322 lines
12 KiB
Python
322 lines
12 KiB
Python
"""
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美股数据服务 - 使用 yfinance 获取美股数据
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"""
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from typing import Optional, Dict, Any, List
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import yfinance as yf
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from datetime import datetime, timedelta
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import pandas as pd
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from app.utils.logger import logger
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class USStockService:
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"""美股数据服务类（支持美股和港股）"""
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def __init__(self):
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"""初始化美股数据服务"""
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self.cache = {} # 简单的内存缓存
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@staticmethod
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def _normalize_hk_symbol(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_stock_info(self, symbol: str) -> Optional[Dict[str, Any]]:
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"""
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获取美股基本信息
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Args:
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symbol: 股票代码（如 AAPL, TSLA 或 0700.HK）
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Returns:
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股票基本信息字典
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"""
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try:
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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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stock = yf.Ticker(normalized_symbol)
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info = stock.info
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if not info or 'symbol' not in info:
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logger.warning(f"未找到股票: {symbol}")
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return None
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# 提取关键信息
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result = {
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"symbol": symbol,
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"name": info.get("longName", info.get("shortName", symbol)),
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"sector": info.get("sector", "未知"),
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"industry": info.get("industry", "未知"),
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"market_cap": info.get("marketCap", 0),
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"current_price": info.get("currentPrice", info.get("regularMarketPrice", 0)),
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"previous_close": info.get("previousClose", 0),
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"open": info.get("open", 0),
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"day_high": info.get("dayHigh", 0),
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"day_low": info.get("dayLow", 0),
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"volume": info.get("volume", 0),
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"avg_volume": info.get("averageVolume", 0),
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"pe_ratio": info.get("trailingPE", 0),
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"forward_pe": info.get("forwardPE", 0),
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"pb_ratio": info.get("priceToBook", 0),
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"dividend_yield": info.get("dividendYield", 0),
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"52_week_high": info.get("fiftyTwoWeekHigh", 0),
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"52_week_low": info.get("fiftyTwoWeekLow", 0),
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"50_day_avg": info.get("fiftyDayAverage", 0),
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"200_day_avg": info.get("twoHundredDayAverage", 0),
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"beta": info.get("beta", 0),
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"eps": info.get("trailingEps", 0),
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"description": info.get("longBusinessSummary", ""),
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}
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logger.info(f"获取美股信息成功: {symbol}")
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return result
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except Exception as e:
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logger.error(f"获取美股信息失败 {symbol}: {e}")
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return None
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def get_historical_data(
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self,
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symbol: str,
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period: str = "1mo",
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interval: str = "1d"
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) -> Optional[pd.DataFrame]:
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"""
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获取美股历史K线数据
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Args:
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symbol: 股票代码
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period: 时间周期 (1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max)
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interval: K线间隔 (1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo)
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Returns:
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包含OHLCV数据的DataFrame
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"""
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try:
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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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stock = yf.Ticker(normalized_symbol)
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hist = stock.history(period=period, interval=interval)
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if hist.empty:
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logger.warning(f"未找到历史数据: {symbol}")
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return None
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logger.info(f"获取美股历史数据成功: {symbol}, 周期: {period}")
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return hist
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except Exception as e:
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logger.error(f"获取美股历史数据失败 {symbol}: {e}")
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return None
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def get_financial_data(self, symbol: str) -> Optional[Dict[str, Any]]:
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"""
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获取美股财务数据
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Args:
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symbol: 股票代码
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Returns:
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财务数据字典
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"""
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try:
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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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stock = yf.Ticker(normalized_symbol)
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# 获取财务报表
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financials = stock.financials
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balance_sheet = stock.balance_sheet
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cashflow = stock.cashflow
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result = {
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"symbol": symbol,
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"income_statement": financials.to_dict() if not financials.empty else {},
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"balance_sheet": balance_sheet.to_dict() if not balance_sheet.empty else {},
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"cash_flow": cashflow.to_dict() if not cashflow.empty else {},
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}
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# 获取关键财务指标
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info = stock.info
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result["key_metrics"] = {
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"revenue": info.get("totalRevenue", 0),
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"gross_profit": info.get("grossProfits", 0),
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"ebitda": info.get("ebitda", 0),
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"net_income": info.get("netIncomeToCommon", 0),
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"total_assets": info.get("totalAssets", 0),
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"total_debt": info.get("totalDebt", 0),
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"total_cash": info.get("totalCash", 0),
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"operating_cash_flow": info.get("operatingCashflow", 0),
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"free_cash_flow": info.get("freeCashflow", 0),
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"roe": info.get("returnOnEquity", 0),
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"roa": info.get("returnOnAssets", 0),
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"profit_margin": info.get("profitMargins", 0),
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"operating_margin": info.get("operatingMargins", 0),
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}
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logger.info(f"获取美股财务数据成功: {symbol}")
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return result
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except Exception as e:
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logger.error(f"获取美股财务数据失败 {symbol}: {e}")
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return None
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def calculate_technical_indicators(self, hist: pd.DataFrame) -> Dict[str, Any]:
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"""
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计算技术指标
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Args:
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hist: 历史数据DataFrame
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Returns:
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技术指标字典
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"""
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try:
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if hist.empty or len(hist) < 20:
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return {}
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close = hist['Close']
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# 计算移动平均线
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ma5 = close.rolling(window=5).mean().iloc[-1] if len(close) >= 5 else None
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ma10 = close.rolling(window=10).mean().iloc[-1] if len(close) >= 10 else None
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ma20 = close.rolling(window=20).mean().iloc[-1] if len(close) >= 20 else None
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ma60 = close.rolling(window=60).mean().iloc[-1] if len(close) >= 60 else None
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# 计算RSI（使用 Wilder's Smoothing 方法）
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delta = 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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rsi = 100 - (100 / (1 + rs))
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rsi_value = rsi.iloc[-1] if len(rsi) >= 14 else None
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# 计算MACD
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exp1 = close.ewm(span=12, adjust=False).mean()
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exp2 = close.ewm(span=26, adjust=False).mean()
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macd = exp1 - exp2
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signal = macd.ewm(span=9, adjust=False).mean()
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macd_value = macd.iloc[-1] if len(macd) >= 26 else None
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signal_value = signal.iloc[-1] if len(signal) >= 26 else None
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# 计算布林带
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bb_middle = close.rolling(window=20).mean()
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bb_std = close.rolling(window=20).std()
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bb_upper = bb_middle + (bb_std * 2)
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bb_lower = bb_middle - (bb_std * 2)
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result = {
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"ma5": float(ma5) if ma5 and not pd.isna(ma5) else None,
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"ma10": float(ma10) if ma10 and not pd.isna(ma10) else None,
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"ma20": float(ma20) if ma20 and not pd.isna(ma20) else None,
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"ma60": float(ma60) if ma60 and not pd.isna(ma60) else None,
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"rsi": float(rsi_value) if rsi_value and not pd.isna(rsi_value) else None,
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"macd": float(macd_value) if macd_value and not pd.isna(macd_value) else None,
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"macd_signal": float(signal_value) if signal_value and not pd.isna(signal_value) else None,
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"bb_upper": float(bb_upper.iloc[-1]) if len(bb_upper) >= 20 and not pd.isna(bb_upper.iloc[-1]) else None,
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"bb_middle": float(bb_middle.iloc[-1]) if len(bb_middle) >= 20 and not pd.isna(bb_middle.iloc[-1]) else None,
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"bb_lower": float(bb_lower.iloc[-1]) if len(bb_lower) >= 20 and not pd.isna(bb_lower.iloc[-1]) else None,
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}
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return result
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except Exception as e:
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logger.error(f"计算技术指标失败: {e}")
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return {}
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def get_comprehensive_analysis(self, symbol: str) -> Optional[Dict[str, Any]]:
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"""
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获取美股综合分析数据
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Args:
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symbol: 股票代码
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Returns:
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综合分析数据字典
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"""
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try:
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# 获取基本信息
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info = self.get_stock_info(symbol)
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if not info:
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return None
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# 获取历史数据
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hist = self.get_historical_data(symbol, period="6mo", interval="1d")
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if hist is None or hist.empty:
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return {
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"success": False,
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"error": "无法获取历史数据"
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}
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# 计算技术指标
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technical = self.calculate_technical_indicators(hist)
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# 获取最近的价格数据
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latest = hist.iloc[-1]
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prev = hist.iloc[-2] if len(hist) > 1 else latest
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# 计算涨跌幅
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change = latest['Close'] - prev['Close']
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change_pct = (change / prev['Close'] * 100) if prev['Close'] != 0 else 0
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result = {
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"success": True,
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"symbol": symbol,
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"name": info["name"],
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"sector": info["sector"],
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"industry": info["industry"],
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"current_price": float(latest['Close']),
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"change": float(change),
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"change_percent": float(change_pct),
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"volume": int(latest['Volume']),
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"market_cap": info["market_cap"],
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"pe_ratio": info["pe_ratio"],
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"pb_ratio": info["pb_ratio"],
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"dividend_yield": info["dividend_yield"],
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"52_week_high": info["52_week_high"],
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"52_week_low": info["52_week_low"],
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"technical_indicators": technical,
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"description": info["description"][:500] if info["description"] else "",
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}
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logger.info(f"获取美股综合分析成功: {symbol}")
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return result
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except Exception as e:
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logger.error(f"获取美股综合分析失败 {symbol}: {e}")
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return {
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"success": False,
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"error": str(e)
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}
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# 创建全局实例
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us_stock_service = USStockService()
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