feat: add dynamic advisor asset allocation

tests/unit/service/test_dynamic_allocation_optimizer.py

@@ -0,0 +1,65 @@
+from decimal import Decimal
+
+from app.service.asset_allocation_service import AssetAllocationService
+from app.service.dynamic_allocation_optimizer import (
+    AssetClassMarketMetric,
+    DynamicAllocationOptimizer,
+)
+
+
+def metric(
+    asset_class: str, return_pct: str, drawdown: str, turnover: str
+) -> AssetClassMarketMetric:
+    return AssetClassMarketMetric(
+        asset_class=asset_class,
+        trailing_120d_return_pct=Decimal(return_pct),
+        max_drawdown_pct=Decimal(drawdown),
+        average_daily_turnover_amount=Decimal(turnover),
+        product_count=2,
+    )
+
+
+def test_strategic_weights_apply_horizon_liquidity_and_drawdown_constraints() -> None:
+    weights = AssetAllocationService._strategic_weights("C1", 6, "daily", Decimal("10"))
+    assert weights == {"cash_management_etf": 65, "bond_etf": 35, "equity_etf": 0}
+
+    weights = AssetAllocationService._strategic_weights("C5", 72, "over_30_days", Decimal("30"))
+    assert weights == {"cash_management_etf": 5, "bond_etf": 10, "equity_etf": 85}
+
+
+def test_optimizer_uses_return_drawdown_and_liquidity_evidence() -> None:
+    metrics = [
+        metric("cash_management_etf", "2", "1", "20000000"),
+        metric("bond_etf", "6", "8", "5000000"),
+        metric("equity_etf", "12", "25", "1000000"),
+    ]
+    result = DynamicAllocationOptimizer.optimize(
+        {"cash_management_etf": 15, "bond_etf": 45, "equity_etf": 40},
+        metrics,
+        return_target_lower_pct=Decimal("6"),
+        max_drawdown_pct=Decimal("15"),
+        liquidity_requirement="within_7_days",
+    )
+
+    assert result.dynamic is True
+    assert sum(result.weights.values()) == 100
+    assert result.weights["equity_etf"] <= 40
+    assert result.metric_coverage_pct == Decimal("100")
+    evidence = {item["asset_class"]: item for item in result.factors}
+    assert evidence["equity_etf"]["composite_score"] is not None
+
+
+def test_optimizer_falls_back_to_static_weights_when_coverage_is_insufficient() -> None:
+    strategic = {"cash_management_etf": 30, "bond_etf": 50, "equity_etf": 20}
+    result = DynamicAllocationOptimizer.optimize(
+        strategic,
+        [metric("bond_etf", "6", "8", "5000000")],
+        return_target_lower_pct=Decimal("6"),
+        max_drawdown_pct=Decimal("15"),
+        liquidity_requirement="within_7_days",
+    )
+
+    assert result.dynamic is False
+    assert result.weights == strategic
+    assert result.metric_coverage_pct == Decimal("33.33333333333333333333333333")
+    assert result.factors[0]["composite_score"] is None