111 lines
5.1 KiB
Python
111 lines
5.1 KiB
Python
"""投顾 Agent 的公共底座实现。"""
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from typing import Any
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from app.core.contracts import AgentDefinition, AgentRequest, CoreResult, RequestContext
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from app.service.agent.implementations.fund_query_demo import FundQueryDemoAgent
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class AdvisorAgent(FundQueryDemoAgent):
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"""通过公共 BaseAgent 链路运行的最小投顾 Agent。"""
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definition = AgentDefinition(
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agent_type="advisor",
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version="0.1.0",
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allowed_roles=("customer", "advisor", "operator", "admin"),
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allowed_portals=("api",),
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allowed_tools=(
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"query_fund_quote", "query_investment_goal", "analyze_portfolio",
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"generate_asset_allocation",
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),
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supported_intents=(
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"fund_quote", "investment_goal", "portfolio_analysis", "asset_allocation",
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),
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)
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async def handle(self, request: AgentRequest, context: RequestContext) -> CoreResult:
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if (
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self._classified_intent is not None
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and self._classified_intent.intent == "investment_goal"
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):
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output = await self.call_tool(
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"query_investment_goal", {}, intent="investment_goal", context=context
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)
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if not isinstance(output, dict):
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return CoreResult(text="当前没有已确认的投资目标，暂不能用于配置或产品推荐。")
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return CoreResult(text=self._describe_goal(output))
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if (
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self._classified_intent is not None
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and self._classified_intent.intent == "portfolio_analysis"
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):
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output = await self.call_tool(
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"analyze_portfolio", {}, intent="portfolio_analysis", context=context
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)
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return CoreResult(text=self._describe_portfolio(output))
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if (
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self._classified_intent is not None
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and self._classified_intent.intent == "asset_allocation"
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):
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output = await self.call_tool(
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"generate_asset_allocation", {}, intent="asset_allocation", context=context
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)
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return CoreResult(text=self._describe_allocation(output))
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return await super().handle(request, context)
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@staticmethod
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def _describe_goal(goal: dict[str, Any]) -> str:
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return (
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f"当前投资目标：年化收益目标 {goal['annualized_return_lower_pct']}%-"
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f"{goal['annualized_return_upper_pct']}%，最大回撤 {goal['max_drawdown_pct']}%，"
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f"流动性要求 {goal['liquidity_requirement']}，投资期限 "
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f"{goal['investment_horizon_months']} 个月，业绩比较基准 {goal['benchmark_name']}。"
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"以上为目标采集结果，不构成收益承诺或交易指令。"
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)
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@staticmethod
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def _describe_portfolio(result: object) -> str:
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if not isinstance(result, dict):
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return "持仓分析暂不可用，请稍后重试。"
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status = result.get("status")
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if status == "no_positions":
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return "当前没有可分析的场内基金持仓。"
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if status == "valuation_required":
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return "当前持仓缺少可用市值，暂不能计算集中度。"
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summary = result.get("summary")
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if not isinstance(summary, dict):
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return "持仓分析数据不完整，请稍后重试。"
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concentration = result.get("product_concentration")
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hhi = concentration.get("hhi") if isinstance(concentration, dict) else None
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return (
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f"持仓分析完成：共 {summary.get('position_count')} 个产品，"
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f"总市值 {summary.get('total_market_value')}，产品集中度 HHI 为 {hhi}。"
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"分析结果仅供参考，不生成交易指令。"
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)
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@staticmethod
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def _describe_allocation(result: object) -> str:
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if not isinstance(result, dict):
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return "资产配置分析暂不可用，请稍后重试。"
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status = result.get("status")
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if status == "profile_required":
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return "当前缺少有效风险画像，暂不能生成资产配置。"
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if status == "investment_goal_required":
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return "当前没有已确认的投资目标，暂不能生成资产配置。"
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if status != "ready":
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return "资产配置分析数据不完整，请稍后重试。"
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allocation = result.get("allocation")
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if not isinstance(allocation, list) or not allocation:
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return "当前没有足够的场内基金数据生成资产配置。"
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parts = [
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f"{item.get('label', item.get('asset_class'))} {item.get('target_pct')}%"
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for item in allocation if isinstance(item, dict)
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]
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optimization = result.get("optimization")
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dynamic = isinstance(optimization, dict) and bool(optimization.get("dynamic"))
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mode = "动态历史因子优化" if dynamic else "静态配置（历史数据覆盖不足）"
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return (
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f"资产配置分析完成（{mode}）：" + "，".join(parts)
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+ "。该结果综合考虑收益目标、最大回撤、流动性和投资期限，"
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"仅供分析参考，不构成交易指令。"
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)
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