修复数据库链接异常。

alpha/requirements.txt

@@ -4,3 +4,4 @@ scipy>=1.10.0
 matplotlib>=3.7.0
 tushare>=1.3.0
 psycopg2-binary>=2.9.0
+python-dotenv>=1.0.0

quantitative_data/.env.example

@@ -4,8 +4,8 @@
 # ========================================
 
 # PostgreSQL 数据库连接
-QUANT_DB_HOST=192.168.27.15
-QUANT_DB_PORT=12345
+QUANT_DB_HOST=192.168.27.11
+QUANT_DB_PORT=5438
 QUANT_DB_NAME=quant_db
 QUANT_DB_USER=postgres
 QUANT_DB_PASSWORD=your_password_here

quantitative_data/config.py

@@ -8,19 +8,48 @@
 设置方式：
   Windows:  set QUANT_DB_PASSWORD=your_password && set TUSHARE_TOKEN=your_token
   Linux:    export QUANT_DB_PASSWORD=your_password && export TUSHARE_TOKEN=your_token
-  也可创建 .env 文件（参考 .env.example）
+   也可创建 .env 文件（参考 .env.example）
 """
 import os
+from pathlib import Path
+from urllib.parse import quote_plus
+
+# 加载 .env 文件中的环境变量
+_LOADED = False
+try:
+    from dotenv import load_dotenv
+    # 使用 resolve() 获取绝对路径，避免 os.chdir() 改变工作目录后 __file__ 变为相对路径
+    env_path = Path(__file__).resolve().parent / ".env"
+    # 如果 resolve 后的路径不存在，尝试从当前文件所在目录查找
+    if not env_path.exists():
+        # 备用方案：也检查 Notebook 的当前工作目录
+        cwd_path = Path.cwd() / ".env"
+        if cwd_path.exists():
+            env_path = cwd_path
+    
+    if env_path.exists():
+        load_dotenv(dotenv_path=env_path, override=True)
+        print(f"✓ 已加载环境变量文件: {env_path}")
+        _LOADED = True
+    else:
+        print(f"⚠ 未找到 .env 文件。检查路径: {Path(__file__).resolve().parent / '.env'}, 当前目录: {Path.cwd() / '.env'}")
+except ImportError:
+    print("⚠ python-dotenv 未安装，无法自动加载 .env 文件。请手动设置环境变量或执行: pip install python-dotenv")
 
 # PostgreSQL Docker 连接配置
+_PASSWORD = os.environ.get("QUANT_DB_PASSWORD", "")
+
 DB_CONFIG = {
-    "host": os.environ.get("QUANT_DB_HOST", "192.168.27.15"),
-    "port": int(os.environ.get("QUANT_DB_PORT", "12345")),
+    "host": os.environ.get("QUANT_DB_HOST", "192.168.27.11"),
+    "port": int(os.environ.get("QUANT_DB_PORT", "5438")),
     "database": os.environ.get("QUANT_DB_NAME", "quant_db"),
     "user": os.environ.get("QUANT_DB_USER", "postgres"),
-    "password": os.environ.get("QUANT_DB_PASSWORD", ""),
+    "password": _PASSWORD,
 }
 
+# 对密码进行 URL 编码（用于 SQLAlchemy 连接串），避免特殊字符导致解析错误
+PASSWORD_ENCODED = quote_plus(_PASSWORD) if _PASSWORD else ""
+
 # Tushare API Token
 TUSHARE_TOKEN = os.environ.get("TUSHARE_TOKEN", "")
 

quantitative_data/importer.py

@@ -1,6 +1,6 @@
 """
 量化数据导入核心模块
-连接 Docker PostgreSQL (192.168.27.15:12345)
+连接 Docker PostgreSQL (192.168.27.11:5438)
 从 Tushare 拉取数据并批量导入
 """
 import time
@@ -15,7 +15,7 @@ from psycopg2 import sql
 from psycopg2.extras import execute_values
 from sqlalchemy import create_engine
 
-from config import DB_CONFIG, TUSHARE_TOKEN, BATCH_SIZE, START_DATE, END_DATE
+from config import DB_CONFIG, TUSHARE_TOKEN, BATCH_SIZE, START_DATE, END_DATE, PASSWORD_ENCODED
 
 # ============================================================
 # 日志配置
@@ -55,7 +55,7 @@ def get_sqlalchemy_engine():
     global _sqlalchemy_engine
     if _sqlalchemy_engine is None:
         db_url = (
-            f"postgresql://{DB_CONFIG['user']}:{DB_CONFIG['password']}"
+            f"postgresql://{DB_CONFIG['user']}:{PASSWORD_ENCODED}"
             f"@{DB_CONFIG['host']}:{DB_CONFIG['port']}/{DB_CONFIG['database']}"
         )
         _sqlalchemy_engine = create_engine(db_url, pool_size=5, max_overflow=10)
@@ -185,6 +185,9 @@ def import_stock_basic():
     导入股票基本信息 (stock_basic)
     Tushare: stock_basic
     """
+    # 确保数据库表结构已初始化
+    init_database()
+
     logger.info("=" * 60)
     logger.info("[1/7] 导入股票基本信息 (stock_basic) ...")
 
@@ -820,7 +823,9 @@ def init_database():
     # 执行 DDL
     conn = get_pg_connection()
     try:
-        with open("schema.sql", "r", encoding="utf-8") as f:
+        import os as _os
+        schema_path = _os.path.join(_os.path.dirname(_os.path.abspath(__file__)), "schema.sql")
+        with open(schema_path, "r", encoding="utf-8") as f:
             ddl_sql = f.read()
 
         # 按分号分割，逐条执行 (忽略被注释掉的分区表DDL)

quantitative_data/requirements.txt

@@ -2,3 +2,4 @@ tushare>=1.4.0
 pandas>=1.5.0
 psycopg2-binary>=2.9.0
 sqlalchemy>=2.0.0
+python-dotenv>=1.0.0

quantitative_data/schema.sql

@@ -1,6 +1,6 @@
 -- ============================================================
 -- 量化投资数据库 Schema
--- PostgreSQL (Docker: 192.168.27.15:12345)
+-- PostgreSQL (Docker: 192.168.27.11:5438)
 -- 数据来源：Tushare
 -- ============================================================
 

quantitative_data/数据批量导入.ipynb

@@ -7,7 +7,7 @@
     "# 量化投资数据批量导入\n",
     "\n",
     "## 目标\n",
-    "将 Tushare 的日线行情数据及公司基本面数据批量导入 Docker PostgreSQL (192.168.27.15:12345)\n",
+    "将 Tushare 的日线行情数据及公司基本面数据批量导入 Docker PostgreSQL (192.168.27.11:5438)\n",
     "\n",
     "## 数据库结构概览\n",
     "\n",
@@ -40,9 +40,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "工作目录: t:\\jupyter\\notebook\\quantitative_data\n",
+      "Python 版本: 3.10.2 (heads/master:d9999f5, Dec 16 2022, 16:20:32) [MSC v.1929 64 bit (AMD64)]\n"
+     ]
+    }
+   ],
    "source": [
     "import sys\n",
     "import os\n",
@@ -53,12 +62,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pandas                   1.5.0\n",
+      "python-dotenv            1.2.2\n",
+      "SQLAlchemy               1.3.24\n",
+      "tushare                  1.2.89\n",
+      "vnpy-tushare             1.2.85.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip available: 22.2.2 -> 26.2\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
    "source": [
     "# 检查依赖包\n",
-    "!pip list | findstr -i \"tushare pandas psycopg2 sqlalchemy\""
+    "!pip list | findstr -i \"tushare pandas psycopg2-binary sqlalchemy python-dotenv\""
    ]
   },
   {
@@ -68,14 +98,22 @@
    "outputs": [],
    "source": [
     "# 如果需要安装依赖，取消注释下面这行\n",
-    "# !pip install -r requirements.txt"
+    "#!pip install -r requirements.txt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "模块导入成功!\n"
+     ]
+    }
+   ],
    "source": [
     "# 导入核心模块\n",
     "from importer import (\n",
@@ -105,9 +143,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✗ 连接失败: connection to server at \"192.168.27.11\", port 5438 failed: fe_sendauth: no password supplied\n",
+      "\n",
+      "请检查:\n",
+      "  1. Docker 容器是否已启动: docker ps | findstr postgres\n",
+      "  2. 环境变量 (.env) 中的连接参数是否正确\n",
+      "  3. 防火墙是否开放 5438 端口\n"
+     ]
+    }
+   ],
    "source": [
     "# 测试数据库连接\n",
     "try:\n",
@@ -125,7 +176,7 @@
     "    print(\"请检查:\")\n",
     "    print(\"  1. Docker 容器是否已启动: docker ps | findstr postgres\")\n",
     "    print(\"  2. 环境变量 (.env) 中的连接参数是否正确\")\n",
-    "    print(\"  3. 防火墙是否开放 12345 端口\")"
+    "    print(\"  3. 防火墙是否开放 5438 端口\")"
    ]
   },
   {
@@ -625,7 +676,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -639,7 +690,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.10.2"
   }
  },
  "nbformat": 4,