张胜宇
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- 分支内容对齐 qyqy_develop b6ec3aa，树完全一致（同步后 git diff 为空） - 覆盖本轮全部交付：客服 Agent 重构（安全路由 / 五出口 / 记忆与画像 / RAG 全链路） + 开发文档 62 份编号体系（D1.1 v1.17 索引） + 新增 D2.10-客服Agent端到端答辩文档-2026-09-21.html - 基线：e239eb7（2026-09-17 品牌口径统一快照），本提交为其直接后继
202 lines
8.9 KiB
Python
202 lines
8.9 KiB
Python
"""Dynamic, analysis-only asset allocation from confirmed goals and history."""
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from collections import defaultdict
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from collections.abc import Callable
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from datetime import UTC, date, datetime
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from decimal import Decimal
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from typing import Any
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from app.core.advisor_allocation_contracts import AssetAllocationQuery
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from app.core.contracts import RequestContext
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from app.infrastructure.db import SessionFactory
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from app.repository.advisor_product_repository import AdvisorProductRepository
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from app.repository.portfolio_analysis_repository import PortfolioAnalysisRepository
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from app.service.authorization_service import AuthorizationService
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from app.service.dynamic_allocation_optimizer import (
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ASSET_CLASSES,
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AssetClassMarketMetric,
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DynamicAllocationOptimizer,
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)
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from app.service.investment_goal_service import InvestmentGoalService
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from app.service.product_governance_monitor_service import SALES_INSTITUTION
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from app.service.profile_governance_service import ProfileGovernanceService
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from app.service.suitability_service import SuitabilityService
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BASE_ALLOCATIONS = {
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"C1": {"cash_management_etf": 50, "bond_etf": 40, "equity_etf": 10},
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"C2": {"cash_management_etf": 30, "bond_etf": 50, "equity_etf": 20},
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"C3": {"cash_management_etf": 15, "bond_etf": 45, "equity_etf": 40},
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"C4": {"cash_management_etf": 10, "bond_etf": 25, "equity_etf": 65},
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"C5": {"cash_management_etf": 5, "bond_etf": 15, "equity_etf": 80},
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}
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ASSET_LABELS = {
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"cash_management_etf": "现金管理类场内基金",
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"bond_etf": "债券类场内基金",
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"equity_etf": "权益类场内基金",
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}
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class AssetAllocationService:
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def __init__(
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self,
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*,
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session_factory: Callable[[], Any] = SessionFactory,
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enforce_profile_governance: bool = False,
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) -> None:
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self.session_factory = session_factory
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self.enforce_profile_governance = enforce_profile_governance
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async def generate_for_agent(
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self, arguments: AssetAllocationQuery, context: RequestContext
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) -> dict[str, object]:
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# 代客：arguments.customer_id 指定客户；留空则以登录用户自身为对象（原行为）。
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customer_id = arguments.customer_id or int(context.user_id)
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if customer_id == int(context.user_id):
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await AuthorizationService.require(context, "asset-allocation:generate:self")
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else:
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await AuthorizationService.require_customer_scope(
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context, "asset-allocation:generate:customer", customer_id
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)
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if self.enforce_profile_governance:
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await ProfileGovernanceService().require_operable(customer_id)
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authority = await SuitabilityService().authority_for_customer(customer_id)
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if authority.customer_risk_level is None:
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return {"status": "profile_required"}
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goal = await InvestmentGoalService().current_for_customer(customer_id, context)
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if goal is None:
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return {"status": "investment_goal_required"}
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horizon = goal.get("investment_horizon_months")
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if not isinstance(horizon, int):
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return {"status": "investment_goal_invalid"}
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risk = f"C{authority.customer_risk_level}"
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liquidity = str(goal["liquidity_requirement"])
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strategic = self._strategic_weights(
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risk, horizon, liquidity, Decimal(str(goal["max_drawdown_pct"]))
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)
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metrics = await self._market_metrics(authority.customer_risk_level)
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optimized = DynamicAllocationOptimizer.optimize(
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strategic,
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metrics,
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return_target_lower_pct=Decimal(str(goal["annualized_return_lower_pct"])),
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max_drawdown_pct=Decimal(str(goal["max_drawdown_pct"])),
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liquidity_requirement=liquidity,
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)
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return {
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"status": "ready",
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"allocation": [
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{"asset_class": key, "label": ASSET_LABELS[key], "target_pct": value}
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for key, value in optimized.weights.items()
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],
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"optimization": {
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"method": "constrained_historical_multi_factor_v1",
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"dynamic": optimized.dynamic,
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"metric_coverage_pct": str(optimized.metric_coverage_pct.quantize(Decimal("0.01"))),
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"strategic_allocation": strategic,
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"factor_evidence": optimized.factors,
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},
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"constraints": {
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"annualized_return_lower_pct": goal["annualized_return_lower_pct"],
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"max_drawdown_pct": goal["max_drawdown_pct"],
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"liquidity_requirement": liquidity,
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"investment_horizon_months": horizon,
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},
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"analysis_only": True,
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}
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async def _market_metrics(self, customer_risk_level: int) -> list[AssetClassMarketMetric]:
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now = datetime.now(UTC).replace(tzinfo=None)
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async with self.session_factory() as session:
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candidates = await AdvisorProductRepository(session).authoritative_tradable_products(
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now, sales_institution=SALES_INSTITUTION, limit=50
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)
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candidates, _excluded = AdvisorProductRepository.hard_suitability_filter(
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candidates, customer_risk_level
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)
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ids = tuple(item.product.id for item in candidates)
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repository = PortfolioAnalysisRepository(session)
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classifications = await repository.latest_asset_classifications(ids, date.today())
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snapshots = await repository.latest_metrics(ids, date.today())
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qualities = await repository.latest_quality(ids, date.today())
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grouped: dict[str, list[AssetClassMarketMetric]] = defaultdict(list)
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for product_id in ids:
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classification = classifications.get(product_id)
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metric = snapshots.get(product_id)
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quality = qualities.get(product_id)
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if (
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classification is None
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or classification.asset_class not in ASSET_CLASSES
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or quality is None
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or quality.status != "accepted"
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or metric is None
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or metric.trailing_120d_return_pct is None
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or metric.max_drawdown_pct is None
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or metric.average_daily_turnover_amount is None
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or metric.observation_count < 20
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):
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continue
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grouped[classification.asset_class].append(
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AssetClassMarketMetric(
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asset_class=classification.asset_class,
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trailing_120d_return_pct=metric.trailing_120d_return_pct,
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max_drawdown_pct=metric.max_drawdown_pct,
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average_daily_turnover_amount=metric.average_daily_turnover_amount,
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product_count=1,
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)
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)
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return [
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AssetClassMarketMetric(
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asset_class=key,
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trailing_120d_return_pct=sum(
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(item.trailing_120d_return_pct for item in rows), Decimal()
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)
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/ len(rows),
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max_drawdown_pct=(
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sum((item.max_drawdown_pct for item in rows), Decimal()) / len(rows)
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),
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average_daily_turnover_amount=sum(
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(item.average_daily_turnover_amount for item in rows), Decimal()
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)
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/ len(rows),
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product_count=len(rows),
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)
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for key, rows in grouped.items()
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]
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@staticmethod
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def _strategic_weights(
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risk: str, horizon: int, liquidity: str, max_drawdown: Decimal
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) -> dict[str, int]:
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weights = dict(BASE_ALLOCATIONS[risk])
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if horizon <= 12:
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AssetAllocationService._move(weights, "equity_etf", "cash_management_etf", 10)
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elif horizon >= 60 and risk != "C1":
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AssetAllocationService._move(weights, "bond_etf", "equity_etf", 5)
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if liquidity == "daily":
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AssetAllocationService._move(weights, "equity_etf", "cash_management_etf", 10)
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AssetAllocationService._move(weights, "bond_etf", "cash_management_etf", 5)
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if max_drawdown <= 10:
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AssetAllocationService._cap_equity(weights, 20)
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elif max_drawdown <= 20:
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AssetAllocationService._cap_equity(weights, 40)
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return weights
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@staticmethod
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def _move(weights: dict[str, int], source: str, target: str, amount: int) -> None:
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moved = min(weights[source], amount)
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weights[source] -= moved
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weights[target] += moved
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@staticmethod
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def _cap_equity(weights: dict[str, int], cap: int) -> None:
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excess = max(0, weights["equity_etf"] - cap)
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weights["equity_etf"] -= excess
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weights["bond_etf"] += excess
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async def asset_allocation_tool(
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arguments: AssetAllocationQuery, context: RequestContext
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) -> dict[str, object]:
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return await AssetAllocationService(enforce_profile_governance=True).generate_for_agent(
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arguments, context
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)
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