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@ -111,7 +111,9 @@ quotes_df %<>%
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na.omit
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```
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Base and quote currencies:
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## Discover Data
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Calculate statistics and `volatility`:
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```{r discover}
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quotes_stats <- quotes_df %>%
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@ -124,14 +126,7 @@ quotes_stats <- quotes_df %>%
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volatility = sd(log_return)
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)
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volatility_threshold <- quotes_stats %>% pull(volatility) %>% quantile(probs = .75)
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quotes_stats %>%
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filter(
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volatility <= volatility_threshold |
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symbol == "USD/RUB"
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) %>%
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mutate(
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`100x Volatility` = volatility*100
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) %>%
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@ -140,14 +135,14 @@ quotes_stats %>%
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gt() %>%
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tab_header(
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title = "The Least Volatile Currencies",
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title = "The Least and The Most Volatile Currencies",
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subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
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) %>%
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fmt_number(
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columns = c(max_price, min_price, max_min_rate, `100x Volatility`)
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columns = c(max_price, min_price, max_min_rate, last_price, `100x Volatility`)
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)
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```
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My broker available pairs:
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My broker trades the following pairs:
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```{r}
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symbols <- c(
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@ -156,9 +151,12 @@ symbols <- c(
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'AED', 'KZT', 'BYN', 'TRY', 'MXN'
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)
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symbols <- str_c("USD", symbols, sep = "/")
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quotes_stats %>%
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filter(
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symbol %in% str_c("USD", symbols, sep = "/")
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symbol %in% symbols
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) %>%
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mutate(
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`100x Volatility` = volatility*100
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@ -172,26 +170,21 @@ quotes_stats %>%
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subtitle = glue("{min(quotes_df$date)} to {max(quotes_df$date)}")
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) %>%
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fmt_number(
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columns = c(max_price, min_price, max_min_rate, `100x Volatility`)
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columns = c(max_price, min_price, last_price, max_min_rate, `100x Volatility`)
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)
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```
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Plot exchange rate for out favorites:
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```{r}
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```
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Define low risk symbols:
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```{r}
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usdrub_vol <- quotes_stats %>% filter(symbol == "USD/RUB") %>% pull(volatility)
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low_risk_symbols <- quotes_stats %>%
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filter(
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symbol %in% str_c("USD", symbols, sep = "/")
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) %>%
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filter(
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volatility <= volatility_threshold |
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symbol == "USD/RUB"
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symbol %in% symbols &
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volatility <= usdrub_vol
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) %>%
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pull(symbol) %>%
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unique
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@ -204,4 +197,26 @@ cat(
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```
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```{r}
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jumper_symbols <- quotes_stats %>% filter(max_min_rate > 2) %>% pull(symbol)
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quotes_df %>%
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filter(symbol %in% low_risk_symbols) %>%
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mutate(
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jumper = if_else(symbol %in% jumper_symbols, T, F)
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) %>%
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group_by(symbol) %>%
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mutate(R = cumsum(return)) %>%
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ggplot +
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geom_line(aes(x = date, y = R, color = symbol)) +
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facet_grid(jumper ~ ., scales = "free") +
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theme_bw()
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```
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