periodReturn calculation method
Closed this issue · 3 comments
Not really an issue, just a confirmation if possible on how for example periodReturn = "weekly" works as I couldn't find in the documentation.
Minimal, reproducible example
In the example below from Yahoo Finance historical data, the weekly return is 33.3%. So it must be taking the Adj Close or Close from previous week close to calculate return - is that correct? As the current week starting on 10th of July shows now change.
| Date | Open | High | Low | Close | Adj Close | Volume |
|---|---|---|---|---|---|---|
| 12/07/2023 | 0.004 | 0.004 | 0.004 | 0.004 | 0.004 | 125000 |
| 11/07/2023 | 0.004 | 0.004 | 0.0035 | 0.004 | 0.004 | 2475371 |
| 10/07/2023 | 0.004 | 0.004 | 0.0035 | 0.004 | 0.004 | 3139964 |
| 7/07/2023 | 0.004 | 0.004 | 0.003 | 0.003 | 0.003 | 1135640 |
library(quantmod)
getSymbols(Symbols = "VAL.AX", src = "yahoo")
weeklyReturn(VAL.AX, subset = '2023-06::', type = 'arithmetic', leading = TRUE)Session Info
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22621)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8 LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] geomtextpath_0.1.1 patchwork_1.1.2 showtext_0.9-5 showtextdb_3.0 sysfonts_0.8.8
[6] ggtext_0.1.2 readxl_1.4.2 scales_1.2.1 forcats_1.0.0 stringr_1.5.0
[11] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[16] ggplot2_3.4.2 tidyverse_2.0.0 tidyquant_1.0.5 quantmod_0.4.20 TTR_0.24.3
[21] PerformanceAnalytics_2.0.4 xts_0.12.1 zoo_1.8-11 lubridate_1.9.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 class_7.3-20 timetk_2.8.1 snakecase_0.11.0 markdown_1.7 gridtext_0.1.5 rstudioapi_0.14
[8] listenv_0.8.0 furrr_0.3.1 farver_2.1.1 alphavantager_0.1.2 dials_1.1.0 scico_1.3.1 ggrepel_0.9.1
[15] bit64_4.0.5 prodlim_2019.11.13 fansi_1.0.3 xml2_1.3.4 codetools_0.2-18 splines_4.2.1 knitr_1.41
[22] jsonlite_1.8.4 workflows_1.1.3 yardstick_1.1.0 tune_1.0.1 compiler_4.2.1 httr_1.4.6 Matrix_1.5-1
[29] fastmap_1.1.0 cli_3.5.0 htmltools_0.5.4 tools_4.2.1 gtable_0.3.1 glue_1.6.2 Rcpp_1.0.9
[36] cellranger_1.1.0 DiceDesign_1.9 vctrs_0.6.2 iterators_1.0.14 parsnip_1.0.4 timeDate_4021.106 gower_1.0.0
[43] xfun_0.39 globals_0.16.1 timechange_0.2.0 lifecycle_1.0.3 future_1.28.0 MASS_7.3-58.1 ipred_0.9-13
[50] vroom_1.6.0 ragg_1.2.5 hms_1.1.3 parallel_4.2.1 yaml_2.3.6 curl_4.3.3 rpart_4.1.16
[57] stringi_1.7.8 foreach_1.5.2 lhs_1.1.6 hardhat_1.2.0 lava_1.6.10 rlang_1.1.1 pkgconfig_2.0.3
[64] commonmark_1.9.0 systemfonts_1.0.4 rsample_1.1.0 evaluate_0.19 lattice_0.20-45 labeling_0.4.2 recipes_1.0.1
[71] cowplot_1.1.1 bit_4.0.4 tidyselect_1.2.0 parallelly_1.32.1 magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[78] pillar_1.9.0 withr_2.5.0 survival_3.4-0 nnet_7.3-18 future.apply_1.9.1 janitor_2.1.0 crayon_1.5.2
[85] MetBrewer_0.2.0 Quandl_2.11.0 utf8_1.2.2 tzdb_0.3.0 rmarkdown_2.19 grid_4.2.1 digest_0.6.31
[92] textshaping_0.3.6 GPfit_1.0-8 munsell_0.5.0 quadprog_1.5-8 Could you give me an example that doesn't require tidyquant? That would make it easier for me to look into your question.
Done :-)
Thanks. It's easier to see what's going on if you use something that trades more often, like SPY. You can see below that the output of weeklyReturn() is the discrete return based on the close price. You could call weeklyReturn(Ad(x)) if you wanted the weekly adjusted returns.
x <- getSymbols("SPY", auto.assign = FALSE)
r <- weeklyReturn(x, subset = '2023-06::')
y <- merge(x, r, join = "right")
tail(merge(y, returns = drop(Cl(y/lag(y)-1))), 20)
## SPY.Open SPY.High SPY.Low SPY.Close SPY.Volume SPY.Adjusted weekly.returns returns
## 2023-06-02 424.50 428.74 423.95 427.92 91366700 426.3363 0.018808687 NA
## 2023-06-09 429.96 431.99 428.87 429.90 85742800 428.3090 0.004626987 0.004626987
## 2023-06-16 443.02 443.61 438.97 439.46 114121300 439.4600 0.022237724 0.022237724
## 2023-06-23 432.93 435.06 432.47 433.21 92074500 433.2100 -0.014222000 -0.014222000
## 2023-06-30 441.44 444.30 441.11 443.28 104921500 443.2800 0.023245095 0.023245095
## 2023-07-07 438.63 442.64 438.30 438.55 86076100 438.5500 -0.010670481 -0.010670481
## 2023-07-14 450.48 451.36 448.49 449.28 69761800 449.2800 0.024467019 0.024467019I'm going to close this since it isn't something that needs to change in quantmod. But I'm happy to continue the conversation.