cost-density-net-rr-calculator
計算交易成本對風險報酬比的非線性衰減影響。將固定佣金與點差整合為「成本密度」指標，揭示停損大小與策略效率的雙曲線關係，識別「獲利事件視界」閾值。
npx skills add majiayu000/claude-skill-registry --skill cost-density-net-rr-calculator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
<essential_principles> **成本密度模型核心原則** **1. 核心公式** 所有計算基於以下關係： ``` Cost Density = (c/V + s) # 成本密度（pips 等效） x = Cost Density / P # 負載係數 RR_net = (RR_g - x) / (1 + x) # 淨風險報酬比 WR_min = (1 + x) / (1 + RR_g) # 最低勝率 P_critical = CostDensity × (RR_g + 2) / RR_g # 效率減半點 ``` **2. 參數定義** | 參數 | 定義 | 單位 | |------|---------------------------|--------------| | RR_g | 毛風險報酬比（目標/停損） | 無單位 | | P | 停損大小 | pips/points | | c | 來回佣金（每手） | 帳戶貨幣 | | s | 來回點差 | pips/points | | V | 每 pip 價值（每手） | 帳戶貨幣/pip | | R | 固定風險（可選，會抵消） | 帳戶貨幣 | **3. 關鍵洞察** - **雙曲線衰減**: P → 0 時，x → ∞，RR_net → -1 - **R 無關性**: RR_net 不依賴固定風險 R - **剪刀效應**: 短時間框架同時增加成本負擔與降低訊號品質 **4. 單位一致性規則** - P 和 s 必須使用相同基準（都是 pips 或都是 points） - c 必須是 round-turn（來回）佣金 - V 必須是每 pip 每手的價值 </essential_principles> <intake> **您想要執行什麼操作？** 1. **Compute** - 計算單一參數組合的成本密度與效率指標 2. **Sweep** - 掃描停損範圍，生成 RR_net/WR_min 曲線表 3. **Analyze** - 解讀結果，提供策略建議 **等待回應後再繼續。** </intake> <routing> | Response | Workflow | Description | |--------------------------------------|----------------------|----------------------| | 1, "compute", "calculate", "single" | workflows/compute.md | 單次計算成本密度指標 | | 2, "sweep", "grid", "curve", "range" | workflows/sweep.md | 網格掃描與閾值搜尋 | | 3, "a
What does the cost-density-net-rr-calculator skill do?
計算交易成本對風險報酬比的非線性衰減影響。將固定佣金與點差整合為「成本密度」指標，揭示停損大小與策略效率的雙曲線關係，識別「獲利事件視界」閾值。
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cost-density-net-rr-calculator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.