251 lines
8.2 KiB
Python
251 lines
8.2 KiB
Python
"""
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YFinance 服务 - 美股数据获取
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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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"""
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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"""
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def __init__(self):
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"""初始化服务"""
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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._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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logger.info("YFinance 服务初始化成功")
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except ImportError:
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logger.error("yfinance 未安装，请运行: pip install yfinance")
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raise
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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
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Args:
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symbol: 股票代码，如 'AAPL'
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Returns:
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行情数据字典
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"""
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try:
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ticker = self.yf.Ticker(symbol)
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# 使用 history 方法获取数据（更可靠，避免 429 错误）
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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"无法获取 {symbol} 的历史数据")
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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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# 过滤掉常见的 429 错误信息
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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，请稍后再试 ({symbol})")
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else:
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logger.error(f"获取 {symbol} 行情失败: {error_msg}")
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return None
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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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# 默认时间周期配置 - 股票不需要太实时的数据
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timeframes = {
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'1w': ('1wk', '2y'), # 周级别，2年
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'1d': ('1d', '6mo'), # 日级别，6个月
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'1h': ('1h', '1mo'), # 小时级别，1个月
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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"""
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cache_key = f"{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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# 获取新数据
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try:
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ticker = self.yf.Ticker(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（兼容现有代码）
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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.error(f"获取数据失败 {cache_key}: {e}")
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return None
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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
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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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# 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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