The toll is charged on every trade. The edge is earned on some of them.

Three things, and they are not variants of each other. Commission is a fee per side, known exactly before you trade and unaffected by anything the market does. The spread is the gap between the best bid and the best offer; you pay half of it against the mid every time you cross to get filled, so a round turn that crosses both ways pays one full spread. Slippage is everything left over — the difference between the price at the touch and the price you actually got. The first is certain, the second is quoted, and the third is a distribution nobody shows you.

Added up, they form a toll charged on every round turn regardless of outcome. That is the whole of the argument, and its consequences are larger than they look, because a trader thinks in target sizes and the toll is charged in trade counts. On the illustrative configuration used throughout this article, a strategy grossing 2.45 ticks a trade pays 2.62 ticks of friction and nets minus 0.17. It does not look like a losing strategy from the inside. It is one.

Friction does not scale with your profits. It scales with your trade count. Any question about what trading costs is therefore really a question about how often you trade — and that has an exact answer.

Key insights

1. 01Friction is paid on every trade and edge is earned on the winners, so a tick of friction must be recovered out of a fraction of your trades. Restoring expectancy costs 1/w ticks of average win, where w is the win rate. At 45% that is 2.22 ticks per tick of toll.
2. 02A fill can never be better than the best price displayed and can always be worse, so the cost distribution has a hard floor and no ceiling. Its mean therefore exceeds its median — on the default configuration the median round turn costs 1.40 ticks and the mean costs 2.62.
3. 03If shorter holding periods capture proportionally smaller moves — the square-root-of-time behaviour of a series with no serial correlation — then gross profit grows with the square root of trade count while friction grows linearly. The break-even frequency is then exactly four times the frequency that maximises net profit.
4. 04Under that same scaling the sustainable trading rate goes as the square of the edge. A 20% smaller edge costs 36% of your sustainable frequency; a doubled edge quadruples it.
5. 05Friction expressed in ticks is independent of position size, so the friction-to-edge ratio cannot be improved by trading bigger — and past the depth resting at the touch it gets strictly worse.

Three separate payments to three separate parties, which is why lumping them together as “costs” hides the thing that matters: they respond to entirely different levers.

Commission · a fee, to whoever clears you

A fixed amount per side, per contract or per share. It does not move with volatility, order size relative to the book, urgency or time of day. It is the only one of the three you can look up in advance and be right about. Expressed in ticks it is 2c / V per round turn, where c is the commission per side per contract and V is the currency value of one tick per contract — and note that the contract count cancels, a fact clause 05 turns into something useful.

Spread · a price, to whoever quoted it

The best bid and the best offer are not the same number. The difference is the spread, and it exists because someone standing ready to trade with you at any moment is granting you an option and must be paid for it. Measured against the mid price, buying at the offer costs half a spread and selling at the bid costs half a spread — so a round turn that crosses in both directions pays exactly one full spread, not two. Traders routinely double-count this, and the resulting error is one of the largest single items in an incorrect cost estimate.

The spread is quoted, so it is visible before you trade. That visibility is the reason it is the friction traders most often model and — as the decomposition in clause 05 shows — it is not usually the largest one.

Slippage · a residual, to nobody in particular

Slippage is what remains after commission and spread are accounted for: the gap between the price you expected at the touch and the price you were actually filled at. It has three distinct sources and they do not behave alike.

• Depth. The size displayed at the best price is finite. An order larger than it fills the surplus at the next price, and the one after that. For a book with roughly D contracts at each level, an order of Q contracts fills at an average of (Q/D − 1) / 2 ticks worse than the touch once Q exceeds D — the arithmetic average of the levels it consumes.
• Latency. The price can move between your decision and your order’s arrival. In the absence of information this is symmetric; in the presence of it, it is not, which is clause 02.
• Selection. A resting order is not filled at a time you choose. It is filled when someone wants to trade against it, which is a systematically different set of moments from “at random”. Clause 07 works that one through.

Put the three together and the toll on one round turn, in ticks, is:

Every symbol in that identity is a number you can set below. Nothing in it is a rate this page has looked up, because rates of this kind are venue- and broker-specific, change without notice, and a page that printed one would be wrong for most readers on the day they arrived.

Three mechanisms, three levers. Commission responds to who clears you. Spread responds to what you trade. Slippage responds to how, when and how large you trade — and it is the only one of the three that is a distribution rather than a number.

Because it has a hard floor and no ceiling, and that is a structural fact rather than an observation about any particular market. A marketable order cannot fill better than the best price currently displayed — that price is by definition the best one available. So the cheapest possible outcome is exactly zero extra ticks, and it happens often. Everything else is worse, and nothing bounds how much worse.

A quantity bounded below and unbounded above has its mean above its median, always. This is not a subtlety; it is the same reason average incomes exceed typical ones. And it means the number a trader forms by watching their own fills — which is a sense of the typical fill — is systematically below the number that determines their annual result, which is the mean.

The median fill is what you notice. The mean fill is what you pay. On the default configuration those two numbers differ by 87%, and no amount of attentiveness at the screen closes the gap, because the gap lives in the trades you remember least.

The distribution, stated

To compute rather than assert, slippage needs a shape. The one used here is the minimal honest one: on each side, with probability k you are filled at the touch and pay nothing extra; with probability (1 − k) you slip, by a whole number of ticks drawn from a geometric distribution with mean m. That gives a spike at zero — most orders in a liquid book do fill at the touch — and a genuine tail, which a single average number cannot. Expected slippage per side is (1 − k) · m ticks, and the two sides of a round turn are drawn independently.

The figure below is not a sample. The distribution of the round-turn toll is the exact convolution of two of those draws plus the deterministic spread and commission, computed over the tick lattice — so there is no seed, no path count and no sampling error to report. Change any control and the shape is recomputed exactly.

Why it is worst exactly when you most want to be filled

The second asymmetry is a conditioning effect and it is larger than the first. Quoting is granting an option, as clause 01 put it, and the fee for granting an option rises with the chance that whoever takes it knows something. That chance is highest around information — a release, an open, a break of a level everyone is watching. So spreads widen and displayed size thins at precisely the moments that generate most signals.

Which means the distribution that applies to your orders is not the distribution that applies to all orders. It is the distribution conditional on your signal having fired, and that condition selects the worse end. The modelled version of this is one control: the share of entries that land in a fast book, where — as stated in the panel — the spread is doubled, slipping becomes three times as likely, and the slip when it happens is half again as large.

One trade in five carries two fifths of the bill. A trader who measures average slippage across all their orders and applies it to their signal-driven ones is using the wrong conditional distribution, and the error is always in the same direction. The multipliers above are stated assumptions, not measurements — the real ones are specific to an instrument, a venue and a time of day, and the only way to know them is to record your own fills against the quote that was displayed when you sent the order.

More than the friction, and by a factor you can compute exactly: 1/w, where w is the win rate. The reason is the one in the headline — the toll is charged on every trade and the edge is earned on the winners — and it means that friction expressed in the units a trader actually thinks in is much larger than friction expressed per trade.

Write the gross expectancy of a strategy that wins w of the time, taking W ticks on a win and giving up L on a loss, and then ask what W has to be to survive a toll of f ticks:

On the default configuration — 45% win rate, 7-tick losses, 2.62 ticks of toll — the frictionless break-even average win is 8.56 ticks. With friction it is 14.38. The toll of 2.62 ticks has raised the required target by 5.82 ticks, which is 2.22 times its own size, because it must be paid out of the 45 trades in a hundred that work rather than the hundred that happen.

A 2.62-tick toll is not a 2.62-tick problem. At a 45% win rate it is a 5.82-tick problem, and at a 30% win rate the same toll would be an 8.73-tick problem. The lower your win rate, the more brutally friction is amplified.

The same statement can be made in win-rate terms instead, and it is no gentler. Holding the 14-tick win and the 7-tick loss fixed, the win rate required to break even after friction is 45.8%. The frictionless requirement it is measured against — 33.3% at a two-to-one payoff, from the break-even identity 1 ÷ (1 + reward:risk) — is not derived here: it is the central result of expectancy versus win rate, and this clause only adds the toll to it. Twelve and a half percentage points of win rate, bought and paid for before the strategy does anything at all.

Notice what this does not say. It does not say the strategy is bad; the gross edge is real and positive. It says that a gross edge and a net edge are different objects and that the gap between them is not small. The relationship between win rate and expectancy, and why the first is a poor guide to the second, is worked through separately in the expectancy article; everything here takes a gross expectancy as given and asks only what the toll does to it.

Start with the version that needs no assumptions at all. Friction is a fixed number of ticks per round turn. Your edge is a number of ticks per round turn. Trading is worth doing when the second exceeds the first, and the break-even is a size of move, not a frequency: any trade whose expected capture is smaller than your round-turn toll is negative-expectancy no matter how skilled you are or how many times you take it.

What turns that into a question about frequency is a single further observation. Trading more often, within the same calendar window, means holding for shorter periods. And the size of the move available over a holding period is not constant — it grows with the period. For a price series with no serial correlation the variance of the move over a period grows in proportion to the period, so the move itself grows in proportion to the square root of it. Halve your holding period and the typical available move shrinks by a factor of about 1.41; your toll does not shrink at all.

Solve it. Setting net to zero gives the break-even frequency; setting its derivative to zero gives the frequency that maximises net. Their ratio is (1 / (1 − a)) raised to the power 1/a — and at the square-root exponent a = 0.5 that expression is exactly 4.

Under square-root scaling, the trading rate at which you make nothing is exactly four times the rate at which you make the most. Long before friction takes all of the edge, it has taken most of it — and nothing about the experience tells you which side of the peak you are on.

The ladder of what is lost on the way is worth reading slowly. At twice the net-maximising rate you keep 82.8% of the achievable net. At three times you keep 46.4%. At four times you keep nothing. The first step costs almost nothing and feels like activity; the third costs everything.

On the default configuration the numbers land where the figure below shows them: the toll is 2.62 ticks against a gross of 2.45 at 40 trades a month, break-even sits at 35 trades a month and the net-maximising rate at about 9. A reader at 40 is a little past break-even, losing about $136 a month; the same strategy at 9 trades a month would make about $458.

The scaling law worth remembering

Rearranging the break-even condition gives something sharper than the 4× result. The sustainable frequency is proportional to the square of the edge per trade (again at a = 0.5). A 20% smaller edge does not cost 20% of your sustainable trading rate; it costs 36% of it. A doubled edge quadruples it. This is the crucial asymmetry: friction is a fixed toll while edge is uncertain and variable, so the strategies with the least margin lose the most frequency, and they lose it fastest.

The third option in the panel exists to make the assumption visible. Set the scaling to “edge independent of rate” and the whole frequency argument dissolves: if your edge per trade genuinely does not shrink as you trade more, then net is linear in trade count and there is no break-even frequency — only a break-even edge, and you are either above it or below it. The readout says so with an em-dash rather than inventing a number. That is the assumption most traders make implicitly, and stating it out loud is most of what this clause is for.

Not where most traders assume. Commission is the friction that gets shopped, because it is the one printed on a page — and on the default configuration it is the smallest of the three, at 15.3% of the toll. The spread takes 45.8% and slippage 38.9%, and between them they account for more than four fifths of a bill that is usually negotiated on its remaining fifth.

The friction you can look up is the one that matters least. The two that matter most are the two you have to measure yourself.

The composition is not fixed, which is what makes the figure worth manipulating rather than reading once. Commission is a flat currency amount, so its share falls as the tick value rises and rises as you trade cheaper instruments. Spread scales with the quoted spread and with how many sides you cross. Slippage scales with urgency, with the share of entries into fast books, and — past a threshold — with size.

Why you cannot outgrow friction by trading bigger

This is the one property of the decomposition that surprises people, and it falls straight out of the identity in clause 01. Spread and slippage are quoted in ticks per round turn and do not care how many contracts you send. Commission is charged per contract — but so is your profit, so the contract count cancels when you express commission in ticks. The result is that the ratio of friction to gross edge is completely independent of position size. Ten times the size is ten times the friction and ten times the edge; the ledger doubles on both sides and the verdict is unchanged.

That holds exactly up to one threshold and then fails hard. Once your order is larger than the size resting at the touch, the surplus fills at worse prices and slippage per contract starts to climb, while your edge per contract does not. On the default configuration the friction-to-gross ratio is 106.9% at every size from 1 to 20 contracts — and 147.8% at 40, where the order is twice the depth. Size is neutral up to the depth available and strictly adverse beyond it, and there is no size at which it helps.

How much size to take for reasons other than friction is a separate question with its own answer; the sizing article takes it up, and nothing here is an argument for any particular size.

The per-trade toll is small enough to ignore, which is exactly why it works. Multiply it by the trade count and it stops being ignorable. On the default configuration — 40 trades a month, 2 contracts, a $10 tick — the annual friction is $25,152. Against a $50,000 account that is 50.3% of the account, per year, paid out in increments of $52.40 that never once felt like a decision.

Nobody would accept a 50% annual fee. Everybody accepts 2.62 ticks a round turn. They are the same transaction at two levels of aggregation, and only one of them is ever presented as a percentage.

Two different costs share the word, and the boundary is worth drawing here rather than leaving a reader to assume this page covers both. Everything above is charged inside trades you are already cleared to take. What it costs to be cleared at all — the price of an evaluation, and that price again on each further attempt — is a separate calculation with a separate answer, and it is worked through in what a challenge actually costs to enter. The two are additive rather than alternatives: one is the price of the seat, the other the price of every trade taken in it, and only their sum describes a year.

And then compounding gets hold of it

The bill above is a straight subtraction, and it is exactly right if your position size is held constant: the friction removes exactly what it charges. But if size is scaled with equity — a sizing choice, not a law, and one treated in its own article — then the friction is not merely subtracted. It is removed from the base that would have compounded. That identity is general and is not this article's to establish: money taken out of a compounding base costs its own future compounding, whatever took it out, and what compounding, inflation, tax and withdrawals actually leave you derives it once, for any withdrawal. What follows is its friction case, and the point specific to friction is that this particular withdrawal is involuntary, arrives on every trade, and is never itemised.

You paid — and you are — poorer than you would have been. The extra — is the return the paid-away money would have earned had it stayed in the account, and it is the reason friction is worse than its own invoice. Note that the friction figure here is smaller than the fixed-size bill above: under proportional sizing the position shrinks as the account does, so each month's toll is levied on a smaller base — the two regimes are charged differently and only one of them belongs under this heading. Note also what this does to the headline return: gross, this configuration compounds to — for the year; net, to —. The entire distance between an outstanding year and a bad one is friction.

The asymmetry that makes it worse for marginal strategies

Friction is a fixed, known, certain toll; edge is an estimate with error around it, so the two do not degrade alike. Read the ladder's middle column against the one to its left. At 10 trades a month the modelled edge nets about $5,472 a year, while half that edge nets minus $408 — the edge fell by half and the net fell through zero, because a fixed toll is subtracted from the margin before it is subtracted from anything else.

It is better, and it is better for a structural reason rather than a careless one: every simplification available in a fill assumption fails in the same direction. That makes the error one-sided by construction, which is a much stronger statement than saying it is noisy.

Why an assumed fill is optimistic by construction

A backtest fills you at a price that existed. It does not check three things, and each check can only ever make the fill worse:

• That enough size was available at that price. If there was not, the surplus filled deeper — never shallower.
• That your resting order reached the front of the queue before the level traded away. If it did not, you were not filled at all, or you were filled later and worse.
• That the quote you filled against was not about to move. If it was, you were filled precisely because someone else knew it — never in spite of that.

There is no mechanism by which an unmodelled detail of execution makes your fill better than assumed. A one-sided error is not a rounding problem; it is a bias, and its magnitude is the distribution in clause 02.

That is a statement about fills only. Whether a strategy’s apparent edge survives out of sample for reasons unrelated to execution is a separate question with its own machinery, and the backtest article handles it; the point here is narrower and more mechanical — even a perfectly honest, perfectly out-of-sample strategy is worth less live than on paper, by exactly the friction it never modelled.

Crossing or resting: a trade-off, not a ranking

Crossing the spread buys certainty at a known price. Resting avoids the crossing cost and buys two uncertainties instead: you may not be filled, and when you are, you are filled selectively. Both of those are costs and neither appears on a statement.

Non-fill is not free. A signal that does not become a trade removes a positive-expectancy opportunity from your record, and its cost is the edge you did not capture. Selective fill is worse still and is the deeper mechanism: a resting bid is hit when sellers are aggressive, which is disproportionately when the price is about to go lower. You are filled preferentially at the moments you would have preferred to skip. Call that drag d, in ticks, and the comparison is a single inequality:

Solve it for the fill rate and the trade-off becomes concrete. With half a tick of adverse selection, resting the entry beats crossing it only above a fill rate of 59.1%. At a quarter of a tick the threshold drops to 50.6%; at a full tick it rises to 88.9%. Those are plausible values on either side of the line, which is the honest conclusion: the sign of the comparison flips inside the range of ordinary experience, so it cannot be settled in the abstract.

Both inputs are measurable and neither can be assumed. The fill rate is countable — the share of your resting orders that ever fill. The drag is the harder one: it is the difference between the average result of your filled resting orders and the average result of the signals you generated, which requires recording the signals you did not trade. That is the entire practical reason the number is so rarely known, and what a trade journal can prove takes up how a record is built that could answer it.

Everything here is arithmetic under a stated model. Here is the whole of it, so that the warrant can be checked rather than assumed.

The model, in full

• Trades are independent and identically distributed, with two outcomes: a win of W ticks or a loss of L ticks, gross of friction. Real distributions have partial exits, scratches and fatter tails.
• Slippage on each side is zero with probability k, otherwise geometric with mean m ticks, and the two sides are independent. The distribution is stated, not fitted to any data. A book that thins with depth has a heavier tail than this one.
• The fast-book state doubles the spread, triples the probability of slipping and multiplies the mean slip by 1.5. Those three multipliers are stated assumptions chosen to be plausible. They are not measurements, and the real values are specific to an instrument, a venue and a time of day.
• The book has even depth D at every level, so book-walking is (Q/D − 1)/2 ticks per side. Real books usually thin with distance, which makes real impact worse.
• The frequency argument assumes gross per trade scales as the holding period to the power a, with a = 0.5 corresponding to a series with no serial correlation and continuous market presence. This is the single largest assumption on the page, it is exposed as a control, and setting it to zero removes the entire frequency conclusion.
• Commission is per side, per contract, flat. Tiered, volume-based and exchange-fee structures are not modelled.
• No rate on this page is a quoted market figure. Tick values, spreads and commissions are inputs. The defaults were chosen to be plausible and round, and they describe no particular instrument, venue or broker.

What this article does not claim

• It does not claim any particular trader’s friction is any particular size. Every figure is the consequence of inputs the reader sets.
• It does not rank crossing against resting, or one order type or venue against another. Clause 07 gives an inequality, not a verdict, and the inequality flips inside the range of ordinary experience.
• It does not say that trading less is better. It says that under a stated scaling assumption there is a rate above which net falls, and it puts the assumption on a control so the claim can be switched off.
• It does not cost anything other than mechanical execution friction. Fees to enter a programme, deviations from a plan, and position sizing are all different accountings with their own pages.

What it does establish

Four things that do not depend on any of the choices above. That friction is charged per trade and edge is earned per winner, so restoring expectancy costs 1/w ticks of average win — that is algebra. That a cost bounded below and unbounded above has a mean exceeding its median — that is a property of any such distribution. That expressing commission in ticks cancels the contract count, so the friction-to-edge ratio is size-invariant up to the depth at the touch — that is the identity in clause 01. And that a linear function will always eventually overtake a concave one, so if gross per trade falls at all with frequency, a break-even rate exists and can be computed.

This article cites no external source because it makes no external claim: it quotes no commission rate, no spread and no tick value as fact. Every result is derived from definitions stated on the page and can be checked with a calculator. The full derivation ledger, with the exact expressions and the verification method for each, is published alongside the article as what-trading-costs-per-trade.references.md. The summary:

• Clause 01 · the friction identitySpread costs half a spread per side against the mid, so X sides cost X · 0.5 · S ticks. Commission of c per side per contract on Q contracts is 2cQ in currency, which is 2c/V ticks — the contract count cancels.
• Clause 02 · the distributionPer side: zero with probability k, otherwise geometric with mean m over whole ticks. The round-turn distribution is the exact convolution of two such draws, computed over the tick lattice and mixed across the calm and fast states. Mean, median and quantiles are read off the accumulated exact distribution, not sampled.
• Clause 03 · the amplificationSetting w·W − (1−w)·L − f to zero gives W = (f + (1−w)L)/w, so the derivative of the required win with respect to f is 1/w.
• Clause 04 · the 4× resultWith gross over a window equal to A·T to the power (1−a) and friction f·T, break-even is at (A/f) to the power 1/a and the peak at ((1−a)A/f) to the power 1/a. Their ratio is (1/(1−a)) to the power 1/a, which is 4 at a = 0.5. Break-even proportional to A to the power 1/a gives the edge-squared scaling at the same exponent.
• Clause 04 · the square root of timeVariances of independent increments add, so the standard deviation of a sum over n increments grows as the square root of n. The size of a typical move over a holding period therefore grows as the square root of that period, for a series with no serial correlation.
• Clause 05 · book-walkingWith D contracts at each level, an order of Q fills levels 0 to Q/D − 1 in equal parts, so the average level consumed is (Q/D − 1)/2 ticks below the touch.
• Clause 06 · compoundingTerminal equity is the account multiplied by (1 + r) to the power 12 at the monthly rate r, computed gross and net. The shortfall exceeds the friction paid by the return the paid-away capital would have earned.
• Clause 07 · the fill inequalityResting the entry beats crossing it when fill · (g − d − 0.5S − c − slip/2) exceeds g − S − c − slip; solving for the fill rate gives the thresholds quoted.

Related reading on this site, where a question genuinely belongs to another page: expectancy versus win rate for why a win rate is a poor guide to profitability; why a backtest stops working for out-of-sample decay, which is a different failure from an optimistic fill; position sizing for how much size to take and what each method assumes; what a bad decision costs for the costs that are not charged mechanically; and what a trade journal can prove for how to build the record that would let you measure your own fill rate and adverse-selection drag.