Initial commit of integrated agent system

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cillin
2026-03-30 17:46:44 +08:00
commit 0fa413380c
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# -*- coding: utf-8 -*-
"""Initialize run-scoped agent workspace assets."""
from pathlib import Path
from typing import Dict, Iterable, Optional
import yaml
from .skills_manager import SkillsManager
from .team_pipeline_config import ensure_team_pipeline_config
class RunWorkspaceManager:
"""Create and maintain run-level prompt asset files for each agent."""
def __init__(self, project_root: Optional[Path] = None):
self.skills_manager = SkillsManager(project_root=project_root)
self.project_root = self.skills_manager.project_root
def get_run_dir(self, config_name: str) -> Path:
return self.project_root / "runs" / config_name
def ensure_run_workspace(self, config_name: str) -> Path:
run_dir = self.get_run_dir(config_name)
run_dir.mkdir(parents=True, exist_ok=True)
self.skills_manager.ensure_activation_manifest(config_name)
ensure_team_pipeline_config(
project_root=self.project_root,
config_name=config_name,
default_analysts=[
"fundamentals_analyst",
"technical_analyst",
"sentiment_analyst",
"valuation_analyst",
],
)
bootstrap_path = run_dir / "BOOTSTRAP.md"
if not bootstrap_path.exists():
bootstrap_path.write_text(
"---\n"
"tickers:\n"
" - AAPL\n"
" - MSFT\n"
" - GOOGL\n"
" - AMZN\n"
" - NVDA\n"
" - META\n"
" - TSLA\n"
" - AMD\n"
" - NFLX\n"
" - AVGO\n"
" - PLTR\n"
" - COIN\n"
"initial_cash: 100000\n"
"margin_requirement: 0.0\n"
"enable_memory: false\n"
"max_comm_cycles: 2\n"
"agent_overrides: {}\n"
"---\n\n"
"# Bootstrap\n\n"
"Use this file to describe run-specific setup notes, preferred tickers,\n"
"risk bounds, or strategy constraints before the first execution.\n\n"
"The YAML front matter above is machine-readable runtime configuration.\n"
"The markdown body below is injected into agent prompts as run context.\n",
encoding="utf-8",
)
return run_dir
def bootstrap_path(self, config_name: str) -> Path:
return self.get_run_dir(config_name) / "BOOTSTRAP.md"
def ensure_agent_assets(
self,
config_name: str,
agent_id: str,
file_contents: Optional[Dict[str, str]] = None,
persona: Optional[Dict[str, object]] = None,
) -> Path:
asset_dir = self.skills_manager.get_agent_asset_dir(
config_name,
agent_id,
)
asset_dir.mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "installed").mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "active").mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "disabled").mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "local").mkdir(parents=True, exist_ok=True)
file_contents = file_contents or self.build_default_agent_files(agent_id=agent_id)
for filename, content in file_contents.items():
legacy_contents = self.build_legacy_agent_file_variants(
agent_id=agent_id,
filename=filename,
persona=persona,
)
self._ensure_file(asset_dir / filename, content, legacy_contents=legacy_contents)
self._ensure_agent_yaml(
asset_dir / "agent.yaml",
agent_id=agent_id,
)
return asset_dir
def build_default_agent_files(
self,
*,
agent_id: str,
persona: Optional[Dict[str, object]] = None,
) -> Dict[str, str]:
"""Build default workspace markdown files for one agent."""
if agent_id.endswith("_analyst"):
return self._build_analyst_files(agent_id=agent_id, persona=persona or {})
if agent_id == "portfolio_manager":
return self._build_portfolio_manager_files()
if agent_id == "risk_manager":
return self._build_risk_manager_files()
return self._build_generic_files(agent_id=agent_id)
def build_legacy_agent_file_variants(
self,
*,
agent_id: str,
filename: str,
persona: Optional[Dict[str, object]] = None,
) -> list[str]:
"""Return known generated legacy variants safe to upgrade in-place."""
persona = persona or {}
variants: list[dict[str, str]] = [
self._build_legacy_english_files(agent_id=agent_id),
self._build_previous_chinese_files(agent_id=agent_id, persona=persona),
]
values: list[str] = []
for item in variants:
content = item.get(filename)
if content:
values.append(content)
return values
def load_agent_file(
self,
*,
config_name: str,
agent_id: str,
filename: str,
) -> str:
"""Load one run-scoped agent workspace file."""
path = self.skills_manager.get_agent_asset_dir(config_name, agent_id) / filename
if not path.exists():
raise FileNotFoundError(f"File not found: {filename}")
return path.read_text(encoding="utf-8")
def update_agent_file(
self,
*,
config_name: str,
agent_id: str,
filename: str,
content: str,
) -> None:
"""Write one run-scoped agent workspace file."""
asset_dir = self.skills_manager.get_agent_asset_dir(config_name, agent_id)
asset_dir.mkdir(parents=True, exist_ok=True)
path = asset_dir / filename
path.write_text(content, encoding="utf-8")
def initialize_default_assets(
self,
config_name: str,
agent_ids: Iterable[str],
analyst_personas: Optional[Dict[str, Dict]] = None,
) -> None:
self.ensure_run_workspace(config_name)
analyst_personas = analyst_personas or {}
for agent_id in agent_ids:
if agent_id.endswith("_analyst"):
persona = analyst_personas.get(agent_id, {})
file_contents = self.build_default_agent_files(
agent_id=agent_id,
persona=persona,
)
else:
persona = None
file_contents = self.build_default_agent_files(agent_id=agent_id)
asset_dir = self.skills_manager.get_agent_asset_dir(config_name, agent_id)
asset_dir.mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "installed").mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "active").mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "disabled").mkdir(parents=True, exist_ok=True)
(asset_dir / "skills" / "local").mkdir(parents=True, exist_ok=True)
for filename, content in file_contents.items():
self._ensure_file(
asset_dir / filename,
content,
legacy_contents=self.build_legacy_agent_file_variants(
agent_id=agent_id,
filename=filename,
persona=persona,
),
)
self._ensure_agent_yaml(asset_dir / "agent.yaml", agent_id=agent_id)
@staticmethod
def _ensure_file(path: Path, content: str, *, legacy_contents: Optional[list[str]] = None) -> None:
if not path.exists():
path.write_text(content, encoding="utf-8")
return
existing = path.read_text(encoding="utf-8")
normalized_existing = existing.strip()
candidates = {item.strip() for item in (legacy_contents or []) if item and item.strip()}
if normalized_existing in candidates:
path.write_text(content, encoding="utf-8")
@staticmethod
def _build_generic_files(agent_id: str) -> Dict[str, str]:
return {
"SOUL.md": (
"# Soul\n\n"
f"你是 `{agent_id}`,语气冷静、客观、专业。保持清晰推理,优先基于数据而不是情绪下结论。\n"
),
"PROFILE.md": (
"# Profile\n\n"
"记录这个 agent 长期稳定的分析风格、偏好、优势与盲点。\n"
),
"AGENTS.md": (
"# Agent Guide\n\n"
"工作要求:\n"
"- 优先使用已激活的技能和工具\n"
"- 结论要明确,过程要可追溯\n"
"- 与其他 agent 协作时保持输入输出简洁\n"
"- 最终输出必须使用简体中文;如需引用英文术语,仅保留专有名词,解释和结论必须用中文\n"
),
"POLICY.md": (
"# Policy\n\n"
"- 给出结论时说明核心驱动因素\n"
"- 明确风险边界和结论失效条件\n"
"- 出现反例时需要纳入最终判断\n"
"- 不要输出英文报告标题、英文摘要或整段英文正文\n"
),
"MEMORY.md": (
"# Memory\n\n"
"记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n"
),
}
@classmethod
def _build_analyst_files(cls, *, agent_id: str, persona: Dict[str, object]) -> Dict[str, str]:
role_name = str(persona.get("name") or agent_id)
focus_items = [
str(item).strip()
for item in persona.get("focus", [])
if str(item).strip()
]
focus_md = "\n".join(f"- {item}" for item in focus_items) or "- 根据当前任务选择最相关的分析维度"
description = str(persona.get("description") or "").strip()
files = cls._build_generic_files(agent_id)
files["SOUL.md"] = (
"# Soul\n\n"
f"你是一位专业的{role_name}\n\n"
"保持谦逊和开放,主动寻找与自己观点相悖的证据,并将其纳入最终评估。"
"你的分析要体现持续演化的投资哲学,而不是一次性的结论。\n"
)
files["PROFILE.md"] = (
"# Profile\n\n"
f"角色定位:{role_name}\n\n"
"你的关注重点:\n"
f"{focus_md}\n\n"
"角色说明:\n"
f"{description or '围绕最关键的基本面、技术面、情绪面或估值因素形成高质量判断。'}\n"
)
files["AGENTS.md"] = (
"# Agent Guide\n\n"
"分析流程:\n"
"- 优先识别真正驱动价值或价格变化的核心变量\n"
"- 使用相关工具和技能补足证据链\n"
"- 给出可验证、可复查、可执行的分析结果\n"
"- 在团队讨论中清晰表达你的论点和反论点\n\n"
"输出要求:\n"
"- 给出明确投资信号:看涨、看跌或中性\n"
"- 包含置信度0-100\n"
"- 如果你确定要分享最终分析,请先给出结论,再给出推理依据\n"
"- 最终输出必须使用简体中文,不要生成英文版 analysis report\n"
)
files["POLICY.md"] = (
"# Policy\n\n"
"- 深化你的投资逻辑,确保每项建议都有清晰、可追溯、可重复的依据\n"
"- 明确风险边界:在什么具体情况下当前结论会失效\n"
"- 做逆向测试:说明市场主流共识与你的不同点\n"
"- 每次分析后反思这次案例如何验证或挑战你现有的信念\n"
"- 即使输入新闻或财报原文是英文,最终表达也必须用中文\n"
)
return files
@classmethod
def _build_portfolio_manager_files(cls) -> Dict[str, str]:
files = cls._build_generic_files("portfolio_manager")
files["SOUL.md"] = (
"# Soul\n\n"
"你是一位负责做出投资决策的投资组合经理。你需要综合多个分析视角,"
"做出保守、明确、资本约束下可执行的组合决策。\n"
)
files["PROFILE.md"] = (
"# Profile\n\n"
"核心职责:\n"
"- 分析分析师和风险管理经理的输入\n"
"- 基于信号和市场情境做出投资决策\n"
"- 使用可用工具记录每个 ticker 的决策\n"
)
files["AGENTS.md"] = (
"# Agent Guide\n\n"
"决策框架:\n"
"- 审阅分析以理解市场观点\n"
"- 在做决策前先考虑风险警告\n"
"- 评估当前投资组合持仓、现金与保证金占用\n"
"- 决策必须与整体投资目标和风险约束一致\n\n"
"决策类型:\n"
'- `long`:看涨,建议买入\n'
'- `short`:看跌,建议卖出或做空\n'
'- `hold`:中性,维持当前持仓\n\n'
"输出要求:\n"
"- 使用 `make_decision` 工具记录每个股票的最终决策\n"
"- 记录完成后给出投资逻辑总结\n"
"- 最终总结必须使用简体中文\n"
)
files["POLICY.md"] = (
"# Policy\n\n"
"- 在决定数量时考虑可用现金,不要超出现金允许范围\n"
"- 考虑做空头寸的保证金要求\n"
"- 仓位规模相对于组合总资产保持保守\n"
"- 始终为决策提供清晰理由\n"
"- 不要输出英文投资报告或英文结论\n"
)
return files
@classmethod
def _build_risk_manager_files(cls) -> Dict[str, str]:
files = cls._build_generic_files("risk_manager")
files["SOUL.md"] = (
"# Soul\n\n"
"你是一位专业的风险管理经理,负责监控投资组合风险并提供风险警告。"
"你的目标不是输出空泛的谨慎,而是给出量化、可执行、可优先级排序的风险意见。\n"
)
files["PROFILE.md"] = (
"# Profile\n\n"
"核心职责:\n"
"- 监控投资组合敞口和集中度风险\n"
"- 评估仓位规模相对于波动性是否合理\n"
"- 评估保证金使用和杠杆水平\n"
"- 识别潜在风险因素并提供警告\n"
"- 基于市场条件建议仓位限制\n"
)
files["AGENTS.md"] = (
"# Agent Guide\n\n"
"决策流程:\n"
"- 优先使用可用的风险工具量化集中度、波动率和保证金压力\n"
"- 结合工具结果与当前市场上下文做判断\n"
"- 生成可操作的风险警告和仓位限制建议\n"
"- 为风险评估提供清晰理由\n\n"
"输出要求:\n"
"- 风险评估要简洁但全面\n"
"- 按严重程度优先排序警告\n"
"- 提供具体、可操作的建议\n"
"- 尽可能包含量化指标\n"
"- 最终风险结论必须使用简体中文\n"
)
files["POLICY.md"] = (
"# Policy\n\n"
"- 先量化,再判断,不要只给抽象风险表述\n"
"- 高严重度风险必须先说\n"
"- 最终结论需要明确仓位限制或调整建议\n"
"- 不要输出英文风险报告或英文摘要\n"
)
return files
@staticmethod
def _build_legacy_english_files(agent_id: str) -> Dict[str, str]:
policy_tail = "Optional run-scoped constraints, limits, or strategy policy.\n\n"
if agent_id == "portfolio_manager":
policy_tail += "Respect cash, margin, and portfolio concentration constraints before recording decisions.\n"
elif agent_id == "risk_manager":
policy_tail += "Use available risk tools before issuing the final risk memo.\n"
elif agent_id.endswith("_analyst"):
policy_tail += "State a clear signal, confidence, and the conditions that would invalidate the thesis.\n"
return {
"SOUL.md": "# Soul\n\nDescribe the agent's temperament, reasoning posture, and voice.\n\n",
"PROFILE.md": "# Profile\n\nTrack this agent's long-lived investment style, preferences, and strengths.\n\n",
"AGENTS.md": "# Agent Guide\n\nDocument how this agent should work, collaborate, and choose tools or skills.\n\n",
"POLICY.md": "# Policy\n\n" + policy_tail,
"MEMORY.md": "# Memory\n\nStore durable lessons, heuristics, and reminders for this agent.\n\n",
}
@classmethod
def _build_previous_chinese_files(cls, *, agent_id: str, persona: Dict[str, object]) -> Dict[str, str]:
if agent_id.endswith("_analyst"):
role_name = str(persona.get("name") or agent_id)
focus_items = [
str(item).strip()
for item in persona.get("focus", [])
if str(item).strip()
]
focus_md = "\n".join(f"- {item}" for item in focus_items) or "- 根据当前任务选择最相关的分析维度"
description = str(persona.get("description") or "").strip()
return {
"SOUL.md": (
"# Soul\n\n"
f"你是一位专业的{role_name}\n\n"
"保持谦逊和开放,主动寻找与自己观点相悖的证据,并将其纳入最终评估。"
"你的分析要体现持续演化的投资哲学,而不是一次性的结论。\n"
),
"PROFILE.md": (
"# Profile\n\n"
f"角色定位:{role_name}\n\n"
"你的关注重点:\n"
f"{focus_md}\n\n"
"角色说明:\n"
f"{description or '围绕最关键的基本面、技术面、情绪面或估值因素形成高质量判断。'}\n"
),
"AGENTS.md": (
"# Agent Guide\n\n"
"分析流程:\n"
"- 优先识别真正驱动价值或价格变化的核心变量\n"
"- 使用相关工具和技能补足证据链\n"
"- 给出可验证、可复查、可执行的分析结果\n"
"- 在团队讨论中清晰表达你的论点和反论点\n\n"
"输出要求:\n"
"- 给出明确投资信号:看涨、看跌或中性\n"
"- 包含置信度0-100\n"
"- 如果你确定要分享最终分析,请先给出结论,再给出推理依据\n"
),
"POLICY.md": (
"# Policy\n\n"
"- 深化你的投资逻辑,确保每项建议都有清晰、可追溯、可重复的依据\n"
"- 明确风险边界:在什么具体情况下当前结论会失效\n"
"- 做逆向测试:说明市场主流共识与你的不同点\n"
"- 每次分析后反思这次案例如何验证或挑战你现有的信念\n"
),
"MEMORY.md": "# Memory\n\n记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n",
}
if agent_id == "portfolio_manager":
return {
"SOUL.md": "# Soul\n\n你是一位负责做出投资决策的投资组合经理。你需要综合多个分析视角,做出保守、明确、资本约束下可执行的组合决策。\n",
"PROFILE.md": "# Profile\n\n核心职责:\n- 分析分析师和风险管理经理的输入\n- 基于信号和市场情境做出投资决策\n- 使用可用工具记录每个 ticker 的决策\n",
"AGENTS.md": "# Agent Guide\n\n决策框架:\n- 审阅分析以理解市场观点\n- 在做决策前先考虑风险警告\n- 评估当前投资组合持仓、现金与保证金占用\n- 决策必须与整体投资目标和风险约束一致\n\n决策类型:\n- `long`:看涨,建议买入\n- `short`:看跌,建议卖出或做空\n- `hold`:中性,维持当前持仓\n\n输出要求:\n- 使用 `make_decision` 工具记录每个股票的最终决策\n- 记录完成后给出投资逻辑总结\n",
"POLICY.md": "# Policy\n\n- 在决定数量时考虑可用现金,不要超出现金允许范围\n- 考虑做空头寸的保证金要求\n- 仓位规模相对于组合总资产保持保守\n- 始终为决策提供清晰理由\n",
"MEMORY.md": "# Memory\n\n记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n",
}
if agent_id == "risk_manager":
return {
"SOUL.md": "# Soul\n\n你是一位专业的风险管理经理,负责监控投资组合风险并提供风险警告。你的目标不是输出空泛的谨慎,而是给出量化、可执行、可优先级排序的风险意见。\n",
"PROFILE.md": "# Profile\n\n核心职责:\n- 监控投资组合敞口和集中度风险\n- 评估仓位规模相对于波动性是否合理\n- 评估保证金使用和杠杆水平\n- 识别潜在风险因素并提供警告\n- 基于市场条件建议仓位限制\n",
"AGENTS.md": "# Agent Guide\n\n决策流程:\n- 优先使用可用的风险工具量化集中度、波动率和保证金压力\n- 结合工具结果与当前市场上下文做判断\n- 生成可操作的风险警告和仓位限制建议\n- 为风险评估提供清晰理由\n\n输出要求:\n- 风险评估要简洁但全面\n- 按严重程度优先排序警告\n- 提供具体、可操作的建议\n- 尽可能包含量化指标\n",
"POLICY.md": "# Policy\n\n- 先量化,再判断,不要只给抽象风险表述\n- 高严重度风险必须先说\n- 最终结论需要明确仓位限制或调整建议\n",
"MEMORY.md": "# Memory\n\n记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n",
}
return cls._build_legacy_english_files(agent_id)
@staticmethod
def _ensure_agent_yaml(path: Path, agent_id: str) -> None:
if path.exists():
return
payload = {
"agent_id": agent_id,
"prompt_files": [
"SOUL.md",
"PROFILE.md",
"AGENTS.md",
"POLICY.md",
"MEMORY.md",
],
"enabled_skills": [],
"disabled_skills": [],
"active_tool_groups": [],
"disabled_tool_groups": [],
}
path.write_text(
yaml.safe_dump(payload, allow_unicode=True, sort_keys=False),
encoding="utf-8",
)
# Backward-compatible alias: code importing WorkspaceManager from this module should continue to work.
WorkspaceManager = RunWorkspaceManager