Currencies Analysis (beta)

src/fx_currencies_anlysis.Rmd

@@ -9,11 +9,14 @@ knitr::opts_chunk$set(echo = T, warning = F)
 
 ***Analysis price of the my list of ~~the most promised cryptotokens~~ currencies.***
 
-## Set envinroment :earth_asia:
+## Prepare
+
+Install packages and set environment :earth_asia:
 
 `install.packages("azuremlsdk")`
 
 ```{r set_envinroment, message=FALSE}
+suppressPackageStartupMessages({
   library(dplyr)
   library(tidyr)
   
@@ -27,25 +30,33 @@ library(glue)
   library(ggplot2)
   
   library(azuremlsdk)
+})
+```
 
-## set params
+
+```{r set_params}
 .azureml_dataset_name <- "Currencies"
+```
 
-## connect to Azure ML workspace
+
+Connect to Azure ML workspace:
+
+```{r azureml_connect}
 ws <- azuremlsdk::load_workspace_from_config()
 sprintf(
   "%s workspace located in %s region", ws$name, ws$location
 )
-
 ```
-## Load dataset
 
 
+## Load dataset
+
 ```{r get_azure_dataset}
 currencies_ds <- azuremlsdk::get_dataset_by_name(ws, name = .azureml_dataset_name)
 currencies_ds$name
 currencies_ds$description
 ```
+Get USD/RUB top higher rates:
 
 ```{r prepare_dataframe}
 quotes_df <- currencies_ds$to_pandas_dataframe()
@@ -71,6 +82,10 @@ quotes_df %>%
   )
 ```
 
+## Preprocessing data
+
+Calculate `Return` and `Log Return` for last 10 years: 
+
 ```{r preprocessing}
 quotes_df %<>% 
   transmute(
@@ -125,7 +140,7 @@ quotes_stats %>%
   
   gt() %>% 
   tab_header(
-    title = "Least Volatility Currencies",
+    title = "The Least Volatile Currencies",
     subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
   ) %>%
   fmt_number(
@@ -138,7 +153,7 @@ My broker available pairs:
 symbols  <- c(
   'RUB', 
   'EUR', 'GBP', 'CHF', 'CNY', 'HKD', 'JPY', 'SEK', 'SGD', 'AUD',  
-  'KZT', 'BYN', 'TRY', 'MXN'
+  'AED', 'KZT', 'BYN', 'TRY', 'MXN'
 )
 
 quotes_stats %>% 
@@ -147,14 +162,13 @@ quotes_stats %>%
   ) %>% 
   mutate(
     `100x Volatility` = volatility*100
-    #risk = if_else(volatility > volatility_threshold, "High", "Medium or low", NA_character_)
   ) %>% 
   arrange(volatility) %>% 
   select(-volatility) %>% 
   
   gt() %>% 
   tab_header(
-    title = "Least Volatility Currencies",
+    title = "The Most Promised Currencies",
     subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
   ) %>%
   fmt_number(