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
581
skills/alphaear-predictor/scripts/utils/database_manager.py
Normal file
581
skills/alphaear-predictor/scripts/utils/database_manager.py
Normal file
@@ -0,0 +1,581 @@
|
||||
import sqlite3
|
||||
import json
|
||||
from datetime import datetime, date
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Optional, Any, Union
|
||||
import pandas as pd
|
||||
from loguru import logger
|
||||
|
||||
class DatabaseManager:
|
||||
"""
|
||||
AlphaEar 数据库管理器 - 负责存储热点数据、搜索缓存和股价数据
|
||||
使用 SQLite 进行持久化存储
|
||||
"""
|
||||
|
||||
def __init__(self, db_path: str = "data/signal_flux.db"):
|
||||
self.db_path = Path(db_path)
|
||||
self.db_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
|
||||
self.conn.row_factory = sqlite3.Row
|
||||
self._init_db()
|
||||
logger.info(f"💾 Database initialized at {self.db_path}")
|
||||
|
||||
def _init_db(self):
|
||||
"""初始化表结构"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
# 1. 每日热点新闻表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS daily_news (
|
||||
id TEXT PRIMARY KEY,
|
||||
source TEXT,
|
||||
rank INTEGER,
|
||||
title TEXT,
|
||||
url TEXT,
|
||||
content TEXT,
|
||||
publish_time TEXT,
|
||||
crawl_time TEXT,
|
||||
sentiment_score REAL,
|
||||
analysis TEXT,
|
||||
meta_data TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 尝试添加 analysis 列（如果表已存在但没有该列）
|
||||
try:
|
||||
cursor.execute("ALTER TABLE daily_news ADD COLUMN analysis TEXT")
|
||||
except:
|
||||
pass # 列已存在
|
||||
|
||||
|
||||
# 2. 搜索缓存表 (原有 JSON 缓存)
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS search_cache (
|
||||
query_hash TEXT PRIMARY KEY,
|
||||
query TEXT,
|
||||
engine TEXT,
|
||||
results TEXT,
|
||||
timestamp TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 2.5 搜索详情表 (展开的搜索结果)
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS search_detail (
|
||||
id TEXT,
|
||||
query_hash TEXT,
|
||||
rank INTEGER,
|
||||
title TEXT,
|
||||
url TEXT,
|
||||
content TEXT,
|
||||
publish_time TEXT,
|
||||
crawl_time TEXT,
|
||||
sentiment_score REAL,
|
||||
source TEXT,
|
||||
meta_data TEXT,
|
||||
PRIMARY KEY (query_hash, id)
|
||||
)
|
||||
""")
|
||||
|
||||
# 3. 股价数据表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS stock_prices (
|
||||
ticker TEXT,
|
||||
date TEXT,
|
||||
open REAL,
|
||||
close REAL,
|
||||
high REAL,
|
||||
low REAL,
|
||||
volume REAL,
|
||||
change_pct REAL,
|
||||
PRIMARY KEY (ticker, date)
|
||||
)
|
||||
""")
|
||||
|
||||
# 4. 股票列表表 (用于检索)
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS stock_list (
|
||||
code TEXT PRIMARY KEY,
|
||||
name TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
# 5. 投资信号表 (ISQ Framework)
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS signals (
|
||||
signal_id TEXT PRIMARY KEY,
|
||||
title TEXT,
|
||||
summary TEXT,
|
||||
transmission_chain TEXT,
|
||||
sentiment_score REAL,
|
||||
confidence REAL,
|
||||
intensity INTEGER,
|
||||
expected_horizon TEXT,
|
||||
price_in_status TEXT,
|
||||
impact_tickers TEXT,
|
||||
industry_tags TEXT,
|
||||
sources TEXT,
|
||||
user_id TEXT,
|
||||
created_at TEXT
|
||||
)
|
||||
""")
|
||||
|
||||
|
||||
|
||||
# 6. 创建索引以优化查询性能
|
||||
cursor.execute("CREATE INDEX IF NOT EXISTS idx_news_crawl_time ON daily_news(crawl_time)")
|
||||
cursor.execute("CREATE INDEX IF NOT EXISTS idx_news_source ON daily_news(source)")
|
||||
cursor.execute("CREATE INDEX IF NOT EXISTS idx_search_cache_timestamp ON search_cache(timestamp)")
|
||||
cursor.execute("CREATE INDEX IF NOT EXISTS idx_stock_prices_ticker_date ON stock_prices(ticker, date)")
|
||||
# 尝试添加 user_id 列到 signals 表
|
||||
try:
|
||||
cursor.execute("ALTER TABLE signals ADD COLUMN user_id TEXT")
|
||||
except:
|
||||
pass
|
||||
|
||||
cursor.execute("CREATE INDEX IF NOT EXISTS idx_signals_user_id ON signals(user_id)")
|
||||
|
||||
self.conn.commit()
|
||||
|
||||
#
|
||||
# self.conn.commit()
|
||||
|
||||
|
||||
# --- 新闻数据操作 ---
|
||||
|
||||
def save_daily_news(self, news_list: List[Dict]) -> int:
|
||||
"""保存热点新闻，包含发布时间与抓取时间"""
|
||||
cursor = self.conn.cursor()
|
||||
count = 0
|
||||
crawl_time = datetime.now().isoformat()
|
||||
|
||||
for news in news_list:
|
||||
try:
|
||||
# 兼容不同来源的 ID 生成逻辑
|
||||
news_id = news.get('id') or f"{news.get('source')}_{news.get('rank')}_{crawl_time[:10]}"
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO daily_news
|
||||
(id, source, rank, title, url, content, publish_time, crawl_time, sentiment_score, meta_data)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
news_id,
|
||||
news.get('source'),
|
||||
news.get('rank'),
|
||||
news.get('title'),
|
||||
news.get('url'),
|
||||
news.get('content', ''),
|
||||
news.get('publish_time'), # 新增支持发布时间
|
||||
crawl_time,
|
||||
news.get('sentiment_score'),
|
||||
json.dumps(news.get('meta_data', {}))
|
||||
))
|
||||
count += 1
|
||||
except sqlite3.Error as e:
|
||||
logger.error(f"Database error saving news item {news.get('title')}: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error saving news item {news.get('title')}: {e}")
|
||||
|
||||
self.conn.commit()
|
||||
return count
|
||||
|
||||
def get_daily_news(self, source: Optional[str] = None, limit: int = 100, days: int = 1) -> List[Dict]:
|
||||
"""获取最近 N 天的热点新闻"""
|
||||
cursor = self.conn.cursor()
|
||||
# 使用 crawl_time 过滤，保证结果的新鲜度
|
||||
time_threshold = (datetime.now().timestamp() - days * 86400)
|
||||
time_threshold_str = datetime.fromtimestamp(time_threshold).isoformat()
|
||||
|
||||
query = "SELECT * FROM daily_news WHERE crawl_time >= ?"
|
||||
params = [time_threshold_str]
|
||||
|
||||
if source:
|
||||
query += " AND source = ?"
|
||||
params.append(source)
|
||||
|
||||
query += " ORDER BY crawl_time DESC, rank LIMIT ?"
|
||||
params.append(limit)
|
||||
|
||||
cursor.execute(query, params)
|
||||
return [dict(row) for row in cursor.fetchall()]
|
||||
|
||||
def lookup_reference_by_url(self, url: str) -> Optional[Dict[str, Any]]:
|
||||
"""Best-effort lookup of a source item by URL.
|
||||
|
||||
This is used to render a stable bibliography from DB-backed metadata.
|
||||
It searches both `daily_news` and `search_detail`.
|
||||
"""
|
||||
url = (url or "").strip()
|
||||
if not url:
|
||||
return None
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
try:
|
||||
cursor.execute(
|
||||
"""
|
||||
SELECT title, source, publish_time, crawl_time, url
|
||||
FROM daily_news
|
||||
WHERE url = ?
|
||||
ORDER BY crawl_time DESC
|
||||
LIMIT 1
|
||||
""",
|
||||
(url,),
|
||||
)
|
||||
row = cursor.fetchone()
|
||||
if row:
|
||||
return dict(row)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
cursor.execute(
|
||||
"""
|
||||
SELECT title, source, publish_time, crawl_time, url
|
||||
FROM search_detail
|
||||
WHERE url = ?
|
||||
ORDER BY crawl_time DESC
|
||||
LIMIT 1
|
||||
""",
|
||||
(url,),
|
||||
)
|
||||
row = cursor.fetchone()
|
||||
if row:
|
||||
return dict(row)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
def delete_news(self, news_id: str) -> bool:
|
||||
"""删除特定新闻"""
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute("DELETE FROM daily_news WHERE id = ?", (news_id,))
|
||||
self.conn.commit()
|
||||
return cursor.rowcount > 0
|
||||
|
||||
def update_news_content(self, news_id: str, content: str = None, analysis: str = None) -> bool:
|
||||
"""更新新闻的内容或分析结果"""
|
||||
cursor = self.conn.cursor()
|
||||
updates = []
|
||||
params = []
|
||||
|
||||
if content is not None:
|
||||
updates.append("content = ?")
|
||||
params.append(content)
|
||||
if analysis is not None:
|
||||
updates.append("analysis = ?")
|
||||
params.append(analysis)
|
||||
|
||||
if not updates:
|
||||
return False
|
||||
|
||||
params.append(news_id)
|
||||
query = f"UPDATE daily_news SET {', '.join(updates)} WHERE id = ?"
|
||||
cursor.execute(query, params)
|
||||
self.conn.commit()
|
||||
return cursor.rowcount > 0
|
||||
|
||||
# --- 搜索缓存辅助 ---
|
||||
|
||||
def get_search_cache(self, query_hash: str, ttl_seconds: Optional[int] = None) -> Optional[Dict]:
|
||||
"""获取搜索缓存 (优先查 search_detail)"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
# 1. 尝试从 search_detail 获取展开的结构化数据
|
||||
cursor.execute("""
|
||||
SELECT * FROM search_detail
|
||||
WHERE query_hash = ?
|
||||
ORDER BY rank
|
||||
""", (query_hash,))
|
||||
details = [dict(row) for row in cursor.fetchall()]
|
||||
|
||||
if details:
|
||||
# 检查 TTL (取第一条的时间)
|
||||
first_time = datetime.fromisoformat(details[0]['crawl_time'])
|
||||
if ttl_seconds and (datetime.now() - first_time).total_seconds() > ttl_seconds:
|
||||
logger.info(f"⌛ Detailed cache expired for hash {query_hash}")
|
||||
pass # Expired, fall through or return None? If Detail expired, Cache likely expired too.
|
||||
# But let's check basic cache just in case metadata differs?
|
||||
# Actually if details exist, we prefer them. If expired, we return None.
|
||||
return None
|
||||
|
||||
logger.info(f"✅ Hit detailed search cache for {query_hash} ({len(details)} items)")
|
||||
# Reconstruct the expected 'results' list format for SearchTools
|
||||
# SearchTools expects a list of dicts.
|
||||
# We return a dict wrapper to match get_search_cache signature returning Dict usually containing 'results' string.
|
||||
# But SearchTools logic:
|
||||
# cache = db.get_search_cache(...)
|
||||
# cached_data = json.loads(cache['results'])
|
||||
|
||||
# To minimize SearchTools changes, we can return a dict mimicking the old structure
|
||||
# OR Change SearchTools to handle list return.
|
||||
# Let's return a special dict that SearchTools can recognize or just format it as before.
|
||||
return {"results": json.dumps(details), "timestamp": details[0]['crawl_time']}
|
||||
|
||||
# 2. Fallback to old table
|
||||
cursor.execute("SELECT * FROM search_cache WHERE query_hash = ?", (query_hash,))
|
||||
row = cursor.fetchone()
|
||||
|
||||
if not row:
|
||||
return None
|
||||
|
||||
row_dict = dict(row)
|
||||
if ttl_seconds:
|
||||
cache_time = datetime.fromisoformat(row_dict['timestamp'])
|
||||
if (datetime.now() - cache_time).total_seconds() > ttl_seconds:
|
||||
logger.info(f"⌛ Cache expired for hash {query_hash}")
|
||||
return None
|
||||
|
||||
return row_dict
|
||||
|
||||
def save_search_cache(self, query_hash: str, query: str, engine: str, results: Union[str, List[Dict]]):
|
||||
"""保存搜索结果 (同时保存到 search_cache 和 search_detail)"""
|
||||
cursor = self.conn.cursor()
|
||||
current_time = datetime.now().isoformat()
|
||||
|
||||
results_str = results if isinstance(results, str) else json.dumps(results)
|
||||
|
||||
# 1. Save summary to search_cache
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO search_cache (query_hash, query, engine, results, timestamp)
|
||||
VALUES (?, ?, ?, ?, ?)
|
||||
""", (query_hash, query, engine, results_str, current_time))
|
||||
|
||||
# 2. Save details to search_detail if results is a list
|
||||
if isinstance(results, list):
|
||||
for item in results:
|
||||
try:
|
||||
item_id = item.get('id') or f"{hash(item.get('url', ''))}"
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO search_detail
|
||||
(id, query_hash, rank, title, url, content, publish_time, crawl_time, sentiment_score, source, meta_data)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
str(item_id),
|
||||
query_hash,
|
||||
item.get('rank', 0),
|
||||
item.get('title'),
|
||||
item.get('url'),
|
||||
item.get('content', ''),
|
||||
item.get('publish_time'),
|
||||
item.get('crawl_time') or current_time,
|
||||
item.get('sentiment_score'),
|
||||
item.get('source'),
|
||||
json.dumps(item.get('meta_data', {}))
|
||||
))
|
||||
except sqlite3.Error as e:
|
||||
logger.error(f"Database error saving search detail {item.get('title')}: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error saving search detail {item.get('title')}: {e}")
|
||||
|
||||
self.conn.commit()
|
||||
|
||||
def find_similar_queries(self, query: str, limit: int = 5) -> List[Dict]:
|
||||
"""模糊搜索相似的已缓存查询"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
# Simple fuzzy match: query in cached OR cached in query
|
||||
q_wild = f"%{query}%"
|
||||
cursor.execute("""
|
||||
SELECT query, query_hash, timestamp, results
|
||||
FROM search_cache
|
||||
WHERE query LIKE ? OR ? LIKE ('%' || query || '%')
|
||||
ORDER BY timestamp DESC
|
||||
LIMIT ?
|
||||
""", (q_wild, query, limit))
|
||||
|
||||
return [dict(row) for row in cursor.fetchall()]
|
||||
|
||||
def search_local_news(self, query: str, limit: int = 5) -> List[Dict]:
|
||||
"""从本地 daily_news 搜索相关新闻"""
|
||||
cursor = self.conn.cursor()
|
||||
q_wild = f"%{query}%"
|
||||
# Search title and content
|
||||
cursor.execute("""
|
||||
SELECT * FROM daily_news
|
||||
WHERE title LIKE ? OR content LIKE ?
|
||||
ORDER BY crawl_time DESC
|
||||
LIMIT ?
|
||||
""", (q_wild, q_wild, limit))
|
||||
return [dict(row) for row in cursor.fetchall()]
|
||||
|
||||
# --- 股票数据操作 ---
|
||||
|
||||
def save_stock_list(self, df: pd.DataFrame):
|
||||
"""保存股票列表到 stock_list 表"""
|
||||
cursor = self.conn.cursor()
|
||||
try:
|
||||
# 清空旧表
|
||||
cursor.execute("DELETE FROM stock_list")
|
||||
|
||||
# 批量插入
|
||||
data = df[['code', 'name']].to_dict('records')
|
||||
cursor.executemany(
|
||||
"INSERT INTO stock_list (code, name) VALUES (:code, :name)",
|
||||
data
|
||||
)
|
||||
self.conn.commit()
|
||||
except sqlite3.Error as e:
|
||||
logger.error(f"Database error saving stock list: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error saving stock list: {e}")
|
||||
|
||||
def search_stock(self, query: str, limit: int = 5) -> List[Dict]:
|
||||
"""模糊搜索股票代码或名称"""
|
||||
cursor = self.conn.cursor()
|
||||
wild = f"%{query}%"
|
||||
cursor.execute("""
|
||||
SELECT code, name FROM stock_list
|
||||
WHERE code LIKE ? OR name LIKE ?
|
||||
LIMIT ?
|
||||
""", (wild, wild, limit))
|
||||
return [dict(row) for row in cursor.fetchall()]
|
||||
|
||||
def get_stock_by_code(self, code: str) -> Optional[Dict[str, str]]:
|
||||
"""精确按代码获取股票信息。
|
||||
|
||||
Args:
|
||||
code: 股票代码（A股6位 / 港股5位），必须为纯数字字符串。
|
||||
|
||||
Returns:
|
||||
dict: {"code": str, "name": str} 或 None。
|
||||
"""
|
||||
if not code:
|
||||
return None
|
||||
clean = "".join([c for c in str(code).strip() if c.isdigit()])
|
||||
if not clean:
|
||||
return None
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute("SELECT code, name FROM stock_list WHERE code = ? LIMIT 1", (clean,))
|
||||
row = cursor.fetchone()
|
||||
return dict(row) if row else None
|
||||
|
||||
def save_stock_prices(self, ticker: str, df: pd.DataFrame):
|
||||
"""保存股价历史数据"""
|
||||
if df.empty:
|
||||
return
|
||||
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
# 确保 DataFrame 有必要的列
|
||||
required_cols = ['date', 'open', 'close', 'high', 'low', 'volume', 'change_pct']
|
||||
for col in required_cols:
|
||||
if col not in df.columns:
|
||||
logger.warning(f"Missing column {col} in stock data for {ticker}")
|
||||
return
|
||||
|
||||
try:
|
||||
for _, row in df.iterrows():
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO stock_prices
|
||||
(ticker, date, open, close, high, low, volume, change_pct)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
ticker,
|
||||
row['date'],
|
||||
row['open'],
|
||||
row['close'],
|
||||
row['high'],
|
||||
row['low'],
|
||||
row['volume'],
|
||||
row['change_pct']
|
||||
))
|
||||
self.conn.commit()
|
||||
except sqlite3.Error as e:
|
||||
logger.error(f"Database error saving stock prices for {ticker}: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error saving stock prices for {ticker}: {e}")
|
||||
|
||||
def get_stock_prices(self, ticker: str, start_date: str, end_date: str) -> pd.DataFrame:
|
||||
"""获取指定日期范围的股价数据"""
|
||||
cursor = self.conn.cursor()
|
||||
|
||||
cursor.execute("""
|
||||
SELECT * FROM stock_prices
|
||||
WHERE ticker = ? AND date >= ? AND date <= ?
|
||||
ORDER BY date
|
||||
""", (ticker, start_date, end_date))
|
||||
|
||||
rows = cursor.fetchall()
|
||||
if not rows:
|
||||
return pd.DataFrame()
|
||||
|
||||
columns = ['ticker', 'date', 'open', 'close', 'high', 'low', 'volume', 'change_pct']
|
||||
return pd.DataFrame([dict(row) for row in rows], columns=columns)
|
||||
|
||||
def execute_query(self, query: str, params: tuple = ()) -> List[Any]:
|
||||
"""执行自定义 SQL 查询"""
|
||||
try:
|
||||
cursor = self.conn.cursor()
|
||||
cursor.execute(query, params)
|
||||
if query.strip().upper().startswith("SELECT"):
|
||||
return cursor.fetchall()
|
||||
else:
|
||||
self.conn.commit()
|
||||
return []
|
||||
except sqlite3.Error as e:
|
||||
logger.error(f"SQL execution failed (Database error): {e}")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"SQL execution failed (Unexpected error): {e}")
|
||||
return []
|
||||
|
||||
# --- 投资信号操作 (ISQ Framework) ---
|
||||
|
||||
def save_signal(self, signal: Dict[str, Any]):
|
||||
"""保存投资信号"""
|
||||
cursor = self.conn.cursor()
|
||||
created_at = datetime.now().isoformat()
|
||||
|
||||
cursor.execute("""
|
||||
INSERT OR REPLACE INTO signals
|
||||
(signal_id, title, summary, transmission_chain, sentiment_score,
|
||||
confidence, intensity, expected_horizon, price_in_status,
|
||||
impact_tickers, industry_tags, sources, user_id, created_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (
|
||||
signal.get('signal_id'),
|
||||
signal.get('title'),
|
||||
signal.get('summary'),
|
||||
json.dumps(signal.get('transmission_chain', [])),
|
||||
signal.get('sentiment_score', 0.0),
|
||||
signal.get('confidence', 0.0),
|
||||
signal.get('intensity', 1),
|
||||
signal.get('expected_horizon', 'T+0'),
|
||||
signal.get('price_in_status', '未知'),
|
||||
json.dumps(signal.get('impact_tickers', [])),
|
||||
json.dumps(signal.get('industry_tags', [])),
|
||||
json.dumps(signal.get('sources', [])),
|
||||
signal.get('user_id'),
|
||||
created_at
|
||||
))
|
||||
self.conn.commit()
|
||||
|
||||
def get_recent_signals(self, limit: int = 20, user_id: Optional[str] = None) -> List[Dict]:
|
||||
"""获取最近的投资信号"""
|
||||
cursor = self.conn.cursor()
|
||||
if user_id:
|
||||
cursor.execute("SELECT * FROM signals WHERE user_id = ? ORDER BY created_at DESC LIMIT ?", (user_id, limit))
|
||||
else:
|
||||
cursor.execute("SELECT * FROM signals ORDER BY created_at DESC LIMIT ?", (limit,))
|
||||
rows = cursor.fetchall()
|
||||
|
||||
signals = []
|
||||
for row in rows:
|
||||
d = dict(row)
|
||||
# 解析 JSON 字段
|
||||
for field in ['transmission_chain', 'impact_tickers', 'industry_tags', 'sources']:
|
||||
if d.get(field):
|
||||
try:
|
||||
d[field] = json.loads(d[field])
|
||||
except:
|
||||
pass
|
||||
signals.append(d)
|
||||
return signals
|
||||
|
||||
def close(self):
|
||||
if self.conn:
|
||||
self.conn.close()
|
||||
logger.info("Database connection closed.")
|
||||
|
||||
Reference in New Issue
Block a user