"""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)