A backtest Sharpe ratio of 13.4 is not evidence of a durable trading edge on its own. If adding realistic trading costs sharply reduces it, the result is a warning to examine turnover, fill assumptions, funding, and how the strategy was selected—not proof that the strategy is worthless or that the revised backtest is accurate. The specific backtest behind this headline cannot be audited without its returns, code, venue, instruments, and execution records.
Why costs can change a backtest so much
A gross backtest often assumes trades happen at a convenient reference price and reports returns before venue charges. A live order instead pays explicit fees and faces execution frictions. The impact is especially important when a strategy trades frequently: small costs on many entries and exits can accumulate into a large drag.
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Sharpe is a ratio of excess return to return variability, with annualization depending on the return frequency and convention. If costs reduce average returns while volatility remains similar, the ratio can fall substantially. Costs can also change the return distribution itself, so recalculating Sharpe from net periodic returns is preferable to applying a simple deduction to the reported ratio.
That makes the decline diagnostic, not conclusive. It may expose an edge that was mostly turnover or optimistic execution, or it may reflect a cost model that does not match the actual venue and order behavior. A very high result also deserves scrutiny if many strategies or parameter settings were tried on the same data.
Which trading costs belong in the model?
Commission: charge each executed side correctly
Commission is the venue’s explicit charge for an execution. Use the schedule that applied to the particular venue, market, account tier, discount, and date, and distinguish maker from taker executions. As an example of how venue-specific rates are, Binance’s public spot schedule displayed a regular-user maker/taker rate of 0.100% / 0.100% before the displayed BNB discount at the time documented by its spot trading fee schedule. That is not a universal crypto rate or a rate to assume for another account or date.
Order type alone does not determine the fee role. Binance explains that market orders are taker trades, while a limit order may be maker if it rests on the book or taker if it executes immediately (Binance maker and taker fee explanation). A model that labels every limit order maker can understate fees; a model that assumes every passive order fills can also overstate trading activity or performance.
Rank #2
Spread and slippage: model the price actually obtainable
Commissions do not capture the difference between a reference price and the price an order can realistically obtain. The bid-ask spread and slippage should be modeled against executable quotes or another clearly stated fill benchmark, rather than assuming every trade fills at a candle close or midpoint.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallExplain how the model handles order size relative to available quote depth, latency, partial fills, and price impact. If the data or model does not represent market impact or the capacity available to larger traders, say so. For example, a 2026 study of crypto perpetual backtests explicitly excludes market impact and institutional capacity; its findings therefore do not establish performance after those effects (study on crypto perpetual backtest protocols).
Rank #3
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Perpetual-futures funding: charge only when exposure crosses settlement
Funding is a transfer between long and short holders of perpetual contracts, not a substitute for commission or an arbitrary flat fee. Binance’s futures FAQ describes funding as “periodic payments transferred between holders of long and short positions in Perpetual Contracts” (Introduction to Binance Futures Funding Rates). Binance says the default funding interval is eight hours, but intervals can vary by contract or change. A position is liable only if it is held at that contract’s scheduled funding time.
Use historical funding rates at the contract’s actual settlement times, matched to the strategy’s positions. A constant funding estimate can miss both the rate’s variation and whether the strategy was exposed when settlement occurred.
Instrument-specific frictions
Leverage, margin, borrowing, liquidation-sensitive positions, and decentralized venues can introduce additional mechanics or charges. Their treatment depends on the product and venue; there is no single universal cost list that can be applied to every crypto strategy. Identify the relevant rules for the instruments actually traded rather than assuming these costs are covered by a generic fee percentage.
What a Sharpe of 13.4 does—and does not—tell you
Annualized Sharpe can be overstated when model selection repeatedly reuses the same data. A 2026 SSRN working paper reported that annualized Sharpe was inflated by 3.6× on average under a naïve protocol compared with a nested walk-forward and cost-aware protocol. The comparison covered six factors and 137 Binance USDT perpetual contracts during 2020–2024; it is a preprint result for that sample, not a multiplier to apply to every strategy (2026 SSRN working paper on Sharpe inflation).
Execution-cost evidence also depends on the market and benchmark. A separate 2026 SSRN working paper reported effective spreads of 253–835 basis points for its studied major-exchange sample and comparison benchmarks. That range is not a universal estimate for crypto spreads (2026 SSRN working paper on effective spreads). A 2026 Management Science study found that gas fees burdened smaller decentralized-exchange trades, while transaction costs were more competitive for larger trades in the studied data; this does not establish that decentralized venues are cheaper for all large trades (Ranaldo and coauthors’ decentralized-exchange transaction-cost study).
One study of a Coinbase Pro fee change found maker-fee increases passed through into higher posted spreads, while the studied taker-fee reduction more than compensated for that spread increase. It also reported lower depth and transaction counts and larger average trade size. Those findings describe that venue and event, not a general rule for fee changes elsewhere (study of a Coinbase Pro fee change).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to audit the before-and-after result
Recompute both versions on the same return series, dates, venue, instruments, capital, and position-sizing rules. Report enough detail for a reader to understand what changed and to reproduce the calculation.
- Return and Sharpe method: state the return frequency, Sharpe formula, annualization convention, and risk-free-rate treatment.
- Test scope: identify the sample dates, instruments, venue, and position-sizing rules.
- Trading intensity: report turnover and whether each result is gross or net of costs.
- Commission: provide the dated fee schedule, account tier or discount, and maker/taker assumptions for executed orders.
- Execution: name the fill benchmark and slippage model; explain assumptions about depth, order size, latency, partial fills, and market impact where relevant.
- Derivatives: state the historical funding data and settlement treatment for perpetual positions.
- Robustness: show results under more than one plausible execution-cost assumption, and include drawdown as well as net return and Sharpe.
When comparing cost models or strategies, keep the sample, capital, and sizing assumptions fixed. Compare net return, drawdown, fee and funding burden, turnover, fill realism, and sensitivity to spread and impact—not just a single Sharpe figure. Passive orders may reduce explicit fees when they earn maker treatment, but they can also have lower fill probability; an unfilled order is not a profitable trade.
What the headline can establish
Without the underlying return series, code, exchange, instrument, fee tier, order log, and funding history, neither the original 13.4 Sharpe nor the size and cause of its decline can be verified. Even a carefully costed net backtest does not validate live profitability: realized fills, available depth, and market conditions can differ from the model. The useful outcome is a transparent net result whose assumptions and sensitivity are visible, rather than a headline ratio treated as a promise.
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