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shadow-account

Shadow Account extracts profitable trading patterns from a user's settlement records into 3-5 plain-language rules, backtests them across China A-shares, Hong Kong, US, and crypto markets, and generates an 8-section PDF report quantifying the profit difference between the user's actual trades and the extracted strategy rules. Use this when a user wants to understand their implicit trading style, measure how much emotion or discipline gaps cost them, or validate their profitability rules across multiple asset classes.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading /tmp/shadow-account && cp -r /tmp/shadow-account/agent/src/skills/shadow-account ~/.claude/skills/shadow-account
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Shadow Account — 影子账户

## 何时触发

当用户说 "提炼我的策略" / "训练影子" / "我的打法回测一下" / "我能多赚多少" / "我的盈利模式" 时,加载此 skill。

**前提**:用户已上传交割单且 `analyze_trade_journal` 已跑过。若没有,先跑 Phase 4a 工具。

## 工作流(四步)

1. `extract_shadow_strategy(journal_path=...)`
   - 返回 `shadow_id` + 3-5 条人话规则
   - 向用户 confirm:"这些规则像你本人吗?" 如果用户说"不像",提高 `min_support` 重跑
2. `run_shadow_backtest(shadow_id=..., journal_path=...)`
   - 返回 per-market 指标 + `delta_pnl` + attribution breakdown
   - 默认四市场并跑(china_a/hk/us/crypto)
3. `render_shadow_report(shadow_id=...)`
   - 生成 HTML + PDF(weasyprint 失败时自动降级成 HTML-only)
   - 返回 `html_path` / `pdf_path` / `delta_pnl`
4. (可选)`scan_shadow_signals(shadow_id=...)` — 今日落在影子入场窗口的标的列表(研究用)

## 产出解读

### 规则卡
每条规则含:`rule_id`、`human_text`(≤30 字)、`support_count`、`coverage_rate`、`holding_days_range`。规则不是"必赚公式",而是"用户盈利时的共性画像"。

### 回测矩阵
- `per_market`:四市场的 Sharpe/年化/最大回撤
- `combined`:合并池表现
- `equity_curve`:净值时序(进入 PDF Section 3)

### 差值归因(PDF Section 5 — gut punch)
所有数值 signed,正值=影子相对赚更多:
- `noise_trades_pnl`:不命中任何规则的真实交易累计 PnL(用户的情绪单)
- `early_exit_pnl`:赢单但持仓 < 规则下限,按不足比例折算的机会成本
- `late_exit_pnl`:亏单但持仓 > 规则上限,按超额比例折算的放大损失
- `overtrading_pnl`:超出规则频率的真实交易 PnL
- `missed_signals_pnl`:残差(shadow_pnl − real_pnl − 上面四项之和)

### 反事实 Top 5
按 `|impact|` 排序,列出 5 条"最该做没做 / 最不该做却做了"的交易,带具体日期、原因。

## 对话模板

**确认规则**:
> 从你 {profitable_roundtrips} 笔盈利回合中提炼出这些规则:{rules}。这些看起来像你本人的打法吗?

**展示差值**(Section 5):
> 影子 PnL **{shadow_pnl:+.0f}** / 你真实 **{real_pnl:+.0f}** / 差值 **{delta_pnl:+.0f}**。其中 **{noise_trades_pnl:+.0f}** 来自不符合你任何盈利规则的"情绪单"。

**今日扫描**(强制附带免责):
> 今日落在你影子入场节奏的标的:{symbols}。**仅研究用,不是买入建议。**

## 规则翻译 Prompt 模板

当 `extract_shadow_strategy` 被调用时,可以注入一个 `llm_translator` callable 以把结构化 entry_condition 翻译成中文自然语言:

```
[上下文] 一位散户的盈利回合中,{N} 笔满足同一组条件:
  market = {market}
  entry_hour ∈ [{hour_min}, {hour_max}]
  持有 {hold_lo}-{hold_hi} 天
[任务] 用 ≤30 字的中文写一条规则,口吻像用户自述的交易习惯,不要堆术语。
[输出] 只返回一行规则文本,不要解释。
```

不注入时走 f-string 模板(见 `extractor._translate_rule`)。

## 红线

- **不落单**:这些工具永远不会对接任何下单通道,仅研究输出
- **不复制他人策略**:Shadow Account 是"用户自己"的影子,不从社区/公开策略提取规则
- **样本不足必报错**:profitable roundtrips < 5 → 直接 raise,不编造
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