simulating_reality/main.R at master · SilasFriby/simulating_reality · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89


rm(list = ls()) # clear work space


######## load packages

library(ggplot2) # sweet plots
library(reshape2) # melt function


######## source functions

source("functions/simulate_GBM.R")


######## initialize

x0 <- 100
mu <- 0.02
sigma <- 0.5
n_sim <- 10
end_time <- 1
dt <- 1 / 200
time_vector <- seq(0, end_time, by = dt)


######## read stock price data

sp_100_data <- read.table("data/sp_100_jan_feb_2018.csv",
                          header = TRUE,
                          sep = ";",
                          dec = ",",
                          stringsAsFactors = FALSE)


######## plot closing price

plot(1:dim(sp_100_data)[1], sp_100_data$Close, type = 'l')


######## simulate paths from GBM


simulated_paths <- simulate_GBM(x0 = x0,
                                mu = mu,
                                sigma = sigma,
                                n_sim = n_sim,
                                time_vector = time_vector)


######### plot simulated paths


#### R plot

plot(time_vector, simulated_paths[, 1], type = "l")

#### ggplot

data_plot <- data.frame('time' = time_vector, "GBM" = simulated_paths)
data_plot <- melt(data_plot,  id = c('time'))

plot_gbm <- ggplot(data_plot, aes(time, value)) +
  geom_line(aes(colour = variable)) +
  theme(legend.position = "none")


#### save ggplot

ggsave(filename = "plots/gbm_paths.png",
       plot = plot_gbm)


#### save simulated paths

write.table(
  simulated_paths,
  "results/simulated_paths.csv",
  sep = ";",
  dec = ",",
  row.names = FALSE
)