# DCA vs Lump Sum (2021-01 edition) Image credit: Chris Liverani

# 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)

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
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. ##### Shitcoin Ninja
###### Crypto Enthusiast

My research interests include blockchain, cryptocurrencies and market analysis.