251 lines
8.4 KiB
Python
251 lines
8.4 KiB
Python
"""新浪财经数据客户端。
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作为东方财富/腾讯的兜底数据源：
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- 分钟 K 线：用于盘中量能证据
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- 实时行情：可按股票列表批量拉取
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新浪接口属于公开网页行情接口，字段稳定性弱于正式数据服务，因此只作为 fallback。
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"""
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import json
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import logging
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import re
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from collections import defaultdict
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import httpx
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import pandas as pd
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from app.data.cache import cache
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from app.data.code_utils import split_ts_code
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from app.data.models import StockQuote
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logger = logging.getLogger(__name__)
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SINA_KLINE_URL = "https://quotes.sina.cn/cn/api/json_v2.php/CN_MarketDataService.getKLineData"
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SINA_QUOTE_URL = "https://hq.sinajs.cn/list="
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HEADERS = {
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"Referer": "https://finance.sina.com.cn",
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"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36",
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}
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def _ts_code_to_sina(ts_code: str) -> str:
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code, market = split_ts_code(ts_code)
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return f"{market.lower()}{code}"
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async def get_min_kline(ts_code: str, period: str = "5", count: int = 48) -> pd.DataFrame:
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"""获取新浪分钟 K 线，返回与东方财富分钟线一致的列。"""
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scale = period if period in {"5", "15", "30", "60"} else "5"
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cache_key = f"sina_min_kline:{ts_code}:{scale}:{count}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached
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params = {
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"symbol": _ts_code_to_sina(ts_code),
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"scale": scale,
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"ma": "no",
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"datalen": str(count),
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}
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try:
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async with httpx.AsyncClient(timeout=10, follow_redirects=True) as client:
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resp = await client.get(SINA_KLINE_URL, params=params, headers=HEADERS)
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resp.raise_for_status()
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rows = _parse_kline_payload(resp.text)
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if not rows:
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return pd.DataFrame()
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result = []
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for item in rows[-count:]:
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amount = item.get("amount")
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volume = item.get("volume", 0)
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result.append({
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"time": item.get("day", ""),
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"open": float(item.get("open", 0) or 0),
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"close": float(item.get("close", 0) or 0),
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"high": float(item.get("high", 0) or 0),
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"low": float(item.get("low", 0) or 0),
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"volume": float(volume or 0),
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# 新浪分钟线常见字段只有 volume；量能分布分析需要 amount 时用 volume 兜底。
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"amount": float(amount if amount not in (None, "") else volume or 0),
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})
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df = pd.DataFrame(result)
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cache.set(cache_key, df, ttl=180)
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return df
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except Exception as e:
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logger.warning("新浪分钟K线获取失败 %s: %s", ts_code, e)
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return pd.DataFrame()
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async def get_realtime_quotes_batch(ts_codes: list[str]) -> dict[str, StockQuote]:
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"""批量获取新浪实时行情。"""
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results: dict[str, StockQuote] = {}
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if not ts_codes:
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return results
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batch_size = 80
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for i in range(0, len(ts_codes), batch_size):
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batch = ts_codes[i:i + batch_size]
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symbols = ",".join(_ts_code_to_sina(code) for code in batch)
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try:
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async with httpx.AsyncClient(timeout=10, follow_redirects=True) as client:
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resp = await client.get(f"{SINA_QUOTE_URL}{symbols}", headers=HEADERS)
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resp.raise_for_status()
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resp.encoding = "gbk"
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quote_map = _parse_quote_payload(resp.text)
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for ts_code in batch:
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data = quote_map.get(_ts_code_to_sina(ts_code))
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quote = _quote_from_fields(ts_code, data)
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if quote:
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results[ts_code] = quote
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except Exception as e:
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logger.warning("新浪批量行情获取失败: %s", e)
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return results
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async def get_sector_realtime_ranking_by_industry(limit: int = 20) -> list[dict]:
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"""用新浪全市场实时行情 + Tushare 静态行业映射聚合今日行业榜。
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这是东方财富板块榜不可用时的 fallback，不依赖 Tushare 当日行情。
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"""
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from app.data.tushare_client import tushare_client
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cache_key = f"sina_sector_industry:{limit}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached
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stock_basic = tushare_client.get_stock_basic()
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if stock_basic.empty or "industry" not in stock_basic.columns:
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logger.warning("新浪行业榜兜底失败: 股票静态行业映射为空")
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return []
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stock_basic = stock_basic.dropna(subset=["industry"])
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code_to_industry = {}
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code_to_name = {}
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for _, row in stock_basic.iterrows():
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ts_code = row.get("ts_code")
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industry = row.get("industry")
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if not ts_code or not industry:
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continue
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code_to_industry[ts_code] = str(industry)
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code_to_name[ts_code] = str(row.get("name") or ts_code)
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quotes = await get_realtime_quotes_batch(list(code_to_industry.keys()))
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if not quotes:
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return []
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grouped = defaultdict(lambda: {
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"amount": 0.0,
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"pct_sum": 0.0,
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"count": 0,
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"up_count": 0,
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"down_count": 0,
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"leading": None,
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})
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for ts_code, quote in quotes.items():
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industry = code_to_industry.get(ts_code)
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if not industry:
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continue
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bucket = grouped[industry]
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bucket["amount"] += quote.amount or 0
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bucket["pct_sum"] += quote.pct_chg
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bucket["count"] += 1
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if quote.pct_chg > 0:
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bucket["up_count"] += 1
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elif quote.pct_chg < 0:
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bucket["down_count"] += 1
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leading = bucket["leading"]
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if leading is None or quote.pct_chg > leading["pct_chg"]:
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bucket["leading"] = {
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"ts_code": ts_code,
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"name": quote.name or code_to_name.get(ts_code, ts_code),
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"pct_chg": quote.pct_chg,
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}
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result = []
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for idx, (industry, bucket) in enumerate(grouped.items(), start=1):
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count = bucket["count"] or 1
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leading = bucket["leading"] or {}
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result.append({
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"sector_code": f"SINA_{idx:03d}",
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"sector_name": industry,
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"board_type": "industry",
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"pct_change": round(bucket["pct_sum"] / count, 2),
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"amount": round(bucket["amount"], 2),
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"turnover_rate": 0,
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"up_count": int(bucket["up_count"]),
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"down_count": int(bucket["down_count"]),
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"leading_stock_name": leading.get("name", ""),
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"leading_stock_code": leading.get("ts_code", ""),
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"leading_stock_pct": float(leading.get("pct_chg", 0) or 0),
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"source": "sina",
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})
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result.sort(key=lambda item: (item["pct_change"], item["amount"]), reverse=True)
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result = result[:limit]
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cache.set(cache_key, result, ttl=180)
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logger.info("新浪行业实时榜兜底: 获取 %s 个行业", len(result))
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return result
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def _parse_kline_payload(text: str) -> list[dict]:
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text = (text or "").strip()
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if not text:
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return []
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if text.startswith("["):
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return json.loads(text)
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start = text.find("[")
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end = text.rfind("]")
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if start >= 0 and end > start:
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return json.loads(text[start:end + 1])
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return []
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def _parse_quote_payload(text: str) -> dict[str, list[str]]:
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result: dict[str, list[str]] = {}
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pattern = re.compile(r'var hq_str_([^=]+)="([^"]*)";')
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for symbol, payload in pattern.findall(text or ""):
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if payload:
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result[symbol] = payload.split(",")
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return result
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def _quote_from_fields(ts_code: str, fields: list[str] | None) -> StockQuote | None:
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if not fields or len(fields) < 32:
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return None
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try:
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name = fields[0]
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open_price = float(fields[1] or 0)
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pre_close = float(fields[2] or 0)
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price = float(fields[3] or 0)
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high = float(fields[4] or 0)
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low = float(fields[5] or 0)
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volume = float(fields[8] or 0)
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amount = float(fields[9] or 0) / 10000
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pct_chg = ((price - pre_close) / pre_close * 100) if pre_close else 0
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if price <= 0:
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return None
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return StockQuote(
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ts_code=ts_code,
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name=name,
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price=price,
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pct_chg=round(pct_chg, 2),
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volume=volume,
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amount=amount,
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turnover_rate=0,
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high=high,
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low=low,
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open=open_price,
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pre_close=pre_close,
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)
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except (IndexError, ValueError):
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return None
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