Merge pull request #19 from codez0mb1e/azure-day-conf

Currency portfolio: assets selection
2024-11-24 11:22:19 +00:00 · 2022-04-04 20:50:14 +03:00 · 2022-04-04 20:50:14 +03:00 · e68f56d9a8
commit e68f56d9a8
parent 687f5fd1c8 8d6730d75b
8 changed files with 2960 additions and 2 deletions
--- a/README.md
+++ b/README.md
@ -23,6 +23,14 @@

 :newspaper: **Последние обновления**:

+Апрель 2022:
+
+- Для формирования низкорискованного валютного портфеля:
+  - добавил скрипт [анализа волатильности валют](src/fx_currencies_analysis.md)
+  - добавил скрипт [моделирования цен валют с использованием метода Монте-Карло](src/fx_currency_portfolio__assets_selection.ipynb)
+
+Март 2022:
+
 - Обновление списка [стейблкоинов](lists.md#stablecoins)
 - Рефакторинг списка [VPN сервисов](lists.md#vpn)
 - Обновлена структура `README.md`
--- a/faq.md
+++ b/faq.md
@ -14,8 +14,15 @@

 ### Как выбрать соотношение рублей, иностранной валюты для наличных средств?

+
 Оценить вероятность, что ЦБ введет фиксированный курс с одновременным запретом покупки/продажи валюты на открытом рынке.

 Чем выше эта вероятность, тем бОльшая доля наличных средств в рублях должна быть в корзине; максимальную долю рублей имеет смысл ограничиться 80%.

 Чем ниже эта вероятность, тем бОльшая доля наличных средств в иностранной валюте должна быть. Минимальная долю рубля имеет смысл ограничить 30%.
+
+### Как сформировать низкорискованный валютный портфель?
+
+1. [Проанализируйте волатильности валют](src/fx_currencies_analysis.md) на основе исторических данных и выберете те, риски по которым ниже рублевых.
+2. [Смоделируйте цены валют](src/fx_currency_portfolio__assets_selection.ipynb), выбранных на шаге 1, с использованием метода Монте-Карло.
+3. Основываясь на результатах моделирования, выберите наиболее подходящии Вам по соотношению доходность/риски валютные пары.
--- a/src/cryptocurrency_portfolio__assets_selection.py
+++ b/src/cryptocurrency_portfolio__assets_selection.py
@ -0,0 +1,188 @@
+#!/usr/bin/python3
+
+
+"""Crypto Currency Portfolio: Assets Selection.
+
+Description:
+    Crypto Currency Selection using monte Carlo simulation.
+"""
+
+# %% Import dependencies ----
+# core
+import os
+import gc
+
+# data science
+import pandas as pd
+import numpy as np
+from scipy.stats import norm
+
+# Cloud integration
+from azureml.core import Workspace, Dataset, VERSION as aml_version
+print(f'Azure ML SDK v{aml_version}')
+
+# network
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# plots
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# show info about python env
+from IPython import sys_info
+print(sys_info())
+
+import warnings
+warnings.filterwarnings("ignore")
+
+
+# %% Set params ----
+symbols = ['BTCUSDT', 'ETHUSDT', 'BNBUSDT', 'SOLUSDT', 'MATICUSDT', 'UNIUSDT']
+
+n_days = int(252) # US market has 252 trading days in a year
+n_iterations = int(1e4)
+
+
+
+# %% Load quotes ----
+def get_quotes(symbol: str) -> pd.DataFrame:
+    df = pd.read_csv(f'https://www.cryptodatadownload.com/cdd/Binance_{symbol}_d.csv', skiprows=[0])
+    df = df.set_index('date')
+    df = df.sort_values(by = 'date')
+
+    return df[['symbol', 'open', 'high', 'low', 'close']]
+
+quotes_data = [get_quotes(s) for s in symbols]
+# row-wise union:
+# pd.concat([get_quotes(s) for s in symbols])
+# column-wise: 
+# pd.concat(list1, axis=1, ignore_index=False)
+
+
+btcusdt_df = quotes_data[0]
+
+pd.concat([
+    btcusdt_df['close'].head(5),
+    btcusdt_df['close'].tail(5)
+])
+
+
+
+# %% Calculate Return
+def get_returns(close_prices) -> pd.Series:
+    return (close_prices/close_prices.shift()) - 1
+
+
+btcusdt_df['diff'] = btcusdt_df['close'].diff()
+btcusdt_df['return'] = get_returns(btcusdt_df['close'])
+
+btcusdt_df[['close', 'diff', 'return']].tail(10)
+
+
+
+# %% Calculate LogReturn
+def get_log_returns(return_prices) -> pd.Series:
+    return np.log(1 + return_prices)
+
+btcusdt_df['log_return'] = btcusdt_df['return'].apply(lambda x: get_log_returns(x))
+btcusdt_df[['close', 'diff', 'return', 'log_return']].tail(10)
+
+
+
+# %% Simulate possible LogReturns
+
+def get_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int) -> pd.Series:
+    u = log_returns.mean()
+    var = log_returns.var()
+    stdev = log_returns.std()
+
+    drift = u - (0.5*var)
+    Z = norm.ppf(np.random.rand(n_days, n_iterations))
+    
+    return np.exp(drift + stdev*Z)
+
+
+btcusd_logreturns = btcusdt_df['log_return'].dropna()
+btcusd_simulated_returns = get_simulated_returns(btcusd_logreturns, n_days, n_iterations)
+
+assert(
+    btcusd_simulated_returns.shape == (n_days, n_iterations)
+)
+
+
+
+# %% Monte carlo simulation functions ----
+def get_breakeven_prob(predicted, threshold = 0):
+    """
+    This function calculated the probability of a stock being above a certain threshhold, which can be defined as a value (final stock price) or return rate (percentage change)
+    """
+    predicted0 = predicted.iloc[0,0]
+    predicted = predicted.iloc[-1]
+    predList = list(predicted)
+
+    over = [(i*100)/predicted0 for i in predList if ((i-predicted0)*100)/predicted0 >= threshold]
+    less = [(i*100)/predicted0 for i in predList if ((i-predicted0)*100)/predicted0 < threshold]
+
+    return (len(over)/(len(over) + len(less)))
+
+
+def monte_carlo_simulation(simulated_returns: pd.Series, last_actual_price: float, n_days: int, plot=True):
+    # Create empty matrix
+    price_list = np.zeros_like(simulated_returns)
+
+    # Put the last actual price in the first row of matrix
+    price_list[0] = last_actual_price
+
+    # Calculate the price of each day
+    for t in range(1, n_days):
+        price_list[t] = price_list[t-1]*simulated_returns[t]
+    
+    # Plot
+    if plot == True:
+        x = pd.DataFrame(price_list).iloc[-1]
+        fig, ax = plt.subplots(1, 2, figsize=(14,4))
+        sns.distplot(x, ax=ax[0])
+        sns.distplot(x, hist_kws={'cumulative': True}, kde_kws={'cumulative': True}, ax=ax[1])
+        plt.xlabel('Stock Price')
+        plt.show()
+    
+
+    print(f"Investment period: {n_days-1}")
+    print(f"Expected Value: ${round(pd.DataFrame(price_list).iloc[-1].mean(),2)}")
+    print(f"Return: {round(100*(pd.DataFrame(price_list).iloc[-1].mean()-price_list[0,1])/pd.DataFrame(price_list).iloc[-1].mean(),2)}%")
+    print(f"Probability of Breakeven: {get_breakeven_prob(pd.DataFrame(price_list))}")
+          
+    return pd.DataFrame(price_list)
+
+
+# %% Run Monte carlo simulation and estimate result
+
+simulated_prices_df = monte_carlo_simulation(
+    btcusd_simulated_returns, 
+    quotes_data[0]['close'].tail(1), 
+    n_days)
+
+
+plt.figure(figsize=(10,6))
+plt.plot(simulated_prices_df.iloc[:, 1:10])
+plt.show()
+
+
+
+# %% Monte Carlo simulation pipeline for multiple tokens ----
+
+n_iterations = int(1e4) #! WARN: set simulations number
+
+returns_data = [get_returns(df['close']) for df in quotes_data]
+log_returns_data = [get_log_returns(r) for r in returns_data]
+simulated_returns_data =  [get_simulated_returns(lr, n_days, n_iterations) for lr in log_returns_data]
+
+
+for i in range(len(simulated_returns_data)):
+    print(f'Starting Monte-Carlo simulation for {symbols[i]} symbol...')
+    prices_ms = monte_carlo_simulation(simulated_returns_data[i], quotes_data[i]['close'].tail(1), n_days, plot=True)
+    
+    plt.figure(figsize=(10,6))
+    plt.plot(prices_ms.iloc[:, 1:50])
+    plt.show()
--- a/src/fx_currencies_analysis.Rmd
+++ b/src/fx_currencies_analysis.Rmd
@ -0,0 +1,248 @@
+---
+title: "Currencies Analysis"
+date: "`r format(Sys.time(), '%d %B, %Y')`"
+output:
+  github_document:
+    toc: false
+    toc_depth: 2
+    fig_width: 9
+    fig_height: 9 
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = T, warning = F)
+```
+
+***Analysis price of the my list of currencies.***
+
+## Prepare
+
+Install packages and set environment :earth_asia:
+
+`install.packages("azuremlsdk")`
+
+```{r set_envinroment, message=FALSE}
+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)
+})
+```
+
+
+```{r set_params}
+.azureml_dataset_name <- "Currencies"
+```
+
+
+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
+
+WARNING: I used `currency exchange rates` data from [Kaggle Dataset](https://www.kaggle.com/datasets/dhruvildave/currency-exchange-rates):
+
+```{r get_azure_dataset}
+currencies_ds <- azuremlsdk::get_dataset_by_name(ws, name = .azureml_dataset_name)
+
+sprintf(
+  "Dataset name: %s. %s",
+  currencies_ds$name, 
+  currencies_ds$description
+)
+```
+
+Get `USD/RUB` top higher rates:
+
+```{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$date)}")
+  ) %>%
+  fmt_date(
+    columns = date,
+    date_style = 6
+  ) %>%
+  fmt_number(
+    columns = c(open, high, low, close)
+  )
+```
+
+## Preprocessing data
+
+Calculate `Return` and `Log Return` for last 10 years: 
+
+```{r preprocessing}
+quotes_df %<>% 
+  transmute(
+    symbol = slug,
+    price = close,
+    date
+  ) %>% 
+  
+  filter(
+    str_detect(symbol, "USD/") &
+    date > max(date) - lubridate::years(10)
+  ) %>% 
+  
+  filter(!(symbol == "USD/RUB" & price < 1)) %>% 
+  
+  arrange(date) %>% 
+  group_by(symbol) %>%
+  
+  mutate(
+    return = c(NA_real_, diff(price))/lag(price),
+    log_return = log(1 + return)
+  ) %>% 
+  na.omit
+```
+
+## Discover Data
+
+Calculate statistics and `volatility`:
+
+```{r discover}
+quotes_stats <- quotes_df %>% 
+
+  summarise(
+    max_price = max(price), 
+    min_price = min(price),
+    last_price = last(price),
+    max_min_rate = max(price)/min(price),
+    volatility = sd(log_return)
+  )
+
+quotes_stats %>% 
+  mutate(
+    `100x Volatility` = volatility*100
+  ) %>% 
+  arrange(volatility) %>% 
+  select(-volatility) %>% 
+  
+  gt() %>% 
+  tab_header(
+    title = "The Least and The Most Volatile Currencies",
+    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
+  ) %>%
+  fmt_number(
+    columns = c(max_price, min_price, max_min_rate, last_price, `100x Volatility`)
+  )
+```
+My broker trades the following pairs:
+
+```{r}
+symbols  <- c(
+  'RUB', 
+  'EUR', 'GBP', 'CHF', 'CNY', 'HKD', 'JPY', 'SEK', 'SGD', 'AUD',  
+  'AED', 'KZT', 'BYN', 'TRY', 'MXN'
+)
+
+symbols <- str_c("USD", symbols, sep = "/")
+
+
+quotes_stats %>% 
+  filter(
+    symbol %in% symbols
+  ) %>% 
+  mutate(
+    `100x Volatility` = volatility*100
+  ) %>% 
+  arrange(volatility) %>% 
+  select(-volatility) %>% 
+  
+  gt() %>% 
+  tab_header(
+    title = "The Most Promised Currencies",
+    subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
+  ) %>%
+  fmt_number(
+    columns = c(max_price, min_price, last_price, max_min_rate, `100x Volatility`)
+  )
+
+```
+Plot exchange rate for out favorites:
+
+Define low risk symbols:
+
+```{r}
+usdrub_vol <- quotes_stats %>% filter(symbol == "USD/RUB") %>% pull(volatility)
+
+low_risk_symbols <- quotes_stats %>% 
+  filter(
+    symbol %in% symbols &
+    volatility <= usdrub_vol
+  ) %>% 
+  pull(symbol) %>% 
+  unique
+
+cat(
+  sprintf(
+    "['%s']",
+    paste(low_risk_symbols, collapse = "', '")
+))
+```
+
+
+```{r}
+jumper_symbols <- quotes_stats %>% filter(max_min_rate > 2) %>% pull(symbol)
+
+quotes_df %>% 
+  filter(symbol %in% low_risk_symbols) %>% 
+  mutate(
+    jumper = if_else(symbol %in% jumper_symbols, "High risk currencies", "Low risk currencies")
+  ) %>% 
+  group_by(symbol) %>% 
+  mutate(R = cumsum(return)) %>% 
+  
+  ggplot + 
+    geom_line(aes(x = date, y = R, color = symbol)) +
+    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
+
+    facet_grid(jumper ~ ., scales = "free") +
+    
+    labs(
+      title = "Currencies Exchange Rates", subtitle = "Return of Investment for last 10 years",
+      x = "", y = "Return of Investment", 
+      caption = currencies_ds$description) +
+    theme_minimal() +
+  
+    theme(
+      legend.position = "top", legend.title = element_blank(),
+      plot.caption = element_text(size = 8)
+    )
+```
+
+
+
+
+
--- a/src/fx_currencies_analysis.md
+++ b/src/fx_currencies_analysis.md
@ -0,0 +1,947 @@
+Currencies Analysis
+================
+04 April, 2022
+
+***Analysis price of the my list of currencies.***
+
+## Prepare
+
+Install packages and set environment :earth asia:
+
+`install.packages("azuremlsdk")`
+
+``` r
+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)
+})
+```
+
+``` r
+.azureml_dataset_name <- "Currencies"
+```
+
+Connect to Azure ML workspace:
+
+``` r
+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](https://www.kaggle.com/datasets/dhruvildave/currency-exchange-rates):
+
+``` r
+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:
+
+``` r
+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
+
+
+<table class="gt_table">
+  <thead class="gt_header">
+    <tr>
+      <th colspan="5" class="gt_heading gt_title gt_font_normal" style>USD/RUB Rate</th>
+    </tr>
+    <tr>
+      <th colspan="5" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>1996-10-30 to 2021-08-30</th>
+    </tr>
+  </thead>
+  <thead class="gt_col_headings">
+    <tr>
+      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">date</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">open</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">high</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">low</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">close</th>
+    </tr>
+  </thead>
+  <tbody class="gt_table_body">
+    <tr><td class="gt_row gt_left">Jan 21, 2016</td>
+<td class="gt_row gt_right">82.06</td>
+<td class="gt_row gt_right">85.82</td>
+<td class="gt_row gt_right">82.06</td>
+<td class="gt_row gt_right">81.82</td></tr>
+    <tr><td class="gt_row gt_left">Jan 22, 2016</td>
+<td class="gt_row gt_right">80.61</td>
+<td class="gt_row gt_right">81.26</td>
+<td class="gt_row gt_right">77.94</td>
+<td class="gt_row gt_right">82.90</td></tr>
+    <tr><td class="gt_row gt_left">Jan 26, 2016</td>
+<td class="gt_row gt_right">81.54</td>
+<td class="gt_row gt_right">82.16</td>
+<td class="gt_row gt_right">78.33</td>
+<td class="gt_row gt_right">79.84</td></tr>
+    <tr><td class="gt_row gt_left">Feb 3, 2016</td>
+<td class="gt_row gt_right">79.56</td>
+<td class="gt_row gt_right">79.75</td>
+<td class="gt_row gt_right">77.87</td>
+<td class="gt_row gt_right">79.71</td></tr>
+    <tr><td class="gt_row gt_left">Feb 10, 2016</td>
+<td class="gt_row gt_right">79.39</td>
+<td class="gt_row gt_right">79.49</td>
+<td class="gt_row gt_right">77.65</td>
+<td class="gt_row gt_right">79.59</td></tr>
+    <tr><td class="gt_row gt_left">Feb 12, 2016</td>
+<td class="gt_row gt_right">79.36</td>
+<td class="gt_row gt_right">79.74</td>
+<td class="gt_row gt_right">78.59</td>
+<td class="gt_row gt_right">79.77</td></tr>
+    <tr><td class="gt_row gt_left">Mar 19, 2020</td>
+<td class="gt_row gt_right">80.92</td>
+<td class="gt_row gt_right">82.07</td>
+<td class="gt_row gt_right">79.24</td>
+<td class="gt_row gt_right">80.92</td></tr>
+    <tr><td class="gt_row gt_left">Mar 23, 2020</td>
+<td class="gt_row gt_right">79.72</td>
+<td class="gt_row gt_right">81.34</td>
+<td class="gt_row gt_right">79.49</td>
+<td class="gt_row gt_right">79.84</td></tr>
+    <tr><td class="gt_row gt_left">Mar 31, 2020</td>
+<td class="gt_row gt_right">79.59</td>
+<td class="gt_row gt_right">79.69</td>
+<td class="gt_row gt_right">77.66</td>
+<td class="gt_row gt_right">79.59</td></tr>
+    <tr><td class="gt_row gt_left">Nov 3, 2020</td>
+<td class="gt_row gt_right">80.55</td>
+<td class="gt_row gt_right">80.57</td>
+<td class="gt_row gt_right">79.05</td>
+<td class="gt_row gt_right">80.52</td></tr>
+  </tbody>
+  
+  
+</table>
+
+
+## Preprocessing data
+
+Calculate `Return` and `Log Return` for last 10 years:
+
+``` r
+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`:
+
+``` r
+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`)
+  )
+```
+
+
+<table class="gt_table">
+  <thead class="gt_header">
+    <tr>
+      <th colspan="6" class="gt_heading gt_title gt_font_normal" style>The Least and The Most Volatile Currencies</th>
+    </tr>
+    <tr>
+      <th colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>2011-09-01 to 2021-08-30</th>
+    </tr>
+  </thead>
+  <thead class="gt_col_headings">
+    <tr>
+      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">symbol</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">max_price</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">min_price</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">last_price</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">max_min_rate</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">100x Volatility</th>
+    </tr>
+  </thead>
+  <tbody class="gt_table_body">
+    <tr><td class="gt_row gt_left">USD/AED</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">0.01</td></tr>
+    <tr><td class="gt_row gt_left">USD/HKD</td>
+<td class="gt_row gt_right">7.85</td>
+<td class="gt_row gt_right">7.75</td>
+<td class="gt_row gt_right">7.79</td>
+<td class="gt_row gt_right">1.01</td>
+<td class="gt_row gt_right">0.03</td></tr>
+    <tr><td class="gt_row gt_left">USD/KWD</td>
+<td class="gt_row gt_right">0.31</td>
+<td class="gt_row gt_right">0.27</td>
+<td class="gt_row gt_right">0.30</td>
+<td class="gt_row gt_right">1.16</td>
+<td class="gt_row gt_right">0.16</td></tr>
+    <tr><td class="gt_row gt_left">USD/CNY</td>
+<td class="gt_row gt_right">7.18</td>
+<td class="gt_row gt_right">6.03</td>
+<td class="gt_row gt_right">6.47</td>
+<td class="gt_row gt_right">1.19</td>
+<td class="gt_row gt_right">0.23</td></tr>
+    <tr><td class="gt_row gt_left">USD/DJF</td>
+<td class="gt_row gt_right">177.72</td>
+<td class="gt_row gt_right">172.00</td>
+<td class="gt_row gt_right">177.50</td>
+<td class="gt_row gt_right">1.03</td>
+<td class="gt_row gt_right">0.28</td></tr>
+    <tr><td class="gt_row gt_left">USD/SGD</td>
+<td class="gt_row gt_right">1.46</td>
+<td class="gt_row gt_right">1.20</td>
+<td class="gt_row gt_right">1.34</td>
+<td class="gt_row gt_right">1.21</td>
+<td class="gt_row gt_right">0.33</td></tr>
+    <tr><td class="gt_row gt_left">USD/SAR</td>
+<td class="gt_row gt_right">3.77</td>
+<td class="gt_row gt_right">3.30</td>
+<td class="gt_row gt_right">3.75</td>
+<td class="gt_row gt_right">1.14</td>
+<td class="gt_row gt_right">0.39</td></tr>
+    <tr><td class="gt_row gt_left">USD/GTQ</td>
+<td class="gt_row gt_right">7.89</td>
+<td class="gt_row gt_right">7.04</td>
+<td class="gt_row gt_right">7.73</td>
+<td class="gt_row gt_right">1.12</td>
+<td class="gt_row gt_right">0.41</td></tr>
+    <tr><td class="gt_row gt_left">USD/ILS</td>
+<td class="gt_row gt_right">4.07</td>
+<td class="gt_row gt_right">3.13</td>
+<td class="gt_row gt_right">3.20</td>
+<td class="gt_row gt_right">1.30</td>
+<td class="gt_row gt_right">0.45</td></tr>
+    <tr><td class="gt_row gt_left">USD/TTD</td>
+<td class="gt_row gt_right">6.78</td>
+<td class="gt_row gt_right">5.93</td>
+<td class="gt_row gt_right">6.76</td>
+<td class="gt_row gt_right">1.14</td>
+<td class="gt_row gt_right">0.47</td></tr>
+    <tr><td class="gt_row gt_left">USD/CAD</td>
+<td class="gt_row gt_right">1.46</td>
+<td class="gt_row gt_right">0.97</td>
+<td class="gt_row gt_right">1.26</td>
+<td class="gt_row gt_right">1.51</td>
+<td class="gt_row gt_right">0.47</td></tr>
+    <tr><td class="gt_row gt_left">USD/MYR</td>
+<td class="gt_row gt_right">4.49</td>
+<td class="gt_row gt_right">2.96</td>
+<td class="gt_row gt_right">4.16</td>
+<td class="gt_row gt_right">1.52</td>
+<td class="gt_row gt_right">0.50</td></tr>
+    <tr><td class="gt_row gt_left">USD/DKK</td>
+<td class="gt_row gt_right">7.15</td>
+<td class="gt_row gt_right">5.18</td>
+<td class="gt_row gt_right">6.30</td>
+<td class="gt_row gt_right">1.38</td>
+<td class="gt_row gt_right">0.51</td></tr>
+    <tr><td class="gt_row gt_left">USD/EUR</td>
+<td class="gt_row gt_right">0.96</td>
+<td class="gt_row gt_right">0.70</td>
+<td class="gt_row gt_right">0.85</td>
+<td class="gt_row gt_right">1.38</td>
+<td class="gt_row gt_right">0.51</td></tr>
+    <tr><td class="gt_row gt_left">USD/CRC</td>
+<td class="gt_row gt_right">619.70</td>
+<td class="gt_row gt_right">478.54</td>
+<td class="gt_row gt_right">619.70</td>
+<td class="gt_row gt_right">1.29</td>
+<td class="gt_row gt_right">0.53</td></tr>
+    <tr><td class="gt_row gt_left">USD/PHP</td>
+<td class="gt_row gt_right">54.23</td>
+<td class="gt_row gt_right">39.75</td>
+<td class="gt_row gt_right">49.71</td>
+<td class="gt_row gt_right">1.36</td>
+<td class="gt_row gt_right">0.54</td></tr>
+    <tr><td class="gt_row gt_left">USD/INR</td>
+<td class="gt_row gt_right">77.57</td>
+<td class="gt_row gt_right">45.70</td>
+<td class="gt_row gt_right">73.29</td>
+<td class="gt_row gt_right">1.70</td>
+<td class="gt_row gt_right">0.54</td></tr>
+    <tr><td class="gt_row gt_left">USD/RON</td>
+<td class="gt_row gt_right">4.54</td>
+<td class="gt_row gt_right">2.93</td>
+<td class="gt_row gt_right">4.18</td>
+<td class="gt_row gt_right">1.55</td>
+<td class="gt_row gt_right">0.55</td></tr>
+    <tr><td class="gt_row gt_left">USD/JPY</td>
+<td class="gt_row gt_right">125.63</td>
+<td class="gt_row gt_right">75.74</td>
+<td class="gt_row gt_right">109.90</td>
+<td class="gt_row gt_right">1.66</td>
+<td class="gt_row gt_right">0.55</td></tr>
+    <tr><td class="gt_row gt_left">USD/GBP</td>
+<td class="gt_row gt_right">0.87</td>
+<td class="gt_row gt_right">0.58</td>
+<td class="gt_row gt_right">0.73</td>
+<td class="gt_row gt_right">1.49</td>
+<td class="gt_row gt_right">0.55</td></tr>
+    <tr><td class="gt_row gt_left">USD/JMD</td>
+<td class="gt_row gt_right">153.88</td>
+<td class="gt_row gt_right">83.37</td>
+<td class="gt_row gt_right">150.53</td>
+<td class="gt_row gt_right">1.85</td>
+<td class="gt_row gt_right">0.56</td></tr>
+    <tr><td class="gt_row gt_left">USD/MKD</td>
+<td class="gt_row gt_right">58.92</td>
+<td class="gt_row gt_right">42.07</td>
+<td class="gt_row gt_right">51.98</td>
+<td class="gt_row gt_right">1.40</td>
+<td class="gt_row gt_right">0.58</td></tr>
+    <tr><td class="gt_row gt_left">USD/MDL</td>
+<td class="gt_row gt_right">20.31</td>
+<td class="gt_row gt_right">11.09</td>
+<td class="gt_row gt_right">17.58</td>
+<td class="gt_row gt_right">1.83</td>
+<td class="gt_row gt_right">0.61</td></tr>
+    <tr><td class="gt_row gt_left">USD/BDT</td>
+<td class="gt_row gt_right">84.72</td>
+<td class="gt_row gt_right">72.39</td>
+<td class="gt_row gt_right">84.72</td>
+<td class="gt_row gt_right">1.17</td>
+<td class="gt_row gt_right">0.62</td></tr>
+    <tr><td class="gt_row gt_left">USD/AUD</td>
+<td class="gt_row gt_right">1.74</td>
+<td class="gt_row gt_right">0.93</td>
+<td class="gt_row gt_right">1.37</td>
+<td class="gt_row gt_right">1.88</td>
+<td class="gt_row gt_right">0.63</td></tr>
+    <tr><td class="gt_row gt_left">USD/SEK</td>
+<td class="gt_row gt_right">10.44</td>
+<td class="gt_row gt_right">6.29</td>
+<td class="gt_row gt_right">8.62</td>
+<td class="gt_row gt_right">1.66</td>
+<td class="gt_row gt_right">0.64</td></tr>
+    <tr><td class="gt_row gt_left">USD/CHF</td>
+<td class="gt_row gt_right">1.03</td>
+<td class="gt_row gt_right">0.79</td>
+<td class="gt_row gt_right">0.92</td>
+<td class="gt_row gt_right">1.31</td>
+<td class="gt_row gt_right">0.64</td></tr>
+    <tr><td class="gt_row gt_left">USD/CZK</td>
+<td class="gt_row gt_right">26.03</td>
+<td class="gt_row gt_right">16.75</td>
+<td class="gt_row gt_right">21.67</td>
+<td class="gt_row gt_right">1.55</td>
+<td class="gt_row gt_right">0.64</td></tr>
+    <tr><td class="gt_row gt_left">USD/BWP</td>
+<td class="gt_row gt_right">12.19</td>
+<td class="gt_row gt_right">6.58</td>
+<td class="gt_row gt_right">11.12</td>
+<td class="gt_row gt_right">1.85</td>
+<td class="gt_row gt_right">0.66</td></tr>
+    <tr><td class="gt_row gt_left">USD/NZD</td>
+<td class="gt_row gt_right">1.78</td>
+<td class="gt_row gt_right">1.13</td>
+<td class="gt_row gt_right">1.43</td>
+<td class="gt_row gt_right">1.57</td>
+<td class="gt_row gt_right">0.66</td></tr>
+    <tr><td class="gt_row gt_left">USD/THB</td>
+<td class="gt_row gt_right">36.43</td>
+<td class="gt_row gt_right">28.07</td>
+<td class="gt_row gt_right">32.45</td>
+<td class="gt_row gt_right">1.30</td>
+<td class="gt_row gt_right">0.67</td></tr>
+    <tr><td class="gt_row gt_left">USD/LKR</td>
+<td class="gt_row gt_right">199.43</td>
+<td class="gt_row gt_right">106.22</td>
+<td class="gt_row gt_right">199.43</td>
+<td class="gt_row gt_right">1.88</td>
+<td class="gt_row gt_right">0.67</td></tr>
+    <tr><td class="gt_row gt_left">USD/KRW</td>
+<td class="gt_row gt_right">1,262.93</td>
+<td class="gt_row gt_right">999.83</td>
+<td class="gt_row gt_right">1,165.89</td>
+<td class="gt_row gt_right">1.26</td>
+<td class="gt_row gt_right">0.70</td></tr>
+    <tr><td class="gt_row gt_left">USD/RSD</td>
+<td class="gt_row gt_right">118.47</td>
+<td class="gt_row gt_right">70.05</td>
+<td class="gt_row gt_right">99.29</td>
+<td class="gt_row gt_right">1.69</td>
+<td class="gt_row gt_right">0.70</td></tr>
+    <tr><td class="gt_row gt_left">USD/UYU</td>
+<td class="gt_row gt_right">45.31</td>
+<td class="gt_row gt_right">18.08</td>
+<td class="gt_row gt_right">42.53</td>
+<td class="gt_row gt_right">2.51</td>
+<td class="gt_row gt_right">0.71</td></tr>
+    <tr><td class="gt_row gt_left">USD/PLN</td>
+<td class="gt_row gt_right">4.28</td>
+<td class="gt_row gt_right">2.87</td>
+<td class="gt_row gt_right">3.86</td>
+<td class="gt_row gt_right">1.49</td>
+<td class="gt_row gt_right">0.72</td></tr>
+    <tr><td class="gt_row gt_left">USD/HUF</td>
+<td class="gt_row gt_right">338.26</td>
+<td class="gt_row gt_right">188.61</td>
+<td class="gt_row gt_right">294.66</td>
+<td class="gt_row gt_right">1.79</td>
+<td class="gt_row gt_right">0.74</td></tr>
+    <tr><td class="gt_row gt_left">USD/MUR</td>
+<td class="gt_row gt_right">42.55</td>
+<td class="gt_row gt_right">26.50</td>
+<td class="gt_row gt_right">42.55</td>
+<td class="gt_row gt_right">1.61</td>
+<td class="gt_row gt_right">0.79</td></tr>
+    <tr><td class="gt_row gt_left">USD/MXN</td>
+<td class="gt_row gt_right">25.34</td>
+<td class="gt_row gt_right">11.98</td>
+<td class="gt_row gt_right">20.14</td>
+<td class="gt_row gt_right">2.11</td>
+<td class="gt_row gt_right">0.80</td></tr>
+    <tr><td class="gt_row gt_left">USD/NIO</td>
+<td class="gt_row gt_right">35.13</td>
+<td class="gt_row gt_right">22.05</td>
+<td class="gt_row gt_right">35.00</td>
+<td class="gt_row gt_right">1.59</td>
+<td class="gt_row gt_right">0.84</td></tr>
+    <tr><td class="gt_row gt_left">USD/KZT</td>
+<td class="gt_row gt_right">454.34</td>
+<td class="gt_row gt_right">174.15</td>
+<td class="gt_row gt_right">427.18</td>
+<td class="gt_row gt_right">2.61</td>
+<td class="gt_row gt_right">0.84</td></tr>
+    <tr><td class="gt_row gt_left">USD/QAR</td>
+<td class="gt_row gt_right">3.90</td>
+<td class="gt_row gt_right">3.00</td>
+<td class="gt_row gt_right">3.64</td>
+<td class="gt_row gt_right">1.30</td>
+<td class="gt_row gt_right">0.95</td></tr>
+    <tr><td class="gt_row gt_left">USD/TRY</td>
+<td class="gt_row gt_right">8.78</td>
+<td class="gt_row gt_right">1.71</td>
+<td class="gt_row gt_right">8.38</td>
+<td class="gt_row gt_right">5.12</td>
+<td class="gt_row gt_right">0.97</td></tr>
+    <tr><td class="gt_row gt_left">USD/ZAR</td>
+<td class="gt_row gt_right">19.25</td>
+<td class="gt_row gt_right">6.98</td>
+<td class="gt_row gt_right">14.66</td>
+<td class="gt_row gt_right">2.76</td>
+<td class="gt_row gt_right">0.99</td></tr>
+    <tr><td class="gt_row gt_left">USD/RUB</td>
+<td class="gt_row gt_right">82.90</td>
+<td class="gt_row gt_right">28.79</td>
+<td class="gt_row gt_right">73.50</td>
+<td class="gt_row gt_right">2.88</td>
+<td class="gt_row gt_right">1.05</td></tr>
+    <tr><td class="gt_row gt_left">USD/ZMW</td>
+<td class="gt_row gt_right">22.64</td>
+<td class="gt_row gt_right">5.11</td>
+<td class="gt_row gt_right">15.82</td>
+<td class="gt_row gt_right">4.43</td>
+<td class="gt_row gt_right">1.06</td></tr>
+    <tr><td class="gt_row gt_left">USD/BRL</td>
+<td class="gt_row gt_right">5.89</td>
+<td class="gt_row gt_right">1.58</td>
+<td class="gt_row gt_right">5.19</td>
+<td class="gt_row gt_right">3.72</td>
+<td class="gt_row gt_right">1.08</td></tr>
+    <tr><td class="gt_row gt_left">USD/ARS</td>
+<td class="gt_row gt_right">97.70</td>
+<td class="gt_row gt_right">4.10</td>
+<td class="gt_row gt_right">97.70</td>
+<td class="gt_row gt_right">23.85</td>
+<td class="gt_row gt_right">1.11</td></tr>
+    <tr><td class="gt_row gt_left">USD/TND</td>
+<td class="gt_row gt_right">3.06</td>
+<td class="gt_row gt_right">1.37</td>
+<td class="gt_row gt_right">2.79</td>
+<td class="gt_row gt_right">2.23</td>
+<td class="gt_row gt_right">1.17</td></tr>
+    <tr><td class="gt_row gt_left">USD/BGN</td>
+<td class="gt_row gt_right">1.87</td>
+<td class="gt_row gt_right">1.21</td>
+<td class="gt_row gt_right">1.66</td>
+<td class="gt_row gt_right">1.55</td>
+<td class="gt_row gt_right">1.28</td></tr>
+    <tr><td class="gt_row gt_left">USD/EGP</td>
+<td class="gt_row gt_right">19.60</td>
+<td class="gt_row gt_right">5.83</td>
+<td class="gt_row gt_right">15.65</td>
+<td class="gt_row gt_right">3.37</td>
+<td class="gt_row gt_right">1.29</td></tr>
+    <tr><td class="gt_row gt_left">USD/NOK</td>
+<td class="gt_row gt_right">11.76</td>
+<td class="gt_row gt_right">5.36</td>
+<td class="gt_row gt_right">8.66</td>
+<td class="gt_row gt_right">2.19</td>
+<td class="gt_row gt_right">1.31</td></tr>
+    <tr><td class="gt_row gt_left">USD/PEN</td>
+<td class="gt_row gt_right">4.11</td>
+<td class="gt_row gt_right">2.38</td>
+<td class="gt_row gt_right">4.07</td>
+<td class="gt_row gt_right">1.72</td>
+<td class="gt_row gt_right">1.34</td></tr>
+    <tr><td class="gt_row gt_left">USD/BYN</td>
+<td class="gt_row gt_right">3.08</td>
+<td class="gt_row gt_right">0.51</td>
+<td class="gt_row gt_right">2.51</td>
+<td class="gt_row gt_right">6.04</td>
+<td class="gt_row gt_right">1.37</td></tr>
+    <tr><td class="gt_row gt_left">USD/MAD</td>
+<td class="gt_row gt_right">10.29</td>
+<td class="gt_row gt_right">7.89</td>
+<td class="gt_row gt_right">8.95</td>
+<td class="gt_row gt_right">1.30</td>
+<td class="gt_row gt_right">1.43</td></tr>
+    <tr><td class="gt_row gt_left">USD/UAH</td>
+<td class="gt_row gt_right">33.50</td>
+<td class="gt_row gt_right">7.80</td>
+<td class="gt_row gt_right">26.92</td>
+<td class="gt_row gt_right">4.30</td>
+<td class="gt_row gt_right">1.83</td></tr>
+    <tr><td class="gt_row gt_left">USD/SDG</td>
+<td class="gt_row gt_right">451.00</td>
+<td class="gt_row gt_right">1.39</td>
+<td class="gt_row gt_right">440.03</td>
+<td class="gt_row gt_right">324.46</td>
+<td class="gt_row gt_right">5.96</td></tr>
+    <tr><td class="gt_row gt_left">USD/BND</td>
+<td class="gt_row gt_right">1.43</td>
+<td class="gt_row gt_right">0.66</td>
+<td class="gt_row gt_right">1.34</td>
+<td class="gt_row gt_right">2.18</td>
+<td class="gt_row gt_right">6.29</td></tr>
+    <tr><td class="gt_row gt_left">USD/XOF</td>
+<td class="gt_row gt_right">647.00</td>
+<td class="gt_row gt_right">58.00</td>
+<td class="gt_row gt_right">555.47</td>
+<td class="gt_row gt_right">11.16</td>
+<td class="gt_row gt_right">6.44</td></tr>
+    <tr><td class="gt_row gt_left">USD/IDR</td>
+<td class="gt_row gt_right">16,504.80</td>
+<td class="gt_row gt_right">892.00</td>
+<td class="gt_row gt_right">14,370.00</td>
+<td class="gt_row gt_right">18.50</td>
+<td class="gt_row gt_right">6.58</td></tr>
+    <tr><td class="gt_row gt_left">USD/HNL</td>
+<td class="gt_row gt_right">24.90</td>
+<td class="gt_row gt_right">3.00</td>
+<td class="gt_row gt_right">23.83</td>
+<td class="gt_row gt_right">8.30</td>
+<td class="gt_row gt_right">8.14</td></tr>
+    <tr><td class="gt_row gt_left">USD/MZN</td>
+<td class="gt_row gt_right">78.49</td>
+<td class="gt_row gt_right">3.30</td>
+<td class="gt_row gt_right">63.11</td>
+<td class="gt_row gt_right">23.78</td>
+<td class="gt_row gt_right">8.77</td></tr>
+    <tr><td class="gt_row gt_left">USD/ETB</td>
+<td class="gt_row gt_right">45.23</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">45.06</td>
+<td class="gt_row gt_right">45.23</td>
+<td class="gt_row gt_right">9.26</td></tr>
+    <tr><td class="gt_row gt_left">USD/TWD</td>
+<td class="gt_row gt_right">33.73</td>
+<td class="gt_row gt_right">1.80</td>
+<td class="gt_row gt_right">27.77</td>
+<td class="gt_row gt_right">18.72</td>
+<td class="gt_row gt_right">9.86</td></tr>
+    <tr><td class="gt_row gt_left">USD/PKR</td>
+<td class="gt_row gt_right">168.15</td>
+<td class="gt_row gt_right">2.00</td>
+<td class="gt_row gt_right">165.63</td>
+<td class="gt_row gt_right">84.07</td>
+<td class="gt_row gt_right">12.69</td></tr>
+    <tr><td class="gt_row gt_left">USD/UZS</td>
+<td class="gt_row gt_right">10,653.20</td>
+<td class="gt_row gt_right">83.00</td>
+<td class="gt_row gt_right">10,646.89</td>
+<td class="gt_row gt_right">128.35</td>
+<td class="gt_row gt_right">12.93</td></tr>
+    <tr><td class="gt_row gt_left">USD/GHS</td>
+<td class="gt_row gt_right">573.00</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">5.98</td>
+<td class="gt_row gt_right">573.00</td>
+<td class="gt_row gt_right">13.74</td></tr>
+    <tr><td class="gt_row gt_left">USD/ISK</td>
+<td class="gt_row gt_right">147.04</td>
+<td class="gt_row gt_right">2.00</td>
+<td class="gt_row gt_right">126.65</td>
+<td class="gt_row gt_right">73.52</td>
+<td class="gt_row gt_right">16.09</td></tr>
+    <tr><td class="gt_row gt_left">USD/PGK</td>
+<td class="gt_row gt_right">3.51</td>
+<td class="gt_row gt_right">0.29</td>
+<td class="gt_row gt_right">3.51</td>
+<td class="gt_row gt_right">11.89</td>
+<td class="gt_row gt_right">16.18</td></tr>
+    <tr><td class="gt_row gt_left">USD/MMK</td>
+<td class="gt_row gt_right">1,642.00</td>
+<td class="gt_row gt_right">6.23</td>
+<td class="gt_row gt_right">1,642.00</td>
+<td class="gt_row gt_right">263.65</td>
+<td class="gt_row gt_right">16.59</td></tr>
+    <tr><td class="gt_row gt_left">USD/CLP</td>
+<td class="gt_row gt_right">867.50</td>
+<td class="gt_row gt_right">5.00</td>
+<td class="gt_row gt_right">782.21</td>
+<td class="gt_row gt_right">173.50</td>
+<td class="gt_row gt_right">18.70</td></tr>
+    <tr><td class="gt_row gt_left">USD/SZL</td>
+<td class="gt_row gt_right">1,189.00</td>
+<td class="gt_row gt_right">1.07</td>
+<td class="gt_row gt_right">14.91</td>
+<td class="gt_row gt_right">1,111.21</td>
+<td class="gt_row gt_right">18.79</td></tr>
+    <tr><td class="gt_row gt_left">USD/XPF</td>
+<td class="gt_row gt_right">119.35</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">100.90</td>
+<td class="gt_row gt_right">119.35</td>
+<td class="gt_row gt_right">19.32</td></tr>
+    <tr><td class="gt_row gt_left">USD/SOS</td>
+<td class="gt_row gt_right">1,670.00</td>
+<td class="gt_row gt_right">6.00</td>
+<td class="gt_row gt_right">571.00</td>
+<td class="gt_row gt_right">278.33</td>
+<td class="gt_row gt_right">29.77</td></tr>
+    <tr><td class="gt_row gt_left">USD/MWK</td>
+<td class="gt_row gt_right">812.43</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">804.55</td>
+<td class="gt_row gt_right">812.43</td>
+<td class="gt_row gt_right">30.83</td></tr>
+    <tr><td class="gt_row gt_left">USD/NGN</td>
+<td class="gt_row gt_right">412.50</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">411.00</td>
+<td class="gt_row gt_right">412.50</td>
+<td class="gt_row gt_right">31.65</td></tr>
+    <tr><td class="gt_row gt_left">USD/VND</td>
+<td class="gt_row gt_right">23,631.00</td>
+<td class="gt_row gt_right">21.00</td>
+<td class="gt_row gt_right">22,775.00</td>
+<td class="gt_row gt_right">1,125.29</td>
+<td class="gt_row gt_right">34.09</td></tr>
+    <tr><td class="gt_row gt_left">USD/COP</td>
+<td class="gt_row gt_right">4,174.75</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">3,805.25</td>
+<td class="gt_row gt_right">1,136.67</td>
+<td class="gt_row gt_right">37.09</td></tr>
+    <tr><td class="gt_row gt_left">USD/IQD</td>
+<td class="gt_row gt_right">1,578.00</td>
+<td class="gt_row gt_right">10.00</td>
+<td class="gt_row gt_right">1,458.00</td>
+<td class="gt_row gt_right">157.80</td>
+<td class="gt_row gt_right">37.10</td></tr>
+    <tr><td class="gt_row gt_left">USD/MGA</td>
+<td class="gt_row gt_right">3,931.18</td>
+<td class="gt_row gt_right">0.30</td>
+<td class="gt_row gt_right">3,808.00</td>
+<td class="gt_row gt_right">12,910.29</td>
+<td class="gt_row gt_right">46.29</td></tr>
+    <tr><td class="gt_row gt_left">USD/SLL</td>
+<td class="gt_row gt_right">10,250.50</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">10,250.50</td>
+<td class="gt_row gt_right">10,250.50</td>
+<td class="gt_row gt_right">47.59</td></tr>
+  </tbody>
+  
+  
+</table>
+
+
+My broker trades the following pairs:
+
+``` r
+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`)
+  )
+```
+
+<div id="arrslcwxgh" style="overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
+
+<table class="gt_table">
+  <thead class="gt_header">
+    <tr>
+      <th colspan="6" class="gt_heading gt_title gt_font_normal" style>The Most Promised Currencies</th>
+    </tr>
+    <tr>
+      <th colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>2011-09-01 to 2021-08-30</th>
+    </tr>
+  </thead>
+  <thead class="gt_col_headings">
+    <tr>
+      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1">symbol</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">max_price</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">min_price</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">last_price</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">max_min_rate</th>
+      <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1">100x Volatility</th>
+    </tr>
+  </thead>
+  <tbody class="gt_table_body">
+    <tr><td class="gt_row gt_left">USD/AED</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">3.67</td>
+<td class="gt_row gt_right">1.00</td>
+<td class="gt_row gt_right">0.01</td></tr>
+    <tr><td class="gt_row gt_left">USD/HKD</td>
+<td class="gt_row gt_right">7.85</td>
+<td class="gt_row gt_right">7.75</td>
+<td class="gt_row gt_right">7.79</td>
+<td class="gt_row gt_right">1.01</td>
+<td class="gt_row gt_right">0.03</td></tr>
+    <tr><td class="gt_row gt_left">USD/CNY</td>
+<td class="gt_row gt_right">7.18</td>
+<td class="gt_row gt_right">6.03</td>
+<td class="gt_row gt_right">6.47</td>
+<td class="gt_row gt_right">1.19</td>
+<td class="gt_row gt_right">0.23</td></tr>
+    <tr><td class="gt_row gt_left">USD/SGD</td>
+<td class="gt_row gt_right">1.46</td>
+<td class="gt_row gt_right">1.20</td>
+<td class="gt_row gt_right">1.34</td>
+<td class="gt_row gt_right">1.21</td>
+<td class="gt_row gt_right">0.33</td></tr>
+    <tr><td class="gt_row gt_left">USD/EUR</td>
+<td class="gt_row gt_right">0.96</td>
+<td class="gt_row gt_right">0.70</td>
+<td class="gt_row gt_right">0.85</td>
+<td class="gt_row gt_right">1.38</td>
+<td class="gt_row gt_right">0.51</td></tr>
+    <tr><td class="gt_row gt_left">USD/JPY</td>
+<td class="gt_row gt_right">125.63</td>
+<td class="gt_row gt_right">75.74</td>
+<td class="gt_row gt_right">109.90</td>
+<td class="gt_row gt_right">1.66</td>
+<td class="gt_row gt_right">0.55</td></tr>
+    <tr><td class="gt_row gt_left">USD/GBP</td>
+<td class="gt_row gt_right">0.87</td>
+<td class="gt_row gt_right">0.58</td>
+<td class="gt_row gt_right">0.73</td>
+<td class="gt_row gt_right">1.49</td>
+<td class="gt_row gt_right">0.55</td></tr>
+    <tr><td class="gt_row gt_left">USD/AUD</td>
+<td class="gt_row gt_right">1.74</td>
+<td class="gt_row gt_right">0.93</td>
+<td class="gt_row gt_right">1.37</td>
+<td class="gt_row gt_right">1.88</td>
+<td class="gt_row gt_right">0.63</td></tr>
+    <tr><td class="gt_row gt_left">USD/SEK</td>
+<td class="gt_row gt_right">10.44</td>
+<td class="gt_row gt_right">6.29</td>
+<td class="gt_row gt_right">8.62</td>
+<td class="gt_row gt_right">1.66</td>
+<td class="gt_row gt_right">0.64</td></tr>
+    <tr><td class="gt_row gt_left">USD/CHF</td>
+<td class="gt_row gt_right">1.03</td>
+<td class="gt_row gt_right">0.79</td>
+<td class="gt_row gt_right">0.92</td>
+<td class="gt_row gt_right">1.31</td>
+<td class="gt_row gt_right">0.64</td></tr>
+    <tr><td class="gt_row gt_left">USD/MXN</td>
+<td class="gt_row gt_right">25.34</td>
+<td class="gt_row gt_right">11.98</td>
+<td class="gt_row gt_right">20.14</td>
+<td class="gt_row gt_right">2.11</td>
+<td class="gt_row gt_right">0.80</td></tr>
+    <tr><td class="gt_row gt_left">USD/KZT</td>
+<td class="gt_row gt_right">454.34</td>
+<td class="gt_row gt_right">174.15</td>
+<td class="gt_row gt_right">427.18</td>
+<td class="gt_row gt_right">2.61</td>
+<td class="gt_row gt_right">0.84</td></tr>
+    <tr><td class="gt_row gt_left">USD/TRY</td>
+<td class="gt_row gt_right">8.78</td>
+<td class="gt_row gt_right">1.71</td>
+<td class="gt_row gt_right">8.38</td>
+<td class="gt_row gt_right">5.12</td>
+<td class="gt_row gt_right">0.97</td></tr>
+    <tr><td class="gt_row gt_left">USD/RUB</td>
+<td class="gt_row gt_right">82.90</td>
+<td class="gt_row gt_right">28.79</td>
+<td class="gt_row gt_right">73.50</td>
+<td class="gt_row gt_right">2.88</td>
+<td class="gt_row gt_right">1.05</td></tr>
+    <tr><td class="gt_row gt_left">USD/BYN</td>
+<td class="gt_row gt_right">3.08</td>
+<td class="gt_row gt_right">0.51</td>
+<td class="gt_row gt_right">2.51</td>
+<td class="gt_row gt_right">6.04</td>
+<td class="gt_row gt_right">1.37</td></tr>
+  </tbody>
+  
+  
+</table>
+</div>
+
+Plot exchange rate for out favorites:
+
+Define low risk symbols:
+
+``` r
+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']
+
+``` r
+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)
+    )
+```
+
+![](fx_currencies_analysis_files/figure-gfm/unnamed-chunk-3-1.png)<!-- -->
--- a/src/fx_currencies_analysis_files/figure-gfm/unnamed-chunk-3-1.png
+++ b/src/fx_currencies_analysis_files/figure-gfm/unnamed-chunk-3-1.png
--- a/src/fx_currency_portfolio__assets_selection.ipynb
+++ b/src/fx_currency_portfolio__assets_selection.ipynb
--- a/src/fx_currency_portfolio__assets_selection.py
+++ b/src/fx_currency_portfolio__assets_selection.py
@ -0,0 +1,242 @@
+#!/usr/bin/python3
+
+"""Currency Portfolio: Assets Selection.
+
+Description:
+    Currency Selection in anti-crisis portfolio using Monte Carlo simulation.
+"""
+
+# %% Import dependencies ----
+# core
+import sys
+import warnings
+import gc
+from IPython import sys_info
+
+# data science
+import pandas as pd
+import numpy as np
+from scipy.stats import norm
+
+# Cloud integration
+from azureml.core import Workspace, Dataset, ComputeTarget, VERSION as aml_version
+print(f'Azure ML SDK v{aml_version}')
+# plots
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# show info about python env
+print(sys_info())
+warnings.filterwarnings("ignore")
+
+
+# %% Set params ----
+n_days = int(252)  # US market has 252 trading days in a year
+n_simulations = int(1e4)  # number of Monte-Carlo simulations
+
+# The most promised currencies (copy this list from fx_currencies_analysis.Rmd)
+symbols = [
+    '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'
+]
+
+
+# %% Connect to Azure ML workspace ----
+ws =  Workspace.from_config()
+print(f"Connected to *{ws.get_details()['friendlyName']}* workspace in *{ws.get_details()['location']}*.")
+
+print('Compute Targets:')
+for compute_name in ws.compute_targets:
+    compute = ws.compute_targets[compute_name]
+    print('\t', compute.name, ':', compute.type)
+
+# > htop
+
+
+# %% Load dateset ----
+currencies_ds = Dataset.get_by_name(ws, name='Currencies')
+currencies_ds.to_pandas_dataframe()
+
+print(f'Dataset name: {currencies_ds.name}. Description: {currencies_ds.description}.')
+print(f'Size of Azure ML dataset object: {sys.getsizeof(currencies_ds)} bytes.')
+
+
+# %% Preprocessing ----
+quotes_df = (currencies_ds
+    # materialize
+    .to_pandas_dataframe()
+    # define format
+    .rename(columns={'slug': 'symbol'})
+    .loc[:, ['symbol', 'date', 'close']]
+    # filter
+    .query("symbol in @symbols")
+    .query("date > '2012-01-01'")
+    # set time index
+    .set_index('date')
+    .sort_values(by='date'))
+
+quotes_df
+
+
+# %% Discover data ----
+quotes_df.groupby('symbol')['close'].agg(['count', 'last'])
+
+
+# %% USD/RUB dataset ----
+usdrub_df = quotes_df[quotes_df.symbol == 'USD/RUB']
+usdrub_df
+
+
+# %% Calculate Return ----
+def calc_returns(close_prices: pd.Series) -> pd.Series:
+    """Calculate Investment Return"""
+    return (close_prices/close_prices.shift()) - 1
+
+
+usdrub_df['diff'] = usdrub_df['close'].diff()
+usdrub_df['return'] = calc_returns(usdrub_df['close'])
+
+usdrub_df[['close', 'diff', 'return']].tail(10)
+
+
+# %% Calculate LogReturn ----
+def calc_log_returns(return_prices: pd.Series) -> pd.Series:
+    """Calculate Log Return"""
+    return np.log(1 + return_prices)
+
+usdrub_df['log_return'] = usdrub_df['return'].apply(lambda x: calc_log_returns(x))
+
+usdrub_df[['close', 'diff', 'return', 'log_return']].tail(10)
+
+
+# %% Simulate possible LogReturns ----
+
+def calc_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int) -> pd.Series:
+    """Calculate Simulated Return"""
+
+    u = log_returns.mean()
+    var = log_returns.var()
+    stdev = log_returns.std()
+
+    drift = u - (0.5*var)
+    Z = norm.ppf(np.random.rand(n_days, n_iterations))
+
+    return np.exp(drift + stdev*Z)
+
+
+usdrub_simulated_returns = calc_simulated_returns(
+    usdrub_df['log_return'].dropna(),
+    n_days,
+    n_simulations)
+
+assert(
+    usdrub_simulated_returns.shape == (n_days, n_simulations)
+    and (usdrub_simulated_returns > 0).all()
+    and (usdrub_simulated_returns < 2).all()
+)
+
+
+# %% Monte carlo simulation evaluation ----
+
+def get_breakeven_prob(pred, risk_free_rate: float = 0.02) -> pd.Series:
+    """
+    Calculation of the probability of a stock being above a certain threshold,
+    which can be defined as a value (final stock price) or return rate (percentage change).
+    """
+
+    init_pred = pred.iloc[0, 0]
+
+    last_pred = pred.iloc[-1]
+    pred_list = list(last_pred)
+
+    over = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred >= risk_free_rate]
+    less = [(p*100)/init_pred for p in pred_list if ((p-init_pred)*100)/init_pred < risk_free_rate]
+
+    return len(over)/(len(over) + len(less))
+
+
+def evaluate_simulation(simulated_returns: pd.Series, last_actual_price: float, n_days: int, plot: bool = True) -> pd.DataFrame:
+    """
+    Evaluate Monte-Carlo simulations result
+    """
+
+    # Create empty matrix
+    price_list = np.zeros_like(simulated_returns)
+
+    # Put the last actual price in the first row,
+    # and calculate the price of each day
+    price_list[0] = last_actual_price
+    for t in range(1, n_days):
+        price_list[t] = price_list[(t - 1)]*simulated_returns[t]
+
+    # convert to temp dataframe
+    price_df = pd.DataFrame(price_list)
+
+    # Plot
+    if plot == True:
+        x = price_df.iloc[-1]
+        fig, ax = plt.subplots(1, 2, figsize=(14, 4))
+        sns.distplot(x, ax=ax[0])
+        sns.distplot(x, hist_kws={'cumulative': True}, kde_kws={'cumulative':True}, ax=ax[1])
+        plt.xlabel('Stock Price')
+        plt.show()
+
+    print('Results:')
+    print(f'\tInvestment period: {n_days-1} days')
+    print(f'\tExpected Value: {round(price_df.iloc[-1].mean(), 2)} per USD')
+    print(f'\tReturn: {round(100*(price_df.iloc[-1].mean() - price_list[0,1])/price_df.iloc[-1].mean(), 2)}%')
+    print(f'\tProbability of Breakeven: {get_breakeven_prob(price_df)}')
+
+    return price_df
+
+
+# %% Run Monte carlo simulation and estimate result ----
+
+usdrub_mc_simulation_df = evaluate_simulation(
+    usdrub_simulated_returns,
+    last_actual_price=usdrub_df['close'].tail(1),
+    n_days=n_days)
+
+
+plt.figure(figsize=(10, 6))
+plt.plot(usdrub_mc_simulation_df.sample(10, axis='columns'))
+plt.title('USD/RUB Price Simulation')
+plt.xlabel('Days')
+plt.ylabel('RUB per $1')
+plt.ylim(10, 300)
+plt.show()
+
+
+# %% Monte Carlo simulation pipeline for multiple tokens ----
+
+# 1. prepare
+quotes_data = [quotes_df.query('symbol == @s') for s in quotes_df.symbol.unique()]
+symbols_list = [df.symbol.unique() for df in quotes_data]
+
+# 2. simulate
+returns_data = [calc_returns(df['close']) for df in quotes_data]
+log_returns_data = [calc_log_returns(r) for r in returns_data]
+simulated_returns_data = [calc_simulated_returns(lr, n_days, n_simulations) for lr in log_returns_data]
+
+assert(
+    len(quotes_data) > 0
+    and len(quotes_data) == len(symbols_list) == len(returns_data) == len(log_returns_data) == len(simulated_returns_data)
+)
+
+# 3. evaluate
+for i in range(len(symbols_list)):
+    print(f'---- Starting Monte-Carlo simulation for {symbols_list[i]} symbol ----')
+
+    prices_ms = evaluate_simulation(simulated_returns_data[i], quotes_data[i]['close'].tail(1), n_days, plot=False)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(prices_ms.sample(50, axis='columns'))
+    plt.title(f'{symbols_list[i]} Price Simulation')
+    plt.xlabel('Days')
+    plt.ylabel('Amount per $1')
+    plt.show()
+
+
+# %% Completed ----
+gc.collect()
+