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A crypto strategy’s Sharpe ratio can fall sharply when commissions, spread, slippage, and—in perpetual futures—funding are included. That drop is a reason to inspect the assumptions, not proof on its own that the strategy is unusable or that the revised backtest is accurate. The Sharpe ratio of 13.4 in this title cannot be independently checked: no strategy code, return series, venue, instrument, or cost inputs are available.
Why trading costs can change a backtest so much
A backtest that records trades at candle closes or midpoint prices may show returns the strategy could not have captured. Real orders pay commissions and transact at executable prices; frequent trading repeats those costs. A derivatives strategy may also make or receive funding payments while it holds a perpetual-futures position.
The key distinction is between a headline result and a credible net result. A large decline after costs can reveal that a strategy depends on high turnover, optimistic fills, or omitted charges. It does not by itself identify which assumption is wrong—or establish that the strategy would lose money live.
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Commissions depend on venue and execution role
Apply the fee schedule that matches the historical venue, account tier, discounts, and order role. As a venue-specific reference, Binance’s public spot schedule displayed a regular-user maker/taker rate of 0.100% / 0.100% before its displayed BNB discount at the time documented by Binance’s trading-fee schedule. That is not a universal crypto fee and may change.
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Order type alone does not determine the fee role. Binance says a market order is always taker; a limit order that rests on the book can be maker, but a limit order that executes immediately can be taker. See Binance’s maker-and-taker explanation. Model the role of each fill rather than labeling every limit order maker.
Spread and slippage depend on executable fills
Commissions are venue charges. Execution costs are the difference between the price your backtest assumes and a realistic fill benchmark; they can include the bid–ask spread and slippage. A close price or midpoint is not automatically an executable price. Document the benchmark and how the model handles quote depth, order size, latency, partial fills, and price impact.
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Published crypto cost estimates are tied to their samples and methods. A 2026 SSRN working paper by Dominik Rösch, Thomas Shohfi, Jenny Stanco, and Michael Walz reports effective spreads of 253–835 basis points for its studied major-exchange sample and comparison benchmarks; that range is not a general estimate for all crypto markets (SSRN paper). A separate 2026 study of crypto perpetual backtests expressly excludes market impact and institutional capacity, so its findings should not be read as accounting for those effects (SSRN paper).
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Perpetual futures have funding transfers
For perpetual contracts, use historical funding rates and the contract’s actual settlement schedule. Binance describes funding as “periodic payments transferred between holders of long and short positions in Perpetual Contracts.” Its default interval is eight hours, but intervals can vary by contract or change. A position is liable only if it is open at the scheduled funding time. Check the relevant contract information and funding history rather than applying a constant fee proxy (Binance Futures funding-rate FAQ).
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Check for other instrument-specific charges
Leverage, margin, borrowing, liquidation-sensitive positions, and decentralized venues can introduce additional mechanics or charges. Their exact treatment depends on the instrument and venue; the cited sources do not establish a complete universal checklist or fee schedule for every product.
How to rebuild the comparison
- Define the backtest. Record the sample dates, instruments, venue, return frequency, position-sizing rules, and whether the results are gross or net. State the Sharpe formula, annualization convention, and risk-free-rate treatment.
- Reconstruct commissions. Use the applicable schedule and account tier for the period being tested. Apply maker or taker treatment to actual modeled executions; do not infer maker status from a limit-order label.
- Choose and disclose a fill benchmark. Specify whether the model starts from bid, ask, midpoint, or another executable-price reference, then explain how it accounts for spread, slippage, order size, depth, latency, partial fills, and price impact where relevant.
- Add historical funding for perpetual positions. Apply the rate and settlement interval for the specific contract only when a position is held at settlement.
- Run more than one plausible cost case. Compare gross performance with net results under multiple defensible execution-cost assumptions. Show how changing spread or slippage assumptions affects the outcome rather than presenting one cost estimate as certain.
- Report the results together. Include net return, Sharpe, drawdown, turnover, commission burden, funding burden, fill assumptions, and sensitivity to execution costs. Keep sample, capital, and sizing assumptions consistent across comparisons.
Why a very high Sharpe still needs validation
Trading costs are only one source of optimism. Repeatedly selecting strategies or parameters on the same data can make a backtest look better than its out-of-sample performance. A 2026 SSRN working paper by Sergey Nefedov compared a naïve protocol with a nested walk-forward, cost-aware protocol across six factors and 137 Binance USDT perpetual contracts during 2020–2024. It reports annualized Sharpe ratios inflated by 3.6× on average under the naïve protocol. That is a study-specific preprint result, not a correction factor to apply to every strategy (SSRN working paper).
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Other market-structure findings also need their original scope. A 2026 *Management Science* study reports that DEX gas fees burdened smaller trades, with more competitive transaction costs for larger trades in the data it studied; this does not establish that decentralized exchanges are cheaper for every large trade (Management Science study). A paper examining a Coinbase Pro fee change found that maker fees passed through to higher posted spreads, while the studied taker-fee reduction more than compensated for that spread increase; depth and transaction counts decreased and average trade size increased. Those results describe a particular venue and event, not a general prediction for fee changes (Coinbase Pro fee-change study).
What can—and cannot—be concluded about a Sharpe of 13.4
Without the strategy’s return series and implementation details, neither the original 13.4 nor the size or cause of its decline after costs can be audited. The useful conclusion is methodological: a net Sharpe is only as credible as its return calculation, cost inputs, and execution assumptions. Even a careful net backtest does not validate live profitability.
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