Create a results-matrix after using for-loops - r

Question

I (being an absolut beginner with R and programming) have to do some analysis with R for my thesis in finance :(
The purpose is to simulate data (stock prices) with a GBM and run over the results 2 trading strategies. Within the GBM I'll have to "play" with the veriables "r" and "sigma" (3 different values for each, thus 9 combinations). Each combination needs to be simulated 10000 times over a period T=10, N=250. To all these simulations 2 trading strategies have to be applied - MACD and RSI (within the TTR-package).
Now I'm facing an issue with writing the code :(
#Geometrical Brownian Motion
Sim <- GBM(x, r, sigma, T, N)
x <-100
r <-0
sigma <-1
T <- 10
N <- 250
#Additional info for RSI-strategy
retSim <- ROC(Sim)
SimRSI <- RSI(Sim, 14,)
SimRSI[is.na(SimRSI)] <- 0
#Create a vector for the results of the inner loop
portfolio <- rep(0:N)
portfolio[1] <- 100
runs <- 10000
#Creating vectors for final results of portfolio and simulation after 10000 runs (only the last value of each of the 10000 simulations and portfolio results of the strategy required)
resultsSimGBM <- rep(0:runs)
resultsRSIr1sig1 <- rep(0:runs)
#orders
buyRSI<-portfolio[i-1]*exp(retSim[i])
holdRSI<-portfolio[i-1]
#Simulation
portfolio[1]<-100
i <- 1
j <- 1
#Second loop
for(j in 0:runs){
#Simulation GBM
x <-100
r <-0
sigma <-1
T <- 10
N <- 250
Sim <- GBM(x, r, sigma, T, N)
retSim <- ROC(Sim)
SimRSI <- RSI(Sim, 14,)
SimRSI[is.na(SimRSI)] <- 0
portfolio[1] <- 100
#First loop
for(i in 2:length(Sim)){
#Apply RSI on GBM
buyRSI<-portfolio[i-1]*exp(retSim[i])
holdRSI<-portfolio[i-1]
if(SimRSI[i-1]<50 && SimRSI[i]>50){portfolio[i]=buyRSI}
if(SimRSI[i-1]>50 && SimRSI[i]<50){portfolio[i]=holdRSI}
if(SimRSI[i-1]<50 && SimRSI[i]<50){portfolio[i]=holdRSI}
if(SimRSI[i-1]>50 && SimRSI[i]>50){portfolio[i]=buyRSI}
i <- i+1
}
resultsRSI[j] <- portfolio[N]
resultsSimGBM[j] <- Sim[N]
j <- j+1
}
Anyway, this is what I have until now and it seems to work. However, into the first (inner) loop, I need to include also the second strategy (which until now, singled out) looked following :
#MACD strategy
portfolioMACD[1]<-100
i <- 1
j <- 1
for(j in 0:runs){
Sim <- BMSim
retSim <- ROC(Sim)
SimMACD <- MACD(Sim, 12, 26, 9, myType="EMA")
DataSimMACD <- data.frame(SimMACD)
DataSimMACD$macd[is.na(DataSimMACD$macd)] <- 0
DataSimMACD$signal[is.na(DataSimMACD$signal)] <- 0
for(i in 2:length(Sim)){
buyMACD<-portfolioMACD[i-1]*exp(retSim[i])
sellMACD<-portfolioMACD[i-1]
holdMACD<-portfolioMACD[i-1]*exp(retSim[i])
if(DataSimMACD$macd[i-1]<DataSimMACD$signal[i-1] && DataSimMACD$macd[i]>DataSimMACD$signal[i]){portfolioMACD[i]=buyMACD}
if(DataSimMACD$macd[i-1]>DataSimMACD$signal[i-1] && DataSimMACD$macd[i]<DataSimMACD$signal[i]){portfolioMACD[i]=sellMACD}
if(DataSimMACD$macd[i-1]>DataSimMACD$signal[i-1] && DataSimMACD$macd[i]>DataSimMACD$signal[i]){portfolioMACD[i]=holdMACD}
if(DataSimMACD$macd[i-1]<DataSimMACD$signal[i-1] && DataSimMACD$macd[i]<DataSimMACD$signal[i]){portfolioMACD[i]=sellMACD}
if(DataSimMACD$macd[i]==DataSimMACD$signal[i]){portfolioMACD[i]=sellMACD}
if(DataSimMACD$macd[i-1]==DataSimMACD$signal[i-1] && DataSimMACD$macd[i]!=DataSimMACD$signal[i]){portfolioMACD[i]=buyMACD}
i <- i+1
}
resultsMACD[j] <- portfolioMACD[length(Sim)]
j <- j+1
}
BASICALLY:
1-One Brownian motion has to consist of 2500 elements, to which both trading strategies have to be applied separately
2-this whole procedure has to be repeated 10000 times for each out of 9 combinations of variables r and sigma (r1sigma1, r1sigma2, r1sigma3,.....,r3sigma3) (this I don't have included in my code yet - no clue how to construct those 2 loops around it all...) :(
3-the endresult should be a 10000x27 matrix with 10000rows (for amount of runs) and 27 colums (9x GBM, RSI, MACD) filled only with the 2500th (endvalue) of each simulation (from point 1.) --> how to do it?
SOS! Could someone of you PLEASE PLEASE PLEASE help me with this mess? I'm completely lost and it's my graduation paper -.-
Every help will be highly praised and deeply appreciated!
Thanks in advance and sorry for the long post.
Cheers from Berlin, Ana :)
EDIT AND ANOTHER SIMPLIFIED EXAMPLE
library(sde)
#Vectors for results
Returns <- rep(0:N)
LogReturns <- rep(0:N)
Simulation <- rep(0:N)
ResultsSimulation <- rep(0:runs)
ResultsReturns <- rep(0:runs)
ResultsLog <- rep(0:runs)
runs=50 #how ofthen the Simulation of GBM should be repeated
i <- 1
j <- 1
#second loop
for(j in 2:runs){
Simulation <- GBM(x, r, sigma, T, N)
x=100
r=0
sigma=1
T=1
N=20
#first loop
for(i in 2:length(BM)){
Returns <- ROC(Simulation)
LogReturns[i+1] <- log(Simulation[i+1]/Simulation[i])
i <- i+1
}
ResultsSimulation[j]<-Simulation[N]
ResultsReturns[j]<-Returns[N]
ResultsLog[j]<-LogReturns[N]
j <- j+1
}
ResultsMatrix <- as.matrix(data.frame(ResultsSimulation, ResultsReturns, ResultsLog))
The structure of this example is basically what I have. I need to construct around it 2 more loops which will do the same simulations and calculations for 3 different "r" values and "sigma" values (variables within the GBM-function). The resuls (the final value of each Simulation and calculation from the first loop) should be saved in separate vectors or in a matrix consisting of those --> thus, 27 vestors of length 50 (3 results for each combination of variables r and sigma)
for example, if sigma=0.1; 0.3; 0,6 and r=0,03; 0,05; 0,08
How to construct those loops around it all and save the data accordingly?
Sorry for the questions guys, but I'm really lost :(
Cheers and thanks a lot in advance! At least for reading ;)

Answer 1

Is this close to what you need? If so, you can add new trading functions to return a portfolio, then just call it (see the example):
warning: It took about 1.5 hours to run with N_SIMS = 100000!
get.simulation.GBM = function(TIME = 10, N = 250, N_SIMS = 1000, x0 = 100, sigma = c(0.1, 0.3, 0.6), r = c(0.03, 0.05, 0.08))
{
require(sde)
params = expand.grid(sigma = sigma, r = r)
# pre-allocate matrix
results = matrix(0, ncol = N_SIMS * nrow(params), nrow = N)
for (i in 1:nrow(params))
{
idx.range = ((i - 1)*N_SIMS + 1):((i - 1)*N_SIMS + N_SIMS)
temp.res = replicate(N_SIMS, GBM(x0, r = params[i, 'r'], sigma = params[i, 'sigma'], T = TIME, N = N - 1 ))
results[, idx.range] = temp.res
}
return(results)
}
apply.MACD = function(serie, nFast = 12, nSlow = 26, nSig = 9, p0 = 100)
{
require(TTR)
roc = ROC(serie)
sim.MACD = MACD(serie, nFast, nSlow, nSig, maType = "EMA")
portfolio = rep(0, length = length(serie))
portfolio[1] = p0
sim.MACD[is.na(sim.MACD)] = 0
sim.MACD = as.data.frame(sim.MACD)
for (i in 2:length(serie))
{
buy = portfolio[i - 1] * exp(roc[i])
sell = portfolio[i - 1]
hold = buy
if(sim.MACD$macd[i - 1] < sim.MACD$signal[i - 1] && sim.MACD$macd[i] > sim.MACD$signal[i]){portfolio[i] = buy}
if(sim.MACD$macd[i - 1] > sim.MACD$signal[i - 1] && sim.MACD$macd[i] < sim.MACD$signal[i]){portfolio[i] = sell}
if(sim.MACD$macd[i - 1] > sim.MACD$signal[i - 1] && sim.MACD$macd[i] > sim.MACD$signal[i]){portfolio[i] = hold}
if(sim.MACD$macd[i - 1] < sim.MACD$signal[i - 1] && sim.MACD$macd[i] < sim.MACD$signal[i]){portfolio[i] = sell}
if(sim.MACD$macd[i] == sim.MACD$signal[i]){portfolio[i] = sell}
if(sim.MACD$macd[i - 1] == sim.MACD$signal[i - 1] && sim.MACD$macd[i] != sim.MACD$signal[i]){portfolio[i] = buy}
}
return(portfolio)
}
apply.RSI = function(serie, p0 = 100, n = 14)
{
require(TTR)
roc = ROC(serie)
sim.RSI = RSI(serie, n = n)
sim.RSI[is.na(sim.RSI)] = 0
portfolio = rep(0, length = length(serie))
portfolio[1] = p0
for (i in 2:length(serie))
{
buy = portfolio[i - 1] * exp(roc[i])
hold = portfolio[i - 1]
if(sim.RSI[i - 1] < 50 && sim.RSI[i] > 50){portfolio[i] = buy}
if(sim.RSI[i - 1] > 50 && sim.RSI[i] < 50){portfolio[i] = hold}
if(sim.RSI[i - 1] < 50 && sim.RSI[i] < 50){portfolio[i] = hold}
if(sim.RSI[i - 1] > 50 && sim.RSI[i] > 50){portfolio[i] = buy}
}
return(portfolio)
}
# Example (this is SLOW)
simulation.matrix = get.simulation.GBM()
portfolio.RSI = apply(simulation.matrix, 2, apply.RSI)
portfolio.MACD = apply(simulation.matrix, 2, apply.MACD)
# if you need only the last values
portfolio.RSI.last = tail(portfolio.RSI, 1)
portfolio.MACD.last = tail(portfolio.MACD, 1)