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+---
+title: "Currencies Analysis"
+output: github_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = T, warning = F)
+```
+
+***Analysis price of the my list of ~~the most promised cryptotokens~~ currencies.***
+
+## Set envinroment :earth_asia:
+
+`install.packages("azuremlsdk")`
+
+```{r set_envinroment, message=FALSE}
+library(dplyr)
+library(tidyr)
+
+library(lubridate)
+library(stringr)
+
+library(gt)
+library(tidyverse)
+library(glue)
+
+library(ggplot2)
+
+library(azuremlsdk)
+
+## set params
+.azureml_dataset_name <- "Currencies"
+
+## connect to Azure ML workspace
+ws <- azuremlsdk::load_workspace_from_config()
+sprintf(
+  "%s workspace located in %s region",ws$name, ws$location
+)
+
+```
+## Load dataset
+
+
+```{r get_azure_dataset}
+currencies_ds <- azuremlsdk::get_dataset_by_name(ws, name = .azureml_dataset_name)
+currencies_ds$name
+currencies_ds$description
+```
+
+```{r prepare_dataframe}
+quotes_df <- currencies_ds$to_pandas_dataframe()
+
+# ~ 20 years, 150 currencies and 1.5M rows
+
+quotes_df %>%
+  filter(slug == "USD/RUB") %>%
+  select(-slug) %>% 
+  top_n(10) %>% 
+  
+  gt() %>%
+  tab_header(
+    title = "USD/RUB Rate",
+    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
+  ) %>%
+  fmt_date(
+    columns = date,
+    date_style = 6
+  ) %>%
+  fmt_number(
+    columns = c(open, high, low, close)
+  )
+```
+
+```{r preprocessing}
+quotes_df %<>% 
+  transmute(
+    symbol = slug,
+    price = close,
+    date
+  ) %>% 
+  
+  filter(
+    str_detect(symbol, "USD/") &
+    date > max(date) - lubridate::years(10)
+  ) %>% 
+  
+  filter(!(symbol == "USD/RUB" & price < 1)) %>% 
+  
+  arrange(date) %>% 
+  group_by(symbol) %>%
+  
+  mutate(
+    return = c(NA_real_, diff(price))/lag(price),
+    log_return = log(1 + return)
+  ) %>% 
+  na.omit
+```
+
+Base and quote currencies:
+
+```{r discover}
+quotes_stats <- quotes_df %>% 
+
+  summarise(
+    max_price = max(price), 
+    min_price = min(price),
+    last_price = last(price),
+    max_min_rate = max(price)/min(price),
+    volatility = sd(log_return)
+  )
+
+volatility_threshold <- quotes_stats %>% pull(volatility) %>% quantile(probs = .75)
+
+
+quotes_stats %>% 
+  filter(
+    volatility <= volatility_threshold | 
+    symbol == "USD/RUB"
+  ) %>% 
+  mutate(
+    `100x Volatility` = volatility*100
+  ) %>% 
+  arrange(volatility) %>% 
+  select(-volatility) %>% 
+  
+  gt() %>% 
+  tab_header(
+    title = "Least Volatility Currencies",
+    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
+  ) %>%
+  fmt_number(
+    columns = c(max_price, min_price, max_min_rate, `100x Volatility`)
+  )
+```
+My broker available pairs:
+
+```{r}
+symbols  <- c(
+  'RUB', 
+  'EUR', 'GBP', 'CHF', 'CNY', 'HKD', 'JPY', 'SEK', 'SGD', 'AUD',  
+  'KZT', 'BYN', 'TRY', 'MXN'
+)
+
+quotes_stats %>% 
+  filter(
+    symbol %in% str_c("USD", symbols, sep = "/")
+  ) %>% 
+  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",
+    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
+  ) %>%
+  fmt_number(
+    columns = c(max_price, min_price, max_min_rate, `100x Volatility`)
+  )
+
+```
+Plot exchange rate for out favorites:
+
+```{r}
+
+```
+
+Define low risk symbols:
+
+```{r}
+low_risk_symbols <- quotes_stats %>% 
+  filter(
+    symbol %in% str_c("USD", symbols, sep = "/")
+  ) %>% 
+  filter(
+    volatility <= volatility_threshold | 
+    symbol == "USD/RUB"
+  ) %>% 
+  pull(symbol) %>% 
+  unique
+
+cat(
+  sprintf(
+    "['%s']",
+    paste(low_risk_symbols, collapse = "', '")
+))
+```
+
+
+