What “research on Hyperliquid” actually means
Research is the work that happens before a strategy is a strategy. You take a hypothesis — “assets paying high funding tend to underperform over the next four hours” — write a factor that encodes it, compute the factor across the HL universe, evaluate whether the cross-section actually has the signal you think it does, and decide whether to keep researching, drop it, or wire it into a deployable book. Most of the calendar in a quant's year is research, not deployment.
On Hyperliquid specifically, research has different inputs than on an equity desk. You have ~200 perp markets with a wide range of listing history. You have hourly funding as a first-class data series, not a footnote. You have a regime structure that flips between extreme-funding, dispersed-funding, and compressed-funding periods within months. And you have venue-level transparency that lets you replay your own fills against the public state. The research toolkit has to make all of that addressable.
The core signal diagnostics
These are the standard diagnostics for triaging a cross-sectional signal. None of them is exotic; the value is in reading them together, because each one hides a failure the others expose.
| Diagnostic | What it tells you |
|---|---|
| Information coefficient (IC) | Per-bar cross-sectional rank correlation between the factor and forward returns. Spearman by default — robust to fat tails. |
| Rolling IC | Mean IC over a sliding window. Reveals whether the factor is stable, decaying, or regime-conditional. |
| IC stability (ICIR-equivalent) | Mean IC divided by standard deviation of IC. The IC-Sharpe — a high-IC factor with unstable ICIR is just lucky. |
| Half-life | How many bars until the IC decays to half its peak value. Sets the natural rebalance horizon. |
| Decay curve | IC at each forward horizon (1 bar, 4 bars, 16 bars, 96 bars). Shows whether you are looking at a fast signal or a slow one. |
| Quantile spread | Forward return of top decile minus bottom decile. Tests for monotonicity — a factor with high IC but no monotone payoff is suspicious. |
| Turnover | Fraction of the cross-section that changes deciles each bar. High turnover means the signal is short-horizon and cost-sensitive. |
| Cost sensitivity | Quantile-spread payoff net of a realistic fee + slippage model. The honest filter — many high-IC signals die when you charge them realistic frictions. |
One practical rule: compute the diagnostics on exactly the factor values your strategy will trade — the same data alignment, the same universe filter, the same bar timing. A factor re-implemented in a notebook with a one-bar alignment difference can show an IC the traded signal never had.
Regime-aware factor research
A factor that has an IC of 0.05 averaged across the full sample and an IC of 0.15 in extreme-funding regimes and -0.02 in compressed-funding regimes is not really one factor. It is a conditional bet on the regime. Crypto markets shift between regimes faster than equities, and any research process that ignores that structure will keep promoting fragile factors.
Useful regime labels come from simple series: mean absolute funding across the universe, the cross-sectional dispersion of funding, realized volatility, average pairwise correlation. Keel's component registry includes detectors of this kind — FundingLevelRegime, FundingDispersionRegime and others — which a strategy can use as a gate or a scaler. In research the same kind of label is a conditioning variable: split the sample by regime and compute IC and rolling IC inside each bucket.
The research flow becomes: compute IC unconditional, then compute IC inside each regime bucket, then decide whether the conditional structure is real (and worth a regime gate) or random (and not worth the complexity).
Hyperliquid-specific considerations
- Funding as alpha, not friction. On most equity desks funding is a borrow cost you subtract at the end. On HL it is a tradeable data series that has its own IC, its own decay, and its own regime structure. Treat funding as a factor input, not a P&L deduction.
- Regime shifts inside months. Equity regime research often works on quarterly buckets. HL regime structure moves on a weekly-to-monthly cadence. Rolling-IC windows of 50 to 200 bars (12 hours to 2 days at 15-minute resolution) are more informative than the typical 252-day rolling window from an equity context.
- Newer listings have short history. A factor IC computed over a universe that includes assets with 30 days of history is dominated by survivorship and selection effects. Restrict each evaluation to a minimum-history universe — the honest version of the IC, not the version that looks best in a screenshot.
- Quoting and tick conventions. Cross-sectional evaluations need consistent units. Standardize price returns, funding rates, and OI deltas across the universe before ranking, so a cross-asset IC computation is not silently comparing percent moves on one asset and basis-point moves on another.
An end-to-end research flow
A typical session: compute the candidate factor across the HL universe; compute IC, ICIR, decay curve, half-life and quantile spread; stratify the IC by regime to check conditional structure; if the unconditional or conditional IC is convincing, wire the signal into a strategy with a forecast-weight aggregator and a vol-targeting overlay; backtest with realistic costs and funding; compare against a benchmark; then decide whether it earns capital. In Keel, that second half is the product: compose the strategy from registered components, backtest it over any window with fees, slippage and hourly funding (every run is kept), and deploy the same pipeline live on Hyperliquid.
Wherever you run the diagnostics, the failure to avoid is the rewrite. The factor you evaluated and the signal you trade should be the same computation; otherwise the IC you measured belongs to a different signal. That rewrite is the most common source of slippage between a research IC and live performance.
Try it
Build a strategy on your factor, backtest it over any window with fees, slippage and hourly funding, and deploy the same pipeline live on Hyperliquid when the research checks out.