Concept
Cost-model Realism
Cost-model Realism is a Performance, Backtesting & Validation concept. First implementations are in the build queue: the write-up leads, the indicators follow.
What is cost-model realism?
Cost-model realism is the discipline of making a backtest's assumed trading costs match what execution would actually have cost: commissions and exchange fees, bid-ask spread, slippage between signal price and fill price, market impact for size, plus the carry items many tests forget, such as funding rates on perpetuals, borrow fees on shorts, and overnight financing. A backtest run at zero or token costs is not a conservative simplification; it is a systematic upward bias, because costs are paid on every trade while edge arrives only on some.
The issue exists because most strategy edges are small relative to round-trip costs, and the relationship is multiplicative with turnover. A signal worth 0.15% per trade is genuinely profitable at 0.05% round-trip cost and a steady loser at 0.25%. Many published anomalies and most high-frequency-looking retail backtests live entirely inside this gap: profitable on paper, negative after honest costs. Realistic modeling means using the spread and fee schedule of the actual venue and account tier, filling market orders at the touch or worse rather than at mid, and recognizing that limit orders avoid spread but introduce non-fill risk that the backtest must also model.
Traders care because cost assumptions are the single easiest place for a backtest to lie, and the lie is invisible on an equity-curve chart. Serious validation therefore pairs a realistic base case with cost sensitivity analysis, re-running the test at multiples of assumed costs to see where profitability dies, and treats a strategy that only works at optimistic costs as unproven. A Sharpe ratio quoted without its cost assumptions is close to meaningless for high-turnover systems.
How traders use it
- As a backtest configuration standard: set commissions from the real fee schedule, model spread by filling at bid or ask rather than mid, and add a per-trade slippage allowance calibrated to the instrument's typical depth and the order size traded.
- As a turnover audit: multiply round-trip cost by annual trade count to get the yearly cost hurdle, then check that gross backtest returns clear it with a margin, since strategies that barely clear it often disappoint live.
- As a stress test via cost sensitivity: re-run the backtest at 1.5x, 2x, and 3x assumed costs and record where expectancy turns negative; a robust strategy degrades gradually rather than flipping sign at a small cost bump.
- As a live calibration loop: once trading begins, compare realized fills to modeled fills and feed the measured slippage back into the cost model, tightening the estimate with real data over time.
- Its main limitation is honest uncertainty about impact: costs for size, stressed markets, and thin sessions are hard to model from historical quotes, so cost models tend to be most reliable for small orders in liquid instruments.
Cost-model realism vs related concepts
Execution Cost Modeling: Closely related but aimed at execution tactics: it models how order type, timing, and slicing change the cost of a given trade. Cost-model realism is the validation-side counterpart, asking whether the backtest's assumed costs are honest at all.
Cost Sensitivity: The stress-testing extension. Realism sets the best-estimate base case; sensitivity analysis perturbs that estimate to find the cost level at which the strategy breaks, which matters because the true future cost is never known exactly.
Related concepts · Validation methodology
Concept family
Performance, Backtesting & Validation
30 concepts mapped · 30 in the Library
Cost-model Realism FAQ
What cost figure should I assume if I don't know my slippage?
A common starting point is to assume fills cross the full quoted spread plus a slippage allowance of a fraction of the spread again, then refine from live fills. For liquid large-cap stocks and major FX pairs this is often small; for small caps, altcoins, and fast markets it can exceed the spread itself.
Do limit orders make costs zero?
No. Limit orders avoid paying the spread but introduce adverse selection: they fill readily when price is moving against you and miss when it runs in your favor. A backtest that assumes every limit order fills is often more biased than one paying market-order costs.
Why did my live results underperform a backtest that included commissions?
Commissions are usually the smallest cost component. Spread, slippage, and funding or borrow charges frequently dominate, and many backtests omit them entirely. Comparing your average live fill to the backtest's assumed fill price usually locates the gap quickly.
How much can costs change a strategy's ranking?
Substantially. After honest costs, high-turnover variants often fall below slower variants of the same idea, so optimizing on gross returns tends to select exactly the parameter sets that costs punish most.
Build Cost-model Realism your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.