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Loading Historical Stock Data

13 Nov 2014

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Historical Stock Data is critical for testing your investment strategies. There are many ways to load Historical Stock Data in to your R session. Below I show 4 different approaches to load historical stock data:

  • Download Historical Stock quotes from Yahoo Fiance with getSymbols function from quantmod package
  • Load Historical Stock data from the csv files you saved from Yahoo Fiance
  • Load Historical Stock data from your custom files
  • Load Historical Stock from one csv file, where each column represents one stock

In the code below I demonstrate these 4 methods and use loaded data to run a back-test:

Loading Historical Prices:

	#*****************************************************************
	# Load historical data
	#****************************************************************** 
	library(SIT)
	load.packages('quantmod')
  
	stock.folder = 'c:\\Stocks\\Data\\'
	tickers = spl('UUP,EMB,HYG')
    
	data <- new.env()
        
	# load historical data, select data load method
	data.load.method = 'basic'	
  
	if(data.load.method == 'basic') {       
        # quantmod - getSymbols
        getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)
	}else if(data.load.method == 'basic.local') {
        # if you saved yahoo historical price files localy
        getSymbols.sit(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T, stock.folder = stock.folder)
	}else if(data.load.method == 'custom.local') {
        # custom format historical price files
        for(n in tickers) {
            data[[n]] = read.xts(paste(stock.folder, n, '.csv', sep=''), format='%m/%d/%Y')
        }   
	}else if(data.load.method == 'custom.one.file') {
        # read from one csv file, column headers are tickers
        filename = 'hex.csv'
        all.data = read.xts(paste(stock.folder, filename, sep=''), format='%m/%d/%Y')
        for(n in names(all.data)) {
            data[[n]] = all.data[,n]
            colnames(data[[n]]) = 'Close'
            data[[n]]$Adjusted = data[[n]]$Open = data[[n]]$High = data[[n]]$Low = data[[n]]$Close
        }
	}       
            
	# prepare data for back test
		for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)                            
	bt.prep(data, align='remove.na')
 
   
	#*****************************************************************
	# Code Strategies
	#******************************************************************
	prices = data$prices  
	n = ncol(prices)
   
	models = list()
   
	# find period ends
	period.ends = endpoints(prices, 'months')
		period.ends = period.ends[period.ends > 0]
       
	obj = portfolio.allocation.helper(data$prices, period.ends=period.ends, lookback.len = 250, 
		min.risk.fns = list(EW=equal.weight.portfolio,
                        RP=risk.parity.portfolio(),
                        MV=min.var.portfolio,
                        MC=min.corr.portfolio)
	)

20 , percent = 9.7% 30 , percent = 23.6% 40 , percent = 37.5% 50 , percent = 51.4% 60 , percent = 65.3% 70 , percent = 79.2% 80 , percent = 93.1%

	models = create.strategies(obj, data)$models

EW , percent = 25% Latest weights :

  EMB HYG UUP
2014-12-05 296.3226 369.099 1405.877

Performance summary : CAGR Best Worst 5.4 2.2 -3.1

RP , percent = 50% Latest weights :

  EMB HYG UUP
2014-12-05 294.9029 402.3187 1286.081

Performance summary : CAGR Best Worst 4.4 1.8 -2.7

MV , percent = 75% Latest weights :

  EMB HYG UUP
2014-12-05 291.1793 336.6892 1553.726

Performance summary : CAGR Best Worst 3 1.7 -2.1

MC , percent = 100% Latest weights :

  EMB HYG UUP
2014-12-05 200.0815 200.7362 2503.768

Performance summary : CAGR Best Worst 2 1.8 -2

Finally let’s make a report:

	#*****************************************************************
	# Create Report
	#******************************************************************        
	plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)	    	
		mtext('Cumulative Performance', side = 2, line = 1)

plot of chunk plot-3

	print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T))
  EW RP MV MC
Period Dec2007 - Dec2014 Dec2007 - Dec2014 Dec2007 - Dec2014 Dec2007 - Dec2014
Cagr 5.41 4.45 2.97 2.02
Sharpe 1.11 0.97 0.67 0.43
DVR 1.05 0.92 0.61 0.31
Volatility 4.87 4.59 4.56 4.95
MaxDD -11.05 -7.78 -6.59 -8.15
AvgDD -0.81 -0.82 -0.91 -1.36
VaR -0.41 -0.41 -0.41 -0.48
CVaR -0.74 -0.72 -0.71 -0.74
Exposure 85.12 85.12 85.12 85.12

(this report was produced on: 2014-12-07)

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