# Dragon Refined Stability Review

## Scope
- branches: `alpha_first_selective_veto` vs `alpha_first_glued_refined_hot_cap`
- execution models: `same_close`, `next_open`, `next_close`
- costs: `0`, `5`, `10`, `20 bps/side`

## Latency Review
- same_close control vs refined: avg_return `2.86%` -> `3.42%`, PF `4.04` -> `5.11`
- next_open control vs refined: avg_return `2.76%` -> `3.31%`, PF `3.78` -> `4.73`
- next_close control vs refined: avg_return `1.98%` -> `2.44%`, PF `2.37` -> `2.78`

## Cost + Next-Open Stress
- next_open + 20 bps/side control CAGR `22.41%` vs refined `25.51%`
- next_open + 20 bps/side control PF `2.92` vs refined `3.64`
- next_open + 20 bps/side control max DD `-28.86%` vs refined `-19.08%`

## Risk Cluster Review
- next_open control max loss streak `10` vs refined `8`
- next_open control worst 5-trade sum `-10.11%` vs refined `-7.96%`
- next_open control short-loss share `90.40%` vs refined `87.99%`
- next_open control worst loss family `glued_buy` vs refined `glued_buy`

## Judgment
- If refined still leads after next-bar execution and cost drag, its edge is less likely to be a same-bar backtest artifact.
- If refined also keeps loss clustering and drawdown no worse than control, the branch is moving closer to a deployable research baseline.