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backend/app/api.py

@@ -19,6 +19,7 @@ from datetime import datetime
 
 import pandas as pd
 from fastapi import APIRouter, Depends, File, HTTPException, Response, UploadFile
+from fastapi.responses import StreamingResponse
 from sqlalchemy import delete, func, select, text
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.sql.elements import TextClause
@@ -518,47 +519,88 @@ async def backtest_event(
 
 
 # ---------- 智能选股 ----------
-@router.post("/screener/run", response_model=ScreenerRunResponse)
+@router.post("/screener/run")
 async def screener_run(
     req: ScreenerRunRequest,
     session: AsyncSession = Depends(get_session),
     user=Depends(require_user),
-) -> ScreenerRunResponse:
+) -> StreamingResponse:
     """自然语言 -> LLM 解析条件 -> 全市场筛选。也可直传 conditions 跳过 LLM（微调再跑）。
-    成功的提问（含解析出的条件与命中数）记录到 screener_queries，供历史一键重跑。"""
-    try:
-        conds = req.conditions or await parse_conditions(req.text)
-        if not conds.indicator and not conds.snapshot:
-            raise HTTPException(status_code=400, detail="AI 未从描述中解析出任何筛选条件，请换种说法")
-        result = await engine.run_screen(session, conds, settings.screener_default_limit)
-        # 相同文本 + 相同条件的上一条不重复记录（一键重跑场景）
-        exists = (
-            await session.execute(
-                select(ScreenerQuery.id).where(
-                    ScreenerQuery.user_id == user.id,
-                    ScreenerQuery.text == req.text.strip(),
-                    ScreenerQuery.conditions_json == json.dumps(conds.model_dump(), ensure_ascii=False),
+
+    NDJSON 流式响应（每行一个 JSON 事件，前端逐行渲染进度）：
+      {"type":"stage","key":"llm|date|prefilter|bars|filter_done|done","msg":"…","ms":123}
+      {"type":"parsed","conditions":{…},"ms":456}          LLM 解析出的结构化条件
+      {"type":"candidates","count":5400,"msg":"…","ms":…}  SQL 预筛后的候选数
+      {"type":"progress","done":500,"total":5400}          逐股指标过滤进度
+      {"type":"result","result":{…ScreenerRunResponse…},"ms":…}
+      {"type":"error","message":"…","code":400}            流中途失败（HTTP 已 200）
+    成功的提问（含解析出的条件与命中数）记录到 screener_queries，供历史一键重跑。
+    """
+    limit = settings.screener_default_limit
+
+    async def gen():
+        try:
+            if req.conditions:
+                conds = req.conditions
+            else:
+                yield _ndjson({"type": "stage", "key": "llm",
+                               "msg": f"AI 解析条件中（{settings.llm_model}）…"})
+                conds = await parse_conditions(req.text)
+            if not conds.indicator and not conds.snapshot:
+                yield _ndjson({"type": "error", "code": 400,
+                               "message": "AI 未从描述中解析出任何筛选条件，请换种说法"})
+                return
+            yield _ndjson({"type": "parsed", "conditions": conds.model_dump()})
+
+            result = None
+            async for ev in engine.run_screen_events(session, conds, limit):
+                if ev["type"] == "result":
+                    result = ev["result"]
+                    yield _ndjson({"type": "stage", "key": "done", "ms": ev.get("ms"),
+                                   "msg": f"筛选完成：{result['total']} 只命中（数据基准 {result['trade_date']:%Y-%m-%d}）"})
+                else:
+                    yield _ndjson(ev)
+
+            if result is None:
+                yield _ndjson({"type": "error", "code": 500, "message": "选股流程未产出结果"})
+                return
+            yield _ndjson({"type": "result", "result": ScreenerRunResponse(**result).model_dump(mode="json")})
+
+            # 相同文本 + 相同条件的上一条不重复记录（一键重跑场景）
+            exists = (
+                await session.execute(
+                    select(ScreenerQuery.id).where(
+                        ScreenerQuery.user_id == user.id,
+                        ScreenerQuery.text == req.text.strip(),
+                        ScreenerQuery.conditions_json == json.dumps(conds.model_dump(), ensure_ascii=False),
+                    )
                 )
-            )
-        ).scalar_one_or_none()
-        if exists is None:
-            session.add(ScreenerQuery(
-                user_id=user.id,
-                text=req.text.strip(),
-                conditions_json=json.dumps(conds.model_dump(), ensure_ascii=False),
-                hit_count=result.get("total", 0),
-            ))
-            await session.commit()
-        return ScreenerRunResponse(**result)
-    except HTTPException:
-        raise
-    except DataNotReadyError as e:
-        raise HTTPException(status_code=409, detail=str(e))
-    except ValueError as e:  # 未知指标/字段、条件为空
-        raise HTTPException(status_code=400, detail=str(e))
-    except ScreenerError as e:
-        code = 503 if "未配置 LLM_API_KEY" in str(e) else 502
-        raise HTTPException(status_code=code, detail=str(e))
+            ).scalar_one_or_none()
+            if exists is None:
+                session.add(ScreenerQuery(
+                    user_id=user.id,
+                    text=req.text.strip(),
+                    conditions_json=json.dumps(conds.model_dump(), ensure_ascii=False),
+                    hit_count=result.get("total", 0),
+                ))
+                await session.commit()
+        except DataNotReadyError as e:
+            yield _ndjson({"type": "error", "code": 409, "message": str(e)})
+        except ValueError as e:  # 未知指标/字段、条件为空
+            yield _ndjson({"type": "error", "code": 400, "message": str(e)})
+        except ScreenerError as e:
+            code = 503 if "未配置 LLM_API_KEY" in str(e) else 502
+            yield _ndjson({"type": "error", "code": code, "message": str(e)})
+        except Exception as e:  # noqa: BLE001
+            yield _ndjson({"type": "error", "code": 500, "message": f"选股失败: {e}"})
+
+    return StreamingResponse(gen(), media_type="application/x-ndjson",
+                             headers={"Cache-Control": "no-store", "X-Accel-Buffering": "no"})
+
+
+def _ndjson(obj: dict) -> str:
+    """dict -> NDJSON 行（json.dumps 保证 default=str 兜底 datetime 等）。"""
+    return json.dumps(obj, ensure_ascii=False, default=str) + "\n"
 
 
 @router.get("/screener/queries", response_model=ScreenerQueryListResponse)