The minimum edge that survives commissions and slippage

A strategy that looks profitable on paper has to clear two payments before any of that profit reaches you: the exchange takes its cut on the way in and on the way out, and the order book takes a smaller cut by giving you a slightly worse fill than the screen showed when you clicked. Both happen on every single trade. Both are silent. And both compound — a strategy that nominally makes 20% a year before costs can easily clear less than half of that after.

The math for how much edge you actually need is straightforward, but most retail traders never run it. They look at a backtest with a 0.5% average per-trade gain and a 78% win rate and assume they're holding a money printer. Then they trade live for three months and watch the equity curve drift sideways or down, and the post-mortem usually blames the strategy. Sometimes it is the strategy. More often, it's that the strategy was always borderline and the costs ate the difference.

This post walks through what those costs actually are on a major crypto exchange, the formula for break-even edge, why high-turnover strategies fail this test more often than low-turnover ones, and why an 80% win rate on small-R trades is structurally more vulnerable to cost drag than a 50% win rate on big-R trades. The interactive at the end takes your strategy's numbers and tells you whether the gross edge survives the round trip.

The two costs, in order

Commissions. Every exchange charges a fee on each fill. On Phemex perpetual contracts, the public fee schedule lists 0.01% for maker fills (orders that add liquidity to the book — limit orders that wait) and 0.06% for taker fills (orders that consume liquidity — market orders, or limits priced through the spread). VIP tiers reduce both, with the highest retail levels reaching about 0.0035% maker and 0.035% taker, but most retail accounts pay the standard rates.

A round trip — open and close — at standard taker rates is 0.06% × 2 = 0.12% of position size, just on commissions. At maker-maker (open with a resting limit, close with another resting limit), it's 0.02%. The difference between maker-maker and taker-taker is 6× — which is large enough that the choice of order type often matters more than the choice of strategy.

The wrinkle: making the maker fill happen requires the price to come to you. If the strategy needs to be in now, you pay taker. So the realistic average for many retail strategies is somewhere between, often something like one taker fill (entry, when timing matters) and one maker fill (exit, when waiting for the target). That averages to about 0.07% round-trip on commissions.

Slippage. This is the gap between the price on screen when you decide to trade and the price your fill actually executes at. It comes from two sources stacked: the bid-ask spread (you cross half of it on entry and half on exit, in expectation), and price drift during the milliseconds-to-seconds between decision and execution.

For BTC perps on Phemex, the typical bid-ask spread during normal hours is around 0.005% to 0.02%, depending on volatility. Crossing half of it costs roughly 0.005% per side, or 0.01% round trip. Larger orders that don't fit at the top-of-book level walk into deeper price levels and pay more. A typical retail-sized perp order on a major coin sees somewhere between 0.02% and 0.05% in execution slippage round-trip on top of the visible spread, more during news events, more again when liquidity thins overnight.

A reasonable average for a retail-sized BTC perp trade, executed during normal hours with mixed maker/taker order types: 0.10% to 0.15% round trip in total friction (commissions + slippage). For ETH it's similar; for SOL and XRP it's slightly higher because the books are thinner; for low-cap altcoin perps it can easily be 0.30% or more.

That number — 0.10% to 0.15% round trip on majors — is the floor your strategy has to beat per trade just to be neutral.

The break-even formula

The formula for whether a strategy survives is depressingly simple:

Net edge per trade = (Win rate × Avg win) − ((1 − Win rate) × Avg loss) − Round-trip costs

If that number is positive, the strategy makes money. If it's negative, the strategy loses money no matter how good the equity curve looked in a no-fees backtest. Run the math:

• Strategy A: 80% win rate, 0.4% average win, 0.4% average loss, 0.12% round-trip cost.

Gross edge = 0.8 × 0.4 − 0.2 × 0.4 = 0.32 − 0.08 = 0.24% per trade. Net after costs = 0.24 − 0.12 = 0.12% per trade. Profitable but barely.

• Strategy B: 80% win rate, 0.2% average win, 0.2% average loss, 0.12% round-trip cost.

Gross edge = 0.8 × 0.2 − 0.2 × 0.2 = 0.16 − 0.04 = 0.12% per trade. Net after costs = 0.12 − 0.12 = 0.00% per trade. Break even. The strategy works for free.

• Strategy C: 80% win rate, 0.1% average win, 0.1% average loss, 0.12% round-trip cost.

Gross edge = 0.08 − 0.02 = 0.06% per trade. Net after costs = −0.06% per trade. The strategy loses money despite winning four out of five times.

Three strategies, all with 80% win rates, with very different fates. Win rate alone tells you nothing about whether costs survive. The product of win rate × average win matters more, and the relationship between average move and the cost floor matters most.

The 80%-win-rate companion post on why a 90% win rate can still lose money covers the broader version of the same math: high win rate combined with small reward is structurally vulnerable to anything that introduces a fixed per-trade cost, which costs always do.

What the cost floor looks like

The shape is what matters. A strategy with 0.5% gross per-trade edge stays comfortably profitable even at high turnover. Drop the gross edge to 0.25% and the curve sags but holds. At 0.12% gross — exactly equal to the cost floor — the strategy crosses break-even somewhere around 500 to 1000 trades per year and goes negative beyond that. Below 0.12% gross, more trading just digs the hole faster.

The intuition: cost is a per-trade tax that doesn't care about the size of your edge on that trade. A high-frequency strategy with thin per-trade edge gets taxed at every step. A low-frequency strategy with fat per-trade edge gets taxed once in a while and has plenty of margin. The same total annual return target therefore gets very different cost drag depending on how it was achieved.

Where retail strategies usually fail this test

A few patterns show up over and over.

Backtests assume zero costs. Most retail backtest tools either ignore commissions entirely or model a single fixed percentage that's optimistic. The published TradingView strategy library, for example, often shows results without per-trade fees. Add 0.12% round-trip and entire categories of strategy that looked profitable become break-even or worse.

Slippage is modelled as the bid-ask spread. It's bigger than that. The visible spread is the cost of an infinitesimal trade. A real retail-sized order walks the book and pays more, especially for thinner instruments. And during news events, when many strategies want to enter at once, slippage can multiply by 5x or 10x for a few minutes.

Funding rates are forgotten. On perpetual contracts, every eight hours one side pays the other. For strategies that hold positions across funding boundaries, this is a real cost (or income) that has to enter the math. Average funding for BTC perps over the long run is positive (longs pay shorts), historically around 0.005% to 0.02% per 8-hour interval, though it can swing far higher during sustained trends. Cumulatively, holding a long perp position for weeks during a bull run can pay 5-15% per year in funding alone.

Slippage gets worse exactly when the strategy thinks it's winning. Most strategies that detect breakouts or volatility expansions try to enter precisely when liquidity is thinnest and other people are doing the same thing. The execution price drifts away from the trigger faster than during quiet conditions. The realised win on these trades is consistently below the theoretical win.

Order type doesn't match the strategy timeframe. A strategy with a 4-hour signal that always enters with market orders is paying taker fees for no reason — a limit just inside the spread would fill within a minute or two on a major and save 5x in fees. Conversely, a fast-reaction strategy that uses limit orders is missing fills when the move is real, and paying full taker plus slippage on the ones it does catch.

The pattern across all of these: cost assumptions in the backtest are systematically lower than cost realities in live trading. The gap between backtest and live performance has many causes; this is the single largest one for most retail strategies.

How institutional traders deal with it

Three angles, none of them subtle.

Lower fees. VIP tiers drop fees substantially for higher volume. On most major crypto venues, the highest tiers are 0.0035%-0.005% maker and around 0.02%-0.035% taker. That's three to five times less than retail. For a high-frequency strategy, this difference alone moves the strategy from money-loser to money-maker.

Maker rebates. Some exchanges go further and pay you to provide liquidity — a negative maker fee. CME futures and several US equity venues operate this way; in crypto, dYdX has run rebate programs and Bybit has had limited maker incentive periods. A strategy that exclusively trades as a maker on a rebate venue earns a small return just from the act of trading, before any directional edge.

Order routing and execution algorithms. Institutions use TWAP, VWAP, iceberg, and proprietary execution algorithms to break large orders into pieces that minimise market impact. A retail order goes in as one chunk and pays whatever slippage that one chunk costs; a 1m USD institutional order might be executed across 30 minutes through dozens of venues to keep average impact at 0.02% or less.

The retail position relative to all of this: you can't get the lowest fee tier without high volume, can't get rebates without market-making infrastructure, and can't run TWAP without engineering. What you can do is pick strategies with enough per-trade edge that the retail cost floor is not a problem.

The interactive

The defaults model a moderately reasonable setup: 80% win rate, 0.4% wins and losses, 500 trades per year, half taker / half maker, and 0.015% slippage per side. Push the win rate up and the average win down — common patterns for "scalping" or "high-frequency" strategies — and watch the verdict deteriorate. Reduce the trade count and increase the average move and watch it improve.

The most useful sliders to play with: the slippage slider, because it's the one most retail traders underestimate, and the trades per year slider, because it's the one most retail traders are tempted to crank up when their strategy is "working." The latter is also where compound return interacts with cost drag — at high turnover, a small drag per trade becomes a large drag per year.

For more on why the trade count itself is a knob worth using carefully, the patience-as-an-edge angle (when published) covers the same idea from the discipline side rather than the cost side. Cross-reference with the losing-trade-recovery post for the human cost of trading too often. Two related calculators that plug into this math directly: the required reward-to-risk to break even and the perpetual futures PnL calculator with fees.