# DCA vs Lump Sum (2021-01 edition)

# Introduction

We are revisiting the Dollar Cost Averaging (**DCA**) vs Lump Sum (**LS**) article, quick and fast with fresh and (less boring) format :)

```
# How much are we going to invest?
investment_value <- 10000
#what is the value of one dca chunk?
dca_n <- 12
dca_by <- 'month'
dca_chunk <- round(investment_value/dca_n, 2)
```

This time, we are investing $10000 in 2 ways:

- Either as a Lump Sum (
**LS**) on a given day - or as dollar-cost averaged (
**DCA**) investment in 12 installments every month. (Each installment represents $833.33)

In order to keep it fair, money not invested yet with **DCA** is considered to be part of the returns. (You still have some installments to make, so you still have the money at present day sitting at 0% valuation)

# Loading the data

For BTCUSD pair, we take prices at daily close value on Bitfinex (easily available at http://www.cryptodatadownload.com/data/bitfinex/ ). If you need, you can easily supplement your own.

```
# Read close prices for the day
data <- read_csv('http://www.cryptodatadownload.com/cdd/Bitfinex_BTCUSD_d.csv', skip = 1) %>%
select(DATE = date, PRICE = close) %>%
arrange(DATE)
```

Our dataset contains

```
# What is the price and date 'today' ?
price_start <- data %>% head(1) %>% pull(PRICE)
date_start <- data %>% head(1) %>% pull(DATE)
price_now <- data %>% tail(1) %>% pull(PRICE)
date_now <- data %>% tail(1) %>% pull(DATE)
```

BTC prices from 2015-02-09 up to 2021-01-10 . With these, we can calculate values of our **LS** and **DCA** investments, started at any given day.

# Calculating present value of investment

Now, let’s calculate present value of an investment made on any given day in past

```
# today's value of our lump sum investment executed on a given day
d1 <- data %>%
mutate(LUMP = round((!!investment_value)/PRICE*(!!price_now))) %>%
# helper - we also calculate today's value of a DCA installment executed on this day
# this way we do not have to recount it multiple times later
mutate(DCA_CHUNK_VALUE = (!!dca_chunk)/PRICE*(!!price_now))
# calcualte leftover value for DCA investments executed late
d1$DCA_LEFTOVER <- vapply(
#for each date
d1$DATE,
function(d) {
# see on which days the DCA installements are made
dates <- seq(d, length.out = dca_n, by = dca_by)
# calculate how many are we missing for a given date
missing_installements <- length(dates[dates > date_now])
# return the leftover
missing_installements * dca_chunk
},
0.01 #return double
)
# today's value of DCA investment, with execution started on a given day
d1$DCA_12M <- vapply(
# for each date
data$DATE,
function(d) {
#calculate the sum of
sum(
#price on 12 months following the date
d1[d1$DATE %in% seq(d, length.out = dca_n, by = dca_by), ] %>%
pull(DCA_CHUNK_VALUE)
) #sum
},
0.01 #return double
)
# summarise DCA earnings
d1$DCA <- round(d1$DCA_12M + d1$DCA_LEFTOVER)
# compare winning strategy
d1$WIN <- case_when(d1$LUMP>d1$DCA ~ 'LUMP', T ~ 'DCA')
```

# Results

## Overall performance

Performance of both strategies can be visualised as follows

```
d1 %>%
ggplot(aes(x = DATE)) +
geom_line(aes(y = LUMP, color = 'LS')) +
geom_line(aes(y = DCA, color = 'DCA, 12 monthly installments')) +
scale_y_log10(labels = scales::comma) +
labs(
title = 'Present value of BTC investment executed on a given day',
y = 'Present value',
x = 'Date',
color = '',
caption = sprintf('Presented by Shitcoin Ninja (%s)', date_now)
) +
theme(legend.position = 'bottom')
```

Aside from the fact, that we did not lose money no matter what strategy or time to invest have we chosen (up to this day™), we can draw several conclusions:

```
res1 <- d1 %>%
group_by(WIN) %>%
tally() %>%
pivot_wider(names_from = WIN, values_from = n)
dca_wins <- res1 %>% pull(DCA)
lump_wins <- res1 %>% pull(LUMP)
total_days <- dca_wins + lump_wins
dca_wins_p <- round(dca_wins/total_days * 100, 2)
lump_wins_p <- round(lump_wins/total_days * 100, 2)
```

From 2015-02-09 to 2021-01-10 (2163 days), investing via **LS** would win 1665 times (76.98%) while investing via **DCA** would win 498 times (23.02)

```
dca_win_by <- d1 %>% filter(WIN == 'DCA') %>% mutate(DCA_WIN_BY = DCA - LUMP) %>% pull(DCA_WIN_BY)
lump_win_by <- d1 %>% filter(WIN == 'LUMP') %>% mutate(LUMP_WIN_BY = LUMP - DCA) %>% pull(LUMP_WIN_BY)
dca_mean_win_by <- round(mean(dca_win_by))
lump_mean_win_by <- round(mean(lump_win_by))
dca_median_win_by <- round(median(dca_win_by))
lump_median_win_by <- round(median(lump_win_by))
```

We can also look at how good or bad these strategies perform on the days they are winning

- On the days
**DCA**wins, it wins over**LS**by $16212 on average (or $13998 in median value) - On the days
**LS**wins, it wins over**DCA**by $165052 on average (or $91952 in median value)

So, not only does **DCA** win only in 23.02% of cases, its performance is on average, 10.18 times **worse** even when it wins, and its median value is 6.57 times **worse**

## Difference in performance illustrated

```
group_transitions = function(x) c(0,cumsum(x[-1L] != x[-length(x)]))+1
d1 %>% mutate(DIFF = LUMP-DCA, SIGN = DIFF < 0) %>%
ggplot(aes(x = DATE)) +
geom_line(aes(y = DIFF, color = WIN, group=group_transitions(SIGN))) +
geom_hline(aes(yintercept = -dca_median_win_by, color = 'DCA', linetype = 'DCA')) +
geom_hline(aes(yintercept = lump_median_win_by, color = 'LUMP', linetype = 'LUMP')) +
scale_linetype_manual(name = "Median performance", values = c(3, 3)) +
scale_y_continuous(labels = scales::comma) +
labs(
title = 'Difference in strategy performance',
y = 'Performance difference (LUMP - DCA)',
x = 'Date',
color = 'Winning strategy',
caption = sprintf('Presented by Shitcoin Ninja (%s)', date_now),
linetype = 'asdf'
) +
theme(legend.position = 'bottom')
```

# Conclusion

If you decide to invest in BTC, Lump Sum yields statistically superior results. Past performance shows, that Lump Sum wins in 76.98% of cases, and when it wins, it performs 10.18 times better.

Source code for this is also available on github. Feel free to copy, change and improve it.