Updated README

README.md

@@ -1,3 +1,4 @@
+
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
 # fixedincome
@@ -25,13 +26,17 @@ with interest rates using `fixedincome`.
 
 You can install from CRAN with:
 
-    install.packages("fixedincome")
+``` r
+install.packages("fixedincome")
+```
 
 You can install the development version of fixedincome from
 [GitHub](https://github.com/) with:
 
-    # install.packages("devtools")
-    devtools::install_github("wilsonfreitas/R-fixedincome")
+``` r
+# install.packages("devtools")
+devtools::install_github("wilsonfreitas/R-fixedincome")
+```
 
 ## Examples
 
@@ -57,148 +62,176 @@ restriction can be reviewed in the future.
 Given that let’s declare an annual spot rate with a `simple`
 compounding, an `actual/360` and the `actual` calendar.
 
-    library(fixedincome)
-    sr <- spotrate(0.06, "simple", "actual/360", "actual")
-    sr
-    #> [1] "0.06 simple actual/360 actual"
+``` r
+library(fixedincome)
+sr <- spotrate(0.06, "simple", "actual/360", "actual")
+sr
+#> [1] "0.06 simple actual/360 actual"
+```
 
 Compound the spot rate for 7 months.
 
-    compound(sr, 7, "months")
-    #> [1] 1.035
+``` r
+compound(sr, 7, "months")
+#> [1] 1.035
+```
 
 Also compound using dates.
 
-    compound(sr, as.Date("2022-02-23"), as.Date("2022-12-28"))
-    #> [1] 1.051333
+``` r
+compound(sr, as.Date("2022-02-23"), as.Date("2022-12-28"))
+#> [1] 1.051333
+```
 
 Spot rates can be put inside data.frames.
 
-    library(dplyr)
-    library(fixedincome)
-
-    df <- tibble(
-      rate = spotrate(rep(10.56 / 100, 5),
-        compounding = "discrete",
-        daycount = "business/252",
-        calendar = "Brazil/ANBIMA"
-      ),
-      terms = term(1:5, "years")
-    )
-
-    df
-    #> # A tibble: 5 x 2
-    #>   rate                                       terms  
-    #>   <SpotRate>                                 <Term> 
-    #> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year 
-    #> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years
-    #> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years
-    #> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years
-    #> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years
+``` r
+library(dplyr)
+library(fixedincome)
+
+df <- tibble(
+  rate = spotrate(rep(10.56 / 100, 5),
+    compounding = "discrete",
+    daycount = "business/252",
+    calendar = "Brazil/ANBIMA"
+  ),
+  terms = term(1:5, "years")
+)
+
+df
+#> # A tibble: 5 x 2
+#>   rate                                       terms  
+#>   <SpotRate>                                 <Term> 
+#> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year 
+#> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years
+#> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years
+#> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years
+#> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years
+```
 
 The tidyverse verbs can be easily used with `SpotRate` and `Term`
 classes.
 
-    df |> mutate(fact = compound(rate, terms))
-    #> # A tibble: 5 x 3
-    #>   rate                                       terms    fact
-    #>   <SpotRate>                                 <Term>  <dbl>
-    #> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year   1.11
-    #> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years  1.22
-    #> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years  1.35
-    #> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years  1.49
-    #> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years  1.65
+``` r
+df |> mutate(fact = compound(rate, terms))
+#> # A tibble: 5 x 3
+#>   rate                                       terms    fact
+#>   <SpotRate>                                 <Term>  <dbl>
+#> 1 0.1056 discrete business/252 Brazil/ANBIMA 1 year   1.11
+#> 2 0.1056 discrete business/252 Brazil/ANBIMA 2 years  1.22
+#> 3 0.1056 discrete business/252 Brazil/ANBIMA 3 years  1.35
+#> 4 0.1056 discrete business/252 Brazil/ANBIMA 4 years  1.49
+#> 5 0.1056 discrete business/252 Brazil/ANBIMA 5 years  1.65
+```
 
 ### Spot rate curves
 
 Let’s create a spot rate curve using web scraping (from B3 website)
 
-    source("examples/utils-functions.R")
-    curve <- get_curve_from_web("2022-02-23")
-    curve
-    #>          SpotRateCurve
-    #> 1 day           0.1065
-    #> 3 days          0.1064
-    #> 25 days         0.1111
-    #> 44 days         0.1138
-    #> 66 days         0.1168
-    #> 87 days         0.1189
-    #> 108 days        0.1207
-    #> 131 days        0.1220
-    #> 152 days        0.1227
-    #> 172 days        0.1235
-    #> # ... with 29 more rows
-    #> discrete business/252 Brazil/ANBIMA 
-    #> Reference date: 2022-02-23
+``` r
+source("examples/utils-functions.R")
+curve <- get_curve_from_web("2022-02-23")
+curve
+#>          SpotRateCurve
+#> 1 day           0.1065
+#> 3 days          0.1064
+#> 25 days         0.1111
+#> 44 days         0.1138
+#> 66 days         0.1168
+#> 87 days         0.1189
+#> 108 days        0.1207
+#> 131 days        0.1220
+#> 152 days        0.1227
+#> 172 days        0.1235
+#> # ... with 29 more rows
+#> discrete business/252 Brazil/ANBIMA 
+#> Reference date: 2022-02-23
+```
 
 `SpotRateCurve` plots can be easily done by calling `plot`.
 
-    plot(curve)
+``` r
+plot(curve)
+```
 
 <img src="man/figures/README-unnamed-chunk-10-1.png" width="100%" />
 
 For another date.
 
-    curve <- get_curve_from_web("2011-02-23")
-    plot(curve)
+``` r
+curve <- get_curve_from_web("2011-02-23")
+plot(curve)
+```
 
 <img src="man/figures/README-unnamed-chunk-11-1.png" width="100%" />
 
 It can show the forward rates for the short term by selecting the first
 two years.
 
-    curve <- get_curve_from_web("2022-02-23")
-    plot(fixedincome::first(curve, "2 years"), show_forward = TRUE)
+``` r
+curve <- get_curve_from_web("2022-02-23")
+plot(fixedincome::first(curve, "2 years"), show_forward = TRUE)
+```
 
 <img src="man/figures/README-unnamed-chunk-12-1.png" width="100%" />
 
 Once interpolation is set, it can be used in the plot.
 
-    interpolation(curve) <- interp_flatforward()
-    plot(
-      fixedincome::first(curve, "2 years"),
-      use_interpolation = TRUE, legend_location = "bottomright"
-    )
+``` r
+interpolation(curve) <- interp_flatforward()
+plot(
+  fixedincome::first(curve, "2 years"),
+  use_interpolation = TRUE, legend_location = "bottomright"
+)
+```
 
 <img src="man/figures/README-unnamed-chunk-13-1.png" width="100%" />
 
 Parametric models like the Nelson-Siegel-Svensson model can be fitted to
 the curve.
 
-    interpolation(curve) <- fit_interpolation(
-      interp_nelsonsiegelsvensson(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), curve
-    )
+``` r
+interpolation(curve) <- fit_interpolation(
+  interp_nelsonsiegelsvensson(0.01, 0.01, 0.01, 0.01, 0.01, 0.01), curve
+)
 
-    interpolation(curve)
-    #> <Interpolation: nelsonsiegelsvensson > 
-    #>  Parameters:
-    #>   beta1   beta2   beta3   beta4 lambda1 lambda2 
-    #> 0.09283 0.01325 0.04613 0.05007 0.00022 0.01092
+interpolation(curve)
+#> <Interpolation: nelsonsiegelsvensson > 
+#>  Parameters:
+#>   beta1   beta2   beta3   beta4 lambda1 lambda2 
+#>   0.300  -0.181  -0.858   0.611   0.053   0.054
+```
 
 Once set to the curve it is used in the plot to show daily forward
 rates.
 
-    plot(curve, use_interpolation = TRUE, show_forward = TRUE)
+``` r
+plot(curve, use_interpolation = TRUE, show_forward = TRUE)
+```
 
 <img src="man/figures/README-unnamed-chunk-15-1.png" width="100%" />
 
 The interpolation can be changed in order to compare different
 interpolations and the effects in forward rates.
 
-    interpolation(curve) <- interp_flatforward()
-    plot(
-      curve,
-      use_interpolation = TRUE, show_forward = TRUE,
-      legend_location = "bottomright"
-    )
+``` r
+interpolation(curve) <- interp_flatforward()
+plot(
+  curve,
+  use_interpolation = TRUE, show_forward = TRUE,
+  legend_location = "bottomright"
+)
+```
 
 <img src="man/figures/README-unnamed-chunk-16-1.png" width="100%" />
 
 Interpolation enables the creation of standardized curves, commonly used
 in risk management to build risk factors.
 
-    risk_terms <- c(1, c(3, 6, 9) * 21, c(1, 5, 10) * 252)
-    risk_curve <- curve[[risk_terms]]
-    plot(risk_curve, use_interpolation = TRUE)
+``` r
+risk_terms <- c(1, c(3, 6, 9) * 21, c(1, 5, 10) * 252)
+risk_curve <- curve[[risk_terms]]
+plot(risk_curve, use_interpolation = TRUE)
+```
 
 <img src="man/figures/README-unnamed-chunk-17-1.png" width="100%" />