Crypto
Dollar-Cost Averaging (DCA) as a System
Same total dollars, same asset, two different entry schedules. Reroll the price path a few times and watch which one wins stop being predictable.
Race lump sum against DCA
$10,000 goes in two ways on the same volatile price path: all at once on day one, or spread evenly across a set number of installments. Set the installment count, run the race, and reroll the path as many times as you want. Track how often each approach actually comes out ahead.
How it works
- DCA replaces one entry decision with many smaller ones. Instead of picking a single moment to deploy all of the capital, it splits the total into equal installments bought at fixed intervals regardless of price, removing the pressure of trying to time any single entry.
- The average cost basis is what actually gets compared. Lump sum locks in day one's price as the entire cost basis. DCA blends together whatever prices happened to print across every installment, which can land above or below where a lump sum would have gone in.
- Mathematically, lump sum wins more often than not in a rising market. If the asset trends up over the full period, more time in the market at the lower early price usually beats a series of installments bought at progressively higher prices. DCA's real edge shows up specifically when the early part of the period is volatile or falling.
- DCA's actual case isn't "better returns," it's smoother entry risk. It reduces the odds of putting all the capital in right before a sharp drop, at the cost of also reducing the odds of putting all the capital in right before a sharp rise. Rerolling the path above a few times is the fastest way to feel that tradeoff instead of just being told about it.
Where this breaks
Treating DCA as a return-maximizing strategy instead of a risk-reducing habit
DCA gets marketed as a way to "beat the market timers," but on average, across most historical stretches of most trending assets, a lump sum invested immediately has a higher expected return than the same capital drip-fed in over time, because more money spends more time invested. The strategy's real job is behavioral: it makes a large, uncertain entry decision small and repeatable enough that an investor actually follows through with it instead of waiting indefinitely for a "better" moment that may never come. Expecting it to outperform lump sum on average is the setup for disappointment, not the strategy failing.