DCA vs Lump Sum

What this comparison actually measures

Two ways of entering the same trade. A lump sum buys everything on the signal date and sells everything on the exit date. A DCA spread splits the same total into equal daily orders across a number of days and uses the average price it paid. Both end up holding the same coin count target; the difference is the entry and exit price they achieved.

That means the question is not “is dollar-cost averaging good?” It is narrower: inside a fixed window with a known start and a known end, did spreading orders add or remove value? The table above answers that per cycle for whatever amount and day counts you choose.

Why the window changes the answer

BTC500 fixes both ends of the trade in advance: buy at T-500, sell at T+500. A DCA spread moves part of the position off those dates, so its result depends on which direction price moved immediately after the signal.

• If price falls after the buy signal, a spread buys more of the position cheaper and beats the lump sum.
• If price rises after the buy signal, a spread pays progressively more and loses to the lump sum.
• On the exit side the effect mirrors: selling into a falling market beats selling all at once, and selling into a rising market loses to it.

Because the four completed cycles had very different paths off the buy date, the winner flips between them. A strategy that wins in two cycles and loses in two is not a rule — it is a coin flip with extra steps, fees and admin.

How the numbers are calculated

1. The investment amount is divided into equal daily slices across the chosen buy days.
2. Each slice is filled at that day's closing price. Days without a price are skipped and the day count drops accordingly, which is why some cycles show fewer days used.
3. The average price paid is the arithmetic mean of the slices, not the price on the first or last day.
4. The same logic is applied to the sell side; a sell-days value of 0 means the whole position exits on the signal date.
5. The multiplier is average sell price divided by average buy price, and the return is that multiplier applied to the amount entered.

The costs a backtest hides

Every extra order has a cost the daily-close model does not see. Exchange fees are charged per fill, the spread is crossed each time, and a large order placed in slices can move the book. On the tax side, more fills usually means more lots, more records and — depending on jurisdiction — more taxable events. None of that appears in the multiplier above, and all of it leans against the DCA side of the comparison.

When spreading orders is the right call

DCA is usually chosen for reasons the table cannot score. A position that is too large to execute in one order, cash that arrives in instalments, or a holder who would abandon the plan after a single bad fill are all legitimate reasons to spread. In those cases the comparison is not “which made more money” but “which version of this plan actually gets completed.” A slightly lower multiplier that you finish beats a higher one you exit early.

Limits of this model

Prices are daily closes, fees and taxes are excluded, and four completed cycles is a small sample. The tool also assumes the full amount is available on day one, which is precisely the situation DCA is meant to avoid. Read the result as a description of the price path, not as a forecast for the next window.

For the underlying dates see Bitcoin halving dates, replay the cycles day by day in the time machine, or run a single entry and exit in the simulator.

DCA vs lump sum — frequently asked questions

Is DCA better than lump sum for Bitcoin?

Neither wins in general. Inside a fixed two-date window the answer depends on the price path between the two dates: spreading buys helps when price falls after the signal, and hurts when price rises. Across the completed BTC500 cycles the split is close to even, which is why this page reports the result per cycle instead of claiming a winner.

Does the BTC500 rule itself use DCA?

No. BTC500 is defined as a single buy 500 days before the halving and a single sell 500 days after it. This page exists to test whether overriding that with a spread of orders would have improved the outcome, and to show that the answer is not consistent.

How many days should the DCA window be?

There is no optimal number that holds across cycles. A short window (1–7 days) behaves almost like a lump sum and mostly changes execution risk. A long window (30–90 days) moves a meaningful part of the position away from the signal date, which is where the differences come from. The page defaults to 30 days as a middle setting, not as a recommendation.

Are fees and taxes included?

No. The comparison uses daily closing prices and ignores exchange fees, bid-ask spreads, slippage and taxes. Those costs rise with the number of orders, so the real-world gap between a lump sum and a 30-day spread is slightly worse for the DCA side than the numbers shown here.

Should I DCA into the next halving window?

That is a personal decision about behaviour and cash flow, not something the historical table settles. Spreading orders can make a large position easier to execute and easier to hold through a drawdown, but it also removes part of the position from the exact signal date. The tool shows the cost of that trade-off in past cycles so it can be judged with numbers rather than intuition.