# Help Bruno 03-09-2021

# Own thoughts: Why not use k-means clustering to determine portfolio size.

library(tidyverse)
library(lubridate)

df = readxl::read_xlsx(choose.files())

df = df %>% 
  select(Px, Rule, `Stop Trigger`)

test = df[, 1]

test2 = test %>% 
  mutate(Rule = NaN,
         Stop = NaN)

test2[1, 2] = 1
test2[1, 3] = test2[1, 1]

test2 = data.frame(Px = rnorm(2500, 100, 15),
                   Cash = rnorm(2500, 100, 2))

temp = data.frame(test2$Px, test2$Cash)
Rets = (temp[-1, ]/temp[-nrow(temp), ])-1

Brunos_rule = function(Px, Cash, start_rule, Bear, Bull){
  temp = data.frame(Px, Cash)
  Rets = (temp[-1, ]/temp[-nrow(temp), ])-1
  df = data.frame(Px) %>% 
    mutate(Rule = NaN,
           Stop = NaN)
  
  df$Rule = start_rule
  df$Stop = df[1, 1]
  
  for(x in 2:nrow(df)){
    if(df$Rule[x-1] == 1){
      df$Rule[x] = if_else(df$Px[x] <= df$Stop[x-1]*(1+Bear), 0, df$Rule[x-1])
      df$Stop[x] = if_else(df$Rule[x] != df$Rule[x-1], df$Px[x], max(df$Stop[x-1], df$Px[x]))
    } else{
      df$Rule[x] = if_else(df$Px[x] > df$Stop[x-1]*(1+Bull), 1, df$Rule[x-1])
      df$Stop[x] = if_else(df$Rule[x] != df$Rule[x-1], df$Px[x], min(df$Stop[x-1], df$Px[x]))
    }
  }
  Ans = if_else(df$Rule[-1] == 1, Rets$Px, Rets$Cash)
  return(Ans)
}
library(tictoc)
tic()
Brunos_rule(test2$Px, test2$Cash, 1, -0.05, 0.05)
toc()

bears = seq(-0.2, -0.05, 0.025)
bulls = seq(0.05, 0.2, 0.025)
combinations = as.data.frame(t(expand.grid(bears, bulls)))
names(combinations) = paste0("Bear", rep(bears, 7), "/Bulls", rep(bulls, each = 7))

results = map_dfc(combinations, function(x){
  ans = Brunos_rule(test2$Px, test2$Cash, 1, x[1], x[2])
})

example = results %>% 
  mutate(Date = ymd("2000-01-01") + 1:2499, .before = 1) %>% 
  gather(key = "iteration", value = "Returns", -1)
  
ggplot(example, aes(x = Date, y = Returns, colour = iteration))+
  geom_line()+
  guides(color = "none")