""" YFinance 服务 - 美股数据获取 支持获取美股的实时行情和历史 K 线数据 """ import pandas as pd from typing import Dict, List, Optional from datetime import datetime, timedelta from app.utils.logger import logger import time class YFinanceService: """YFinance 服务类""" def __init__(self): """初始化服务""" try: import yfinance as yf self.yf = yf self._cache = {} # 数据缓存 self._cache_time = {} # 缓存时间 self._cache_ttl = 300 # 缓存有效期（秒） logger.info("YFinance 服务初始化成功") except ImportError: logger.error("yfinance 未安装，请运行: pip install yfinance") raise def get_ticker(self, symbol: str) -> Optional[Dict]: """ 获取股票实时行情 Args: symbol: 股票代码，如 'AAPL' Returns: 行情数据字典 """ try: ticker = self.yf.Ticker(symbol) # 使用 history 方法获取数据（更可靠，避免 429 错误） hist = ticker.history(period="2d", interval="1h") if hist.empty: logger.warning(f"无法获取 {symbol} 的历史数据") return None latest = hist.iloc[-1] return { 'symbol': symbol, 'lastPrice': float(latest['Close']), 'priceChange': float(latest['Close'] - latest['Open']), 'priceChangePercent': float((latest['Close'] - latest['Open']) / latest['Open'] * 100) if latest['Open'] > 0 else 0, 'volume': int(latest['Volume']), 'high': float(latest['High']), 'low': float(latest['Low']), 'open': float(latest['Open']), 'prevClose': float(latest['Close']), 'timestamp': datetime.now().isoformat() } except Exception as e: error_msg = str(e) # 过滤掉常见的 429 错误信息 if "429" in error_msg or "Too Many Requests" in error_msg: logger.warning(f"YFinance API 限流，请稍后再试 ({symbol})") else: logger.error(f"获取 {symbol} 行情失败: {error_msg}") return None def get_multi_timeframe_data( self, symbol: str, timeframes: Optional[Dict[str, tuple]] = None ) -> Dict[str, pd.DataFrame]: """ 获取多时间周期的 K 线数据 Args: symbol: 股票代码 timeframes: 时间周期配置 {'1d': ('1d', '3mo'), ...} Returns: 多时间周期数据字典 {'1d': df, '1h': df, ...} """ if timeframes is None: # 默认时间周期配置 - 股票不需要太实时的数据 timeframes = { '1w': ('1wk', '2y'), # 周级别，2年 '1d': ('1d', '6mo'), # 日级别，6个月 '1h': ('1h', '1mo'), # 小时级别，1个月 } result = {} for tf_name, (interval, period) in timeframes.items(): try: df = self._get_cached_data(symbol, interval, period) if df is not None and not df.empty: result[tf_name] = df logger.debug(f"获取 {symbol} {tf_name} 数据成功: {len(df)} 条") else: logger.warning(f"获取 {symbol} {tf_name} 数据失败或为空") except Exception as e: logger.error(f"获取 {symbol} {tf_name} 数据出错: {e}") return result def _get_cached_data( self, symbol: str, interval: str, period: str ) -> Optional[pd.DataFrame]: """获取带缓存的数据""" cache_key = f"{symbol}_{interval}_{period}" now = datetime.now() # 检查缓存 if cache_key in self._cache: cache_time = self._cache_time.get(cache_key) if cache_time and (now - cache_time).total_seconds() < self._cache_ttl: logger.debug(f"使用缓存数据: {cache_key}") return self._cache[cache_key] # 获取新数据 try: ticker = self.yf.Ticker(symbol) df = ticker.history(period=period, interval=interval) if df.empty: return None # 转换数据格式（兼容现有代码） df = self._format_dataframe(df) # 更新缓存 self._cache[cache_key] = df self._cache_time[cache_key] = now return df except Exception as e: logger.error(f"获取数据失败 {cache_key}: {e}") return None def _format_dataframe(self, df: pd.DataFrame) -> pd.DataFrame: """ 格式化 DataFrame 以兼容现有代码 yfinance 原始格式: - 列名大写: Open, High, Low, Close, Volume - 索引是 Datetime 转换后格式: - 列名小写: open, high, low, close, volume - 重置索引，time 作为一列 - 添加技术指标 """ df = df.copy() # 列名转为小写 df.columns = [col.lower() for col in df.columns] # 重置索引 df = df.reset_index() # 重命名日期列 if 'date' in df.columns: df = df.rename(columns={'date': 'time'}) elif 'datetime' in df.columns: df = df.rename(columns={'datetime': 'time'}) # 删除不需要的列 cols_to_keep = ['time', 'open', 'high', 'low', 'close', 'volume'] df = df[[col for col in cols_to_keep if col in df.columns]] # 添加技术指标（与 binance_service 一致） df = self._add_indicators(df) return df def _add_indicators(self, df: pd.DataFrame) -> pd.DataFrame: """ 添加技术指标到 DataFrame Args: df: 原始数据 Returns: 添加了技术指标的 DataFrame """ df = df.copy() # 移动平均线 df['ma5'] = df['close'].rolling(window=5).mean() df['ma10'] = df['close'].rolling(window=10).mean() df['ma20'] = df['close'].rolling(window=20).mean() df['ma50'] = df['close'].rolling(window=50).mean() # RSI（使用 Wilder's Smoothing 方法） delta = df['close'].diff() gain = delta.where(delta > 0, 0) loss = -delta.where(delta < 0, 0) # 使用 EMA (Wilder's Smoothing) 而不是简单平均 avg_gain = gain.ewm(alpha=1/14, adjust=False).mean() avg_loss = loss.ewm(alpha=1/14, adjust=False).mean() rs = avg_gain / avg_loss df['rsi'] = 100 - (100 / (1 + rs)) # MACD (使用与 binance_service 相同的计算方法) ema_fast = df['close'].ewm(span=12, adjust=False).mean() ema_slow = df['close'].ewm(span=26, adjust=False).mean() df['macd'] = ema_fast - ema_slow df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean() df['macd_hist'] = df['macd'] - df['macd_signal'] # ATR high_low = df['high'] - df['low'] high_close = abs(df['high'] - df['close'].shift()) low_close = abs(df['low'] - df['close'].shift()) true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1) df['atr'] = true_range.rolling(window=14).mean() # KDJ 指标 low_min = df['low'].rolling(window=9).min() high_max = df['high'].rolling(window=9).max() rsv = (df['close'] - low_min) / (high_max - low_min) * 100 df['k'] = rsv.ewm(com=2, adjust=False).mean() df['d'] = df['k'].ewm(com=2, adjust=False).mean() df['j'] = 3 * df['k'] - 2 * df['d'] return df def clear_cache(self): """清空缓存""" self._cache.clear() self._cache_time.clear() logger.info("YFinance 缓存已清空") # 全局单例 _yfinance_service: Optional[YFinanceService] = None def get_yfinance_service() -> YFinanceService: """获取 YFinance 服务单例""" global _yfinance_service if _yfinance_service is None: _yfinance_service = YFinanceService() return _yfinance_service