from datetime import datetime, timedelta from typing import List, Dict, Optional import akshare as ak import yfinance as yf import pandas as pd import re import sqlite3 import requests as _requests from requests.exceptions import RequestException from loguru import logger from .database_manager import DatabaseManager import os from contextlib import contextmanager class EastMoneyDirect: """东方财富 HTTP 直接调用 —— 作为 akshare 的零依赖降级方案。 仅使用 requests，无需 API Key，国内网络直连。 """ KLINE_URL = "https://push2his.eastmoney.com/api/qt/stock/kline/get" LIST_URL = "https://push2.eastmoney.com/api/qt/clist/get" UT = "fa5fd1943c7b386f172d6893dbfba10b" @staticmethod def _secid(ticker: str) -> str: """将纯数字 ticker 转为东方财富 secid 格式。 A股: 6开头 -> 1.{ticker}(上交所) | 其他 -> 0.{ticker}(深交所) 港股: 5位数字 -> 116.{ticker} """ if len(ticker) == 5: return f"116.{ticker}" if ticker.startswith(('6', '9')): return f"1.{ticker}" return f"0.{ticker}" @classmethod def fetch_kline(cls, ticker: str, start_date: str, end_date: str) -> pd.DataFrame: """获取 K 线数据，返回与 akshare 对齐的 DataFrame。 Args: ticker: 纯数字股票代码 start_date: YYYYMMDD end_date: YYYYMMDD """ params = { 'secid': cls._secid(ticker), 'fields1': 'f1,f2,f3,f4,f5,f6', 'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61', 'klt': '101', # 日K 'fqt': '1', # 前复权 'beg': start_date, 'end': end_date, 'lmt': '1000', 'ut': cls.UT, } resp = _requests.get(cls.KLINE_URL, params=params, timeout=10) resp.raise_for_status() data = resp.json().get('data') if not data or not data.get('klines'): return pd.DataFrame() # kline 格式: "日期,开盘,收盘,最高,最低,成交量,成交额,振幅,涨跌幅,涨跌额,换手率" rows = [k.split(',') for k in data['klines']] df = pd.DataFrame(rows, columns=[ '日期', '开盘', '收盘', '最高', '最低', '成交量', '成交额', '振幅', '涨跌幅', '涨跌额', '换手率' ]) # 转为数值类型 for col in ['开盘', '收盘', '最高', '最低', '成交量', '涨跌幅']: df[col] = pd.to_numeric(df[col], errors='coerce') return df @classmethod def fetch_stock_list(cls, market: str = 'a') -> pd.DataFrame: """获取股票列表。 Args: market: 'a' for A股, 'hk' for 港股 """ if market == 'a': fs = 'm:0+t:6,m:0+t:80,m:1+t:2,m:1+t:23' else: fs = 'm:128+t:3,m:128+t:4,m:128+t:1,m:128+t:2' all_items = [] page = 1 while True: params = { 'pn': str(page), 'pz': '5000', 'po': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'fid': 'f12', 'fs': fs, 'fields': 'f12,f14', 'ut': cls.UT, } resp = _requests.get(cls.LIST_URL, params=params, timeout=15) resp.raise_for_status() data = resp.json().get('data', {}) diff = data.get('diff', []) if not diff: break for item in diff: all_items.append({'code': item.get('f12', ''), 'name': item.get('f14', '')}) total = data.get('total', 0) if page * 5000 >= total: break page += 1 return pd.DataFrame(all_items) @contextmanager def temporary_no_proxy(): """Context manager to temporarily unset proxy environment variables.""" proxies = {k: os.environ.get(k) for k in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']} for k in proxies: if k in os.environ: del os.environ[k] try: yield finally: for k, v in proxies.items(): if v is not None: os.environ[k] = v class StockTools: """金融分析股票工具 - 结合高性能数据库缓存与增量更新""" def __init__(self, db: DatabaseManager, auto_update: bool = True): """ 初始化股票工具 Args: db: 数据库管理器 auto_update: 是否在列表为空时自动更新，默认 True """ self.db = db if auto_update: self._check_and_update_stock_list() def _check_and_update_stock_list(self, force: bool = False): """检查并更新股票列表。仅在列表为空或 force=True 时从网络拉取。""" # 直接查询表中记录数 cursor = self.db.conn.cursor() cursor.execute("SELECT COUNT(*) FROM stock_list") count = cursor.fetchone()[0] if count > 0 and not force: logger.info(f"ℹ️ Stock list already cached ({count} stocks)") return logger.info("📡 Updating A-share and HK-share stock list...") df_combined = None # === 主路径: akshare === try: def fetch_data_ak(): df_a = ak.stock_zh_a_spot_em() df_a = df_a[['代码', '名称']].copy() df_a.columns = ['code', 'name'] df_hk = ak.stock_hk_spot_em() df_hk = df_hk[['代码', '名称']].copy() df_hk.columns = ['code', 'name'] return pd.concat([df_a, df_hk], ignore_index=True) try: df_combined = fetch_data_ak() except (RequestException, Exception) as e: if "Proxy" in str(e) or "proxy" in str(e): logger.warning(f"⚠️ Proxy error detected: {e}. Retrying with proxy disabled...") with temporary_no_proxy(): df_combined = fetch_data_ak() else: raise e logger.info(f"✅ akshare: fetched {len(df_combined)} stocks.") except Exception as e: logger.warning(f"⚠️ akshare stock list failed: {e}. Trying EastMoney direct...") # === 降级路径: 东方财富直接 HTTP === if df_combined is None or df_combined.empty: try: df_a = EastMoneyDirect.fetch_stock_list('a') df_hk = EastMoneyDirect.fetch_stock_list('hk') df_combined = pd.concat([df_a, df_hk], ignore_index=True) logger.info(f"✅ EastMoney direct: fetched {len(df_combined)} stocks.") except Exception as e2: logger.error(f"❌ All stock list sources failed. akshare + EastMoney: {e2}") return if df_combined is not None and not df_combined.empty: self.db.save_stock_list(df_combined) logger.info(f"✅ Cached {len(df_combined)} stocks to database.") def search_ticker(self, query: str, limit: int = 5) -> List[Dict]: """ 模糊搜索 A 股股票代码或名称，支持常见缩写。 """ # 清洗后缀 (如 CATL.SZ -> CATL, 000001.SZ -> 000001) clean_query = re.sub(r'\.(SZ|SH|HK|US)$', '', query, flags=re.IGNORECASE) # 常见缩写映射 aliases = { "CATL": "宁德时代", "BYD": "比亚迪", "TSLA": "特斯拉", "Moutai": "贵州茅台", "Tencent": "腾讯", "Alibaba": "阿里巴巴", "Meituan": "美团", } search_query = aliases.get(clean_query.upper(), clean_query) # Robustness: if regex-like ticker code is embedded in query (e.g. "300364 中文在线"), try to extract it if not search_query.isdigit(): # Extract explicit 5-6 digit codes match = re.search(r'\b(\d{5,6})\b', clean_query) if match: search_query = match.group(1) res = self.db.search_stock(search_query, limit) if not res and search_query.isalpha(): # Robustness: mock search hit for alphabetic US tickers return [{"code": search_query.upper(), "name": search_query.upper()}] return res def get_stock_price( self, ticker: str, start_date: Optional[str] = None, end_date: Optional[str] = None, force_sync: bool = False, ) -> pd.DataFrame: """ 获取指定股票的历史价格数据。优先从本地缓存读取，缺失时自动从网络补齐。 Args: ticker: 股票代码，如 "600519"（贵州茅台）或 "000001"（平安银行）。 start_date: 开始日期，格式 "YYYY-MM-DD"。默认为 90 天前。 end_date: 结束日期，格式 "YYYY-MM-DD"。默认为今天。 Returns: 包含 date, open, close, high, low, volume, change_pct 列的 DataFrame。 """ now = datetime.now() if not end_date: end_date = now.strftime('%Y-%m-%d') if not start_date: start_date = (now - timedelta(days=90)).strftime('%Y-%m-%d') df_db = self.db.get_stock_prices(ticker, start_date, end_date) need_update = False if df_db.empty: need_update = True else: db_latest = pd.to_datetime(df_db['date'].max()) req_latest = pd.to_datetime(end_date) if (req_latest - db_latest).days > 2: need_update = True if force_sync: need_update = True if need_update: logger.info(f"📡 Data stale or missing for {ticker}, syncing from network...") is_us_stock = bool(re.search(r'[a-zA-Z]', ticker)) and not bool(re.search(r'\d{5,6}', ticker)) if is_us_stock: clean_ticker = ticker.upper() else: # 清洗 ticker，确保只包含数字（Akshare A 股接口通常只需要数字代码） clean_ticker = "".join(filter(str.isdigit, ticker)) if not clean_ticker: logger.warning(f"⚠️ Unsupported ticker format: {ticker}") return df_db try: s_fmt = start_date.replace("-", "") e_fmt = end_date.replace("-", "") df_remote = None def fetch_data_akshare(): """主路径: akshare""" if is_us_stock: return _fetch_data_yfinance() if len(clean_ticker) == 5: return ak.stock_hk_hist( symbol=clean_ticker, period="daily", start_date=s_fmt, end_date=e_fmt, adjust="qfq" ) else: return ak.stock_zh_a_hist( symbol=clean_ticker, period="daily", start_date=s_fmt, end_date=e_fmt, adjust="qfq" ) def _fetch_data_yfinance(): """美股路径: yfinance""" yf_ticker = yf.Ticker(clean_ticker) end_dt = datetime.strptime(end_date, "%Y-%m-%d") + timedelta(days=1) df_us = yf_ticker.history(start=start_date, end=end_dt.strftime("%Y-%m-%d")) if df_us.empty: return pd.DataFrame() df_us = df_us.reset_index() date_col = 'Date' if 'Date' in df_us.columns else df_us.columns[0] df_us = df_us.rename(columns={ 'Open': 'open', 'Close': 'close', 'High': 'high', 'Low': 'low', 'Volume': 'volume' }) if pd.api.types.is_datetime64_any_dtype(df_us[date_col]): df_us['date'] = df_us[date_col].dt.strftime('%Y-%m-%d') else: df_us['date'] = pd.to_datetime(df_us[date_col]).dt.strftime('%Y-%m-%d') df_us['change_pct'] = df_us['close'].pct_change() * 100 df_us['change_pct'] = df_us['change_pct'].fillna(0) return df_us[['date', 'open', 'close', 'high', 'low', 'volume', 'change_pct']] def fetch_data_eastmoney(): """降级路径: 东方财富直接 HTTP""" logger.info(f"📡 Trying EastMoney direct for {clean_ticker}...") return EastMoneyDirect.fetch_kline(clean_ticker, s_fmt, e_fmt) # === 多源尝试: akshare → 东方财富直接 === try: try: df_remote = fetch_data_akshare() except (RequestException, Exception) as e: if "Proxy" in str(e) or "proxy" in str(e): logger.warning(f"⚠️ Proxy error detected: {e}. Retrying with proxy disabled...") with temporary_no_proxy(): df_remote = fetch_data_akshare() else: raise e except Exception as e: logger.warning(f"⚠️ akshare failed for {clean_ticker}: {e}") if not is_us_stock: try: df_remote = fetch_data_eastmoney() except Exception as e2: logger.warning(f"⚠️ EastMoney direct also failed for {clean_ticker}: {e2}") raise e # 抛出原始错误 if df_remote is not None and not df_remote.empty: if not is_us_stock: df_remote = df_remote.rename(columns={ '日期': 'date', '开盘': 'open', '收盘': 'close', '最高': 'high', '最低': 'low', '成交量': 'volume', '涨跌幅': 'change_pct' }) # 确保日期格式正确 df_remote['date'] = pd.to_datetime(df_remote['date']).dt.strftime('%Y-%m-%d') # 只有在获取到有意义的数据时才保存 self.db.save_stock_prices(clean_ticker, df_remote) # 保存时使用清洗后的 clean_ticker # 重新查询数据库返回结果，保证一致性 return self.db.get_stock_prices(clean_ticker, start_date, end_date) else: logger.warning(f"⚠️ Akshare returned empty data for {clean_ticker}") except KeyError as e: # Akshare 有时在某些股票无数据时会抛出 KeyError logger.warning(f"⚠️ Akshare data missing for {clean_ticker}: {e}") except (RequestException, ConnectionError) as e: logger.error(f"❌ Network error during Akshare sync for {clean_ticker}: {e}") except sqlite3.Error as e: logger.error(f"❌ Database error during Akshare sync for {clean_ticker}: {e}") except Exception as e: logger.error(f"❌ Unexpected error during Akshare sync for {clean_ticker}: {e}") return df_db def get_stock_analysis(ticker: str, db: DatabaseManager) -> str: """ 生成指定股票的分析摘要报告。 Args: ticker: 股票代码 db: 数据库管理器实例 Returns: Markdown 格式的分析报告，包含价格走势和关键指标。 """ tools = StockTools(db) df = tools.get_stock_price(ticker) if df.empty: return f"❌ 未能获取 {ticker} 的股价数据。" latest = df.iloc[-1] change = ((latest['close'] - df.iloc[0]['close']) / df.iloc[0]['close']) * 100 report = [ f"## 📊 {ticker} 分析报告", f"- **查询时段**: {df.iloc[0]['date']} -> {latest['date']}", f"- **当前价**: ¥{latest['close']:.2f}", f"- **时段涨跌**: {change:+.2f}%", f"- **最高/最低**: ¥{df['high'].max():.2f} / ¥{df['low'].min():.2f}", "\n### 最近交易概览", "```", df.tail(5)[['date', 'close', 'change_pct', 'volume']].to_string(index=False), "```" ] return "\n".join(report)