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Currencies Analysis (beta)
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@ -9,43 +9,54 @@ knitr::opts_chunk$set(echo = T, warning = F)
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***Analysis price of the my list of ~~the most promised cryptotokens~~ currencies.***
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## Set envinroment :earth_asia:
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## Prepare
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Install packages and set environment :earth_asia:
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`install.packages("azuremlsdk")`
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```{r set_envinroment, message=FALSE}
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library(dplyr)
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library(tidyr)
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suppressPackageStartupMessages({
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library(dplyr)
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library(tidyr)
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library(lubridate)
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library(stringr)
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library(gt)
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library(tidyverse)
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library(glue)
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library(ggplot2)
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library(azuremlsdk)
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})
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```
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library(lubridate)
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library(stringr)
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library(gt)
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library(tidyverse)
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library(glue)
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library(ggplot2)
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library(azuremlsdk)
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## set params
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```{r set_params}
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.azureml_dataset_name <- "Currencies"
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```
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## connect to Azure ML workspace
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Connect to Azure ML workspace:
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```{r azureml_connect}
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ws <- azuremlsdk::load_workspace_from_config()
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sprintf(
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"%s workspace located in %s region",ws$name, ws$location
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"%s workspace located in %s region", ws$name, ws$location
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)
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```
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## Load dataset
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## Load dataset
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```{r get_azure_dataset}
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currencies_ds <- azuremlsdk::get_dataset_by_name(ws, name = .azureml_dataset_name)
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currencies_ds$name
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currencies_ds$description
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```
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Get USD/RUB top higher rates:
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```{r prepare_dataframe}
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quotes_df <- currencies_ds$to_pandas_dataframe()
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@ -71,6 +82,10 @@ quotes_df %>%
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)
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```
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## Preprocessing data
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Calculate `Return` and `Log Return` for last 10 years:
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```{r preprocessing}
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quotes_df %<>%
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transmute(
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@ -125,7 +140,7 @@ quotes_stats %>%
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gt() %>%
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tab_header(
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title = "Least Volatility Currencies",
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title = "The Least Volatile Currencies",
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subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
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) %>%
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fmt_number(
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@ -138,7 +153,7 @@ My broker available pairs:
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symbols <- c(
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'RUB',
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'EUR', 'GBP', 'CHF', 'CNY', 'HKD', 'JPY', 'SEK', 'SGD', 'AUD',
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'KZT', 'BYN', 'TRY', 'MXN'
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'AED', 'KZT', 'BYN', 'TRY', 'MXN'
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)
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quotes_stats %>%
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@ -147,14 +162,13 @@ quotes_stats %>%
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) %>%
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mutate(
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`100x Volatility` = volatility*100
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#risk = if_else(volatility > volatility_threshold, "High", "Medium or low", NA_character_)
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) %>%
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arrange(volatility) %>%
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select(-volatility) %>%
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gt() %>%
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tab_header(
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title = "Least Volatility Currencies",
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title = "The Most Promised Currencies",
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subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
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) %>%
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fmt_number(
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1999
src/fx_currencies_anlysis.md
Normal file
1999
src/fx_currencies_anlysis.md
Normal file
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