453 lines
16 KiB
Python
453 lines
16 KiB
Python
"""东方财富数据客户端
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通过 push2.eastmoney.com / push2his.eastmoney.com 获取实时数据：
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- 板块实时涨跌幅排名（行业/概念板块）
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- A 股分钟级 K 线数据（盘中量能分布分析）
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免费接口，无需认证，注意限频。
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"""
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import logging
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import httpx
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import pandas as pd
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from datetime import datetime
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from app.data.cache import cache
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from app.config import settings
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from app.db.error_logger import log_error
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logger = logging.getLogger(__name__)
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# 东方财富接口
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EASTMONEY_KLINE_URL = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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SECTOR_LIST_URL = "https://push2.eastmoney.com/api/qt/clist/get"
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HEADERS = {
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"Referer": "https://finance.eastmoney.com",
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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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SECTOR_HEADERS = {
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"Referer": "https://data.eastmoney.com",
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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_eastmoney(ts_code: str) -> str:
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"""600519.SH -> 1.600519 (上海=1, 深圳=0)"""
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code, market = ts_code.split(".")
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prefix = "1" if market == "SH" else "0"
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return f"{prefix}.{code}"
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async def get_sector_realtime_ranking(
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fs: str = "m:90+t:2",
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sort_by: str = "f3",
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descending: bool = True,
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page_size: int = 100,
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notify: bool = True,
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) -> list[dict]:
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"""获取东方财富板块实时涨跌幅排名
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一次请求获取所有板块的实时数据，盘中和盘后均可使用。
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替代通过腾讯批量获取成分股行情再手动计算的方式。
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Args:
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fs: 板块类型
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- "m:90+t:2": 行业板块（东方财富分类，约496个）
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- "m:90+t:3": 概念板块（约493个）
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- "m:90+t:1": 地域板块（约31个）
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sort_by: 排序字段，默认 f3=涨跌幅
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descending: True=降序（涨幅从高到低）
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page_size: 返回板块数量
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Returns:
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list[dict]，每个 dict 包含:
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- sector_code: 东方财富板块代码 (如 "BK0428")
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- sector_name: 板块名称 (如 "酿酒行业")
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- pct_change: 实时涨跌幅 %
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- amount: 成交额（元）
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- turnover_rate: 换手率 %
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- up_count: 上涨家数
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- down_count: 下跌家数
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- leading_stock_name: 领涨股名称 (f128)
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- leading_stock_code: 领涨股代码 (f140)
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- leading_stock_pct: 领涨股涨跌幅 % (f141)
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"""
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cache_key = f"sector_rt:{fs}:{sort_by}"
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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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"pn": "1",
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"pz": str(page_size),
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# 东方财富列表接口里，po=1 对应降序，po=0 对应升序。
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# 之前这里写反后，会把“涨幅榜”实际拿成跌幅靠前列表。
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"po": "1" if descending else "0",
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"np": "1",
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"ut": "b1f8f8f8",
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"fltt": "2",
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"invt": "2",
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"fid": sort_by,
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"fs": fs,
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"fields": "f2,f3,f4,f6,f8,f12,f14,f104,f105,f128,f140,f141",
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}
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try:
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board_type = "industry" if fs == "m:90+t:2" else "concept" if fs == "m:90+t:3" else "region" if fs == "m:90+t:1" else "unknown"
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async with httpx.AsyncClient() as client:
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resp = await client.get(
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SECTOR_LIST_URL,
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params=params,
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headers=SECTOR_HEADERS,
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timeout=10,
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follow_redirects=True,
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)
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data = _parse_eastmoney_json(resp, "板块实时排名")
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items = data.get("data", {}).get("diff", [])
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if not items:
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logger.debug("东方财富板块实时排名无数据")
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return []
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result = []
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for item in items:
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# f3 可能是 "-"（停牌等异常情况）
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pct = item.get("f3")
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if pct == "-" or pct is None:
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pct = 0.0
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result.append({
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"sector_code": item.get("f12", ""),
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"sector_name": item.get("f14", ""),
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"board_type": board_type,
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"pct_change": float(pct),
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"amount": float(item.get("f6", 0) or 0),
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"turnover_rate": float(item.get("f8", 0) or 0),
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"up_count": int(item.get("f104", 0) or 0),
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"down_count": int(item.get("f105", 0) or 0),
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"leading_stock_name": item.get("f128", ""),
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"leading_stock_code": item.get("f140", ""),
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"leading_stock_pct": float(item.get("f141", 0) or 0),
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})
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# 缓存：盘中 60 秒，盘后 300 秒
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ttl = 60 if _is_trading_hours() else 300
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cache.set(cache_key, result, ttl)
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logger.info(f"东方财富板块实时排名: 获取 {len(result)} 个板块")
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return result
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except Exception as e:
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logger.error(f"东方财富板块实时排名获取失败: {e}")
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if notify:
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await log_error(
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"eastmoney",
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f"东方财富板块实时排名获取失败: {e}",
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detail=f"fs={fs}, sort_by={sort_by}, page_size={page_size}",
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)
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return []
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async def get_a_share_realtime_ranking(
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sort_by: str = "f3",
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descending: bool = True,
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page_size: int = 6000,
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) -> list[dict]:
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"""获取 A 股实时行情列表，用于市场温度和实时候选召回。"""
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cache_key = f"ashare_rt:{sort_by}:{descending}:{page_size}"
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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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fs = "m:0+t:6,m:0+t:80,m:0+t:81+s:2048,m:1+t:2,m:1+t:23"
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fields = "f2,f3,f6,f8,f9,f12,f14,f20,f21,f23,f62"
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try:
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result = []
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page = 1
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page_size_per_request = min(max(page_size, 200), 500)
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async with httpx.AsyncClient() as client:
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while len(result) < page_size:
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params = {
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"pn": str(page),
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"pz": str(page_size_per_request),
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"po": "1" if descending else "0",
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"np": "1",
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"ut": "b1f8f8f8",
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"fltt": "2",
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"invt": "2",
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"fid": sort_by,
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"fs": fs,
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"fields": fields,
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}
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resp = await client.get(
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SECTOR_LIST_URL,
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params=params,
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headers=SECTOR_HEADERS,
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timeout=12,
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follow_redirects=True,
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)
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data = _parse_eastmoney_json(resp, f"A股实时行情 第{page}页")
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items = data.get("data", {}).get("diff", [])
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if not items:
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break
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before_count = len(result)
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for item in items:
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pct = item.get("f3")
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price = item.get("f2")
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code = str(item.get("f12", "") or "")
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if not code or pct == "-" or price == "-" or pct is None or price is None:
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continue
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result.append({
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"ts_code": _eastmoney_code_to_ts(code),
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"name": item.get("f14", ""),
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"price": float(price or 0),
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"pct_chg": float(pct or 0),
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"amount": float(item.get("f6", 0) or 0),
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"turnover_rate": float(item.get("f8", 0) or 0),
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"pe": _safe_float(item.get("f9")),
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"pb": _safe_float(item.get("f23")),
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"total_mv": _safe_float(item.get("f20")),
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"circ_mv": _safe_float(item.get("f21")),
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"main_net_inflow": _safe_float(item.get("f62")) or 0,
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})
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if len(result) == before_count or len(items) < page_size_per_request:
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break
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page += 1
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# 去重，避免跨页或接口异常重复
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deduped = {}
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for item in result:
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deduped[item["ts_code"]] = item
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result = list(deduped.values())[:page_size]
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ttl = 60 if _is_trading_hours() else 300
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cache.set(cache_key, result, ttl)
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logger.info("东方财富A股实时行情: 获取 %s 只", len(result))
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return result
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except Exception as e:
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logger.error(f"东方财富A股实时行情获取失败: {e}")
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await log_error(
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"eastmoney",
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f"东方财富A股实时行情获取失败: {e}",
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detail=f"sort_by={sort_by}, page_size={page_size}",
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)
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return []
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def _eastmoney_code_to_ts(code: str) -> str:
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if code.startswith("6"):
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return f"{code}.SH"
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return f"{code}.SZ"
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def _safe_float(value) -> float | None:
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if value in (None, "", "-"):
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return None
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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async def get_min_kline(
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ts_code: str,
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period: str = "5",
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count: int = 48,
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) -> pd.DataFrame:
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"""获取分钟级 K 线数据
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Args:
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ts_code: Tushare 格式代码，如 600519.SH
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period: 分钟周期 ("1", "5", "15", "30", "60")
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count: 请求条数（5分钟×48=4小时≈一个交易日）
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Returns:
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DataFrame with columns: time, open, close, high, low, volume, amount
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空 DataFrame if failed
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"""
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cache_key = f"min_kline:{ts_code}:{period}"
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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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secid = _ts_code_to_eastmoney(ts_code)
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params = {
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"secid": secid,
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"fields1": "f1,f2,f3,f4,f5,f6",
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"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
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"klt": period, # 分钟周期
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"fqt": "1", # 前复权
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"beg": "0",
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"end": "20500101",
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"lmt": str(count), # 返回条数限制
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}
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try:
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async with httpx.AsyncClient() as client:
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resp = await client.get(
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EASTMONEY_KLINE_URL,
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params=params,
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headers=HEADERS,
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timeout=10,
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follow_redirects=True,
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)
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data = _parse_eastmoney_json(resp, f"分钟K线 {ts_code}")
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klines = data.get("data", {}).get("klines", [])
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if not klines:
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logger.debug(f"东方财富分钟K线 {ts_code} 无数据")
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return pd.DataFrame()
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# 解析：格式 "2026-04-15 09:30,5.71,5.73,5.74,5.70,12345,70000.00"
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rows = []
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for line in klines:
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parts = line.split(",")
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if len(parts) < 7:
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continue
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rows.append({
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"time": parts[0],
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"open": float(parts[1]),
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"close": float(parts[2]),
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"high": float(parts[3]),
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"low": float(parts[4]),
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"volume": float(parts[5]),
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"amount": float(parts[6]),
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})
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df = pd.DataFrame(rows)
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if df.empty:
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return df
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# 缓存（分钟数据盘中3分钟过期，盘后30分钟过期）
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ttl = 180 if _is_trading_hours() else 1800
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cache.set(cache_key, df, ttl)
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return df
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except Exception as e:
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logger.warning(f"东方财富分钟K线获取失败 {ts_code}: {e}，尝试新浪兜底")
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try:
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from app.data.sina_client import get_min_kline as get_sina_min_kline
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fallback_df = await get_sina_min_kline(ts_code, period=period, count=count)
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if not fallback_df.empty:
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logger.info("新浪分钟K线兜底成功 %s: %s 条", ts_code, len(fallback_df))
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return fallback_df
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except Exception as fallback_error:
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logger.warning("新浪分钟K线兜底异常 %s: %s", ts_code, fallback_error)
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logger.error(f"分钟K线双源获取失败 {ts_code}: 东方财富={e}")
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await log_error(
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"market_data",
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f"分钟K线双源获取失败 {ts_code}: {e}",
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detail=f"primary=eastmoney, fallback=sina, period={period}, count={count}",
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)
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return pd.DataFrame()
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def _parse_eastmoney_json(resp: httpx.Response, label: str) -> dict:
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"""解析东方财富 JSON 响应，遇到 302/HTML 等非 JSON 情况给出更清晰日志。"""
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resp.raise_for_status()
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content_type = resp.headers.get("content-type", "")
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text_preview = (resp.text or "")[:160].replace("\n", " ").replace("\r", " ")
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if "json" not in content_type.lower() and not resp.text.strip().startswith("{"):
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raise ValueError(
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f"{label} 返回非JSON响应(status={resp.status_code}, content_type={content_type}, body={text_preview})"
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)
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try:
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return resp.json()
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except Exception as e:
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raise ValueError(
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f"{label} JSON解析失败(status={resp.status_code}, content_type={content_type}, body={text_preview})"
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) from e
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def analyze_intraday_volume_distribution(min_df: pd.DataFrame) -> dict:
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"""分析盘中量能分布（基于5分钟K线）
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Returns:
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{
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"morning_volume_ratio": float, # 上午成交额占比
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"afternoon_volume_ratio": float, # 下午成交额占比
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"opening_strength": float, # 开盘30分钟成交额/全天占比
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"closing_strength": float, # 尾盘30分钟成交额/全天占比
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"volume_trend": str, # "morning_dominant" / "afternoon_dominant" / "balanced"
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"key_periods": list[str], # 关键放量时段描述
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}
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"""
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if min_df.empty or len(min_df) < 4:
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return {
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"morning_volume_ratio": 0.5,
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"afternoon_volume_ratio": 0.5,
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"opening_strength": 0.25,
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"closing_strength": 0.25,
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"volume_trend": "balanced",
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"key_periods": [],
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}
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total_amount = min_df["amount"].sum()
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if total_amount == 0:
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return {
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"morning_volume_ratio": 0.5,
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"afternoon_volume_ratio": 0.5,
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"opening_strength": 0.25,
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"closing_strength": 0.25,
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"volume_trend": "balanced",
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"key_periods": [],
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}
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# 按时间段分割
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morning = min_df[min_df["time"].str.contains("09:|10:|11:0", na=False)]
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afternoon = min_df[min_df["time"].str.contains("13:|14:0|14:1|14:2|14:3|14:4|14:5", na=False)]
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morning_ratio = morning["amount"].sum() / total_amount if not morning.empty else 0.5
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afternoon_ratio = afternoon["amount"].sum() / total_amount if not afternoon.empty else 0.5
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# 开盘30分钟（9:30-10:00）
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opening_30 = min_df[min_df["time"].str.contains("09:3|09:4|09:5|10:0", na=False)]
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opening_strength = opening_30["amount"].sum() / total_amount if not opening_30.empty else 0.25
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# 尾盘30分钟（14:30-15:00）
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closing_30 = min_df[min_df["time"].str.contains("14:3|14:4|14:5|15:0", na=False)]
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closing_strength = closing_30["amount"].sum() / total_amount if not closing_30.empty else 0.25
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# 量能趋势判断
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if morning_ratio > 0.6:
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volume_trend = "morning_dominant"
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elif afternoon_ratio > 0.6:
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volume_trend = "afternoon_dominant"
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else:
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volume_trend = "balanced"
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# 关键放量时段
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key_periods = []
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avg_amount = min_df["amount"].mean()
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hot_bars = min_df[min_df["amount"] > avg_amount * 2]
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for _, bar in hot_bars.iterrows():
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time_str = bar["time"].split(" ")[1] if " " in bar["time"] else bar["time"]
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pct = bar["amount"] / total_amount * 100
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||
key_periods.append(f"{time_str}放量(占比{pct:.0f}%)")
|
||
|
||
return {
|
||
"morning_volume_ratio": round(morning_ratio, 2),
|
||
"afternoon_volume_ratio": round(afternoon_ratio, 2),
|
||
"opening_strength": round(opening_strength, 2),
|
||
"closing_strength": round(closing_strength, 2),
|
||
"volume_trend": volume_trend,
|
||
"key_periods": key_periods[:3],
|
||
}
|
||
|
||
|
||
def _is_trading_hours() -> bool:
|
||
"""简单判断是否在交易时间"""
|
||
now = datetime.now()
|
||
if now.weekday() >= 5: # 周六周日
|
||
return False
|
||
hour = now.hour
|
||
minute = now.minute
|
||
# 9:30 - 11:30 或 13:00 - 15:00
|
||
if (hour == 9 and minute >= 30) or (hour == 10) or (hour == 11 and minute <= 30):
|
||
return True
|
||
if (hour == 13) or (hour == 14) or (hour == 15 and minute == 0):
|
||
return True
|
||
return False
|