Currencies Analysis

04 April, 2022

Analysis price of the my list of currencies.

Prepare

Install packages and set environment :earth asia:

install.packages("azuremlsdk")

options(max.print = 1e3, scipen = 999, width = 1e2)
options(stringsAsFactors = F)

suppressPackageStartupMessages({
  library(dplyr)
  library(tidyr)
  
  library(lubridate)
  library(stringr)
  
  library(gt)
  library(tidyverse)
  library(glue)
  
  library(ggplot2)
  
  library(azuremlsdk)
})

.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
)

## [1] "portf-opt-ws workspace located in westeurope region"

Load dataset

WARNING: I used currency exchange rates data from Kaggle Dataset:

currencies_ds <- azuremlsdk::get_dataset_by_name(ws, name = .azureml_dataset_name)

sprintf(
  "Dataset name: %s. %s",
  currencies_ds$name, 
  currencies_ds$description
)

## [1] "Dataset name: Currencies. Source: https://www.kaggle.com/datasets/dhruvildave/currency-exchange-rates"

Get USD/RUB top higher rates:

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)
  )

## Selecting by close

USD/RUB Rate
1996-10-30 to 2021-08-30
date	open	high	low	close
Jan 21, 2016	82.06	85.82	82.06	81.82
Jan 22, 2016	80.61	81.26	77.94	82.90
Jan 26, 2016	81.54	82.16	78.33	79.84
Feb 3, 2016	79.56	79.75	77.87	79.71
Feb 10, 2016	79.39	79.49	77.65	79.59
Feb 12, 2016	79.36	79.74	78.59	79.77
Mar 19, 2020	80.92	82.07	79.24	80.92
Mar 23, 2020	79.72	81.34	79.49	79.84
Mar 31, 2020	79.59	79.69	77.66	79.59
Nov 3, 2020	80.55	80.57	79.05	80.52

Preprocessing data

Calculate Return and Log Return for last 10 years:

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

Discover Data

Calculate statistics and volatility:

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)
  )

quotes_stats %>% 
  mutate(
    `100x Volatility` = volatility*100
  ) %>% 
  arrange(volatility) %>% 
  select(-volatility) %>% 
  
  gt() %>% 
  tab_header(
    title = "The Least and The Most Volatile Currencies",
    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
  ) %>%
  fmt_number(
    columns = c(max_price, min_price, max_min_rate, last_price, `100x Volatility`)
  )

The Least and The Most Volatile Currencies
2011-09-01 to 2021-08-30
symbol	max_price	min_price	last_price	max_min_rate	100x Volatility
USD/AED	3.67	3.67	3.67	1.00	0.01
USD/HKD	7.85	7.75	7.79	1.01	0.03
USD/KWD	0.31	0.27	0.30	1.16	0.16
USD/CNY	7.18	6.03	6.47	1.19	0.23
USD/DJF	177.72	172.00	177.50	1.03	0.28
USD/SGD	1.46	1.20	1.34	1.21	0.33
USD/SAR	3.77	3.30	3.75	1.14	0.39
USD/GTQ	7.89	7.04	7.73	1.12	0.41
USD/ILS	4.07	3.13	3.20	1.30	0.45
USD/TTD	6.78	5.93	6.76	1.14	0.47
USD/CAD	1.46	0.97	1.26	1.51	0.47
USD/MYR	4.49	2.96	4.16	1.52	0.50
USD/DKK	7.15	5.18	6.30	1.38	0.51
USD/EUR	0.96	0.70	0.85	1.38	0.51
USD/CRC	619.70	478.54	619.70	1.29	0.53
USD/PHP	54.23	39.75	49.71	1.36	0.54
USD/INR	77.57	45.70	73.29	1.70	0.54
USD/RON	4.54	2.93	4.18	1.55	0.55
USD/JPY	125.63	75.74	109.90	1.66	0.55
USD/GBP	0.87	0.58	0.73	1.49	0.55
USD/JMD	153.88	83.37	150.53	1.85	0.56
USD/MKD	58.92	42.07	51.98	1.40	0.58
USD/MDL	20.31	11.09	17.58	1.83	0.61
USD/BDT	84.72	72.39	84.72	1.17	0.62
USD/AUD	1.74	0.93	1.37	1.88	0.63
USD/SEK	10.44	6.29	8.62	1.66	0.64
USD/CHF	1.03	0.79	0.92	1.31	0.64
USD/CZK	26.03	16.75	21.67	1.55	0.64
USD/BWP	12.19	6.58	11.12	1.85	0.66
USD/NZD	1.78	1.13	1.43	1.57	0.66
USD/THB	36.43	28.07	32.45	1.30	0.67
USD/LKR	199.43	106.22	199.43	1.88	0.67
USD/KRW	1,262.93	999.83	1,165.89	1.26	0.70
USD/RSD	118.47	70.05	99.29	1.69	0.70
USD/UYU	45.31	18.08	42.53	2.51	0.71
USD/PLN	4.28	2.87	3.86	1.49	0.72
USD/HUF	338.26	188.61	294.66	1.79	0.74
USD/MUR	42.55	26.50	42.55	1.61	0.79
USD/MXN	25.34	11.98	20.14	2.11	0.80
USD/NIO	35.13	22.05	35.00	1.59	0.84
USD/KZT	454.34	174.15	427.18	2.61	0.84
USD/QAR	3.90	3.00	3.64	1.30	0.95
USD/TRY	8.78	1.71	8.38	5.12	0.97
USD/ZAR	19.25	6.98	14.66	2.76	0.99
USD/RUB	82.90	28.79	73.50	2.88	1.05
USD/ZMW	22.64	5.11	15.82	4.43	1.06
USD/BRL	5.89	1.58	5.19	3.72	1.08
USD/ARS	97.70	4.10	97.70	23.85	1.11
USD/TND	3.06	1.37	2.79	2.23	1.17
USD/BGN	1.87	1.21	1.66	1.55	1.28
USD/EGP	19.60	5.83	15.65	3.37	1.29
USD/NOK	11.76	5.36	8.66	2.19	1.31
USD/PEN	4.11	2.38	4.07	1.72	1.34
USD/BYN	3.08	0.51	2.51	6.04	1.37
USD/MAD	10.29	7.89	8.95	1.30	1.43
USD/UAH	33.50	7.80	26.92	4.30	1.83
USD/SDG	451.00	1.39	440.03	324.46	5.96
USD/BND	1.43	0.66	1.34	2.18	6.29
USD/XOF	647.00	58.00	555.47	11.16	6.44
USD/IDR	16,504.80	892.00	14,370.00	18.50	6.58
USD/HNL	24.90	3.00	23.83	8.30	8.14
USD/MZN	78.49	3.30	63.11	23.78	8.77
USD/ETB	45.23	1.00	45.06	45.23	9.26
USD/TWD	33.73	1.80	27.77	18.72	9.86
USD/PKR	168.15	2.00	165.63	84.07	12.69
USD/UZS	10,653.20	83.00	10,646.89	128.35	12.93
USD/GHS	573.00	1.00	5.98	573.00	13.74
USD/ISK	147.04	2.00	126.65	73.52	16.09
USD/PGK	3.51	0.29	3.51	11.89	16.18
USD/MMK	1,642.00	6.23	1,642.00	263.65	16.59
USD/CLP	867.50	5.00	782.21	173.50	18.70
USD/SZL	1,189.00	1.07	14.91	1,111.21	18.79
USD/XPF	119.35	1.00	100.90	119.35	19.32
USD/SOS	1,670.00	6.00	571.00	278.33	29.77
USD/MWK	812.43	1.00	804.55	812.43	30.83
USD/NGN	412.50	1.00	411.00	412.50	31.65
USD/VND	23,631.00	21.00	22,775.00	1,125.29	34.09
USD/COP	4,174.75	3.67	3,805.25	1,136.67	37.09
USD/IQD	1,578.00	10.00	1,458.00	157.80	37.10
USD/MGA	3,931.18	0.30	3,808.00	12,910.29	46.29
USD/SLL	10,250.50	1.00	10,250.50	10,250.50	47.59

My broker trades the following pairs:

symbols  <- c(
  'RUB', 
  'EUR', 'GBP', 'CHF', 'CNY', 'HKD', 'JPY', 'SEK', 'SGD', 'AUD',  
  'AED', 'KZT', 'BYN', 'TRY', 'MXN'
)

symbols <- str_c("USD", symbols, sep = "/")


quotes_stats %>% 
  filter(
    symbol %in% symbols
  ) %>% 
  mutate(
    `100x Volatility` = volatility*100
  ) %>% 
  arrange(volatility) %>% 
  select(-volatility) %>% 
  
  gt() %>% 
  tab_header(
    title = "The Most Promised Currencies",
    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
  ) %>%
  fmt_number(
    columns = c(max_price, min_price, last_price, max_min_rate, `100x Volatility`)
  )

The Most Promised Currencies
2011-09-01 to 2021-08-30
symbol	max_price	min_price	last_price	max_min_rate	100x Volatility
USD/AED	3.67	3.67	3.67	1.00	0.01
USD/HKD	7.85	7.75	7.79	1.01	0.03
USD/CNY	7.18	6.03	6.47	1.19	0.23
USD/SGD	1.46	1.20	1.34	1.21	0.33
USD/EUR	0.96	0.70	0.85	1.38	0.51
USD/JPY	125.63	75.74	109.90	1.66	0.55
USD/GBP	0.87	0.58	0.73	1.49	0.55
USD/AUD	1.74	0.93	1.37	1.88	0.63
USD/SEK	10.44	6.29	8.62	1.66	0.64
USD/CHF	1.03	0.79	0.92	1.31	0.64
USD/MXN	25.34	11.98	20.14	2.11	0.80
USD/KZT	454.34	174.15	427.18	2.61	0.84
USD/TRY	8.78	1.71	8.38	5.12	0.97
USD/RUB	82.90	28.79	73.50	2.88	1.05
USD/BYN	3.08	0.51	2.51	6.04	1.37

Plot exchange rate for out favorites:

Define low risk symbols:

usdrub_vol <- quotes_stats %>% filter(symbol == "USD/RUB") %>% pull(volatility)

low_risk_symbols <- quotes_stats %>% 
  filter(
    symbol %in% symbols &
    volatility <= usdrub_vol
  ) %>% 
  pull(symbol) %>% 
  unique

cat(
  sprintf(
    "['%s']",
    paste(low_risk_symbols, collapse = "', '")
))

## ['USD/AED', 'USD/AUD', 'USD/CHF', 'USD/CNY', 'USD/EUR', 'USD/GBP', 'USD/HKD', 'USD/JPY', 'USD/KZT', 'USD/MXN', 'USD/RUB', 'USD/SEK', 'USD/SGD', 'USD/TRY']

jumper_symbols <- quotes_stats %>% filter(max_min_rate > 2) %>% pull(symbol)

quotes_df %>% 
  filter(symbol %in% low_risk_symbols) %>% 
  mutate(
    jumper = if_else(symbol %in% jumper_symbols, "High risk currencies", "Low risk currencies")
  ) %>% 
  group_by(symbol) %>% 
  mutate(R = cumsum(return)) %>% 
  
  ggplot + 
    geom_line(aes(x = date, y = R, color = symbol)) +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +

    facet_grid(jumper ~ ., scales = "free") +
    
    labs(
      title = "Currencies Exchange Rates", subtitle = "Return of Investment for last 10 years",
      x = "", y = "Return of Investment", 
      caption = currencies_ds$description) +
    theme_minimal() +
  
    theme(
      legend.position = "top", legend.title = element_blank(),
      plot.caption = element_text(size = 8)
    )

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fx_currencies_analysis.md

fx_currencies_analysis.md

Currencies Analysis

Prepare

Load dataset

Preprocessing data

Discover Data

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fx_currencies_analysis.md

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Currencies Analysis

Prepare

Load dataset

Preprocessing data

Discover Data