Competition - bitcoin

Should your fund invest in Bitcoin?

📖 Background

You work as an analyst at an investment fund in New York. Your CFO wants to explore if it is a good idea to invest some of the fund's assets in Bitcoin. You have to prepare a report on this asset and how it compares to the stock market in general.

💾 The data


#library(gridExtra) gridExtra::grid.arrange()

install.packages('tidyquant')
install.packages('ggdark')

library(tidyverse)
library(tidyquant)

Bitcoin daily data in US dollars

"date" - date from September 17, 2014 to November 17, 2021
"open" - the price at the beginning of the trading day
"high" - the highest price reached that day
"low" - the lowest price reached that day
"close" - the price at the closing of the trading day
"volume" - how many Bitcoin were traded that day


bitcoin <- read_csv("./data/bitcoin-usd.csv", show_col_types = FALSE) %>% 
    drop_na() %>% glimpse()

S&P 500 daily data

"date" - date from September 17, 2014 to November 17, 2021
"open" - the index level at the beginning of the trading day
"high" - the highest level reached that day
"low" - the lowest level reached that day
"close" - the level at the closing of the trading day
"volume" - how many shares in the companies that make up the index were traded that day


sp500 <- read_csv("./data/sp500.csv", show_col_types = FALSE) %>% 
    glimpse()

Inflation and gold as monthly data

"date" - date from September, 2014 to November, 2021
"gold_usd" - price in usd of gold for that month
"cpi_us" - the inflation index for the US for that month (cpi = consumer price index)

CPI data from the U.S. Bureau of Labor Statistics (https://www.bls.gov/cpi/). Publicly available information.


monthly_data <- read_csv("./data/monthly_data.csv", show_col_types = FALSE) %>% 
    glimpse()

Overall growth per week & total percent increase since September 2014


# growth days = close - open / days
#((60276 - 457) / 2615) * 7
#paste0(scales::comma((60276 - 457) / 457 * 100, 1), "%")

bitcoin %>%
    filter(date > "2021-06-01") %>%
    ggplot(aes(date, close, volume = volume, 
               color_up = "#7FFF00", color_down = "red", 
               fill_up = "#7FFF00", fill_down = "red")) + 
    geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
    geom_smooth(method = 'lm', formula = 'y~x', se = FALSE) +
    scale_y_continuous(breaks = seq(0, 100000, 5000), labels = scales::dollar) +
    scale_x_date(date_labels = '%b %y', date_breaks = '1 week') +
    ggdark::dark_theme_classic() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = paste0("Bitcoin Jun 2021-Present"), y = '', x = '',
         caption = paste0("*overall growth rate since ", 
                          format(as.Date(dplyr::first(bitcoin$date)), "%b %d,%Y") )) +
    annotate("text", x = as.Date('2021-07-01'), y = 60000 , 
             label = paste0("Overall growth: $", 
            round(((dplyr::last(bitcoin$close) - dplyr::first(bitcoin$close)) / nrow(bitcoin)) * 7, 2),
                            "/week")) +
    annotate("text", x = as.Date('2021-07-01'), y = 55000,
             label = paste0("Percent increase: ",
             round((dplyr::last(bitcoin$close) - dplyr::first(bitcoin$close)) / dplyr::first(bitcoin$close) 
             * 100, 2), "%"))

monthly_data %>%
    ggplot(aes(date, gold_usd)) +
    geom_point(color = 'chartreuse') +
    geom_line(color = 'chartreuse') +
    geom_smooth(method = 'lm', formula = 'y~x', se = FALSE) +
    scale_y_continuous(breaks = seq(0, 3000, 100), labels = scales::dollar) +
    scale_x_date(date_labels = '%b %y', date_breaks = '4 months') +
    ggdark::dark_theme_classic() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
    labs(title = "Gold 2014-Present", y = '', x = '') +
    annotate("text", x = min(monthly_data$date), y = max(monthly_data$gold_usd),
             hjust = -0.42, vjust = 5.42,
             label = paste0("Overall growth: $", 
    round(((last(monthly_data$gold_usd) - first(monthly_data$gold_usd)) / nrow(monthly_data)) / 4.333, 2), 
        "/week")) +
    annotate("text", x = min(monthly_data$date), y = max(monthly_data$gold_usd), 
             hjust = -0.42, vjust = 10.42, 
             label = paste0("Percent increase: ",
             round((last(monthly_data$gold_usd) - first(monthly_data$gold_usd)) / first(monthly_data$gold_usd)
                   * 100, 2), "%"))

sp500 %>%
    filter(date > '2021-06-01') %>%
    ggplot(aes(factor(date), close, volume = volume, 
               color_up = "#7FFF00", color_down = "red", 
               fill_up = "#7FFF00", fill_down = "red")) + 
    geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
    scale_y_continuous(breaks = seq(0, 5000, 100), labels = scales::comma) +
    ggdark::dark_theme_classic() +
    theme(axis.text.x = element_blank()) +
    labs(title = "S&P 500 Jun 2021-Present", y = '', x = '',
         caption = paste0("*overall growth rate since ", 
                          format(as.Date(dplyr::first(sp500$date)), "%b %d,%Y") )) +
    annotate("text", x = '2021-07-01', y = 4650,
             label = paste0("Overall growth: ", 
                            round((last(sp500$close) - first(sp500$close)) / nrow(sp500) * 5, 2), 
                            " points/week")) +
    annotate("text", x = '2021-07-01', y = 4600,
             label = paste0("Percent increase: ",
                            round((last(sp500$close) - first(sp500$close)) / first(sp500$close) * 100, 2)
                            , "%"))

Everything goes up, makes sense to put your life savings into Bitcoin ASAP.

Go big or go home.

Bitcoin runs the world.

💪 Competition challenge

Create a report that covers the following:

How does the performance of Bitcoin compare to the S&P 500 and the price of gold?
Analyze Bitcoin's returns and volatility profile. Do you believe it could help improve the performance of a portfolio? Do you believe Bitcoin could be used as a hedge versus inflation?
The CFO is looking to lower volatility in the fund. Explore building a portfolio using some or all of these assets. Make a recommendation that minimizes overall risk.

🧑‍⚖️ Judging criteria

Recommendations (35%)

Clarity of recommendations - how clear and well presented the recommendation is.
Quality of recommendations - are appropriate analytic techniques used & are the conclusions valid?
Number of relevant insights found for the target audience.

Story telling (30%)

How well the data and insights are connected to the recommendation.
How the narrative and whole report connects together.
Balancing making the report in depth enough but also concise.

Visualizations (25%)

Appropriateness of visualization used.
Clarity of insight from visualization.

Upvotes (10%)

Upvoting - most upvoted entries get the most points.

✅ Checklist before publishing into the competition

Rename your workspace to make it descriptive of your work. N.B. you should leave the notebook name as notebook.Rmd.
Remove redundant cells like the judging criteria so the workbook is focused on your story.
Make sure the workbook reads well and explains how you found your insights.
Check that all the cells run without error.