How different teams put HyperBasis data to work. These are research workflows, not promises of returns — no outcome is ever guaranteed.
Quant teams screen the RWA universe for deviation dislocations between Hyperliquid and TradFi, then test whether those dislocations revert. The leaderboard's actionable view pre-filters for data quality, liquidity, funding alignment and friction cost; the backtest page lets researchers simulate the mean-reversion logic historically before risking capital.
Funding is carry. The 10-minute funding snapshot history, percentiles and z-scores let teams classify whether an instrument's current funding is normal, stretched or extreme relative to its own history — the input to carry and basis decisions.
Risk teams watch the tracked pool's position changes and liquidation-price clusters to understand where forced selling could concentrate. This is context for risk sizing, not a directional prediction.
The capacity score, depth-1% and slippage matrix (from $10k to $500k) help desks size orders realistically on thin RWA books before touching the market.
The OVNT page compares Hyperliquid's 24/7 session against the TradFi close, flagging perps that mispriced overnight news — relevant to anyone carrying RWA exposure through market-closed hours.
Institutions license the underlying database for backtesting, seasonality and correlation studies under the Data License Agreement (resale and AI-model training prohibited).