tidyquant part 3
## Download financial data with tidyquant ## Perform various data manipulations with dplyr ## join and pivot datasets, CAPM computation ## Athanassios Stavrakoudis ## 29 Oct 2021 # load required libraries library(tidyquant) library(tidyverse) # store in a variable for further use FB <- tq_get("FB", from = "2015-01-01", to = Sys.Date()) # min max per quarter max_close_FB <- FB %>% tq_transmute(select = close, mutate_fun = apply.quarterly, FUN = max, col_rename = "max_close") min_close_FB <- FB %>% tq_transmute(select = close, mutate_fun = apply.quarterly, FUN = min, col_rename = "min_close") mm_close_FB <- inner_join(min_close_FB, max_close_FB, by = "date") mm_close_FB # compute daily returns of Nasdaq and FB, adjusted prices # Capital asset pricing model NDAQ <- tq_get("NDAQ", get = "stock.prices", from = "2015-01-01", to = Sys.Date()) Ra <- FB %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "daily", col_rename = "rFB") Rb <- NDAQ %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "daily", col_rename = "rNDAQ") RaRb <- left_join(Ra, Rb, by = "date") RaRb # Compute CAPM RaRb %>% tq_performance(Ra = rFB, Rb = rNDAQ, performance_fun = table.CAPM) # transpose the table results RaRb %>% tq_performance(Ra = rFB, Rb = rNDAQ, performance_fun = table.CAPM) %>% t()
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Αθανάσιο Σταυρακούδη
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