Hypotheses
Cost-Regularised Deep Momentum LSTM on a Hyperliquid Daily Long–Short Panel
Derived by the Research Lead from Lim, Zohren and Roberts, Enhancing Time Series Momentum Strategies Using Deep Neural Networks: test direct LSTM position outputs trained on turnover-adjusted Sharpe within…
Hypothesis
Derived by the Research Lead from Lim, Zohren and Roberts, Enhancing Time Series Momentum Strategies Using Deep Neural Networks: test direct LSTM position outputs trained on turnover-adjusted Sharpe within volatility-scaled time-series momentum. Use daily LTC, XRP and ADA Hyperliquid perpetuals. Preserve the actual recurrent architecture, the source's 15% annualised volatility target and its 10-bps one-way turnover penalty. Adapt the original continuous-futures universe, initial training period and exposure caps explicitly. Implement the LSTM and Adam using permitted NumPy operations; do not substitute an MLP, classifier or heuristic. The supplied full text resolves details absent from the abstract. All four supplied figures were inspected: they show asset-level performance dispersion, turnover, cost sensitivity and regularisation comparisons, rather than marked trading entries. Their reported performance is unverified on these markets. Continuous-market orders execute at signal-bar closes, the registry's equivalent of next-bar-open execution.
Analysis
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
Backtest and paper results are hypothetical. Trading involves risk of loss.