Market Orders vs. Limit Orders for Trading Bots: Do Lower Fees Actually Improve Returns?

A limit price does not guarantee a maker fill

A marketable buy limit can take an existing offer immediately. A resting limit can later provide liquidity. Some orders can contain both kinds of fill, so the relevant fee belongs to the execution, not merely the label on the submitted order. Coinbase describes this split explicitly. Binance’s LIMIT_MAKER instruction instead rejects an order that would immediately trade as a taker. Rejection avoids that execution; it does not deliver a free fill at the desired price. [1] [2]

A limit controls the permitted execution price, not the return after entry or the probability of execution. A market order also does not guarantee any particular price or a full fill under every venue condition. Our question is which complete execution policy serves a trading rule, not which order name sounds cheaper. The order-placement research of Cont and Kukanov treats fees, queues, order flow and execution risk together; its results do not supply a universal rule for this BTC example. [7]

Hold the signals and exit policy fixed before comparing entries

We specified two policies and five non-overlapping synthetic BTC/USDT spot episodes before calculating their outcomes. The cases were deliberately designed to expose mechanisms, not randomly sampled to estimate their frequency. Each policy receives the same five buy decisions, 60,000 milliseconds apart, starts with 10,000 USDT, and targets 0.02 BTC per episode. The quote at order arrival is 49,990 bid / 50,010 ask. The market policy buys the full target at the ask; the passive policy rests at the bid without topping up or chasing.

Both sell whatever they actually own at the episode’s declared bid at 10,000 milliseconds. An unfilled entry stays cash. The two accounts carry all previous results forward, with no deposits, borrowing, funding or compounding of the fixed target quantity. Quantities and prices are assumed valid under the hypothetical filters. Sufficient displayed liquidity is assumed for each modeled market execution. The tape is exogenous: neither policy changes later prices, order flow or other traders’ actions. That restriction prevents a claim about actual market impact or capacity.

Work through the queue instead of assigning fills from a candle low

The model uses a simple first-in, first-out queue at one price level. Eligible aggressive sell volume consumes the quantity ahead before reaching our order. We give no credit for other traders canceling ahead and assume no hidden quantity. These are model choices, not facts inferred about a venue. Coinbase’s published matching rules illustrate why time and displayed priority matter, while also describing details such as iceberg priority that this simple model omits. [3]

In Case C, 0.040 BTC of executable sell flow arrives at 200 ms. After consuming 0.030 BTC ahead, 0.010 BTC fills our 0.020 target; the remaining 0.010 is canceled. A candle low of 49,990 cannot reveal that quantity. Nor can aggregate trade data alone tell us precisely which cancellations occurred ahead of our order. A credible historical reconstruction needs event ordering and justified queue assumptions.

Case E has no eligible sell flow before cancellation. Its price declines only after the cancellation has become effective. In Case D, sell flow arrives at 550 ms, after our request but before cancellation takes effect. The complete 0.020 BTC fills and is acknowledged before the common exit. This is why the time of a cancellation request cannot be treated as the time an order stopped existing.

Lower fees coexist with a losing passive policy

The executed decimal calculation charges each fill once and carries cash and inventory through every episode. Market entries acquire 0.100 BTC across the five decisions; passive entries acquire 0.050 BTC. Passive order participation is 3/5, or 60%, because two orders fill fully and one partly. Its quantity fill rate is 0.050/0.100, or 50%. Reporting only the three filled orders would remove the missed opportunities from the comparison.

Market gross price P&L is −13.00000 USDT and fees are 9.98900, giving −22.98900 net. Passive gross P&L is −13.50000 and fees are 2.98590, giving −16.48590 net. Ending equities are 9,977.01100 and 9,983.51410: returns of −0.229890% and −0.164859% on the common starting capital. Passive has lower fees but slightly worse gross P&L, despite trading half the quantity. These designed cases cannot establish an expected return, drawdown distribution or statistical edge. A no-trade cash baseline earns zero in this no-interest exercise and therefore finishes above both policies; that is an accounting benchmark, not a finding about the underlying market strategy.

Only part of the difference is a fee saving

First match the 0.050 BTC actually bought passively to the same quantities in the market policy, within each episode and with identical exits. On that matched quantity, buying at 49,990 instead of 50,010 improves gross price P&L by 1.00000 USDT. Entry fee savings are 0.050 × (50,010 × 0.001 − 49,990 × 0.0002) = 2.00060. Exit fees cancel in this matched comparison because quantity, exit price and taker rate match.

The remaining difference comes from exposure not taken. The missing market-policy quantities would have earned +7.78980 in A and +0.89790 in the unfilled half of C, but lost 12.19020 in E. Their combined net result is −3.50250. Not taking them therefore adds +3.50250 to passive relative performance. We did not identify which signals to skip in advance; these are an after-the-calculation accounting decomposition, not a proposed profitable filter.

The apparent total fee reduction of 7.00310 is larger than the matched-entry saving because the passive policy trades less and pays fewer exit fees. Calling all of that reduction better execution would confuse lower activity with a cheaper execution of the same exposure. The actual fill prices already include the bid/ask difference: do not deduct a spread estimate again. Real fee assets, discounts and additional charges must be reconciled from the account; Binance’s commission documentation explains why the effective charge is not always a single advertised maker/taker rate. [4]

A favorable entry price can arrive with unfavorable information

Cases B and D show the passive buyer receiving inventory before a lower exit price. The lower entry price and fee help relative to immediate market entry, but do not make those positions profitable. This illustrates adverse-selection risk: the events that reach a resting buy order can be associated with a worse subsequent price. These five chosen episodes do not estimate that association or its causal magnitude. On real data, examine post-fill price movement alongside fill probability and retain the unfilled signals as well.

In D, a system that treats the 500-ms cancellation request as success records no entry, even though 0.020 BTC fills at 550 ms. The correct account includes the −6.99396 USDT result. Binance explains that fills can occur while cancellation is in flight, and its user-data reports identify executed quantities and order states. [5] [6] Use a durable decision record, reconcile the order and position, and suppress blind retries while status is uncertain. A client order identifier helps identify an order; it is not by itself a complete application-level duplicate-prevention design.

The queue assumption changes the result more than removing the maker fee

We reran the same five episodes with 0, 0.01, 0.03 and 0.05 BTC ahead, leaving prices, flows, policies and fees unchanged. The passive outcomes are not monotone in queue size. A shorter queue captures more of the designed winners, but some fills also belong to losing episodes. At the longest queue only 0.010 BTC fills, so the small loss partly reflects not trading. It is not evidence that joining a worse queue is advantageous.

With the base 0.03-BTC queue and a zero maker fee, passive net P&L improves only from −16.48590 to −15.98600. Eliminating that fee saves 0.49990 and still leaves a loss. This sensitivity holds fills fixed; a real fee change or different order size could change behavior and liquidity. We report every planned queue case rather than selecting the best one as an optimized policy.

An unfinished position still belongs in account equity

At 9,000 ms in E, before its scheduled 10,000-ms exit, suppose its available bid is already 49,500. The market account holds 0.020 BTC and 8,988.00100 USDT cash. Marked inventory is 990.00000, so equity is 9,978.00100. At this instant the passive account is flat with 9,983.51410. Dropping the market account’s open position would understate equity; showing only closed-trade profit would omit its current price loss.

The scheduled market sale of that inventory produces a further 0.99000 fee, reconciling marked equity to the final 9,977.01100. The pre-exit mark is not an executed liquidation result. We supply discrete transaction and endpoint records, not a complete intra-episode valuation series, so we do not report maximum drawdown or time-weighted exposure. Total executed turnover is 9,989.00000 USDT for market and 4,985.50000 for passive. Our forward-testing guide develops the full cash-and-inventory audit.

What a real comparison would need to record

Freeze the eligible signal set, arrival convention, order price, lifetime, partial-fill treatment, fallback and exit rule before evaluating the policy. A policy that waits and then crosses the spread is a third candidate, not the passive policy tested here. Give it its own rules and cost record. Separate entry execution from urgent risk reduction: the opportunity cost of an unfilled exit can differ substantially from an unfilled optional entry.

For historical work, obtain timestamped order-book and trade events at adequate depth, check sequence gaps and justify queue assumptions, latency, cancellation effects and impact. A sampled quote or a candle alone cannot identify the counterfactual fill. For a prospective pilot, retain every eligible intent, submission, rejection, partial fill, cancellation and final position. Declare fees and fee assets, then compare net whole-account outcomes with uncertainty appropriate to their dependence. The edge-or-luck article explains why many related fills do not become many independent tests.

ChatGPT or Claude can help specify those questions; Codex or Claude Code can help audit the event logic and arithmetic. Our investigation used AI assistance and executed decimal calculations on the constructed tape. It did not compare model products or connect a funded account. Require the collaborator to preserve unresolved events rather than inventing favorable fills.

SPECIFY: Freeze signal eligibility, arrival time, price, lifetime, partial-fill treatment, cancellation, fallback and exit rules. Identify missing decisions.
IMPLEMENT: Preserve the approved policy and the complete event order; do not fill a limit merely because a candle touches its price.
RECONCILE: Track cash, inventory, fees and unresolved orders. Distinguish a cancellation request from its confirmed effect.
REPORT: Keep all eligible signals and decompose matched-fill price/fee effects from missed or avoided positions. Separate observed fills from modeled counterfactuals.

Choose an execution policy, not the lowest line in a fee schedule

The passive policy loses less on this constructed tape, but only 2.00060 USDT of its 6.50310 relative improvement is the matched-entry fee saving. Missing a winner hurts; avoiding a loser helps; queue and cancellation assumptions determine which inventory exists. Lower fees do not settle any of those questions.

The defensible next step for a real bot is a versioned execution comparison on its own eligible decisions, with justified fills and account reconciliation. A useful policy must satisfy execution urgency, capital and risk constraints as well as after-cost economics. Our results establish how to account for the differences, not which order type will make a future trading system profitable.