Crypto DCA Simulator

Dollar-cost averaging beats most market-timing strategies not because it finds the best entry — it doesn't — but because it removes the need to find one. Buying a fixed dollar amount at regular intervals means you acquire more coins when prices are low and fewer when prices are high, which pushes the average cost basis below the arithmetic mean of the prices you bought at.

Set a starting price, an ending price, how many periods you want to simulate, and how much you invest each period. Load Sample runs 12 monthly $100 BTC purchases from $30,000 to $65,000: total invested $1,200, average cost basis roughly $43,600, and a portfolio worth about $2,055 at the final price — a gain of $855 on $1,200 in. The simulation also shows how the same $1,200 would have done as a lump sum on day one, so you can compare both strategies directly. Nothing is sent to a server; all arithmetic runs in your browser.

The Formula: How DCA Cost Basis Is Calculated

• Price interpolation: each period's price is linearly interpolated between the start and end price, so the simulation is fully deterministic — no randomness, no live data feed.
• Coins per period: each purchase buys investmentAmount ÷ periodPrice coins. The per-period row shows exactly how many coins each purchase adds.
• Average cost basis: total invested ÷ total coins accumulated. In a rising market the DCA average sits below the simple average of prices because more coins are purchased at the earlier, lower prices.
• Lump-sum comparison: the same total investment made entirely at the starting price. The "vs Lump Sum" figure tells you which strategy would have performed better at the current price you enter.

Worked Example: ETH Bear Market

Click Load Sample twice to reach the ETH bear market scenario: 24 weekly $50 purchases from $2,000 down to $900. Total invested is $1,200. Because you buy more ETH at every price drop, the average cost basis lands well below $1,450 (the arithmetic midpoint). If ETH recovers to $900 at the end, you are near break-even; if it recovers higher, DCA outperforms the equivalent lump-sum entry at $2,000. Set the current price field to any recovery value to model the exit.

Accuracy Limits and Input Constraints

Periods are capped at 1,000 to keep the table responsive; the schedule shows the first 24 rows for long runs. Coin amounts are displayed to 8 decimal places for sub-cent holdings, matching Bitcoin's satoshi precision. The lump-sum comparison always uses period-1's interpolated price as the entry point, not a separate input. If you want to model a specific lump-sum entry price, set the start price to that value. Fractional periods are not supported; enter a whole number.