feat: 预测性成本智能MVP — KPI趋势预测引擎(线性回归/移动平均)+API+前端Tab+pytest覆盖

backend/app/api/predict.py

@@ -714,3 +714,53 @@ def api_growth_quality(request: Request, data: dict):
         }
     except Exception as e:
         raise HTTPException(400, f"增长质量诊断失败: {str(e)}")
+
+
+# ── 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,
+)
+
+
+@router.get("/kpi-forecast")
+def api_kpi_forecast(
+    kpi_code: str,
+    periods: int = 3,
+    model: str = "linear",
+    entity_id: int = Depends(get_entity_id),
+    db: Session = Depends(get_db),
+):
+    """单个财务KPI预测 — 线性回归/移动平均，多租户隔离（entity_id 权限校验）"""
+    if periods < 0 or periods > 24:
+        raise HTTPException(400, "periods 必须在 0~24 之间")
+    if model not in MODELS:
+        raise HTTPException(400, f"不支持的模型: {model}，可选: {'/'.join(MODELS)}")
+    result = forecast_kpi(entity_id, kpi_code, db, periods=periods, model=model)
+    if result is None:
+        raise HTTPException(
+            404,
+            f"KPI {kpi_code} 在企业 entity_id={entity_id} 下不存在，或历史数据不足（至少2条）",
+        )
+    return result
+
+
+@router.get("/kpi-forecast/finance")
+def api_kpi_forecast_finance(
+    periods: int = 3,
+    model: str = "linear",
+    entity_id: int = Depends(get_entity_id),
+    db: Session = Depends(get_db),
+):
+    """批量预测该企业全部财务维度KPI（历史≥3条），按可预测性排序"""
+    if periods < 0 or periods > 24:
+        raise HTTPException(400, "periods 必须在 0~24 之间")
+    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)
+    return {
+        "entity_id": entity_id,
+        "model": model,
+        "periods": periods,
+        "total": len(results),
+        "data": results,
+    }