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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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closed as primarily opinion-based by skoestlmeier, Alex C, byouness, LocalVolatility, Lliane Feb 8 at 3:06

Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ 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 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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