refactor: establish standalone application boundary

backend/features/accounts/application.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from typing import Any
+
+from backend.features.accounts.service import AccountService
+
+
+class AccountApplicationMixin:
+    def bind_user(self, user_id: int) -> None:
+        self._request_context.user_id = int(user_id)
+        encrypted = self.database.get_user_credentials(int(user_id))
+        self._request_context.credentials = self.vault.decrypt_json(encrypted) if encrypted else {}
+        self._request_context.access = self.database.user_access(int(user_id)) or {}
+
+    @property
+    def current_user_id(self) -> int:
+        user_id = getattr(self._request_context, "user_id", 0)
+        if not user_id:
+            raise ValueError("当前请求尚未绑定账号。")
+        return int(user_id)
+
+    def membership(self) -> dict[str, Any]:
+        return self.accounts.membership()
+
+    def admin_users(self) -> list[dict[str, Any]]:
+        return self.accounts.admin_users(self._platform_usage_today_for_user)
+
+    def update_membership(self, payload: dict[str, Any]) -> None:
+        self.accounts.update_membership(payload)
+
+    def register_account(self, username: str, password: str) -> dict[str, Any]:
+        return self.accounts.register(username, password)
+
+    def login_account(self, username: str, password: str) -> dict[str, Any]:
+        return self.accounts.login(username, password)
+
+    def change_password(self, current_password: str, new_password: str) -> None:
+        self.accounts.change_password(current_password, new_password)
+
+    def create_account_session(self, user: dict[str, Any]) -> dict[str, Any]:
+        return self.accounts.create_session(user)
+
+    @staticmethod
+    def _validate_account_input(username: str, password: str) -> None:
+        AccountService.validate_input(username, password)
+
+    def save_birth_profile(self, payload: dict[str, Any]) -> dict[str, Any]:
+        return self.accounts.save_birth_profile(payload)
+
+    def stored_birth_profile(self) -> dict[str, str] | None:
+        return self.accounts.stored_birth_profile()
+
+    def account_personal_field(
+        self,
+        current_date: str,
+        current_field: dict[str, Any],
+        public: bool = False,
+    ) -> dict[str, Any] | None:
+        return self.accounts.personal_field(current_date, current_field, public)
+
+    @staticmethod
+    def _public_personal_profile(personal: dict[str, Any]) -> dict[str, Any]:
+        return AccountService.public_personal_profile(personal)

backend/features/accounts/routes.py

@@ -0,0 +1,22 @@
+from __future__ import annotations
+
+
+class AccountRoutesMixin:
+    def _handle_accounts_public_get(self, parsed) -> bool:
+        if parsed.path == "/api/auth/me":
+            self.auth_me()
+            return True
+        return False
+
+    def _handle_accounts_get(self, parsed) -> bool:
+        if parsed.path == "/api/account/status":
+            self.send_json({"ok": True, **self.application_service.status()})
+            return True
+        return False
+
+    def _handle_accounts_delete(self, parsed) -> bool:
+        if parsed.path == "/api/account/birth-profile":
+            deleted = self.application_service.database.delete_user_birth_profile(self.application_service.current_user_id)
+            self.send_json({"ok": True, "deleted": deleted})
+            return True
+        return False

backend/features/alerts/routes.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+import re
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+
+
+class AlertRoutesMixin:
+    def _handle_alerts_get(self, parsed) -> bool:
+        if parsed.path == "/api/alerts":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.alert_center(
+                        query.get("status", ["all"])[0],
+                        query.get("as_of", [date.today().isoformat()])[0],
+                    )
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False
+
+    def _handle_alerts_post(self, parsed) -> bool:
+        alert_read_match = re.fullmatch(r"/api/alerts/(\d+)/read", parsed.path)
+        if alert_read_match:
+            self.send_json(
+                {"ok": True, **self.application_service.mark_alert_read(int(alert_read_match.group(1)))}
+            )
+            return True
+        if parsed.path == "/api/alerts/read-all":
+            body = self.read_json_body(True)
+            self.send_json(
+                {"ok": True, **self.application_service.mark_all_alerts_read(str(body.get("as_of") or ""))}
+            )
+            return True
+        return False
+
+    def _handle_alerts_delete(self, parsed) -> bool:
+        alert_match = re.fullmatch(r"/api/alerts/(\d+)", parsed.path)
+        if alert_match:
+            self.send_json(
+                {"ok": True, **self.application_service.delete_alert(int(alert_match.group(1)))}
+            )
+            return True
+        return False

backend/features/auction/routes.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+from backend.data.providers.tushare_client import TushareError
+
+
+class AuctionRoutesMixin:
+    def _handle_auction_get(self, parsed) -> bool:
+        if parsed.path == "/api/auction":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.auction_center(
+                        query.get("trade_date", [date.today().isoformat()])[0],
+                        query.get("force", ["0"])[0] == "1",
+                    )
+                )
+            except (ValueError, TushareError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False

backend/features/dragon_tiger/routes.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import json
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+from backend.bootstrap.config import validate_text
+
+
+class DragonTigerRoutesMixin:
+    def _handle_dragon_tiger_get(self, parsed) -> bool:
+        if parsed.path == "/api/dragon-tiger":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            force = query.get("force", ["0"])[0] == "1"
+            try:
+                self.send_json(self.application_service.get_dragon_tiger(trade_date, force))
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/dragon-tiger/profiles":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.get_hot_money_profiles(
+                        query.get("force", ["0"])[0] == "1"
+                    )
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/seat-aliases":
+            self.send_json({"items": self.application_service.database.list_seat_aliases()})
+            return True
+        return False
+
+    def save_seat_alias(self) -> None:
+        try:
+            body = self.read_json_body()
+            seat_name = validate_text(body.get("seat_name"), "席位名称", 200, required=True)
+            alias = validate_text(body.get("alias"), "席位别名", 50, required=True)
+            self.application_service.database.save_seat_alias(seat_name, alias)
+            self.send_json({"ok": True})
+        except (ValueError, json.JSONDecodeError) as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)

backend/features/heaven/manual.py

@@ -0,0 +1,412 @@
+from __future__ import annotations
+
+import copy
+from typing import Any
+
+from backend.bootstrap.config import validate_text
+from backend.data.providers.tushare_client import _sector_coverage_issue
+from backend.features.heaven.engine import _market_line_scores, _score_to_line
+
+
+class HeavenManualMixin:
+    @staticmethod
+    def _heaven_manual_schema(market_mode: str) -> dict[str, dict[str, Any]]:
+        intraday = market_mode == "intraday"
+        fields = {
+            "stock_amount_percentile": {"line": 1, "label": "成交额全市场分位", "unit": "%", "min": 0, "max": 100},
+            "stock_turnover_rate": {"line": 1, "label": "个股换手率", "unit": "%", "min": 0, "max": 100},
+            "stock_turnover_relative": {"line": 1, "label": "相对市场换手", "unit": "倍", "min": 0, "max": 20},
+            "stock_volume_activity_ratio": {"line": 1, "label": "同进度量能", "unit": "倍", "min": 0, "max": 20},
+            "stock_seal_amount_million": {"line": 1, "label": "封单金额", "unit": "万元", "min": 0, "max": 100000000},
+            "stock_open_times": {"line": 1, "label": "开板次数", "unit": "次", "min": 0, "max": 100, "integer": True},
+            "stock_change": {"line": 2, "label": "个股涨跌幅", "unit": "%", "min": -100, "max": 100},
+            "stock_streak": {"line": 2, "label": "连板高度", "unit": "板", "min": 0, "max": 100, "integer": True},
+            "stock_status": {"line": 2, "label": "个股状态", "type": "select", "options": ["普通", "涨停", "炸板", "跌停"]},
+            "sector_name": {"line": [3, 4], "label": "申万二级行业", "type": "text", "max_length": 50},
+            "sector_up_count": {"line": 3, "label": "行业上涨家数", "unit": "家", "min": 0, "max": 10000, "integer": True},
+            "sector_down_count": {"line": 3, "label": "行业下跌家数", "unit": "家", "min": 0, "max": 10000, "integer": True},
+            "sector_coverage": {"line": 3, "label": "成分行情覆盖率", "unit": "%", "min": 0, "max": 100},
+            "sector_relative_turnover": {"line": 3, "label": "行业相对市场换手", "unit": "倍", "min": 0, "max": 20},
+            "sector_member_equal_change": {"line": 3, "label": "成分等权涨跌幅", "unit": "%", "min": -100, "max": 100},
+            "sector_change": {"line": 4, "label": "申万官方涨跌幅", "unit": "%", "min": -100, "max": 100},
+            "sector_leading_pct": {"line": [3, 4], "label": "行业领涨股涨跌幅", "unit": "%", "min": -100, "max": 100},
+            "market_sentiment_score": {"line": 5, "label": "市场情绪温度", "unit": "分", "min": 0, "max": 100},
+            "market_seal_rate": {"line": 5, "label": "封板率", "unit": "%", "min": 0, "max": 100},
+            "market_amount_billion": {"line": 5, "label": "两市成交额", "unit": "亿元", "min": 0, "max": 10000000},
+            "market_recent_average_amount_billion": {"line": 5, "label": "近期平均成交额", "unit": "亿元", "min": 0, "max": 10000000},
+            "market_up_count": {"line": 5, "label": "上涨家数", "unit": "家", "min": 0, "max": 10000, "integer": True},
+            "market_down_count": {"line": 5, "label": "下跌家数", "unit": "家", "min": 0, "max": 10000, "integer": True},
+            "market_limit_up_count": {"line": 5, "label": "涨停家数", "unit": "家", "min": 0, "max": 10000, "integer": True},
+            "market_limit_down_count": {"line": 5, "label": "跌停家数", "unit": "家", "min": 0, "max": 10000, "integer": True},
+            "index_sh_change": {"line": 6, "label": "上证指数涨跌幅", "unit": "%", "min": -20, "max": 20},
+            "index_sz_change": {"line": 6, "label": "深证成指涨跌幅", "unit": "%", "min": -20, "max": 20},
+            "index_cy_change": {"line": 6, "label": "创业板指涨跌幅", "unit": "%", "min": -20, "max": 20},
+            "note": {"line": [], "label": "补录说明", "type": "text", "max_length": 200},
+        }
+        if intraday:
+            for key in ("stock_seal_amount_million", "stock_open_times"):
+                fields.pop(key)
+        else:
+            for key in ("stock_turnover_relative", "stock_volume_activity_ratio", "sector_relative_turnover"):
+                fields.pop(key)
+        return fields
+
+    @classmethod
+    def _validate_heaven_manual_data(
+        cls, raw: Any, market_mode: str
+    ) -> dict[str, Any]:
+        if raw in (None, ""):
+            return {}
+        if not isinstance(raw, dict):
+            raise ValueError("六爻补录数据格式不正确。")
+        schema = cls._heaven_manual_schema(market_mode)
+        unknown = set(raw) - set(schema)
+        if unknown:
+            raise ValueError(f"六爻补录包含未知字段：{next(iter(sorted(unknown)))}")
+        values: dict[str, Any] = {}
+        for key, value in raw.items():
+            if value is None or (isinstance(value, str) and not value.strip()):
+                continue
+            spec = schema[key]
+            if spec.get("type") == "text":
+                values[key] = validate_text(value, spec["label"], int(spec["max_length"]))
+                continue
+            if spec.get("type") == "select":
+                text = str(value).strip()
+                if text not in spec["options"]:
+                    raise ValueError(f"{spec['label']}不在允许范围内。")
+                values[key] = text
+                continue
+            try:
+                number = float(value)
+            except (TypeError, ValueError) as exc:
+                raise ValueError(f"{spec['label']}必须是数字。") from exc
+            if number < float(spec["min"]) or number > float(spec["max"]):
+                raise ValueError(
+                    f"{spec['label']}应在 {spec['min']} 至 {spec['max']} 之间。"
+                )
+            values[key] = int(number) if spec.get("integer") else number
+        return values
+
+    @staticmethod
+    def _apply_heaven_manual_data(
+        dashboard: dict[str, Any],
+        index_context: dict[str, Any],
+        sector: dict[str, Any] | None,
+        stock: dict[str, Any] | None,
+        manual_data: dict[str, Any],
+        market_mode: str,
+        trade_date: str,
+        stock_code: str,
+    ) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any], dict[str, Any]]:
+        dashboard = copy.deepcopy(dashboard)
+        index_context = copy.deepcopy(index_context or {})
+        sector = copy.deepcopy(sector or {})
+        stock = copy.deepcopy(stock or {})
+        overview = dashboard.setdefault("overview", {})
+
+        stock_map = {
+            "stock_amount_percentile": "amount_percentile",
+            "stock_turnover_rate": "turnover_rate",
+            "stock_turnover_relative": "turnover_relative",
+            "stock_volume_activity_ratio": "volume_activity_ratio",
+            "stock_seal_amount_million": "seal_amount_million",
+            "stock_open_times": "open_times",
+            "stock_change": "change",
+            "stock_streak": "streak",
+            "stock_status": "status",
+        }
+        sector_map = {
+            "sector_name": "name",
+            "sector_up_count": "up_count",
+            "sector_down_count": "down_count",
+            "sector_coverage": "coverage",
+            "sector_relative_turnover": "relative_turnover",
+            "sector_member_equal_change": "member_equal_change",
+            "sector_change": "change",
+            "sector_leading_pct": "leading_pct",
+        }
+        overview_map = {
+            "market_sentiment_score": "sentiment_score",
+            "market_seal_rate": "seal_rate",
+            "market_amount_billion": "amount_billion",
+            "market_recent_average_amount_billion": "recent_average_amount_billion",
+            "market_up_count": "up_count",
+            "market_down_count": "down_count",
+            "market_limit_up_count": "limit_up_count",
+            "market_limit_down_count": "limit_down_count",
+        }
+        for manual_key, target in stock_map.items():
+            if manual_key in manual_data:
+                stock[target] = manual_data[manual_key]
+        for manual_key, target in sector_map.items():
+            if manual_key in manual_data:
+                sector[target] = manual_data[manual_key]
+        for manual_key, target in overview_map.items():
+            if manual_key in manual_data:
+                overview[target] = manual_data[manual_key]
+
+        if any(key.startswith("stock_") for key in manual_data):
+            stock.setdefault("code", stock_code)
+            stock.setdefault("name", stock_code or "--")
+            stock["_quantitative_mode"] = "intraday" if market_mode == "intraday" else "historical"
+            if market_mode == "intraday" and "stock_volume_activity_ratio" in manual_data:
+                stock["activity_source"] = "user_supplied"
+        if any(key.startswith("sector_") for key in manual_data):
+            sector["_quantitative_mode"] = "intraday" if market_mode == "intraday" else "historical"
+            sector.setdefault("taxonomy", "sw_l2")
+
+        index_keys = (
+            ("index_sh_change", "000001.SH", "上证指数"),
+            ("index_sz_change", "399001.SZ", "深证成指"),
+            ("index_cy_change", "399006.SZ", "创业板指"),
+        )
+        rows = {str(row.get("ts_code") or row.get("code") or ""): dict(row) for row in index_context.get("indices") or []}
+        for manual_key, code, name in index_keys:
+            if manual_key not in manual_data:
+                continue
+            row = rows.get(code, {"ts_code": code, "name": name})
+            row.update({"pct_chg": manual_data[manual_key], "trade_date": trade_date})
+            rows[code] = row
+        ordered_rows = [rows.get(code) for _, code, _ in index_keys]
+        if all(ordered_rows):
+            index_context["indices"] = ordered_rows
+            changes = [float(row.get("pct_chg") or 0) for row in ordered_rows]
+            aggregate = dict(index_context.get("aggregate") or {})
+            aggregate["average_pct_chg"] = sum(changes) / 3
+            index_context["aggregate"] = aggregate
+        return dashboard, index_context, sector, stock
+
+    @classmethod
+    def _heaven_line_checks(
+        cls,
+        trade_date: str,
+        dashboard: dict[str, Any],
+        recent_history: list[dict[str, Any]],
+        index_context: dict[str, Any],
+        sector: dict[str, Any],
+        stock: dict[str, Any],
+        market_mode: str,
+        manual_data: dict[str, Any],
+    ) -> list[dict[str, Any]]:
+        intraday = market_mode == "intraday"
+        closed = market_mode == "closed"
+        schema = cls._heaven_manual_schema(market_mode)
+        required = {
+            1: (["stock_amount_percentile", "stock_turnover_relative", "stock_volume_activity_ratio"] if intraday else ["stock_amount_percentile", "stock_turnover_rate", "stock_seal_amount_million", "stock_open_times"]),
+            2: ["stock_change", "stock_streak", "stock_status"],
+            3: (["sector_name", "sector_up_count", "sector_down_count", "sector_coverage", "sector_relative_turnover"] if intraday else ["sector_name", "sector_up_count", "sector_down_count", "sector_coverage", "sector_member_equal_change", "sector_leading_pct"]),
+            4: ["sector_name", "sector_change", "sector_leading_pct"],
+            5: ["market_sentiment_score", "market_seal_rate", "market_amount_billion", "market_recent_average_amount_billion", "market_up_count", "market_down_count", "market_limit_up_count", "market_limit_down_count"],
+            6: ["index_sh_change", "index_sz_change", "index_cy_change"],
+        }
+        names = {
+            1: ("初爻", "个股内核", "成交活跃、换手与量能"),
+            2: ("二爻", "个股外显", "涨跌、连板与状态"),
+            3: ("三爻", "行业内核", "行业宽度与成交活跃"),
+            4: ("四爻", "行业外显", "行业涨跌与领涨表现"),
+            5: ("五爻", "市场内核", "情绪、封板、成交与市场宽度"),
+            6: ("上爻", "指数外显", "三大指数当日涨跌"),
+        }
+
+        index_date = str(index_context.get("trade_date") or "").replace("-", "")
+        index_rows = list(index_context.get("indices") or [])
+        index_dates = {str(row.get("trade_date") or "").replace("-", "") for row in index_rows}
+        index_issues = []
+        if len(index_rows) < 3:
+            index_issues.append(f"三大指数仅取得 {len(index_rows)}/3 条行情")
+        elif index_date != trade_date or index_dates != {trade_date}:
+            actual_dates = "、".join(sorted(value for value in index_dates if value)) or "未知"
+            index_issues.append(f"指数实际日期为 {actual_dates}，目标交易日为 {trade_date}")
+        elif not index_context.get("precise"):
+            index_issues.append("三大指数行情未通过完整性校验")
+        elif intraday and not index_context.get("realtime"):
+            index_issues.append("盘中缺少可核验的实时指数行情")
+        elif not intraday and (index_context.get("realtime") or str(index_context.get("source") or "") != "tushare"):
+            index_issues.append("收盘或历史行情不是官方指数日线")
+
+        sector_date = str(sector.get("trade_date") or "").replace("-", "")
+        sector_coverage = float(sector.get("coverage") or 0)
+        sector_explained_count = int(
+            sector.get("explained_count")
+            if sector.get("explained_count") is not None
+            else sector.get("quote_count") or 0
+        )
+        sector_explained_coverage = float(
+            sector.get("explained_coverage")
+            if sector.get("explained_coverage") is not None
+            else sector_coverage
+        )
+        sector_coverage_issue = _sector_coverage_issue(
+            int(sector.get("member_count") or 0),
+            int(sector.get("quote_count") or 0),
+            sector_explained_coverage,
+            sector_explained_count,
+        )
+        sector_common = []
+        if not sector:
+            sector_common.append("未取得申万二级行业归属")
+        elif sector.get("taxonomy") != "sw_l2":
+            sector_common.append("行业分类不是申万二级")
+        elif sector_date != trade_date:
+            sector_common.append("行业行情日期与目标交易日不一致")
+        elif intraday and not sector.get("realtime"):
+            sector_common.append("盘中行业行情不是申万实时行情")
+        elif market_mode == "historical" and sector.get("realtime"):
+            sector_common.append("历史行业行情不能使用实时快照")
+        elif closed and sector.get("realtime") and not sector.get("finalized"):
+            sector_common.append("收盘行业实时行情尚未形成15:00最终快照")
+        sector_inner = list(sector_common)
+        sector_outer = list(sector_common)
+        if not sector.get("inner_precise", sector.get("precise")):
+            sector_inner.append(str(sector.get("inner_error") or sector.get("error") or "行业内核数据未通过校验"))
+        if not sector.get("outer_precise", sector.get("precise")):
+            sector_outer.append(str(sector.get("outer_error") or sector.get("error") or "行业外显数据未通过校验"))
+        if sector and sector_coverage_issue and sector_coverage_issue not in sector_inner:
+            sector_inner.append(sector_coverage_issue)
+        if sector.get("realtime") and not sector.get("relative_turnover"):
+            sector_inner.append("缺少行业相对全市场换手活跃度")
+
+        stock_date = str(stock.get("trade_date") or "").replace("-", "")
+        stock_common = []
+        if not stock.get("code"):
+            stock_common.append("尚未载入有效个股")
+        elif stock_date != trade_date:
+            stock_common.append(f"个股实际日期为 {stock_date or '未知'}，目标交易日为 {trade_date}")
+        elif not stock.get("precise"):
+            stock_common.append("个股行情未通过完整性校验")
+        elif intraday and not stock.get("realtime"):
+            stock_common.append("盘中个股行情不是实时行情")
+        elif not intraday and (stock.get("realtime") or str(stock.get("data_source") or "") != "tushare"):
+            stock_common.append("收盘或历史个股行情不是官方日线")
+        stock_inner = list(stock_common)
+        if intraday and stock.get("turnover_source") in {None, "", "unavailable"}:
+            stock_inner.append("缺少可核验的实时换手率")
+        if intraday and stock.get("activity_source") in {None, "", "unavailable"}:
+            stock_inner.append("缺少同时间进度量能基准")
+
+        overview = dashboard.get("overview") or {}
+        market_key_map = {
+            "market_sentiment_score": "sentiment_score", "market_seal_rate": "seal_rate",
+            "market_amount_billion": "amount_billion", "market_recent_average_amount_billion": "recent_average_amount_billion",
+            "market_up_count": "up_count", "market_down_count": "down_count",
+            "market_limit_up_count": "limit_up_count", "market_limit_down_count": "limit_down_count",
+        }
+        market_issues = []
+        for manual_key, source_key in market_key_map.items():
+            if source_key == "recent_average_amount_billion":
+                history_values = [item.get("amount_billion") for item in recent_history[:-1] if item.get("amount_billion") is not None]
+                if source_key not in overview and not history_values:
+                    market_issues.append(f"缺少{schema[manual_key]['label']}")
+            elif source_key not in overview or overview.get(source_key) is None:
+                market_issues.append(f"缺少{schema[manual_key]['label']}")
+
+        automatic_issues = {
+            1: stock_inner, 2: stock_common, 3: sector_inner,
+            4: sector_outer, 5: market_issues, 6: index_issues,
+        }
+        limits = list(dashboard.get("limits") or [])
+        scores = _market_line_scores(dashboard, recent_history, index_context, sector, stock, limits)
+
+        value_map: dict[str, Any] = {
+            "stock_amount_percentile": stock.get("amount_percentile"),
+            "stock_turnover_rate": stock.get("turnover_rate"),
+            "stock_turnover_relative": stock.get("turnover_relative"),
+            "stock_volume_activity_ratio": stock.get("volume_activity_ratio"),
+            "stock_seal_amount_million": stock.get("seal_amount_million"),
+            "stock_open_times": stock.get("open_times"),
+            "stock_change": stock.get("change"), "stock_streak": stock.get("streak"),
+            "stock_status": stock.get("status"), "sector_name": sector.get("name"),
+            "sector_up_count": sector.get("up_count"), "sector_down_count": sector.get("down_count"),
+            "sector_coverage": sector.get("coverage"), "sector_relative_turnover": sector.get("relative_turnover"),
+            "sector_member_equal_change": sector.get("member_equal_change"),
+            "sector_change": sector.get("change"), "sector_leading_pct": sector.get("leading_pct"),
+            "market_sentiment_score": overview.get("sentiment_score"), "market_seal_rate": overview.get("seal_rate"),
+            "market_amount_billion": overview.get("amount_billion"),
+            "market_recent_average_amount_billion": overview.get("recent_average_amount_billion"),
+            "market_up_count": overview.get("up_count"), "market_down_count": overview.get("down_count"),
+            "market_limit_up_count": overview.get("limit_up_count"), "market_limit_down_count": overview.get("limit_down_count"),
+        }
+        history_values = [float(item.get("amount_billion")) for item in recent_history[:-1] if item.get("amount_billion") is not None]
+        if value_map["market_recent_average_amount_billion"] is None and history_values:
+            value_map["market_recent_average_amount_billion"] = sum(history_values) / len(history_values)
+        if value_map["stock_amount_percentile"] is None and not intraday:
+            amount = float(stock.get("amount_billion") or 0)
+            amounts = [float(item.get("amount_billion") or 0) for item in limits if item.get("amount_billion") is not None]
+            value_map["stock_amount_percentile"] = (
+                sum(item <= amount for item in amounts) / len(amounts) * 100 if amounts else None
+            )
+        row_by_code = {str(row.get("ts_code") or row.get("code") or ""): row for row in index_context.get("indices") or []}
+        value_map.update({
+            "index_sh_change": (row_by_code.get("000001.SH") or {}).get("pct_chg"),
+            "index_sz_change": (row_by_code.get("399001.SZ") or {}).get("pct_chg"),
+            "index_cy_change": (row_by_code.get("399006.SZ") or {}).get("pct_chg"),
+        })
+
+        def missing_value(key: str) -> bool:
+            value = value_map.get(key)
+            return value is None or (isinstance(value, str) and not value.strip())
+
+        invalid_fields = {
+            line_number: {key for key in keys if missing_value(key)}
+            for line_number, keys in required.items()
+        }
+        if stock_common:
+            invalid_fields[1].update(required[1])
+            invalid_fields[2].update(required[2])
+        else:
+            if intraday and stock.get("turnover_source") in {None, "", "unavailable"}:
+                invalid_fields[1].add("stock_turnover_relative")
+            if intraday and stock.get("activity_source") in {None, "", "unavailable"}:
+                invalid_fields[1].add("stock_volume_activity_ratio")
+
+        if sector_common:
+            invalid_fields[3].update(required[3])
+            invalid_fields[4].update(required[4])
+        else:
+            if not sector.get("inner_precise", sector.get("precise")) or sector_coverage_issue:
+                invalid_fields[3].update(key for key in required[3] if key != "sector_name")
+            if sector.get("realtime") and not sector.get("relative_turnover"):
+                invalid_fields[3].add("sector_relative_turnover")
+            # The official SW index supplies only the sector's external change. A valid
+            # membership name and member-stock leader remain usable when that quote fails.
+            if not sector.get("outer_precise", sector.get("precise")):
+                invalid_fields[4].add("sector_change")
+
+        if index_issues:
+            invalid_fields[6].update(required[6])
+
+        checks = []
+        for line_number in range(1, 7):
+            manual_keys = [key for key in required[line_number] if key in manual_data]
+            unresolved_fields = [
+                key for key in required[line_number]
+                if key in invalid_fields[line_number] and key not in manual_data
+            ]
+            hard_missing_identity = line_number in {1, 2} and not stock.get("code")
+            passed = not hard_missing_identity and not unresolved_fields
+            status = "manual" if passed and manual_keys else "passed" if passed else "failed"
+            reasons = [] if passed else [
+                *( ["请先输入并载入股票代码或名称"] if hard_missing_identity else automatic_issues[line_number] ),
+                *( ["需补充：" + "、".join(schema[key]["label"] for key in unresolved_fields)] if unresolved_fields else [] ),
+            ]
+            score = float(scores[line_number - 1]["score"])
+            position, layer, formula = names[line_number]
+            checks.append({
+                "line": line_number, "position": position, "layer": layer, "formula": formula,
+                "status": status, "passed": passed, "reasons": reasons,
+                "score": round(score, 3) if passed else None,
+                "line_value": _score_to_line(score) if passed else None,
+                "evidence": scores[line_number - 1]["evidence"] if passed else [],
+                "fields": [
+                    {
+                        "key": key, "label": schema[key]["label"], "unit": schema[key].get("unit", ""),
+                        "type": schema[key].get("type", "number"), "options": schema[key].get("options", []),
+                        "value": value_map.get(key), "manual": key in manual_data,
+                        "required": True, "min": schema[key].get("min"), "max": schema[key].get("max"),
+                        "integer": bool(schema[key].get("integer")),
+                    }
+                    for key in required[line_number]
+                ],
+            })
+        return checks

backend/features/heaven/market_context.py

@@ -0,0 +1,338 @@
+from __future__ import annotations
+
+import re
+from datetime import datetime, timedelta
+from typing import Any
+
+from backend.bootstrap.config import (
+    normalize_date,
+    tushare_code,
+    validate_stock_code,
+    validate_text,
+)
+from backend.data.providers.tushare_client import TushareError
+
+
+class HeavenMarketContextMixin:
+    def _resolve_heaven_stock_code(self, query: str) -> str:
+        raw = validate_text(query, "股票代码或名称", 30, required=True)
+        code_match = re.fullmatch(r"(\d{6})(?:\.(?:SH|SZ|BJ))?", raw.upper())
+        if code_match:
+            return validate_stock_code(code_match.group(1))
+
+        candidates = self.database.search_stock_master(raw)
+        exact = [item for item in candidates if str(item.get("name") or "").casefold() == raw.casefold()]
+        if not exact and self.configured:
+            try:
+                rows = self._tushare_client().query(
+                    "stock_basic",
+                    {"name": raw, "list_status": "L"},
+                    "ts_code,symbol,name,industry,market,list_date",
+                )
+            except TushareError:
+                rows = []
+            if rows:
+                self.database.upsert_stock_master(rows)
+                candidates = self.database.search_stock_master(raw)
+                exact = [
+                    item
+                    for item in candidates
+                    if str(item.get("name") or "").casefold() == raw.casefold()
+                ]
+
+        matches = exact or candidates
+        if len(matches) == 1:
+            return validate_stock_code(str(matches[0].get("code") or ""))
+        if len(matches) > 1:
+            choices = "、".join(
+                f"{item.get('name') or '--'}（{item.get('code') or '--'}）"
+                for item in matches[:5]
+            )
+            raise ValueError(f"匹配到多只股票：{choices}。请输入六位股票代码。")
+        raise ValueError(f"未找到股票“{raw}”，请检查名称或输入六位股票代码。")
+
+    def _heaven_stock_context(
+        self,
+        stock_code: str,
+        trade_date: str,
+        dashboard: dict[str, Any],
+        market_mode: str,
+    ) -> dict[str, Any]:
+        """Return the only stock contract accepted by heaven trend."""
+        pool_row = next(
+            (
+                dict(row) for key in ("limits", "broken", "down_limits")
+                for row in dashboard.get(key) or []
+                if str(row.get("code") or "") == stock_code
+            ),
+            {},
+        )
+        if market_mode == "intraday":
+            if self.configured:
+                try:
+                    quote = self._tushare_client().realtime_stock_quote(
+                        tushare_code(stock_code),
+                        trade_date,
+                    )
+                    return {
+                        **quote,
+                        "status": pool_row.get("status") or "普通",
+                        "seal_amount_million": pool_row.get("seal_amount_million") or 0,
+                        "open_times": pool_row.get("open_times") or 0,
+                        "streak": pool_row.get("streak") or 0,
+                        "precise": True,
+                    }
+                except TushareError:
+                    pass
+            if pool_row:
+                return {
+                    **pool_row,
+                    "data_source": "dashboard_rt" if dashboard.get("meta", {}).get("realtime") else "dashboard",
+                    "trade_date": trade_date,
+                    "realtime": bool(dashboard.get("meta", {}).get("realtime")),
+                    "precise": False,
+                }
+            return {
+                "code": stock_code,
+                "name": "--",
+                "sector": "其他",
+                "trade_date": trade_date,
+                "realtime": False,
+                "precise": False,
+            }
+
+        detail = self.get_stock_detail(stock_code, trade_date, force=True)
+        detail_meta = detail.get("meta") or {}
+        stock = detail.get("stock") or {}
+        resolved_date = normalize_date(str(detail_meta.get("trade_date") or trade_date))
+        source = str(detail_meta.get("source") or "")
+        return {
+            "code": stock_code,
+            "name": stock.get("name") or pool_row.get("name") or "--",
+            "sector": stock.get("industry") or pool_row.get("sector") or "其他",
+            "status": pool_row.get("status") or "普通",
+            "change": stock.get("change") or 0,
+            "turnover_rate": stock.get("turnover_rate") or 0,
+            "amount_billion": stock.get("amount_billion") or 0,
+            "seal_amount_million": pool_row.get("seal_amount_million") or 0,
+            "open_times": pool_row.get("open_times") or 0,
+            "streak": pool_row.get("streak") or 0,
+            "data_source": source,
+            "trade_date": resolved_date,
+            "realtime": False,
+            "precise": source == "tushare" and resolved_date == trade_date,
+        }
+
+    def _heaven_index_context(
+        self,
+        trade_date: str,
+        dashboard: dict[str, Any],
+        market_mode: str = "historical",
+    ) -> dict[str, Any]:
+        cached = self.database.get_data_snapshot("heaven_indices", trade_date)
+        cached_valid = False
+        if cached:
+            cached_rows = list(cached.get("indices") or [])
+            cached_dates = {
+                str(row.get("trade_date") or "").replace("-", "")
+                for row in cached_rows
+            }
+            cached_valid = (
+                len(cached_rows) == 3
+                and cached_dates == {trade_date}
+                and bool(cached.get("precise"))
+                and not cached.get("realtime")
+                and str(cached.get("source") or "") == "tushare"
+                and int(cached.get("schema_version") or 0) >= 3
+            )
+        if market_mode != "intraday" and cached_valid:
+            return cached
+
+        if not self.configured:
+            error = "Tushare Token 未配置"
+        else:
+            try:
+                client = self._tushare_client()
+                if market_mode == "intraday":
+                    payload = self._aggregate_index_context(trade_date)
+                    payload["schema_version"] = 3
+                    return payload
+                payload = client.market_indices(trade_date)
+                payload["schema_version"] = 3
+                if market_mode == "closed":
+                    payload["finalized"] = True
+                self.database.save_data_snapshot(
+                    "heaven_indices",
+                    trade_date,
+                    str(payload.get("source") or "tushare"),
+                    payload,
+                )
+                return payload
+            except Exception as exc:
+                error = str(exc)
+        overview = dashboard.get("overview") or {}
+        up_count = float(overview.get("up_count") or 0)
+        down_count = float(overview.get("down_count") or 0)
+        breadth = (up_count - down_count) / max(up_count + down_count, 1)
+        return {
+            "source": "market_breadth_proxy",
+            "trade_date": trade_date,
+            "realtime": False,
+            "precise": False,
+            "schema_version": 3,
+            "notice": f"指数数据不可用，当前以市场宽度代理：{error}",
+            "indices": [],
+            "aggregate": {
+                "average_pct_chg": round(breadth * 2.5, 3),
+                "average_return_5d": 0,
+                "average_return_20d": 0,
+            },
+        }
+
+    def _aggregate_index_context(
+        self,
+        trade_date: str,
+        tushare_error: str = "",
+    ) -> dict[str, Any]:
+        quotes = self.realtime_aggregator.tencent_indices()
+        epochs = [int(item.get("quote_time_epoch") or 0) for item in quotes]
+        quote_dates = {
+            datetime.fromtimestamp(epoch).astimezone().strftime("%Y%m%d")
+            for epoch in epochs if epoch
+        }
+        if len(quotes) != 3 or quote_dates != {trade_date}:
+            raise ValueError("腾讯三大指数日期与目标交易日不一致")
+        now = datetime.now().astimezone()
+        max_skew = 120 if now.hour >= 15 else 15
+        if max(epochs) - min(epochs) > max_skew:
+            raise ValueError(f"腾讯三大指数时间差超过{max_skew}秒")
+
+        code_map = {
+            "000001": "000001.SH",
+            "399001": "399001.SZ",
+            "399006": "399006.SZ",
+        }
+        client = self._tushare_client()
+        indices = []
+        start_date = (
+            datetime.strptime(trade_date, "%Y%m%d") - timedelta(days=20)
+        ).strftime("%Y%m%d")
+        for quote in quotes:
+            ts_code = code_map[str(quote.get("code") or "")]
+            history = client.query(
+                "index_daily",
+                {"ts_code": ts_code, "start_date": start_date, "end_date": trade_date},
+                "ts_code,trade_date,close,pct_chg",
+            )
+            history.sort(key=lambda item: str(item.get("trade_date") or ""))
+            completed_closes = [
+                float(item.get("close") or 0)
+                for item in history
+                if str(item.get("trade_date") or "") < trade_date
+                and float(item.get("close") or 0) > 0
+            ]
+            close_5d = (
+                completed_closes[-5]
+                if len(completed_closes) >= 5
+                else completed_closes[0] if completed_closes else 0
+            )
+            close = float(quote.get("price") or 0)
+            indices.append(
+                {
+                    "ts_code": ts_code,
+                    "name": quote.get("name") or ts_code,
+                    "trade_date": trade_date,
+                    "close": close,
+                    "pct_chg": round(float(quote.get("change") or 0), 3),
+                    "return_5d": round((close / close_5d - 1) * 100, 3) if close_5d else 0,
+                    "return_20d": 0,
+                    "amount_billion": float(quote.get("amount_billion") or 0),
+                    "quote_time": quote.get("quote_time") or "",
+                }
+            )
+        return {
+            "trade_date": trade_date,
+            "source": "+".join(
+                sorted({str(item.get("source") or "web_quote") for item in quotes})
+                + ["tushare_index_daily"]
+            ),
+            "realtime": True,
+            "precise": True,
+            "indices": indices,
+            "aggregate": {
+                "average_pct_chg": round(
+                    sum(item["pct_chg"] for item in indices) / len(indices), 3
+                ),
+                "average_return_5d": round(
+                    sum(item["return_5d"] for item in indices) / len(indices), 3
+                ),
+                "average_return_20d": 0,
+            },
+            "quote_time_skew_seconds": max(epochs) - min(epochs),
+            "notice": (
+                "指数实时行情来自腾讯行情，5日趋势来自Tushare历史指数。"
+                + (f" Tushare实时指数未使用：{tushare_error}" if tushare_error else "")
+            ),
+        }
+
+    def _heaven_sector_context(
+        self,
+        identifier: str,
+        trade_date: str,
+        market_mode: str = "historical",
+    ) -> dict[str, Any] | None:
+        """Return the Shenwan L2 sector context for heaven trend.
+
+        观势行业层只使用申万二级行业。外显盘中使用 rt_sw_k、历史使用
+        sw_daily；内核独立使用目标日期成分股行情聚合。收盘过渡期在
+        sw_daily 入库前接受同日15:00后的 rt_sw_k 收盘快照。
+        """
+        cache_key = f"{trade_date}:{identifier.strip().lower()}"
+        cached = self.database.get_data_snapshot("heaven_sector", cache_key)
+        cached_date = str((cached or {}).get("trade_date") or "").replace("-", "")
+        cached_valid = bool(
+            cached
+            and cached_date == trade_date
+            and cached.get("taxonomy") == "sw_l2"
+            and cached.get("inner_precise", cached.get("precise"))
+            and cached.get("outer_precise", cached.get("precise"))
+            and not cached.get("realtime")
+            and int(cached.get("schema_version") or 0) >= 6
+        )
+        if market_mode != "intraday" and cached_valid:
+            return cached
+        if not self.configured:
+            return None
+        try:
+            payload = self._tushare_client().sw_sector_snapshot(
+                tushare_code(identifier),
+                trade_date,
+                realtime_expected=market_mode == "intraday",
+                allow_realtime_close=market_mode == "closed",
+            )
+        except TushareError as exc:
+            if cached_valid:
+                return cached
+            return {
+                "name": "",
+                "code": "",
+                "taxonomy": "sw_l2",
+                "source": "tushare",
+                "trade_date": trade_date,
+                "realtime": market_mode == "intraday",
+                "precise": False,
+                "inner_precise": False,
+                "outer_precise": False,
+                "coverage": 0,
+                "member_count": 0,
+                "quote_count": 0,
+                "error": f"申万二级行业数据获取失败：{exc}",
+            }
+        if not payload.get("realtime") and payload.get("precise"):
+            self.database.save_data_snapshot(
+                "heaven_sector",
+                cache_key,
+                str(payload.get("source") or "tushare"),
+                payload,
+            )
+        return payload

backend/features/heaven/readings.py

@@ -0,0 +1,220 @@
+from __future__ import annotations
+
+import json
+import secrets
+from datetime import date
+from typing import Any
+
+from backend.bootstrap.config import normalize_date
+from backend.features.heaven.agent import HeavenAgentError, interpret_heaven
+from backend.features.heaven.engine import (
+    build_five_phase_field,
+    hexagram_from_lines,
+)
+from backend.features.market import MarketServiceMixin
+
+
+class HeavenReadingMixin:
+    def heaven_personal(self, payload: dict[str, Any]) -> dict[str, Any]:
+        trade_date = normalize_date(str(payload.get("trade_date") or date.today().isoformat()))
+        field = build_five_phase_field(
+            trade_date,
+            self.database.list_sector_phase_overrides(),
+        )
+        personal = self.account_personal_field(trade_date, field, public=True)
+        if not personal:
+            raise ValueError("请先在账号设置中保存个人命理资料。")
+        return personal
+
+    def heaven_hexagram(self, raw_lines: Any) -> dict[str, Any]:
+        if not isinstance(raw_lines, list):
+            raise ValueError("六爻起卦结果格式不正确。")
+        try:
+            lines = [int(value) for value in raw_lines]
+        except (TypeError, ValueError) as exc:
+            raise ValueError("六爻必须由六、七、八、九组成。") from exc
+        return hexagram_from_lines(lines)
+
+    def heaven_readings(
+        self, mode: str, context_date: str = "", limit: int = 100
+    ) -> dict[str, Any]:
+        mode = str(mode or "").strip()
+        if mode not in {"trend", "fortune", "heart"}:
+            raise ValueError("解读记录类型不正确。")
+        normalized_date = normalize_date(context_date) if context_date else ""
+        return {
+            "mode": mode,
+            "items": self.database.list_heaven_readings(
+                self.current_user_id, mode, normalized_date, limit
+            ),
+        }
+
+    @staticmethod
+    def _heaven_reading_identity(
+        mode: str, context_date: str, context: dict[str, Any]
+    ) -> tuple[str, str]:
+        display_date = MarketServiceMixin._display_compact_date(context_date)
+        if mode == "trend":
+            stock = (context.get("selected_focus") or {}).get("stock") or {}
+            code = str(stock.get("code") or "").strip()
+            name = str(stock.get("name") or "").strip()
+            hexagram = context.get("hexagram") or {}
+            transformed = hexagram.get("transformed") or {}
+            subject = " ".join(item for item in (code, name) if item) or "观势"
+            detail = f"{display_date} · {hexagram.get('name') or '--'} → {transformed.get('name') or '--'}"
+            return subject, detail
+        if mode == "fortune":
+            field = context.get("five_phase_field") or {}
+            pillars = field.get("pillars") or {}
+            dominant = (field.get("balance") or [{}])[0]
+            subject = f"{display_date} 观气"
+            detail = (
+                f"{pillars.get('year') or '--'}年 · {pillars.get('month') or '--'}月 · "
+                f"{pillars.get('day') or '--'}日 · {dominant.get('element') or '--'}气偏显"
+            )
+            return subject, detail
+        hexagram = context.get("hexagram") or {}
+        transformed = hexagram.get("transformed") or {}
+        return (
+            f"{display_date} 观心",
+            f"{hexagram.get('name') or '--'} → {transformed.get('name') or '--'}",
+        )
+
+    def heaven_interpret(self, payload: dict[str, Any]) -> dict[str, Any]:
+        mode = str(payload.get("mode") or "").strip()
+        if mode not in {"trend", "fortune", "heart"}:
+            raise ValueError("问天解读模式不正确。")
+        trade_date = normalize_date(str(payload.get("trade_date") or date.today().isoformat()))
+        if mode == "fortune":
+            existing = self.database.latest_heaven_reading(
+                self.current_user_id, "fortune", trade_date
+            )
+            if self._legacy_truncated_heaven_reading(existing):
+                self.database.delete_heaven_reading(
+                    self.current_user_id, int(existing["id"])
+                )
+                existing = None
+            if existing:
+                return {
+                    "answer": existing["answer"],
+                    "mode": mode,
+                    "compiler": "stored",
+                    "notice": "",
+                    "reading": existing,
+                    "reused": True,
+                }
+        if mode in {"trend", "fortune"}:
+            setup = self.heaven_setup(
+                trade_date,
+                str(payload.get("sector") or ""),
+                str(payload.get("stock_code") or ""),
+                payload.get("manual_data"),
+            )
+            if mode == "trend":
+                chart = setup["chart"]
+                if not chart.get("available"):
+                    issues = "；".join((chart.get("quality") or {}).get("issues") or [])
+                    raise ValueError(f"观势数据未通过六爻校验，暂不解势：{issues}")
+                hexagram_context = json.loads(json.dumps(chart["hexagram"], ensure_ascii=False))
+                for line in hexagram_context.get("lines", []):
+                    line.pop("evidence", None)
+                    line.pop("score", None)
+                    line.pop("talent", None)
+                    line.pop("layer", None)
+                    line.pop("role", None)
+                    if not line.get("moving"):
+                        line.pop("text", None)
+                        line.pop("image", None)
+                        line.pop("line_name", None)
+                context = {
+                    "data_trade_date": setup["trade_date"],
+                    "selected_focus": {
+                        "sector": chart.get("sector") or "",
+                        "stock": chart.get("stock") or {},
+                    },
+                    "hexagram": hexagram_context,
+                    "movement": chart.get("movement") or {},
+                }
+            else:
+                personal_profile = self.account_personal_field(
+                    setup["calendar_date"],
+                    setup["field"],
+                    public=False,
+                )
+                fortune_field = json.loads(json.dumps(setup["field"], ensure_ascii=False))
+                catalog = fortune_field.pop("sector_catalog", [])
+                dominant_elements = {
+                    item.get("element") for item in fortune_field.get("balance", [])[:2]
+                }
+                fortune_field["industry_affinity"] = [
+                    {
+                        "element": group.get("element"),
+                        "examples": [
+                            item.get("name")
+                            for item in group.get("industries", [])[:8]
+                            if item.get("name")
+                        ],
+                    }
+                    for group in catalog
+                    if group.get("element") in dominant_elements
+                ]
+                context = {
+                    "calendar_date": setup["calendar_date"],
+                    "five_phase_field": fortune_field,
+                    "personal_profile": personal_profile,
+                }
+                context_date = setup["calendar_date"]
+            if mode == "trend":
+                context_date = setup["trade_date"]
+        else:
+            context = {
+                "hexagram": self.heaven_hexagram(payload.get("lines")),
+                "ritual": "用户已完成30秒静心、六次三枚铜钱起卦，并在心中察看第一念。问题未输入。",
+            }
+            context_date = trade_date
+        result, compiler = self._call_heaven_agent(mode, context)
+        subject, subject_detail = self._heaven_reading_identity(
+            mode, context_date, context
+        )
+        dedupe_key = (
+            f"fortune:{context_date}"
+            if mode == "fortune"
+            else f"{mode}:{context_date}:{secrets.token_urlsafe(12)}"
+        )
+        reading = self.database.save_heaven_reading(
+            self.current_user_id,
+            mode,
+            context_date,
+            subject,
+            subject_detail,
+            str(result.get("answer") or ""),
+            context,
+            dedupe_key,
+        )
+        return {
+            **result,
+            "mode": mode,
+            "compiler": compiler,
+            "notice": "智能解读已自动切换可用服务。" if compiler == "fallback" else "",
+            "reading": reading,
+            "reused": False,
+        }
+
+    @staticmethod
+    def _legacy_truncated_heaven_reading(reading: dict[str, Any] | None) -> bool:
+        return bool(reading and str(reading.get("answer") or "").rstrip().endswith("……"))
+
+    def _call_heaven_agent(self, mode: str, context: dict[str, Any]) -> tuple[dict[str, Any], str]:
+        result = self.llm_gateway.call(
+            f"heaven_{mode}",
+            f"heaven-{mode}-v1",
+            lambda profile: interpret_heaven(
+                mode,
+                context,
+                profile.api_key,
+                profile.base_url,
+                profile.model,
+            ),
+            (HeavenAgentError,),
+        )
+        return result.value, result.role

backend/features/heaven/routes.py

@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+import json
+import re
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs, unquote
+from backend.bootstrap.config import validate_text
+
+
+class HeavenRoutesMixin:
+    def _handle_heaven_get(self, parsed) -> bool:
+        if parsed.path == "/api/heaven/readings":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.heaven_readings(
+                        query.get("mode", [""])[0],
+                        query.get("context_date", [""])[0],
+                        int(query.get("limit", ["100"])[0]),
+                    )
+                )
+            except (TypeError, ValueError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/heaven/setup":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            sector_name = query.get("sector", [""])[0]
+            stock_code = query.get("stock_code", [""])[0]
+            manual_data = None
+            manual_text = query.get("manual_data", [""])[0]
+            if manual_text:
+                try:
+                    manual_data = json.loads(manual_text)
+                except json.JSONDecodeError:
+                    self.send_json({"error": "六爻补录数据格式不正确。"}, HTTPStatus.BAD_REQUEST)
+                    return True
+            try:
+                self.send_json(
+                    self.application_service.heaven_setup(
+                        trade_date,
+                        sector_name,
+                        stock_code,
+                        manual_data,
+                    )
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False
+
+    def _handle_heaven_delete(self, parsed) -> bool:
+        heaven_reading_match = re.fullmatch(r"/api/heaven/readings/(\d+)", parsed.path)
+        if heaven_reading_match:
+            deleted = self.application_service.database.delete_heaven_reading(
+                self.application_service.current_user_id, int(heaven_reading_match.group(1))
+            )
+            self.send_json({"ok": True, "deleted": deleted})
+            return True
+        sector_phase_match = re.fullmatch(r"/api/heaven/sector-phases/(.+)", parsed.path)
+        if sector_phase_match:
+            name = unquote(sector_phase_match.group(1)).strip()
+            deleted = self.application_service.database.delete_sector_phase_override(name)
+            self.send_json({"ok": True, "deleted": deleted})
+            return True
+        return False
+
+    def save_sector_phase_override(self) -> None:
+        try:
+            body = self.read_json_body()
+            name = validate_text(body.get("name"), "行业或题材名称", 50, required=True)
+            element = str(body.get("element") or "").strip()
+            if element not in {"木", "火", "土", "金", "水"}:
+                raise ValueError("五行归类必须是木、火、土、金或水。")
+            self.application_service.database.save_sector_phase_override(name, element)
+            self.send_json({"ok": True})
+        except (ValueError, json.JSONDecodeError) as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)

backend/features/heaven/trend.py

@@ -0,0 +1,358 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+
+from backend.bootstrap.config import normalize_date
+from backend.data.providers.tushare_client import _sector_coverage_issue
+from backend.features.heaven.engine import build_five_phase_field, build_market_hexagram
+
+
+class HeavenTrendMixin:
+    def heaven_setup(
+        self,
+        trade_date: str,
+        sector_name: str = "",
+        stock_code: str = "",
+        manual_data: dict[str, Any] | None = None,
+    ) -> dict[str, Any]:
+        normalized_date = normalize_date(trade_date)
+        dashboard = self.get_dashboard(normalized_date)
+        data_date = normalize_date(str(dashboard.get("meta", {}).get("trade_date") or normalized_date))
+        recent_history = self.database.snapshot_summaries(data_date, 10)
+        market_mode = self._heaven_market_mode(data_date, dashboard)
+        manual_data = self._validate_heaven_manual_data(manual_data, market_mode)
+        index_context = self._heaven_index_context(data_date, dashboard, market_mode)
+        external_stock = None
+        normalized_stock_code = ""
+        if stock_code.strip():
+            normalized_stock_code = self._resolve_heaven_stock_code(stock_code)
+            external_stock = self._heaven_stock_context(
+                normalized_stock_code,
+                data_date,
+                dashboard,
+                market_mode,
+            )
+        external_sector = None
+        if normalized_stock_code and self.configured:
+            external_sector = self._heaven_sector_context(
+                normalized_stock_code,
+                data_date,
+                market_mode,
+            )
+            if external_sector and external_stock:
+                external_stock["sector"] = external_sector.get("name") or external_stock.get("sector")
+        dashboard, index_context, external_sector, external_stock = self._apply_heaven_manual_data(
+            dashboard,
+            index_context,
+            external_sector,
+            external_stock,
+            manual_data,
+            market_mode,
+            data_date,
+            normalized_stock_code,
+        )
+        if external_sector and external_stock:
+            external_stock["sector"] = external_sector.get("name") or external_stock.get("sector")
+        sector_input = str((external_sector or {}).get("name") or sector_name.strip())
+        if not normalized_stock_code:
+            data_checks = []
+            chart = {
+                "available": False,
+                "selection_required": True,
+                "data_trade_date": data_date,
+                "sector": "",
+                "sector_code": "",
+                "sector_taxonomy": "",
+                "stock": {"code": "", "name": "", "status": ""},
+                "quality": {
+                    "status": "awaiting_selection",
+                    "issues": [],
+                    "principle": "",
+                    "sources": [],
+                },
+                "index_context": index_context,
+            }
+        else:
+            data_checks = self._heaven_line_checks(
+                data_date,
+                dashboard,
+                recent_history,
+                index_context,
+                external_sector or {},
+                external_stock or {},
+                market_mode,
+                manual_data,
+            )
+            quality_issues = [
+                f"{check['position']}·{check['layer']}：{'；'.join(check['reasons'])}"
+                for check in data_checks
+                if not check["passed"]
+            ]
+            if quality_issues:
+                chart = {
+                    "available": False,
+                    "selection_required": False,
+                    "data_trade_date": data_date,
+                    "sector": str((external_sector or {}).get("name") or sector_input or "--"),
+                    "sector_code": str((external_sector or {}).get("code") or ""),
+                    "sector_taxonomy": str((external_sector or {}).get("taxonomy") or ""),
+                    "stock": {
+                        "code": normalized_stock_code,
+                        "name": str((external_stock or {}).get("name") or "--"),
+                        "status": str((external_stock or {}).get("status") or ""),
+                    },
+                    "quality": {
+                        "status": "blocked",
+                        "issues": quality_issues,
+                        "principle": "六爻任一层缺少同日、同口径的有效数据，本系统不成卦。",
+                        "sources": self._heaven_trend_sources(
+                            data_date, index_context, external_sector, external_stock
+                        ),
+                    },
+                    "index_context": index_context,
+                }
+            else:
+                chart = build_market_hexagram(
+                    dashboard,
+                    recent_history,
+                    index_context,
+                    sector_input,
+                    normalized_stock_code,
+                    external_stock,
+                    external_sector,
+                )
+                chart["available"] = True
+                chart["selection_required"] = False
+                manual_active = any(check["status"] == "manual" for check in data_checks)
+                chart["quality"] = {
+                    "status": "manual" if manual_active else "verified",
+                    "issues": [],
+                    "principle": (
+                        "自动行情与用户补充数据均已通过同一套量化公式校验。"
+                        if manual_active
+                        else "指数、板块、个股均已通过同日同口径校验。"
+                    ),
+                    "sources": [
+                        *self._heaven_trend_sources(
+                            data_date, index_context, external_sector, external_stock
+                        ),
+                        *([{
+                            "lines": "补录爻位",
+                            "layer": "用户补充",
+                            "realtime": market_mode == "intraday",
+                            "detail": str(manual_data.get("note") or "量化数据经原公式重新计算"),
+                        }] if manual_active else []),
+                    ],
+                }
+        chart["data_checks"] = data_checks
+        chart["manual_data"] = manual_data
+        sector_phase_overrides = self.database.list_sector_phase_overrides()
+        field = build_five_phase_field(
+            normalized_date,
+            sector_phase_overrides,
+        )
+        personal_profile = self.account_personal_field(
+            normalized_date,
+            field,
+            public=True,
+        )
+        daily_fortune_reading = self.database.latest_heaven_reading(
+            self.current_user_id, "fortune", normalized_date
+        )
+        if self._legacy_truncated_heaven_reading(daily_fortune_reading):
+            daily_fortune_reading = None
+        return {
+            "trade_date": data_date,
+            "calendar_date": normalized_date,
+            "market_mode": market_mode,
+            "chart": chart,
+            "field": field,
+            "personal_profile": personal_profile,
+            "daily_fortune_reading": daily_fortune_reading,
+            "sector_phase_overrides": [
+                {"name": name, "element": element}
+                for name, element in sector_phase_overrides.items()
+            ],
+            "llm": {
+                "configured": self.llm_configured,
+                "model": self.llm_primary_model if self.llm_configured else "",
+                "fallback_configured": self.llm_fallback_configured,
+                "fallback_model": self.llm_fallback_model if self.llm_fallback_configured else "",
+            },
+        }
+
+    @staticmethod
+    def _heaven_market_mode(
+        trade_date: str,
+        dashboard: dict[str, Any],
+        now: datetime | None = None,
+    ) -> str:
+        """区分盘中、今日收盘和历史，避免把 rt_k 数据来源误当成交易状态。"""
+        now = now or datetime.now().astimezone()
+        if trade_date != now.strftime("%Y%m%d"):
+            return "historical"
+        meta = dashboard.get("meta") or {}
+        status = str(meta.get("market_status") or "").lower()
+        local_time = now.time().replace(tzinfo=None)
+        if status == "closed" or local_time > datetime.strptime("15:05", "%H:%M").time():
+            return "closed"
+        if status in {"trading", "auction", "pre_open"} or (
+            bool(meta.get("realtime"))
+            and local_time >= datetime.strptime("09:15", "%H:%M").time()
+        ):
+            return "intraday"
+        return "historical"
+
+    @staticmethod
+    def _heaven_trend_sources(
+        trade_date: str,
+        index_context: dict[str, Any],
+        sector: dict[str, Any] | None,
+        stock: dict[str, Any] | None,
+    ) -> list[dict[str, Any]]:
+        sector = sector or {}
+        stock = stock or {}
+        return [
+            {
+                "lines": "五爻、上爻",
+                "layer": "指数",
+                "source": index_context.get("source") or "unavailable",
+                "trade_date": index_context.get("trade_date") or "",
+                "realtime": bool(index_context.get("realtime")),
+                "detail": f"三大指数 {len(index_context.get('indices') or [])}/3",
+            },
+            {
+                "lines": "三爻、四爻",
+                "layer": "行业",
+                "source": sector.get("source") or "unavailable",
+                "trade_date": sector.get("trade_date") or "",
+                "realtime": bool(sector.get("realtime")),
+                "detail": (
+                    f"申万二级 {sector.get('name') or '--'} {sector.get('code') or '--'} "
+                    f"成分覆盖 {int(sector.get('quote_count') or 0)}/{int(sector.get('member_count') or 0)}"
+                ),
+            },
+            {
+                "lines": "初爻、二爻",
+                "layer": "个股",
+                "source": stock.get("data_source") or "unavailable",
+                "trade_date": stock.get("trade_date") or trade_date,
+                "realtime": bool(stock.get("realtime")),
+                "detail": (
+                    f"{stock.get('name') or '--'}；换手基准 "
+                    f"{stock.get('capital_trade_date') or '--'}"
+                ),
+            },
+        ]
+
+    @staticmethod
+    def _heaven_trend_quality_issues(
+        trade_date: str,
+        dashboard: dict[str, Any],
+        index_context: dict[str, Any],
+        sector: dict[str, Any] | None,
+        stock: dict[str, Any] | None,
+        market_mode: str = "historical",
+    ) -> list[str]:
+        issues: list[str] = []
+        intraday = market_mode == "intraday"
+        closed = market_mode == "closed"
+        if intraday:
+            meta = dashboard.get("meta") or {}
+            market_status = str(meta.get("market_status") or "")
+            now = datetime.now().astimezone()
+            try:
+                updated_at = datetime.fromisoformat(str(meta.get("updated_at") or ""))
+                if updated_at.tzinfo is None:
+                    updated_at = updated_at.replace(tzinfo=now.tzinfo)
+                snapshot_age = (now - updated_at.astimezone(now.tzinfo)).total_seconds()
+            except ValueError:
+                snapshot_age = float("inf")
+            if market_status in {"trading", "auction", "pre_open"} and snapshot_age > 120:
+                issues.append("主行情快照超过2分钟，请点击顶部刷新")
+        # 收盘后不再用 dashboard.market_status 作为阻断条件。盘后同步可能将
+        # rt_k 快照替换成同日盘后日线而不带该字段；六爻数据本身的日期、
+        # 完整性和来源校验已足以判断是否可以成卦。
+
+        index_date = str(index_context.get("trade_date") or "").replace("-", "")
+        index_rows = list(index_context.get("indices") or [])
+        index_row_dates = {
+            str(row.get("trade_date") or "").replace("-", "") for row in index_rows
+        }
+        if not index_context.get("precise") or len(index_rows) < 3:
+            issues.append("指数层缺少三大指数的有效行情")
+        elif index_date != trade_date or index_row_dates != {trade_date}:
+            issues.append("指数行情与目标交易日不一致")
+        elif intraday and not index_context.get("realtime"):
+            issues.append("盘中指数层缺少可核验的实时行情")
+        elif not intraday and (
+            index_context.get("realtime")
+            or str(index_context.get("source") or "") != "tushare"
+        ):
+            issues.append("历史/收盘指数层必须使用 Tushare 官方指数日线")
+
+        sector = sector or {}
+        sector_date = str(sector.get("trade_date") or "").replace("-", "")
+        sector_coverage = float(sector.get("coverage") or 0)
+        sector_explained_count = int(
+            sector.get("explained_count")
+            if sector.get("explained_count") is not None
+            else sector.get("quote_count") or 0
+        )
+        sector_explained_coverage = float(
+            sector.get("explained_coverage")
+            if sector.get("explained_coverage") is not None
+            else sector_coverage
+        )
+        sector_coverage_issue = _sector_coverage_issue(
+            int(sector.get("member_count") or 0),
+            int(sector.get("quote_count") or 0),
+            sector_explained_coverage,
+            sector_explained_count,
+        )
+        if not sector:
+            issues.append("行业层缺少申万二级行业归属")
+        elif sector.get("taxonomy") != "sw_l2":
+            issues.append("行业层必须使用申万二级行业分类")
+        elif sector_date != trade_date:
+            issues.append("行业行情与目标交易日不一致")
+        elif intraday and not sector.get("realtime"):
+            issues.append("盘中行业层缺少申万实时行情")
+        elif market_mode == "historical" and sector.get("realtime"):
+            issues.append("历史行业层不能使用实时快照")
+        elif closed and sector.get("realtime") and not sector.get("finalized"):
+            issues.append("收盘行业层缺少15:00最终快照")
+        if not sector.get("inner_precise", sector.get("precise")):
+            issues.append("行业内核缺少可核验的成分行情")
+        if not sector.get("outer_precise", sector.get("precise")):
+            issues.append("行业外显缺少申万官方行情")
+        if sector and sector_coverage_issue:
+            issues.append(sector_coverage_issue)
+        if sector.get("realtime") and not sector.get("relative_turnover"):
+            issues.append("行业内核缺少相对全市场换手活跃度")
+
+        stock = stock or {}
+        stock_date = str(stock.get("trade_date") or "").replace("-", "")
+        if not stock or not stock.get("code"):
+            issues.append("个股层尚未载入有效标的")
+        elif not stock.get("precise"):
+            issues.append("个股层缺少可核验的行情数据")
+        elif stock_date != trade_date:
+            issues.append("个股行情与目标交易日不一致")
+        elif intraday and not stock.get("realtime"):
+            issues.append("盘中个股层不是 rt_k 实时行情")
+        elif not intraday and (
+            stock.get("realtime")
+            or str(stock.get("data_source") or "") != "tushare"
+        ):
+            issues.append("历史/收盘个股层必须使用 Tushare 官方日线")
+        if intraday and stock and not stock.get("turnover_source"):
+            issues.append("个股内核缺少可核验的实时换手率")
+        elif intraday and stock.get("turnover_source") == "unavailable":
+            issues.append("个股内核缺少流通股本，无法计算实时换手率")
+        if intraday and stock.get("activity_source") == "unavailable":
+            issues.append("个股内核缺少近5日量能基准")
+        elif intraday and not stock.get("activity_source"):
+            issues.append("个股内核缺少同时间进度量能")
+        return issues

backend/features/market/insights_auction.py

@@ -0,0 +1,221 @@
+from __future__ import annotations
+
+import copy
+from datetime import datetime
+from statistics import median
+from typing import Any
+
+from backend.data.numbers import non_nan_number as _number
+from backend.data.providers.tushare_client import TushareError
+from backend.features.market.insights_context import _display_date
+
+
+class MarketAuctionInsightsMixin:
+    def auction_center(
+        self,
+        requested_date: str,
+        force: bool = False,
+        user_id: int = 0,
+    ) -> dict[str, Any]:
+        trade_date, previous_date = self._trade_context(requested_date)
+        session = self._auction_session(requested_date, trade_date)
+        phase = str(session["phase"])
+        ifind_ready = bool(self.ifind and self.ifind.configured)
+        live_dynamic = phase == "observing" and ifind_ready
+        use_ifind_snapshot = phase in {"observing", "selection", "finalized"} and ifind_ready
+        data_date = previous_date if phase == "pending" or (phase == "observing" and not live_dynamic) else trade_date
+        carried_forward = data_date != trade_date
+        cache_key = data_date
+        if not force and not live_dynamic:
+            cached = self.database.get_data_snapshot("auction_center_v6", cache_key)
+            if cached:
+                result = copy.deepcopy(cached)
+                result["meta"] = {
+                    **result.get("meta", {}),
+                    **session,
+                    "requested_date": _display_date(requested_date),
+                    "trade_date": _display_date(data_date),
+                    "carried_forward": carried_forward,
+                    "available": bool((result.get("summary") or {}).get("stock_count")),
+                    "cached": True,
+                }
+                return self._with_auction_watchlist(result, data_date, user_id)
+
+        if use_ifind_snapshot:
+            rows = self._dynamic_auction_rows(data_date, previous_date, user_id)
+        else:
+            rows = []
+        if not rows and not live_dynamic:
+            try:
+                rows = self.client.query("stk_auction", {"trade_date": data_date})
+            except TushareError:
+                rows = self.database.auction_factors_for_date(data_date)
+        if not rows:
+            return {
+                "meta": {
+                    **session,
+                    "requested_date": _display_date(requested_date),
+                    "trade_date": _display_date(data_date),
+                    "carried_forward": carried_forward,
+                    "available": False,
+                    "cached": False,
+                    "notice": "该交易日暂无可用竞价快照",
+                    "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+                },
+                "summary": {
+                    "stock_count": 0, "up_count": 0, "down_count": 0,
+                    "limit_open_count": 0, "strong_open_count": 0,
+                    "median_change": 0, "amount_billion": 0,
+                    "candidate_count": 0, "focus_count": 0, "one_price_count": 0,
+                },
+                "expectations": {"超预期": 0, "符合预期": 0, "低于预期": 0},
+                "candidate_meta": {"baseline_date": _display_date(previous_date)},
+                "themes": {"carry": [], "new_themes": []},
+                "amount_history": self._auction_amount_history(data_date),
+                "news_feedback": {"available": False, "message": "隔夜消息反馈暂不可用"},
+                "focus_rows": [], "one_price_rows": [], "rows": [],
+                "watchlist_rows": [], "watchlist_missing_count": 0,
+            }
+
+        master = self._stock_master()
+        try:
+            limit_rows = self.client.query(
+                "stk_limit",
+                {"trade_date": data_date},
+                "trade_date,ts_code,up_limit,down_limit",
+            )
+        except TushareError:
+            limit_rows = []
+        limit_map = {str(item.get("ts_code") or ""): item for item in limit_rows}
+        normalized = []
+        for row in rows:
+            ts_code = str(row.get("ts_code") or "")
+            stock = master.get(ts_code)
+            price = _number(row.get("price"))
+            pre_close = _number(row.get("pre_close"))
+            list_date = str((stock or {}).get("list_date") or "")
+            if (
+                not stock
+                or price <= 0
+                or pre_close <= 0
+                or (list_date and list_date >= data_date)
+            ):
+                continue
+            change = (price / pre_close - 1) * 100
+            amount_million = _number(row.get("amount")) / 1_000_000
+            volume_ratio = _number(row.get("volume_ratio"))
+            turnover_rate = _number(row.get("turnover_rate"))
+            up_limit = _number((limit_map.get(ts_code) or {}).get("up_limit"))
+            is_one_price = bool(
+                up_limit > 0 and abs(price - up_limit) <= max(0.001, up_limit * 0.00005)
+            )
+            normalized.append(
+                {
+                    "code": str(stock.get("code") or ts_code.split(".")[0]),
+                    "ts_code": ts_code,
+                    "name": str(stock.get("name") or "--"),
+                    "sector": str(stock.get("industry") or "其他"),
+                    "price": round(price, 2),
+                    "pre_close": round(pre_close, 2),
+                    "change": round(change, 2),
+                    "volume_ten_thousand": round(_number(row.get("vol")) / 10_000, 2),
+                    "amount_million": round(amount_million, 2),
+                    "turnover_rate": round(turnover_rate, 4),
+                    "volume_ratio": round(volume_ratio, 2),
+                    "up_limit": round(up_limit, 2) if up_limit else None,
+                    "is_one_price": is_one_price,
+                    "signal": (
+                        "竞价涨停" if change >= 9.5 else
+                        "强势高开" if change >= 3 else
+                        "高开" if change > 0.2 else
+                        "深度低开" if change <= -3 else
+                        "低开" if change < -0.2 else "平开"
+                    ),
+                }
+            )
+        normalized.sort(key=lambda item: (item["amount_million"], item["volume_ratio"]), reverse=True)
+        self.database.upsert_auction_factors(rows)
+        changes = [item["change"] for item in normalized]
+        total = len(normalized)
+        _, baseline_date = self._trade_context(data_date)
+        candidates, candidate_meta, focus_rows = self._auction_candidates(normalized, baseline_date)
+        candidate_map = {str(item.get("code") or ""): item for item in candidates}
+        one_price_rows = []
+        for row in normalized:
+            if not row.get("is_one_price"):
+                continue
+            enriched = candidate_map.get(str(row.get("code") or ""), {})
+            one_price_rows.append(
+                {
+                    **row,
+                    **enriched,
+                    "attention_score": None,
+                    "expectation": "",
+                    "expected_change": None,
+                    "expectation_reason": "竞价价格封于当日涨停价，已从普通异动评分中隔离",
+                }
+            )
+        one_price_codes = {str(item.get("code") or "") for item in one_price_rows}
+        candidates = [item for item in candidates if str(item.get("code") or "") not in one_price_codes]
+        focus_rows = [item for item in focus_rows if str(item.get("code") or "") not in one_price_codes]
+        one_price_rows.sort(
+            key=lambda item: (
+                bool(item.get("is_market_core")),
+                _number(item.get("prior_streak")),
+                _number(item.get("amount_million")),
+            ),
+            reverse=True,
+        )
+        expectations = {
+            label: sum(item.get("expectation") == label for item in candidates)
+            for label in ("超预期", "符合预期", "低于预期")
+        }
+        prior_snapshot = self.database.get_snapshot(baseline_date) or {}
+        themes = self._auction_theme_evidence(prior_snapshot, candidates + one_price_rows)
+        self._ensure_auction_amount_history(data_date)
+        amount_history = self._auction_amount_history(data_date)
+        prior_amounts = [item["amount_billion"] for item in amount_history[:-1]]
+        current_amount = round(sum(item["amount_million"] for item in normalized) / 100, 2)
+        previous_amount = prior_amounts[-1] if prior_amounts else 0
+        five_day_amounts = prior_amounts[-5:]
+        five_day_average = sum(five_day_amounts) / len(five_day_amounts) if five_day_amounts else 0
+        result = {
+            "meta": {
+                "requested_date": _display_date(requested_date),
+                "trade_date": _display_date(data_date),
+                "carried_forward": carried_forward,
+                "available": bool(normalized),
+                **session,
+                "cached": False,
+                "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+            },
+            "summary": {
+                "stock_count": total,
+                "up_count": sum(value > 0.2 for value in changes),
+                "down_count": sum(value < -0.2 for value in changes),
+                "limit_open_count": len(one_price_rows),
+                "strong_open_count": sum(value >= 3 for value in changes),
+                "median_change": round(median(changes), 2) if changes else 0,
+                "amount_billion": current_amount,
+                "amount_change_previous": round((current_amount / previous_amount - 1) * 100, 1) if previous_amount else None,
+                "amount_change_5d": round((current_amount / five_day_average - 1) * 100, 1) if five_day_average else None,
+                "candidate_count": len(candidates),
+                "focus_count": len(focus_rows),
+                "one_price_count": len(one_price_rows),
+            },
+            "expectations": expectations,
+            "candidate_meta": candidate_meta,
+            "themes": themes,
+            "amount_history": amount_history,
+            "news_feedback": {
+                "available": False,
+                "message": "隔夜消息反馈暂不可用",
+                "detail": "待稳定的新闻与公告数据接入后开放",
+            },
+            "focus_rows": focus_rows,
+            "one_price_rows": one_price_rows,
+            "rows": candidates,
+        }
+        if not live_dynamic:
+            self.database.save_data_snapshot("auction_center_v6", cache_key, "market", result)
+        return self._with_auction_watchlist(result, data_date, user_id)

backend/features/market/insights_auction_data.py

@@ -0,0 +1,318 @@
+from __future__ import annotations
+
+import copy
+from datetime import datetime, time as dt_time, timedelta
+from typing import Any
+
+from backend.data.numbers import non_nan_number as _number
+from backend.data.providers.ifind_client import IfindError
+from backend.data.providers.tushare_client import TushareError
+from backend.features.market.insights_context import CHINA_TIMEZONE, _display_date
+
+
+class MarketAuctionDataMixin:
+    def _auction_session(self, requested_date: str, trade_date: str) -> dict[str, Any]:
+        now = self._now_provider()
+        if now.tzinfo is None:
+            now = now.replace(tzinfo=CHINA_TIMEZONE)
+        else:
+            now = now.astimezone(CHINA_TIMEZONE)
+        requested = str(requested_date or "").replace("-", "")
+        today = now.strftime("%Y%m%d")
+        if requested != today or trade_date != today:
+            return {
+                "phase": "archive",
+                "actionable": False,
+                "next_transition_at": "",
+            }
+
+        local_time = now.time().replace(tzinfo=None)
+        transitions = (
+            (dt_time(9, 15), "pending", dt_time(9, 15)),
+            (dt_time(9, 25), "observing", dt_time(9, 25)),
+            (dt_time(9, 30), "selection", dt_time(9, 30)),
+        )
+        for boundary, phase, next_boundary in transitions:
+            if local_time < boundary:
+                transition = now.replace(
+                    hour=next_boundary.hour,
+                    minute=next_boundary.minute,
+                    second=0,
+                    microsecond=0,
+                )
+                return {
+                    "phase": phase,
+                    "actionable": phase == "selection",
+                    "next_transition_at": transition.isoformat(timespec="seconds"),
+                }
+        return {
+            "phase": "finalized",
+            "actionable": False,
+            "next_transition_at": "",
+        }
+
+    def _auction_amount_history(self, trade_date: str) -> list[dict[str, Any]]:
+        dates = self.database.auction_factor_dates(trade_date, 10)
+        stock_list_dates = {
+            str(item.get("ts_code") or ""): str(item.get("list_date") or "")
+            for item in self.database.list_stock_master()
+            if item.get("ts_code")
+        }
+        history = []
+        for current_date in dates:
+            rows = [
+                row for row in self.database.auction_factors_for_date(current_date)
+                if (
+                    str(row.get("ts_code") or "") in stock_list_dates
+                    and (
+                        not stock_list_dates[str(row.get("ts_code") or "")]
+                        or stock_list_dates[str(row.get("ts_code") or "")] < current_date
+                    )
+                )
+            ]
+            history.append(
+                {
+                    "trade_date": _display_date(current_date),
+                    "amount_billion": round(sum(_number(row.get("amount")) for row in rows) / 100_000_000, 2),
+                    "stock_count": len(rows),
+                }
+            )
+        return history
+
+    def _ensure_auction_amount_history(self, trade_date: str, target_days: int = 10) -> None:
+        existing = set(self.database.auction_factor_dates(trade_date, target_days + 5))
+        if len(existing) >= target_days:
+            return
+        end = datetime.strptime(trade_date, "%Y%m%d")
+        start = (end - timedelta(days=35)).strftime("%Y%m%d")
+        try:
+            calendar = self.client.query(
+                "trade_cal",
+                {
+                    "exchange": "SSE",
+                    "start_date": start,
+                    "end_date": trade_date,
+                    "is_open": 1,
+                },
+                "cal_date,is_open",
+            )
+        except TushareError:
+            return
+        dates = sorted(
+            str(item.get("cal_date") or "")
+            for item in calendar
+            if int(_number(item.get("is_open"))) == 1 and item.get("cal_date")
+        )[-target_days:]
+        for current_date in dates:
+            if current_date in existing:
+                continue
+            try:
+                rows = self.client.query(
+                    "stk_auction",
+                    {"trade_date": current_date},
+                    "ts_code,trade_date,vol,price,amount,pre_close,turnover_rate,volume_ratio,float_share",
+                )
+            except TushareError:
+                break
+            if rows:
+                self.database.upsert_auction_factors(rows)
+                existing.add(current_date)
+
+    def _with_auction_watchlist(
+        self,
+        result: dict[str, Any],
+        trade_date: str,
+        user_id: int,
+    ) -> dict[str, Any]:
+        personalized = copy.deepcopy(result)
+        if not user_id:
+            personalized["watchlist_rows"] = []
+            personalized["watchlist_missing_count"] = 0
+            return personalized
+        watched = self.database.list_watchlist(user_id)
+        if not watched:
+            personalized["watchlist_rows"] = []
+            personalized["watchlist_missing_count"] = 0
+            return personalized
+
+        public_rows = {
+            str(item.get("code") or ""): item
+            for item in (
+                list(personalized.get("rows") or [])
+                + list(personalized.get("one_price_rows") or [])
+            )
+        }
+        factors = {
+            str(item.get("ts_code") or "").split(".")[0]: item
+            for item in self.database.auction_factors_for_date(trade_date)
+        }
+        master = {
+            str(item.get("ts_code") or "").split(".")[0]: item
+            for item in self.database.list_stock_master()
+        }
+        rows = []
+        missing = 0
+        for item in watched:
+            code = str(item.get("code") or "")
+            if code in public_rows:
+                rows.append({**public_rows[code], "is_watchlist": True})
+                continue
+            factor = factors.get(code)
+            if not factor:
+                missing += 1
+                rows.append(
+                    {
+                        "code": code,
+                        "name": str(item.get("name") or "--"),
+                        "sector": str(item.get("sector") or "其他"),
+                        "available": False,
+                        "is_watchlist": True,
+                    }
+                )
+                continue
+            stock = master.get(code, {})
+            price = _number(factor.get("price"))
+            pre_close = _number(factor.get("pre_close"))
+            change = (price / pre_close - 1) * 100 if price > 0 and pre_close > 0 else 0
+            row = {
+                "code": code,
+                "ts_code": str(factor.get("ts_code") or ""),
+                "name": str(item.get("name") or stock.get("name") or "--"),
+                "sector": str(item.get("sector") or stock.get("industry") or "其他"),
+                "price": round(price, 2),
+                "pre_close": round(pre_close, 2),
+                "change": round(change, 2),
+                "amount_million": round(_number(factor.get("amount")) / 1_000_000, 2),
+                "turnover_rate": round(_number(factor.get("turnover_rate")), 4),
+                "volume_ratio": round(_number(factor.get("volume_ratio")), 2),
+                "candidate_sources": ["我的自选"],
+                "source_label": "我的自选",
+                "prior_streak": 0,
+                "concepts": [],
+                "expected_change": 0.0,
+                "core_tags": [],
+                "is_market_core": False,
+                "is_watchlist": True,
+                "available": True,
+            }
+            actual_strength = change + self._auction_confirmation(row)
+            row["actual_strength"] = round(actual_strength, 2)
+            row["expectation"] = self._expectation_label(actual_strength, 0.0)
+            row["attention_score"] = self._attention_score(row, 0.0, [], ["我的自选"], 0, False)
+            direction = "高于" if change > 0 else "低于" if change < 0 else "贴合"
+            row["expectation_reason"] = f"自选观察；竞价涨幅{direction}个人观察基准{abs(change):.1f}个百分点，量比{row['volume_ratio']:.2f}"
+            rows.append(row)
+        rows.sort(
+            key=lambda row: (bool(row.get("available", True)), _number(row.get("attention_score"))),
+            reverse=True,
+        )
+        personalized["watchlist_rows"] = rows
+        personalized["watchlist_missing_count"] = missing
+        return personalized
+
+    def _dynamic_auction_rows(
+        self,
+        trade_date: str,
+        baseline_date: str,
+        user_id: int,
+    ) -> list[dict[str, Any]]:
+        if not self.ifind or not self.ifind.configured:
+            return []
+        master = self._stock_master()
+        placeholders = [
+            {
+                "code": str(item.get("code") or ts_code.split(".")[0]),
+                "ts_code": ts_code,
+                "name": str(item.get("name") or "--"),
+                "sector": str(item.get("industry") or "其他"),
+            }
+            for ts_code, item in master.items()
+        ]
+        candidates, _, _ = self._auction_candidates(placeholders, baseline_date)
+        selected_codes = {
+            str(item.get("ts_code") or "")
+            for item in candidates
+            if item.get("ts_code")
+        }
+        if user_id:
+            watched = {str(item.get("code") or "") for item in self.database.list_watchlist(user_id)}
+            selected_codes.update(
+                ts_code for ts_code in master if ts_code.split(".")[0] in watched
+            )
+        selected_codes.discard("")
+        if not selected_codes:
+            return []
+
+        display_date = _display_date(trade_date)
+        now = self._now_provider()
+        if now.tzinfo is None:
+            now = now.replace(tzinfo=CHINA_TIMEZONE)
+        else:
+            now = now.astimezone(CHINA_TIMEZONE)
+        end_time = min(now.time().replace(tzinfo=None), dt_time(9, 25))
+        end_stamp = f"{display_date} {end_time.strftime('%H:%M:%S')}"
+        start_stamp = f"{display_date} 09:15:00"
+        snapshot_rows: list[dict[str, Any]] = []
+        ordered_codes = sorted(selected_codes)
+        for index in range(0, len(ordered_codes), 80):
+            try:
+                snapshot_rows.extend(
+                    self.ifind.snapshots(
+                        ordered_codes[index:index + 80],
+                        [
+                            "latest", "volume", "amount", "preClose",
+                            "bid1", "bidSize1", "ask1", "askSize1",
+                        ],
+                        start_stamp,
+                        end_stamp,
+                        cache_ttl=8,
+                    )
+                )
+            except IfindError:
+                continue
+
+        latest: dict[str, dict[str, Any]] = {}
+        for row in snapshot_rows:
+            ts_code = str(row.get("thscode") or "")
+            previous = latest.get(ts_code) or {}
+            if (
+                ts_code
+                and _number(row.get("latest")) > 0
+                and str(row.get("time") or "") >= str(previous.get("time") or "")
+            ):
+                latest[ts_code] = row
+        prior_factors = {
+            str(item.get("ts_code") or ""): item
+            for item in self.database.auction_factors_for_date(baseline_date)
+        }
+        normalized = []
+        for ts_code, row in latest.items():
+            price = _number(row.get("latest"))
+            pre_close = _number(row.get("preClose"))
+            volume = _number(row.get("volume"))
+            bid_size = _number(row.get("bidSize1"))
+            ask_size = _number(row.get("askSize1"))
+            if volume <= 0 and bid_size > 0 and ask_size > 0:
+                volume = min(bid_size, ask_size)
+            amount = _number(row.get("amount"))
+            if amount <= 0 and price > 0 and volume > 0:
+                amount = price * volume
+            prior_volume = _number((prior_factors.get(ts_code) or {}).get("vol"))
+            normalized.append(
+                {
+                    "ts_code": ts_code,
+                    "trade_date": trade_date,
+                    "vol": volume,
+                    "price": price,
+                    "amount": amount,
+                    "pre_close": pre_close,
+                    "turnover_rate": 0,
+                    "volume_ratio": volume / prior_volume if prior_volume > 0 else 0,
+                    "float_share": 0,
+                    "bid_size1": bid_size,
+                    "ask_size1": ask_size,
+                    "snapshot_time": str(row.get("time") or ""),
+                    "dynamic": True,
+                }
+            )
+        return normalized

backend/features/market/insights_auction_scoring.py

@@ -0,0 +1,355 @@
+from __future__ import annotations
+
+from statistics import median
+from typing import Any
+
+from backend.data.numbers import non_nan_number as _number
+from backend.features.market.insights_context import _display_date
+
+
+class MarketAuctionScoringMixin:
+    @staticmethod
+    def _expectation_label(actual_strength: float, expected_change: float) -> str:
+        difference = actual_strength - expected_change
+        if difference >= 1.5:
+            return "超预期"
+        if difference <= -1.5:
+            return "低于预期"
+        return "符合预期"
+
+    @staticmethod
+    def _auction_confirmation(row: dict[str, Any]) -> float:
+        volume_ratio = _number(row.get("volume_ratio"))
+        turnover_rate = _number(row.get("turnover_rate"))
+        amount_million = _number(row.get("amount_million"))
+        return (
+            (0.6 if volume_ratio >= 2 else 0.3 if volume_ratio >= 1.2 else -0.5 if volume_ratio < 0.6 else 0)
+            + (0.25 if turnover_rate >= 0.15 else -0.25 if turnover_rate < 0.03 else 0)
+            + (0.3 if amount_million >= 20 else 0.15 if amount_million >= 5 else -0.3 if amount_million < 1 else 0)
+        )
+
+    @staticmethod
+    def _attention_score(
+        row: dict[str, Any],
+        expected_change: float,
+        core_tags: list[str],
+        sources: list[str],
+        prior_streak: int,
+        strong_sector: bool,
+    ) -> float:
+        if core_tags:
+            identity_score = 35.0
+        elif prior_streak >= 2:
+            identity_score = 27.0
+        elif any(source in {"昨日涨停", "昨日炸板"} for source in sources):
+            identity_score = 21.0
+        else:
+            identity_score = 14.0
+        deviation_score = min(30.0, abs(_number(row.get("change")) - expected_change) * 5)
+        volume_score = min(10.0, max(0.0, _number(row.get("volume_ratio"))) / 2 * 10)
+        amount_score = min(6.0, max(0.0, _number(row.get("amount_million"))) / 10 * 6)
+        turnover_score = min(4.0, max(0.0, _number(row.get("turnover_rate"))) / 0.2 * 4)
+        theme_score = 15.0 if strong_sector else 7.0 if row.get("concepts") else 0.0
+        return round(min(100.0, identity_score + deviation_score + volume_score + amount_score + turnover_score + theme_score), 1)
+
+    def _auction_candidates(
+        self,
+        rows: list[dict[str, Any]],
+        baseline_date: str,
+    ) -> tuple[list[dict[str, Any]], dict[str, Any], list[dict[str, Any]]]:
+        """Build a narrow, explainable universe from prior limits, breaks and top-20 hot lists."""
+        snapshot = self.database.get_snapshot(baseline_date) or {}
+        prior_limits = list(snapshot.get("limits") or [])
+        prior_broken = list(snapshot.get("broken") or [])
+        prior_sectors = list(snapshot.get("sectors") or [])
+        strong_sector_names = {
+            str(item.get("name") or "") for item in prior_sectors[:5] if item.get("name")
+        }
+        ths_rows, dc_rows, errors = self._hot_rows(baseline_date)
+        candidates: dict[str, dict[str, Any]] = {}
+        core_tags: dict[str, set[str]] = {}
+
+        def ensure_candidate(item: dict[str, Any]) -> dict[str, Any] | None:
+            code = str(item.get("code") or str(item.get("ts_code") or "").split(".")[0])
+            if not code:
+                return None
+            return candidates.setdefault(
+                code,
+                {
+                    "sources": [],
+                    "streak": 0,
+                    "sector": str(item.get("sector") or "其他"),
+                    "name": str(item.get("name") or item.get("ts_name") or "--"),
+                    "concepts": [],
+                    "ths_rank": None,
+                    "dc_rank": None,
+                },
+            )
+
+        for item in prior_limits:
+            candidate = ensure_candidate(item)
+            if candidate is None:
+                continue
+            candidate["sources"].append("昨日涨停")
+            candidate["streak"] = max(1, int(_number(item.get("streak"), 1)))
+
+        for item in prior_broken:
+            candidate = ensure_candidate(item)
+            if candidate is not None and "昨日炸板" not in candidate["sources"]:
+                candidate["sources"].append("昨日炸板")
+
+        limit_streaks = [max(1, int(_number(item.get("streak"), 1))) for item in prior_limits]
+        highest_streak = max(limit_streaks, default=0)
+        for item in prior_limits:
+            code = str(item.get("code") or "")
+            streak = max(1, int(_number(item.get("streak"), 1)))
+            if streak >= 3:
+                core_tags.setdefault(code, set()).add("三板以上")
+            if highest_streak and streak == highest_streak:
+                core_tags.setdefault(code, set()).add("市场最高板")
+
+        for sector in prior_sectors[:5]:
+            name = str(sector.get("name") or "")
+            members = [item for item in prior_limits if str(item.get("sector") or "其他") == name]
+            if not members:
+                continue
+            leader = max(
+                members,
+                key=lambda item: (
+                    int(_number(item.get("streak"), 1)),
+                    _number(item.get("amount_billion")),
+                    -_number(item.get("open_times")),
+                ),
+            )
+            core_tags.setdefault(str(leader.get("code") or ""), set()).add("题材核心")
+
+        leadership = sorted(
+            prior_limits,
+            key=lambda item: (
+                int(_number(item.get("streak"), 1)),
+                str(item.get("sector") or "") in strong_sector_names,
+                _number(item.get("amount_billion")),
+            ),
+            reverse=True,
+        )
+        if leadership:
+            core_tags.setdefault(str(leadership[0].get("code") or ""), set()).add("市场领涨")
+
+        hot_records: dict[str, dict[str, Any]] = {}
+
+        for source, hot_rows, data_type in (
+            ("同花顺热榜", ths_rows, "热股"),
+            ("东方财富热榜", dc_rows, "A股市场"),
+        ):
+            for item in hot_rows:
+                if str(item.get("data_type") or "") != data_type:
+                    continue
+                ts_code = str(item.get("ts_code") or "")
+                code = ts_code.split(".")[0]
+                rank = max(1, int(_number(item.get("rank"), 9999)))
+                if not code or rank > 20:
+                    continue
+                hot = hot_records.setdefault(
+                    code,
+                    {
+                        "name": str(item.get("ts_name") or "--"),
+                        "concepts": [],
+                        "ths_rank": None,
+                        "dc_rank": None,
+                    },
+                )
+                hot["ths_rank" if source == "同花顺热榜" else "dc_rank"] = rank
+                if source == "同花顺热榜":
+                    hot["concepts"] = self._parse_concepts(item.get("concept"))
+
+        ranked_hot = sorted(
+            hot_records.items(),
+            key=lambda pair: (
+                ((21 - (pair[1].get("ths_rank") or 21)) / 20)
+                + ((21 - (pair[1].get("dc_rank") or 21)) / 20)
+                + (0.35 if pair[1].get("ths_rank") and pair[1].get("dc_rank") else 0)
+            ),
+            reverse=True,
+        )
+        for code, _ in ranked_hot[:5]:
+            core_tags.setdefault(code, set()).add("人气前5")
+
+        for code, hot in hot_records.items():
+            ranks = [rank for rank in (hot.get("ths_rank"), hot.get("dc_rank")) if isinstance(rank, int)]
+            dual = len(ranks) == 2
+            if not ranks or (min(ranks) > 10 and not dual and code not in candidates and code not in core_tags):
+                continue
+            candidate = candidates.setdefault(
+                code,
+                {
+                    "sources": [],
+                    "streak": 0,
+                    "sector": "其他",
+                    "name": hot["name"],
+                    "concepts": [],
+                    "ths_rank": None,
+                    "dc_rank": None,
+                },
+            )
+            candidate["ths_rank"] = hot.get("ths_rank")
+            candidate["dc_rank"] = hot.get("dc_rank")
+            candidate["concepts"] = hot.get("concepts") or []
+            if hot.get("ths_rank") and "同花顺热榜" not in candidate["sources"]:
+                candidate["sources"].append("同花顺热榜")
+            if hot.get("dc_rank") and "东方财富热榜" not in candidate["sources"]:
+                candidate["sources"].append("东方财富热榜")
+
+        normalized = []
+        for row in rows:
+            candidate = candidates.get(str(row.get("code") or ""))
+            if not candidate:
+                continue
+            streak = int(candidate["streak"])
+            expected_change = {1: 1.5, 2: 3.0, 3: 4.0}.get(streak, 5.0 if streak else 0.5)
+            ranks = [
+                rank for rank in (candidate.get("ths_rank"), candidate.get("dc_rank"))
+                if isinstance(rank, int)
+            ]
+            if len(ranks) == 2:
+                expected_change += 0.8
+            elif ranks:
+                best_rank = min(ranks)
+                expected_change += 0.7 if best_rank <= 10 else 0.4 if best_rank <= 30 else 0.2
+            expected_change = min(expected_change, 6.5)
+
+            volume_ratio = _number(row.get("volume_ratio"))
+            turnover_rate = _number(row.get("turnover_rate"))
+            amount_million = _number(row.get("amount_million"))
+            confirmation = self._auction_confirmation(row)
+            actual_strength = _number(row.get("change")) + confirmation
+            label = self._expectation_label(actual_strength, expected_change)
+            is_broken = "昨日炸板" in candidate["sources"] and "昨日涨停" not in candidate["sources"]
+            identity = f"昨日{streak}板" if streak > 1 else "昨日首板" if streak == 1 else "昨日炸板" if is_broken else "人气榜标的"
+            popularity = "，双榜共识" if len(ranks) == 2 else "，热榜靠前" if ranks and min(ranks) <= 10 else ""
+            difference = _number(row.get("change")) - expected_change
+            direction = "高于" if difference > 0 else "低于" if difference < 0 else "贴合"
+            reason = (
+                f"{identity}{popularity}；竞价涨幅{direction}预期中枢"
+                f"{abs(difference):.1f}个百分点，量比{volume_ratio:.2f}"
+            )
+            tags = sorted(core_tags.get(str(row.get("code") or ""), set()))
+            scored_row = {
+                **row,
+                "concepts": candidate["concepts"],
+            }
+            attention_score = self._attention_score(
+                scored_row,
+                expected_change,
+                tags,
+                candidate["sources"],
+                streak,
+                str(candidate.get("sector") or row.get("sector") or "") in strong_sector_names,
+            )
+            normalized.append(
+                {
+                    **scored_row,
+                    "sector": candidate["sector"] if candidate["sector"] != "其他" else row.get("sector", "其他"),
+                    "candidate_sources": candidate["sources"],
+                    "source_label": " · ".join(candidate["sources"]),
+                    "prior_streak": streak,
+                    "concepts": candidate["concepts"],
+                    "expected_change": round(expected_change, 2),
+                    "actual_strength": round(actual_strength, 2),
+                    "expectation": label,
+                    "attention_score": attention_score,
+                    "core_tags": tags,
+                    "is_market_core": bool(tags),
+                    "expectation_reason": reason,
+                }
+            )
+        normalized.sort(key=lambda item: (_number(item.get("attention_score")), _number(item.get("amount_million"))), reverse=True)
+        matched_top = {
+            str(item.get("code") or "")
+            for item in sorted(
+                (item for item in normalized if item.get("expectation") == "符合预期"),
+                key=lambda item: _number(item.get("attention_score")),
+                reverse=True,
+            )[:20]
+        }
+        focus_candidates = [
+            item for item in normalized
+            if item.get("is_market_core")
+            or (_number(item.get("attention_score")) >= 55 and item.get("expectation") != "符合预期")
+            or str(item.get("code") or "") in matched_top
+        ]
+        mandatory = [item for item in focus_candidates if item.get("is_market_core")]
+        mandatory_codes = {str(item.get("code") or "") for item in mandatory}
+        optional = [item for item in focus_candidates if str(item.get("code") or "") not in mandatory_codes]
+        focus_rows = sorted(mandatory, key=lambda item: _number(item.get("attention_score")), reverse=True)
+        focus_rows.extend(optional[:max(0, 30 - len(focus_rows))])
+        focus_rows.sort(key=lambda item: _number(item.get("attention_score")), reverse=True)
+        return normalized, {
+            "baseline_date": _display_date(baseline_date),
+            "prior_limit_count": len(prior_limits),
+            "prior_broken_count": len(prior_broken),
+            "hot_candidate_count": sum(
+                any(source in {"同花顺热榜", "东方财富热榜"} for source in item["sources"])
+                for item in candidates.values()
+            ),
+            "core_count": sum(bool(item.get("is_market_core")) for item in normalized),
+            "notice": "；".join(errors),
+        }, focus_rows
+
+    @staticmethod
+    def _auction_theme_evidence(
+        prior_snapshot: dict[str, Any],
+        candidate_rows: list[dict[str, Any]],
+    ) -> dict[str, list[dict[str, Any]]]:
+        prior_sectors = list(prior_snapshot.get("sectors") or [])
+        carry = []
+        for sector in prior_sectors[:10]:
+            name = str(sector.get("name") or "其他")
+            matched = [row for row in candidate_rows if str(row.get("sector") or "其他") == name]
+            changes = [_number(row.get("change")) for row in matched]
+            middle = median(changes) if changes else -10.0
+            positive_rate = sum(value > 0.2 for value in changes) / len(changes) * 100 if changes else 0.0
+            if middle >= 2 and positive_rate >= 60:
+                status = "强承接"
+            elif middle >= 0 and positive_rate >= 50:
+                status = "有承接"
+            elif middle > -2:
+                status = "分歧"
+            else:
+                status = "承接弱"
+            carry.append(
+                {
+                    "name": name,
+                    "status": status,
+                    "prior_limit_count": int(_number(sector.get("count"))),
+                    "leader": str(sector.get("leader") or "--"),
+                    "matched_count": len(matched),
+                    "median_change": round(middle, 2) if matched else None,
+                    "positive_rate": round(positive_rate, 1),
+                    "amount_million": round(sum(_number(row.get("amount_million")) for row in matched), 2),
+                }
+            )
+
+        concept_groups: dict[str, list[dict[str, Any]]] = {}
+        prior_names = {str(item.get("name") or "") for item in prior_sectors}
+        for row in candidate_rows:
+            for concept in row.get("concepts") or []:
+                if concept and concept not in prior_names:
+                    concept_groups.setdefault(str(concept), []).append(row)
+        new_themes = []
+        for name, members in concept_groups.items():
+            unique = {str(item.get("code") or ""): item for item in members}
+            values = list(unique.values())
+            changes = [_number(item.get("change")) for item in values]
+            if len(values) < 2 or median(changes) < 2 or sum(value > 0.2 for value in changes) / len(values) < 0.67:
+                continue
+            new_themes.append(
+                {
+                    "name": name,
+                    "stock_count": len(values),
+                    "median_change": round(median(changes), 2),
+                    "amount_million": round(sum(_number(item.get("amount_million")) for item in values), 2),
+                    "leaders": [str(item.get("name") or "--") for item in sorted(values, key=lambda value: _number(value.get("change")), reverse=True)[:3]],
+                }
+            )
+        new_themes.sort(key=lambda item: (item["stock_count"], item["median_change"], item["amount_million"]), reverse=True)
+        return {"carry": carry, "new_themes": new_themes[:8]}

backend/features/market/insights_context.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import json
+from datetime import datetime, timedelta, timezone
+from typing import TYPE_CHECKING, Any, Callable
+
+from backend.data.providers.ifind_client import IfindHttpClient
+from backend.data.providers.tushare_client import TushareClient, TushareError
+
+if TYPE_CHECKING:
+    from database import ReviewDatabase
+
+
+CHINA_TIMEZONE = timezone(timedelta(hours=8))
+
+def _display_date(value: str) -> str:
+    text = str(value or "").replace("-", "")
+    if len(text) != 8:
+        return str(value or "")
+    return f"{text[:4]}-{text[4:6]}-{text[6:]}"
+
+
+class MarketInsightsContextMixin:
+    def __init__(
+        self,
+        database: ReviewDatabase,
+        client: TushareClient,
+        now_provider: Callable[[], datetime] | None = None,
+        ifind: IfindHttpClient | None = None,
+    ) -> None:
+        self.database = database
+        self.client = client
+        self._now_provider = now_provider or (lambda: datetime.now(CHINA_TIMEZONE))
+        self.ifind = ifind
+
+    def _trade_context(self, requested_date: str) -> tuple[str, str]:
+        """Resolve trading dates without making cached feature pages depend on Tushare uptime."""
+        requested = str(requested_date or "").replace("-", "")
+        try:
+            return self.client.resolve_trade_context(requested)
+        except TushareError:
+            latest = self.database.get_latest_real_snapshot(requested) or {}
+            trade_date = str(
+                (latest.get("meta") or {}).get("trade_date")
+                or latest.get("_snapshot_date")
+                or requested
+            ).replace("-", "")
+            previous = self.database.get_latest_real_snapshot(trade_date, strictly_before=True) or {}
+            previous_date = str(
+                (previous.get("meta") or {}).get("trade_date")
+                or previous.get("_snapshot_date")
+                or ""
+            ).replace("-", "")
+            return trade_date, previous_date
+
+    def _latest_feature_snapshot(self, kind: str, trade_date: str) -> dict[str, Any] | None:
+        return self.database.get_latest_data_snapshot(kind, "", trade_date)
+
+    def _stock_master(self) -> dict[str, dict[str, Any]]:
+        rows = self.database.list_stock_master()
+        if not rows:
+            rows = self.client.query(
+                "stock_basic",
+                {"list_status": "L"},
+                "ts_code,name,industry,market,list_date",
+            )
+            self.database.upsert_stock_master(rows)
+            rows = self.database.list_stock_master()
+        return {str(row.get("ts_code") or ""): row for row in rows}
+
+    @staticmethod
+    def _parse_concepts(value: Any) -> list[str]:
+        if isinstance(value, list):
+            return [str(item) for item in value if str(item).strip()]
+        text = str(value or "").strip()
+        if not text:
+            return []
+        try:
+            parsed = json.loads(text)
+            if isinstance(parsed, list):
+                return [str(item) for item in parsed if str(item).strip()]
+        except json.JSONDecodeError:
+            pass
+        return [part.strip() for part in text.split(",") if part.strip()]

backend/features/market/insights_popularity.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import copy
+from datetime import datetime, timedelta
+from typing import Any
+
+from backend.data.numbers import non_nan_number as _number
+from backend.data.providers.tushare_client import TushareError
+from backend.features.market.insights_context import _display_date
+
+
+class MarketPopularityInsightsMixin:
+    def popularity(self, requested_date: str, force: bool = False) -> dict[str, Any]:
+        trade_date, previous_date = self._trade_context(requested_date)
+        if not force:
+            cached = self.database.get_data_snapshot("popularity_v1", trade_date)
+            if cached:
+                result = copy.deepcopy(cached)
+                result["meta"] = {**result.get("meta", {}), "cached": True}
+                return result
+
+        ths_rows, dc_rows, errors = self._hot_rows(trade_date)
+        actual_date = trade_date
+        carried_forward = False
+        if not ths_rows and not dc_rows and previous_date:
+            ths_rows, dc_rows, errors = self._hot_rows(previous_date)
+            actual_date = previous_date
+            carried_forward = bool(ths_rows or dc_rows)
+        if not ths_rows and not dc_rows:
+            fallback = self._latest_feature_snapshot("popularity_v1", trade_date)
+            if fallback:
+                result = copy.deepcopy(fallback)
+                result["meta"] = {
+                    **result.get("meta", {}),
+                    "requested_date": _display_date(requested_date),
+                    "carried_forward": True,
+                    "cached": True,
+                    "notice": "当前榜单暂不可用，展示最近有效快照",
+                }
+                return result
+            return {
+                "meta": {
+                    "requested_date": _display_date(requested_date),
+                    "trade_date": _display_date(trade_date),
+                    "previous_trade_date": _display_date(previous_date),
+                    "carried_forward": False,
+                    "cached": False,
+                    "notice": "该交易日暂无可用人气榜",
+                    "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+                },
+                "summary": {"ths_count": 0, "dc_count": 0, "dual_count": 0},
+                "combined": [], "ths": [], "dc": [],
+            }
+
+        prior_request = (datetime.strptime(actual_date, "%Y%m%d") - timedelta(days=1)).strftime("%Y%m%d")
+        prior_date, _ = self._trade_context(prior_request)
+        previous_ths, previous_dc, _ = self._hot_rows(prior_date)
+        ths = self._normalize_hot(ths_rows, "热股", previous_ths)
+        dc = self._normalize_hot(dc_rows, "A股市场", previous_dc)
+        ths_map = {item["ts_code"]: item for item in ths}
+        dc_map = {item["ts_code"]: item for item in dc}
+        combined = []
+        for ts_code in set(ths_map) | set(dc_map):
+            ths_item = ths_map.get(ts_code)
+            dc_item = dc_map.get(ts_code)
+            base = ths_item or dc_item or {}
+            ths_rank = int(ths_item["rank"]) if ths_item else None
+            dc_rank = int(dc_item["rank"]) if dc_item else None
+            score = (
+                (101 - (ths_rank or 101)) * 0.5
+                + (201 - (dc_rank or 201)) * 0.25
+            )
+            combined.append(
+                {
+                    **base,
+                    "ths_rank": ths_rank,
+                    "dc_rank": dc_rank,
+                    "score": round(score, 2),
+                    "dual_source": bool(ths_item and dc_item),
+                    "concepts": (ths_item or {}).get("concepts") or [],
+                }
+            )
+        combined.sort(key=lambda item: (item["dual_source"], item["score"]), reverse=True)
+        for index, item in enumerate(combined, 1):
+            item["rank"] = index
+        result = {
+            "meta": {
+                "requested_date": _display_date(requested_date),
+                "trade_date": _display_date(actual_date),
+                "previous_trade_date": _display_date(prior_date),
+                "carried_forward": carried_forward,
+                "cached": False,
+                "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+                "notice": "；".join(errors),
+            },
+            "summary": {
+                "ths_count": len(ths),
+                "dc_count": len(dc),
+                "dual_count": sum(item["dual_source"] for item in combined),
+            },
+            "combined": combined[:200],
+            "ths": ths,
+            "dc": dc,
+        }
+        self.database.save_data_snapshot("popularity_v1", trade_date, "market", result)
+        return result
+
+    def _hot_rows(self, trade_date: str) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[str]]:
+        errors = []
+        try:
+            ths = self.client.query("ths_hot", {"trade_date": trade_date})
+        except TushareError:
+            ths = []
+            errors.append("同花顺榜单暂不可用")
+        try:
+            dc = self.client.query("dc_hot", {"trade_date": trade_date})
+        except TushareError:
+            dc = []
+            errors.append("东方财富榜单暂不可用")
+        return ths, dc, errors
+
+    def _normalize_hot(
+        self,
+        rows: list[dict[str, Any]],
+        data_type: str,
+        previous_rows: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        previous = {
+            str(row.get("ts_code") or ""): int(_number(row.get("rank")))
+            for row in previous_rows
+            if str(row.get("data_type") or "") == data_type
+        }
+        items = []
+        for row in rows:
+            if str(row.get("data_type") or "") != data_type:
+                continue
+            rank = int(_number(row.get("rank")))
+            ts_code = str(row.get("ts_code") or "")
+            prior_rank = previous.get(ts_code)
+            items.append(
+                {
+                    "rank": rank,
+                    "ts_code": ts_code,
+                    "code": ts_code.split(".")[0],
+                    "name": str(row.get("ts_name") or "--"),
+                    "change": round(_number(row.get("pct_change")), 2),
+                    "price": round(_number(row.get("current_price")), 2),
+                    "hot": round(_number(row.get("hot")), 1),
+                    "rank_change": (prior_rank - rank) if prior_rank else None,
+                    "concepts": self._parse_concepts(row.get("concept")),
+                    "reason": str(row.get("rank_reason") or ""),
+                    "rank_time": str(row.get("rank_time") or ""),
+                }
+            )
+        items.sort(key=lambda item: item["rank"])
+        return items

backend/features/market/insights_themes.py

@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import copy
+from datetime import datetime, timedelta
+from typing import Any
+
+from backend.data.numbers import non_nan_number as _number
+from backend.data.providers.tushare_client import TushareError
+from backend.features.market.insights_context import _display_date
+
+
+class MarketThemeInsightsMixin:
+    def _theme_directory(self) -> list[dict[str, Any]]:
+        cached = self.database.get_data_snapshot("theme_directory_v1", "ths") or {}
+        if cached.get("items"):
+            return list(cached["items"])
+        rows = self.client.query(
+            "ths_index", {}, "ts_code,name,count,exchange,list_date,type"
+        )
+        items = [
+            {
+                "code": str(row.get("ts_code") or ""),
+                "name": str(row.get("name") or ""),
+                "member_count": int(_number(row.get("count"))),
+                "list_date": str(row.get("list_date") or ""),
+            }
+            for row in rows
+            if str(row.get("type") or "").upper() == "N"
+            and str(row.get("exchange") or "").upper() == "A"
+            and row.get("ts_code")
+            and row.get("name")
+        ]
+        self.database.save_data_snapshot(
+            "theme_directory_v1", "ths", "market", {"items": items}
+        )
+        return items
+
+    def theme_library(self, requested_date: str, force: bool = False) -> dict[str, Any]:
+        trade_date, previous_date = self._trade_context(requested_date)
+        if not force:
+            cached = self.database.get_data_snapshot("theme_library_v1", trade_date)
+            if cached:
+                result = copy.deepcopy(cached)
+                result["meta"] = {**result.get("meta", {}), "cached": True}
+                return result
+
+        try:
+            daily = self.client.query(
+                "ths_daily",
+                {"trade_date": trade_date},
+                "ts_code,trade_date,open,high,low,close,pre_close,pct_change,vol,turnover_rate",
+            )
+        except TushareError:
+            fallback = self._latest_feature_snapshot("theme_library_v1", trade_date)
+            if fallback:
+                result = copy.deepcopy(fallback)
+                result["meta"] = {
+                    **result.get("meta", {}),
+                    "requested_date": _display_date(requested_date),
+                    "carried_forward": True,
+                    "cached": True,
+                    "notice": "当前题材行情暂不可用，展示最近有效快照",
+                }
+                return result
+            daily = []
+        actual_date = trade_date
+        carried_forward = False
+        if not daily and previous_date:
+            try:
+                daily = self.client.query(
+                    "ths_daily",
+                    {"trade_date": previous_date},
+                    "ts_code,trade_date,open,high,low,close,pre_close,pct_change,vol,turnover_rate",
+                )
+            except TushareError:
+                daily = []
+            actual_date = previous_date
+            carried_forward = bool(daily)
+        daily_map = {str(row.get("ts_code") or ""): row for row in daily}
+        try:
+            hot_rows = self.client.query("ths_hot", {"trade_date": actual_date})
+        except TushareError:
+            hot_rows = []
+        hot_map = {
+            str(row.get("ts_code") or ""): int(_number(row.get("rank")))
+            for row in hot_rows
+            if str(row.get("data_type") or "") == "概念板块"
+        }
+        items = []
+        for item in self._theme_directory():
+            quote = daily_map.get(item["code"], {})
+            items.append(
+                {
+                    **item,
+                    "change": round(_number(quote.get("pct_change")), 2),
+                    "close": round(_number(quote.get("close")), 3),
+                    "turnover_rate": round(_number(quote.get("turnover_rate")), 2),
+                    "volume": round(_number(quote.get("vol")), 2),
+                    "hot_rank": hot_map.get(item["code"]),
+                    "has_quote": bool(quote),
+                }
+            )
+        items.sort(
+            key=lambda item: (
+                item["has_quote"],
+                item["hot_rank"] is not None,
+                -(item["hot_rank"] or 9999),
+                item["change"],
+            ),
+            reverse=True,
+        )
+        quoted = [item for item in items if item["has_quote"]]
+        result = {
+            "meta": {
+                "requested_date": _display_date(requested_date),
+                "trade_date": _display_date(actual_date),
+                "carried_forward": carried_forward,
+                "cached": False,
+                "notice": "" if quoted else "该交易日暂无题材行情，已保留题材目录",
+                "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+            },
+            "summary": {
+                "theme_count": len(items),
+                "quoted_count": len(quoted),
+                "up_count": sum(item["change"] > 0 for item in quoted),
+                "down_count": sum(item["change"] < 0 for item in quoted),
+                "hot_count": len(hot_map),
+            },
+            "items": items,
+        }
+        self.database.save_data_snapshot("theme_library_v1", trade_date, "market", result)
+        return result
+
+    def theme_detail(self, code: str, requested_date: str) -> dict[str, Any]:
+        code = str(code or "").strip().upper()
+        library = self.theme_library(requested_date)
+        theme = next((item for item in library["items"] if item["code"] == code), None)
+        if not theme:
+            raise ValueError("未找到对应题材。")
+        actual_date = str(library["meta"]["trade_date"]).replace("-", "")
+        detail_key = f"{actual_date}:{code}"
+        cached_detail = self.database.get_data_snapshot("theme_detail_v1", detail_key)
+        if cached_detail:
+            return cached_detail
+        try:
+            members = self.client.query(
+                "ths_member", {"ts_code": code, "is_new": "Y"}, "ts_code,con_code,con_name"
+            )
+        except TushareError:
+            members = []
+        bars = self.database.daily_bars_for_date(actual_date)
+        if not bars:
+            bars = self.client.query(
+                "daily",
+                {"trade_date": actual_date},
+                "ts_code,trade_date,open,high,low,close,pct_chg,vol,amount",
+            )
+            self.database.upsert_daily_bars(bars)
+        bar_map = {str(row.get("ts_code") or ""): row for row in bars}
+        normalized_members = []
+        for member in members:
+            ts_code = str(member.get("con_code") or "")
+            quote = bar_map.get(ts_code, {})
+            normalized_members.append(
+                {
+                    "code": ts_code.split(".")[0],
+                    "ts_code": ts_code,
+                    "name": str(member.get("con_name") or "--"),
+                    "price": round(_number(quote.get("close")), 2),
+                    "change": round(_number(quote.get("pct_chg")), 2),
+                    "amount_billion": round(_number(quote.get("amount")) / 100_000, 2),
+                    "has_quote": bool(quote),
+                }
+            )
+        normalized_members.sort(
+            key=lambda item: (item["has_quote"], item["change"], item["amount_billion"]),
+            reverse=True,
+        )
+        end = datetime.strptime(actual_date, "%Y%m%d")
+        try:
+            history = self.client.query(
+                "ths_daily",
+                {
+                    "ts_code": code,
+                    "start_date": (end - timedelta(days=190)).strftime("%Y%m%d"),
+                    "end_date": actual_date,
+                },
+                "ts_code,trade_date,open,high,low,close,pct_change,vol,turnover_rate",
+            )
+        except TushareError:
+            history = []
+        history.sort(key=lambda row: str(row.get("trade_date") or ""))
+        series = [
+            {
+                "trade_date": _display_date(str(row.get("trade_date") or "")),
+                "open": _number(row.get("open")),
+                "high": _number(row.get("high")),
+                "low": _number(row.get("low")),
+                "close": _number(row.get("close")),
+                "change": _number(row.get("pct_change")),
+                "volume": _number(row.get("vol")),
+            }
+            for row in history[-90:]
+        ]
+        result = {
+            "meta": {
+                "trade_date": _display_date(actual_date),
+                "notice": "" if members or history else "题材成分与走势暂不可用",
+            },
+            "theme": theme,
+            "series": series,
+            "members": normalized_members,
+            "summary": {
+                "member_count": len(normalized_members),
+                "up_count": sum(item["change"] > 0 for item in normalized_members if item["has_quote"]),
+                "down_count": sum(item["change"] < 0 for item in normalized_members if item["has_quote"]),
+                "quoted_count": sum(item["has_quote"] for item in normalized_members),
+            },
+        }
+        if members or history:
+            self.database.save_data_snapshot("theme_detail_v1", detail_key, "market", result)
+        return result

backend/features/market/routes.py

@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+import re
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+from backend.data.providers.tushare_client import TushareError
+from backend.features.market import ChartDataError
+
+
+class MarketRoutesMixin:
+    def _handle_market_get(self, parsed) -> bool:
+        if parsed.path == "/api/dashboard":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                self.send_json(self.application_service.get_dashboard(trade_date, False))
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            except Exception as exc:
+                self.send_json({"error": f"数据加载失败：{exc}"}, HTTPStatus.INTERNAL_SERVER_ERROR)
+            return True
+        if parsed.path == "/api/realtime-aggregate/health":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    {
+                        "ok": True,
+                        "aggregate": self.application_service.realtime_aggregate_health(
+                            query.get("sector", [""])[0]
+                        ),
+                    }
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/search":
+            query = parse_qs(parsed.query)
+            search_query = query.get("q", [""])[0]
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                self.send_json(self.application_service.search_entities(search_query, trade_date))
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/search/detail":
+            query = parse_qs(parsed.query)
+            entity_type = query.get("type", [""])[0]
+            identifier = query.get("id", [""])[0]
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                self.send_json(
+                    self.application_service.get_search_detail(entity_type, identifier, trade_date)
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            except TushareError as exc:
+                self.send_json({"error": f"行情加载失败：{exc}"}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/chart/intraday":
+            query = parse_qs(parsed.query)
+            entity_type = query.get("type", [""])[0]
+            identifier = query.get("id", [""])[0]
+            try:
+                self.send_json(self.application_service.get_intraday_chart(entity_type, identifier))
+            except (ValueError, ChartDataError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        stock_preview_match = re.fullmatch(r"/api/stock/(\d{6})/preview", parsed.path)
+        if stock_preview_match:
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            force = query.get("force", ["0"])[0] == "1"
+            try:
+                self.send_json(
+                    self.application_service.get_stock_preview(stock_preview_match.group(1), trade_date, force)
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        stock_match = re.fullmatch(r"/api/stock/(\d{6})", parsed.path)
+        if stock_match:
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            force = query.get("force", ["0"])[0] == "1"
+            try:
+                self.send_json(self.application_service.get_stock_detail(stock_match.group(1), trade_date, force))
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False

backend/features/mentor/routes.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import json
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+
+
+class MentorRoutesMixin:
+    def _handle_mentor_get(self, parsed) -> bool:
+        if parsed.path == "/api/mentors/setup":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                self.send_json(self.application_service.mentor_setup(trade_date))
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/mentors/messages":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    {
+                        "items": self.application_service.mentor_messages(
+                            query.get("mentor_id", [""])[0],
+                            query.get("trade_date", [date.today().isoformat()])[0],
+                        )
+                    }
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False
+
+    def _handle_mentor_post(self, parsed) -> bool:
+        if parsed.path == "/api/mentors/preferences":
+            try:
+                result = self.application_service.save_mentor_preferences(self.read_json_body())
+                self.send_json({"ok": True, **result})
+            except (ValueError, json.JSONDecodeError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False
+
+    def _handle_mentor_delete(self, parsed) -> bool:
+        if parsed.path == "/api/mentors/messages":
+            query = parse_qs(parsed.query)
+            try:
+                deleted = self.application_service.clear_mentor_messages(
+                    query.get("mentor_id", [""])[0],
+                    query.get("trade_date", [date.today().isoformat()])[0],
+                )
+                self.send_json({"ok": True, "deleted": deleted})
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False

backend/features/pools/routes.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+import json
+from http import HTTPStatus
+
+
+class PoolRoutesMixin:
+    def save_reason(self) -> None:
+        try:
+            body = self.read_json_body()
+            self.application_service.save_reason(
+                str(body.get("trade_date") or ""),
+                str(body.get("code") or ""),
+                str(body.get("reason") or ""),
+            )
+            self.send_json({"ok": True})
+        except (ValueError, json.JSONDecodeError) as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)

backend/features/popularity/routes.py

@@ -0,0 +1,23 @@
+from __future__ import annotations
+
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+from backend.data.providers.tushare_client import TushareError
+
+
+class PopularityRoutesMixin:
+    def _handle_popularity_get(self, parsed) -> bool:
+        if parsed.path == "/api/popularity":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.popularity(
+                        query.get("trade_date", [date.today().isoformat()])[0],
+                        query.get("force", ["0"])[0] == "1",
+                    )
+                )
+            except (ValueError, TushareError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False

backend/features/review/routes.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import re
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+
+
+class ReviewRoutesMixin:
+    def _handle_review_get(self, parsed) -> bool:
+        if parsed.path == "/api/trades":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.trade_entries(
+                        query.get("start_date", [""])[0],
+                        query.get("end_date", [""])[0],
+                        query.get("code", [""])[0],
+                    )
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/assistant/messages":
+            self.send_json({"items": self.application_service.assistant_messages()})
+            return True
+        if parsed.path == "/api/watchlist":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.review_watchlist(
+                        query.get("trade_date", [date.today().isoformat()])[0]
+                    )
+                )
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/notes":
+            query = parse_qs(parsed.query)
+            code = query.get("code", [""])[0]
+            trade_date = query.get("trade_date", [""])[0].replace("-", "")
+            scope = query.get("scope", ["all"])[0]
+            if scope not in {"all", "daily", "stock"}:
+                self.send_json({"error": "复盘记录范围不支持。"}, HTTPStatus.BAD_REQUEST)
+                return True
+            self.send_json(
+                {
+                    "items": self.application_service.database.list_notes(
+                        self.application_service.current_user_id, code, trade_date, scope
+                    )
+                }
+            )
+            return True
+        return False
+
+    def _handle_review_delete(self, parsed) -> bool:
+        if parsed.path == "/api/assistant/messages":
+            deleted = self.application_service.clear_assistant_messages()
+            self.send_json({"ok": True, "deleted": deleted})
+            return True
+        watchlist_match = re.fullmatch(r"/api/watchlist/(\d{6})", parsed.path)
+        if watchlist_match:
+            deleted = self.application_service.database.delete_watchlist(
+                self.application_service.current_user_id, watchlist_match.group(1)
+            )
+            self.send_json({"ok": True, "deleted": deleted})
+            return True
+        note_match = re.fullmatch(r"/api/notes/(\d+)", parsed.path)
+        if note_match:
+            deleted = self.application_service.database.delete_note(
+                self.application_service.current_user_id, int(note_match.group(1))
+            )
+            self.send_json({"ok": True, "deleted": deleted})
+            return True
+        trade_match = re.fullmatch(r"/api/trades/(\d+)", parsed.path)
+        if trade_match:
+            self.send_json(
+                {"ok": True, **self.application_service.delete_trade_entry(int(trade_match.group(1)))}
+            )
+            return True
+        return False

backend/features/rotation/routes.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+
+
+class RotationRoutesMixin:
+    def _handle_rotation_get(self, parsed) -> bool:
+        if parsed.path == "/api/rotation/history":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                self.send_json(self.application_service.rotation_history(trade_date, 9))
+            except (TypeError, ValueError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/rotation/members":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.rotation_sector_members(
+                        query.get("trade_date", [date.today().isoformat()])[0],
+                        query.get("sector", [""])[0],
+                    )
+                )
+            except (TypeError, ValueError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False

backend/features/screener/backtest.py

@@ -0,0 +1,141 @@
+from __future__ import annotations
+
+import statistics
+from collections import defaultdict
+from datetime import datetime
+from typing import Any
+
+from backend.data.numbers import finite_number as _number
+from backend.features.screener.factors import FactorBuilder
+from backend.features.screener.formula import FormulaEvaluator
+from database import ReviewDatabase
+
+
+class BacktestRunner:
+    def __init__(
+        self,
+        database: ReviewDatabase,
+        factor_builder: FactorBuilder,
+        formula_evaluator: FormulaEvaluator,
+    ) -> None:
+        self.database = database
+        self.factor_builder = factor_builder
+        self.formula_evaluator = formula_evaluator
+        self._backtest_factor_cache: dict[tuple[str, int], list[dict[str, Any]]] = {}
+
+    def build_factors(
+        self, trade_date: str, history_days: int
+    ) -> tuple[list[dict[str, Any]], str]:
+        return self.factor_builder.build_factors(
+            trade_date, history_days=history_days
+        )
+
+    def apply_formula(
+        self, rows: list[dict[str, Any]], formula: dict[str, Any], regime: str
+    ) -> list[dict[str, Any]]:
+        return self.formula_evaluator.apply_formula(rows, formula, regime)
+
+    def backtest(self, trade_date: str, formula: dict[str, Any]) -> dict[str, Any]:
+        meta = formula.get("meta") or {}
+        history_days = max(21, min(260, int(meta.get("history_days") or 80)))
+        holding_days = max(1, min(30, int(meta.get("backtest_days") or 3)))
+        take_profit = max(0.5, min(50.0, float(meta.get("take_profit") or 3)))
+        stop_loss = min(-0.5, max(-50.0, float(meta.get("stop_loss") or -3)))
+        dates = self.database.factor_dates(trade_date, history_days + holding_days + 20)
+        eligible_dates = dates[:-holding_days] if len(dates) > holding_days else []
+        frequency = str(meta.get("frequency") or "每日")
+        if "月" in frequency:
+            grouped = {}
+            for value in eligible_dates:
+                grouped[value[:6]] = value
+            evaluation_dates = list(grouped.values())[-8:]
+        elif "双周" in frequency:
+            weekly_dates = []
+            grouped = {}
+            for value in eligible_dates:
+                parsed = datetime.strptime(value, "%Y%m%d")
+                grouped[parsed.strftime("%G-%V")] = value
+            weekly_dates = list(grouped.values())
+            evaluation_dates = weekly_dates[-16::2][-8:]
+        elif "周" in frequency:
+            grouped = {}
+            for value in eligible_dates:
+                parsed = datetime.strptime(value, "%Y%m%d")
+                grouped[parsed.strftime("%G-%V")] = value
+            evaluation_dates = list(grouped.values())[-8:]
+        else:
+            evaluation_dates = eligible_dates[-8:]
+        wins = 0
+        losses = 0
+        samples = 0
+        returns = []
+        drawdowns = []
+        all_data = self.database.load_factor_data(
+            trade_date, history_days + holding_days + 20
+        )
+        bars_by_code: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for row in all_data["bars"]:
+            bars_by_code[row["ts_code"]].append(row)
+        for bars in bars_by_code.values():
+            bars.sort(key=lambda item: item["trade_date"])
+
+        for current_date in evaluation_dates:
+            try:
+                cache_key = (current_date, history_days)
+                factors = self._backtest_factor_cache.get(cache_key)
+                if factors is None:
+                    factors, _ = self.build_factors(
+                        current_date, history_days=history_days
+                    )
+                    if len(self._backtest_factor_cache) >= 64:
+                        self._backtest_factor_cache.pop(
+                            next(iter(self._backtest_factor_cache))
+                        )
+                    self._backtest_factor_cache[cache_key] = factors
+            except ValueError:
+                continue
+            selected = self.apply_formula(factors, {**formula, "limit": min(10, formula["limit"])}, "backtest")
+            for candidate in selected:
+                bars = bars_by_code.get(candidate["ts_code"], [])
+                index = next((i for i, row in enumerate(bars) if row["trade_date"] == current_date), -1)
+                future = bars[index + 1:index + 1 + holding_days] if index >= 0 else []
+                if len(future) < holding_days:
+                    continue
+                entry = candidate["price"]
+                won = False
+                lost = False
+                for day in future:
+                    low_return = (_number(day["low"]) / entry - 1) * 100
+                    high_return = (_number(day["high"]) / entry - 1) * 100
+                    if low_return <= stop_loss:
+                        lost = True
+                        break
+                    if high_return >= take_profit:
+                        won = True
+                        break
+                if won:
+                    wins += 1
+                elif lost:
+                    losses += 1
+                samples += 1
+                returns.append((_number(future[-1]["close"]) / entry - 1) * 100)
+                drawdowns.append(min((_number(day["low"]) / entry - 1) * 100 for day in future))
+        return {
+            "samples": samples,
+            "wins": wins,
+            "losses": losses,
+            "win_rate": round(wins / samples * 100, 1) if samples else 0,
+            "average_3d_return": round(statistics.fmean(returns), 2) if returns else 0,
+            "average_holding_return": round(statistics.fmean(returns), 2) if returns else 0,
+            "average_drawdown": round(statistics.fmean(drawdowns), 2) if drawdowns else 0,
+            "evaluation_days": len(evaluation_dates),
+            "frequency": frequency,
+            "holding_days": holding_days,
+            "take_profit": take_profit,
+            "stop_loss": stop_loss,
+            "definition": (
+                f"收盘后选股，未来{holding_days}日先触及+{take_profit:g}%且未先触及"
+                f"{stop_loss:g}%计为成功；同日双触发按失败处理。"
+            ),
+            "approximate": True,
+        }

backend/features/screener/catalog.py

@@ -0,0 +1,707 @@
+from __future__ import annotations
+
+from backend.features.screener.strategies import ADVANCED_CURATED_STRATEGIES
+
+
+REGIMES = {
+    "ice": "冰点",
+    "repair": "修复",
+    "fermentation": "发酵",
+    "climax": "高潮",
+    "divergence": "分化",
+    "retreat": "退潮",
+}
+
+FACTOR_FIELDS = {
+    "close": "收盘价",
+    "pct_chg": "当日涨幅",
+    "return_5d": "5日涨幅",
+    "return_10d": "10日涨幅",
+    "return_20d": "20日涨幅",
+    "return_60d": "60日涨幅",
+    "return_5d_rank": "5日涨幅排名",
+    "momentum_60_5": "中期动量",
+    "momentum_60_5_rank": "中期动量排名",
+    "above_ma20": "站上20日线",
+    "rsi_6": "RSI(6)",
+    "ma60_slope": "60日线斜率",
+    "ma20_slope_5d": "20日线5日斜率",
+    "ma_bull_alignment": "均线多头排列",
+    "drawdown_from_high_250": "距250日高点回撤",
+    "donchian_breakout_pct": "唐奇安突破幅度",
+    "range_20d": "20日振幅",
+    "rs_high_120": "RS线120日新高",
+    "excess_return_60d": "60日超额收益",
+    "weekly_trend_signal": "周线趋势信号",
+    "daily_buy_trigger": "日线买点",
+    "weekly_amount_trend": "周成交趋势",
+    "volume_ratio_5d": "5日量比",
+    "turnover_5d": "5日累计换手",
+    "volatility_10d": "10日波动率",
+    "amount_billion": "成交额",
+    "turnover_rate": "换手率",
+    "circ_mv_billion": "流通市值",
+    "net_flow_million": "主力净流入",
+    "large_flow_million": "大单净流入",
+    "net_flow_5d_million": "5日主力净流入",
+    "flow_to_circ_mv_5d": "5日净流入占流通市值",
+    "sector_strength": "板块强度",
+    "sector_return_5d": "行业5日涨幅",
+    "sector_return_20d": "行业20日涨幅",
+    "sector_momentum_rank": "行业20日动量排名",
+    "sector_stock_momentum_rank": "行业内个股动量排名",
+    "sector_net_flow_5d_million": "行业5日主力净流入",
+    "sector_flow_rank": "行业资金流排名",
+    "sector_prosperity_rank": "行业景气度排名",
+    "sector_trend_rank": "行业趋势排名",
+    "sector_crowding_rank": "行业拥挤度排名",
+    "sector_composite_score": "行业三维综合分",
+    "sector_limit_count": "板块涨停数",
+    "sector_up_count": "板块强势股数",
+    "relative_strength": "相对强度",
+    "limit_streak": "连板高度",
+    "auction_change": "竞价涨幅",
+    "auction_amount_million": "竞价成交额",
+    "auction_turnover_rate": "竞价换手率",
+    "auction_volume_ratio": "竞价量比",
+    "total_mv_billion": "总市值",
+    "pe_ttm": "市盈率TTM",
+    "pb": "市净率",
+    "ps_ttm": "市销率TTM",
+    "dividend_yield_ttm": "股息率TTM",
+    "dividend_years": "近年持续分红",
+    "roe": "净资产收益率",
+    "roa": "总资产收益率",
+    "roic": "投入资本回报率",
+    "gross_margin": "销售毛利率",
+    "netprofit_yoy": "净利润同比",
+    "revenue_yoy": "营业收入同比",
+    "ocf_to_opincome": "经营现金流质量",
+    "earnings_surprise_pct": "业绩超预期幅度",
+    "earnings_days_since_announce": "业绩公告后天数",
+    "earnings_event_quality": "业绩事件质量",
+    "popularity_score": "人气榜热度",
+    "popularity_rank_change": "人气排名跃升",
+    "popularity_dual_source": "双榜共识",
+    "institution_net_buy_million": "机构席位净买入",
+    "institution_seat_count": "机构席位数",
+    "style_size_fit": "大小盘风格匹配",
+    "style_growth_fit": "成长价值风格匹配",
+    "style_fit_score": "当前风格匹配度",
+    "factor_value_score": "价值因子分",
+    "factor_growth_score": "成长因子分",
+    "factor_quality_score": "质量因子分",
+    "factor_momentum_score": "动量因子分",
+    "factor_sentiment_score": "交易情绪因子分",
+    "multi_factor_composite": "动态多因子综合分",
+    "relative_position_60": "60日相对位置",
+    "max_abs_change_15d": "15日最大波动",
+    "close_to_high_15d": "距15日高点",
+    "close_to_high_60d": "距60日高点",
+    "no_limit_30d": "近30日无涨停",
+    "had_limit_80d": "近80日曾涨停",
+    "previous_first_limit": "昨日首板",
+    "previous_limit_signal": "昨日涨停或触板",
+    "previous_limit_streak": "昨日连板高度",
+    "previous_amount_billion": "昨日成交额",
+    "is_limit_up_today": "当日涨停",
+    "is_limit_down_today": "当日跌停",
+    "sector_breadth_ma20": "行业20日线宽度",
+    "no_limit_down_20d": "近20日无跌停",
+    "financial_risk": "财务风险标记",
+    "is_market_height": "当前市场最高板",
+    "new_space_board": "新晋空间板",
+    "max_continuous_board_10d": "近10日最高连板",
+    "dragon_first_yin": "龙头首阴",
+    "yin_day_pct": "首阴跌幅",
+    "vol_vs_previous": "较前日量能",
+    "broken_reversal": "断板反包",
+    "days_since_broken": "断板后天数",
+    "close_above_broken_high": "收复断板高点",
+    "vol_vs_broken_day": "较断板日量能",
+    "recent_limit_up_5d": "近5日涨停次数",
+    "intraday_min_pct": "盘中最大跌幅",
+    "lower_shadow_ratio": "下影线实体比",
+}
+
+FACTOR_GROUPS = {
+    "行情动量": [
+        "close", "pct_chg", "return_5d", "return_10d", "return_20d", "return_60d",
+        "return_5d_rank", "momentum_60_5", "momentum_60_5_rank", "above_ma20",
+        "rsi_6", "ma60_slope", "ma20_slope_5d", "ma_bull_alignment",
+        "drawdown_from_high_250", "donchian_breakout_pct", "range_20d",
+        "rs_high_120", "excess_return_60d", "weekly_trend_signal",
+        "daily_buy_trigger", "weekly_amount_trend", "relative_strength",
+        "relative_position_60", "close_to_high_15d", "close_to_high_60d",
+    ],
+    "量价交易": [
+        "volume_ratio_5d", "turnover_5d", "volatility_10d", "amount_billion", "turnover_rate",
+        "net_flow_million", "large_flow_million", "net_flow_5d_million",
+        "flow_to_circ_mv_5d", "previous_amount_billion",
+        "intraday_min_pct", "lower_shadow_ratio", "vol_vs_previous", "vol_vs_broken_day",
+    ],
+    "板块结构": [
+        "sector_strength", "sector_return_5d", "sector_return_20d", "sector_momentum_rank",
+        "sector_stock_momentum_rank", "sector_net_flow_5d_million", "sector_flow_rank",
+        "sector_prosperity_rank", "sector_trend_rank", "sector_crowding_rank",
+        "sector_composite_score",
+        "sector_limit_count", "sector_up_count", "sector_breadth_ma20",
+        "limit_streak", "previous_limit_streak", "previous_first_limit", "previous_limit_signal",
+        "is_limit_up_today", "is_limit_down_today",
+        "no_limit_30d", "had_limit_80d", "max_abs_change_15d", "no_limit_down_20d",
+        "is_market_height", "new_space_board", "max_continuous_board_10d",
+        "dragon_first_yin", "yin_day_pct", "broken_reversal", "days_since_broken",
+        "close_above_broken_high", "recent_limit_up_5d",
+    ],
+    "竞价因子": [
+        "auction_change", "auction_amount_million", "auction_turnover_rate", "auction_volume_ratio",
+    ],
+    "估值规模": [
+        "circ_mv_billion", "total_mv_billion", "pe_ttm", "pb", "ps_ttm",
+        "dividend_yield_ttm", "dividend_years",
+    ],
+    "财务质量": [
+        "roe", "roa", "roic", "gross_margin", "netprofit_yoy", "revenue_yoy",
+        "ocf_to_opincome", "financial_risk",
+        "earnings_surprise_pct", "earnings_days_since_announce", "earnings_event_quality",
+    ],
+    "特色数据": [
+        "popularity_score", "popularity_rank_change", "popularity_dual_source",
+        "institution_net_buy_million", "institution_seat_count",
+        "style_size_fit", "style_growth_fit", "style_fit_score",
+        "factor_value_score", "factor_growth_score", "factor_quality_score",
+        "factor_momentum_score", "factor_sentiment_score", "multi_factor_composite",
+    ],
+}
+
+ALLOWED_OPERATORS = {">", ">=", "<", "<=", "==", "!=", "between", "in"}
+
+
+BUILTIN_STRATEGIES = [
+    {
+        "name": "冰点抗跌先手",
+        "description": "寻找冰点中保持相对强度、低波动且有板块承接的个股，允许无结果。",
+        "regimes": ["ice"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "pct_chg", "op": "between", "value": [-3, 7]},
+                {"field": "return_5d", "op": ">=", "value": -5},
+                {"field": "amount_billion", "op": ">=", "value": 1},
+                {"field": "volatility_10d", "op": "<=", "value": 7},
+            ],
+            "score": [
+                {"field": "relative_strength", "weight": 0.30, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.25, "direction": "desc"},
+                {"field": "volume_ratio_5d", "weight": 0.20, "direction": "desc"},
+                {"field": "volatility_10d", "weight": 0.15, "direction": "asc"},
+                {"field": "amount_billion", "weight": 0.10, "direction": "desc"},
+            ],
+            "limit": 12,
+            "min_score": 0.58,
+        },
+    },
+    {
+        "name": "修复先锋",
+        "description": "筛选率先站回趋势、温和放量并获得板块共振的修复前排。",
+        "regimes": ["repair"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "pct_chg", "op": "between", "value": [1, 9.7]},
+                {"field": "return_5d", "op": ">", "value": 0},
+                {"field": "above_ma20", "op": "==", "value": 1},
+                {"field": "volume_ratio_5d", "op": ">=", "value": 1.05},
+            ],
+            "score": [
+                {"field": "sector_strength", "weight": 0.28, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.24, "direction": "desc"},
+                {"field": "volume_ratio_5d", "weight": 0.18, "direction": "desc"},
+                {"field": "net_flow_million", "weight": 0.16, "direction": "desc"},
+                {"field": "amount_billion", "weight": 0.14, "direction": "desc"},
+            ],
+            "limit": 15,
+            "min_score": 0.54,
+        },
+    },
+    {
+        "name": "主线发酵跟随",
+        "description": "在主线扩散期寻找趋势、成交承载和板块涨停梯队共同增强的个股。",
+        "regimes": ["fermentation"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "pct_chg", "op": "between", "value": [0, 9.8]},
+                {"field": "return_5d", "op": ">=", "value": 3},
+                {"field": "above_ma20", "op": "==", "value": 1},
+                {"field": "amount_billion", "op": ">=", "value": 2},
+            ],
+            "score": [
+                {"field": "sector_limit_count", "weight": 0.25, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.24, "direction": "desc"},
+                {"field": "return_10d", "weight": 0.20, "direction": "desc"},
+                {"field": "amount_billion", "weight": 0.16, "direction": "desc"},
+                {"field": "large_flow_million", "weight": 0.15, "direction": "desc"},
+            ],
+            "limit": 15,
+            "min_score": 0.55,
+        },
+    },
+    {
+        "name": "高潮核心去后排",
+        "description": "高潮阶段只保留容量、趋势和辨识度较高的核心，降低后排跟风权重。",
+        "regimes": ["climax"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "pct_chg", "op": "between", "value": [-2, 7]},
+                {"field": "return_10d", "op": ">=", "value": 5},
+                {"field": "above_ma20", "op": "==", "value": 1},
+                {"field": "amount_billion", "op": ">=", "value": 5},
+            ],
+            "score": [
+                {"field": "amount_billion", "weight": 0.28, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.22, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.20, "direction": "desc"},
+                {"field": "volatility_10d", "weight": 0.15, "direction": "asc"},
+                {"field": "limit_streak", "weight": 0.15, "direction": "desc"},
+            ],
+            "limit": 10,
+            "min_score": 0.62,
+        },
+    },
+    {
+        "name": "分化承接回流",
+        "description": "寻找分化中仍有趋势承接、板块强度和资金回流的核心候选。",
+        "regimes": ["divergence"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "pct_chg", "op": "between", "value": [-3, 7]},
+                {"field": "return_5d", "op": ">", "value": 0},
+                {"field": "above_ma20", "op": "==", "value": 1},
+                {"field": "volume_ratio_5d", "op": "between", "value": [0.7, 3.5]},
+            ],
+            "score": [
+                {"field": "relative_strength", "weight": 0.28, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.24, "direction": "desc"},
+                {"field": "net_flow_million", "weight": 0.20, "direction": "desc"},
+                {"field": "volatility_10d", "weight": 0.16, "direction": "asc"},
+                {"field": "amount_billion", "weight": 0.12, "direction": "desc"},
+            ],
+            "limit": 12,
+            "min_score": 0.57,
+        },
+    },
+    {
+        "name": "退潮防守观察",
+        "description": "退潮期采用高门槛防守筛选，结果为空代表当前不宜主动出击。",
+        "regimes": ["retreat"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 180},
+            "filters": [
+                {"field": "pct_chg", "op": "between", "value": [-2, 4]},
+                {"field": "return_5d", "op": ">=", "value": -2},
+                {"field": "above_ma20", "op": "==", "value": 1},
+                {"field": "volatility_10d", "op": "<=", "value": 4.5},
+                {"field": "amount_billion", "op": ">=", "value": 2},
+            ],
+            "score": [
+                {"field": "volatility_10d", "weight": 0.30, "direction": "asc"},
+                {"field": "relative_strength", "weight": 0.25, "direction": "desc"},
+                {"field": "amount_billion", "weight": 0.20, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.15, "direction": "desc"},
+                {"field": "net_flow_million", "weight": 0.10, "direction": "desc"},
+            ],
+            "limit": 8,
+            "min_score": 0.68,
+        },
+    },
+    {
+        "name": "竞价强势确认",
+        "description": "用竞价涨幅、成交承载和量比确认修复或发酵阶段的主动进攻标的。",
+        "regimes": ["repair", "fermentation", "divergence"],
+        "formula": {
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "auction_change", "op": "between", "value": [1, 7]},
+                {"field": "auction_amount_million", "op": ">=", "value": 3},
+                {"field": "auction_volume_ratio", "op": ">=", "value": 0.8},
+                {"field": "amount_billion", "op": ">=", "value": 1},
+            ],
+            "score": [
+                {"field": "auction_amount_million", "weight": 0.26, "direction": "desc"},
+                {"field": "auction_volume_ratio", "weight": 0.22, "direction": "desc"},
+                {"field": "auction_change", "weight": 0.18, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.18, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.16, "direction": "desc"},
+            ],
+            "limit": 15,
+            "min_score": 0.56,
+        },
+    },
+]
+
+for _strategy in BUILTIN_STRATEGIES:
+    _strategy["formula"].setdefault("meta", {
+        "library": "smart", "category": "周期策略", "quality": "系统",
+        "frequency": "每日", "risk": "随市场阶段", "data_group": "行情因子",
+    })
+
+
+CURATED_STRATEGIES = [
+    {
+        "name": "连续分红质量",
+        "description": "寻找持续派息、盈利质量稳定且波动可控的长期现金回报型公司。",
+        "regimes": list(REGIMES),
+        "formula": {
+            "meta": {"library": "curated", "category": "红利价值", "quality": "A", "frequency": "月度", "risk": "中低", "data_group": "估值与财务"},
+            "universe": {"exclude_st": True, "listed_days_min": 1095},
+            "filters": [
+                {"field": "dividend_years", "op": ">=", "value": 4},
+                {"field": "dividend_yield_ttm", "op": ">=", "value": 2},
+                {"field": "roe", "op": ">=", "value": 6},
+                {"field": "pb", "op": "between", "value": [0.1, 4]},
+            ],
+            "score": [
+                {"field": "dividend_yield_ttm", "weight": 0.30, "direction": "desc"},
+                {"field": "roe", "weight": 0.24, "direction": "desc"},
+                {"field": "ocf_to_opincome", "weight": 0.18, "direction": "desc"},
+                {"field": "volatility_10d", "weight": 0.16, "direction": "asc"},
+                {"field": "total_mv_billion", "weight": 0.12, "direction": "desc"},
+            ], "limit": 20, "min_score": 0.52,
+        },
+    },
+    {
+        "name": "ROIC质量低波",
+        "description": "以投入资本回报、毛利率和估值为核心，寻找低波动的高质量公司。",
+        "regimes": ["ice", "repair", "divergence", "retreat"],
+        "formula": {
+            "meta": {"library": "curated", "category": "质量价值", "quality": "A-", "frequency": "月度", "risk": "中低", "data_group": "估值与财务"},
+            "universe": {"exclude_st": True, "listed_days_min": 730},
+            "filters": [
+                {"field": "roic", "op": ">=", "value": 6},
+                {"field": "gross_margin", "op": ">=", "value": 15},
+                {"field": "pe_ttm", "op": "between", "value": [1, 45]},
+                {"field": "amount_billion", "op": ">=", "value": 1},
+            ],
+            "score": [
+                {"field": "roic", "weight": 0.28, "direction": "desc"},
+                {"field": "gross_margin", "weight": 0.22, "direction": "desc"},
+                {"field": "ps_ttm", "weight": 0.18, "direction": "asc"},
+                {"field": "volatility_10d", "weight": 0.18, "direction": "asc"},
+                {"field": "total_mv_billion", "weight": 0.14, "direction": "desc"},
+            ], "limit": 20, "min_score": 0.54,
+        },
+    },
+    {
+        "name": "低估值现金流白马",
+        "description": "筛选估值克制、经营现金流健康、资产回报稳定的大中型公司。",
+        "regimes": ["ice", "repair", "divergence", "retreat"],
+        "formula": {
+            "meta": {"library": "curated", "category": "现金流价值", "quality": "A-", "frequency": "月度", "risk": "中低", "data_group": "估值与财务"},
+            "universe": {"exclude_st": True, "listed_days_min": 730},
+            "filters": [
+                {"field": "pb", "op": "between", "value": [0.1, 1.8]},
+                {"field": "roa", "op": ">=", "value": 3},
+                {"field": "ocf_to_opincome", "op": ">", "value": 0},
+                {"field": "netprofit_yoy", "op": ">=", "value": -15},
+                {"field": "total_mv_billion", "op": ">=", "value": 100},
+            ],
+            "score": [
+                {"field": "roa", "weight": 0.26, "direction": "desc"},
+                {"field": "ocf_to_opincome", "weight": 0.24, "direction": "desc"},
+                {"field": "pb", "weight": 0.20, "direction": "asc"},
+                {"field": "total_mv_billion", "weight": 0.16, "direction": "desc"},
+                {"field": "volatility_10d", "weight": 0.14, "direction": "asc"},
+            ], "limit": 20, "min_score": 0.53,
+        },
+    },
+    {
+        "name": "高增长合理估值",
+        "description": "在收入和利润同步增长的公司中，优先选择估值合理、趋势得到确认的标的。",
+        "regimes": ["repair", "fermentation", "divergence"],
+        "formula": {
+            "meta": {"library": "curated", "category": "成长质量", "quality": "B+", "frequency": "月度", "risk": "中", "data_group": "估值与财务"},
+            "universe": {"exclude_st": True, "listed_days_min": 365},
+            "filters": [
+                {"field": "pe_ttm", "op": "between", "value": [1, 35]},
+                {"field": "revenue_yoy", "op": ">=", "value": 10},
+                {"field": "netprofit_yoy", "op": ">=", "value": 15},
+                {"field": "roe", "op": ">=", "value": 5},
+                {"field": "amount_billion", "op": ">=", "value": 1},
+            ],
+            "score": [
+                {"field": "netprofit_yoy", "weight": 0.27, "direction": "desc"},
+                {"field": "revenue_yoy", "weight": 0.23, "direction": "desc"},
+                {"field": "roe", "weight": 0.20, "direction": "desc"},
+                {"field": "pe_ttm", "weight": 0.16, "direction": "asc"},
+                {"field": "relative_strength", "weight": 0.14, "direction": "desc"},
+            ], "limit": 20, "min_score": 0.55,
+        },
+    },
+    {
+        "name": "行业宽度主线",
+        "description": "从行业站上20日线的覆盖率和板块强度出发，筛选主线中的强势个股。",
+        "regimes": ["repair", "fermentation", "climax", "divergence"],
+        "formula": {
+            "meta": {"library": "curated", "category": "行业轮动", "quality": "B+", "frequency": "每周", "risk": "中", "data_group": "行情与行业"},
+            "universe": {"exclude_st": True, "listed_days_min": 180},
+            "filters": [
+                {"field": "sector_breadth_ma20", "op": ">=", "value": 55},
+                {"field": "sector_strength", "op": ">=", "value": 55},
+                {"field": "above_ma20", "op": "==", "value": 1},
+                {"field": "amount_billion", "op": ">=", "value": 2},
+            ],
+            "score": [
+                {"field": "sector_breadth_ma20", "weight": 0.28, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.24, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.20, "direction": "desc"},
+                {"field": "sector_limit_count", "weight": 0.16, "direction": "desc"},
+                {"field": "amount_billion", "weight": 0.12, "direction": "desc"},
+            ], "limit": 20, "min_score": 0.56,
+        },
+    },
+    {
+        "name": "首板低开",
+        "description": "昨日首板且位置不高，次日竞价温和低开并具备成交承载时进入候选。",
+        "regimes": ["ice", "repair", "divergence"],
+        "formula": {
+            "meta": {"library": "curated", "category": "短线竞价", "quality": "B+", "frequency": "每日9:25", "risk": "高", "data_group": "行情与竞价"},
+            "universe": {"exclude_st": True, "listed_days_min": 250},
+            "filters": [
+                {"field": "previous_first_limit", "op": "==", "value": 1},
+                {"field": "auction_change", "op": "between", "value": [-4.5, -2.5]},
+                {"field": "relative_position_60", "op": "<=", "value": 0.55},
+                {"field": "previous_amount_billion", "op": ">=", "value": 1},
+            ],
+            "score": [
+                {"field": "auction_amount_million", "weight": 0.28, "direction": "desc"},
+                {"field": "previous_amount_billion", "weight": 0.24, "direction": "desc"},
+                {"field": "relative_position_60", "weight": 0.20, "direction": "asc"},
+                {"field": "sector_strength", "weight": 0.16, "direction": "desc"},
+                {"field": "auction_volume_ratio", "weight": 0.12, "direction": "desc"},
+            ], "limit": 12, "min_score": 0.50,
+        },
+    },
+    {
+        "name": "小碎步临界突破",
+        "description": "寻找近期窄幅爬升、接近阶段高点且具备历史活跃记忆的突破候选。",
+        "regimes": ["repair", "fermentation", "divergence"],
+        "formula": {
+            "meta": {"library": "curated", "category": "形态突破", "quality": "B+", "frequency": "每日", "risk": "中高", "data_group": "历史行情"},
+            "universe": {"exclude_st": True, "listed_days_min": 250},
+            "filters": [
+                {"field": "no_limit_30d", "op": "==", "value": 1},
+                {"field": "had_limit_80d", "op": "==", "value": 1},
+                {"field": "max_abs_change_15d", "op": "<=", "value": 3},
+                {"field": "close_to_high_15d", "op": ">=", "value": 0.98},
+                {"field": "close_to_high_60d", "op": ">=", "value": 0.90},
+            ],
+            "score": [
+                {"field": "close_to_high_15d", "weight": 0.26, "direction": "desc"},
+                {"field": "volume_ratio_5d", "weight": 0.22, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.20, "direction": "desc"},
+                {"field": "max_abs_change_15d", "weight": 0.18, "direction": "asc"},
+                {"field": "circ_mv_billion", "weight": 0.14, "direction": "asc"},
+            ], "limit": 15, "min_score": 0.54,
+        },
+    },
+    {
+        "name": "连板龙头",
+        "description": "从昨日连板梯队中按高度、板块热度和成交承载筛选辨识度前排。",
+        "regimes": ["fermentation", "climax", "divergence"],
+        "formula": {
+            "meta": {"library": "curated", "category": "连板接力", "quality": "B", "frequency": "每日", "risk": "很高", "data_group": "涨停结构"},
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "previous_limit_streak", "op": ">=", "value": 2},
+                {"field": "previous_amount_billion", "op": ">=", "value": 1},
+            ],
+            "score": [
+                {"field": "previous_limit_streak", "weight": 0.34, "direction": "desc"},
+                {"field": "sector_limit_count", "weight": 0.24, "direction": "desc"},
+                {"field": "previous_amount_billion", "weight": 0.18, "direction": "desc"},
+                {"field": "turnover_rate", "weight": 0.14, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.10, "direction": "desc"},
+            ], "limit": 10, "min_score": 0.50,
+        },
+    },
+    {
+        "name": "微盘三正",
+        "description": "以正估值、正盈利和正经营现金流约束微盘暴露，保留明确风险提示。",
+        "regimes": ["repair", "fermentation"],
+        "formula": {
+            "meta": {"library": "curated", "category": "小盘质量", "quality": "B", "frequency": "每周", "risk": "高", "data_group": "估值与财务"},
+            "universe": {"exclude_st": True, "listed_days_min": 365},
+            "filters": [
+                {"field": "pb", "op": ">", "value": 0},
+                {"field": "roe", "op": ">", "value": 0},
+                {"field": "ocf_to_opincome", "op": ">", "value": 0},
+                {"field": "circ_mv_billion", "op": "between", "value": [5, 100]},
+                {"field": "amount_billion", "op": ">=", "value": 0.5},
+            ],
+            "score": [
+                {"field": "circ_mv_billion", "weight": 0.32, "direction": "asc"},
+                {"field": "roe", "weight": 0.24, "direction": "desc"},
+                {"field": "ocf_to_opincome", "weight": 0.20, "direction": "desc"},
+                {"field": "turnover_rate", "weight": 0.14, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.10, "direction": "desc"},
+            ], "limit": 20, "min_score": 0.52,
+        },
+    },
+    {
+        "name": "首板高开弱转强",
+        "description": "昨日涨停或触板后，使用9:25最终竞价涨幅、量比和板块承接确认强度。",
+        "regimes": ["repair", "fermentation", "divergence"],
+        "formula": {
+            "meta": {"library": "curated", "category": "短线竞价", "quality": "B-", "frequency": "每日9:25", "risk": "高", "data_group": "行情与竞价"},
+            "universe": {"exclude_st": True, "listed_days_min": 120},
+            "filters": [
+                {"field": "previous_limit_signal", "op": "==", "value": 1},
+                {"field": "auction_change", "op": "between", "value": [1, 6]},
+                {"field": "auction_volume_ratio", "op": ">=", "value": 0.8},
+                {"field": "previous_amount_billion", "op": "between", "value": [3, 25]},
+            ],
+            "score": [
+                {"field": "auction_amount_million", "weight": 0.28, "direction": "desc"},
+                {"field": "auction_volume_ratio", "weight": 0.24, "direction": "desc"},
+                {"field": "auction_change", "weight": 0.18, "direction": "desc"},
+                {"field": "sector_strength", "weight": 0.17, "direction": "desc"},
+                {"field": "relative_strength", "weight": 0.13, "direction": "desc"},
+            ], "limit": 15, "min_score": 0.52,
+        },
+    },
+]
+
+CURATED_STRATEGIES.extend(ADVANCED_CURATED_STRATEGIES)
+
+STRATEGY_ENVIRONMENT_NOTES = {
+    "连续分红质量": (
+        "防守市、低利率环境与中长期配置窗口",
+        "风险偏好快速上升时，稳健资产的价格弹性通常落后",
+    ),
+    "ROIC质量低波": (
+        "震荡偏弱、重视盈利质量与回撤控制的市场",
+        "主题快速扩散或高弹性行情中，低波筛选可能错过进攻方向",
+    ),
+    "低估值现金流白马": (
+        "估值修复、价值回归及防守配置阶段",
+        "低估值可能来自基本面持续走弱，需警惕价值陷阱",
+    ),
+    "高增长合理估值": (
+        "业绩驱动、成长风格占优且趋势获得确认的阶段",
+        "增长预期下修或估值快速收缩时，回撤可能明显放大",
+    ),
+    "行业宽度主线": (
+        "主线清晰、行业内部多数个股同步走强的行情",
+        "板块快速轮动时，宽度信号容易在确认后迅速衰减",
+    ),
+    "首板低开": (
+        "情绪修复期的分歧转一致与首板次日承接",
+        "退潮加速或低开缺少量能承接时，弱势可能继续扩大",
+    ),
+    "小碎步临界突破": (
+        "趋势蓄势、波动收敛后临近突破的结构市",
+        "无量突破或指数剧烈震荡时，容易形成冲高回落",
+    ),
+    "连板龙头": (
+        "高度拓展、题材梯队完整且接力情绪活跃的阶段",
+        "亏钱效应扩散或高位股集中退潮时，接力风险很高",
+    ),
+    "微盘三正": (
+        "小盘风格活跃、流动性宽松且风险偏好较高的行情",
+        "风格切向大盘或微盘流动性收缩时，组合波动会显著上升",
+    ),
+    "首板高开弱转强": (
+        "竞价承接明确、短线情绪修复或主线发酵阶段",
+        "高开缺乏板块共振时，竞价强势可能转为盘中兑现",
+    ),
+    "中期动量·强者恒强": (
+        "趋势延续、主升段及强弱分化清晰的行情",
+        "无趋势震荡或快速轮动中，动量信号容易反复失效",
+    ),
+    "强者回调": (
+        "主升趋势未破、强势股完成良性回踩的窗口",
+        "趋势已反转时，回调信号可能演变为下跌中继",
+    ),
+    "超跌反转": (
+        "急跌后恐慌释放充分、市场进入修复预期的阶段",
+        "单边下跌初段容易过早介入，超跌不等于止跌",
+    ),
+    "相对强度新高": (
+        "指数偏弱但结构性主线明确，或机构抱团强化的行情",
+        "基准快速补涨或强势方向瓦解时，相对优势可能迅速消失",
+    ),
+    "均线多头排列": (
+        "中期趋势向上、回撤有序的趋势市与主升段",
+        "高位趋势末端或宽幅震荡中，均线信号通常反应滞后",
+    ),
+    "唐奇安通道突破": (
+        "整理末端、放量突破并启动新趋势的行情",
+        "无量突破和宽幅震荡环境中，假突破出现概率较高",
+    ),
+    "周线趋势·日线买点": (
+        "中期趋势稳定、日线回踩或再启动的多周期共振阶段",
+        "周线拐点尚未确认时，日线信号可能只是短暂反抽",
+    ),
+    "空间板": (
+        "市场高度持续拓展、板块梯队完整的强接力环境",
+        "高度压缩或亏钱效应扩散时，最高板的补跌风险极高",
+    ),
+    "龙头首阴": (
+        "主线龙头仍有辨识度、首次分歧后存在回流预期的阶段",
+        "题材退潮或龙头地位被替代后，首阴可能只是下跌起点",
+    ),
+    "断板反包": (
+        "强势题材分歧后快速修复、核心股重新获得资金承接时",
+        "板块强度不足或反包缩量时，形态持续性通常较弱",
+    ),
+    "核按钮反核": (
+        "恐慌释放后出现明确承接、短线情绪转暖的窗口",
+        "系统性退潮中深水拉回可能只是日内脉冲，隔日风险较高",
+    ),
+    "行业动量轮动": (
+        "主线相对清晰、行业趋势能够延续两周以上的结构市",
+        "行业轮动速度过快或前三名差距很小时，动量优势容易迅速衰减",
+    ),
+    "主力资金行业流入": (
+        "板块轮动初期、资金先于价格形成连续净流入的阶段",
+        "资金流口径可能受大宗交易和短期对倒影响，单日突增不代表趋势",
+    ),
+    "景气-趋势-拥挤三维行业打分": (
+        "行业景气与价格趋势同向、但交易拥挤尚未达到极端的结构市",
+        "财务披露存在滞后，行业快速反转时三维综合分可能反应偏慢",
+    ),
+    "大小盘/成长价值风格切换(元策略)": (
+        "大小盘或成长价值风格形成持续相对强弱的阶段",
+        "风格快速往返切换时，近20日相对表现容易产生滞后信号",
+    ),
+    "业绩超预期漂移(SUE/PEAD)": (
+        "业绩披露窗口中，快报相对预告继续上修且价格尚未充分兑现时",
+        "预告与快报口径可能不同，公告后高开兑现会削弱漂移效应",
+    ),
+    "多因子综合打分(IC动态加权)": (
+        "因子表现具备一定延续性、市场并非由单一极端主题主导时",
+        "近期有效因子可能快速失效，动态权重不能消除风格突变风险",
+    ),
+    "热度突增潜伏(另类数据)": (
+        "人气快速抬升但股价尚未明显启动的题材萌芽与扩散初期",
+        "榜单热度可能由短期讨论驱动，缺少价格确认时误报率较高",
+    ),
+    "机构榜溢价": (
+        "机构专用席位在相对低位形成明确净买入、且成交承载正常时",
+        "高位机构榜可能对应兑现或对倒，席位净买入不等于持续锁仓",
+    ),
+}
+
+for strategy in CURATED_STRATEGIES:
+    suitable_environment, failure_risk = STRATEGY_ENVIRONMENT_NOTES[strategy["name"]]
+    strategy["formula"]["meta"].update(
+        {
+            "suitable_environment": suitable_environment,
+            "failure_risk": failure_risk,
+        }
+    )
+
+BUILTIN_STRATEGIES.extend(CURATED_STRATEGIES)

backend/features/screener/compiler.py

@@ -4,7 +4,7 @@ import json
 from typing import Any
 
 from backend.llm import transport as llm_transport
-from backend.features.screener.engine import FACTOR_FIELDS, REGIMES
+from backend.features.screener.catalog import FACTOR_FIELDS, REGIMES
 
 
 class LLMCompilerError(RuntimeError):

backend/features/screener/data_sync.py

@@ -0,0 +1,342 @@
+from __future__ import annotations
+
+import statistics
+from datetime import datetime, timedelta
+from typing import Any
+
+from backend.data.numbers import finite_number as _number
+from backend.data.providers.tushare_client import TushareClient, TushareError
+from backend.features.screener.indicators import _optional_number
+from database import ReviewDatabase
+
+
+def _quarter_periods(trade_date: str, count: int) -> list[str]:
+    current = datetime.strptime(trade_date, "%Y%m%d")
+    quarter_ends = ((3, 31), (6, 30), (9, 30), (12, 31))
+    periods = []
+    year = current.year
+    while len(periods) < count:
+        for month, day in reversed(quarter_ends):
+            value = datetime(year, month, day)
+            if value <= current:
+                periods.append(value.strftime("%Y%m%d"))
+                if len(periods) == count:
+                    break
+        year -= 1
+    return sorted(periods)
+
+
+def _earnings_event_rows(
+    forecasts: list[dict[str, Any]], expresses: list[dict[str, Any]], trade_date: str,
+) -> list[dict[str, Any]]:
+    forecast_map: dict[tuple[str, str], dict[str, Any]] = {}
+    for row in forecasts:
+        key = (str(row.get("ts_code") or ""), str(row.get("end_date") or ""))
+        ann_date = str(row.get("ann_date") or "")
+        if not all(key) or not ann_date or ann_date > trade_date:
+            continue
+        previous = forecast_map.get(key)
+        if previous is None or ann_date > str(previous.get("ann_date") or ""):
+            forecast_map[key] = row
+    result = []
+    for row in expresses:
+        ts_code = str(row.get("ts_code") or "")
+        end_date = str(row.get("end_date") or "")
+        ann_date = str(row.get("ann_date") or "")
+        forecast = forecast_map.get((ts_code, end_date))
+        if not forecast or not ts_code or not end_date or not ann_date or ann_date > trade_date:
+            continue
+        lower = _optional_number(forecast.get("net_profit_min"))
+        upper = _optional_number(forecast.get("net_profit_max"))
+        forecast_profit = statistics.fmean(
+            value for value in (lower, upper) if value is not None
+        ) if lower is not None or upper is not None else None
+        actual_profit = _optional_number(row.get("n_income"))
+        if forecast_profit in (None, 0) or actual_profit is None:
+            continue
+        # forecast is reported in ten-thousand yuan while express uses yuan.
+        if abs(actual_profit) > max(abs(forecast_profit), 1) * 100:
+            actual_profit /= 10000
+        surprise_pct = (actual_profit / forecast_profit - 1) * 100
+        result.append(
+            {
+                "end_date": end_date,
+                "ann_date": ann_date,
+                "ts_code": ts_code,
+                "forecast_profit": forecast_profit,
+                "actual_profit": actual_profit,
+                "surprise_pct": surprise_pct,
+                "revenue_yoy": _optional_number(row.get("yoy_sales")),
+                "netprofit_yoy": _optional_number(row.get("yoy_net_profit")),
+                "source": "forecast+express",
+            }
+        )
+    return result
+
+
+def _popularity_factor_rows(
+    trade_date: str,
+    ths_rows: list[dict[str, Any]],
+    dc_rows: list[dict[str, Any]],
+    previous_ths: list[dict[str, Any]],
+    previous_dc: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    def ranks(rows: list[dict[str, Any]], data_type: str) -> dict[str, int]:
+        result = {}
+        for row in rows:
+            if data_type and str(row.get("data_type") or "") != data_type:
+                continue
+            ts_code = str(row.get("ts_code") or "")
+            rank = int(_number(row.get("rank")))
+            if ts_code and rank > 0:
+                result[ts_code] = rank
+        return result
+
+    ths = ranks(ths_rows, "热股")
+    dc = ranks(dc_rows, "A股市场")
+    previous_ths_map = ranks(previous_ths, "热股")
+    previous_dc_map = ranks(previous_dc, "A股市场")
+    result = []
+    for ts_code in set(ths) | set(dc):
+        ths_rank = ths.get(ts_code)
+        dc_rank = dc.get(ts_code)
+        current_best = min(value for value in (ths_rank, dc_rank) if value is not None)
+        previous_candidates = [
+            value for value in (previous_ths_map.get(ts_code), previous_dc_map.get(ts_code))
+            if value is not None
+        ]
+        previous_best = min(previous_candidates) if previous_candidates else None
+        score = (101 - (ths_rank or 101)) * 0.5 + (201 - (dc_rank or 201)) * 0.25
+        result.append(
+            {
+                "trade_date": trade_date,
+                "ts_code": ts_code,
+                "ths_rank": ths_rank,
+                "dc_rank": dc_rank,
+                "combined_score": round(score, 2),
+                "rank_change": (
+                    previous_best - current_best
+                    if previous_best is not None
+                    else min(30, max(0, 31 - current_best))
+                    if previous_ths_map or previous_dc_map else 0
+                ),
+                "dual_source": bool(ths_rank and dc_rank),
+            }
+        )
+    return result
+
+
+class FactorDataService:
+    def __init__(self, database: ReviewDatabase, client: TushareClient) -> None:
+        self.database = database
+        self.client = client
+
+    def sync(self, requested_date: str, lookback: int = 45) -> dict[str, Any]:
+        lookback = max(25, min(260, int(lookback)))
+        trade_date, _ = self.client.resolve_trade_context(requested_date)
+        end = datetime.strptime(trade_date, "%Y%m%d")
+        start = (end - timedelta(days=max(100, lookback * 2 + 20))).strftime("%Y%m%d")
+        calendar = self.client.query(
+            "trade_cal",
+            {"exchange": "SSE", "start_date": start, "end_date": trade_date, "is_open": 1},
+            "cal_date,is_open",
+        )
+        dates = sorted(row["cal_date"] for row in calendar if row.get("is_open") == 1)[-lookback:]
+        existing = set(self.database.factor_dates(trade_date, lookback + 10))
+        dates_to_fetch = [value for value in dates if value not in existing or value == trade_date]
+        auction_source_dates = dates[-min(80, len(dates)):]
+        existing_auction = set(self.database.auction_factor_dates(trade_date, 90))
+        auction_dates_to_fetch = [
+            value for value in auction_source_dates
+            if value not in existing_auction or value == trade_date
+        ]
+        long_calendar = self.client.query(
+            "trade_cal",
+            {
+                "exchange": "SSE",
+                "start_date": datetime(end.year - 5, 1, 1).strftime("%Y%m%d"),
+                "end_date": trade_date,
+                "is_open": 1,
+            },
+            "cal_date,is_open",
+        )
+        last_open_by_year: dict[str, str] = {}
+        last_open_by_month: dict[str, str] = {}
+        for row in long_calendar:
+            if row.get("is_open") == 1 and row.get("cal_date"):
+                value = str(row["cal_date"])
+                last_open_by_year[value[:4]] = max(last_open_by_year.get(value[:4], ""), value)
+                last_open_by_month[value[:6]] = max(last_open_by_month.get(value[:6], ""), value)
+        valuation_dates = set(dates[-min(80, len(dates)):])
+        valuation_dates.update(last_open_by_year.values())
+        valuation_dates.update(last_open_by_month.values())
+        existing_indicators = set(self.database.daily_indicator_dates(trade_date, 500))
+        indicator_dates_to_fetch = sorted(
+            value for value in valuation_dates if value not in existing_indicators or value == trade_date
+        )
+
+        master = self.client.query(
+            "stock_basic",
+            {"list_status": "L"},
+            "ts_code,name,industry,market,list_date",
+        )
+        master_count = self.database.upsert_stock_master(master)
+        bar_count = 0
+        for current_date in dates_to_fetch:
+            rows = self.client.query(
+                "daily",
+                {"trade_date": current_date},
+                "ts_code,trade_date,open,high,low,close,pct_chg,vol,amount",
+            )
+            bar_count += self.database.upsert_daily_bars(rows)
+
+        indicator_count = 0
+        for current_date in indicator_dates_to_fetch:
+            indicators = self.client.query(
+                "daily_basic",
+                {"trade_date": current_date},
+                "ts_code,trade_date,turnover_rate,volume_ratio,total_mv,circ_mv,"
+                "pe_ttm,pb,ps_ttm,dv_ttm",
+            )
+            indicator_count += self.database.upsert_daily_indicators(indicators)
+
+        notices = []
+        benchmark_count = 0
+        try:
+            benchmark_rows = self.client.query(
+                "index_daily",
+                {"ts_code": "000300.SH", "start_date": dates[0], "end_date": trade_date},
+                "ts_code,trade_date,close,pct_chg",
+            )
+            benchmark_count = self.database.upsert_benchmark_bars(benchmark_rows)
+        except TushareError as exc:
+            notices.append(f"沪深300基准暂不可用：{exc}")
+        fundamental_count = 0
+        existing_periods = set(self.database.fundamental_periods())
+        for period in _quarter_periods(trade_date, 9):
+            if period in existing_periods and period < trade_date[:4] + "0101":
+                continue
+            try:
+                rows = self.client.query(
+                    "fina_indicator_vip",
+                    {"period": period},
+                    "ts_code,ann_date,end_date,roe,roa,roic,grossprofit_margin,"
+                    "netprofit_yoy,or_yoy,ocf_to_opincome",
+                )
+            except TushareError as exc:
+                notices.append(f"财务质量接口不可用：{exc}")
+                break
+            published = [
+                row for row in rows
+                if not row.get("ann_date") or str(row.get("ann_date")) <= trade_date
+            ]
+            published.sort(key=lambda row: str(row.get("ann_date") or ""))
+            fundamental_count += self.database.upsert_fundamental_indicators(published)
+        auction_count = 0
+        auction_dates = 0
+        for current_date in auction_dates_to_fetch:
+            try:
+                auction_rows = self.client.query(
+                    "stk_auction",
+                    {"trade_date": current_date},
+                    "ts_code,trade_date,vol,price,amount,pre_close,turnover_rate,volume_ratio,float_share",
+                )
+                if auction_rows:
+                    auction_count += self.database.upsert_auction_factors(auction_rows)
+                    auction_dates += 1
+            except TushareError as exc:
+                notices.append(f"竞价因子接口不可用：{exc}")
+                break
+        moneyflow_count = 0
+        moneyflow_dates = 0
+        for current_date in dates[-min(5, len(dates)):]:
+            try:
+                moneyflow = self.client.query(
+                    "moneyflow",
+                    {"trade_date": current_date},
+                    "ts_code,trade_date,buy_sm_amount,sell_sm_amount,buy_md_amount,sell_md_amount,"
+                    "buy_lg_amount,sell_lg_amount,buy_elg_amount,sell_elg_amount,net_mf_amount",
+                )
+                moneyflow_count += self.database.upsert_moneyflow(moneyflow)
+                if moneyflow:
+                    moneyflow_dates += 1
+            except TushareError as exc:
+                notices.append(f"资金流接口不可用：{exc}")
+                break
+
+        earnings_count = 0
+        forecasts: list[dict[str, Any]] = []
+        expresses: list[dict[str, Any]] = []
+        for period in _quarter_periods(trade_date, 5):
+            try:
+                forecast_rows = self.client.query(
+                    "forecast_vip",
+                    {"period": period},
+                    "ts_code,ann_date,end_date,net_profit_min,net_profit_max,last_parent_net,p_change_min,p_change_max",
+                )
+                express_rows = self.client.query(
+                    "express_vip",
+                    {"period": period},
+                    "ts_code,ann_date,end_date,n_income,yoy_net_profit,yoy_sales",
+                )
+            except TushareError as exc:
+                notices.append(f"业绩事件接口不可用：{exc}")
+                break
+            forecasts.extend(forecast_rows)
+            expresses.extend(express_rows)
+        if forecasts and expresses:
+            earnings_count = self.database.upsert_earnings_events(
+                _earnings_event_rows(forecasts, expresses, trade_date)
+            )
+
+        popularity_count = 0
+        previous_trade_date = dates[-2] if len(dates) >= 2 else ""
+        try:
+            ths_rows = self.client.query("ths_hot", {"trade_date": trade_date})
+            dc_rows = self.client.query("dc_hot", {"trade_date": trade_date})
+            previous_ths = (
+                self.client.query("ths_hot", {"trade_date": previous_trade_date})
+                if previous_trade_date else []
+            )
+            previous_dc = (
+                self.client.query("dc_hot", {"trade_date": previous_trade_date})
+                if previous_trade_date else []
+            )
+            popularity_count = self.database.upsert_popularity_factors(
+                _popularity_factor_rows(
+                    trade_date, ths_rows, dc_rows, previous_ths, previous_dc
+                )
+            )
+        except TushareError as exc:
+            notices.append(f"人气榜因子不可用：{exc}")
+
+        institution_count = 0
+        try:
+            institution_rows = self.client.query(
+                "top_inst",
+                {"trade_date": trade_date},
+                "trade_date,ts_code,exalter,buy,sell,net_buy,side,reason",
+            )
+            institution_count = self.database.upsert_lhb_institutions(institution_rows)
+        except TushareError as exc:
+            notices.append(f"机构席位明细不可用：{exc}")
+
+        return {
+            "trade_date": trade_date,
+            "calendar_dates": len(dates),
+            "fetched_dates": len(dates_to_fetch),
+            "stocks": master_count,
+            "bars": bar_count,
+            "benchmark_bars": benchmark_count,
+            "indicators": indicator_count,
+            "indicator_dates": len(indicator_dates_to_fetch),
+            "fundamentals": fundamental_count,
+            "moneyflow": moneyflow_count,
+            "moneyflow_dates": moneyflow_dates,
+            "auction_rows": auction_count,
+            "auction_dates": auction_dates,
+            "earnings_events": earnings_count,
+            "popularity_rows": popularity_count,
+            "institution_rows": institution_count,
+            "notice": "；".join(notices),
+        }

backend/features/screener/factors.py

@@ -0,0 +1,562 @@
+from __future__ import annotations
+
+import statistics
+from collections import defaultdict
+from datetime import datetime
+from typing import Any
+
+from backend.data.numbers import finite_number as _number
+from backend.features.screener.indicators import (
+    _available_percentile_map,
+    _broken_reversal_metrics,
+    _ending_streak,
+    _is_limit_bar,
+    _limit_threshold,
+    _macd_last,
+    _macd_series,
+    _max_streak,
+    _optional_number,
+    _pearson,
+    _percentile_map,
+    _rounded_optional,
+    _rsi,
+    _touched_limit_bar,
+    _weekly_series,
+)
+from database import ReviewDatabase
+
+
+class FactorBuilder:
+    def __init__(self, database: ReviewDatabase) -> None:
+        self.database = database
+
+    def build_factors(
+        self,
+        trade_date: str,
+        realtime_snapshot: dict[str, Any] | None = None,
+        history_days: int = 80,
+    ) -> tuple[list[dict[str, Any]], str]:
+        history_days = max(21, min(260, int(history_days)))
+        data = self.database.load_factor_data(trade_date, history_days)
+        dates = [value for value in data["dates"] if value <= trade_date]
+        if len(dates) < 21:
+            raise ValueError("历史行情不足 21 个交易日，请先同步因子数据。")
+        history_date = dates[-1]
+        realtime_map = {
+            str(row.get("ts_code") or ""): row
+            for row in (realtime_snapshot or {}).get("rows") or []
+        }
+        realtime_date = str((realtime_snapshot or {}).get("trade_date") or "")
+        use_realtime = bool(realtime_map and realtime_date == trade_date and history_date < trade_date)
+        actual_date = trade_date if use_realtime else history_date
+        master = {row["ts_code"]: row for row in data["master"]}
+        indicators = {row["ts_code"]: row for row in data["indicators"]}
+        fundamentals = {row["ts_code"]: row for row in data.get("fundamentals", [])}
+        indicator_history: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for row in data.get("indicator_history", []):
+            indicator_history[str(row.get("ts_code") or "")].append(row)
+        indicator_series: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for row in data.get("indicator_series", []):
+            indicator_series[str(row.get("ts_code") or "")].append(row)
+        benchmark_by_date = {
+            str(row.get("trade_date") or ""): _number(row.get("close"))
+            for row in data.get("benchmarks", [])
+            if _number(row.get("close")) > 0
+        }
+        moneyflow = {row["ts_code"]: row for row in data["moneyflow"]}
+        moneyflow_history: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for row in data.get("moneyflow_history", []):
+            moneyflow_history[str(row.get("ts_code") or "")].append(row)
+        auction = {
+            row["ts_code"]: row
+            for row in data.get("auction", [])
+            if str(row.get("trade_date") or "") == actual_date
+        }
+        earnings_events: dict[str, dict[str, Any]] = {}
+        for row in data.get("earnings_events", []):
+            ts_code = str(row.get("ts_code") or "")
+            ann_date = str(row.get("ann_date") or "")
+            if ann_date <= actual_date and (
+                ts_code not in earnings_events
+                or ann_date > str(earnings_events[ts_code].get("ann_date") or "")
+            ):
+                earnings_events[ts_code] = row
+        popularity = {
+            str(row.get("ts_code") or ""): row
+            for row in data.get("popularity", [])
+        }
+        institutions = {
+            str(row.get("ts_code") or ""): row
+            for row in data.get("institutions", [])
+        }
+        grouped: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for row in data["bars"]:
+            if row["trade_date"] <= history_date:
+                grouped[row["ts_code"]].append(row)
+
+        snapshot = self.database.get_snapshot(actual_date) or {}
+        limit_map: dict[str, tuple[str, int]] = {}
+        for key, status in (("limits", "涨停"), ("broken", "炸板"), ("down_limits", "跌停")):
+            for row in snapshot.get(key) or []:
+                limit_map[str(row.get("code"))] = (status, int(row.get("streak") or 0))
+
+        factors = []
+        current_day = datetime.strptime(actual_date, "%Y%m%d")
+        for ts_code, bars in grouped.items():
+            bars.sort(key=lambda item: item["trade_date"])
+            if len(bars) < 21 or bars[-1]["trade_date"] != history_date:
+                continue
+            info = master.get(ts_code)
+            if not info:
+                continue
+            historical_closes = [_number(item["close"]) for item in bars]
+            historical_volumes = [_number(item["vol"]) for item in bars]
+            realtime = realtime_map.get(ts_code) if use_realtime else None
+            current = realtime or bars[-1]
+            closes = historical_closes + ([_number(realtime["close"])] if realtime else [])
+            volumes = historical_volumes + ([_number(realtime["vol"])] if realtime else [])
+            if closes[-1] <= 0:
+                continue
+            returns_10 = [_number(item["pct_chg"]) for item in bars[-10:]]
+            if realtime:
+                returns_10 = returns_10[-9:] + [_number(realtime.get("pct_chg"))]
+            previous_volume = statistics.fmean(volumes[-6:-1]) if any(volumes[-6:-1]) else 0
+            indicator = indicators.get(ts_code, {})
+            fundamental = fundamentals.get(ts_code, {})
+            flow = moneyflow.get(ts_code, {})
+            flow_history = moneyflow_history.get(ts_code, [])
+            auction_row = auction.get(ts_code, {})
+            list_date = str(info.get("list_date") or "")
+            try:
+                listed_days = (current_day - datetime.strptime(list_date, "%Y%m%d")).days
+            except ValueError:
+                listed_days = 9999
+            code = str(info.get("code") or ts_code.split(".")[0])
+            status, streak = limit_map.get(code, ("", 0))
+            name = str(info.get("name") or "--")
+            shape_rows = bars + ([realtime] if realtime else [])
+            shape_close = [_number(item.get("close")) for item in shape_rows]
+            shape_high = [_number(item.get("high") or item.get("close")) for item in shape_rows]
+            shape_low = [_number(item.get("low") or item.get("close")) for item in shape_rows]
+            shape_changes = [_number(item.get("pct_chg")) for item in shape_rows]
+            position_rows = shape_rows[-60:]
+            position_high = max((_number(item.get("high") or item.get("close")) for item in position_rows), default=0)
+            position_low = min((_number(item.get("low") or item.get("close")) for item in position_rows), default=0)
+            relative_position = (
+                (closes[-1] - position_low) / (position_high - position_low)
+                if position_high > position_low else 0.5
+            )
+            previous_index = len(bars) - 1 if realtime else len(bars) - 2
+            previous_bar = bars[previous_index] if previous_index >= 0 else {}
+            previous_limit = _is_limit_bar(bars, previous_index, code, name)
+            previous_touched = _touched_limit_bar(bars, previous_index, code, name)
+            recent_prior_signal = any(
+                _is_limit_bar(bars, index, code, name)
+                or _touched_limit_bar(bars, index, code, name)
+                for index in range(max(0, previous_index - 2), previous_index)
+            )
+            previous_streak = 0
+            streak_index = previous_index
+            while streak_index >= 0 and _is_limit_bar(bars, streak_index, code, name):
+                previous_streak += 1
+                streak_index -= 1
+            limit_flags = [
+                _is_limit_bar(shape_rows, index, code, name)
+                for index in range(len(shape_rows))
+            ]
+            annual_dividend_rows = indicator_history.get(ts_code, [])
+            dividend_years = sum(
+                1 for item in annual_dividend_rows if _optional_number(item.get("dv_ttm")) not in (None, 0)
+            )
+            current_streak = _ending_streak(limit_flags)
+            prior_streak = _ending_streak(limit_flags, len(limit_flags) - 2)
+            streak = max(streak, current_streak)
+            return_60d = (
+                (closes[-1] / closes[-61] - 1) * 100 if len(closes) >= 61 and closes[-61] else 0
+            )
+            momentum_60_5 = (
+                (closes[-6] / closes[-61] - 1) * 100 if len(closes) >= 61 and closes[-61] else 0
+            )
+            ma20 = statistics.fmean(closes[-20:])
+            ma60 = statistics.fmean(closes[-60:]) if len(closes) >= 60 else ma20
+            prior_ma20 = statistics.fmean(closes[-25:-5]) if len(closes) >= 25 else ma20
+            prior_ma60 = statistics.fmean(closes[-65:-5]) if len(closes) >= 65 else ma60
+            ma20_slope = (ma20 / prior_ma20 - 1) * 100 if prior_ma20 else 0
+            ma60_slope = (ma60 / prior_ma60 - 1) * 100 if prior_ma60 else 0
+            ma_values = [statistics.fmean(closes[-window:]) for window in (5, 10, 20, 60)]
+            high_250 = max(shape_high[-250:]) if len(shape_high) >= 250 else max(shape_high)
+            drawdown_250 = (1 - closes[-1] / high_250) * 100 if high_250 else 100
+            prior_high_20 = max(shape_high[-21:-1]) if len(shape_high) >= 21 else 0
+            breakout_pct = (closes[-1] / prior_high_20 - 1) * 100 if prior_high_20 else 0
+            prior_lows_20 = shape_low[-21:-1]
+            range_20d = (
+                (prior_high_20 / min(prior_lows_20) - 1) * 100
+                if prior_lows_20 and min(prior_lows_20) > 0 else 100
+            )
+            turnover_rows = sorted(
+                indicator_series.get(ts_code, []), key=lambda item: str(item.get("trade_date") or "")
+            )
+            turnover_values = [_number(item.get("turnover_rate")) for item in turnover_rows[-5:]]
+            if realtime and _number(realtime.get("turnover_rate")):
+                turnover_values = turnover_values[-4:] + [_number(realtime.get("turnover_rate"))]
+            turnover_5d = sum(turnover_values)
+            rs_values = [
+                _number(item.get("close")) / benchmark_by_date[str(item.get("trade_date"))]
+                for item in shape_rows[-120:]
+                if benchmark_by_date.get(str(item.get("trade_date"))) and _number(item.get("close")) > 0
+            ]
+            benchmark_60 = [
+                benchmark_by_date.get(str(item.get("trade_date")))
+                for item in shape_rows[-61:]
+                if benchmark_by_date.get(str(item.get("trade_date")))
+            ]
+            benchmark_return_60 = (
+                (benchmark_60[-1] / benchmark_60[0] - 1) * 100
+                if len(benchmark_60) >= 61 and benchmark_60[0] else 0
+            )
+            weekly_closes, weekly_amounts = _weekly_series(shape_rows)
+            weekly_dif, weekly_dea = _macd_last(weekly_closes)
+            daily_dif, daily_dea = _macd_series(closes)
+            daily_cross = (
+                len(daily_dif) >= 2 and daily_dif[-1] > daily_dea[-1]
+                and daily_dif[-2] <= daily_dea[-2]
+            )
+            current_open = _number(current.get("open"))
+            daily_pullback = closes[-1] >= ma20 and current_open <= ma20 * 1.02 and closes[-1] > current_open
+            previous_close = closes[-2] if len(closes) >= 2 else closes[-1]
+            intraday_min = (
+                (_number(current.get("low")) / previous_close - 1) * 100 if previous_close else 0
+            )
+            body = abs(closes[-1] - current_open)
+            lower_shadow = max(0.0, min(current_open, closes[-1]) - _number(current.get("low")))
+            lower_shadow_ratio = lower_shadow / body if body > 0 else (10.0 if lower_shadow > 0 else 0.0)
+            previous_volume_value = volumes[-2] if len(volumes) >= 2 else 0
+            vol_vs_previous = volumes[-1] / previous_volume_value if previous_volume_value else 0
+            broken = _broken_reversal_metrics(shape_rows, limit_flags, code, name)
+            netprofit_yoy = _optional_number(fundamental.get("netprofit_yoy"))
+            earnings_event = earnings_events.get(ts_code, {})
+            announcement_date = str(earnings_event.get("ann_date") or "")
+            earnings_days = (
+                sum(1 for value in dates if announcement_date < value <= actual_date)
+                if announcement_date and announcement_date <= actual_date
+                else None
+            )
+            announcement_bar = next(
+                (item for item in shape_rows if str(item.get("trade_date") or "") == announcement_date),
+                None,
+            )
+            announcement_bad = False
+            if announcement_bar is not None:
+                bar_index = shape_rows.index(announcement_bar)
+                prior_volumes = [
+                    _number(item.get("vol")) for item in shape_rows[max(0, bar_index - 5):bar_index]
+                    if _number(item.get("vol")) > 0
+                ]
+                volume_baseline = statistics.fmean(prior_volumes) if prior_volumes else 0
+                announcement_bad = (
+                    _number(announcement_bar.get("close")) < _number(announcement_bar.get("open"))
+                    and _number(announcement_bar.get("pct_chg")) < 0
+                    and volume_baseline > 0
+                    and _number(announcement_bar.get("vol")) / volume_baseline >= 1.8
+                )
+            popularity_row = popularity.get(ts_code)
+            institution_row = institutions.get(ts_code)
+            factors.append(
+                {
+                    "code": code,
+                    "ts_code": ts_code,
+                    "name": name,
+                    "sector": info.get("industry") or "其他",
+                    "market": info.get("market") or "--",
+                    "listed_days": listed_days,
+                    "close": round(closes[-1], 2),
+                    "price": round(closes[-1], 2),
+                    "pct_chg": round(_number(current["pct_chg"]), 2),
+                    "return_5d": round((closes[-1] / closes[-6] - 1) * 100, 2),
+                    "return_10d": round((closes[-1] / closes[-11] - 1) * 100, 2),
+                    "return_20d": round((closes[-1] / closes[-21] - 1) * 100, 2),
+                    "return_60d": round(return_60d, 2),
+                    "momentum_60_5": round(momentum_60_5, 2),
+                    "above_ma20": int(closes[-1] > ma20),
+                    "rsi_6": round(_rsi(closes, 6), 2),
+                    "ma60_slope": round(ma60_slope, 3),
+                    "ma20_slope_5d": round(ma20_slope, 3),
+                    "ma_bull_alignment": int(ma_values[0] > ma_values[1] > ma_values[2] > ma_values[3]),
+                    "drawdown_from_high_250": round(drawdown_250, 2),
+                    "donchian_breakout_pct": round(breakout_pct, 2),
+                    "range_20d": round(range_20d, 2),
+                    "rs_high_120": int(len(rs_values) >= 120 and rs_values[-1] >= max(rs_values)),
+                    "excess_return_60d": round(return_60d - benchmark_return_60, 2),
+                    "weekly_trend_signal": int(len(weekly_closes) >= 30 and weekly_dif > 0 and weekly_dea > 0),
+                    "daily_buy_trigger": int(daily_cross or daily_pullback),
+                    "weekly_amount_trend": int(
+                        len(weekly_amounts) >= 5
+                        and weekly_amounts[-1] >= statistics.fmean(weekly_amounts[-5:-1])
+                    ),
+                    "volume_ratio_5d": round(volumes[-1] / previous_volume, 2) if previous_volume else 0,
+                    "turnover_5d": round(turnover_5d, 2),
+                    "volatility_10d": round(statistics.pstdev(returns_10), 2),
+                    "amount_billion": round(
+                        _number(current["amount"]) / (100000000 if realtime else 100000), 2
+                    ),
+                    "turnover_rate": round(
+                        _number(realtime.get("turnover_rate"))
+                        if realtime else _number(indicator.get("turnover_rate")),
+                        2,
+                    ),
+                    "circ_mv_billion": round(_number(indicator.get("circ_mv")) / 10000, 2),
+                    "total_mv_billion": round(_number(indicator.get("total_mv")) / 10000, 2),
+                    "pe_ttm": _rounded_optional(indicator.get("pe_ttm"), 2),
+                    "pb": _rounded_optional(indicator.get("pb"), 2),
+                    "ps_ttm": _rounded_optional(indicator.get("ps_ttm"), 2),
+                    "dividend_yield_ttm": _rounded_optional(indicator.get("dv_ttm"), 2),
+                    "dividend_years": dividend_years,
+                    "roe": _rounded_optional(fundamental.get("roe"), 2),
+                    "roa": _rounded_optional(fundamental.get("roa"), 2),
+                    "roic": _rounded_optional(fundamental.get("roic"), 2),
+                    "gross_margin": _rounded_optional(fundamental.get("grossprofit_margin"), 2),
+                    "netprofit_yoy": _rounded_optional(fundamental.get("netprofit_yoy"), 2),
+                    "revenue_yoy": _rounded_optional(fundamental.get("or_yoy"), 2),
+                    "ocf_to_opincome": _rounded_optional(fundamental.get("ocf_to_opincome"), 2),
+                    "earnings_surprise_pct": _rounded_optional(earnings_event.get("surprise_pct"), 2),
+                    "earnings_days_since_announce": earnings_days,
+                    "earnings_event_quality": int(not announcement_bad) if earnings_days is not None else None,
+                    "popularity_score": _rounded_optional(
+                        popularity_row.get("combined_score") if popularity_row else None, 2
+                    ),
+                    "popularity_rank_change": (
+                        int(popularity_row["rank_change"])
+                        if popularity_row and popularity_row.get("rank_change") is not None else None
+                    ),
+                    "popularity_dual_source": (
+                        int(bool(popularity_row.get("dual_source"))) if popularity_row else None
+                    ),
+                    "institution_net_buy_million": (
+                        round(_number(institution_row.get("net_buy_amount")) / 1_000_000, 2)
+                        if institution_row else None
+                    ),
+                    "institution_seat_count": (
+                        int(institution_row.get("seat_count") or 0) if institution_row else None
+                    ),
+                    "net_flow_million": round(_number(flow.get("net_mf_amount")) / 100, 2),
+                    "large_flow_million": round(_number(flow.get("large_net_amount")) / 100, 2),
+                    "net_flow_5d_million": round(
+                        sum(_number(item.get("net_mf_amount")) for item in flow_history) / 100,
+                        2,
+                    ),
+                    "flow_to_circ_mv_5d": round(
+                        sum(_number(item.get("net_mf_amount")) for item in flow_history)
+                        / _number(indicator.get("circ_mv")) * 100,
+                        4,
+                    ) if _number(indicator.get("circ_mv")) else 0,
+                    "limit_status": status,
+                    "limit_streak": streak,
+                    "is_limit_up_today": int(limit_flags[-1]),
+                    "is_limit_down_today": int(_number(current.get("pct_chg")) <= -_limit_threshold(code, name)),
+                    "auction_change": round(_number(auction_row.get("change")), 2),
+                    "auction_amount_million": round(_number(auction_row.get("amount")) / 1_000_000, 2),
+                    "auction_turnover_rate": round(_number(auction_row.get("turnover_rate")), 4),
+                    "auction_volume_ratio": round(_number(auction_row.get("volume_ratio")), 2),
+                    "relative_position_60": round(relative_position, 4),
+                    "max_abs_change_15d": round(max((abs(value) for value in shape_changes[-15:]), default=0), 2),
+                    "close_to_high_15d": round(closes[-1] / max(shape_high[-15:]), 4) if shape_high[-15:] and max(shape_high[-15:]) else 0,
+                    "close_to_high_60d": round(closes[-1] / max(shape_high[-60:]), 4) if shape_high[-60:] and max(shape_high[-60:]) else 0,
+                    "no_limit_30d": int(not any(limit_flags[-30:])),
+                    "had_limit_80d": int(any(limit_flags[-80:-30] if len(limit_flags) > 30 else [])),
+                    "no_limit_down_20d": int(not any(
+                        _number(item.get("pct_chg")) <= -_limit_threshold(code, name)
+                        for item in shape_rows[-20:]
+                    )),
+                    "financial_risk": int(
+                        "ST" in name.upper() or "退" in name
+                        or (netprofit_yoy is not None and netprofit_yoy <= -100)
+                    ),
+                    "prior_limit_streak": prior_streak,
+                    "max_continuous_board_10d": _max_streak(limit_flags[-10:]),
+                    "dragon_first_yin": int(
+                        prior_streak >= 3 and not limit_flags[-1] and closes[-1] < current_open
+                    ),
+                    "yin_day_pct": round(_number(current.get("pct_chg")), 2),
+                    "vol_vs_previous": round(vol_vs_previous, 3),
+                    "broken_reversal": broken["signal"],
+                    "days_since_broken": broken["days"],
+                    "close_above_broken_high": broken["recovered"],
+                    "vol_vs_broken_day": broken["volume_ratio"],
+                    "recent_limit_up_5d": sum(limit_flags[-5:]),
+                    "intraday_min_pct": round(intraday_min, 2),
+                    "lower_shadow_ratio": round(lower_shadow_ratio, 2),
+                    "previous_first_limit": int(previous_limit and not recent_prior_signal),
+                    "previous_limit_signal": int((previous_limit or previous_touched) and not recent_prior_signal),
+                    "previous_limit_streak": previous_streak,
+                    "previous_amount_billion": round(_number(previous_bar.get("amount")) / 100000, 2),
+                }
+            )
+
+        market_return = statistics.fmean(row["return_5d"] for row in factors) if factors else 0
+        sectors: dict[str, list[dict[str, Any]]] = defaultdict(list)
+        for row in factors:
+            sectors[row["sector"]].append(row)
+        sector_metrics = []
+        market_amount = sum(max(0.0, row["amount_billion"]) for row in factors)
+        for sector_name, sector_rows in sectors.items():
+            average_return = statistics.fmean(row["return_5d"] for row in sector_rows)
+            average_return_20d = statistics.fmean(row["return_20d"] for row in sector_rows)
+            sector_net_flow = sum(row["net_flow_5d_million"] for row in sector_rows)
+            limit_count = sum(row["limit_status"] == "涨停" or row["pct_chg"] >= 9.5 for row in sector_rows)
+            up_count = sum(row["pct_chg"] >= 5 for row in sector_rows)
+            breadth_ma20 = sum(row["above_ma20"] for row in sector_rows) / max(len(sector_rows), 1) * 100
+            sector_growth = [
+                statistics.fmean(values)
+                for row in sector_rows
+                if (values := [
+                    value for value in (row.get("revenue_yoy"), row.get("netprofit_yoy"))
+                    if value is not None
+                ])
+            ]
+            prosperity_raw = statistics.median(sector_growth) if sector_growth else -100.0
+            average_turnover = statistics.fmean(row["turnover_rate"] for row in sector_rows)
+            amount_share = (
+                sum(max(0.0, row["amount_billion"]) for row in sector_rows) / market_amount * 100
+                if market_amount else 0.0
+            )
+            crowding_raw = average_turnover + amount_share
+            trend_raw = average_return_20d + breadth_ma20 / 10
+            strength = min(100, max(0, 50 + average_return * 4 + limit_count * 3 + up_count * 0.6))
+            sector_metrics.append(
+                {
+                    "ts_code": sector_name,
+                    "sector_return_20d": average_return_20d,
+                    "sector_net_flow_5d_million": sector_net_flow,
+                    "sector_prosperity_raw": prosperity_raw,
+                    "sector_trend_raw": trend_raw,
+                    "sector_crowding_raw": crowding_raw,
+                }
+            )
+            stock_momentum_ranks = _percentile_map(sector_rows, "return_20d", "desc")
+            for row in sector_rows:
+                row["sector_strength"] = round(strength, 1)
+                row["sector_return_5d"] = round(average_return, 2)
+                row["sector_return_20d"] = round(average_return_20d, 2)
+                row["sector_net_flow_5d_million"] = round(sector_net_flow, 2)
+                row["sector_stock_momentum_rank"] = round(
+                    stock_momentum_ranks.get(row["ts_code"], 0.0), 4
+                )
+                row["sector_limit_count"] = limit_count
+                row["sector_up_count"] = up_count
+                row["sector_breadth_ma20"] = round(breadth_ma20, 1)
+                row["relative_strength"] = round(row["return_5d"] - market_return, 2)
+        sector_momentum_ranks = _percentile_map(
+            sector_metrics, "sector_return_20d", "desc"
+        )
+        sector_flow_ranks = _percentile_map(
+            sector_metrics, "sector_net_flow_5d_million", "desc"
+        )
+        sector_prosperity_ranks = _percentile_map(
+            sector_metrics, "sector_prosperity_raw", "desc"
+        )
+        sector_trend_ranks = _percentile_map(
+            sector_metrics, "sector_trend_raw", "desc"
+        )
+        sector_crowding_ranks = _percentile_map(
+            sector_metrics, "sector_crowding_raw", "desc"
+        )
+        for sector_name, sector_rows in sectors.items():
+            prosperity_rank = sector_prosperity_ranks.get(sector_name, 0.0)
+            trend_rank = sector_trend_ranks.get(sector_name, 0.0)
+            crowding_rank = sector_crowding_ranks.get(sector_name, 0.0)
+            composite_score = (
+                prosperity_rank * 0.40 + trend_rank * 0.30 + (1 - crowding_rank) * 0.30
+            )
+            for row in sector_rows:
+                row["sector_momentum_rank"] = round(
+                    sector_momentum_ranks.get(sector_name, 0.0), 4
+                )
+                row["sector_flow_rank"] = round(
+                    sector_flow_ranks.get(sector_name, 0.0), 4
+                )
+                row["sector_prosperity_rank"] = round(prosperity_rank, 4)
+                row["sector_trend_rank"] = round(trend_rank, 4)
+                row["sector_crowding_rank"] = round(crowding_rank, 4)
+                row["sector_composite_score"] = round(composite_score, 4)
+
+        factor_specs = {
+            "factor_value_score": (("pe_ttm", "asc"), ("pb", "asc"), ("dividend_yield_ttm", "desc")),
+            "factor_growth_score": (("revenue_yoy", "desc"), ("netprofit_yoy", "desc")),
+            "factor_quality_score": (("roe", "desc"), ("roic", "desc"), ("gross_margin", "desc")),
+            "factor_momentum_score": (("momentum_60_5", "desc"), ("relative_strength", "desc")),
+            "factor_sentiment_score": (("turnover_rate", "desc"), ("volume_ratio_5d", "desc")),
+        }
+        for output_field, specs in factor_specs.items():
+            maps = [_available_percentile_map(factors, field, direction) for field, direction in specs]
+            for row in factors:
+                values = [mapping.get(row["ts_code"]) for mapping in maps]
+                available = [value for value in values if value is not None]
+                row[output_field] = round(statistics.fmean(available), 4) if available else None
+
+        return_rank_map = _available_percentile_map(factors, "return_20d", "desc")
+        factor_weights = {}
+        for output_field in factor_specs:
+            pairs = [
+                (row.get(output_field), return_rank_map.get(row["ts_code"]))
+                for row in factors
+                if row.get(output_field) is not None and return_rank_map.get(row["ts_code"]) is not None
+            ]
+            correlation = _pearson([pair[0] for pair in pairs], [pair[1] for pair in pairs])
+            factor_weights[output_field] = max(0.05, correlation)
+        factor_weight_total = sum(factor_weights.values()) or 1
+        for row in factors:
+            weighted = [
+                (row.get(field), weight)
+                for field, weight in factor_weights.items()
+                if row.get(field) is not None
+            ]
+            row["multi_factor_composite"] = round(
+                sum(value * weight for value, weight in weighted)
+                / (sum(weight for _, weight in weighted) or factor_weight_total),
+                4,
+            ) if weighted else None
+
+        size_ranks = _available_percentile_map(factors, "total_mv_billion", "desc")
+        large_rows = [row for row in factors if (size_ranks.get(row["ts_code"]) or 0) >= 0.70]
+        small_rows = [
+            row for row in factors
+            if size_ranks.get(row["ts_code"]) is not None
+            and size_ranks[row["ts_code"]] <= 0.30
+        ]
+        large_return = statistics.fmean(row["return_20d"] for row in large_rows) if large_rows else 0
+        small_return = statistics.fmean(row["return_20d"] for row in small_rows) if small_rows else 0
+        prefer_large = large_return >= small_return
+        growth_rows = [row for row in factors if (row.get("factor_growth_score") or 0) >= 0.70]
+        value_rows = [row for row in factors if (row.get("factor_value_score") or 0) >= 0.70]
+        growth_return = statistics.fmean(row["return_20d"] for row in growth_rows) if growth_rows else 0
+        value_return = statistics.fmean(row["return_20d"] for row in value_rows) if value_rows else 0
+        prefer_growth = growth_return >= value_return
+        for row in factors:
+            size_rank = size_ranks.get(row["ts_code"])
+            row["style_size_fit"] = round(
+                size_rank if prefer_large else 1 - size_rank, 4
+            ) if size_rank is not None else None
+            style_factor = "factor_growth_score" if prefer_growth else "factor_value_score"
+            row["style_growth_fit"] = row.get(style_factor)
+            style_values = [
+                value for value in (row.get("style_size_fit"), row.get("style_growth_fit"))
+                if value is not None
+            ]
+            row["style_fit_score"] = round(statistics.fmean(style_values), 4) if style_values else None
+        momentum_ranks = _percentile_map(factors, "momentum_60_5", "desc")
+        return_ranks = _percentile_map(factors, "return_5d", "desc")
+        market_height = max((int(row.get("limit_streak") or 0) for row in factors), default=0)
+        prior_market_height = max((int(row.get("prior_limit_streak") or 0) for row in factors), default=0)
+        for row in factors:
+            row["momentum_60_5_rank"] = round(momentum_ranks.get(row["ts_code"], 0.0), 4)
+            row["return_5d_rank"] = round(return_ranks.get(row["ts_code"], 0.0), 4)
+            is_height = market_height >= 2 and int(row.get("limit_streak") or 0) == market_height
+            row["is_market_height"] = int(is_height)
+            row["new_space_board"] = int(
+                is_height
+                and not (
+                    prior_market_height >= 2
+                    and int(row.get("prior_limit_streak") or 0) == prior_market_height
+                )
+            )
+        return factors, actual_date

backend/features/screener/formula.py

@@ -0,0 +1,146 @@
+from __future__ import annotations
+
+import copy
+from typing import Any
+
+from backend.features.screener.catalog import (
+    ALLOWED_OPERATORS,
+    BUILTIN_STRATEGIES,
+    FACTOR_FIELDS,
+    REGIMES,
+)
+from backend.features.screener.indicators import _matches, _percentile_map, _risk_flags
+
+
+class FormulaEvaluator:
+    def validate_formula(self, formula: dict[str, Any]) -> dict[str, Any]:
+        if not isinstance(formula, dict):
+            raise ValueError("选股公式必须是 JSON 对象。")
+        result = copy.deepcopy(formula)
+        universe = result.setdefault("universe", {})
+        universe["exclude_st"] = bool(universe.get("exclude_st", True))
+        universe["listed_days_min"] = max(0, min(5000, int(universe.get("listed_days_min", 120))))
+        filters = result.setdefault("filters", [])
+        if not isinstance(filters, list) or len(filters) > 20:
+            raise ValueError("筛选条件必须是列表，且不能超过 20 条。")
+        for condition in filters:
+            field = condition.get("field")
+            operator = condition.get("op")
+            if field not in FACTOR_FIELDS:
+                raise ValueError(f"不支持的选股因子：{field}")
+            if operator not in ALLOWED_OPERATORS:
+                raise ValueError(f"不支持的运算符：{operator}")
+            if "value" not in condition:
+                raise ValueError(f"因子 {field} 缺少比较值。")
+        scores = result.setdefault("score", [])
+        if not isinstance(scores, list) or not scores or len(scores) > 12:
+            raise ValueError("评分因子应为 1 至 12 条。")
+        for item in scores:
+            if item.get("field") not in FACTOR_FIELDS:
+                raise ValueError(f"不支持的评分因子：{item.get('field')}")
+            item["weight"] = float(item.get("weight", 0))
+            if item["weight"] <= 0 or item["weight"] > 1:
+                raise ValueError("评分权重必须大于 0 且不超过 1。")
+            if item.get("direction", "desc") not in {"asc", "desc"}:
+                raise ValueError("评分方向只能是 asc 或 desc。")
+            item["direction"] = item.get("direction", "desc")
+        result["limit"] = max(1, min(50, int(result.get("limit", 15))))
+        result["min_score"] = max(0, min(1, float(result.get("min_score", 0))))
+        return result
+
+    def apply_formula(
+        self, rows: list[dict[str, Any]], formula: dict[str, Any], regime: str
+    ) -> list[dict[str, Any]]:
+        universe = formula["universe"]
+        eligible = []
+        score_fields = [item["field"] for item in formula["score"]]
+        for row in rows:
+            name = str(row.get("name") or "")
+            if universe.get("exclude_st") and ("ST" in name.upper() or "退" in name):
+                continue
+            if row.get("listed_days", 0) < universe.get("listed_days_min", 0):
+                continue
+            if any(row.get(field) is None for field in score_fields):
+                continue
+            if all(_matches(row.get(item["field"]), item["op"], item["value"]) for item in formula["filters"]):
+                eligible.append(row)
+        if not eligible:
+            return []
+
+        percentiles = {
+            item["field"]: _percentile_map(eligible, item["field"], item["direction"])
+            for item in formula["score"]
+        }
+        weight_total = sum(item["weight"] for item in formula["score"])
+        results = []
+        for row in eligible:
+            contributions = []
+            score = 0.0
+            for item in formula["score"]:
+                percentile = percentiles[item["field"]].get(row["ts_code"], 0.5)
+                points = percentile * item["weight"] / weight_total
+                score += points
+                contributions.append(
+                    {
+                        "field": item["field"],
+                        "label": FACTOR_FIELDS[item["field"]],
+                        "value": row.get(item["field"], 0),
+                        "points": round(points * 100, 1),
+                    }
+                )
+            if score < formula["min_score"]:
+                continue
+            contributions.sort(key=lambda item: item["points"], reverse=True)
+            item = dict(row)
+            item["score"] = round(score, 4)
+            item["score_display"] = round(score * 100, 1)
+            item["contributions"] = contributions
+            item["reason"] = "、".join(entry["label"] for entry in contributions[:3])
+            include_regime_risk = formula.get("meta", {}).get("library") != "curated"
+            item["risk_flags"] = _risk_flags(row, regime, include_regime_risk)
+            results.append(item)
+        results.sort(key=lambda item: item["score"], reverse=True)
+        return results[: formula["limit"]]
+
+
+def compile_local_strategy(prompt: str, regime: str) -> dict[str, Any]:
+    base = next((item for item in BUILTIN_STRATEGIES if regime in item["regimes"]), BUILTIN_STRATEGIES[1])
+    formula = copy.deepcopy(base["formula"])
+    description = prompt.strip() or base["description"]
+    lowered = description.lower()
+    if "低吸" in description:
+        formula["filters"] = [item for item in formula["filters"] if item["field"] != "pct_chg"]
+        formula["filters"].append({"field": "pct_chg", "op": "between", "value": [-3, 3]})
+    if "放量" in description:
+        formula["filters"].append({"field": "volume_ratio_5d", "op": ">=", "value": 1.2})
+    if "强势" in description or "突破" in description:
+        formula["filters"].append({"field": "return_5d", "op": ">=", "value": 5})
+    if "低波" in description or "稳健" in description:
+        formula["score"].append({"field": "volatility_10d", "weight": 0.18, "direction": "asc"})
+    if "资金" in description or "主力" in description:
+        formula["score"].append({"field": "net_flow_million", "weight": 0.18, "direction": "desc"})
+    if "小市值" in description or "小盘" in description:
+        formula["score"].append({"field": "circ_mv_billion", "weight": 0.15, "direction": "asc"})
+    if "竞价" in description:
+        formula["filters"].extend(
+            [
+                {"field": "auction_change", "op": "between", "value": [0.5, 8]},
+                {"field": "auction_amount_million", "op": ">=", "value": 2},
+            ]
+        )
+        formula["score"].extend(
+            [
+                {"field": "auction_volume_ratio", "weight": 0.20, "direction": "desc"},
+                {"field": "auction_amount_million", "weight": 0.18, "direction": "desc"},
+            ]
+        )
+    if "少量" in description or "精选" in description:
+        formula["limit"] = min(formula["limit"], 8)
+    formula["score"] = formula["score"][:12]
+    return {
+        "name": f"{REGIMES.get(regime, regime)}自定义策略",
+        "description": description,
+        "regimes": [regime],
+        "formula": formula,
+        "compiler": "local_template",
+    }

backend/features/screener/indicators.py

@@ -0,0 +1,238 @@
+from __future__ import annotations
+
+import math
+import statistics
+from datetime import datetime
+from typing import Any
+
+from backend.data.numbers import finite_number as _number
+
+
+def _optional_number(value: Any) -> float | None:
+    if value in (None, ""):
+        return None
+    try:
+        result = float(value)
+    except (TypeError, ValueError):
+        return None
+    return result if math.isfinite(result) else None
+
+
+def _rounded_optional(value: Any, digits: int = 2) -> float | None:
+    parsed = _optional_number(value)
+    return round(parsed, digits) if parsed is not None else None
+
+
+def _limit_threshold(code: str, name: str) -> float:
+    if code.startswith(("4", "8")):
+        return 29.0
+    if code.startswith(("30", "68")):
+        return 19.0
+    return 9.5
+
+
+def _ending_streak(flags: list[bool], end_index: int | None = None) -> int:
+    if not flags:
+        return 0
+    index = len(flags) - 1 if end_index is None else min(end_index, len(flags) - 1)
+    streak = 0
+    while index >= 0 and flags[index]:
+        streak += 1
+        index -= 1
+    return streak
+
+
+def _max_streak(flags: list[bool]) -> int:
+    best = current = 0
+    for value in flags:
+        current = current + 1 if value else 0
+        best = max(best, current)
+    return best
+
+
+def _rsi(values: list[float], period: int = 6) -> float:
+    if len(values) <= period:
+        return 50.0
+    changes = [values[index] - values[index - 1] for index in range(len(values) - period, len(values))]
+    gains = sum(max(change, 0.0) for change in changes) / period
+    losses = sum(max(-change, 0.0) for change in changes) / period
+    if losses == 0:
+        return 100.0 if gains > 0 else 50.0
+    return 100 - 100 / (1 + gains / losses)
+
+
+def _ema(values: list[float], period: int) -> list[float]:
+    if not values:
+        return []
+    alpha = 2 / (period + 1)
+    result = [values[0]]
+    for value in values[1:]:
+        result.append(value * alpha + result[-1] * (1 - alpha))
+    return result
+
+
+def _macd_series(values: list[float]) -> tuple[list[float], list[float]]:
+    fast = _ema(values, 12)
+    slow = _ema(values, 26)
+    dif = [left - right for left, right in zip(fast, slow)]
+    return dif, _ema(dif, 9)
+
+
+def _macd_last(values: list[float]) -> tuple[float, float]:
+    dif, dea = _macd_series(values)
+    return (dif[-1], dea[-1]) if dif and dea else (0.0, 0.0)
+
+
+def _weekly_series(rows: list[dict[str, Any]]) -> tuple[list[float], list[float]]:
+    weeks: dict[str, tuple[float, float]] = {}
+    for row in rows:
+        trade_date = str(row.get("trade_date") or "")
+        try:
+            key = datetime.strptime(trade_date, "%Y%m%d").strftime("%G-%V")
+        except ValueError:
+            continue
+        close = _number(row.get("close"))
+        amount = _number(row.get("amount"))
+        previous = weeks.get(key, (close, 0.0))
+        weeks[key] = (close, previous[1] + amount)
+    ordered = list(weeks.values())
+    return [item[0] for item in ordered], [item[1] for item in ordered]
+
+
+def _broken_reversal_metrics(
+    rows: list[dict[str, Any]], flags: list[bool], code: str, name: str,
+) -> dict[str, Any]:
+    result = {"signal": 0, "days": 0, "recovered": 0, "volume_ratio": 0.0}
+    if not rows or not flags[-1]:
+        return result
+    current_close = _number(rows[-1].get("close"))
+    current_volume = _number(rows[-1].get("vol"))
+    for days in range(1, 4):
+        index = len(rows) - 1 - days
+        if index <= 0 or flags[index] or _ending_streak(flags, index - 1) < 2:
+            continue
+        broken_high = _number(rows[index].get("high"))
+        broken_volume = _number(rows[index].get("vol"))
+        recovered = int(current_close >= broken_high > 0)
+        volume_ratio = current_volume / broken_volume if broken_volume else 0.0
+        return {
+            "signal": int(recovered and volume_ratio >= 1),
+            "days": days,
+            "recovered": recovered,
+            "volume_ratio": round(volume_ratio, 3),
+        }
+    return result
+
+
+def _is_limit_bar(rows: list[dict[str, Any]], index: int, code: str, name: str) -> bool:
+    if index < 0 or index >= len(rows):
+        return False
+    return _number(rows[index].get("pct_chg")) >= _limit_threshold(code, name)
+
+
+def _touched_limit_bar(rows: list[dict[str, Any]], index: int, code: str, name: str) -> bool:
+    if index <= 0 or index >= len(rows):
+        return False
+    previous_close = _number(rows[index - 1].get("close"))
+    high = _number(rows[index].get("high"))
+    if previous_close <= 0 or high <= 0:
+        return False
+    touched_change = (high / previous_close - 1) * 100
+    return touched_change >= _limit_threshold(code, name)
+
+
+def _matches(actual: Any, operator: str, expected: Any) -> bool:
+    if actual is None:
+        return False
+    try:
+        if operator == "between":
+            return float(expected[0]) <= float(actual) <= float(expected[1])
+        if operator == "in":
+            return actual in expected
+        if operator == ">":
+            return float(actual) > float(expected)
+        if operator == ">=":
+            return float(actual) >= float(expected)
+        if operator == "<":
+            return float(actual) < float(expected)
+        if operator == "<=":
+            return float(actual) <= float(expected)
+        if operator == "==":
+            return actual == expected or float(actual) == float(expected)
+        if operator == "!=":
+            return actual != expected
+    except (TypeError, ValueError, IndexError):
+        return False
+    return False
+
+
+def _percentile_map(rows: list[dict[str, Any]], field: str, direction: str) -> dict[str, float]:
+    ordered = sorted(rows, key=lambda item: _number(item.get(field)))
+    denominator = max(1, len(ordered) - 1)
+    result = {}
+    for index, row in enumerate(ordered):
+        percentile = index / denominator
+        result[row["ts_code"]] = 1 - percentile if direction == "asc" else percentile
+    return result
+
+
+def _available_percentile_map(
+    rows: list[dict[str, Any]], field: str, direction: str,
+) -> dict[str, float | None]:
+    available = [row for row in rows if row.get(field) is not None]
+    result: dict[str, float | None] = {
+        str(row.get("ts_code") or ""): None for row in rows
+    }
+    if not available:
+        return result
+    ordered = sorted(available, key=lambda item: _number(item.get(field)))
+    denominator = max(1, len(ordered) - 1)
+    for index, row in enumerate(ordered):
+        percentile = 0.5 if len(ordered) == 1 else index / denominator
+        result[str(row.get("ts_code") or "")] = (
+            1 - percentile if direction == "asc" else percentile
+        )
+    return result
+
+
+def _pearson(first: list[float], second: list[float]) -> float:
+    if len(first) != len(second) or len(first) < 20:
+        return 0.0
+    first_mean = statistics.fmean(first)
+    second_mean = statistics.fmean(second)
+    numerator = sum(
+        (left - first_mean) * (right - second_mean)
+        for left, right in zip(first, second)
+    )
+    left_sum = sum((value - first_mean) ** 2 for value in first)
+    right_sum = sum((value - second_mean) ** 2 for value in second)
+    denominator = math.sqrt(left_sum * right_sum)
+    return numerator / denominator if denominator else 0.0
+
+
+def _risk_flags(
+    row: dict[str, Any], regime: str, include_regime_risk: bool = True
+) -> list[str]:
+    flags = []
+    if row.get("pct_chg", 0) >= 9.5:
+        flags.append("当日接近涨停，次日存在高开与无法成交风险")
+    if row.get("return_10d", 0) >= 25:
+        flags.append("短期累计涨幅较高")
+    if row.get("volatility_10d", 0) >= 7:
+        flags.append("波动率偏高")
+    if row.get("amount_billion", 0) < 1:
+        flags.append("成交承载力偏弱")
+    if include_regime_risk and regime == "retreat":
+        flags.append("市场处于退潮阶段，策略可能选择空仓")
+    return flags
+
+
+def _regime_reason(regime: str) -> str:
+    return {
+        "ice": "情绪和赚钱效应处于低位，重点观察率先抗跌与转折信号。",
+        "repair": "核心指标从低位改善，适合观察率先修复且有板块共振的方向。",
+        "fermentation": "赚钱效应扩散，主线和梯队持续增强。",
+        "climax": "情绪处于高位，后排跟风与兑现风险同时上升。",
+        "divergence": "指数或核心仍强，但广度、封板质量开始分化。",
+        "retreat": "情绪指标继续走弱，应提高筛选门槛并接受无候选结果。",
+    }.get(regime, "市场阶段待确认。")

backend/features/screener/regime.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from typing import Any
+
+from backend.data.numbers import finite_number as _number
+from backend.features.screener.catalog import REGIMES
+from backend.features.screener.indicators import _regime_reason
+from backend.features.sentiment.engine import build_sentiment_history, latest_contiguous_history
+from database import ReviewDatabase
+
+
+class RegimeDetector:
+    def __init__(self, database: ReviewDatabase) -> None:
+        self.database = database
+
+    def detect_regime(self, trade_date: str) -> dict[str, Any]:
+        series = latest_contiguous_history(
+            build_sentiment_history(self.database.list_snapshot_payloads(trade_date, 260))
+        )
+        if not series:
+            return {
+                "id": "repair", "label": REGIMES["repair"], "confidence": 25,
+                "reason": "复盘快照不足，暂按中性修复处理。", "evidence": [], "history": [],
+            }
+        current = series[-1]
+        previous = series[-2] if len(series) > 1 else current
+        score = _number(current.get("score"))
+        previous_score = _number(previous.get("score"))
+        delta = score - previous_score
+        seal_rate = _number(current.get("seal_rate"))
+        limit_up = _number(current.get("limit_up_count"))
+        broken = _number(current.get("broken_count"))
+        regime = next(
+            (key for key, label in REGIMES.items() if label == current.get("phase")),
+            "divergence",
+        )
+        confidence = min(92, 45 + len(series[-8:]) * 5 + min(abs(delta), 12))
+        evidence = [
+            f"情绪温度 {score:.0f}，较前一交易日 {delta:+.0f}，{current.get('direction') or '持平'}",
+            f"封板率 {seal_rate:.1f}%",
+            f"涨停 {limit_up:.0f} 家，炸板 {broken:.0f} 家",
+        ]
+        return {
+            "id": regime,
+            "label": REGIMES[regime],
+            "confidence": round(confidence),
+            "reason": _regime_reason(regime),
+            "evidence": evidence,
+            "history": [
+                {"trade_date": item["trade_date"], "score": _number(item.get("score"))}
+                for item in series[-8:]
+            ],
+        }

backend/features/screener/repository.py

@@ -433,11 +433,6 @@ class ScreenerRepositoryMixin:
         }
 
     def snapshot_summaries(self, end_date: str, limit: int = 10) -> list[dict[str, Any]]:
-        try:
-            from sentiment_engine import build_sentiment_history
-        except ModuleNotFoundError:
-            from .sentiment_engine import build_sentiment_history
-
         series = build_sentiment_history(self.list_snapshot_payloads(end_date, 260))
         return [
             {

backend/features/screener/routes.py

@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import json
+import re
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+
+
+class ScreenerRoutesMixin:
+    def _handle_screener_get(self, parsed) -> bool:
+        if parsed.path == "/api/screener/setup":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                self.send_json(self.application_service.screener_setup(trade_date))
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/screener/tracking":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.screener_tracking(int(query.get("limit", ["12"])[0]))
+                )
+            except (TypeError, ValueError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False
+
+    def _handle_screener_post(self, parsed) -> bool:
+        if parsed.path == "/api/screener/tracking":
+            try:
+                result = self.application_service.add_screener_tracking(self.read_json_body())
+                self.send_json({"ok": True, **result})
+            except (ValueError, json.JSONDecodeError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False
+
+    def _handle_screener_delete(self, parsed) -> bool:
+        strategy_match = re.fullmatch(r"/api/screener/strategies/(\d+)", parsed.path)
+        if strategy_match:
+            try:
+                result = self.application_service.delete_screener_strategy(int(strategy_match.group(1)))
+                self.send_json({"ok": True, **result})
+            except ValueError as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        tracking_match = re.fullmatch(r"/api/screener/tracking/(\d+)", parsed.path)
+        if tracking_match:
+            result = self.application_service.remove_screener_tracking(int(tracking_match.group(1)))
+            self.send_json({"ok": True, **result})
+            return True
+        return False
+
+    def sync_screener_data(self) -> None:
+        try:
+            body = self.read_json_body()
+            result = self.application_service.sync_screener_data(
+                str(body.get("trade_date") or date.today().isoformat()),
+                int(body.get("lookback") or 45),
+            )
+            self.send_json({"ok": True, "result": result})
+        except ValueError as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+        except Exception as exc:
+            self.send_json({"error": f"因子数据同步失败：{exc}"}, HTTPStatus.INTERNAL_SERVER_ERROR)
+
+    def compile_screener_strategy(self) -> None:
+        try:
+            body = self.read_json_body()
+            result = self.application_service.compile_screener_strategy(
+                str(body.get("prompt") or ""), str(body.get("regime") or "")
+            )
+            self.send_json({"ok": True, "strategy": result})
+        except ValueError as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+
+    def save_screener_strategy(self) -> None:
+        try:
+            body = self.read_json_body()
+            result = self.application_service.save_screener_strategy(body)
+            self.send_json({"ok": True, **result})
+        except ValueError as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+
+    def run_screener(self) -> None:
+        try:
+            body = self.read_json_body()
+            result = self.application_service.run_screener(body)
+            self.send_json({"ok": True, "result": result})
+        except ValueError as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+        except Exception as exc:
+            self.send_json({"error": f"选股执行失败：{exc}"}, HTTPStatus.INTERNAL_SERVER_ERROR)
+
+    def refresh_screener_tracking(self) -> None:
+        try:
+            body = self.read_json_body(True)
+            trade_date = str(body.get("trade_date") or date.today().isoformat())
+            self.send_json({"ok": True, **self.application_service.refresh_screener_tracking(trade_date)})
+        except (ValueError, json.JSONDecodeError) as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+        except Exception as exc:
+            self.send_json({"error": f"跟踪刷新失败：{exc}"}, HTTPStatus.INTERNAL_SERVER_ERROR)

backend/features/screener/selection.py

@@ -0,0 +1,138 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+
+from backend.bootstrap.config import display_compact_date as _display_date
+from backend.features.screener.backtest import BacktestRunner
+from backend.features.screener.catalog import REGIMES
+from backend.features.screener.factors import FactorBuilder
+from backend.features.screener.formula import FormulaEvaluator
+from database import ReviewDatabase
+
+
+class SelectionRunner:
+    def __init__(
+        self,
+        database: ReviewDatabase,
+        factor_builder: FactorBuilder,
+        formula_evaluator: FormulaEvaluator,
+        backtest_runner: BacktestRunner,
+    ) -> None:
+        self.database = database
+        self.factor_builder = factor_builder
+        self.formula_evaluator = formula_evaluator
+        self.backtest_runner = backtest_runner
+
+    def validate_formula(self, formula: dict[str, Any]) -> dict[str, Any]:
+        return self.formula_evaluator.validate_formula(formula)
+
+    def build_factors(
+        self,
+        trade_date: str,
+        realtime_snapshot: dict[str, Any] | None,
+        history_days: int,
+    ) -> tuple[list[dict[str, Any]], str]:
+        return self.factor_builder.build_factors(
+            trade_date, realtime_snapshot, history_days
+        )
+
+    def apply_formula(
+        self, rows: list[dict[str, Any]], formula: dict[str, Any], regime: str
+    ) -> list[dict[str, Any]]:
+        return self.formula_evaluator.apply_formula(rows, formula, regime)
+
+    def backtest(self, trade_date: str, formula: dict[str, Any]) -> dict[str, Any]:
+        return self.backtest_runner.backtest(trade_date, formula)
+
+    def screen(
+        self, user_id: int, trade_date: str, formula: dict[str, Any], regime: str,
+        strategy_name: str, run_backtest: bool = True,
+        realtime_snapshot: dict[str, Any] | None = None,
+        mode: str = "smart",
+        prepared_factors: list[dict[str, Any]] | None = None,
+        prepared_date: str = "",
+    ) -> dict[str, Any]:
+        mode = mode if mode in {"smart", "curated", "quant"} else "smart"
+        formula = self.validate_formula(formula)
+        if prepared_factors is None:
+            history_days = int((formula.get("meta") or {}).get("history_days") or 80)
+            factors, actual_date = self.build_factors(
+                trade_date, realtime_snapshot, history_days
+            )
+        else:
+            factors = prepared_factors
+            actual_date = prepared_date or trade_date
+        candidates = self.apply_formula(factors, formula, regime)
+        backtest = self.backtest(actual_date, formula) if run_backtest else None
+        required_fields = sorted({
+            str(item.get("field") or "")
+            for item in list(formula.get("filters") or []) + list(formula.get("score") or [])
+            if item.get("field")
+        })
+        complete_rows = sum(
+            1 for row in factors
+            if all(row.get(field) is not None for field in required_fields)
+        )
+        coverage = round(complete_rows / len(factors) * 100, 1) if factors else 0.0
+        health_status = "normal" if candidates else "no_signal"
+        if backtest and backtest["samples"] >= 20:
+            for candidate in candidates:
+                estimate = backtest["win_rate"] * 0.65 + candidate["score"] * 100 * 0.35
+                candidate["historical_probability"] = round(min(95, max(5, estimate)), 1)
+                candidate["probability_samples"] = backtest["samples"]
+        else:
+            for candidate in candidates:
+                candidate["historical_probability"] = None
+                candidate["probability_samples"] = backtest["samples"] if backtest else 0
+        result = {
+            "meta": {
+                "trade_date": _display_date(actual_date),
+                "regime": regime,
+                "regime_label": REGIMES.get(regime, regime),
+                "strategy_name": strategy_name,
+                "mode": mode,
+                "library_version": int(
+                    (formula.get("meta") or {}).get("library_version") or 0
+                ),
+                "universe_count": len(factors),
+                "candidate_count": len(candidates),
+                "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+                "health": {
+                    "status": health_status,
+                    "required_field_count": len(required_fields),
+                    "complete_rows": complete_rows,
+                    "universe_rows": len(factors),
+                    "coverage": coverage,
+                    "signal_count": len(candidates),
+                },
+                "selection_source": (
+                    "tushare_rt_k+history" if realtime_snapshot else "historical_eod"
+                ),
+                "realtime": bool(realtime_snapshot),
+                "history_cutoff": (
+                    str(realtime_snapshot.get("previous_trade_date") or "")
+                    if realtime_snapshot else actual_date
+                ),
+                "factor_freshness": {
+                    "realtime": [
+                        "价格", "涨跌幅", "成交量", "成交额", "换手率",
+                        "均线位置", "5/10日动量", "板块强度", "开盘竞价",
+                    ] if realtime_snapshot else [],
+                    "historical": ["历史波动率", "流通市值", "资金流", "竞价因子", "回测"],
+                },
+            },
+            "formula": formula,
+            "candidates": candidates,
+            "backtest": backtest,
+            "disclaimer": (
+                "候选仅由策略条件与当日数据计算；历史统计不代表未来收益。"
+                if mode == "curated"
+                else "概率为历史条件估计，不代表未来收益；退潮或样本不足时允许无候选。"
+            ),
+        }
+        run_id = self.database.save_screener_run(
+            user_id, actual_date, regime, strategy_name, formula, result, mode
+        )
+        result["meta"]["run_id"] = run_id
+        return result

backend/features/screener/service.py

@@ -12,13 +12,9 @@ from backend.features.screener.compiler import (
     LLMCompilerError,
     compile_strategy_with_llm,
 )
-from backend.features.screener.engine import (
-    FACTOR_FIELDS,
-    FACTOR_GROUPS,
-    REGIMES,
-    FactorDataService,
-    compile_local_strategy,
-)
+from backend.features.screener.catalog import FACTOR_FIELDS, FACTOR_GROUPS, REGIMES
+from backend.features.screener.data_sync import FactorDataService
+from backend.features.screener.formula import compile_local_strategy
 
 
 SCREENER_LIBRARY_VERSION = 8

backend/features/sentiment/routes.py

@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+
+
+class SentimentRoutesMixin:
+    def _handle_sentiment_get(self, parsed) -> bool:
+        if parsed.path == "/api/sentiment/history":
+            query = parse_qs(parsed.query)
+            trade_date = query.get("trade_date", [date.today().isoformat()])[0]
+            try:
+                limit = int(query.get("limit", ["20"])[0])
+                self.send_json(self.application_service.sentiment_history(trade_date, limit))
+            except (TypeError, ValueError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False

backend/features/system/routes.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from datetime import datetime
+from http import HTTPStatus
+
+
+class SystemRoutesMixin:
+    def _handle_system_public_get(self, parsed) -> bool:
+        if parsed.path == "/api/health":
+            self.send_json(
+                {
+                    "ok": True,
+                    "storage": "sqlite",
+                    "account_required": True,
+                    "time": datetime.now().astimezone().isoformat(timespec="seconds"),
+                }
+            )
+            return True
+        return False
+
+    def _handle_system_get(self, parsed) -> bool:
+        if parsed.path == "/api/admin/settings":
+            self.send_json(
+                {"ok": True, **self.application_service.system_status(), "users": self.application_service.admin_users()}
+            )
+            return True
+        return False
+
+    def backfill_data(self) -> None:
+        try:
+            body = self.read_json_body()
+            results = self.application_service.backfill(
+                str(body.get("start_date") or ""),
+                str(body.get("end_date") or ""),
+            )
+            self.send_json({"ok": True, "results": results})
+        except ValueError as exc:
+            self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+        except Exception as exc:
+            self.send_json({"error": f"历史回补失败：{exc}"}, HTTPStatus.INTERNAL_SERVER_ERROR)

backend/features/system/service.py

@@ -0,0 +1,254 @@
+from __future__ import annotations
+
+import re
+import secrets
+from typing import Any
+
+from backend.bootstrap.config import TOKEN_PATTERN, validate_text
+
+
+class SystemServiceMixin:
+    def _load_system_credentials(self, environment: dict[str, str]) -> dict[str, Any]:
+        encrypted = self.database.get_system_setting("credentials")
+        current = self.vault.decrypt_json(encrypted) if encrypted else {}
+        changed = False
+        first_user_id = self.database.first_user_id()
+        first_personal: dict[str, Any] = {}
+        if first_user_id:
+            first_encrypted = self.database.get_user_credentials(first_user_id)
+            first_personal = self.vault.decrypt_json(first_encrypted) if first_encrypted else {}
+        defaults = {
+            "tushare_token": environment.get("tushare_token") or first_personal.get("tushare_token") or "",
+            "ifind_refresh_token": environment.get("ifind_refresh_token") or "",
+            "ifind_access_token": environment.get("ifind_access_token") or "",
+            "platform_llm_primary_api_key": environment.get("platform_llm_primary_api_key") or first_personal.get("llm_primary_api_key") or "",
+            "platform_llm_primary_base_url": environment.get("platform_llm_primary_base_url") or first_personal.get("llm_primary_base_url") or "https://api.openai.com/v1",
+            "platform_llm_primary_model": environment.get("platform_llm_primary_model") or first_personal.get("llm_primary_model") or "",
+            "platform_llm_fallback_api_key": environment.get("platform_llm_fallback_api_key") or first_personal.get("llm_fallback_api_key") or "",
+            "platform_llm_fallback_base_url": environment.get("platform_llm_fallback_base_url") or first_personal.get("llm_fallback_base_url") or "",
+            "platform_llm_fallback_model": environment.get("platform_llm_fallback_model") or first_personal.get("llm_fallback_model") or "",
+            "member_daily_limit": 50,
+            "background_refresh_enabled": True,
+        }
+        for key, value in defaults.items():
+            if key not in current:
+                current[key] = value
+                changed = True
+        if not isinstance(current.get("llm_models"), list):
+            migrated_models: list[dict[str, str]] = []
+            for role, label in (("primary", "原主模型"), ("fallback", "原辅助模型")):
+                profile = {
+                    "api_key": str(current.get(f"platform_llm_{role}_api_key") or ""),
+                    "base_url": str(current.get(f"platform_llm_{role}_base_url") or ""),
+                    "model": str(current.get(f"platform_llm_{role}_model") or ""),
+                }
+                if profile["api_key"] or profile["model"]:
+                    model_id = f"migrated-{role}"
+                    migrated_models.append(
+                        {"id": model_id, "name": label, **profile}
+                    )
+                    current[f"{role}_model_id"] = model_id
+            current["llm_models"] = migrated_models
+            current.setdefault("primary_model_id", "")
+            current.setdefault("fallback_model_id", "")
+            changed = True
+        if changed or not encrypted:
+            self.database.save_system_setting("credentials", self.vault.encrypt_json(current))
+        for row in self.database.list_user_credentials():
+            personal = self.vault.decrypt_json(str(row.get("encrypted_payload") or ""))
+            if "tushare_token" in personal:
+                personal.pop("tushare_token", None)
+                self.database.save_user_credentials(
+                    int(row["user_id"]), self.vault.encrypt_json(personal)
+                )
+        return current
+
+    def _save_system_credentials(self, credentials: dict[str, Any]) -> None:
+        with self.system_lock:
+            self.database.save_system_setting("credentials", self.vault.encrypt_json(credentials))
+            self._system_credentials = dict(credentials)
+            if hasattr(self, "ifind"):
+                self.ifind.set_credentials(
+                    str(credentials.get("ifind_refresh_token") or ""),
+                    str(credentials.get("ifind_access_token") or ""),
+                )
+
+    @property
+    def configured(self) -> bool:
+        return bool(self.token)
+
+    def _credentials(self) -> dict[str, str]:
+        credentials = getattr(self._request_context, "credentials", {})
+        return {
+            "llm_primary_api_key": str(credentials.get("llm_primary_api_key") or ""),
+            "llm_primary_base_url": str(
+                credentials.get("llm_primary_base_url") or "https://api.openai.com/v1"
+            ),
+            "llm_primary_model": str(credentials.get("llm_primary_model") or ""),
+            "llm_fallback_api_key": str(credentials.get("llm_fallback_api_key") or ""),
+            "llm_fallback_base_url": str(credentials.get("llm_fallback_base_url") or ""),
+            "llm_fallback_model": str(credentials.get("llm_fallback_model") or ""),
+        }
+
+    def _save_credentials(self, credentials: dict[str, str]) -> None:
+        self.database.save_user_credentials(
+            self.current_user_id,
+            self.vault.encrypt_json(credentials),
+        )
+        self._request_context.credentials = dict(credentials)
+
+    @property
+    def token(self) -> str:
+        return str(self._system_credentials.get("tushare_token") or "")
+
+    def system_status(self) -> dict[str, Any]:
+        platform = self._platform_llm_profile()
+        model_pool = []
+        for item in self._system_credentials.get("llm_models") or []:
+            if not isinstance(item, dict):
+                continue
+            profile = {
+                "api_key": str(item.get("api_key") or ""),
+                "base_url": str(item.get("base_url") or ""),
+                "model": str(item.get("model") or ""),
+            }
+            model_pool.append(
+                {
+                    "id": str(item.get("id") or ""),
+                    "name": str(item.get("name") or ""),
+                    "base_url": profile["base_url"],
+                    "model": profile["model"],
+                    "configured": self._profile_configured(profile),
+                }
+            )
+        return {
+            "data": {
+                "configured": self.configured,
+                "ifind": self.ifind.status(),
+                "background_refresh_enabled": bool(
+                    self._system_credentials.get("background_refresh_enabled", True)
+                ),
+                **self.database.status(),
+                "jobs": self.jobs.repository.recent(12),
+            },
+            "llm": {
+                "primary_configured": self._profile_configured(platform["primary"]),
+                "fallback_configured": self._profile_configured(platform["fallback"]),
+                "models": model_pool,
+                "primary_model_id": str(self._system_credentials.get("primary_model_id") or ""),
+                "fallback_model_id": str(self._system_credentials.get("fallback_model_id") or ""),
+            },
+            "membership": {
+                "member_daily_limit": max(
+                    1, int(self._system_credentials.get("member_daily_limit") or 50)
+                )
+            },
+        }
+
+    def save_system_settings(self, payload: dict[str, Any]) -> dict[str, Any]:
+        current = dict(self._system_credentials)
+        token = str(payload.get("tushare_token") or current.get("tushare_token") or "").strip()
+        if token and not TOKEN_PATTERN.fullmatch(token):
+            raise ValueError("Tushare Token 格式不正确。")
+        ifind_refresh_token = str(
+            payload.get("ifind_refresh_token")
+            or current.get("ifind_refresh_token")
+            or ""
+        ).strip()
+        if ifind_refresh_token and (
+            len(ifind_refresh_token) > 2048
+            or any(character.isspace() for character in ifind_refresh_token)
+        ):
+            raise ValueError("iFinD Refresh Token 格式不正确。")
+        existing_models = {
+            str(item.get("id") or ""): item
+            for item in current.get("llm_models") or []
+            if isinstance(item, dict) and item.get("id")
+        }
+        raw_models = payload.get("models")
+        models: list[dict[str, str]] = []
+        if raw_models is not None:
+            if not isinstance(raw_models, list) or len(raw_models) > 20:
+                raise ValueError("模型池格式不正确，最多可保存 20 个模型。")
+            seen_ids: set[str] = set()
+            seen_names: set[str] = set()
+            for index, raw in enumerate(raw_models, start=1):
+                if not isinstance(raw, dict):
+                    raise ValueError("模型池条目格式不正确。")
+                model_id = str(raw.get("id") or f"model-{secrets.token_hex(6)}").strip()
+                if not re.fullmatch(r"[A-Za-z0-9_-]{3,80}", model_id) or model_id in seen_ids:
+                    raise ValueError("模型 ID 不正确或重复。")
+                name = validate_text(raw.get("name"), f"模型 {index} 名称", 50, required=True)
+                normalized_name = name.casefold()
+                if normalized_name in seen_names:
+                    raise ValueError("模型名称不能重复。")
+                profile = self._validate_llm_profile(
+                    raw,
+                    existing_models.get(model_id) or {},
+                    required=True,
+                    label=name,
+                )
+                models.append({"id": model_id, "name": name, **profile})
+                seen_ids.add(model_id)
+                seen_names.add(normalized_name)
+        else:
+            models = [dict(item) for item in existing_models.values()]
+        model_ids = {item["id"] for item in models}
+        primary_model_id = str(
+            payload.get("primary_model_id", current.get("primary_model_id") or "") or ""
+        ).strip()
+        fallback_model_id = str(
+            payload.get("fallback_model_id", current.get("fallback_model_id") or "") or ""
+        ).strip()
+        if models and primary_model_id not in model_ids:
+            raise ValueError("请从模型池选择主模型。")
+        if not models:
+            primary_model_id = ""
+            fallback_model_id = ""
+        if fallback_model_id and fallback_model_id not in model_ids:
+            raise ValueError("辅助模型不在模型池中。")
+        if fallback_model_id and fallback_model_id == primary_model_id:
+            raise ValueError("主模型与辅助模型不能相同。")
+        try:
+            daily_limit = max(
+                1,
+                min(
+                    1000,
+                    int(payload.get("member_daily_limit", current.get("member_daily_limit") or 50)),
+                ),
+            )
+        except (TypeError, ValueError) as exc:
+            raise ValueError("会员每日额度应为 1 至 1000。") from exc
+        current.update(
+            {
+                "tushare_token": token,
+                "ifind_refresh_token": ifind_refresh_token,
+                "llm_models": models,
+                "primary_model_id": primary_model_id,
+                "fallback_model_id": fallback_model_id,
+                "member_daily_limit": daily_limit,
+                "background_refresh_enabled": bool(
+                    payload.get(
+                        "background_refresh_enabled",
+                        current.get("background_refresh_enabled", True),
+                    )
+                ),
+            }
+        )
+        self._save_system_credentials(current)
+        return self.system_status()
+
+    def status(self) -> dict[str, Any]:
+        llm_access = self.llm_access_status()
+        return {
+            "configured": self.configured,
+            "mode": "tushare" if self.configured else "unavailable",
+            "llm_configured": self.llm_configured,
+            "llm_model": self.llm_primary_model if self.llm_configured else "",
+            "llm_fallback_configured": self.llm_fallback_configured,
+            "llm_fallback_model": self.llm_fallback_model if self.llm_fallback_configured else "",
+            "llm_access": llm_access,
+            "birth_profile_configured": bool(self.stored_birth_profile()),
+            "birth_profile": self.stored_birth_profile(),
+            **self.database.status(),
+        }

backend/features/themes/routes.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from datetime import date
+from http import HTTPStatus
+from urllib.parse import parse_qs
+from backend.data.providers.tushare_client import TushareError
+
+
+class ThemeRoutesMixin:
+    def _handle_themes_get(self, parsed) -> bool:
+        if parsed.path == "/api/themes":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.theme_library(
+                        query.get("trade_date", [date.today().isoformat()])[0],
+                        query.get("force", ["0"])[0] == "1",
+                    )
+                )
+            except (ValueError, TushareError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        if parsed.path == "/api/themes/detail":
+            query = parse_qs(parsed.query)
+            try:
+                self.send_json(
+                    self.application_service.theme_detail(
+                        query.get("code", [""])[0],
+                        query.get("trade_date", [date.today().isoformat()])[0],
+                    )
+                )
+            except (ValueError, TushareError) as exc:
+                self.send_json({"error": str(exc)}, HTTPStatus.BAD_REQUEST)
+            return True
+        return False