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I see at least two problems. The R package quantstrat is poorly documented. And one must have dividends adjusted data. Otherwise the test results will be irrelevant.

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  • $\begingroup$ No matter what package you're using, dividends adjusted data are going to be a prerequisite usually. It might be caricature but no matter what package you're using, it's also likely to be poorly documented. $\endgroup$ – Lliane Feb 8 '19 at 3:06
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It depends, what alternatives do you have? Quantstrat is useful in certain situations, and the authors did their best to give R a useful backtesting package. You are right, the learning curve is a little bit steep since and it's implementation would perhaps be a little bit less awkward in a typical object oriented design. I have switched to Backtrader in Python which is equally as frustrating at times, although better documented. Regardless, any backtesting library you decide to learn will probably have a learning curve, so it depends what you want to use it for. If your goal is to backtest generally simple trading strategies on a single stock, it will do the job quite well. Adjusted data is reletively easy to come by. I recommend the alphavantage api which is also supported in the quantmod library by setting source='av'

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