chore: 重构前基线 — 9 个 sync task 全部 ok + akshare 移除 + mairui 资金流接入

状态： - 9 个 sync task（stock_basic / kline_daily / kline_index / kline_5min / moneyflow / industry_sector / sector_features / share_snapshot / market_regime） - 数据源：baostock + mairui + 雪球(pysnowball) + 新浪（4 个） - 项目级约束：永远不用 akshare（已落实） - kline_5min 改用 DB 快照统一全量/增量逻辑 - 零后端 Chrome 扩展 xueqiu_sync（独立项目）
2026-06-15 15:36:09 +08:00
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+"""新浪财经数据源。
+
+提供：
+  - 日 K 线（主源，免费，无 token）
+  - 指数日 K 线
+
+接口：https://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.getKLineData
+"""
+from __future__ import annotations
+
+import json
+import threading
+import time
+import urllib.request
+from typing import Any
+
+import pandas as pd
+
+from app.core.datasource.base import DataSource
+from app.core.datasource.utils import (
+    code6_to_sina,
+    filter_date_range,
+    normalize_kline,
+)
+
+
+class SinaSource(DataSource):
+    key = "datasource_xinlang"
+    name = "新浪财经"
+    provides = ["kline_daily", "index_daily"]
+    requires_credential = False
+
+    _HEADERS = {
+        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
+        "Referer": "https://finance.sina.com.cn",
+    }
+    _BASE_URL = (
+        "https://money.finance.sina.com.cn/quotes_service/api/json_v2.php"
+        "/CN_MarketData.getKLineData"
+    )
+
+    # RPS 限流（线程安全）
+    # 设为 3 防止触发新浪反爬（单 IP 频率过高会被临时封禁）
+    _RPS_LIMIT = 3.0
+    _rate_lock = threading.Lock()
+    _last_request_ts = 0.0
+
+    @classmethod
+    def _wait_rps(cls):
+        """全局 RPS 限流：所有线程共享同一个速率限制。"""
+        with cls._rate_lock:
+            now = time.time()
+            elapsed = now - cls._last_request_ts
+            min_interval = 1.0 / cls._RPS_LIMIT
+            if elapsed < min_interval:
+                time.sleep(min_interval - elapsed)
+            cls._last_request_ts = time.time()
+
+    def is_available(self) -> tuple[bool, str]:
+        return True, "ok"
+
+    def health_check(self) -> dict[str, Any]:
+        try:
+            url = (
+                f"{self._BASE_URL}?symbol=sh600036&scale=240&ma=no&datalen=5"
+            )
+            req = urllib.request.Request(url, headers=self._HEADERS)
+            with urllib.request.urlopen(req, timeout=15) as resp:
+                data = json.loads(resp.read().decode("utf-8"))
+            if not data or not isinstance(data, list):
+                return {"success": False, "message": "新浪财经返回数据格式异常"}
+            return {"success": True, "message": f"连接成功，获取到 {len(data)} 条 K 线数据"}
+        except Exception as e:
+            return {"success": False, "message": f"新浪财经连接失败: {e}"}
+
+    def _fetch(self, symbol: str, datalen: int = 5000, retry: int = 2) -> list:
+        self._wait_rps()
+        url = f"{self._BASE_URL}?symbol={symbol}&scale=240&ma=no&datalen={int(max(datalen, 260))}"
+        last_err = None
+        for attempt in range(retry + 1):
+            try:
+                req = urllib.request.Request(url, headers=self._HEADERS)
+                with urllib.request.urlopen(req, timeout=20) as resp:
+                    raw = resp.read().decode("gbk", errors="replace")
+                    return json.loads(raw)
+            except Exception as e:
+                last_err = e
+                if attempt < retry:
+                    time.sleep(0.5 * (attempt + 1))  # 短暂退避后重试
+                continue
+        if last_err:
+            from app.core.utils.logging import get_logger
+            get_logger("sina").debug(f"sina fetch {symbol} failed after {retry+1} attempts: {last_err}")
+        return []
+
+    def fetch_kline_daily(self, code6: str, start: str, end: str) -> pd.DataFrame:
+        symbol = code6_to_sina(code6)
+        data = self._fetch(symbol)
+        if not data or not isinstance(data, list):
+            return pd.DataFrame()
+        records = []
+        for item in data:
+            try:
+                records.append({
+                    "trade_date": item["day"],
+                    "open": item["open"],
+                    "high": item["high"],
+                    "low": item["low"],
+                    "close": item["close"],
+                    "volume": item["volume"],
+                })
+            except KeyError:
+                continue
+        return filter_date_range(normalize_kline(pd.DataFrame(records)), start, end)
+
+    def fetch_index_daily(self, index_code: str, start: str, end: str) -> pd.DataFrame:
+        if index_code.startswith(("5", "6", "9")):
+            symbol = f"sh{index_code}"
+        else:
+            symbol = f"sz{index_code}"
+        data = self._fetch(symbol)
+        if not data or not isinstance(data, list):
+            return pd.DataFrame()
+        records = []
+        for item in data:
+            try:
+                records.append({
+                    "trade_date": item["day"],
+                    "open": float(item["open"]),
+                    "high": float(item["high"]),
+                    "low": float(item["low"]),
+                    "close": float(item["close"]),
+                    "volume": float(item["volume"]),
+                })
+            except (KeyError, ValueError, TypeError):
+                continue
+        return filter_date_range(normalize_kline(pd.DataFrame(records)), start, end)