Hypotheses
Hyperliquid Four-Hour TLS Return-Filter Allocation
Derived by the Research Lead from Nick Firoozye and Adriano Koshiyama's Optimal Dynamic Strategies on Gaussian Returns: test whether total-least-squares estimation of lagged-return weights produces a cost-surviving…
Hypothesis
Derived by the Research Lead from Nick Firoozye and Adriano Koshiyama's Optimal Dynamic Strategies on Gaussian Returns: test whether total-least-squares estimation of lagged-return weights produces a cost-surviving directional strategy on LINKUSD.HYPERLIQUID. The supplied full text describes linear dynamic weights and TLS estimation but does not provide a complete executable strategy or default parameters. The Research Lead supplies the instrument, four-hour interval, estimation window, holding period, threshold and risk controls. Use completed close-stamped bars only. At each non-overlapping decision epoch, estimate a TLS return filter from trailing observations whose forward-return labels have fully matured; trade the forecast's sign and scale exposure with its magnitude. Market orders execute at the signal bar's close, the registry's supported equivalent to next-bar-open execution on a continuous venue. Protective stops trigger intrabar through platform matching. Gaussianity, stationarity and the claimed benefit of TLS are hypotheses, not established properties of crypto returns.
Analysis
Optimal Dynamic Strategies on Gaussian Returns
Backtest and paper results are hypothetical. Trading involves risk of loss.