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quantmodyahoo-financequantitative-financetradingalgorithmic-trading

Use of adjusted vs.anadjusted prices for stock strategy backtesting?

This is more of a methodological (rather than a programming) issue, yet it feels SO is the right place for it. Following the ups and downs after Yahoo changed its defaults in May 2017 for fetching daily data (discussed on https://github.com/joshuaulrich/quantmod/issues/174, http://blog.fosstrading.com/2017/06/yahoo-finance-alternatives.html and also on SO Why Open,High,Low prices are wrong when using quantmod?) I am probably not the only one not 100% certain which data to use in a backtesting procedure and whether quantmod getSymbols.yahoo and adjustOHLC still provide the relevant data for quality backtesting. Quantmod 0.4.11 also includes AlphaVantage as (adjusted stock) data provider, but I am not familiar with their reliability. How to prepare the (stock and index) data obtained from getSymbols calls? Which data ((stock & dividends) adjusted or unadjusted) should be used? Which transformations do you use? The adjustOHLC function also contains a bug, as it is not split adjusted (easily seen on AAPL by calling

 getSymbols(AAPL)
 chart_Series(adjustOHLC(AAPL))

and observing a jump in 2014.

Solution

  • You should always use adjusted prices. Most of the time when data provider doesn't have adjusted prices then usually provider's close prices are adjusted. There is no point doing backtests on a raw close prices data. I've once made a mistake by downloading close prices instead of adjusted and at the end of backtesting, my strategy told me that among all S&P composites Master Card was the worst performer. After looking at the MA chart it was obvious why.

    enter image description here

    Beacuse of a split on January 22, 2014 my data had a single return over -90%! In conclusion raw close data for backtesting might give you utterly false results.

    How to deal with splits

    Divide every price before a split by split ratio. For example Master Card had 1:10 split ratio so you should divide every price before 21.01.2014 by 10. It's very easy to find splits in a data, you just have to look for returns around or below -50%.

    Dividends

    Subtract from every price before dividend day dividend amount. To find dividends days you need dividends calendar, it's impossible to find them by yourself.