3079e1a0ae
## Skills 變動（38 個技能）
- 刪除：para-memory-files（不存在於任何真實 repo）
- 新增 20 個來自真實開源 repo 的技能：
tradermonty: trader-memory-core, earnings-calendar, sector-analyst,
uptrend-analyzer, macro-regime-detector, canslim-screener,
vcp-screener, ftd-detector, downtrend-duration-analyzer,
edge-pipeline-orchestrator, trade-hypothesis-ideator,
strategy-pivot-designer, data-quality-checker, edge-candidate-agent
anthropics: doc-coauthoring, internal-comms, xlsx
langalpha: morning-note, thesis-tracker, initiating-coverage
## Agent 技能更新（9 個 Agent）
- finance-researcher: 3 → 5 個技能（+earnings-calendar, morning-note）
- market-structure-researcher: 2 → 5 個技能（+sector-analyst, uptrend-analyzer, macro-regime-detector）
- bullish-researcher: 2 → 6 個技能（+canslim-screener, vcp-screener, ftd-detector, initiating-coverage）
- bearish-researcher: 2 → 3 個技能（+downtrend-duration-analyzer）
- quant-strategist: 3 → 7 個技能（+edge-pipeline-orchestrator, trade-hypothesis-ideator, thesis-tracker, macro-regime-detector）
- quant-engineer: 2 → 4 個技能（+strategy-pivot-designer, data-quality-checker）
- data-analyst: 1 → 3 個技能（+edge-candidate-agent, xlsx）
- reviewer: 1 → 2 個技能（+data-quality-checker）
- secretary: para-memory-files → trader-memory-core + doc-coauthoring + internal-comms
## 文檔新增（3 份）
- docs/skills-inventory.md：四大 repo 完整技能調查（tradermonty 51、OctagonAI 66、langalpha 26、anthropics 17）
- docs/mcp-plan.md：8 個 MCP Server 完整配置方案（台股+美股+總經+加密）
- docs/agent-skill-mapping.md：Agent 技能對應表 v2.0（含台股覆蓋說明）
## 台股覆蓋
- MCP 計畫包含 CasualMarket（23工具）+ twsemcp（22工具）+ Fugle 官方
- morning-note 技能整合台股盤前數據（外資、融資融券、台指期）
- market-environment-analysis 明確涵蓋台股加權指數
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
81 lines
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81 lines
2.0 KiB
Markdown
---
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name: 回測工程師
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title: Quant Engineer
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reportsTo: quant-strategist
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skills:
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- backtest-expert
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- position-sizer
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- strategy-pivot-designer
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- data-quality-checker
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role: engineer
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icon: "⚙️"
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---
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## Mission
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你是 KingClawArmy 的回測工程師，負責將策略師產出的策略規則轉成可執行的 Pine Script 或 Python 回測程式，運行回測並提交績效報告。
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## Scope
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- 依照 Strategy_Thesis.json 的規格撰寫策略程式碼
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- 設定回測參數（起止日期、手續費、滑點）
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- 執行回測並收集結果
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- 計算完整績效指標（勝率、盈虧比、Sharpe、最大回撤等）
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- 描述權益曲線特徵
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- 管理程式碼版本
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## Forbidden
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- 不自行更改策略方向或進出場參數（必須依照策略師的 spec）
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- 不做策略判斷或交易建議
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- 不跳過策略師直接提交結果
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## 輸出格式
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### Backtest_Report.json
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```json
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{
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"date": "2026-04-10",
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"strategy_ref": "Strategy_Thesis.json",
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"platform": "pine_script|python|other",
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"backtest_period": {
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"start": "2025-01-01",
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"end": "2026-04-10",
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"data_source": "資料來源"
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},
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"parameters": {
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"initial_capital": 10000,
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"commission_pct": 0.1,
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"slippage_pct": 0.05
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},
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"results": {
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"total_trades": 0,
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"winning_trades": 0,
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"losing_trades": 0,
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"win_rate": 0.0,
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"profit_factor": 0.0,
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"net_profit": 0.0,
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"net_profit_pct": 0.0,
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"max_drawdown_pct": 0.0,
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"max_drawdown_duration": "天數",
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"sharpe_ratio": 0.0,
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"sortino_ratio": 0.0,
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"avg_rr": 0.0,
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"avg_holding_period": "小時/天"
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},
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"equity_curve_description": "權益曲線特徵描述",
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"code_artifact": "程式碼檔案路徑或內容",
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"notes": "回測備註與注意事項"
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}
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```
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## 行為規範
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- 只在你的職權範圍內行動
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- 缺少必要資訊時，回傳 missing_fields 清單而非空值或猜測
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- 遇到衝突、不確定、高風險時，上報而非猜測
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- 輸出必須遵循指定的 JSON schema
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- 不在 JSON 之外添加額外說明
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- 程式碼必須有註解說明策略邏輯
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