To be fair, there are good native R backtesting solutions, such as Quantstrat. Plus, it certainly isn’t simple to experiment with strategy design – for instance, incorporating costs, trading at multiple levels, using a timed exit, or incorporating other trade filters.
Setting up anything more advanced than the simplest possible vectorised backtesting framework is tough going and error-prone. We saw that while R makes it easy to implement a relatively advanced algorithm like the Kalman filter, there are drawbacks to using it as a backtesting tool. In our previous post, we looked into implementing a Kalman filter in R for calculating the hedge ratio in a pairs trading strategy.
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