feat: 预测性成本智能·宏观敏感性因素联动(IMA 2026.7完整版)

- 内置3宏观因素: 原油价格/美元汇率/CPI通胀率
- 敏感性引擎: 按KPI类别推断弹性(成本类油价0.15/利润类0.12/营收类0.08), 方向+因素涨KPI涨
- 负值KPI(亏损)方向反转修复: 油价涨→净利更亏
- API: GET /predict/kpi-forecast/sensitivity?pct=10 → KPI×因素矩阵(±pct调整后预测)
- 前端: 敏感性幅度选择(±5/10/20%) + 敏感性矩阵表(同向/反向+↑↓调整值)
- 诚实标注: 模型弹性(规则推断,非历史回归), 后续可用宏观历史数据回归校准
- 验证: Chrome实测页面+API矩阵, pytest 46 passed

backend/app/api/predict.py

@@ -1,6 +1,6 @@
 """预测模拟API — 管理会计OS"""
 import logging
-from fastapi import APIRouter, HTTPException, Depends, Request
+from fastapi import APIRouter, HTTPException, Depends, Request, Query
 from app.utils.predict_engine import (
     cvp_analysis, npv, irr,
     sensitivity_analysis, scenario_analysis,
@@ -719,6 +719,7 @@ def api_growth_quality(request: Request, data: dict):
 # ── KPI趋势预测（预测性成本智能 MVP） ────────────────────────────
 from app.utils.kpi_forecast_engine import (  # noqa: E402
     MODELS, forecast_kpi, forecast_finance_kpis,
+    MACRO_FACTORS, factor_sensitivity_for_kpi, adjusted_next_with_factor,
 )
 
 
@@ -764,3 +765,56 @@ def api_kpi_forecast_finance(
         "total": len(results),
         "data": results,
     }
+
+
+@router.get("/kpi-forecast/sensitivity")
+def api_kpi_forecast_sensitivity(
+    pct: float = Query(10, description="宏观因素变动幅度% (±)"),
+    periods: int = Query(3),
+    model: str = Query("linear"),
+    entity_id: int = Depends(get_entity_id),
+    db: Session = Depends(get_db),
+):
+    """宏观敏感性因素联动（IMA 2026.7）— 财务KPI × 宏观因素(油价/汇率/CPI)敏感性矩阵
+    输出：每个KPI的预测值 + 各因素 ±pct% 情景下的调整后预测值
+    MVP：弹性系数为规则推断（按KPI类别），诚实标注"模型弹性"非历史回归"""
+    if abs(pct) > 50:
+        raise HTTPException(400, "pct 必须在 ±50 以内")
+    if model not in MODELS:
+        raise HTTPException(400, f"不支持的模型: {model}，可选: {'/'.join(MODELS)}")
+    results = forecast_finance_kpis(entity_id, db, periods=periods, model=model)
+    matrix = []
+    for r in results:
+        kpi_info = r.get("kpi", {})
+        sens = factor_sensitivity_for_kpi(kpi_info.get("name", ""), kpi_info.get("code", ""))
+        next_val = r.get("next_target")
+        factor_effects = []
+        for s in sens:
+            up_val = adjusted_next_with_factor(next_val, pct, s["direction"], s["elasticity"])
+            down_val = adjusted_next_with_factor(next_val, -pct, s["direction"], s["elasticity"])
+            factor_effects.append({
+                "factor_key": s["factor_key"],
+                "factor_name": s["factor_name"],
+                "factor_unit": s["factor_unit"],
+                "direction": s["direction"],
+                "elasticity": s["elasticity"],
+                "adj_up": up_val,
+                "adj_down": down_val,
+            })
+        matrix.append({
+            "kpi": kpi_info,
+            "category": sens[0]["category"] if sens else "profit",
+            "next_target": next_val,
+            "confidence": r.get("confidence"),
+            "trend": r.get("trend"),
+            "factors": factor_effects,
+        })
+    return {
+        "entity_id": entity_id,
+        "model": model,
+        "periods": periods,
+        "pct": pct,
+        "factors": MACRO_FACTORS,
+        "total": len(matrix),
+        "data": matrix,
+    }