SakurasanandClaude
548ebee47f
后端加载优化（盘中原本每次访问都重新聚合，耗时 10-30 秒）： - 图聚合结果与题材股票子层盘中加 60 秒缓存 - 缓存过期时返回旧数据并后台幂等重建（stale-while-revalidate），打开即秒开 - 缓存基础支持秒级 TTL（set_cache 新增 ttl_seconds） - 响应体瘦身：移除 edges（前端从 stocks[].themeCodes 重建）、 themes 精简字段、stocks 按 limit 裁剪，JSON 从数 MB 降至数百 KB 前端 Canvas 渲染重构： - d3-force 布局保留，SVG 渲染层替换为 Canvas 双缓冲（静止态离屏层 drawImage） - tick 由每帧 setState 改为 rAF 合帧，拖拽/缩放期间零 React 重渲染，800 节点流畅 交互优化： - 手机：新增 +/−/适应 缩放按钮、命中半径放大至 22px、tap/drag 6px 阈值区分、 双指捏合缩放、触屏禁用 hover 避免与选中冲突 - Mac 触控板：双指滚动=平移、捏合(ctrlKey)=缩放、滚轮缩放保留、点空白取消选中 Co-Authored-By: Claude <noreply@anthropic.com>
409 lines
15 KiB
Python
409 lines
15 KiB
Python
"""东方财富题材数据服务：题材列表、题材详情、题材相关股票
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逆向自 emrnweb.eastmoney.com/investment 的 H5 接口：
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- 题材列表: POST https://emcfgdata.eastmoney.com/api/themeInvest/getThemeList
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- 题材详情: GET https://emcfgdata.securities.eastmoney.com/api/themeInvest/getDetail/{themeCode}
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- 相关股票: POST https://emcfgdata.eastmoney.com/api/themeInvest/getStockList
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两个 POST 接口需要「移动端包装结构」：
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{args:{...业务参数}, appKey, client, clientVersion, clientType, randomCode, timestamp}
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"""
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import asyncio
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import json
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import random
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import string
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import time
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from datetime import datetime, time as dtime, timedelta, timezone
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from typing import Optional
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import httpx
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from services.cache import get_cache, set_cache
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# ---- 东方财富移动端配置中心域名 ----
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_PZ_URL = "https://emcfgdata.eastmoney.com"
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_PZ_CDN_URL = "https://emcfgdata.securities.eastmoney.com"
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# appKey 与页面场景对应：题材列表索引页 / 题材详情页
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_APP_KEY_INDEX = "rn-themeIndex"
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_APP_KEY_DETAIL = "rn-themeDetail"
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_HEADERS = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36",
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"Origin": "https://emrnweb.eastmoney.com",
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"Referer": "https://emrnweb.eastmoney.com/",
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"Accept": "application/json",
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"Accept-Language": "zh-CN,zh;q=0.9",
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}
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# ---- 交易时段感知缓存 ----
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_CST = timezone(timedelta(hours=8)) # 北京时间
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_TRADING_MORNING = (dtime(9, 30), dtime(11, 30))
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_TRADING_AFTERNOON = (dtime(13, 0), dtime(15, 0))
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def _is_trading_time() -> bool:
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"""判断当前是否为 A 股交易时段（周一至周五 9:30-11:30 / 13:00-15:00）"""
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now = datetime.now(_CST)
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if now.weekday() >= 5:
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return False
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t = now.time()
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return (_TRADING_MORNING[0] <= t <= _TRADING_MORNING[1]
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or _TRADING_AFTERNOON[0] <= t <= _TRADING_AFTERNOON[1])
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def _dynamic_ttl() -> int:
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"""盘中返回 2 分钟缓存 TTL，非交易时段 18 小时（覆盖到下一交易日）"""
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return 0 if _is_trading_time() else 18
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# ---- 请求封装 ----
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def _build_payload(args: Optional[dict] = None, app_key: str = _APP_KEY_INDEX) -> dict:
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"""构建东方财富移动端请求包装结构"""
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return {
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"args": args or {},
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"appKey": app_key,
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"client": "iOS",
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"clientVersion": "8.3",
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"clientType": "cfw",
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"randomCode": "".join(random.choices(string.ascii_uppercase + string.ascii_lowercase + string.digits, k=16)),
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"timestamp": int(time.time() * 1000),
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}
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async def _post(path: str, args: dict, app_key: str = _APP_KEY_INDEX) -> Optional[dict]:
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"""POST 到配置中心接口，返回 data 层 JSON"""
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payload = _build_payload(args, app_key)
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try:
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async with httpx.AsyncClient(timeout=15) as client:
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resp = await client.post(_PZ_URL + path, json=payload, headers=_HEADERS)
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if resp.status_code != 200:
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return None
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body = resp.json()
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except Exception as e:
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print(f"[themes] POST {path} 失败: {e}")
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return None
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if body.get("code") != 0:
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print(f"[themes] POST {path} 返回错误: {body.get('message')}")
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return None
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return body.get("data")
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async def _get_cdn(path: str, app_key: str = _APP_KEY_DETAIL) -> Optional[dict]:
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"""GET 到配置中心 CDN 接口（题材详情），data 包装结构放 query 参数"""
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payload = _build_payload({}, app_key)
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params = {"data": json.dumps(payload, ensure_ascii=False)}
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try:
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async with httpx.AsyncClient(timeout=15) as client:
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resp = await client.get(_PZ_CDN_URL + path, params=params, headers=_HEADERS)
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if resp.status_code != 200:
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return None
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body = resp.json()
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except Exception as e:
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print(f"[themes] GET {path} 失败: {e}")
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return None
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if body.get("code") != 0:
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print(f"[themes] GET {path} 返回错误: {body.get('message')}")
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return None
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return body.get("data")
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# ---- 题材列表 ----
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# sortField 映射（题材列表页）：1=涨幅(bf3) 3=强度(strengthValue) 4=热度排名(hotRank) 5=成交额(fex5)
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_LIST_PAGE_SIZE = 500
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async def fetch_theme_list(sort_field: int = 1, asc: bool = False) -> list[dict]:
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"""获取全部题材列表（内部循环分页拉全，约 623 个，最多 2 页）
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Args:
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sort_field: 排序字段 1/3/4/5
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asc: True=升序, False=降序
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"""
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cache_key = f"theme_list:{sort_field}:{asc}"
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cached = get_cache(cache_key)
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if cached is not None:
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return json.loads(cached)
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sort = 1 if asc else -1
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# hotRank 数值越小越热，"热度降序(最热在前)" 需反转为接口升序
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if sort_field == 4:
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sort = -sort
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items: list[dict] = []
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page = 1
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total = None
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for _ in range(5): # 安全上限
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data = await _post(
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"/api/themeInvest/getThemeList",
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{"pageSize": _LIST_PAGE_SIZE, "pageNum": page, "sort": sort, "sortField": sort_field},
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)
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if not data:
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break
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if total is None:
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total = data.get("total", 0)
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page_items = data.get("list", [])
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if not page_items:
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break
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items.extend(page_items)
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if len(items) >= total:
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break
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page += 1
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if items:
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ttl = _dynamic_ttl()
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if ttl > 0:
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set_cache(cache_key, json.dumps(items, ensure_ascii=False), ttl_hours=ttl)
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return items
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# ---- 题材详情 ----
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async def fetch_theme_detail(theme_code: str) -> Optional[dict]:
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"""获取题材详情（简介 + 热点事件 + 相关新闻），缓存 1 小时"""
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cache_key = f"theme_detail:{theme_code}"
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cached = get_cache(cache_key)
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if cached is not None:
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return json.loads(cached)
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data = await _get_cdn(f"/api/themeInvest/getDetail/{theme_code}", app_key=_APP_KEY_DETAIL)
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if not data:
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return None
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set_cache(cache_key, json.dumps(data, ensure_ascii=False), ttl_hours=1)
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return data
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# ---- 题材相关股票 ----
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_STOCK_PAGE_SIZE = 100
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async def fetch_theme_stocks(theme_code: str) -> dict:
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"""获取题材下全部相关股票（分页拉全），返回 {stockList, statistic, total}"""
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cache_key = f"theme_stocks:{theme_code}"
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cached = get_cache(cache_key)
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if cached is not None:
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return json.loads(cached)
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stock_list: list[dict] = []
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statistic = {}
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total = 0
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page = 1
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for _ in range(20): # 安全上限
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data = await _post(
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"/api/themeInvest/getStockList",
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{"themeCode": theme_code, "pageSize": _STOCK_PAGE_SIZE, "pageNum": page, "sort": -1, "sortField": "f3"},
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app_key=_APP_KEY_DETAIL,
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)
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if not data:
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break
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if not statistic and data.get("statistic"):
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statistic = data["statistic"]
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page_items = data.get("stockList", [])
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if not page_items:
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break
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stock_list.extend(page_items)
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total = data.get("total", 0)
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if len(stock_list) >= total:
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break
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page += 1
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result = {"stockList": stock_list, "statistic": statistic, "total": total}
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if stock_list:
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ttl_s = _graph_ttl_seconds()
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if ttl_s > 0:
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set_cache(cache_key, json.dumps(result, ensure_ascii=False), ttl_seconds=ttl_s)
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return result
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# ---- 热点穿透：题材-股票 网状关系图数据 ----
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# 合并采样：涨幅榜 Top N + 热度榜 Top N 去重合并，
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# 避免单一榜单导致股票覆盖题材数被低估（如有研新材覆盖 10+ 题材，仅涨幅榜只能采到 2 个）。
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# 盘中图聚合结果/题材股票子层的缓存秒数。交易时段数据波动快，用 60s 短缓存；
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# 非交易时段缓存 18 小时（覆盖到下一交易日）。
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_GRAPH_CACHE_SECONDS = 60
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def _graph_ttl_seconds() -> int:
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"""图聚合结果与题材股票子层的缓存秒数：盘中 60 秒，非盘中 18 小时"""
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return _GRAPH_CACHE_SECONDS if _is_trading_time() else 18 * 3600
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# 后台重建锁：cache_key -> asyncio.Lock，幂等去重，防止并发重复聚合
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_REBUILD_LOCKS: dict[str, asyncio.Lock] = {}
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def _set_graph_cache(cache_key: str, data: dict) -> None:
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"""写入图聚合缓存（存 data + built_at + expires_at，epoch 秒）"""
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ttl_s = _graph_ttl_seconds()
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if ttl_s <= 0:
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return
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now = time.time()
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entry = {"data": data, "built_at": now, "expires_at": now + ttl_s}
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set_cache(cache_key, json.dumps(entry, ensure_ascii=False), ttl_seconds=ttl_s)
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def _get_graph_cache(cache_key: str) -> tuple[Optional[dict], Optional[float]]:
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"""读取图聚合缓存，返回 (data, expires_at)；无缓存/损坏返回 (None, None)"""
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cached = get_cache(cache_key)
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if cached is None:
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return None, None
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try:
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entry = json.loads(cached)
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return entry.get("data"), entry.get("expires_at")
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except (json.JSONDecodeError, TypeError):
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return None, None
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async def _build_theme_graph(sort_field: int, top_n: int) -> dict:
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"""构建完整图数据（全量统计，边由前端从 stocks[].themeCodes 重建）
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Args:
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sort_field: 题材排序 1=涨幅, 4=热度（当前榜在前，另一榜合并补充）
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top_n: 每个榜单的题材数量（1-60）
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Returns:
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{
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"themes": [{themeCode, themeName, stockCount}],
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"stocks": [{securityCode, securityName, coverCount, f3, f2, f62, f100, themeCodes[]}],
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"stats": {themeCount, stockCount, coreCount, maxCover}
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}
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"""
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# 1. 合并采样涨幅榜 + 热度榜（当前榜优先在前，另一榜补充），按 themeCode 去重
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primary_list = await fetch_theme_list(sort_field, asc=False)
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other_field = 4 if sort_field == 1 else 1
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other_list = await fetch_theme_list(other_field, asc=False)
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merged: dict[str, dict] = {}
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for t in primary_list[:top_n]:
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merged.setdefault(t["themeCode"], t)
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for t in other_list[:top_n]:
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merged.setdefault(t["themeCode"], t)
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themes = list(merged.values())
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if not themes:
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return {"themes": [], "stocks": [], "stats": {}}
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# 2. 并发拉取每题材股票（限流保护）
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sem = asyncio.Semaphore(5)
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async def _fetch_with_limit(code: str):
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async with sem:
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return await fetch_theme_stocks(code)
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results = await asyncio.gather(*[_fetch_with_limit(t["themeCode"]) for t in themes])
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# 3. 构建 M:N 关系：统计每股覆盖的题材数
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theme_map = {t["themeCode"]: t for t in themes}
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stock_map: dict[str, dict] = {} # securityCode -> stock dict
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for t, res in zip(themes, results):
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stock_list = res.get("stockList", [])
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theme_map[t["themeCode"]]["stockCount"] = len(stock_list)
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for s in stock_list:
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code = s["securityCode"]
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if code not in stock_map:
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stock_map[code] = {
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"securityCode": code,
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"securityName": s.get("securityName", ""),
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"coverCount": 0,
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"f3": s.get("f3"),
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"f2": s.get("f2"),
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"f62": s.get("f62"),
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"f100": s.get("f100", ""),
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"themeCodes": [],
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}
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stock_map[code]["coverCount"] += 1
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stock_map[code]["themeCodes"].append(t["themeCode"])
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# 4. 排序：覆盖题材数越多（穿透越强）排越前
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stocks = sorted(stock_map.values(), key=lambda x: (-x["coverCount"], -(x.get("f3") or 0)))
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stats = {
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"themeCount": len(themes),
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"stockCount": len(stocks),
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"coreCount": sum(1 for s in stocks if s["coverCount"] >= 2),
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"maxCover": max((s["coverCount"] for s in stocks), default=1),
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}
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return {
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"themes": [
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{"themeCode": t["themeCode"], "themeName": t["themeName"], "stockCount": t["stockCount"]}
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for t in theme_map.values()
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],
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"stocks": stocks,
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"stats": stats,
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}
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def _trim_graph_result(result: dict, limit: int) -> dict:
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"""按 limit 裁剪 stocks（保留穿透度最高的 N 只），仅影响下发体积，不影响 coverCount 统计"""
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return {
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"themes": result.get("themes", []),
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"stocks": result.get("stocks", [])[:limit],
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"stats": result.get("stats", {}),
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}
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def _spawn_rebuild(cache_key: str, sort_field: int, top_n: int) -> None:
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"""幂等触发后台重建：已有重建任务在跑则跳过"""
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lock = _REBUILD_LOCKS.setdefault(cache_key, asyncio.Lock())
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if lock.locked():
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return
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asyncio.create_task(_rebuild_task(cache_key, sort_field, top_n, lock))
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async def _rebuild_task(cache_key: str, sort_field: int, top_n: int, lock: asyncio.Lock) -> None:
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"""后台重建：拿锁后 double-check 缓存是否已被刷新，避免重复聚合"""
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async with lock:
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try:
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data, expires_at = _get_graph_cache(cache_key)
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if data is not None and expires_at and expires_at > time.time():
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return # 已被其他任务刷新
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data = await _build_theme_graph(sort_field, top_n)
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_set_graph_cache(cache_key, data)
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print(f"[themes] graph 后台重建完成: {cache_key}")
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except Exception as e:
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print(f"[themes] graph 后台重建失败: {cache_key} {e}")
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async def fetch_theme_graph(sort_field: int = 1, top_n: int = 30, limit: int = 1000) -> dict:
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"""获取热点穿透图数据（盘中 60s 缓存 + stale-while-revalidate）
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缓存命中且未过期 → 直接返回；已过期 → 返回旧数据并后台异步重建（秒开）；
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无缓存 → 同步构建（并发下加锁去重）。返回前按 limit 裁剪 stocks。
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Args:
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sort_field: 题材排序 1=涨幅, 4=热度
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top_n: 每个榜单的题材数量（1-60）
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limit: 下发 stocks 上限（穿透度最高的 N 只）
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"""
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cache_key = f"theme_graph:{sort_field}:{top_n}"
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data, expires_at = _get_graph_cache(cache_key)
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if data is not None:
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# 有缓存：新鲜直接返回；过期返回旧数据并后台刷新
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if not (expires_at and expires_at > time.time()):
|
||
_spawn_rebuild(cache_key, sort_field, top_n)
|
||
return _trim_graph_result(data, limit)
|
||
|
||
# 无缓存：同步构建（并发下加锁去重）
|
||
lock = _REBUILD_LOCKS.setdefault(cache_key, asyncio.Lock())
|
||
async with lock:
|
||
data, expires_at = _get_graph_cache(cache_key)
|
||
if data is not None:
|
||
# 等待锁期间已被其他请求写入
|
||
if not (expires_at and expires_at > time.time()):
|
||
_spawn_rebuild(cache_key, sort_field, top_n)
|
||
return _trim_graph_result(data, limit)
|
||
data = await _build_theme_graph(sort_field, top_n)
|
||
_set_graph_cache(cache_key, data)
|
||
return _trim_graph_result(data, limit)
|