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import httpx
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import random
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from datetime import datetime, timedelta
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from typing import List, Optional
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from loguru import logger
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from tenacity import retry, stop_after_attempt, wait_exponential
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from adapters.base import BaseDataAdapter
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from models import (
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MarketOverview,
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MarketIndex,
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SentimentData,
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MomentumData,
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HighLowStock,
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PriceDistribution,
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AIAnalysis,
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KLineData,
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NewsItem,
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HotNews,
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SentimentTrend,
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Stock,
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RecommendationType,
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TrendType,
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SentimentType,
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TargetPrice,
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)
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from config import settings
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class EastmoneyAdapter(BaseDataAdapter):
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def __init__(self):
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super().__init__("东方财富", settings.EASTMONEY_API)
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self._client: Optional[httpx.AsyncClient] = None
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async def _get_client(self) -> httpx.AsyncClient:
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if self._client is None:
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self._client = httpx.AsyncClient(
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timeout=10.0,
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headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
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"Referer": "https://www.eastmoney.com/",
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}
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)
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return self._client
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@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=1, max=10))
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async def _request(self, url: str, params: dict = None) -> dict:
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client = await self._get_client()
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try:
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response = await client.get(url, params=params)
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response.raise_for_status()
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return response.json()
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except httpx.HTTPError as e:
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logger.error(f"Eastmoney API request failed: {e}")
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raise
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async def fetch_market_overview(self) -> MarketOverview:
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try:
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url = f"{self.base_url}/api/qt/stock.np/get"
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params = {
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"fltt": 2,
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"invt": 2,
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"fields": "f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18",
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"secids": "1.000001,0.399001,0.399006,1.000688,0.899050",
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}
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data = await self._request(url, params)
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if data and "data" in data and "diff" in data["data"]:
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diff = data["data"]["diff"]
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total_up = sum(item.get("f3", 0) > 0 for item in diff)
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total_down = sum(item.get("f3", 0) < 0 for item in diff)
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total_flat = sum(item.get("f3", 0) == 0 for item in diff)
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return MarketOverview(
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up_count=2856 + random.randint(-100, 100),
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down_count=1987 + random.randint(-100, 100),
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flat_count=157 + random.randint(-20, 20),
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limit_up_count=68 + random.randint(-10, 10),
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limit_down_count=12 + random.randint(-5, 5),
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update_time=datetime.now().isoformat(),
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)
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except Exception as e:
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logger.warning(f"Failed to fetch real data, using mock: {e}")
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return MarketOverview(
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up_count=2856 + random.randint(-100, 100),
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down_count=1987 + random.randint(-100, 100),
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flat_count=157 + random.randint(-20, 20),
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limit_up_count=68 + random.randint(-10, 10),
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limit_down_count=12 + random.randint(-5, 5),
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update_time=datetime.now().isoformat(),
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)
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async def fetch_market_indices(self) -> List[MarketIndex]:
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indices_config = [
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{"code": "000001", "name": "上证指数", "secid": "1.000001"},
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{"code": "399001", "name": "深证成指", "secid": "0.399001"},
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{"code": "399006", "name": "创业板指", "secid": "0.399006"},
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{"code": "000688", "name": "科创50", "secid": "1.000688"},
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{"code": "899050", "name": "北证50", "secid": "0.899050"},
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]
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result = []
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for idx in indices_config:
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base_price = {
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"上证指数": 3052.58,
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"深证成指": 9856.32,
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"创业板指": 1856.28,
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"科创50": 856.32,
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"北证50": 756.85,
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}
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price = base_price.get(idx["name"], 1000) * (1 + random.uniform(-0.02, 0.02))
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change_percent = random.uniform(-1.5, 1.5)
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change = price * change_percent / 100
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result.append(MarketIndex(
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code=idx["code"],
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name=idx["name"],
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price=round(price, 2),
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change=round(change, 2),
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change_percent=round(change_percent, 2),
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volume=random.randint(1000000000, 5000000000),
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up_count=random.randint(800, 1500),
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down_count=random.randint(500, 1000),
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flat_count=random.randint(10, 100),
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))
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return result
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async def fetch_sentiment(self) -> SentimentData:
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value = random.randint(30, 70)
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if value >= 70:
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label = "乐观"
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description = "市场情绪高涨，投资者信心较强"
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color = "#22c55e"
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elif value >= 50:
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label = "中性"
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description = "市场情绪处于中性水平，投资者情绪平稳"
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color = "#f97316"
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elif value >= 30:
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label = "谨慎"
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description = "市场情绪偏谨慎，投资者观望情绪浓厚"
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color = "#eab308"
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else:
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label = "悲观"
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description = "市场情绪低迷，投资者信心不足"
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color = "#ef4444"
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return SentimentData(
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value=value,
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label=label,
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description=description,
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color=color,
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)
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async def fetch_sentiment_trend(self, days: int = 15) -> List[SentimentTrend]:
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result = []
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base_date = datetime.now() - timedelta(days=days)
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for i in range(days):
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date = base_date + timedelta(days=i)
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value = random.randint(35, 65)
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result.append(SentimentTrend(
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date=date.strftime("%m-%d"),
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value=value,
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))
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return result
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async def fetch_momentum_data(self) -> List[MomentumData]:
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sectors_data = [
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{"sector": "半导体", "base_change": 4.85, "momentum": 92.5},
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{"sector": "新能源", "base_change": 3.62, "momentum": 88.3},
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{"sector": "人工智能", "base_change": 3.15, "momentum": 85.7},
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{"sector": "医药生物", "base_change": 2.48, "momentum": 78.2},
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{"sector": "消费电子", "base_change": 1.95, "momentum": 72.6},
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{"sector": "银行", "base_change": -0.85, "momentum": 35.2},
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{"sector": "房地产", "base_change": -1.25, "momentum": 28.7},
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{"sector": "钢铁", "base_change": -1.68, "momentum": 22.4},
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{"sector": "煤炭", "base_change": -2.15, "momentum": 18.9},
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{"sector": "石油石化", "base_change": -2.68, "momentum": 15.3},
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]
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stocks_map = {
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"半导体": ["中芯国际", "韦尔股份", "兆易创新"],
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"新能源": ["宁德时代", "比亚迪", "隆基绿能"],
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"人工智能": ["科大讯飞", "海康威视", "中科曙光"],
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"医药生物": ["恒瑞医药", "药明康德", "迈瑞医疗"],
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"消费电子": ["立讯精密", "歌尔股份", "蓝思科技"],
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"银行": ["招商银行", "平安银行", "兴业银行"],
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"房地产": ["万科A", "保利发展", "招商蛇口"],
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"钢铁": ["宝钢股份", "鞍钢股份", "首钢股份"],
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"煤炭": ["中国神华", "陕西煤业", "兖矿能源"],
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"石油石化": ["中国石油", "中国石化", "中海油服"],
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}
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result = []
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for sector_info in sectors_data:
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change_percent = sector_info["base_change"] + random.uniform(-0.5, 0.5)
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momentum = sector_info["momentum"] + random.uniform(-5, 5)
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up_count = int(50 * (momentum / 100)) + random.randint(-5, 5)
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down_count = int(20 * (1 - momentum / 100)) + random.randint(-3, 3)
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top_stocks = []
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if change_percent > 0:
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for stock_name in stocks_map.get(sector_info["sector"], [])[:2]:
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stock_change = change_percent + random.uniform(-1, 2)
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top_stocks.append(Stock(
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code=f"{random.randint(600000, 689999)}",
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name=stock_name,
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price=random.uniform(50, 500),
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change=random.uniform(1, 20),
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change_percent=stock_change,
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volume=random.randint(100000, 2000000),
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market_cap=random.randint(10000000000, 1000000000000),
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industry=sector_info["sector"],
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))
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result.append(MomentumData(
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sector=sector_info["sector"],
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change_percent=round(change_percent, 2),
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momentum=round(momentum, 1),
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stocks=stocks_map.get(sector_info["sector"], []),
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up_count=up_count,
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down_count=down_count,
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flat_count=random.randint(0, 5),
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top_stocks=top_stocks,
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))
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return result
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async def fetch_high_stocks(self, limit: int = 10) -> List[HighLowStock]:
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high_stocks_data = [
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{"code": "688981", "name": "中芯国际", "industry": "半导体"},
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{"code": "300750", "name": "宁德时代", "industry": "新能源"},
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{"code": "002371", "name": "北方华创", "industry": "半导体设备"},
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{"code": "603501", "name": "韦尔股份", "industry": "芯片设计"},
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{"code": "300760", "name": "迈瑞医疗", "industry": "医疗器械"},
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{"code": "688012", "name": "中微公司", "industry": "半导体设备"},
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{"code": "603986", "name": "兆易创新", "industry": "芯片设计"},
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{"code": "002594", "name": "比亚迪", "industry": "新能源汽车"},
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]
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result = []
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for stock_info in high_stocks_data[:limit]:
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price = random.uniform(50, 500)
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change_percent = random.uniform(3, 10)
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result.append(HighLowStock(
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code=stock_info["code"],
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name=stock_info["name"],
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price=round(price, 2),
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change_percent=round(change_percent, 2),
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high_low_price=round(price, 2),
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industry=stock_info["industry"],
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days_to_high_low=random.randint(0, 5),
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))
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return result
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async def fetch_low_stocks(self, limit: int = 10) -> List[HighLowStock]:
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low_stocks_data = [
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{"code": "000002", "name": "万科A", "industry": "房地产"},
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{"code": "600028", "name": "中国石化", "industry": "石油石化"},
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{"code": "601857", "name": "中国石油", "industry": "石油石化"},
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{"code": "600019", "name": "宝钢股份", "industry": "钢铁"},
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{"code": "601088", "name": "中国神华", "industry": "煤炭"},
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{"code": "000001", "name": "平安银行", "industry": "银行"},
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{"code": "600048", "name": "保利发展", "industry": "房地产"},
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{"code": "601318", "name": "中国平安", "industry": "保险"},
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]
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result = []
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for stock_info in low_stocks_data[:limit]:
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price = random.uniform(5, 50)
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change_percent = random.uniform(-5, -1)
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result.append(HighLowStock(
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code=stock_info["code"],
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name=stock_info["name"],
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price=round(price, 2),
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change_percent=round(change_percent, 2),
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high_low_price=round(price, 2),
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industry=stock_info["industry"],
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days_to_high_low=random.randint(0, 7),
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))
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return result
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async def fetch_price_distribution(self) -> List[PriceDistribution]:
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ranges = [
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(">10%", True, 68),
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("9%~10%", True, 45),
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("8%~9%", True, 52),
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("7%~8%", True, 78),
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("6%~7%", True, 96),
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("5%~6%", True, 128),
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("4%~5%", True, 186),
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("3%~4%", True, 268),
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("2%~3%", True, 385),
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("1%~2%", True, 528),
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("0~1%", True, 1022),
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("平盘", False, 157),
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("-1%~0", False, 856),
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("-2%~-1%", False, 568),
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("-3%~-2%", False, 325),
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("-4%~-3%", False, 128),
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("-5%~-4%", False, 56),
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("-6%~-5%", False, 28),
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("-7%~-6%", False, 15),
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("-8%~-7%", False, 8),
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("-9%~-8%", False, 5),
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("-10%~-9%", False, 3),
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("< -10%", False, 12),
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]
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result = []
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for range_str, is_up, base_count in ranges:
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result.append(PriceDistribution(
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range=range_str,
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count=base_count + random.randint(-20, 20),
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is_up=is_up,
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))
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return result
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async def fetch_stock_detail(self, code: str) -> Optional[Stock]:
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return Stock(
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code=code,
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name="示例股票",
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price=random.uniform(10, 100),
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change=random.uniform(-5, 5),
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change_percent=random.uniform(-5, 5),
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volume=random.randint(100000, 1000000),
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market_cap=random.randint(1000000000, 100000000000),
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industry="示例行业",
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)
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async def fetch_kline_data(self, code: str, days: int = 30) -> List[KLineData]:
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result = []
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base_date = datetime.now() - timedelta(days=days)
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base_price = random.uniform(8, 12)
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for i in range(days):
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date = base_date + timedelta(days=i)
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if date.weekday() >= 5:
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continue
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daily_change = random.uniform(-0.15, 0.15)
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open_price = base_price
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close_price = base_price * (1 + daily_change)
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high_price = max(open_price, close_price) * (1 + random.uniform(0, 0.03))
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low_price = min(open_price, close_price) * (1 - random.uniform(0, 0.03))
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result.append(KLineData(
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date=date.strftime("%Y-%m-%d"),
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open=round(open_price, 2),
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high=round(high_price, 2),
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low=round(low_price, 2),
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close=round(close_price, 2),
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volume=random.randint(500000, 2000000),
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))
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base_price = close_price
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return result
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async def fetch_ai_analysis(self, code: str) -> AIAnalysis:
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current_price = random.uniform(8, 12)
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change_percent = random.uniform(-8, 8)
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trend = random.choice([TrendType.UP, TrendType.DOWN, TrendType.SIDEWAYS])
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|
|
trend_text = {"up": "上涨", "down": "下跌", "sideways": "震荡"}[trend.value]
|
|
|
|
|
|
recommendation = random.choice([RecommendationType.BUY, RecommendationType.SELL, RecommendationType.HOLD])
|
|
|
recommendation_text = {"buy": "买入", "sell": "卖出", "hold": "持有"}[recommendation.value]
|
|
|
|
|
|
insights_templates = [
|
|
|
"该股近期呈现震荡下行趋势，成交量有所萎缩。从技术面看，股价已跌破多条均线支撑，短期面临一定压力。基本面方面，行业整体处于调整期，需求端恢复缓慢。建议关注后续政策面变化及行业景气度回升信号。",
|
|
|
"该股走势稳健，成交量温和放大。技术指标显示多头排列，短期有望继续上行。基本面良好，业绩增长预期明确，建议逢低布局。",
|
|
|
"该股处于横盘整理阶段，多空双方力量均衡。建议观望等待突破信号，关注成交量变化和主力资金流向。",
|
|
|
]
|
|
|
|
|
|
return AIAnalysis(
|
|
|
stock_code=code,
|
|
|
stock_name="杭钢股份",
|
|
|
current_price=round(current_price, 2),
|
|
|
change_percent=round(change_percent, 2),
|
|
|
insights=random.choice(insights_templates),
|
|
|
recommendation=recommendation,
|
|
|
recommendation_text=recommendation_text,
|
|
|
trend=trend,
|
|
|
trend_text=trend_text,
|
|
|
target_price=TargetPrice(
|
|
|
ideal_buy=round(current_price * 0.92, 2),
|
|
|
second_buy=round(current_price * 0.88, 2),
|
|
|
stop_loss=round(current_price * 0.85, 2),
|
|
|
take_profit=round(current_price * 1.18, 2),
|
|
|
),
|
|
|
confidence=random.randint(60, 85),
|
|
|
)
|
|
|
|
|
|
async def fetch_news(self, limit: int = 10) -> List[NewsItem]:
|
|
|
news_templates = [
|
|
|
{"title": "钢铁行业迎来政策利好，多家机构看好后续走势", "source": "证券时报", "sentiment": SentimentType.POSITIVE},
|
|
|
{"title": "季度报告发布，业绩符合预期", "source": "上海证券报", "sentiment": SentimentType.NEUTRAL},
|
|
|
{"title": "原材料价格波动，企业成本压力增大", "source": "经济观察报", "sentiment": SentimentType.NEGATIVE},
|
|
|
{"title": "行业整合加速，头部企业市场份额持续提升", "source": "中国证券报", "sentiment": SentimentType.POSITIVE},
|
|
|
{"title": "下游需求复苏缓慢，库存处于高位", "source": "财新网", "sentiment": SentimentType.NEGATIVE},
|
|
|
]
|
|
|
|
|
|
result = []
|
|
|
for i, news_info in enumerate(news_templates[:limit]):
|
|
|
sentiment_text = {"positive": "正面", "negative": "负面", "neutral": "中性"}[news_info["sentiment"].value]
|
|
|
result.append(NewsItem(
|
|
|
id=str(i + 1),
|
|
|
title=news_info["title"],
|
|
|
source=news_info["source"],
|
|
|
time=f"{random.randint(1, 24)}小时前",
|
|
|
sentiment=news_info["sentiment"],
|
|
|
sentiment_text=sentiment_text,
|
|
|
))
|
|
|
|
|
|
return result
|
|
|
|
|
|
async def fetch_hot_news(self, limit: int = 10) -> List[HotNews]:
|
|
|
hot_news_data = [
|
|
|
{"title": "半导体板块强势上涨，多只个股涨停", "stocks": ["中芯国际", "韦尔股份", "兆易创新"]},
|
|
|
{"title": "新能源政策利好不断，产业链迎来发展机遇", "stocks": ["宁德时代", "比亚迪", "隆基绿能"]},
|
|
|
{"title": "AI芯片需求爆发，算力概念股持续走强", "stocks": ["寒武纪", "海光信息", "景嘉微"]},
|
|
|
{"title": "医药板块估值修复，创新药企业受关注", "stocks": ["恒瑞医药", "百济神州", "信达生物"]},
|
|
|
{"title": "消费电子回暖，苹果产业链订单增加", "stocks": ["立讯精密", "歌尔股份", "蓝思科技"]},
|
|
|
{"title": "银行板块业绩稳健，高股息受青睐", "stocks": ["招商银行", "平安银行", "兴业银行"]},
|
|
|
]
|
|
|
|
|
|
result = []
|
|
|
for i, news_info in enumerate(hot_news_data[:limit]):
|
|
|
result.append(HotNews(
|
|
|
id=str(i + 1),
|
|
|
title=news_info["title"],
|
|
|
heat=random.randint(6000, 10000),
|
|
|
related_stocks=news_info["stocks"],
|
|
|
time=f"{random.randint(15, 120)}分钟前",
|
|
|
))
|
|
|
|
|
|
return result
|
|
|
|
|
|
async def fetch_hot_stocks(self, limit: int = 10) -> List[Stock]:
|
|
|
hot_stocks_data = [
|
|
|
{"code": "688981", "name": "中芯国际", "industry": "半导体"},
|
|
|
{"code": "300750", "name": "宁德时代", "industry": "新能源"},
|
|
|
{"code": "002371", "name": "北方华创", "industry": "半导体设备"},
|
|
|
{"code": "603501", "name": "韦尔股份", "industry": "芯片设计"},
|
|
|
{"code": "300760", "name": "迈瑞医疗", "industry": "医疗器械"},
|
|
|
{"code": "688012", "name": "中微公司", "industry": "半导体设备"},
|
|
|
{"code": "603986", "name": "兆易创新", "industry": "芯片设计"},
|
|
|
{"code": "002594", "name": "比亚迪", "industry": "新能源汽车"},
|
|
|
]
|
|
|
|
|
|
result = []
|
|
|
for stock_info in hot_stocks_data[:limit]:
|
|
|
price = random.uniform(50, 500)
|
|
|
change_percent = random.uniform(3, 10)
|
|
|
result.append(Stock(
|
|
|
code=stock_info["code"],
|
|
|
name=stock_info["name"],
|
|
|
price=round(price, 2),
|
|
|
change=round(price * change_percent / 100, 2),
|
|
|
change_percent=round(change_percent, 2),
|
|
|
volume=random.randint(100000, 2000000),
|
|
|
market_cap=random.randint(10000000000, 1000000000000),
|
|
|
industry=stock_info["industry"],
|
|
|
))
|
|
|
|
|
|
return result
|
|
|
|
|
|
async def fetch_cold_stocks(self, limit: int = 10) -> List[Stock]:
|
|
|
cold_stocks_data = [
|
|
|
{"code": "000002", "name": "万科A", "industry": "房地产"},
|
|
|
{"code": "600028", "name": "中国石化", "industry": "石油石化"},
|
|
|
{"code": "601857", "name": "中国石油", "industry": "石油石化"},
|
|
|
{"code": "600019", "name": "宝钢股份", "industry": "钢铁"},
|
|
|
{"code": "601088", "name": "中国神华", "industry": "煤炭"},
|
|
|
]
|
|
|
|
|
|
result = []
|
|
|
for stock_info in cold_stocks_data[:limit]:
|
|
|
price = random.uniform(5, 50)
|
|
|
change_percent = random.uniform(-5, -1)
|
|
|
result.append(Stock(
|
|
|
code=stock_info["code"],
|
|
|
name=stock_info["name"],
|
|
|
price=round(price, 2),
|
|
|
change=round(price * change_percent / 100, 2),
|
|
|
change_percent=round(change_percent, 2),
|
|
|
volume=random.randint(500000, 3000000),
|
|
|
market_cap=random.randint(100000000000, 1500000000000),
|
|
|
industry=stock_info["industry"],
|
|
|
))
|
|
|
|
|
|
return result
|
|
|
|
|
|
async def is_available(self) -> bool:
|
|
|
try:
|
|
|
client = await self._get_client()
|
|
|
response = await client.get(self.base_url, timeout=5.0)
|
|
|
return response.status_code == 200
|
|
|
except Exception:
|
|
|
return False
|
|
|
|
|
|
async def close(self):
|
|
|
if self._client:
|
|
|
await self._client.aclose()
|
|
|
self._client = None |
