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16a3e7f20d
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3ed0ce1613
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import sqlite3
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db_path = 'data/futures_analysis.db'
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conn = sqlite3.connect(db_path)
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print('=== AI模型配置 ===')
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cursor = conn.execute("SELECT * FROM ai_model_configs")
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cols = [desc[0] for desc in cursor.description]
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print(f'字段: {cols}')
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rows = cursor.fetchall()
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print(f'记录数: {len(rows)}')
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for row in rows:
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print(dict(zip(cols, row)))
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print('\n=== 分析设置 ===')
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cursor = conn.execute("SELECT * FROM analysis_settings")
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rows = cursor.fetchall()
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for row in rows:
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print(row)
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conn.close()
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@ -1,55 +1,32 @@
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import sqlite3
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import sqlite3
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import os
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import os
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from pathlib import Path
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db_path = Path(__file__).parent / "data" / "futures_analysis.db"
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db_path = 'data/futures_analysis.db'
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print("=" * 60)
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print(f'数据库文件路径: {os.path.abspath(db_path)}')
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print("数据库信息检查")
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print(f'数据库存在: {os.path.exists(db_path)}')
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print("=" * 60)
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print(f"数据库路径: {db_path}")
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if os.path.exists(db_path):
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print(f"文件存在: {db_path.exists()}")
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print(f'数据库大小: {os.path.getsize(db_path)} 字节')
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if db_path.exists():
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conn = sqlite3.connect(db_path)
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print(f"文件大小: {db_path.stat().st_size} bytes")
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cursor = conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
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print(f"最后修改: {db_path.stat().st_mtime}")
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tables = [row[0] for row in cursor.fetchall()]
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print(f'数据库表列表: {tables}')
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conn = sqlite3.connect(str(db_path))
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cursor = conn.cursor()
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if 'ai_analysis_cache' in tables:
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cursor = conn.execute("SELECT COUNT(*) FROM ai_analysis_cache")
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# 获取所有表
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
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tables = cursor.fetchall()
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print(f"\n数据库表 ({len(tables)}):")
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for table in tables:
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table_name = table[0]
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cursor.execute(f"SELECT COUNT(*) FROM {table_name}")
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count = cursor.fetchone()[0]
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count = cursor.fetchone()[0]
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print(f" - {table_name}: {count} 条记录")
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print(f'AI分析记录数: {count}')
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# 检查AI模型配置
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cursor = conn.execute("SELECT id, symbol, created_at FROM ai_analysis_cache ORDER BY created_at DESC LIMIT 5")
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print("\n" + "=" * 60)
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records = cursor.fetchall()
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print("AI模型配置检查")
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print(f'最近5条记录:')
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print("=" * 60)
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for r in records:
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try:
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print(f' ID: {r[0]}, 合约: {r[1]}, 时间: {r[2]}')
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cursor.execute("SELECT id, provider, model_name, is_active, enabled FROM ai_model_configs")
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else:
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models = cursor.fetchall()
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print('❌ ai_analysis_cache 表不存在!')
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print(f"AI模型数量: {len(models)}")
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if models:
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for m in models:
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print(f" ID: {m[0]}")
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print(f" Provider: {m[1]}")
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print(f" Model Name: {m[2]}")
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print(f" Active: {m[3]}")
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print(f" Enabled: {m[4]}")
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print()
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else:
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print(" 没有配置AI模型!")
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except sqlite3.OperationalError as e:
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print(f" 查询失败: {e}")
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conn.close()
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conn.close()
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else:
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else:
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print("数据库文件不存在!")
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print('❌ 数据库文件不存在!')
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print("=" * 60)
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{
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"models": [
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{
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"model_name": "qwen3.6-plus",
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"provider": "bailian",
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"api_key": "sk-sp-51d0695ab1114470b913146d21baf68f",
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"api_base": "https://coding.dashscope.aliyuncs.com/v1",
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"model_id": "qwen3.6-plus",
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"temperature": 0.7,
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"max_tokens": 2000,
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"enabled": true
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}
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],
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"active_model": "bailian",
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"analysis_settings": {
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"enable_technical_analysis": true,
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"enable_fundamental_analysis": false,
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"enable_sentiment_analysis": false,
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"risk_tolerance": "medium",
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"max_position_pct": 10
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}
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}
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