Add per-agent skill workspaces and TraderView management

2026-03-17 13:55:14 +08:00
parent 1f5ee3698e
commit 2daf5717ba
35 changed files with 4774 additions and 331 deletions
--- a/backend/skills/builtin/valuation_review/SKILL.md
+++ b/backend/skills/builtin/valuation_review/SKILL.md
@@ -1,21 +1,31 @@
 ---
-name: valuation_review
-description: Estimate fair value and margin of safety using multiple valuation lenses.
+name: 估值分析
+description: 当用户要求“估值分析”“看合理价值”“判断高估低估”“测算安全边际”或“比较多种估值方法”时，应使用此技能。
+version: 1.0.0
 ---

-# Valuation Review
+# 估值分析

-Use this skill when the task requires determining whether a stock is cheap, expensive, or fairly priced.
+当用户需要判断一只股票是低估、高估还是定价合理时，使用这个技能。

-## Workflow
+## 工作流程

-1. Use more than one valuation method when possible.
-2. Compare intrinsic value estimates with current market pricing.
-3. Explain the key assumptions behind the valuation view.
-4. State the margin of safety and what could compress or expand it.
+1. 条件允许时，使用不止一种估值方法。
+2. 对比内在价值估计与当前市场价格。
+3. 解释估值判断背后的关键假设。
+4. 明确安全边际，以及哪些因素会压缩或扩大它。

-## Guardrails
+## 可复用资源

- Treat valuation as a range, not a single precise number.
- Call out assumption sensitivity.
- Avoid high-confidence calls when inputs are sparse or unstable.
+- `scripts/dcf_report.py`
+  用于贴现现金流估值的确定性计算和报告生成。
+- `scripts/owner_earnings_report.py`
+  用于 owner earnings 估值的确定性计算和报告生成。
+- `scripts/multiple_valuation_report.py`
+  用于 EV/EBITDA 和 Residual Income 两类估值报告生成。
+
+## 约束
+
+- 将估值视为区间，而不是一个精确点值。
+- 明确说明假设敏感性。
+- 当输入稀疏或不稳定时，避免给出高置信度判断。
--- a/backend/skills/builtin/valuation_review/init.py
+++ b/backend/skills/builtin/valuation_review/init.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
--- a/backend/skills/builtin/valuation_review/scripts/init.py
+++ b/backend/skills/builtin/valuation_review/scripts/init.py
@@ -0,0 +1 @@
+# -*- coding: utf-8 -*-
--- a/backend/skills/builtin/valuation_review/scripts/dcf_report.py
+++ b/backend/skills/builtin/valuation_review/scripts/dcf_report.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Deterministic DCF report helpers for the valuation_review skill."""
+
+from __future__ import annotations
+
+import json
+from typing import Iterable
+
+
+def build_dcf_report(rows: Iterable[dict], current_date: str) -> str:
+    """Render a DCF valuation report from normalized row inputs."""
+    lines = [f"=== DCF Valuation Analysis ({current_date}) ===\n"]
+
+    for row in rows:
+        error = row.get("error")
+        ticker = row["ticker"]
+        if error:
+            lines.append(f"{ticker}: {error}\n")
+            continue
+
+        current_fcf = float(row["current_fcf"])
+        growth_rate = float(row["growth_rate"])
+        market_cap = float(row["market_cap"])
+        discount_rate = float(row.get("discount_rate", 0.10))
+        terminal_growth = float(row.get("terminal_growth", 0.03))
+        num_years = int(row.get("num_years", 5))
+
+        pv_fcf = sum(
+            current_fcf
+            * (1 + growth_rate) ** year
+            / (1 + discount_rate) ** year
+            for year in range(1, num_years + 1)
+        )
+        terminal_fcf = (
+            current_fcf
+            * (1 + growth_rate) ** num_years
+            * (1 + terminal_growth)
+        )
+        terminal_value = terminal_fcf / (discount_rate - terminal_growth)
+        pv_terminal = terminal_value / (1 + discount_rate) ** num_years
+        enterprise_value = pv_fcf + pv_terminal
+        value_gap = (enterprise_value - market_cap) / market_cap * 100
+
+        if value_gap > 20:
+            assessment = "SIGNIFICANTLY UNDERVALUED"
+        elif value_gap > 0:
+            assessment = "POTENTIALLY UNDERVALUED"
+        elif value_gap > -20:
+            assessment = "POTENTIALLY OVERVALUED"
+        else:
+            assessment = "SIGNIFICANTLY OVERVALUED"
+
+        lines.append(f"{ticker}:")
+        lines.append(f"  Current FCF: ${current_fcf:,.0f}")
+        lines.append(f"  DCF Enterprise Value: ${enterprise_value:,.0f}")
+        lines.append(f"  Market Cap: ${market_cap:,.0f}")
+        lines.append(f"  Value Gap: {value_gap:+.1f}% -> {assessment}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def main() -> None:
+    """Read normalized rows from stdin and emit a text report."""
+    payload = json.load(__import__("sys").stdin)
+    print(build_dcf_report(payload["rows"], payload["current_date"]))
+
+
+if __name__ == "__main__":
+    main()
--- a/backend/skills/builtin/valuation_review/scripts/multiple_valuation_report.py
+++ b/backend/skills/builtin/valuation_review/scripts/multiple_valuation_report.py
@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Deterministic multiple-based valuation helpers for the valuation_review skill."""
+
+from __future__ import annotations
+
+import json
+from typing import Iterable
+
+
+def build_ev_ebitda_report(rows: Iterable[dict], current_date: str) -> str:
+    """Render an EV/EBITDA valuation report from normalized row inputs."""
+    lines = [f"=== EV/EBITDA Valuation ({current_date}) ===\n"]
+
+    for row in rows:
+        error = row.get("error")
+        ticker = row["ticker"]
+        if error:
+            lines.append(f"{ticker}: {error}\n")
+            continue
+
+        current_multiple = float(row["current_multiple"])
+        median_multiple = float(row["median_multiple"])
+        current_ebitda = float(row["current_ebitda"])
+        market_cap = float(row["market_cap"])
+        net_debt = float(row["net_debt"])
+
+        implied_ev = median_multiple * current_ebitda
+        implied_equity = max(implied_ev - net_debt, 0.0)
+        value_gap = (
+            (implied_equity - market_cap) / market_cap * 100
+            if market_cap > 0
+            else 0.0
+        )
+        multiple_discount = (
+            (median_multiple - current_multiple) / median_multiple * 100
+        )
+
+        if multiple_discount > 10:
+            assessment = "TRADING BELOW HISTORICAL MULTIPLE"
+        elif multiple_discount > -10:
+            assessment = "NEAR HISTORICAL AVERAGE"
+        else:
+            assessment = "TRADING ABOVE HISTORICAL MULTIPLE"
+
+        lines.append(f"{ticker}:")
+        lines.append(f"  Current EV/EBITDA: {current_multiple:.1f}x")
+        lines.append(f"  Historical Median: {median_multiple:.1f}x")
+        lines.append(f"  Multiple vs History: {multiple_discount:+.1f}%")
+        lines.append(f"  Implied Equity Value: ${implied_equity:,.0f}")
+        lines.append(f"  Value Gap: {value_gap:+.1f}% -> {assessment}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def build_residual_income_report(rows: Iterable[dict], current_date: str) -> str:
+    """Render a residual income valuation report from normalized row inputs."""
+    lines = [f"=== Residual Income Valuation ({current_date}) ===\n"]
+
+    for row in rows:
+        error = row.get("error")
+        ticker = row["ticker"]
+        if error:
+            lines.append(f"{ticker}: {error}\n")
+            continue
+
+        book_value = float(row["book_value"])
+        initial_ri = float(row["initial_ri"])
+        market_cap = float(row["market_cap"])
+        cost_of_equity = float(row.get("cost_of_equity", 0.10))
+        bv_growth = float(row.get("bv_growth", 0.03))
+        terminal_growth = float(row.get("terminal_growth", 0.03))
+        num_years = int(row.get("num_years", 5))
+        margin_of_safety = float(row.get("margin_of_safety", 0.20))
+
+        pv_ri = sum(
+            initial_ri * (1 + bv_growth) ** year / (1 + cost_of_equity) ** year
+            for year in range(1, num_years + 1)
+        )
+        terminal_ri = initial_ri * (1 + bv_growth) ** (num_years + 1)
+        terminal_value = terminal_ri / (cost_of_equity - terminal_growth)
+        pv_terminal = terminal_value / (1 + cost_of_equity) ** num_years
+        intrinsic_value = (book_value + pv_ri + pv_terminal) * (
+            1 - margin_of_safety
+        )
+        value_gap = (intrinsic_value - market_cap) / market_cap * 100
+
+        lines.append(f"{ticker}:")
+        lines.append(f"  Book Value: ${book_value:,.0f}")
+        lines.append(f"  Residual Income: ${initial_ri:,.0f}")
+        lines.append(
+            f"  Intrinsic Value (w/ 20% MoS): ${intrinsic_value:,.0f}",
+        )
+        lines.append(f"  Value Gap: {value_gap:+.1f}%")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def main() -> None:
+    """Read normalized rows from stdin and emit one selected text report."""
+    payload = json.load(__import__("sys").stdin)
+    mode = payload["mode"]
+    if mode == "ev_ebitda":
+        print(build_ev_ebitda_report(payload["rows"], payload["current_date"]))
+        return
+    if mode == "residual_income":
+        print(build_residual_income_report(payload["rows"], payload["current_date"]))
+        return
+    raise ValueError(f"Unsupported mode: {mode}")
+
+
+if __name__ == "__main__":
+    main()
--- a/backend/skills/builtin/valuation_review/scripts/owner_earnings_report.py
+++ b/backend/skills/builtin/valuation_review/scripts/owner_earnings_report.py
@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""Deterministic owner earnings valuation helpers for the valuation_review skill."""
+
+from __future__ import annotations
+
+import json
+from typing import Iterable
+
+
+def build_owner_earnings_report(rows: Iterable[dict], current_date: str) -> str:
+    """Render an owner earnings valuation report from normalized row inputs."""
+    lines = [f"=== Owner Earnings Valuation ({current_date}) ===\n"]
+
+    for row in rows:
+        error = row.get("error")
+        ticker = row["ticker"]
+        if error:
+            lines.append(f"{ticker}: {error}\n")
+            continue
+
+        owner_earnings = float(row["owner_earnings"])
+        growth_rate = float(row["growth_rate"])
+        market_cap = float(row["market_cap"])
+        required_return = float(row.get("required_return", 0.15))
+        margin_of_safety = float(row.get("margin_of_safety", 0.25))
+        num_years = int(row.get("num_years", 5))
+
+        pv_earnings = sum(
+            owner_earnings
+            * (1 + growth_rate) ** year
+            / (1 + required_return) ** year
+            for year in range(1, num_years + 1)
+        )
+        terminal_growth = min(growth_rate, 0.03)
+        terminal_earnings = (
+            owner_earnings
+            * (1 + growth_rate) ** num_years
+            * (1 + terminal_growth)
+        )
+        terminal_value = terminal_earnings / (
+            required_return - terminal_growth
+        )
+        pv_terminal = terminal_value / (1 + required_return) ** num_years
+        intrinsic_value = (pv_earnings + pv_terminal) * (1 - margin_of_safety)
+        value_gap = (intrinsic_value - market_cap) / market_cap * 100
+
+        if value_gap > 20:
+            assessment = "SIGNIFICANTLY UNDERVALUED"
+        elif value_gap > 0:
+            assessment = "POTENTIALLY UNDERVALUED"
+        elif value_gap > -20:
+            assessment = "POTENTIALLY OVERVALUED"
+        else:
+            assessment = "SIGNIFICANTLY OVERVALUED"
+
+        lines.append(f"{ticker}:")
+        lines.append(f"  Owner Earnings: ${owner_earnings:,.0f}")
+        lines.append(
+            f"  Intrinsic Value (w/ 25% MoS): ${intrinsic_value:,.0f}",
+        )
+        lines.append(f"  Market Cap: ${market_cap:,.0f}")
+        lines.append(f"  Value Gap: {value_gap:+.1f}% -> {assessment}")
+        lines.append("")
+
+    return "\n".join(lines)
+
+
+def main() -> None:
+    """Read normalized rows from stdin and emit a text report."""
+    payload = json.load(__import__("sys").stdin)
+    print(build_owner_earnings_report(payload["rows"], payload["current_date"]))
+
+
+if __name__ == "__main__":
+    main()