Import 9 alphaear finance skills
- 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
This commit is contained in:
Kunthawat Greethong
0
skills/alphaear-stock/scripts/__init__.py
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0
skills/alphaear-stock/scripts/__init__.py
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119
skills/alphaear-stock/scripts/database_manager.py
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skills/alphaear-stock/scripts/database_manager.py
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@@ -0,0 +1,119 @@
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import sqlite3
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from pathlib import Path
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from typing import List, Dict, Optional
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import pandas as pd
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from loguru import logger
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class DatabaseManager:
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"""
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AlphaEar Stock Database Manager
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Reduced version for alphaear-stock skill
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"""
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def __init__(self, db_path: str = "data/signal_flux.db"):
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self.db_path = Path(db_path)
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self.db_path.parent.mkdir(parents=True, exist_ok=True)
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self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
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self.conn.row_factory = sqlite3.Row
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self._init_db()
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logger.debug(f"💾 Stock Database initialized at {self.db_path}")
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def _init_db(self):
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"""Initialize stock-related tables"""
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cursor = self.conn.cursor()
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# Stock Prices Table
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS stock_prices (
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ticker TEXT,
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date TEXT,
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open REAL,
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close REAL,
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high REAL,
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low REAL,
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volume REAL,
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change_pct REAL,
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PRIMARY KEY (ticker, date)
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)
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""")
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# Stock List Table
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cursor.execute("""
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CREATE TABLE IF NOT EXISTS stock_list (
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code TEXT PRIMARY KEY,
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name TEXT
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)
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""")
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cursor.execute("CREATE INDEX IF NOT EXISTS idx_stock_prices_ticker_date ON stock_prices(ticker, date)")
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self.conn.commit()
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# --- Stock Operations ---
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def save_stock_list(self, df: pd.DataFrame):
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cursor = self.conn.cursor()
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try:
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cursor.execute("DELETE FROM stock_list")
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data = df[['code', 'name']].to_dict('records')
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cursor.executemany(
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"INSERT INTO stock_list (code, name) VALUES (:code, :name)",
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data
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)
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self.conn.commit()
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except Exception as e:
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logger.error(f"Error saving stock list: {e}")
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def search_stock(self, query: str, limit: int = 5) -> List[Dict]:
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cursor = self.conn.cursor()
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wild = f"%{query}%"
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cursor.execute("""
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SELECT code, name FROM stock_list
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WHERE code LIKE ? OR name LIKE ?
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LIMIT ?
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""", (wild, wild, limit))
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return [dict(row) for row in cursor.fetchall()]
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def get_stock_by_code(self, code: str) -> Optional[Dict[str, str]]:
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if not code: return None
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clean = "".join([c for c in str(code).strip() if c.isdigit()])
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if not clean: return None
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cursor = self.conn.cursor()
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cursor.execute("SELECT code, name FROM stock_list WHERE code = ? LIMIT 1", (clean,))
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row = cursor.fetchone()
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return dict(row) if row else None
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def save_stock_prices(self, ticker: str, df: pd.DataFrame):
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if df.empty: return
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cursor = self.conn.cursor()
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try:
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for _, row in df.iterrows():
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cursor.execute("""
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INSERT OR REPLACE INTO stock_prices
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(ticker, date, open, close, high, low, volume, change_pct)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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ticker, row['date'], row['open'], row['close'],
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row['high'], row['low'], row['volume'], row['change_pct']
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))
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self.conn.commit()
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except Exception as e:
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logger.error(f"Error saving prices for {ticker}: {e}")
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def get_stock_prices(self, ticker: str, start_date: str, end_date: str) -> pd.DataFrame:
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cursor = self.conn.cursor()
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cursor.execute("""
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SELECT * FROM stock_prices
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WHERE ticker = ? AND date >= ? AND date <= ?
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ORDER BY date
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""", (ticker, start_date, end_date))
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rows = cursor.fetchall()
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if not rows: return pd.DataFrame()
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columns = ['ticker', 'date', 'open', 'close', 'high', 'low', 'volume', 'change_pct']
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return pd.DataFrame([dict(row) for row in rows], columns=columns)
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def close(self):
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if self.conn:
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self.conn.close()
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419
skills/alphaear-stock/scripts/stock_tools.py
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skills/alphaear-stock/scripts/stock_tools.py
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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 yfinance as yf
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import pandas as pd
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import re
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import sqlite3
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import requests as _requests
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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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class EastMoneyDirect:
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"""东方财富 HTTP 直接调用 —— 作为 akshare 的零依赖降级方案。
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仅使用 requests，无需 API Key，国内网络直连。
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"""
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KLINE_URL = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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LIST_URL = "https://push2.eastmoney.com/api/qt/clist/get"
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UT = "fa5fd1943c7b386f172d6893dbfba10b"
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@staticmethod
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def _secid(ticker: str) -> str:
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"""将纯数字 ticker 转为东方财富 secid 格式。
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A股: 6开头 -> 1.{ticker}(上交所) | 其他 -> 0.{ticker}(深交所)
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港股: 5位数字 -> 116.{ticker}
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"""
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if len(ticker) == 5:
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return f"116.{ticker}"
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if ticker.startswith(('6', '9')):
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return f"1.{ticker}"
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return f"0.{ticker}"
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@classmethod
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def fetch_kline(cls, ticker: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""获取 K 线数据，返回与 akshare 对齐的 DataFrame。
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Args:
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ticker: 纯数字股票代码
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start_date: YYYYMMDD
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end_date: YYYYMMDD
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"""
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params = {
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'secid': cls._secid(ticker),
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'fields1': 'f1,f2,f3,f4,f5,f6',
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'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61',
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'klt': '101', # 日K
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'fqt': '1', # 前复权
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'beg': start_date,
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'end': end_date,
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'lmt': '1000',
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'ut': cls.UT,
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}
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resp = _requests.get(cls.KLINE_URL, params=params, timeout=10)
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resp.raise_for_status()
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data = resp.json().get('data')
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if not data or not data.get('klines'):
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return pd.DataFrame()
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# 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"
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rows = [k.split(',') for k in data['klines']]
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df = pd.DataFrame(rows, columns=[
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'日期', '开盘', '收盘', '最高', '最低', '成交量',
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'成交额', '振幅', '涨跌幅', '涨跌额', '换手率'
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])
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# 转为数值类型
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for col in ['开盘', '收盘', '最高', '最低', '成交量', '涨跌幅']:
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df[col] = pd.to_numeric(df[col], errors='coerce')
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return df
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@classmethod
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def fetch_stock_list(cls, market: str = 'a') -> pd.DataFrame:
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"""获取股票列表。
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Args:
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market: 'a' for A股, 'hk' for 港股
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"""
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if market == 'a':
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fs = 'm:0+t:6,m:0+t:80,m:1+t:2,m:1+t:23'
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else:
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fs = 'm:128+t:3,m:128+t:4,m:128+t:1,m:128+t:2'
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all_items = []
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page = 1
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while True:
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params = {
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'pn': str(page), 'pz': '5000', 'po': '1', 'np': '1',
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'fltt': '2', 'invt': '2', 'fid': 'f12',
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'fs': fs, 'fields': 'f12,f14',
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'ut': cls.UT,
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}
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resp = _requests.get(cls.LIST_URL, params=params, timeout=15)
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resp.raise_for_status()
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data = resp.json().get('data', {})
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diff = data.get('diff', [])
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if not diff:
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break
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for item in diff:
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all_items.append({'code': item.get('f12', ''), 'name': item.get('f14', '')})
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total = data.get('total', 0)
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if page * 5000 >= total:
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break
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page += 1
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return pd.DataFrame(all_items)
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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...")
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df_combined = None
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# === 主路径: akshare ===
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try:
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def fetch_data_ak():
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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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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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return pd.concat([df_a, df_hk], ignore_index=True)
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try:
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df_combined = fetch_data_ak()
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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_ak()
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else:
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raise e
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logger.info(f"✅ akshare: fetched {len(df_combined)} stocks.")
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except Exception as e:
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logger.warning(f"⚠️ akshare stock list failed: {e}. Trying EastMoney direct...")
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# === 降级路径: 东方财富直接 HTTP ===
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if df_combined is None or df_combined.empty:
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try:
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df_a = EastMoneyDirect.fetch_stock_list('a')
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df_hk = EastMoneyDirect.fetch_stock_list('hk')
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df_combined = pd.concat([df_a, df_hk], ignore_index=True)
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logger.info(f"✅ EastMoney direct: fetched {len(df_combined)} stocks.")
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except Exception as e2:
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logger.error(f"❌ All stock list sources failed. akshare + EastMoney: {e2}")
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return
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if df_combined is not None and not df_combined.empty:
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self.db.save_stock_list(df_combined)
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logger.info(f"✅ Cached {len(df_combined)} stocks to database.")
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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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res = self.db.search_stock(search_query, limit)
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if not res and search_query.isalpha():
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# Robustness: mock search hit for alphabetic US tickers
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return [{"code": search_query.upper(), "name": search_query.upper()}]
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return res
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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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is_us_stock = bool(re.search(r'[a-zA-Z]', ticker)) and not bool(re.search(r'\d{5,6}', ticker))
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if is_us_stock:
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clean_ticker = ticker.upper()
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else:
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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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logger.warning(f"⚠️ Unsupported ticker format: {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_akshare():
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"""主路径: akshare"""
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if is_us_stock:
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return _fetch_data_yfinance()
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if len(clean_ticker) == 5:
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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"
|
||||
)
|
||||
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)
|
||||
Reference in New Issue
Block a user