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The PMwR package, which I maintain, provides such computations. The package is on CRAN and GitHub/GitLab. Some example code: library("PMwR") trades <- read.table(text=" timestamp , instrument , amount , price 2019-06-25 , Amazon , 20 , 1878 2019-06-26 , Amazon , -10 , 1902 2019-07-01 , Amazon , -10 , 1921 2019-04-15 , ...

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Since the discussion above is not coming to an end, I am going to show a summary of my assumptions. If you don not agree, let me know where exactly you do not agree. 1) The return i) the simple annual return of a 1-year period is: $$r_{1989}=value_{1989}/value_{1988}-1$$ for $$value_{1988}=100, value_{1989}=110, value_{1990}=100$$ we get $$r_{1989}=... 2 You do not have to think too much about return formulas and get confused, just go to the basics. Return is simply:$$ Return = Ending Value / StartingValue - 1  Log returns are used in places where it provides model simplicity in defining returns in a logarithmic format. Also when assumptions are made on log-normality rather than normality. Log returns ...

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If you calculate (1+r1)*(1+r2)-1 = 11.1435% that gives you the TWR (Time Weighted Return) which is one widely used measure of return. It treats all periods equally, no matter the assets involved. I am not familiar with the calculation you do in B17 and C18. If I compute the IRR (internal rate of return) for the cash flows [-1000,-20000,+25810] I get 21.65%...

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