Most published strategies don’t fail in the market; they fail in the spreadsheet cell the authors left empty. Costs are where academic alpha goes to die, and the arithmetic is brutally simple: annual cost drag ≈ annual turnover × per-trade cost. A strategy turning over 20× a year at 15 bps per side pays ~6%/yr before earning anything. That single line disqualifies more papers than any statistical test — which is why the cost question is check #3 in our backtest framework and a core input to the rigor score.
The four components, separated
Lumping “costs” into one number hides which one is eating you. Keep them separate:
1. Fees and commissions — the only component you know exactly: exchange fees, commissions, regulatory charges, borrow costs on shorts (routinely forgotten and often the largest line for crowded shorts), and funding rates for perpetuals.
2. Spread — you cross half the bid-ask on every entry and exit as a taker. For liquid US large caps this is ~1–2 bps; for small caps, tens of bps; for long-tail crypto and prediction markets, it can exceed the entire claimed edge. Backtests filling at mid-price assume this component is zero — the single most common cost lie.
3. Market impact — the cost of your own size moving the price. The workhorse empirical model is the square-root law: impact scales roughly with σ · √(Q/V) — order size Q relative to daily volume V, scaled by volatility. Its consequences are what matter: impact grows sublinearly but relentlessly with size; doubling capital does not halve returns, but it always lowers them; and every strategy has a capacity at which impact equals edge. Impact modeling done properly is a microstructure and optimal-execution discipline — at research stage you mostly need to know whether you’re in the regime where it binds (roughly: orders above ~1–5% of ADV).
4. Timing/implementation slippage — the drift between decision price and fill price: signal-to-order latency, partial fills, and the gap risk on stops. Detected by the same one-bar-lag test that exposes look-ahead — the two failures are cousins.
Honest research-stage cost ranges
Order-of-magnitude per-side costs for a small (impact-free) taker, September 2026; use as priors, not gospel, and verify against your own fills:
| Instrument class | All-in per side | Notes |
|---|---|---|
| US large-cap equities | 1–5 bps | spread-dominated; near-zero commissions |
| US small caps | 10–50+ bps | spread + borrow on shorts |
| Major FX | 0.5–2 bps | venue-dependent |
| Liquid futures | 0.5–2 bps | half-tick spreads |
| BTC/ETH majors | 2–10 bps | taker fees dominate; maker rebates change the game |
| Long-tail crypto | 25–200 bps | spread + thin books |
| Options | wide | spread as % of premium is the killer; model per-contract |
Two uses for this table. First, fill the empty cell: when a paper reports gross returns, apply these and see what survives — most stat-arb and intraday results shrink dramatically. Second, bound your own optimism: if your backtest’s assumed cost sits below the table’s floor for your asset class, you’re not conservative, you’re wrong.
The sensitivity curve beats the point estimate
Because every number above is uncertain, the robust practice isn’t a better point estimate — it’s the cost-sensitivity curve: P&L at 1×, 2×, 3× assumed costs, required by Gate 1 of the production checklist. The shape tells you what you own: a strategy losing 20% of its return at 2× costs is robust; one going negative is a cost model with delusions. Pair it with turnover reporting — the strategy that halves its Sharpe when you halve its turnover was mostly trading for the thrill.
Two design implications fall straight out: prefer signal constructions that trade less (rebalance bands beat calendar rebalancing; signal averaging beats signal chasing), and remember that costs are the one strategy input you can engineer — execution quality, maker vs taker, and patience are alpha sources with none of alpha’s decay.
Live costs are the ongoing measurement
After go-live, realized cost per trade — actual fills vs decision price, from your reconciliation loop — becomes a first-class metric. It closes the loop: your backtest’s cost assumption stops being a prior and becomes a measured, auditable number, in exactly the spirit of assumed vs measured that runs through everything we publish.
Run your numbers: transaction-cost break-even calculator → · capacity estimator → · Where impact modeling lives: Market microstructure hub → · HFT & execution hub → · The arithmetic’s next step: statistical vs economic significance → · From the corpus: VWAP prediction: methods and benchmarks →