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

Currency portfolio: assets selection
This commit is contained in:
Dmitry Petukhov 2022-04-04 20:50:14 +03:00 committed by GitHub
commit e68f56d9a8
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 2960 additions and 2 deletions

View File

@ -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`

11
faq.md
View File

@ -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%.
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.

View File

@ -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()

248
src/fx_currencies_analysis.Rmd Executable file
View File

@ -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)
)
```

View File

@ -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)<!-- -->

Binary file not shown.

After

Width:  |  Height:  |  Size: 209 KiB

File diff suppressed because one or more lines are too long

View File

@ -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()