Researching Intraday vs Multi-Day Alpha
Intraday and multi-day signals aren't the same research problem at different speeds — they need different data, different backtests, and different definitions of what counts as a win.
Prerequisites: Framing a Research Question, Choosing a Signal Horizon
A researcher asked to look for "an intraday edge" and one asked to look for "a two-week edge" are not doing the same job with a different clock. The data, the backtest, and the bar for success are different at every step, and treating one like a faster version of the other is how projects quietly fail.
Different data, different backtest
A multi-day signal is built on daily closes, held through overnight risk, and evaluated against a universe that turns over slowly. The backtest can be a clean cross-sectional exercise: rank names today, hold for a week, measure next week's return. Costs matter but a rough model — a fixed number of basis points per side — is usually good enough to decide if the idea is alive.
An intraday signal lives inside a single trading session and dies or matures within minutes to hours. It needs tick or quote-level data, a realistic queue and fill model, and a cost estimate that accounts for the fact that you are often trading against the very short-term price move you're trying to capture — market impact is not a footnote, it is most of the return. A backtest that ignores the order book will pass signals that cannot survive contact with a real exchange.
What "it works" means changes
For a multi-day signal, a monthly information coefficient of 0.03 with modest turnover is a real result. For an intraday signal, IC per observation is nearly meaningless on its own — what matters is the IC accumulated across the thousands of independent bets a day produces, and whether the edge survives being executed in size without moving the price you're trying to trade at. A signal with a tiny per-trade edge can still be excellent if it fires often enough and costs stay below the edge; a signal with a large per-trade edge is worthless if it can only be captured a few times a day.
Intraday alpha is a market-microstructure problem wearing a forecasting hat — the backtest has to model the order book, not just the price. Multi-day alpha is closer to a cross-sectional forecasting problem where a coarse cost model suffices for a first cut.
A worked comparison
Suppose a researcher has two candidate ideas. Idea A: order-flow imbalance predicts the next 5 minutes of return, with an IC of 0.06 measured tick-by-tick, but each signal is stale within 90 seconds. Idea B: analyst-estimate revisions predict the next 10 trading days, with an IC of 0.02 measured daily. Idea A needs a fill-probability model and realistic latency assumptions before the 0.06 means anything — spread alone can erase it. Idea B can be sanity-checked in an afternoon with daily bars and a flat cost assumption, because overnight and multi-day holding smooths out the microstructure noise that would dominate Idea A's evaluation.
Before building either backtest, ask what unit of "cost" the idea is being compared against — cents of spread and impact for intraday, basis points of round-trip cost for multi-day. Using the wrong unit is the fastest way to green-light a dead idea.
Related concepts
Practice in interviews
Further reading
- Kissell, The Science of Algorithmic Trading and Portfolio Management (ch. on short-horizon alpha)