MilindRCodes · June 9, 2016 05:45 · Jun 9, 2016
diff --git a/Shorting at High.R b/Shorting at High.R
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+#####################################################################################
+#                         Strategy - Shorting at High                               #
+#####################################################################################
+
+# Upload the libraries and make other initial settings ##########################################################################################
+
+library(xlsx);library(zoo);library(quantmod);library(rowr);library(TTR);
+
+system.time({
+
+######################################## Part 1 of the coDe ############################################
+
+# Set the parameters here ###########################################################################################################################################
+
+pc_up_level = 2 # Set the high percentage level here 
+noDays = 2      # No of lookback days, Google finance can go back upto 15 days for 1-minute data.
+
+# Read the xlsx file that contains the Stock tickers that are to be backtested ##################################################################################################################################################################
+
+test_tickers = read.csv("F&O Stock List.csv")
+symbol = as.character(test_tickers[,4])           
+
+
+leverage = as.character(test_tickers[,5])         
+
+sig_finds = data.frame(0,0,0,0,0,0)
+colnames(sig_finds) = c("Ticker","Leverage","Previous Price","Current Price","Abs Change","% Change")
+
+for(s in 1:length(symbol)){
+
+print(s) 
+
+source("Stock price data.R") # Custom function to download stock data
+intraday_price_data(symbol[s],noDays)
+
+dirPath = paste(getwd(),"/",sep="")                # Specify the directory 
+fileName = paste(dirPath,symbol[s],".csv",sep="")  # Specify the filename 
+data = as.data.frame(read.csv(fileName))           # Read the downloaded file
+
+# When the code is run on the trading day, the last price will point
+# to yesterday's closing price. This price is to be extracted to compute
+# the % change in today's current price.
+data_close =subset(data, select=(CLOSE), subset=(TIME == 1530))
+pdp = tail(data_close,1)[,1]
+
+# When the code is run on the trading day, the last price will point
+# to the latest price. Use this price to compute the latest percentage 
+# change for each stock in the stock list.
+
+#(Please note: when the code is run during non-trading hours,
+#(both the pdp and the lp will be the same.)
+lp = tail(data,1)[,3]
+
+# Compute Percentage change for each stock & include in the dataframe only if significant #############################################
+
+# Compute absolute and the percentage change
+apc = round((lp - pdp),2)
+pc = round(((lp - pdp) / pdp) * 100,2)
+
+if (pc > pc_up_level ){
+
+# Create a empty data frame, only if the Percentage change is significant
+# fill the data frame with symbols price changes
+sig_finds_temp = data.frame(0,0,0,0,0,0)  
+
+# Only if the change is significant, the respective stock data will be entered
+# in the data frame being created below.
+colnames(sig_finds_temp) = c("Ticker","Leverage","Previous Price","Current Price","Abs Change","% Change")
+sig_finds_temp = c(symbol[s],leverage[s],pdp,lp,apc,pc) 
+sig_finds = rbind(sig_finds, sig_finds_temp)  # combines the previous and current data frame
+rm(sig_finds_temp)                            # deletes the temporary data frame 
+
+print(sig_finds)
+
+}
+
+# Deletes the downloaded stock price files for each ticker after processing the data
+unlink(fileName)     
+
+}  # Closes the for loop
+
+# Write the results in an excel sheet
+write.xlsx(sig_finds,"Shorting at High.xlsx") 
+
+######################################## Part 2 of the code ############################################
+
+# Compute the different metrics which will be used in evaluating the best stock to Short #################################################
+
+source("Stock price data.R")         # Following lines pull NIFTY data
+daily_price_data("NIFTY",1)          # Number of years to lookback into eg. 1 = 1 year
+
+dirPath = paste(getwd(),"/",sep="") 
+fileName = paste(dirPath,"NIFTY",".csv",sep="")
+
+nifty_data = as.data.frame(read.csv(file = fileName))
+
+test_tickers = read.xlsx("Shorting at High.xlsx",header=TRUE, 1, startRow=1, as.data.frame=TRUE)
+t = nrow(test_tickers)
+
+test_tickers$Count = 0
+test_tickers$Avg_high = 0
+test_tickers$Avg_decline = 0
+test_tickers$Next_3d_Change = 0
+test_tickers$Low_1Yr = 0
+test_tickers$High_1Yr = 0
+test_tickers$Neg_Last15_days = 0
+test_tickers$RSI = 0
+test_tickers$NIFTY_Correlation = 0
+
+symbol = as.character(test_tickers[2:t,2])
+noDays = 1
+
+for (s in 1:length(symbol)){
+
+  print(s)
+
+  table_sig = data.frame(0,0,0,0,0,0,0)  
+  colnames(table_sig) = c("Date","Days High in %","Days Close in %","RSI","Entry Price","Exit Price","Profit/Loss")
+
+  source("Stock price data.R")
+  daily_price_data(symbol[s],noDays)                 # Using the built-in function
+  dirPath = paste(getwd(),"/",sep="")  
+  fileName = paste(dirPath,symbol[s],".csv",sep="") 
+  data = as.data.frame(read.csv(file = fileName))    # Read the file 
+  N  = nrow(data)
+
+  # Initializing variables to zero
+  nc = 0
+  Cum_hc_diff = 0 
+  Cum_threshold_per = 0  
+  Cum_next_3d_change = 0 
+
+  diff = data$HIGH - data$CLOSE
+
+  rsi_data = RSI(data$CLOSE, n = 14, maType="WMA", wts=data[,"VOLUME"])
+
+  for (i in 2:N) {
+
+    days_close = (data$CLOSE[i] - data$CLOSE[i-1])*100/data$CLOSE[i-1]
+    days_high = (data$HIGH[i] - data$CLOSE[i-1])*100/data$CLOSE[i-1]
+
+    condition = ((days_high > pc_up_level) == TRUE)
+    if (condition)
+    {
+      nc  = nc + 1 
+
+      date_table = data$DATE[i]
+
+      high_per_table = round(days_high,2)
+      close_price_per_table = format(round(days_close,2),nsmall = 2)
+
+      rsi_table = format(round(rsi_data[i],2),nsmall = 2)
+
+      entry_price =  format(round(data$CLOSE[i-1]*(1+(pc_up_level/100)),2),nsmall = 2)
+      exit_price =  format(round(data$CLOSE[i],2),nsmall = 2)
+      pl_trade = format(round(((data$CLOSE[i-1]*(1+(pc_up_level/100))) - data$CLOSE[i]),2),nsmall = 2)
+
+      hc_diff = diff[i]  # diff between day's high and day's close whenever the price crossed the threshold level.
+      Cum_hc_diff = Cum_hc_diff + hc_diff  # Cumulative of the diff for all trades
+      Average_hc_diff = round(Cum_hc_diff / nc , 2) # Computing the average for diff between high and low.
+
+      Cum_threshold_per = Cum_threshold_per + days_high  # Computes the total of Day's high's whenever price crossed the threshold.
+      Average_threshold_per =   round(Cum_threshold_per / nc , 2)  # Average of High's
+
+      if ( i < (N - 5)){   # Captures the average next 3-day price movement
+        next_3d_change = data$CLOSE[i+3] - data$CLOSE[i]
+        Cum_next_3d_change = Cum_next_3d_change + (data$CLOSE[i+3] - data$CLOSE[i])
+        Average_next_3d_change =  round(Cum_next_3d_change / nc,2)
+      }
+
+      # Create a temporary dataframe to add values and then later merge with the final table 
+      table_temp = data.frame(0,0,0,0,0,0,0)  
+      colnames(table_temp) = c("Date","Days High in %","Days Close in %","RSI","Entry Price","Exit Price","Profit/Loss")
+      table_temp = c(date_table,high_per_table,close_price_per_table,rsi_table,entry_price,exit_price,pl_trade) 
+      table_sig = rbind(table_sig, table_temp) 
+      rm(table_temp)
+
+    } 
+
+
+  }
+
+  table_sig = table_sig[order(table_sig$Date),]
+
+  name = paste(symbol[s]," past performance",".xlsx",sep="")
+  write.xlsx(table_sig,name) # Write the results in an excel sheet
+
+  # load it back to format the excel sheet for column width
+  wb = loadWorkbook(name)
+  sheets = getSheets(wb)
+  autoSizeColumn(sheets[[1]], colIndex=1:16)
+  saveWorkbook(wb,name)
+
+  # Price performance in the last 15 days, checks for many days the price change was negative.  
+  nc_last_15 = 0 
+  for ( p in (N-15):N){
+
+    condition_1 = (((data$CLOSE[p] - data$CLOSE[p-1])< 0 ) == TRUE )
+    if (condition_1)
+    {
+      nc_last_15 = nc_last_15 + 1
+    }
+  }  
+
+  # Correlation between the ticker and NIFTY 
+  Cor_coeff = round(cor(data$CLOSE , nifty_data$CLOSE),2)
+
+  # Filling the Shorting at High.xlsx with the computed metrics
+
+  e = s + 1
+  test_tickers$Count[e] = nc
+  test_tickers$Avg_high[e] = format(Average_threshold_per,nsmall = 2) # Indicates the Avg. percentage above the threshold in the past
+  test_tickers$Avg_decline[e] = format(Average_hc_diff,nsmall = 2)    # Indicates the Avg. decline in Absolute rupee terms from the Avg. high to the closing price in the past  test_tickers$Next_3d_Change[e] = round(Average_next_3d_change,2) # Indicates the next 3 day price movement.
+  test_tickers$Next_3d_Change[e] = format(Average_next_3d_change,nsmall = 2)
+  test_tickers$Low_1Yr[e] = format(min(data$CLOSE),nsmall = 2)        # 1 year low price
+  test_tickers$High_1Yr[e] = format(max(data$CLOSE),nsmall = 2)       # 1 year high price
+  test_tickers$Neg_Last15_days[e] = nc_last_15     # Indicates how many times in the last 15 days has the price been negative.
+  test_tickers$RSI[e] = round(tail(rsi_data,1),2)
+  test_tickers$NIFTY_Correlation[e] = Cor_coeff    # Computes the correlation between the Stock and NIFTY.
+
+  unlink(fileName) # Deletes the downloaded stock price files for each ticker after processing the data
+
+  rm("data","table_sig")
+
+}  # This closes the for loop 
+
+test_tickers = test_tickers[-1,-1]
+colnames(test_tickers) = c("Ticker", "Leverage","Previous Price","Current Price","Abs. Change","% Change","Count","Avg.High %","Abs Avg.Decline","Next 3-d price move","1-yr low","1-yr high","-Ve last 15-days","RSI","Nifty correlation")
+
+# Write and format the final output file ##########################################################################################
+
+write.xlsx(test_tickers,"Shorting at High.xlsx") # Write the final output back to the same file.
+
+wb = loadWorkbook("Shorting at High.xlsx")
+sheets = getSheets(wb)
+autoSizeColumn(sheets[[1]], colIndex=1:19)
+saveWorkbook(wb,"Shorting at High.xlsx")
+
+#options(warn = oldw)
+
+})
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