commit;

src/app.py

@@ -2,6 +2,7 @@
 import sys
 import os
 from datetime import datetime, timedelta
+from typing import List
 import pandas as pd
 import uuid
 import json
@@ -74,6 +75,9 @@ from src.tushare_scripts.chip_distribution_collector import collect_chip_distrib
 from src.tushare_scripts.stock_factor_collector import collect_stock_factor
 from src.tushare_scripts.stock_factor_pro_collector import collect_stock_factor_pro
 
+# 导入科技主题基本面因子选股策略
+from src.quantitative_analysis.tech_fundamental_factor_strategy_v3 import TechFundamentalFactorStrategy
+
 # 设置日志
 logging.basicConfig(
     level=logging.INFO,
@@ -460,7 +464,7 @@ def run_chip_distribution_collection():
         #     mode='full'
         # )
         collect_chip_distribution(db_url=db_url, tushare_token=TUSHARE_TOKEN, mode='full',
-                                  start_date='2022-01-03', end_date='2022-12-31')
+                                  start_date='2021-01-03', end_date='2021-09-30')
         # collect_chip_distribution(
         #     db_url=db_url,
         #     tushare_token=TUSHARE_TOKEN,
@@ -3780,6 +3784,76 @@ def analyze_stock_overlap():
         }), 500
 
 
+@app.route('/scheduler/techFundamentalStrategy/batch', methods=['GET', 'POST'])
+def run_tech_fundamental_strategy_batch():
+    """批量运行科技主题基本面因子选股策略并保存到数据库"""
+    try:
+        # 获取参数
+        start_date = request.args.get('start_date') or (request.get_json() if request.is_json else {}).get('start_date')
+        end_date = request.args.get('end_date') or (request.get_json() if request.is_json else {}).get('end_date')
+        
+        if not start_date or not end_date:
+            return jsonify({"status": "error", "message": "缺少参数: start_date 和 end_date"}), 400
+        
+        # 验证日期
+        start_dt = datetime.strptime(start_date, '%Y-%m-%d')
+        end_dt = datetime.strptime(end_date, '%Y-%m-%d')
+        
+        # 数据库连接（用于检查交易日数据）
+        from sqlalchemy import create_engine
+        db_url = "mysql+pymysql://fac_pattern:Chlry$%.8_app@192.168.16.153:3307/my_quant_db"
+        engine = create_engine(db_url)
+        
+        # 循环日期区间
+        success_count = 0
+        failed_count = 0
+        skipped_count = 0
+        current_date = start_dt
+        
+        while current_date <= end_dt:
+            trade_date = current_date.strftime('%Y-%m-%d')
+            trade_datetime = f"{trade_date} 00:00:00"
+            
+            # 检查该日期是否有数据
+            check_query = text("""
+                SELECT COUNT(0) as cnt 
+                FROM gp_day_data 
+                WHERE `timestamp` = :trade_datetime
+            """)
+            
+            with engine.connect() as conn:
+                result = conn.execute(check_query, {"trade_datetime": trade_datetime}).fetchone()
+                count = result[0] if result else 0
+            
+            # 只有当有数据时才运行策略
+            if count > 0:
+                try:
+                    strategy = TechFundamentalFactorStrategy(target_date=trade_date)
+                    strategy.run_strategy()
+                    strategy.close_connections()
+                    success_count += 1
+                except Exception as e:
+                    failed_count += 1
+                    logger.error(f"日期 {trade_date} 处理失败: {str(e)}")
+            else:
+                skipped_count += 1
+            
+            current_date += timedelta(days=1)
+        
+        engine.dispose()
+        
+        return jsonify({
+            "status": "success",
+            "success_count": success_count,
+            "failed_count": failed_count,
+            "skipped_count": skipped_count
+        })
+        
+    except Exception as e:
+        logger.error(f"批量运行失败: {str(e)}")
+        return jsonify({"status": "error", "message": str(e)}), 500
+
+
 if __name__ == '__main__':
 
     # 启动Web服务器

src/quantitative_analysis/average_distance_factor.py

@@ -55,16 +55,32 @@ class AverageDistanceFactor:
             print(f"获取股票列表失败: {e}")
             return []
     
-    def get_stock_data(self, symbols, days=20):
-        """获取股票的历史数据"""
+    def get_stock_data(self, symbols, days=20, end_date=None):
+        """
+        获取股票的历史数据
+        
+        Args:
+            symbols: 股票代码列表
+            days: 需要获取的天数
+            end_date: 结束日期（datetime对象或字符串），如果为None则使用今天
+        """
         if not symbols:
             return pd.DataFrame()
         
-        # 计算开始日期
-        end_date = datetime.now()
-        start_date = end_date - timedelta(days=days * 2)  # 多取一些数据以防节假日
+        # 计算结束日期（及时数据使用目标日期当天）
+        if end_date is None:
+            end_date = datetime.now()
+        elif isinstance(end_date, str):
+            end_date = datetime.strptime(end_date, '%Y-%m-%d')
         
-        # 构建SQL查询
+        # 及时数据（日线数据）应该包含目标日期当天的数据
+        # 例如：如果目标日期是2025-11-01，则使用2025-11-01及之前的数据
+        query_end_date = end_date
+        
+        # 计算开始日期（多取一些数据以防节假日）
+        start_date = query_end_date - timedelta(days=days * 2)
+        
+        # 构建SQL查询（使用 <= 包含目标日期当天）
         symbols_str = "', '".join(symbols)
         query = f"""
         SELECT symbol, timestamp, volume, open, high, low, close, 
@@ -72,28 +88,36 @@ class AverageDistanceFactor:
         FROM gp_day_data 
         WHERE symbol IN ('{symbols_str}')
         AND timestamp >= '{start_date.strftime('%Y-%m-%d')}'
+        AND timestamp <= '{end_date.strftime('%Y-%m-%d')}'
         ORDER BY symbol, timestamp DESC
         """
         
         try:
             df = pd.read_sql(query, self.engine)
-            print(f"获取到 {len(df)} 条历史数据")
             return df
         except Exception as e:
-            print(f"获取历史数据失败: {e}")
             return pd.DataFrame()
     
     def calculate_technical_indicators(self, df, days=20):
-        """计算技术指标"""
+        """
+        计算技术指标
+        
+        Args:
+            df: 股票历史数据DataFrame（应已按目标日期筛选）
+            days: 计算指标使用的天数（取目标日期前N天）
+        """
         result_data = []
         
         for symbol in df['symbol'].unique():
             stock_data = df[df['symbol'] == symbol].copy()
+            # 按时间升序排序，确保时间顺序正确
             stock_data = stock_data.sort_values('timestamp')
             
-            # 只取最近N天的数据
+            # 只取目标日期前N个交易日的数据（包括目标日期当天）
+            # tail取最后N行，即最接近目标日期的N个交易日（包括目标日期当天）
             stock_data = stock_data.tail(days)
             
+            # 检查数据是否足够（需要至少有days个交易日的数据）
             if len(stock_data) < days:
                 continue  # 数据不足，跳过
             

src/quantitative_analysis/company_lifecycle_factor.py

@@ -8,7 +8,11 @@ import sys
 import os
 
 # 添加项目根目录到路径
-project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+# __file__ 是当前文件路径，例如: /app/src/quantitative_analysis/company_lifecycle_factor.py
+# 需要获取项目根目录: /app
+current_file_dir = os.path.dirname(os.path.abspath(__file__))  # /app/src/quantitative_analysis
+src_dir = os.path.dirname(current_file_dir)  # /app/src
+project_root = os.path.dirname(src_dir)  # /app (项目根目录)
 sys.path.append(project_root)
 
 # 导入配置
@@ -16,7 +20,7 @@ try:
     from valuation_analysis.config import MONGO_CONFIG2
 except ImportError:
     import importlib.util
-    config_path = os.path.join(project_root, 'valuation_analysis', 'config.py')
+    config_path = os.path.join(src_dir, 'valuation_analysis', 'config.py')
     spec = importlib.util.spec_from_file_location("config", config_path)
     config_module = importlib.util.module_from_spec(spec)
     spec.loader.exec_module(config_module)
@@ -27,7 +31,12 @@ try:
     from tools.stock_code_formatter import StockCodeFormatter
 except ImportError:
     import importlib.util
-    formatter_path = os.path.join(os.path.dirname(project_root), 'tools', 'stock_code_formatter.py')
+    # project_root 已经是项目根目录，直接拼接 tools 目录
+    formatter_path = os.path.join(project_root, 'tools', 'stock_code_formatter.py')
+    
+    if not os.path.exists(formatter_path):
+        raise ImportError(f"无法找到 stock_code_formatter.py，路径: {formatter_path}")
+    
     spec = importlib.util.spec_from_file_location("stock_code_formatter", formatter_path)
     formatter_module = importlib.util.module_from_spec(spec)
     spec.loader.exec_module(formatter_module)
@@ -120,7 +129,7 @@ class CompanyLifecycleFactor:
             annual_data = self.collection.find_one(query)
             
             if annual_data:
-                logger.info(f"找到年报数据: {stock_code} (标准化后: {normalized_code}) - {report_date}")
+                # logger.info(f"找到年报数据: {stock_code} (标准化后: {normalized_code}) - {report_date}")
                 return annual_data
             else:
                 logger.warning(f"未找到年报数据: {stock_code} (标准化后: {normalized_code}) - {report_date}")

src/tushare_scripts/chip_distribution_collector.py

@@ -464,11 +464,11 @@ if __name__ == "__main__":
     # collect_chip_distribution(db_url, tushare_token, mode='full')
     
     # 3. 采集指定日期的数据
-    collect_chip_distribution(db_url, tushare_token, mode='daily', date='2025-11-24')
+    # collect_chip_distribution(db_url, tushare_token, mode='daily', date='2025-11-25')
     
     # 4. 采集指定日期范围的数据
-    # collect_chip_distribution(db_url, tushare_token, mode='full',
-    #                          start_date='2021-11-01', end_date='2021-11-30')
+    collect_chip_distribution(db_url, tushare_token, mode='full',
+                             start_date='2021-01-03', end_date='2021-09-30')
     
     # 5. 调整批量入库大小（默认100只股票一批）
     # collect_chip_distribution(db_url, tushare_token, mode='daily', batch_size=200)

src/tushare_scripts/ths_concept_member_collector.py

@@ -0,0 +1,242 @@
+# coding:utf-8
+"""
+同花顺概念板块成分股采集工具
+功能：从 Tushare 获取同花顺概念板块成分股数据并落库
+API 文档: https://tushare.pro/document/2?doc_id=261
+说明：每次调用时全量覆盖，不需要每日更新
+"""
+
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+
+import pandas as pd
+import tushare as ts
+from sqlalchemy import create_engine, text
+
+# 添加项目根目录到路径，确保能够读取配置
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(PROJECT_ROOT)
+
+from src.scripts.config import TUSHARE_TOKEN
+
+
+class THSConceptMemberCollector:
+    """同花顺概念板块成分股采集器"""
+
+    def __init__(self, db_url: str, tushare_token: str, table_name: str = "ths_concept_member"):
+        """
+        Args:
+            db_url: 数据库连接 URL
+            tushare_token: Tushare Token
+            table_name: 目标表名，默认 ths_concept_member
+        """
+        self.engine = create_engine(
+            db_url,
+            pool_size=5,
+            max_overflow=10,
+            pool_recycle=3600,
+        )
+        self.table_name = table_name
+
+        ts.set_token(tushare_token)
+        self.pro = ts.pro_api()
+
+        print("=" * 60)
+        print("同花顺概念板块成分股采集工具")
+        print(f"目标数据表: {self.table_name}")
+        print("=" * 60)
+
+    @staticmethod
+    def convert_tushare_code_to_db(ts_code: str) -> str:
+        """
+        将 Tushare 代码（600000.SH）转换为数据库代码（SH600000）
+        """
+        if not ts_code or "." not in ts_code:
+            return ts_code
+        base, market = ts_code.split(".")
+        return f"{market}{base}"
+
+    def fetch_concept_index_list(self) -> pd.DataFrame:
+        """
+        获取所有同花顺概念板块列表
+        API: ths_index
+        文档: https://tushare.pro/document/2?doc_id=259
+        
+        Returns:
+            概念板块列表 DataFrame
+        """
+        try:
+            print("正在获取同花顺概念板块列表...")
+            df = self.pro.ths_index(
+                exchange='A',
+                type='N',  # N=概念板块
+            )
+            if df.empty:
+                print("未获取到概念板块数据")
+                return pd.DataFrame()
+            print(f"成功获取 {len(df)} 个概念板块")
+            return df
+        except Exception as exc:
+            print(f"获取概念板块列表失败: {exc}")
+            return pd.DataFrame()
+
+    def fetch_concept_members(self, ts_code: str) -> pd.DataFrame:
+        """
+        获取指定概念板块的成分股
+        API: ths_member
+        文档: https://tushare.pro/document/2?doc_id=261
+        
+        Args:
+            ts_code: 概念板块代码，如 '885800.TI'
+            
+        Returns:
+            成分股列表 DataFrame
+        """
+        try:
+            df = self.pro.ths_member(ts_code=ts_code)
+            return df
+        except Exception as exc:
+            print(f"获取概念板块 {ts_code} 成分股失败: {exc}")
+            return pd.DataFrame()
+
+    def transform_data(self, member_df: pd.DataFrame, concept_name: str = "") -> pd.DataFrame:
+        """
+        将 Tushare 返回的数据转换为数据库入库格式
+        
+        Args:
+            member_df: 成分股信息（包含 ts_code, con_code, con_name 等）
+            concept_name: 概念板块名称（从概念板块列表中获取）
+            
+        Returns:
+            转换后的 DataFrame
+        """
+        if member_df.empty:
+            return pd.DataFrame()
+
+        # 检查必要字段
+        if "ts_code" not in member_df.columns:
+            print(f"警告: 未找到概念板块代码字段(ts_code)，可用字段: {member_df.columns.tolist()}")
+            return pd.DataFrame()
+        if "con_code" not in member_df.columns:
+            print(f"警告: 未找到股票代码字段(con_code)，可用字段: {member_df.columns.tolist()}")
+            return pd.DataFrame()
+        
+        result = pd.DataFrame()
+        # ts_code 是概念板块代码
+        result["concept_code"] = member_df["ts_code"]
+        result["concept_name"] = concept_name
+        # con_code 是股票代码
+        result["stock_ts_code"] = member_df["con_code"]
+        result["stock_symbol"] = member_df["con_code"].apply(self.convert_tushare_code_to_db)
+        # con_name 是股票名称
+        result["stock_name"] = member_df["con_name"] if "con_name" in member_df.columns else ""
+        # is_new 是否最新（接口有返回，但weight、in_date、out_date暂无数据，暂不存储）
+        result["is_new"] = member_df["is_new"] if "is_new" in member_df.columns else None
+        result["created_at"] = datetime.now()
+        result["updated_at"] = datetime.now()
+        return result
+
+    def save_dataframe(self, df: pd.DataFrame) -> None:
+        """
+        将数据写入数据库
+        """
+        if df.empty:
+            return
+        df.to_sql(self.table_name, self.engine, if_exists="append", index=False)
+
+    def run_full_collection(self) -> None:
+        """
+        执行全量覆盖采集：
+        - 清空目标表
+        - 获取所有概念板块列表
+        - 遍历每个概念板块，获取成分股数据
+        - 全量写入数据库
+        """
+        print("=" * 60)
+        print("开始执行全量覆盖采集（同花顺概念板块成分股）")
+        print("=" * 60)
+
+        try:
+            # 清空表
+            with self.engine.begin() as conn:
+                conn.execute(text(f"TRUNCATE TABLE {self.table_name}"))
+            print(f"{self.table_name} 已清空")
+
+            # 获取所有概念板块列表
+            concept_list_df = self.fetch_concept_index_list()
+            if concept_list_df.empty:
+                print("未获取到概念板块列表，采集终止")
+                return
+
+            total_records = 0
+            success_count = 0
+            failed_count = 0
+
+            # 遍历每个概念板块，获取成分股
+            for idx, row in concept_list_df.iterrows():
+                concept_code = row["ts_code"]
+                concept_name = row["name"]
+                
+                print(f"\n[{idx + 1}/{len(concept_list_df)}] 正在采集: {concept_name} ({concept_code})")
+                
+                try:
+                    # 获取该概念板块的成分股
+                    member_df = self.fetch_concept_members(concept_code)
+                    if member_df.empty:
+                        print(f"  概念板块 {concept_name} 无成分股数据")
+                        failed_count += 1
+                        continue
+
+                    # 转换数据格式（传入概念板块名称）
+                    result_df = self.transform_data(member_df, concept_name)
+                    if result_df.empty:
+                        print(f"  概念板块 {concept_name} 数据转换失败")
+                        failed_count += 1
+                        continue
+
+                    # 保存数据
+                    self.save_dataframe(result_df)
+                    total_records += len(result_df)
+                    success_count += 1
+                    print(f"  成功采集 {len(result_df)} 只成分股")
+                    
+                except Exception as exc:
+                    print(f"  采集 {concept_name} ({concept_code}) 失败: {exc}")
+                    failed_count += 1
+                    continue
+
+            print("\n" + "=" * 60)
+            print("全量覆盖采集完成")
+            print(f"总概念板块数: {len(concept_list_df)}")
+            print(f"成功采集: {success_count}")
+            print(f"失败: {failed_count}")
+            print(f"累计成分股记录: {total_records}")
+            print("=" * 60)
+        except Exception as exc:
+            print(f"全量采集失败: {exc}")
+            import traceback
+            traceback.print_exc()
+        finally:
+            self.engine.dispose()
+
+
+def collect_ths_concept_member(
+    db_url: str,
+    tushare_token: str,
+):
+    """
+    采集入口 - 全量覆盖采集
+    """
+    collector = THSConceptMemberCollector(db_url, tushare_token)
+    collector.run_full_collection()
+
+
+if __name__ == "__main__":
+    # DB_URL = "mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj"
+    DB_URL = "mysql+pymysql://fac_pattern:Chlry$%.8_app@192.168.16.153:3307/my_quant_db"
+    TOKEN = TUSHARE_TOKEN
+
+    # 执行全量覆盖采集
+    collect_ths_concept_member(DB_URL, TOKEN)

src/tushare_scripts/ths_index_collector.py

@@ -0,0 +1,225 @@
+# coding:utf-8
+"""
+同花顺概念和行业指数采集工具
+功能：从 Tushare 获取同花顺概念和行业指数数据并落库
+API 文档: https://tushare.pro/document/2?doc_id=259
+说明：每次调用时全量覆盖，不需要每日更新
+"""
+
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+
+import pandas as pd
+import tushare as ts
+from sqlalchemy import create_engine, text
+
+# 添加项目根目录到路径，确保能够读取配置
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(PROJECT_ROOT)
+
+from src.scripts.config import TUSHARE_TOKEN
+
+
+class THSIndexCollector:
+    """同花顺概念和行业指数采集器"""
+
+    def __init__(self, db_url: str, tushare_token: str, table_name: str = "ths_index"):
+        """
+        Args:
+            db_url: 数据库连接 URL
+            tushare_token: Tushare Token
+            table_name: 目标表名，默认 ths_index
+        """
+        self.engine = create_engine(
+            db_url,
+            pool_size=5,
+            max_overflow=10,
+            pool_recycle=3600,
+        )
+        self.table_name = table_name
+
+        ts.set_token(tushare_token)
+        self.pro = ts.pro_api()
+
+        print("=" * 60)
+        print("同花顺概念和行业指数采集工具")
+        print(f"目标数据表: {self.table_name}")
+        print("=" * 60)
+
+    def fetch_index_list(
+        self,
+        exchange: Optional[str] = None,
+        index_type: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """
+        获取同花顺概念和行业指数列表
+        API: ths_index
+        文档: https://tushare.pro/document/2?doc_id=259
+        
+        Args:
+            exchange: 市场类型 A-a股 HK-港股 US-美股，None表示全部
+            index_type: 指数类型 N-概念指数 I-行业指数 R-地域指数 S-同花顺特色指数 
+                       ST-同花顺风格指数 TH-同花顺主题指数 BB-同花顺宽基指数，None表示全部
+        
+        Returns:
+            指数列表 DataFrame
+        """
+        try:
+            params = {}
+            if exchange:
+                params['exchange'] = exchange
+            if index_type:
+                params['type'] = index_type
+            
+            print(f"正在获取同花顺指数列表...")
+            if params:
+                print(f"筛选条件: {params}")
+            
+            df = self.pro.ths_index(**params)
+            if df.empty:
+                print("未获取到指数数据")
+                return pd.DataFrame()
+            print(f"成功获取 {len(df)} 个指数")
+            return df
+        except Exception as exc:
+            print(f"获取指数列表失败: {exc}")
+            return pd.DataFrame()
+
+    def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        将 Tushare 返回的数据转换为数据库入库格式
+        
+        Args:
+            df: 指数信息 DataFrame
+            
+        Returns:
+            转换后的 DataFrame
+        """
+        if df.empty:
+            return pd.DataFrame()
+
+        result = pd.DataFrame()
+        result["ts_code"] = df["ts_code"]
+        result["name"] = df["name"]
+        result["count"] = df.get("count", None)
+        result["exchange"] = df.get("exchange", None)
+        result["list_date"] = pd.to_datetime(df["list_date"], format="%Y%m%d", errors='coerce') if "list_date" in df.columns else None
+        result["type"] = df.get("type", None)
+        result["created_at"] = datetime.now()
+        result["updated_at"] = datetime.now()
+        return result
+
+    def save_dataframe(self, df: pd.DataFrame) -> None:
+        """
+        将数据写入数据库
+        """
+        if df.empty:
+            return
+        df.to_sql(self.table_name, self.engine, if_exists="append", index=False)
+
+    def run_full_collection(
+        self,
+        exchange: Optional[str] = None,
+        index_type: Optional[str] = None,
+    ) -> None:
+        """
+        执行全量覆盖采集：
+        - 清空目标表
+        - 获取所有指数数据
+        - 全量写入数据库
+        
+        Args:
+            exchange: 市场类型，None表示全部
+            index_type: 指数类型，None表示全部
+        """
+        print("=" * 60)
+        print("开始执行全量覆盖采集（同花顺概念和行业指数）")
+        print("=" * 60)
+
+        try:
+            # 清空表
+            with self.engine.begin() as conn:
+                # 如果指定了筛选条件，只删除符合条件的记录；否则清空全部
+                if exchange or index_type:
+                    delete_conditions = []
+                    params = {}
+                    if exchange:
+                        delete_conditions.append("exchange = :exchange")
+                        params["exchange"] = exchange
+                    if index_type:
+                        delete_conditions.append("type = :type")
+                        params["type"] = index_type
+                    delete_sql = text(f"DELETE FROM {self.table_name} WHERE {' AND '.join(delete_conditions)}")
+                    conn.execute(delete_sql, params)
+                    print(f"{self.table_name} 已删除符合条件的旧数据")
+                else:
+                    conn.execute(text(f"TRUNCATE TABLE {self.table_name}"))
+                    print(f"{self.table_name} 已清空")
+            
+            # 获取指数列表
+            index_df = self.fetch_index_list(exchange=exchange, index_type=index_type)
+            if index_df.empty:
+                print("未获取到指数数据，采集终止")
+                return
+
+            # 转换数据格式
+            result_df = self.transform_data(index_df)
+            if result_df.empty:
+                print("数据转换失败")
+                return
+
+            # 保存数据
+            self.save_dataframe(result_df)
+            print(f"成功写入 {len(result_df)} 条记录")
+            
+            # 按类型统计
+            if "type" in result_df.columns:
+                type_stats = result_df.groupby("type").size()
+                print("\n按类型统计:")
+                for idx, count in type_stats.items():
+                    print(f"  {idx}: {count} 个")
+            
+            print("\n" + "=" * 60)
+            print("全量覆盖采集完成")
+            print("=" * 60)
+        except Exception as exc:
+            print(f"全量采集失败: {exc}")
+            import traceback
+            traceback.print_exc()
+        finally:
+            self.engine.dispose()
+
+
+def collect_ths_index(
+    db_url: str,
+    tushare_token: str,
+    exchange: Optional[str] = None,
+    index_type: Optional[str] = None,
+):
+    """
+    采集入口 - 全量覆盖采集
+    
+    Args:
+        db_url: 数据库连接URL
+        tushare_token: Tushare Token
+        exchange: 市场类型 A-a股 HK-港股 US-美股，None表示全部
+        index_type: 指数类型 N-概念指数 I-行业指数 R-地域指数 S-同花顺特色指数 
+                   ST-同花顺风格指数 TH-同花顺主题指数 BB-同花顺宽基指数，None表示全部
+    """
+    collector = THSIndexCollector(db_url, tushare_token)
+    collector.run_full_collection(exchange=exchange, index_type=index_type)
+
+
+if __name__ == "__main__":
+    # DB_URL = "mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj"
+    DB_URL = "mysql+pymysql://fac_pattern:Chlry$%.8_app@192.168.16.153:3307/my_quant_db"
+    TOKEN = TUSHARE_TOKEN
+
+    # 执行全量覆盖采集（获取所有类型的指数）
+    collect_ths_index(DB_URL, TOKEN)
+    
+    # 如果只想获取特定类型，可以这样调用：
+    # collect_ths_index(DB_URL, TOKEN, exchange='A', index_type='N')  # 只获取A股概念指数
+

src/tushare_scripts/trade_cal_collector.py

@@ -0,0 +1,236 @@
+# coding:utf-8
+"""
+交易日历采集工具
+功能：从 Tushare 获取交易日历数据并落库
+API 文档: https://tushare.pro/document/2?doc_id=26
+说明：每次调用时全量覆盖，不需要每日更新
+"""
+
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+
+import pandas as pd
+import tushare as ts
+from sqlalchemy import create_engine, text
+
+# 添加项目根目录到路径，确保能够读取配置
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(PROJECT_ROOT)
+
+from src.scripts.config import TUSHARE_TOKEN
+
+
+class TradeCalCollector:
+    """交易日历采集器"""
+
+    def __init__(self, db_url: str, tushare_token: str, table_name: str = "trade_cal"):
+        """
+        Args:
+            db_url: 数据库连接 URL
+            tushare_token: Tushare Token
+            table_name: 目标表名，默认 trade_cal
+        """
+        self.engine = create_engine(
+            db_url,
+            pool_size=5,
+            max_overflow=10,
+            pool_recycle=3600,
+        )
+        self.table_name = table_name
+
+        ts.set_token(tushare_token)
+        self.pro = ts.pro_api()
+
+        print("=" * 60)
+        print("交易日历采集工具")
+        print(f"目标数据表: {self.table_name}")
+        print("=" * 60)
+
+    def fetch_trade_cal(
+        self,
+        exchange: Optional[str] = None,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """
+        获取交易日历数据
+        API: trade_cal
+        文档: https://tushare.pro/document/2?doc_id=26
+        
+        Args:
+            exchange: 交易所代码 SSE-上交所 SZSE-深交所 BSE-北交所，None表示全部
+            start_date: 开始日期，格式YYYYMMDD，None表示不限制
+            end_date: 结束日期，格式YYYYMMDD，None表示不限制
+        
+        Returns:
+            交易日历 DataFrame
+        """
+        try:
+            params = {}
+            if exchange:
+                params['exchange'] = exchange
+            if start_date:
+                params['start_date'] = start_date
+            if end_date:
+                params['end_date'] = end_date
+            
+            print("正在获取交易日历数据...")
+            if params:
+                print(f"筛选条件: {params}")
+            
+            df = self.pro.trade_cal(**params)
+            if df.empty:
+                print("未获取到交易日历数据")
+                return pd.DataFrame()
+            print(f"成功获取 {len(df)} 条交易日历记录")
+            return df
+        except Exception as exc:
+            print(f"获取交易日历数据失败: {exc}")
+            return pd.DataFrame()
+
+    def transform_data(self, df: pd.DataFrame) -> pd.DataFrame:
+        """
+        将 Tushare 返回的数据转换为数据库入库格式
+        
+        Args:
+            df: 交易日历 DataFrame
+            
+        Returns:
+            转换后的 DataFrame
+        """
+        if df.empty:
+            return pd.DataFrame()
+
+        result = pd.DataFrame()
+        result["exchange"] = df["exchange"]
+        result["cal_date"] = pd.to_datetime(df["cal_date"], format="%Y%m%d", errors='coerce')
+        result["is_open"] = df.get("is_open", None)
+        result["pretrade_date"] = pd.to_datetime(df["pretrade_date"], format="%Y%m%d", errors='coerce') if "pretrade_date" in df.columns else None
+        result["created_at"] = datetime.now()
+        result["updated_at"] = datetime.now()
+        return result
+
+    def save_dataframe(self, df: pd.DataFrame) -> None:
+        """
+        将数据写入数据库
+        """
+        if df.empty:
+            return
+        df.to_sql(self.table_name, self.engine, if_exists="append", index=False)
+
+    def run_full_collection(
+        self,
+        exchange: Optional[str] = None,
+        start_date: Optional[str] = None,
+        end_date: Optional[str] = None,
+    ) -> None:
+        """
+        执行全量覆盖采集：
+        - 清空目标表
+        - 获取交易日历数据
+        - 全量写入数据库
+        
+        Args:
+            exchange: 交易所代码，None表示全部
+            start_date: 开始日期，格式YYYYMMDD，None表示不限制
+            end_date: 结束日期，格式YYYYMMDD，None表示不限制
+        """
+        print("=" * 60)
+        print("开始执行全量覆盖采集（交易日历）")
+        print("=" * 60)
+
+        try:
+            # 清空表
+            with self.engine.begin() as conn:
+                # 如果指定了筛选条件，只删除符合条件的记录；否则清空全部
+                if exchange or start_date or end_date:
+                    delete_conditions = []
+                    params = {}
+                    if exchange:
+                        delete_conditions.append("exchange = :exchange")
+                        params["exchange"] = exchange
+                    if start_date:
+                        delete_conditions.append("cal_date >= :start_date")
+                        params["start_date"] = datetime.strptime(start_date, "%Y%m%d").date()
+                    if end_date:
+                        delete_conditions.append("cal_date <= :end_date")
+                        params["end_date"] = datetime.strptime(end_date, "%Y%m%d").date()
+                    delete_sql = text(f"DELETE FROM {self.table_name} WHERE {' AND '.join(delete_conditions)}")
+                    conn.execute(delete_sql, params)
+                    print(f"{self.table_name} 已删除符合条件的旧数据")
+                else:
+                    conn.execute(text(f"TRUNCATE TABLE {self.table_name}"))
+                    print(f"{self.table_name} 已清空")
+            
+            # 获取交易日历数据
+            cal_df = self.fetch_trade_cal(exchange=exchange, start_date=start_date, end_date=end_date)
+            if cal_df.empty:
+                print("未获取到交易日历数据，采集终止")
+                return
+
+            # 转换数据格式
+            result_df = self.transform_data(cal_df)
+            if result_df.empty:
+                print("数据转换失败")
+                return
+
+            # 保存数据
+            self.save_dataframe(result_df)
+            print(f"成功写入 {len(result_df)} 条记录")
+            
+            # 按交易所统计
+            if "exchange" in result_df.columns:
+                print("\n按交易所统计:")
+                for exchange_name in result_df["exchange"].unique():
+                    exchange_data = result_df[result_df["exchange"] == exchange_name]
+                    total_days = len(exchange_data)
+                    trade_days = int(exchange_data["is_open"].sum()) if "is_open" in exchange_data.columns and exchange_data["is_open"].notna().any() else 0
+                    min_date = exchange_data["cal_date"].min()
+                    max_date = exchange_data["cal_date"].max()
+                    print(f"  {exchange_name}: 总天数={total_days}, 交易日={trade_days}, 日期范围={min_date} ~ {max_date}")
+            
+            print("\n" + "=" * 60)
+            print("全量覆盖采集完成")
+            print("=" * 60)
+        except Exception as exc:
+            print(f"全量采集失败: {exc}")
+            import traceback
+            traceback.print_exc()
+        finally:
+            self.engine.dispose()
+
+
+def collect_trade_cal(
+    db_url: str,
+    tushare_token: str,
+    exchange: Optional[str] = None,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+):
+    """
+    采集入口 - 全量覆盖采集
+    
+    Args:
+        db_url: 数据库连接URL
+        tushare_token: Tushare Token
+        exchange: 交易所代码 SSE-上交所 SZSE-深交所 BSE-北交所，None表示全部
+        start_date: 开始日期，格式YYYYMMDD，None表示不限制
+        end_date: 结束日期，格式YYYYMMDD，None表示不限制
+    """
+    collector = TradeCalCollector(db_url, tushare_token)
+    collector.run_full_collection(exchange=exchange, start_date=start_date, end_date=end_date)
+
+
+if __name__ == "__main__":
+    # DB_URL = "mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj"
+    DB_URL = "mysql+pymysql://fac_pattern:Chlry$%.8_app@192.168.16.153:3307/my_quant_db"
+    TOKEN = TUSHARE_TOKEN
+
+    # 执行全量覆盖采集（获取所有交易所的交易日历，使用默认日期范围）
+    collect_trade_cal(DB_URL, TOKEN)
+    
+    # 如果只想获取特定交易所，可以这样调用：
+    # collect_trade_cal(DB_URL, TOKEN, exchange='SSE')
+