From fatfingererr-macro-skills
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 claudepluginhub joshuarweaver/cascade-code-general-misc-1 --plugin fatfingererr-macro-skillsThis skill uses the workspace's default tool permissions.
<essential_principles>
examples/eurusd_low_cost.jsonexamples/high_frequency_warning.jsonexamples/xauusd_default.jsonmanifest.jsonreferences/formulas.mdreferences/theory.mdscripts/cost_density.pyscripts/cost_density.tsskill.yamltemplates/input-schema.yamltemplates/output-schema.yamlworkflows/analyze.mdworkflows/compute.mdworkflows/sweep.mdCreates new Angular apps using Angular CLI with flags for routing, SSR, SCSS, prefixes, and AI config. Follows best practices for modern TypeScript/Angular development. Use when starting Angular projects.
Generates Angular code and provides architectural guidance for projects, components, services, reactivity with signals, forms, dependency injection, routing, SSR, ARIA accessibility, animations, Tailwind styling, testing, and CLI tooling.
Executes ctx7 CLI to fetch up-to-date library documentation, manage AI coding skills (install/search/generate/remove/suggest), and configure Context7 MCP. Useful for current API refs, skill handling, or agent setup.
<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
4. 單位一致性規則
等待回應後再繼續。
| 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, "analyze", "interpret", "explain" | workflows/analyze.md | 結果解讀與策略建議 |讀取工作流程後，請完全遵循其步驟。
<reference_index>
參考文件 (references/)
| 文件 | 內容 |
|---|---|
| formulas.md | 完整公式推導與數學證明 |
| theory.md | 市場微結構理論背景與文獻 |
| </reference_index> |
<workflows_index>
| Workflow | Purpose |
|---|---|
| compute.md | 單次計算成本密度與效率指標 |
| sweep.md | 網格掃描與閾值搜尋 |
| analyze.md | 結果解讀與策略建議 |
| </workflows_index> |
<templates_index>
| Template | Purpose |
|---|---|
| output-schema.yaml | 輸出 JSON schema |
| input-schema.yaml | 輸入參數 schema |
| </templates_index> |
<scripts_index>
| Script | Purpose |
|---|---|
| cost_density.py | Python 計算實作 |
| cost_density.ts | TypeScript 計算實作 |
| </scripts_index> |
<quick_start> 快速計算（XAU/USD 範例）：
輸入：
{
"RR_g": 3.0,
"c": 7.0,
"s": 1.5,
"V": 10.0,
"P": 20
}
計算：
cost_density = 7.0/10.0 + 1.5 # = 2.2 pips
x = 2.2 / 20 # = 0.11
RR_net = (3.0 - 0.11) / (1 + 0.11) # = 2.60
WR_min = (1 + 0.11) / (1 + 3.0) # = 27.7%
P_critical = 2.2 * (3.0 + 2) / 3.0 # = 3.67 pips
輸出：
{
"cost_density": 2.2,
"x": 0.11,
"RR_net": 2.60,
"WR_min": 0.277,
"P_critical": 3.67,
"Loss_RR": 0.133
}
</quick_start>
<success_criteria> Skill 成功執行時：