439 lines
15 KiB
Python
439 lines
15 KiB
Python
"""
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YFinance 服务 - 美股港股数据获取
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支持获取美股的实时行情和历史 K 线数据
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备用数据源：Stooq
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"""
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import pandas as pd
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from typing import Dict, List, Optional
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from datetime import datetime, timedelta
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from app.utils.logger import logger
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import time
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class YFinanceService:
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"""YFinance 服务类（支持 Stooq 备用）"""
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def __init__(self):
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"""初始化服务"""
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# 初始化 YFinance
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try:
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import yfinance as yf
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self.yf = yf
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self._yf_available = True
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logger.info("YFinance 服务初始化成功")
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except ImportError:
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logger.warning("yfinance 未安装")
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self._yf_available = False
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# 初始化 Stooq（备用）
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try:
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import pandas_datareader.data as web
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self.web = web
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self._stooq_available = True
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logger.info("Stooq 备用数据源初始化成功")
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except ImportError:
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logger.warning("pandas_datareader 未安装，Stooq 备用不可用")
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self._stooq_available = False
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if not self._yf_available and not self._stooq_available:
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raise Exception("没有可用的数据源，请安装 yfinance 或 pandas_datareader")
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self._cache = {} # 数据缓存
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self._cache_time = {} # 缓存时间
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self._cache_ttl = 300 # 缓存有效期（秒）
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def _normalize_hk_symbol(self, symbol: str) -> str:
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"""
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标准化港股代码格式为 yfinance 要求的格式
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- 4位及以下：左侧补零到4位，如 700.HK → 0700.HK, 5.HK → 0005.HK
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- 5位及以上：去掉前导零，如 09618.HK → 9618.HK
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"""
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if not symbol.endswith('.HK'):
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return symbol
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# 分离代码和后缀
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code_part = symbol[:-3] # 去掉 .HK
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suffix = '.HK'
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# 如果是纯数字代码
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if code_part.isdigit():
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# 4位及以下：补零到4位
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if len(code_part) <= 4:
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normalized_code = code_part.zfill(4)
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# 5位及以上：去掉前导零
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else:
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normalized_code = code_part.lstrip('0') or '0'
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else:
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normalized_code = code_part
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return normalized_code + suffix
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def get_ticker(self, symbol: str) -> Optional[Dict]:
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"""
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获取股票实时行情（优先使用 YFinance，失败则使用 Stooq）
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Args:
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symbol: 股票代码，如 'AAPL' 或 '0700.HK'
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Returns:
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行情数据字典
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"""
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# 优先使用 YFinance
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if self._yf_available:
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result = self._get_yf_ticker(symbol)
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if result:
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return result
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logger.info(f"YFinance 获取失败，尝试使用 Stooq 备用数据源 ({symbol})")
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# 备用使用 Stooq
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if self._stooq_available:
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result = self._get_stooq_ticker(symbol)
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if result:
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return result
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return None
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def _get_yf_ticker(self, symbol: str) -> Optional[Dict]:
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"""使用 YFinance 获取行情"""
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try:
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normalized_symbol = self._normalize_hk_symbol(symbol)
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ticker = self.yf.Ticker(normalized_symbol)
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hist = ticker.history(period="2d", interval="1h")
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if hist.empty:
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logger.warning(f"YFinance 无法获取 {symbol} 的数据")
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return None
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latest = hist.iloc[-1]
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return {
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'symbol': symbol,
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'lastPrice': float(latest['Close']),
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'priceChange': float(latest['Close'] - latest['Open']),
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'priceChangePercent': float((latest['Close'] - latest['Open']) / latest['Open'] * 100) if latest['Open'] > 0 else 0,
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'volume': int(latest['Volume']),
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'high': float(latest['High']),
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'low': float(latest['Low']),
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'open': float(latest['Open']),
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'prevClose': float(latest['Close']),
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'timestamp': datetime.now().isoformat()
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}
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except Exception as e:
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error_msg = str(e)
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if "429" in error_msg or "Too Many Requests" in error_msg:
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logger.warning(f"YFinance API 限流 ({symbol})")
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else:
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logger.debug(f"YFinance 获取失败 ({symbol}): {error_msg}")
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return None
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def _get_stooq_ticker(self, symbol: str) -> Optional[Dict]:
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"""使用 Stooq 获取行情（备用）"""
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try:
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# Stooq 使用的港股格式
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stooq_symbol = self._convert_to_stooq_symbol(symbol)
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# 获取最近几天的数据
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start_date = (datetime.now() - timedelta(days=5)).strftime('%Y-%m-%d')
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df = self.web.DataReader(stooq_symbol, 'stooq', start=start_date)
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if df.empty:
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logger.warning(f"Stooq 无法获取 {symbol} 的数据")
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return None
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# Stooq 返回的数据是倒序的，取第一行（最新）
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latest = df.iloc[-1]
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return {
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'symbol': symbol,
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'lastPrice': float(latest['Close']),
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'priceChange': float(latest['Close'] - latest['Open']),
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'priceChangePercent': float((latest['Close'] - latest['Open']) / latest['Open'] * 100) if latest['Open'] > 0 else 0,
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'volume': int(latest['Volume']),
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'high': float(latest['High']),
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'low': float(latest['Low']),
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'open': float(latest['Open']),
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'prevClose': float(latest['Close']),
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'timestamp': datetime.now().isoformat(),
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'source': 'stooq' # 标记数据来源
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}
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except Exception as e:
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logger.error(f"Stooq 获取 {symbol} 行情失败: {e}")
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return None
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def _convert_to_stooq_symbol(self, symbol: str) -> str:
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"""
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转换股票代码为 Stooq 格式
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美股：AAPL -> AAPL.US
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港股：0700.HK -> 0700.HK
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"""
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if symbol.endswith('.HK'):
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return symbol
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elif '.' in symbol:
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# 其他格式保持不变
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return symbol
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else:
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# 美股添加 .US 后缀
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return f"{symbol}.US"
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def get_multi_timeframe_data(
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self,
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symbol: str,
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timeframes: Optional[Dict[str, tuple]] = None
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) -> Dict[str, pd.DataFrame]:
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"""
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获取多时间周期的 K 线数据
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Args:
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symbol: 股票代码
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timeframes: 时间周期配置 {'1d': ('1d', '3mo'), ...}
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Returns:
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多时间周期数据字典 {'1d': df, '1h': df, ...}
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"""
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if timeframes is None:
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# 技术面分析时间周期：1h、1d、1w
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timeframes = {
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'1w': ('1wk', '2y'), # 周级别，2年 - 长期趋势
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'1d': ('1d', '6mo'), # 日级别，6个月 - 中期趋势
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'1h': ('1h', '1mo'), # 小时级别，1个月 - 短期趋势
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}
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result = {}
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for tf_name, (interval, period) in timeframes.items():
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try:
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df = self._get_cached_data(symbol, interval, period)
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if df is not None and not df.empty:
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result[tf_name] = df
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logger.debug(f"获取 {symbol} {tf_name} 数据成功: {len(df)} 条")
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else:
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logger.warning(f"获取 {symbol} {tf_name} 数据失败或为空")
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except Exception as e:
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logger.error(f"获取 {symbol} {tf_name} 数据出错: {e}")
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return result
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def _get_cached_data(
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self,
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symbol: str,
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interval: str,
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period: str
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) -> Optional[pd.DataFrame]:
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"""获取带缓存的数据（优先 YFinance，失败则使用 Stooq）"""
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# 标准化港股代码格式
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normalized_symbol = self._normalize_hk_symbol(symbol)
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cache_key = f"{normalized_symbol}_{interval}_{period}"
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now = datetime.now()
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# 检查缓存
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if cache_key in self._cache:
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cache_time = self._cache_time.get(cache_key)
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if cache_time and (now - cache_time).total_seconds() < self._cache_ttl:
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logger.debug(f"使用缓存数据: {cache_key}")
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return self._cache[cache_key]
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# 优先使用 YFinance
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if self._yf_available:
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df = self._get_yf_data(symbol, interval, period, cache_key, now)
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if df is not None:
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return df
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logger.info(f"YFinance 获取历史数据失败，尝试 Stooq ({symbol})")
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# 备用使用 Stooq
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if self._stooq_available:
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df = self._get_stooq_data(symbol, interval, period, cache_key, now)
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if df is not None:
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logger.info(f"✓ 使用 Stooq 数据源 ({symbol})")
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return df
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return None
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def _get_yf_data(
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self,
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symbol: str,
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interval: str,
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period: str,
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cache_key: str,
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now: datetime
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) -> Optional[pd.DataFrame]:
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"""使用 YFinance 获取历史数据"""
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try:
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normalized_symbol = self._normalize_hk_symbol(symbol)
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ticker = self.yf.Ticker(normalized_symbol)
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df = ticker.history(period=period, interval=interval)
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if df.empty:
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return None
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# 转换数据格式
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df = self._format_dataframe(df)
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# 更新缓存
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self._cache[cache_key] = df
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self._cache_time[cache_key] = now
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return df
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except Exception as e:
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logger.debug(f"YFinance 获取历史数据失败: {e}")
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return None
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def _get_stooq_data(
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self,
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symbol: str,
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interval: str,
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period: str,
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cache_key: str,
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now: datetime
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) -> Optional[pd.DataFrame]:
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"""使用 Stooq 获取历史数据（备用）"""
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try:
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# 转换为 Stooq 格式
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stooq_symbol = self._convert_to_stooq_symbol(symbol)
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# 将 period 转换为天数
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period_days = self._period_to_days(period)
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start_date = (datetime.now() - timedelta(days=period_days)).strftime('%Y-%m-%d')
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# 获取数据
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df = self.web.DataReader(stooq_symbol, 'stooq', start=start_date)
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if df.empty:
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return None
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# Stooq 数据是倒序的，需要反转
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df = df.iloc[::-1]
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# 转换数据格式
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df = self._format_dataframe(df)
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# 更新缓存
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self._cache[cache_key] = df
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self._cache_time[cache_key] = now
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return df
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except Exception as e:
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logger.debug(f"Stooq 获取历史数据失败: {e}")
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return None
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def _period_to_days(self, period: str) -> int:
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"""将 YFinance period 格式转换为天数"""
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period_map = {
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'1mo': 30,
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'3mo': 90,
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'6mo': 180,
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'1y': 365,
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'2y': 730,
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}
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return period_map.get(period, 180) # 默认6个月
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def _format_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
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"""
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格式化 DataFrame 以兼容现有代码
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yfinance 原始格式:
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- 列名大写: Open, High, Low, Close, Volume
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- 索引是 Datetime
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转换后格式:
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- 列名小写: open, high, low, close, volume
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- 重置索引，time 作为一列
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- 添加技术指标
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"""
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df = df.copy()
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# 列名转为小写
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df.columns = [col.lower() for col in df.columns]
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# 重置索引
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df = df.reset_index()
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# 重命名日期列
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if 'date' in df.columns:
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df = df.rename(columns={'date': 'time'})
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elif 'datetime' in df.columns:
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df = df.rename(columns={'datetime': 'time'})
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# 删除不需要的列
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cols_to_keep = ['time', 'open', 'high', 'low', 'close', 'volume']
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df = df[[col for col in cols_to_keep if col in df.columns]]
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# 添加技术指标（与 binance_service 一致）
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df = self._add_indicators(df)
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return df
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def _add_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
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"""
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添加技术指标到 DataFrame
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Args:
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df: 原始数据
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Returns:
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添加了技术指标的 DataFrame
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"""
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df = df.copy()
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# 移动平均线（简单移动平均 MA）
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df['ma5'] = df['close'].rolling(window=5).mean()
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df['ma10'] = df['close'].rolling(window=10).mean()
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df['ma20'] = df['close'].rolling(window=20).mean()
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df['ma50'] = df['close'].rolling(window=50).mean()
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# 指数移动平均线（EMA）- 用于趋势判断
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df['ema20'] = df['close'].ewm(span=20, adjust=False).mean()
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df['ema50'] = df['close'].ewm(span=50, adjust=False).mean()
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df['ema200'] = df['close'].ewm(span=200, adjust=False).mean()
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# RSI（使用 Wilder's Smoothing 方法）
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delta = df['close'].diff()
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gain = delta.where(delta > 0, 0)
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loss = -delta.where(delta < 0, 0)
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# 使用 EMA (Wilder's Smoothing) 而不是简单平均
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avg_gain = gain.ewm(alpha=1/14, adjust=False).mean()
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avg_loss = loss.ewm(alpha=1/14, adjust=False).mean()
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rs = avg_gain / avg_loss
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df['rsi'] = 100 - (100 / (1 + rs))
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# MACD (使用与 binance_service 相同的计算方法)
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ema_fast = df['close'].ewm(span=12, adjust=False).mean()
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ema_slow = df['close'].ewm(span=26, adjust=False).mean()
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df['macd'] = ema_fast - ema_slow
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df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean()
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df['macd_hist'] = df['macd'] - df['macd_signal']
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# ATR
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high_low = df['high'] - df['low']
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high_close = abs(df['high'] - df['close'].shift())
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low_close = abs(df['low'] - df['close'].shift())
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true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
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df['atr'] = true_range.rolling(window=14).mean()
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# KDJ 指标
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low_min = df['low'].rolling(window=9).min()
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high_max = df['high'].rolling(window=9).max()
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rsv = (df['close'] - low_min) / (high_max - low_min) * 100
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df['k'] = rsv.ewm(com=2, adjust=False).mean()
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df['d'] = df['k'].ewm(com=2, adjust=False).mean()
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df['j'] = 3 * df['k'] - 2 * df['d']
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return df
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def clear_cache(self):
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"""清空缓存"""
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self._cache.clear()
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self._cache_time.clear()
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logger.info("YFinance 缓存已清空")
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# 全局单例
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_yfinance_service: Optional[YFinanceService] = None
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def get_yfinance_service() -> YFinanceService:
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"""获取 YFinance 服务单例"""
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global _yfinance_service
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if _yfinance_service is None:
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_yfinance_service = YFinanceService()
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return _yfinance_service
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