Bybit EU adds optional Auto-Earn to scheduled crypto purchases

What Auto-Earn adds to a DCA bot

Bybit EU introduced Auto-Earn for its DCA Bot on 31 August 2026. DCA, or dollar-cost averaging, means buying according to a repeated schedule. The new optional setting adds a second step: eligible purchased assets enter a rewards product. The announcement supports plans of up to five tokens, with schedules from every ten minutes to every four weeks. Turning on a schedule determines when purchases are attempted; it does not guarantee a favorable average price.

Funding, allocation and the ownership change

The official product guide says purchases use USDC and incur the normal spot trading fees, without an additional bot trading fee. A maximum investment amount can stop further purchases. Insufficient USDC suspends the bot rather than terminating it; it can resume at the next scheduled time after funding is restored. Auto-Earn allocation can fail eligibility, minimum-size or pool-capacity requirements, so a completed purchase does not prove a rewards subscription occurred.

The guide’s disclosure says subscribing transfers legal and beneficial ownership of the assets to Bybit EU. The user retains a contractual claim, rather than custody ownership or security rights, and MiCA client-asset protections do not apply to that rewards arrangement. This is a substantive change after the spot purchase, not merely an extra percentage displayed beside an otherwise unchanged holding.

Four scheduled purchases with changing prices

Consider an original example allocating twenty-four USDC on each of four dates to one fictional token. Ignore all fees and assume every purchase completes. At prices of two, three, four and three USDC, the plan acquires twelve, eight, six and eight tokens respectively. Total spending is ninety-six USDC and the total acquired is thirty-four tokens. The average cost is approximately 2.82 USDC per token.

That average comes from dividing total spending by total quantity. Averaging the four displayed prices instead would produce three USDC, which does not describe these equal-spending purchases. More tokens were acquired when the price was lower. The example explains the accounting of a scheduled purchase plan, not why the fictional token should rise afterward.

If the final market price is two USDC, the thirty-four tokens are worth sixty-eight before costs. The plan has spread its purchases across dates and still has a twenty-eight-USDC decline relative to spending. If the ending price is four, the same quantity is worth one hundred thirty-six. The schedule determines the purchase pattern, while the ending price determines the valuation of the accumulated quantity.

A missing purchase changes the actual plan

Now imagine the third scheduled purchase in that example does not occur. The completed spending would be seventy-two USDC and the acquired quantity twenty-eight tokens, not the ninety-six and thirty-four shown in the original plan. A dashboard should be read from completed transactions rather than from the number of dates that elapsed.

This distinction becomes especially useful when funding is intermittent. Adding money after a missed date does not make the historical price on that date available again. The next completed purchase belongs to its actual execution time and price. Someone comparing the plan with a backtest should therefore use the real fills, including missing ones, rather than assuming the intended calendar was executed perfectly.

Rewards need their own reconciliation

For a separate invented rewards illustration, suppose the holder has thirty-four purchased tokens and later receives two tenths of a token as rewards. The resulting quantity is thirty-four point two. The reward amount should be identifiable separately from tokens bought with the original budget, otherwise the apparent average purchase price can become misleading.

A person might want to compare purchase performance, rewards received and current total value. Those are three useful figures, but they answer different questions. The first measures the buying sequence, the second measures the additional product outcome, and the third combines quantities with a current price. The Auto-Earn release brings the workflows together in one interface; understanding the result still requires recognizing which event created each part of the balance and which terms govern it.