Simple Trading Strategy

library(ggplot2)
library(plotly)
library(rvest)
library(pbapply)
library(TTR)
library(dygraphs)
library(lubridate)
library(tidyquant)
library(timetk)
library(htmlwidgets)
library(kableExtra)
options(dplyr.print_max = 1e9)
pacman::p_load(dygraphs,DT,tidyverse,janitor,ggthemes,scales,ggpubr,viridis)

theme_set(theme_pubclean())

0.0.0.1 Description of A simple Trading Strategy

The simple strategy follows that given a list of stocks, we want download is’s associated data in terms of open and closing prices from an API such as Yahoo Finance. Afterwards we would want to find those stocks has closed at 70 day high (i.e. the last closing price to be the #highest over the past 70 days), and over this period the 50-day MA always #stays above the 100-day MA.

start<-as.Date("2010-01-10")
end<-Sys.Date()




simple_strategy= function(start_date,end_date,tickers){

library(tidyquant) 
library(zoo)  
library(furrr) 
library(tidyverse)  
  


start<-as.Date(start_date)
end <-as.Date(end_date)

p  <- tidyquant::tq_get(tickers , get = "stock.prices", from = start,
           to =end)


p= na.omit(p)
#p <-quantmod:: getSymbols("AMZ", from = start,
#            to = end, warnings = FALSE, src = 'yahoo',
#           auto.assign = TRUE)
#p= get(p)

#p=na.approx(p)



prices <-  p %>%
    tidyquant::tq_mutate(select = close, mutate_fun = SMA, n = 100) %>%
    dplyr::rename(SMA100 = SMA) %>%
    tidyquant::tq_mutate(select = close, mutate_fun = SMA, n = 50) %>%
    dplyr::rename(SMA50 = SMA)


prices<-
   prices%>% dplyr::mutate(rolling70= c(rep(NA,69),rollmax(close, 70)))


df=
prices %>% dplyr::filter(close==rolling70)%>%
      dplyr::filter(SMA50>SMA100)

 
 return(df) 
  
}





# for a single stock ,we can run the strategy function below which returns a dataframe which meets the strategy conditions
tickers <- c("GOOGL")
re=simple_strategy(start,end,tickers)




#For multiple  stocks we can take advantage of functional programming where we can repeat the function for many stock indicators using a parallel version of the map function of the purrr package. The parallel version of the purrr package is the furrr package.
ticker= c("FB", "AAPL", "MSFT","GOOGL","TSLA","AMZ")

result =furrr::future_map_dfr(ticker,
                      function(x) simple_strategy(start_date=start,
                                      end_date=end,
                                      tickers=x))


#Equivalently, uisng a for loop
#result= list()
#for( i in 1:length(ticker)){
#result[[i]]  = simple_strategy(start,end,ticker[i])
#}



result %>%  head()%>%
  kable(escape = F, align = "c") %>%
  kable_styling(c("striped", "condensed"), full_width = F)

symbol	date	open	high	low	close	volume	adjusted	SMA100	SMA50	rolling70
FB	2012-12-13	27.59	28.75	27.43	28.24	81051600	28.24	21.9831	22.7988	28.24
FB	2013-01-04	28.01	28.93	27.83	28.76	72715400	28.76	22.4882	24.7086	28.76
FB	2013-01-07	28.69	29.79	28.65	29.42	83781800	29.42	22.5643	24.9106	29.42
FB	2013-01-09	29.67	30.60	29.49	30.59	104787700	30.59	22.7410	25.2490	30.59
FB	2013-01-10	30.60	31.45	30.28	31.30	95316400	31.30	22.8420	25.4238	31.30
FB	2013-01-11	31.28	31.96	31.10	31.72	89598000	31.72	22.9605	25.6194	31.72

library(viridis)
library(scales)

#start <- end - weeks(24)


start<-as.Date("2010-01-10")
end<-Sys.Date()


ticker= c("FB", "AAPL", "MSFT","GOOGL")

 p1 <- tidyquant::tq_get(ticker, 
                        get = "stock.prices", 
                        from = start,
                         to =end) %>%
    #filter(date >= start - days(2 * 20)) %>%
    ggplot(aes(x = date, y = close, 
               open = open, high = high, low = low, close = close, 
               group = symbol)) +
    geom_barchart( colour_up = "#FDE725FF",
  colour_down = "#440154FF",
  fill_up = "#FDE725FF",
  fill_down = "#440154FF") +
    geom_bbands(ma_fun = SMA, sd = 2, n = 50, linetype = 5,color_ma = "#55C667FF") +
    labs(title = "50 Day Simple Moving Average of Several Stocks ", 
         subtitle = "BBands with SMA Applied", 
         y = "Closing Price", x = "") + 
   
    coord_x_date(xlim = c(start, end)) +
    facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
  
    #scale_color_viridis_d(option="D") +
  
  scale_x_date(labels = date_format("%d-%m-%Y"),date_breaks = "2 year") +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
   labs(caption = "www.restsanalytics.com")

    
p1

#(gg <- ggplotly(p1, tooltip = "symbol"))
#gg=highlight(gg, "plotly_hover")
#htmlwidgets::saveWidget(as_widget(gg), "p1.html")
#gg

ticker= c("FB", "AAPL", "MSFT","GOOGL")

p2 <-  tidyquant::tq_get(ticker, 
                        get = "stock.prices", 
                        from = start,
                         to =end) %>%
    #filter(date >= start - days(2 * 20)) %>%
    ggplot(aes(x = date, y = close, 
               open = open, high = high, low = low, close = close, 
               group = symbol)) +
    geom_barchart( colour_up = "#FDE725FF",
  colour_down = "#440154FF",
  fill_up = "#FDE725FF",
  fill_down = "#440154FF") +
    geom_bbands(ma_fun = SMA, sd = 2, n = 100, linetype = 5,color_ma = "#55C667FF") +
    labs(title = "100 Day Simple Moving Average of Several Stocks ", 
         subtitle = "BBands with SMA Applied", 
         y = "Closing Price", x = "") + 
   
    coord_x_date(xlim = c(start, end)) +
    facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
  
    #scale_color_viridis_d(option="D") +
  
  scale_x_date(labels = date_format("%d-%m-%Y"),date_breaks = "2 year") +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
   labs(caption = "www.restsanalytics.com")

#gg <- ggplotly(p2, tooltip = "symbol")

#gg=highlight(gg, "plotly_hover")

#htmlwidgets::saveWidget(as_widget(gg), "p2.html")

p2