What does funding cost do to a backtest?
Perpetual futures charge a funding payment between longs and shorts at regular intervals, and a strategy holding positions across those intervals pays or receives it — which a backtest ignoring funding silently omits from every held position.
The size is not trivial for anything held longer than a few hours. Funding is typically settled every eight hours, and during a strongly trending period the rate on the crowded side can be substantial. A strategy holding a long through a euphoric run is paying for the privilege on every settlement.
The direction of the error is systematic rather than random. Trend-following strategies tend to be positioned on the crowded side, which is the side that pays — so ignoring funding flatters exactly the strategies most likely to be affected.
Modelling it requires the historical funding rate series for the specific instrument and exchange, applied at each settlement the position was open across. It is bookkeeping rather than difficulty, and it is the single most commonly omitted cost in crypto backtesting.
Which exchange's prices are you testing on?
Crypto has no consolidated tape, so every exchange has its own book and its own price — and the differences are larger and more persistent than the equivalent variation between forex brokers.
The practical consequence is that a strategy tested on one exchange's history and traded on another is being estimated rather than measured. For wide-stop strategies the gap is noise; for anything tight it is a meaningful fraction of the edge.
Liquidity differs as much as price. A strategy that works on the deepest venue may be untradeable at size on a thinner one, and the backtest — which fills any order instantly at the printed price — will not mention it.
The discipline is to test on the exchange you will trade, model that venue's fee schedule including any maker-taker distinction, and treat results from an aggregated index price as indicative rather than executable.
Does 24/7 trading actually change anything?
It removes weekend gaps and the session structure that forex strategies often rely on, and it replaces them with a different kind of unevenness that is easy to miss.
The good news first: no weekend gap risk. A forex position held over Friday faces a Monday open that can be far from Friday's close with no opportunity to exit. Crypto trades through, so stops behave as modelled.
The complication is that continuous trading is not uniform trading. Volume and volatility still follow a strong daily and weekly shape driven by where the participants are awake, and a strategy assuming every hour is equivalent will find its fills and its spreads are not.
The other consequence is subtle and affects any calculation involving periods. Annualising a Sharpe from daily returns uses 252 for equities and 365 for crypto, and using the wrong constant misstates the figure by roughly 20% — a difference large enough to change how a result is read.
What about tokens that stop existing?
Crypto has the equity survivorship problem in a more severe form: tokens are delisted, chains are abandoned, and projects fail completely, so a universe built from today's listings excludes every one that did not make it.
Testing an altcoin strategy against the coins currently listed on a major exchange is testing against a set pre-filtered for survival — and unlike equities, where a failure often means an acquisition at a discount, crypto failures frequently go to effectively zero.
The bias is largest for exactly the strategies people most want to test. A rule that buys sharp declines looks superb when every decline in the sample eventually recovered, and the ones that did not recover are the tokens that were removed from the list you built the universe from.
The requirement is the same as for equities: a point-in-time universe including delisted assets with their real final outcomes. Without it, the result is an upper bound and should be reported as one.
Do the standard method rules still apply?
Every one of them, and more strictly rather than less: out-of-sample validation, counting variants, realistic costs and adequate sample size are unchanged by the asset class.
One thing that genuinely differs is the amount of history available. Most crypto instruments have a few years rather than decades, and much of that history sits inside one or two enormous directional regimes. A strategy validated only across a bull market has met one condition.
That shortage makes the trial count more dangerous rather than less. Fewer independent periods and the same number of parameter combinations means a higher chance that the best variant is fitted, which is the opposite of the intuition that a newer market is easier to find edges in.
The compensating advantage is frequency. Continuous trading and high volatility mean trades accumulate quickly, so a strategy's sample size can reach a usable level in months — provided the regime coverage is acknowledged for what it is.