58f9380ec4
- alphaear-deepear-lite: DeepEar Lite API integration - alphaear-logic-visualizer: Draw.io XML finance diagrams - alphaear-news: Real-time finance news (10+ sources) - alphaear-predictor: Kronos time-series forecasting - alphaear-reporter: Professional financial reports - alphaear-search: Web search + local RAG - alphaear-sentiment: FinBERT/LLM sentiment analysis - alphaear-signal-tracker: Signal evolution tracking - alphaear-stock: A-Share/HK/US stock data Updates: - All scripts updated to use universal .env path - Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example - Updated load_dotenv() to use ~/.config/opencode/.env
258 lines
9.8 KiB
Python
258 lines
9.8 KiB
Python
from datetime import datetime, timedelta
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from typing import List, Dict, Optional
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import akshare as ak
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import pandas as pd
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import re
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import sqlite3
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from requests.exceptions import RequestException
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from loguru import logger
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from .database_manager import DatabaseManager
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import os
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from contextlib import contextmanager
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@contextmanager
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def temporary_no_proxy():
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"""Context manager to temporarily unset proxy environment variables."""
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proxies = {k: os.environ.get(k) for k in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']}
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for k in proxies:
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if k in os.environ:
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del os.environ[k]
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try:
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yield
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finally:
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for k, v in proxies.items():
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if v is not None:
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os.environ[k] = v
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class StockTools:
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"""金融分析股票工具 - 结合高性能数据库缓存与增量更新"""
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def __init__(self, db: DatabaseManager, auto_update: bool = True):
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"""
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初始化股票工具
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Args:
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db: 数据库管理器
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auto_update: 是否在列表为空时自动更新，默认 True
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"""
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self.db = db
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if auto_update:
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self._check_and_update_stock_list()
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def _check_and_update_stock_list(self, force: bool = False):
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"""检查并更新股票列表。仅在列表为空或 force=True 时从网络拉取。"""
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# 直接查询表中记录数
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cursor = self.db.conn.cursor()
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cursor.execute("SELECT COUNT(*) FROM stock_list")
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count = cursor.fetchone()[0]
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if count > 0 and not force:
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logger.info(f"ℹ️ Stock list already cached ({count} stocks)")
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return
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logger.info("📡 Updating A-share and HK-share stock list from akshare...")
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def fetch_data():
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# A-share
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df_a = ak.stock_zh_a_spot_em()
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df_a = df_a[['代码', '名称']].copy()
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df_a.columns = ['code', 'name']
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# HK-share
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df_hk = ak.stock_hk_spot_em()
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df_hk = df_hk[['代码', '名称']].copy()
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df_hk.columns = ['code', 'name']
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# Combine
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return pd.concat([df_a, df_hk], ignore_index=True)
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try:
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try:
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df_combined = fetch_data()
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except (RequestException, Exception) as e:
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if "Proxy" in str(e) or "proxy" in str(e):
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logger.warning(f"⚠️ Proxy error detected: {e}. Retrying with proxy disabled...")
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with temporary_no_proxy():
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df_combined = fetch_data()
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else:
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raise e
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self.db.save_stock_list(df_combined)
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logger.info(f"✅ Cached {len(df_combined)} stocks (A-share + HK) to database.")
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except Exception as e:
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logger.error(f"❌ Failed to sync stock list: {e}")
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def search_ticker(self, query: str, limit: int = 5) -> List[Dict]:
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"""
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模糊搜索 A 股股票代码或名称，支持常见缩写。
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"""
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# 清洗后缀 (如 CATL.SZ -> CATL, 000001.SZ -> 000001)
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clean_query = re.sub(r'\.(SZ|SH|HK|US)$', '', query, flags=re.IGNORECASE)
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# 常见缩写映射
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aliases = {
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"CATL": "宁德时代",
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"BYD": "比亚迪",
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"TSLA": "特斯拉",
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"Moutai": "贵州茅台",
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"Tencent": "腾讯",
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"Alibaba": "阿里巴巴",
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"Meituan": "美团",
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}
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search_query = aliases.get(clean_query.upper(), clean_query)
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# Robustness: if regex-like ticker code is embedded in query (e.g. "300364 中文在线"), try to extract it
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if not search_query.isdigit():
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# Extract explicit 5-6 digit codes
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match = re.search(r'\b(\d{5,6})\b', clean_query)
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if match:
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search_query = match.group(1)
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return self.db.search_stock(search_query, limit)
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def get_stock_price(
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self,
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ticker: str,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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force_sync: bool = False,
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) -> pd.DataFrame:
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"""
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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。
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Args:
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ticker: 股票代码，如 "600519"（贵州茅台）或 "000001"（平安银行）。
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start_date: 开始日期，格式 "YYYY-MM-DD"。默认为 90 天前。
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end_date: 结束日期，格式 "YYYY-MM-DD"。默认为今天。
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Returns:
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包含 date, open, close, high, low, volume, change_pct 列的 DataFrame。
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"""
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now = datetime.now()
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if not end_date:
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end_date = now.strftime('%Y-%m-%d')
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if not start_date:
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start_date = (now - timedelta(days=90)).strftime('%Y-%m-%d')
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df_db = self.db.get_stock_prices(ticker, start_date, end_date)
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need_update = False
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if df_db.empty:
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need_update = True
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else:
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db_latest = pd.to_datetime(df_db['date'].max())
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req_latest = pd.to_datetime(end_date)
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if (req_latest - db_latest).days > 2:
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need_update = True
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if force_sync:
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need_update = True
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if need_update:
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logger.info(f"📡 Data stale or missing for {ticker}, syncing from network...")
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# 清洗 ticker，确保只包含数字（Akshare A 股接口通常只需要数字代码）
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clean_ticker = "".join(filter(str.isdigit, ticker))
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if not clean_ticker:
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# Non A/H numeric tickers are not supported by the current data source.
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logger.warning(f"⚠️ Unsupported ticker format (A/H only): {ticker}")
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return df_db
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try:
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s_fmt = start_date.replace("-", "")
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e_fmt = end_date.replace("-", "")
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df_remote = None
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def fetch_data():
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if len(clean_ticker) == 5:
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# HK Stock
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return ak.stock_hk_hist(
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symbol=clean_ticker, period="daily",
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start_date=s_fmt, end_date=e_fmt,
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adjust="qfq"
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)
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else:
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# A-share Stock
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return ak.stock_zh_a_hist(
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symbol=clean_ticker, period="daily",
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start_date=s_fmt, end_date=e_fmt,
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adjust="qfq"
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)
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try:
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df_remote = fetch_data()
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except (RequestException, Exception) as e:
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if "Proxy" in str(e) or "proxy" in str(e):
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logger.warning(f"⚠️ Proxy error detected: {e}. Retrying with proxy disabled...")
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with temporary_no_proxy():
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df_remote = fetch_data()
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else:
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raise e
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if df_remote is not None and not df_remote.empty:
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df_remote = df_remote.rename(columns={
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'日期': 'date', '开盘': 'open', '收盘': 'close',
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'最高': 'high', '最低': 'low', '成交量': 'volume',
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'涨跌幅': 'change_pct'
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})
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# 确保日期格式正确
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df_remote['date'] = pd.to_datetime(df_remote['date']).dt.strftime('%Y-%m-%d')
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# 只有在获取到有意义的数据时才保存
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self.db.save_stock_prices(clean_ticker, df_remote) # 保存时使用清洗后的 clean_ticker
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# 重新查询数据库返回结果，保证一致性
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return self.db.get_stock_prices(clean_ticker, start_date, end_date)
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else:
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logger.warning(f"⚠️ Akshare returned empty data for {clean_ticker}")
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except KeyError as e:
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# Akshare 有时在某些股票无数据时会抛出 KeyError
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logger.warning(f"⚠️ Akshare data missing for {clean_ticker}: {e}")
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except (RequestException, ConnectionError) as e:
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logger.error(f"❌ Network error during Akshare sync for {clean_ticker}: {e}")
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except sqlite3.Error as e:
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logger.error(f"❌ Database error during Akshare sync for {clean_ticker}: {e}")
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except Exception as e:
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logger.error(f"❌ Unexpected error during Akshare sync for {clean_ticker}: {e}")
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return df_db
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def get_stock_analysis(ticker: str, db: DatabaseManager) -> str:
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"""
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生成指定股票的分析摘要报告。
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Args:
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ticker: 股票代码
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db: 数据库管理器实例
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Returns:
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Markdown 格式的分析报告，包含价格走势和关键指标。
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"""
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tools = StockTools(db)
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df = tools.get_stock_price(ticker)
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if df.empty:
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return f"❌ 未能获取 {ticker} 的股价数据。"
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latest = df.iloc[-1]
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change = ((latest['close'] - df.iloc[0]['close']) / df.iloc[0]['close']) * 100
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report = [
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f"## 📊 {ticker} 分析报告",
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f"- **查询时段**: {df.iloc[0]['date']} -> {latest['date']}",
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f"- **当前价**: ¥{latest['close']:.2f}",
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f"- **时段涨跌**: {change:+.2f}%",
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f"- **最高/最低**: ¥{df['high'].max():.2f} / ¥{df['low'].min():.2f}",
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"\n### 最近交易概览",
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"```",
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df.tail(5)[['date', 'close', 'change_pct', 'volume']].to_string(index=False),
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"```"
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]
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return "\n".join(report)
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