knitr::opts_chunk$set(echo = TRUE) library(quantmod) library("PerformanceAnalytics") library("Hmisc") library(kableExtra) library(PMwR) managers <- readRDS("../data/myCota.rds") df <- managers[, c(7,10)] df$Cota <- PMwR::returns(df$cota) df$Ibov <- PMwR::returns(df$IBOV) managers <- df[2:nrow(df), c(3,4)] #managers <- managers[, c("varPerc", "rIBOV")] colnames(managers) <- c("Cota", "IBOV") managers.length = dim(managers)[1] trailing12.rows = ((managers.length - 11):managers.length) trailing36.rows = ((managers.length - 35):managers.length) trailing60.rows = ((managers.length - 59):managers.length)
Primeiramente vejamos um resumo da cota comparada com o IBOV
charts.PerformanceSummary(managers[,c("Cota", "IBOV")], colorset = rich6equal)
O cálculo aqui não está correto
# prettify with format.df in hmisc package df <- table.CalendarReturns(managers[,c(1,2)], as.perc = TRUE) df %>% kableExtra::kbl(booktabs = T, escape = F, digits = c(2,2,2), align = "r")%>% kableExtra::column_spec(1, bold=T) %>% kableExtra::collapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width = F,font_size = 12) # }
df <- table.Stats(managers[,c("Cota", "IBOV")]) df %>% kableExtra::kbl(booktabs = T, escape = F, digits = c(2,2,2), align = "r")%>% kableExtra::column_spec(1, bold=T) %>% kableExtra::collapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width =F,font_size = 12) # }
chart.Boxplot(managers[trailing36.rows, c("Cota", "IBOV")])
layout(rbind(c(1,2),c(3,4))) chart.Histogram(managers[,1,drop=F], main = "Plain", methods = NULL) chart.Histogram(managers[,1,drop=F], main = "Density", breaks=40, methods = c("add.density", "add.normal")) chart.Histogram(managers[,1,drop=F], main = "Skew and Kurt", methods = c("add.centered", "add.rug")) chart.Histogram(managers[,1,drop=F], main = "Risk Measures", methods = c("add.risk"))
chart.RiskReturnScatter(managers[trailing36.rows,1:2], Rf=.03/12, main = "Trailing 36")
charts.RollingPerformance(managers[, c("Cota", "IBOV")])
chart.RelativePerformance(managers[ ,"Cota", drop = FALSE], managers[ , "IBOV", drop = FALSE],colorset = rich12equal)
df <- table.CAPM(managers[trailing36.rows, c("Cota", "IBOV")], managers[ trailing36.rows, 2, drop=FALSE], Rf=.03/12) df %>% kableExtra::kbl(booktabs = T, escape = F, digits = c(2,2,2), align = "r")%>% kableExtra::column_spec(1, bold=T) %>% kableExtra::collapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width =F,font_size = 12)
charts.RollingRegression(managers[ ,"Cota", drop = FALSE], managers[ , "IBOV", drop = FALSE],colorset = rich12equal)
chart.RollingCorrelation(managers[ ,"Cota", drop = FALSE], managers[ , "IBOV", drop = FALSE],colorset = rich12equal)
df <- table.Correlation(managers[ ,"Cota", drop = FALSE], managers[ , "IBOV", drop = FALSE], legend.loc = "lowerleft") df %>% kableExtra::kbl(booktabs = T, escape = F, digits = c(2,2,2), align = "r")%>% kableExtra::column_spec(1, bold=T) %>% kableExtra::collapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width =F,font_size = 12)
df <- table.DownsideRisk(managers[,1:2],Rf=.03/12) df %>% kableExtra::kbl(booktabs = T, escape = F, digits = c(2,2,2), align = "r")%>% kableExtra::column_spec(1, bold=T) %>% kableExtra::collapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width =F,font_size = 12)
df <- table.Drawdowns(managers[,1,drop=F]) df %>% kableExtra::kbl(booktabs = T, escape = F, digits = c(2,2,2), align = "r")%>% kableExtra::column_spec(1, bold=T) %>% kableExtra::collapse_rows(columns = 1, latex_hline = "major", valign = "middle")%>% kableExtra::kable_styling(bootstrap_options = c("striped", "hover"), full_width =F,font_size = 12)
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