Order Book Reconstruction from On-Chain Data on Hyperliquid

Abstract Hyperliquid is the first production perpetuals venue whose central limit order book lives entirely on an app-specific L1: every order placement, modification, cancellation, and fill is a committed transaction. This paper asks whether the resulting on-chain event stream is sufficient to reconstruct full market microstructure state — order flow imbalance (OFI), book pressure, queue position — at the fidelity required for adversarial signal extraction, and whether those reconstructed signals carry forward-looking information about short-horizon returns. We treat this as a methodology pilot. Using the Hyperliquid L1 JSON-RPC and websocket event streams as the source of truth, we describe a deterministic book-reconstruction algorithm, derive OFI and book-pressure signals from the reconstructed state, and contextualize them with 944,150 funding-rate observations and live cross-venue spreads from our production PerpsTrader database. We find that reconstruction is architecturally complete (every state transition is observable on-chain), that the resulting signals are theoretically well-specified, and that the magnitude of Hyperliquid↔Binance cross-venue spreads (SOPH 38.78%, TON 36.72% annualized in our sample) is consistent with exploitable structural divergence. We do not claim statistical significance for return predictability: the in-house trade sample is too small (10 fills) for that. The contribution is the reconstruction methodology and the explicit enumeration of what additional data is needed to elevate each hypothesis from pilot to confirmatory. Hypotheses H1 — Architectural completeness (strong support expected). Hyperliquid's L1 event stream is sufficient to reconstruct the full resting-book state at every committed height, with no information loss relative to a hypothetical off-chain matching engine snapshot. This is a structural claim that follows directly from Hyperliquid's design (HyperBFT consensus, on-chain CLOB); we verify it empirically by demonstrating event-driven reconstruction parity. H2 — Forward-looking signal content (moderate support, data-limited). Reconstructed on-chain book pressure and OFI correlate with subsequent short-horizon (1–10 second) mid-price returns at magnitudes comparable to published off-chain CEX studies (typically ρ ∈ 0.05 to 0.20). We expect a positive but noisy correlation; the pilot data is insufficient to establish significance. H3 — Cross-venue structural decomposition (exploratory — data may be insufficient). Persistent Hyperliquid↔Binance price spreads are partly attributable to divergence in reconstructed book pressure between the two venues, and partly to funding-rate and settlement-latency differentials. This decomposition requires synchronized multi-venue book snapshots we do not yet have; we present the framing only. Data Provenance All data was collected in real-time from Hyperliquid, Binance, and Asterdex via their respective public APIs, ingested through the PerpsTrader production system. Nothing in this paper is simulated, backtested, or synthetically generated. The three data layers are: | Source | Type | Window | Volume | Role in this paper | |--------|------|--------|--------|--------------------| | Hyperliquid L1 RPC + WS | Real-time order events | Continuous | Event stream | Reconstruction source of truth | | Funding rate DB | Real-time API collection | Multi-month | 944,150 observations | Cross-venue context | | Cross-exchange spreads | Live quote sampling | Multi-month | Top-10 by annualized spread | Structural divergence evidence | | In-house trades | Production fills | 2026-06-05 | 10 fills | Signal ground truth (limited) | The funding database averages −0.4527% annualized across 944,150 observations, with extreme dispersion at the tail: MOVE averages −36.96% annualized (min −2166.65%), HEMI averages +10.61%. The top persistent positive-funding names (HEMI, PURR, MANTA, XMR, ZRO) are the natural short-carry candidates; the deep negative outliers (MOVE, STABLE, SOPH, LAYER) are the long-carry candidates. These funding observations frame — but do not by themselves constitute — the order-book analysis below. Analysis 1. Why Hyperliquid is structurally different On a traditional centralized exchange (Binance, Bybit, OKX), the order book is an in-memory data structure inside the matching engine. The public observes a snapshot of the top of book via REST and a delta stream via websocket, but the matching engine's authoritative state is off-chain. Trade prints are observable; resting orders are observable only as a delayed, rate-limited shadow. Hyperliquid inverts this. The matching engine runs as part of L1 consensus (HyperBFT). Every order placement, cancellation, modification, and fill is a committed transaction. The order book is the chain state at a given height. This has three consequences for microstructure research: 1. Reconstruction is information-preserving by construction. There is no "true" book state that exists off-chain and is