DCA：为什么一个乏味的计划常常获胜

Dollar-cost averaging (DCA) means buying fixed amounts on a fixed schedule — every week, every month — instead of trying to pick one good entry. You give up the chance of a perfect entry and, in exchange, you stop needing one. Your average buy price converges toward the average price of the period, which is the only entry nobody has to predict.

Why the boring version wins for most people

• It removes the timing decision. Lump-sum buyers must be right about “now”; a DCA buyer only has to be right about “eventually”. That is a much weaker, much safer claim to need.
• It is stable under stress. A rule that executes the same way in a green month and a blood-red month is worth more than a brilliant plan you abandon halfway. Module m8 covers why discipline, not math, is what usually breaks.
• Volatility works for the schedule, not against it. Fixed-amount buying automatically acquires more units when price is low and fewer when it is high — with zero forecasting.

What DCA does NOT do — read this twice

• It does not protect a bad asset. Averaging into something that trends to zero averages your losses all the way down. DCA is an entry discipline, not a quality filter.
• It is not automatically better than lump-sum. In a market that rises without you, waiting to spread entries can underperform buying once — this is documented in the finance literature and true in crypto’s bull phases.
• It does not replace exits or risk rules. DCA tells you when to buy; it says nothing about stops (m3), position size (m1) or when to leave.

You do not have to take this on faith. Backtest a fixed-schedule buy plan against lump-sum entries on the same historical candles in the Strategy Lab — paper replay, no real money: /app/lab. Preset rows on the analyzer board carry the standing methodology caveat: they are a signal-replay simulation — figures vary run-to-run and are not live performance. Where the paper result disagrees with your intuition, believe the replay — that is the entire point of paper-first workflows.