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Add per-agent skill workspaces and TraderView management

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cillin
2026-03-17 13:55:14 +08:00
parent 1f5ee3698e
commit 2daf5717ba
35 changed files with 4774 additions and 331 deletions
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1
backend/skills/builtin/valuation_review/scripts/__init__.py Normal file
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# -*- coding: utf-8 -*-

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backend/skills/builtin/valuation_review/scripts/dcf_report.py Normal file
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#!/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()

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backend/skills/builtin/valuation_review/scripts/multiple_valuation_report.py Normal file
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#!/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()

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backend/skills/builtin/valuation_review/scripts/owner_earnings_report.py Normal file
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#!/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()