feat: integrate iFinD data and refine intelligent workspaces

tushare_client.py

@@ -1326,6 +1326,58 @@ class TushareClient:
             "methodology": "同花顺行业最新成分股的 rt_k 等权涨跌、宽度与成交额聚合",
         }
 
+    def hot_money_profiles(self) -> dict[str, Any]:
+        rows = self.query("hm_list", {}, "name,desc,orgs")
+        profiles: list[dict[str, Any]] = []
+        seen_names: set[str] = set()
+        for row in rows:
+            name = str(row.get("name") or "").strip()
+            if not name or name in seen_names:
+                continue
+            seen_names.add(name)
+            description = _text(row.get("desc"))
+            organization_text = _text(row.get("orgs"))
+            parsed_organizations: Any = None
+            if organization_text.startswith("["):
+                try:
+                    parsed_organizations = json.loads(organization_text)
+                except json.JSONDecodeError:
+                    parsed_organizations = None
+            organization_parts = (
+                parsed_organizations
+                if isinstance(parsed_organizations, list)
+                else re.split(r"[,，;；\n]+", organization_text)
+            )
+            organizations = list(dict.fromkeys(
+                _text(part)
+                for part in organization_parts
+                if _text(part)
+            ))
+            profiles.append(
+                {
+                    "id": f"hot-money-profile-{len(profiles) + 1}",
+                    "name": name,
+                    "description": description,
+                    "organizations": organizations,
+                    "organization_count": len(organizations),
+                }
+            )
+        return {
+            "meta": {
+                "source": "tushare",
+                "status": "success" if profiles else "empty",
+                "schema_version": 1,
+                "updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
+                "notice": "",
+            },
+            "summary": {
+                "profile_count": len(profiles),
+                "described_count": sum(bool(item["description"]) for item in profiles),
+                "organization_count": sum(item["organization_count"] for item in profiles),
+            },
+            "profiles": profiles,
+        }
+
     def dragon_tiger(self, requested_date: str) -> dict[str, Any]:
         trade_date, _ = self.resolve_trade_context(requested_date)
         detail_rows = self.query(