0.63 

使用OP的原始代码

 user  system elapsed 
67.38    0.00   67.52 

注意:上面显示的时间是system.time的第三小节.每个代码的前两个度量均被丢弃.我用过r <- sqrt(2)，v <- sqrt(3)和dt <- pi.在他的原始代码中，我还用S[i-1]替换了ifelse(i==1,1,S[i-1])，并预先分配了U.

Notes: The times shown above are the third measures of system.time. The first two measures for each code were discarded. I've used r <- sqrt(2), v <- sqrt(3) and dt <- pi. In his original code, I've also replaced S[i-1] for ifelse(i==1,1,S[i-1]), and preallocated U.

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