The appeal of multi-asset screening is obvious: capital flows to wherever the opportunity is. The hidden cost is comparability — a momentum signal on a currency pair and one on a large-cap equity are not the same measurement, and a process that ignores that will allocate on noise.
Make the ranking mean something
- ▸Normalize before you compare. Rank candidates on a scale-free measure — a z-score, a percentile within the symbol's own history, a risk-adjusted return — rather than raw numbers that carry each asset class's units.
- ▸Costs are per-asset facts. Spreads, commissions, and financing differ wildly between a forex pair and an equity. A screen that applies one cost assumption everywhere will systematically favor whichever class it underpriced.
- ▸Session and calendar differences. Crypto trades continuously, equities do not, forex follows its own clock. Signals computed across these calendars need explicit handling or the screen is comparing different amounts of time.
- ▸Correlation, not just score. The fifth-best candidate that moves with your first four adds less than its rank suggests. Cross-asset correlation is the check that turns a list into a portfolio.
From screen to portfolio
The workflow that holds up is a funnel: screen wide across assets with one normalized ranking, backtest the survivors with per-asset costs on both engines, then look at the combined book in a single portfolio view before committing. When every step happens in one system, the comparison survives the trip — and the positions you end up paper trading are positions you can actually explain. The alternative is four spreadsheets and a reconciliation ritual every rebalance, which is where most multi-asset processes quietly die.