commit;

requirements.txt

@@ -24,3 +24,4 @@ pyyaml==6.0.3
 xtquant==250516.1.1
 tushare>=1.4.24
 akshare==1.17.82
+seaborn==0.13.2

src/app.py

@@ -682,18 +682,34 @@ def precalculate_industry_crowding_batch():
         from src.valuation_analysis.industry_analysis import IndustryAnalyzer
 
         analyzer = IndustryAnalyzer()
-        # 固定行业和概念板块
+        
-        industries = ["煤炭开采", "焦炭加工", "油气开采", "石油化工", "油服工程", "日用化工", "化纤", "化学原料", "化学制品", "塑料", "橡胶", "农用化工", "非金属材料", "冶钢原料", "普钢", "特钢", "工业金属", "贵金属", "能源金属", "稀有金属", "金属新材料", "水泥", "玻璃玻纤", "装饰建材", "种植业", "养殖业", "林业", "渔业", "饲料", "农产品加工", "动物保健", "酿酒", "饮料乳品", "调味品", "休闲食品", "食品加工", "纺织制造", "服装家纺", "饰品", "造纸", "包装印刷", "家居用品", "文娱用品", "白色家电", "黑色家电", "小家电", "厨卫电器", "家电零部件", "一般零售", "商业物业经营", "专业连锁", "贸易", "电子商务", "乘用车", "商用车", "汽车零部件", "汽车服务", "摩托车及其他", "化学制药", "生物制品", "中药", "医药商业", "医疗器械", "医疗服务", "医疗美容", "电机制造", "电池", "电网设备", "光伏设备", "风电设备", "其他发电设备", "地面兵装", "航空装备", "航天装备", "航海装备", "军工电子", "轨交设备", "通用设备", "专用设备", "工程机械", "自动化设备", "半导体", "消费电子", "光学光电", "元器件", "其他电子", "通信设备", "通信工程", "电信服务", "IT设备", "软件服务", "云服务", "产业互联网", "游戏", "广告营销", "影视院线", "数字媒体", "出版业", "广播电视", "全国性银行", "地方性银行", "证券", "保险", "多元金融", "房屋建设", "基础建设", "专业工程", "工程咨询服务", "装修装饰", "房地产开发", "房产服务", "体育", "教育培训", "酒店餐饮", "旅游", "专业服务", "公路铁路", "航空机场", "航运港口", "物流", "电力", "燃气", "水务", "环保设备", "环境治理", "环境监测", "综合类"]
+        # 从数据库查询所有行业
-        concepts = ["通达信88", "海峡西岸", "海南自贸", "一带一路", "上海自贸", "雄安新区", "粤港澳", "ST板块", "次新股", "含H股", "含B股", "含GDR", "含可转债", "国防军工", "军民融合", "大飞机", "稀缺资源", "5G概念", "碳中和", "黄金概念", "物联网", "创投概念", "航运概念", "铁路基建", "高端装备", "核电核能", "光伏", "风电", "锂电池概念", "燃料电池", "HJT电池", "固态电池", "钠电池", "钒电池", "TOPCon电池", "钙钛矿电池", "BC电池", "氢能源", "稀土永磁", "盐湖提锂", "锂矿", "水利建设", "卫星导航", "可燃冰", "页岩气", "生物疫苗", "基因概念", "维生素", "仿制药", "创新药", "免疫治疗", "CXO概念", "节能环保", "食品安全", "白酒概念", "代糖概念", "猪肉", "鸡肉", "水产品", "碳纤维", "石墨烯", "3D打印", "苹果概念", "阿里概念", "腾讯概念", "小米概念", "百度概念", "华为鸿蒙", "华为海思", "华为汽车", "华为算力", "特斯拉概念", "消费电子概念", "汽车电子", "无线耳机", "生物质能", "地热能", "充电桩", "新能源车", "换电概念", "高压快充", "草甘膦", "安防服务", "垃圾分类", "核污染防治", "风沙治理", "乡村振兴", "土地流转", "体育概念", "博彩概念", "赛马概念", "分散染料", "聚氨酯", "云计算", "边缘计算", "网络游戏", "信息安全", "国产软件", "大数据", "数据中心", "芯片", "MCU芯片", "汽车芯片", "存储芯片", "互联金融", "婴童概念", "养老概念", "网红经济", "民营医院", "特高压", "智能电网", "智能穿戴", "智能交通", "智能家居", "智能医疗", "智慧城市", "智慧政务", "机器人概念", "机器视觉", "超导概念", "职业教育", "物业管理概念", "虚拟现实", "数字孪生", "钛金属", "钴金属", "镍金属", "氟概念", "磷概念", "无人机", "PPP概念", "新零售", "跨境电商", "量子科技", "无人驾驶", "ETC概念", "胎压监测", "OLED概念", "MiniLED", "MicroLED", "超清视频", "区块链", "数字货币", "人工智能", "租购同权", "工业互联", "知识产权", "工业大麻", "工业气体", "人造肉", "预制菜", "种业", "化肥概念", "操作系统", "光刻机", "第三代半导体", "远程办公", "口罩防护", "医废处理", "虫害防治", "超级电容", "C2M概念", "地摊经济", "冷链物流", "抖音概念", "降解塑料", "医美概念", "人脑工程", "烟草概念", "新型烟草", "有机硅概念", "新冠检测", "BIPV概念", "地下管网", "储能", "新材料", "工业母机", "一体压铸", "汽车热管理", "汽车拆解", "NMN概念", "国资云", "元宇宙概念", "NFT概念", "云游戏", "天然气", "绿色电力", "培育钻石", "信创", "幽门螺杆菌", "电子纸", "新冠药概念", "免税概念", "PVDF概念", "装配式建筑", "绿色建筑", "东数西算", "跨境支付CIPS", "中俄贸易", "电子身份证", "家庭医生", "辅助生殖", "肝炎概念", "新型城镇", "粮食概念", "超临界发电", "虚拟电厂", "动力电池回收", "PCB概念", "先进封装", "热泵概念", "EDA概念", "光热发电", "供销社", "Web3概念", "DRG-DIP", "AIGC概念", "复合铜箔", "数据确权", "数据要素", "POE胶膜", "血氧仪", "旅游概念", "中特估", "ChatGPT概念", "CPO概念", "数字水印", "毫米波雷达", "工业软件", "6G概念", "时空大数据", "可控核聚变", "知识付费", "算力租赁", "光通信", "混合现实", "英伟达概念", "减速器", "减肥药", "合成生物", "星闪概念", "液冷服务器", "新型工业化", "短剧游戏", "多模态AI", "PEEK材料", "小米汽车概念", "飞行汽车", "Sora概念", "人形机器人", "AI手机PC", "低空经济", "铜缆高速连接", "军工信息化", "玻璃基板", "商业航天", "车联网", "财税数字化", "折叠屏", "AI眼镜", "智谱AI", "IP经济", "宠物经济", "小红书概念", "AI智能体", "DeepSeek概念", "AI医疗概念", "海洋经济", "外骨骼机器人", "军贸概念"]
+        industry_list = analyzer.get_industry_list()
+        industries = [item['name'] for item in industry_list]
+        logger.info(f"从数据库获取到 {len(industries)} 个行业")
+        
+        # 从数据库查询所有概念板块
+        concept_list = analyzer.get_concept_list()
+        concepts = [item['name'] for item in concept_list]
+        logger.info(f"从数据库获取到 {len(concepts)} 个概念板块")
 
         # 批量计算行业和概念板块拥挤度
         analyzer.batch_calculate_industry_crowding(industries, concepts)
         
-        logger.info("批量计算行业和概念板块的拥挤度指标完成")
+        logger.info(f"批量计算完成：{len(industries)} 个行业和 {len(concepts)} 个概念板块的拥挤度指标")
     except Exception as e:
         logger.error(f"批量计算行业拥挤度指标失败: {str(e)}")
+        import traceback
+        logger.error(traceback.format_exc())
         return jsonify({
-        "status": "success"
+            "status": "error",
+            "message": str(e)
+        }), 500
+    
+    return jsonify({
+        "status": "success",
+        "industries_count": len(industries) if 'industries' in locals() else 0,
+        "concepts_count": len(concepts) if 'concepts' in locals() else 0
     }), 200
 
 @app.route('/')

src/quantitative_analysis/company_lifecycle_factor.py

@@ -28,7 +28,7 @@ except ImportError:
 
 # 导入股票代码格式化工具
 try:
-    from tools.stock_code_formatter import StockCodeFormatter
+    from src.tools.stock_code_formatter import StockCodeFormatter
 except ImportError:
     import importlib.util
     # project_root 已经是项目根目录，直接拼接 tools 目录

src/quantitative_analysis/financial_indicator_analyzer.py

@@ -9,7 +9,6 @@
 
 import sys
 import pymongo
-import datetime
 import logging
 from typing import Dict, List, Optional, Union
 from pathlib import Path
@@ -24,7 +23,7 @@ sys.path.append(str(project_root))
 from src.valuation_analysis.config import MONGO_CONFIG2, DB_URL
 
 # 导入股票代码格式转换工具
-from tools.stock_code_formatter import StockCodeFormatter
+from src.tools.stock_code_formatter import StockCodeFormatter
 
 # 设置日志
 logging.basicConfig(
@@ -478,8 +477,6 @@ class FinancialIndicatorAnalyzer:
         Returns:
             List[str]: 季度列表，格式为 YYYY-MM-DD
         """
-        from datetime import datetime, timedelta
-        import calendar
 
         quarters = []
         year, month, day = map(int, start_date.split('-'))

src/quantitative_analysis/overlap_analyzer.py

@@ -12,8 +12,7 @@ import logging
 from typing import Dict, List, Optional, Tuple
 from pathlib import Path
 from sqlalchemy import create_engine, text
-import pandas as pd
+from datetime import datetime
-from datetime import datetime, timedelta
 
 # 添加项目根路径到Python路径
 project_root = Path(__file__).parent.parent.parent
@@ -33,7 +32,7 @@ except ImportError:
 
 # 导入股票代码格式转换工具
 try:
-    from tools.stock_code_formatter import StockCodeFormatter
+    from src.tools.stock_code_formatter import StockCodeFormatter
 except ImportError:
     # 如果上面的导入失败，尝试直接导入
     import importlib.util

src/tushare_scripts/ths_industry_member_collector.py

@@ -0,0 +1,243 @@
+# coding:utf-8
+"""
+同花顺行业板块成分股采集工具
+功能：从 Tushare 获取同花顺行业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 THSIndustryMemberCollector:
+    """同花顺行业板块成分股采集器"""
+
+    def __init__(self, db_url: str, tushare_token: str, table_name: str = "ths_industry_member"):
+        """
+        Args:
+            db_url: 数据库连接 URL
+            tushare_token: Tushare Token
+            table_name: 目标表名，默认 ths_industry_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("同花顺行业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_industry_index_list(self) -> pd.DataFrame:
+        """
+        获取所有同花顺行业板块列表
+        API: ths_index
+        文档: https://tushare.pro/document/2?doc_id=259
+        
+        Returns:
+            行业板块列表 DataFrame
+        """
+        try:
+            print("正在获取同花顺行业板块列表...")
+            df = self.pro.ths_index(
+                exchange='A',
+                type='I',  # I=行业板块
+            )
+            if df.empty:
+                print("未获取到行业板块数据")
+                return pd.DataFrame()
+            print(f"成功获取 {len(df)} 个行业板块")
+            return df
+        except Exception as exc:
+            print(f"获取行业板块列表失败: {exc}")
+            return pd.DataFrame()
+
+    def fetch_industry_members(self, ts_code: str) -> pd.DataFrame:
+        """
+        获取指定行业板块的成分股
+        API: ths_member
+        文档: https://tushare.pro/document/2?doc_id=261
+        
+        Args:
+            ts_code: 行业板块代码，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"获取行业板块 {ts_code} 成分股失败: {exc}")
+            return pd.DataFrame()
+
+    def transform_data(self, member_df: pd.DataFrame, industry_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）
+            industry_name: 行业板块名称（从行业板块列表中获取）
+            
+        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: 未找到行业板块代码字段(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 是行业板块代码
+        result["industry_code"] = member_df["ts_code"]
+        result["industry_name"] = industry_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
+        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
+        - 获取所有行业板块列表
+        - 遍历每个行业板块，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（同花顺行业板块成分股）")
+        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")
+
+            # 获取所有行业板块列表
+            industry_list_df = self.fetch_industry_index_list()
+            if industry_list_df.empty:
+                print("未获取到行业板块列表，采集终止")
+                return
+
+            total_records = 0
+            success_count = 0
+            failed_count = 0
+
+            # 遍历每个行业板块，获取成分股
+            for idx, row in industry_list_df.iterrows():
+                industry_code = row["ts_code"]
+                industry_name = row["name"]
+                
+                print(f"\n[{idx + 1}/{len(industry_list_df)}] 正在采集: {industry_name} ({industry_code})")
+                
+                try:
+                    # 获取该行业板块的成分股
+                    member_df = self.fetch_industry_members(industry_code)
+                    if member_df.empty:
+                        print(f"  行业板块 {industry_name} 无成分股数据")
+                        failed_count += 1
+                        continue
+
+                    # 转换数据格式（传入行业板块名称）
+                    result_df = self.transform_data(member_df, industry_name)
+                    if result_df.empty:
+                        print(f"  行业板块 {industry_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 {industry_name} ({industry_code}) 失败: {exc}")
+                    failed_count += 1
+                    continue
+
+            print("\n" + "=" * 60)
+            print("全量覆盖采集完成")
+            print(f"总行业板块数: {len(industry_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_industry_member(
+    db_url: str,
+    tushare_token: str,
+):
+    """
+    采集入口 - 全量覆盖采集
+    """
+    collector = THSIndustryMemberCollector(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_industry_member(DB_URL, TOKEN)
+

src/tushare_scripts/transfer_ths_to_gp_bk.py

@@ -0,0 +1,300 @@
+# coding:utf-8
+"""
+同花顺板块数据转存脚本
+功能：将 ths_industry_member 和 ths_concept_member 表的数据转存到 gp_hybk 和 gp_gnbk 表。但是提前需要处理ths_concept_member_collector.py和ths_industry_member_collector.py里面的数据。
+说明：全量清理目标表后重新插入，保持数据结构对齐
+"""
+
+import os
+import sys
+import re
+from typing import Optional
+
+import pandas as pd
+from sqlalchemy import create_engine, text
+
+# 添加项目根目录到路径
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(PROJECT_ROOT)
+
+
+class THSToGPBKTransfer:
+    """同花顺板块数据转存器"""
+
+    def __init__(self, db_url: str):
+        """
+        Args:
+            db_url: 数据库连接 URL
+        """
+        self.engine = create_engine(
+            db_url,
+            pool_size=5,
+            max_overflow=10,
+            pool_recycle=3600,
+        )
+
+        print("=" * 60)
+        print("同花顺板块数据转存工具")
+        print("=" * 60)
+
+    @staticmethod
+    def extract_bk_code(code: str) -> Optional[int]:
+        """
+        从板块代码中提取数字部分（如：885800.TI -> 885800）
+        
+        Args:
+            code: 板块代码，如 '885800.TI' 或 '885800'
+            
+        Returns:
+            提取的数字，如果无法提取则返回 None
+        """
+        if not code:
+            return None
+        # 提取数字部分
+        match = re.search(r'(\d+)', str(code))
+        if match:
+            try:
+                return int(match.group(1))
+            except ValueError:
+                return None
+        return None
+
+    def transfer_industry_data(self) -> dict:
+        """
+        将 ths_industry_member 表数据转存到 gp_hybk 表
+        
+        Returns:
+            转存结果统计
+        """
+        print("\n" + "=" * 60)
+        print("开始转存行业板块数据 (ths_industry_member -> gp_hybk)")
+        print("=" * 60)
+
+        result = {
+            'success': False,
+            'source_count': 0,
+            'transferred_count': 0,
+            'error': None
+        }
+
+        try:
+            # 1. 读取源表数据
+            print("正在读取 ths_industry_member 表数据...")
+            source_query = text("""
+                SELECT 
+                    industry_code,
+                    industry_name,
+                    stock_symbol,
+                    stock_name
+                FROM ths_industry_member
+                WHERE industry_code IS NOT NULL 
+                  AND stock_symbol IS NOT NULL
+            """)
+            
+            with self.engine.connect() as conn:
+                source_df = pd.read_sql(source_query, conn)
+            
+            result['source_count'] = len(source_df)
+            print(f"源表共 {result['source_count']} 条记录")
+
+            if source_df.empty:
+                print("源表无数据，转存终止")
+                result['success'] = True
+                return result
+
+            # 2. 数据转换
+            print("正在转换数据格式...")
+            target_df = pd.DataFrame()
+            target_df['bk_code'] = source_df['industry_code'].apply(self.extract_bk_code)
+            target_df['bk_name'] = source_df['industry_name']
+            target_df['gp_code'] = source_df['stock_symbol']
+            target_df['gp_name'] = source_df['stock_name']
+            
+            # 过滤掉 bk_code 为 None 的记录
+            target_df = target_df[target_df['bk_code'].notna()]
+
+            print(f"转换后共 {len(target_df)} 条有效记录")
+
+            # 3. 清空目标表
+            print("正在清空 gp_hybk 表...")
+            with self.engine.begin() as conn:
+                conn.execute(text("TRUNCATE TABLE gp_hybk"))
+            print("gp_hybk 表已清空")
+
+            # 4. 插入数据
+            print("正在插入数据到 gp_hybk 表...")
+            target_df.to_sql(
+                'gp_hybk',
+                self.engine,
+                if_exists='append',
+                index=False,
+                method='multi',
+                chunksize=1000
+            )
+            
+            result['transferred_count'] = len(target_df)
+            result['success'] = True
+            print(f"成功转存 {result['transferred_count']} 条记录到 gp_hybk 表")
+
+        except Exception as e:
+            result['error'] = str(e)
+            print(f"转存行业板块数据失败: {e}")
+            import traceback
+            traceback.print_exc()
+
+        return result
+
+    def transfer_concept_data(self) -> dict:
+        """
+        将 ths_concept_member 表数据转存到 gp_gnbk 表
+        
+        Returns:
+            转存结果统计
+        """
+        print("\n" + "=" * 60)
+        print("开始转存概念板块数据 (ths_concept_member -> gp_gnbk)")
+        print("=" * 60)
+
+        result = {
+            'success': False,
+            'source_count': 0,
+            'transferred_count': 0,
+            'error': None
+        }
+
+        try:
+            # 1. 读取源表数据
+            print("正在读取 ths_concept_member 表数据...")
+            source_query = text("""
+                SELECT 
+                    concept_code,
+                    concept_name,
+                    stock_symbol,
+                    stock_name
+                FROM ths_concept_member
+                WHERE concept_code IS NOT NULL 
+                  AND stock_symbol IS NOT NULL
+            """)
+            
+            with self.engine.connect() as conn:
+                source_df = pd.read_sql(source_query, conn)
+            
+            result['source_count'] = len(source_df)
+            print(f"源表共 {result['source_count']} 条记录")
+
+            if source_df.empty:
+                print("源表无数据，转存终止")
+                result['success'] = True
+                return result
+
+            # 2. 数据转换
+            print("正在转换数据格式...")
+            target_df = pd.DataFrame()
+            target_df['bk_code'] = source_df['concept_code'].apply(self.extract_bk_code)
+            target_df['bk_name'] = source_df['concept_name']
+            target_df['gp_code'] = source_df['stock_symbol']
+            target_df['gp_name'] = source_df['stock_name']
+            
+            # 过滤掉 bk_code 为 None 的记录
+            target_df = target_df[target_df['bk_code'].notna()]
+
+            print(f"转换后共 {len(target_df)} 条有效记录")
+
+            # 3. 清空目标表
+            print("正在清空 gp_gnbk 表...")
+            with self.engine.begin() as conn:
+                conn.execute(text("TRUNCATE TABLE gp_gnbk"))
+            print("gp_gnbk 表已清空")
+
+            # 4. 插入数据
+            print("正在插入数据到 gp_gnbk 表...")
+            target_df.to_sql(
+                'gp_gnbk',
+                self.engine,
+                if_exists='append',
+                index=False,
+                method='multi',
+                chunksize=1000
+            )
+            
+            result['transferred_count'] = len(target_df)
+            result['success'] = True
+            print(f"成功转存 {result['transferred_count']} 条记录到 gp_gnbk 表")
+
+        except Exception as e:
+            result['error'] = str(e)
+            print(f"转存概念板块数据失败: {e}")
+            import traceback
+            traceback.print_exc()
+
+        return result
+
+    def run_full_transfer(self) -> dict:
+        """
+        执行全量转存：行业 + 概念
+        
+        Returns:
+            转存结果汇总
+        """
+        print("\n" + "=" * 60)
+        print("开始执行全量转存")
+        print("=" * 60)
+
+        summary = {
+            'industry': None,
+            'concept': None,
+            'all_success': False
+        }
+
+        # 转存行业数据
+        industry_result = self.transfer_industry_data()
+        summary['industry'] = industry_result
+
+        # 转存概念数据
+        concept_result = self.transfer_concept_data()
+        summary['concept'] = concept_result
+
+        # 汇总结果
+        summary['all_success'] = industry_result.get('success') and concept_result.get('success')
+
+        # 打印汇总
+        print("\n" + "=" * 60)
+        print("转存完成汇总")
+        print("=" * 60)
+        print(f"行业板块转存: {'成功' if industry_result.get('success') else '失败'}")
+        if industry_result.get('success'):
+            print(f"  - 源表记录数: {industry_result.get('source_count', 0)}")
+            print(f"  - 转存记录数: {industry_result.get('transferred_count', 0)}")
+        if industry_result.get('error'):
+            print(f"  - 错误: {industry_result.get('error')}")
+
+        print(f"\n概念板块转存: {'成功' if concept_result.get('success') else '失败'}")
+        if concept_result.get('success'):
+            print(f"  - 源表记录数: {concept_result.get('source_count', 0)}")
+            print(f"  - 转存记录数: {concept_result.get('transferred_count', 0)}")
+        if concept_result.get('error'):
+            print(f"  - 错误: {concept_result.get('error')}")
+
+        print("=" * 60)
+
+        return summary
+
+
+def transfer_ths_to_gp_bk(db_url: str):
+    """
+    转存入口函数
+    
+    Args:
+        db_url: 数据库连接 URL
+    """
+    transfer = THSToGPBKTransfer(db_url)
+    transfer.run_full_transfer()
+
+
+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"
+    
+    # 执行全量转存
+    transfer_ths_to_gp_bk(DB_URL)
+

src/valuation_analysis/industry_analysis.py

@@ -14,10 +14,21 @@ import datetime
 import logging
 import json
 import redis
-import time
+from typing import Tuple, Dict, List
-from typing import Tuple, Dict, List, Optional, Union
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pathlib import Path
+import os
+import sys
 
-from .config import DB_URL, OUTPUT_DIR, LOG_FILE
+# 处理相对导入和绝对导入
+try:
+    from .config import DB_URL, OUTPUT_DIR, LOG_FILE
+except ImportError:
+    # 直接执行时使用绝对导入
+    current_dir = Path(__file__).parent
+    sys.path.insert(0, str(current_dir.parent.parent))
+    from src.valuation_analysis.config import DB_URL, OUTPUT_DIR, LOG_FILE
 
 # 配置日志
 logging.basicConfig(
@@ -59,6 +70,132 @@ class IndustryAnalyzer:
         )
         logger.info("行业估值分析器初始化完成")
     
+    def _setup_chinese_font(self):
+        """
+        设置matplotlib中文字体，解决中文显示乱码问题
+        使用setup_fonts工具确保字体存在，并优先使用项目目录中的字体文件
+        """
+        try:
+            from matplotlib import font_manager
+            from matplotlib.font_manager import FontProperties
+            
+            # 1. 确保字体文件存在（使用setup_fonts工具）
+            try:
+                # 获取项目路径
+                current_file = Path(__file__)
+                project_root = current_file.parent.parent
+                setup_fonts_path = project_root / "fundamentals_llm" / "setup_fonts.py"
+                
+                if setup_fonts_path.exists():
+                    # 动态导入setup_fonts模块
+                    import importlib.util
+                    spec = importlib.util.spec_from_file_location("setup_fonts", str(setup_fonts_path))
+                    setup_fonts = importlib.util.module_from_spec(spec)
+                    spec.loader.exec_module(setup_fonts)
+                    
+                    # 确保字体已安装到fundamentals_llm/fonts目录
+                    font_dir = project_root / "fundamentals_llm" / "fonts"
+                    font_file = setup_fonts.install_font(str(font_dir))
+                    
+                    if font_file and os.path.exists(font_file):
+                        logger.info(f"setup_fonts已确保字体存在: {font_file}")
+                    else:
+                        logger.warning("setup_fonts未能安装字体，尝试查找现有字体")
+                else:
+                    logger.warning(f"未找到setup_fonts.py: {setup_fonts_path}")
+            except Exception as e:
+                logger.warning(f"调用setup_fonts失败: {e}，尝试查找现有字体")
+            
+            # 2. 查找项目中的字体文件（优先使用）
+            current_file = Path(__file__)
+            project_root = current_file.parent.parent
+            
+            # 可能的字体文件位置
+            font_paths = [
+                project_root / "fundamentals_llm" / "fonts" / "simhei.ttf",  # 现有字体目录
+                project_root / "valuation_analysis" / "fonts" / "simhei.ttf",  # 本模块字体目录
+            ]
+            
+            font_file = None
+            for font_path in font_paths:
+                if font_path.exists():
+                    font_file = str(font_path)
+                    logger.info(f"找到项目字体文件: {font_file}")
+                    break
+            
+            # 3. 如果找到项目字体文件，直接使用
+            if font_file and os.path.exists(font_file):
+                try:
+                    # 使用字体文件路径直接注册
+                    font_prop = FontProperties(fname=font_file)
+                    # 获取字体名称
+                    font_name = font_prop.get_name()
+                    # 设置matplotlib使用该字体
+                    plt.rcParams['font.sans-serif'] = [font_name] + plt.rcParams['font.sans-serif']
+                    logger.info(f"成功使用项目字体: {font_name} (来自: {font_file})")
+                except Exception as e:
+                    logger.warning(f"加载项目字体文件失败: {e}，尝试使用系统字体")
+                    font_file = None
+            
+            # 4. 如果项目字体不可用，尝试使用系统字体
+            if font_file is None:
+                import platform
+                system = platform.system()
+                font_candidates = []
+                if system == "Windows":
+                    font_candidates = [
+                        'Microsoft YaHei',
+                        'SimHei',
+                        'SimSun',
+                        'KaiTi',
+                        'FangSong'
+                    ]
+                elif system == "Darwin":  # macOS
+                    font_candidates = [
+                        'PingFang SC',
+                        'STHeiti',
+                        'Arial Unicode MS',
+                        'Heiti SC'
+                    ]
+                else:  # Linux
+                    font_candidates = [
+                        'WenQuanYi Micro Hei',
+                        'WenQuanYi Zen Hei',
+                        'Noto Sans CJK SC',
+                        'Droid Sans Fallback'
+                    ]
+                
+                # 尝试设置系统字体
+                for font_name in font_candidates:
+                    try:
+                        font_list = [f.name for f in font_manager.fontManager.ttflist]
+                        if font_name in font_list:
+                            plt.rcParams['font.sans-serif'] = [font_name] + plt.rcParams['font.sans-serif']
+                            logger.info(f"使用系统字体: {font_name}")
+                            break
+                    except Exception as e:
+                        continue
+            
+            # 5. 如果都没找到，使用默认设置
+            if 'font.sans-serif' not in plt.rcParams or len(plt.rcParams['font.sans-serif']) == 0:
+                plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS', 'DejaVu Sans']
+                logger.warning("使用默认中文字体设置")
+            
+            # 解决负号显示问题
+            plt.rcParams['axes.unicode_minus'] = False
+            
+            # 清除matplotlib字体缓存，强制重新加载字体
+            try:
+                font_manager._rebuild()
+                logger.info("已清除matplotlib字体缓存")
+            except Exception as e:
+                logger.warning(f"清除字体缓存失败: {e}")
+            
+        except Exception as e:
+            logger.warning(f"设置中文字体失败，使用默认设置: {e}")
+            plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS', 'DejaVu Sans']
+            plt.rcParams['axes.unicode_minus'] = False
+    
     def get_industry_list(self) -> List[Dict]:
         """
         获取所有行业列表
@@ -1234,3 +1371,559 @@ class IndustryAnalyzer:
         except Exception as e:
             logger.error(f"筛选拥挤度缓存时出错: {e}")
         return result
+    
+    def _get_all_industries_concepts(self, days: int = 120) -> Tuple[List[str], List[str]]:
+        """
+        从Redis缓存中获取所有有效的行业和概念板块
+        
+        Args:
+            days: 需要分析的天数，用于验证数据有效性
+            
+        Returns:
+            (all_industries, all_concepts) 元组
+        """
+        try:
+            # 计算结束日期和开始日期
+            end_date = datetime.datetime.now()
+            start_date = end_date - datetime.timedelta(days=days)
+            
+            all_industries = []
+            all_concepts = []
+            
+            # 获取所有行业和概念板块的缓存key
+            industry_keys = redis_client.keys('industry_crowding:*')
+            concept_keys = redis_client.keys('concept_crowding:*')
+            
+            # 处理行业
+            for key in industry_keys:
+                try:
+                    name = key.split(':', 1)[1]
+                    cached_data = redis_client.get(key)
+                    if not cached_data:
+                        continue
+                    
+                    df = pd.DataFrame(json.loads(cached_data))
+                    if df.empty:
+                        continue
+                    
+                    # 确保trade_date是日期类型
+                    if 'trade_date' in df.columns:
+                        df['trade_date'] = pd.to_datetime(df['trade_date'])
+                    
+                    # 筛选近120天的数据
+                    df = df[df['trade_date'] >= start_date]
+                    df = df.sort_values('trade_date')
+                    
+                    if len(df) < 10:  # 数据点太少，跳过
+                        continue
+                    
+                    all_industries.append(name)
+                except Exception as e:
+                    logger.warning(f"处理行业缓存 {key} 时出错: {e}")
+                    continue
+            
+            # 处理概念板块
+            for key in concept_keys:
+                try:
+                    name = key.split(':', 1)[1]
+                    cached_data = redis_client.get(key)
+                    if not cached_data:
+                        continue
+                    
+                    df = pd.DataFrame(json.loads(cached_data))
+                    if df.empty:
+                        continue
+                    
+                    # 确保trade_date是日期类型
+                    if 'trade_date' in df.columns:
+                        df['trade_date'] = pd.to_datetime(df['trade_date'])
+                    
+                    # 筛选近120天的数据
+                    df = df[df['trade_date'] >= start_date]
+                    df = df.sort_values('trade_date')
+                    
+                    if len(df) < 10:  # 数据点太少，跳过
+                        continue
+                    
+                    all_concepts.append(name)
+                except Exception as e:
+                    logger.warning(f"处理概念缓存 {key} 时出错: {e}")
+                    continue
+            
+            logger.info(f"获取到 {len(all_industries)} 个行业和 {len(all_concepts)} 个概念板块")
+            return all_industries, all_concepts
+            
+        except Exception as e:
+            logger.error(f"获取行业/概念板块列表失败: {e}")
+            return [], []
+    
+    def _select_top_industries_concepts(self, count: int = 40, days: int = 120) -> Tuple[List[str], List[str]]:
+        """
+        从Redis缓存中选择拥挤度变化幅度最大的行业和概念板块
+        
+        Args:
+            count: 选择的行业/概念数量
+            days: 需要分析的天数
+            
+        Returns:
+            (selected_industries, selected_concepts) 元组
+        """
+        try:
+            # 计算结束日期和开始日期
+            end_date = datetime.datetime.now()
+            start_date = end_date - datetime.timedelta(days=days)
+            
+            industry_scores = []
+            concept_scores = []
+            
+            # 获取所有行业和概念板块的缓存key
+            industry_keys = redis_client.keys('industry_crowding:*')
+            concept_keys = redis_client.keys('concept_crowding:*')
+            
+            # 处理行业
+            for key in industry_keys:
+                try:
+                    name = key.split(':', 1)[1]
+                    cached_data = redis_client.get(key)
+                    if not cached_data:
+                        continue
+                    
+                    df = pd.DataFrame(json.loads(cached_data))
+                    if df.empty:
+                        continue
+                    
+                    # 确保trade_date是日期类型
+                    if 'trade_date' in df.columns:
+                        df['trade_date'] = pd.to_datetime(df['trade_date'])
+                    
+                    # 筛选近120天的数据
+                    df = df[df['trade_date'] >= start_date]
+                    df = df.sort_values('trade_date')
+                    
+                    if len(df) < 10:  # 数据点太少，跳过
+                        continue
+                    
+                    # 计算拥挤度变化幅度（最大值-最小值）
+                    percentile_values = df['percentile'].values
+                    if len(percentile_values) > 0:
+                        change_range = float(percentile_values.max() - percentile_values.min())
+                        current_value = float(percentile_values[-1])
+                        
+                        # 综合评分：变化幅度 + 当前值的绝对值（突出极端情况）
+                        score = change_range * 0.7 + abs(current_value - 50) * 0.3
+                        
+                        industry_scores.append({
+                            'name': name,
+                            'score': score,
+                            'change_range': change_range,
+                            'current': current_value
+                        })
+                except Exception as e:
+                    logger.warning(f"处理行业缓存 {key} 时出错: {e}")
+                    continue
+            
+            # 处理概念板块
+            for key in concept_keys:
+                try:
+                    name = key.split(':', 1)[1]
+                    cached_data = redis_client.get(key)
+                    if not cached_data:
+                        continue
+                    
+                    df = pd.DataFrame(json.loads(cached_data))
+                    if df.empty:
+                        continue
+                    
+                    # 确保trade_date是日期类型
+                    if 'trade_date' in df.columns:
+                        df['trade_date'] = pd.to_datetime(df['trade_date'])
+                    
+                    # 筛选近120天的数据
+                    df = df[df['trade_date'] >= start_date]
+                    df = df.sort_values('trade_date')
+                    
+                    if len(df) < 10:  # 数据点太少，跳过
+                        continue
+                    
+                    # 计算拥挤度变化幅度
+                    percentile_values = df['percentile'].values
+                    if len(percentile_values) > 0:
+                        change_range = float(percentile_values.max() - percentile_values.min())
+                        current_value = float(percentile_values[-1])
+                        
+                        # 综合评分
+                        score = change_range * 0.7 + abs(current_value - 50) * 0.3
+                        
+                        concept_scores.append({
+                            'name': name,
+                            'score': score,
+                            'change_range': change_range,
+                            'current': current_value
+                        })
+                except Exception as e:
+                    logger.warning(f"处理概念缓存 {key} 时出错: {e}")
+                    continue
+            
+            # 按评分排序，选择前count个
+            industry_scores.sort(key=lambda x: x['score'], reverse=True)
+            concept_scores.sort(key=lambda x: x['score'], reverse=True)
+            
+            selected_industries = [item['name'] for item in industry_scores[:count]]
+            selected_concepts = [item['name'] for item in concept_scores[:count]]
+            
+            logger.info(f"选择了 {len(selected_industries)} 个行业和 {len(selected_concepts)} 个概念板块")
+            
+            return selected_industries, selected_concepts
+            
+        except Exception as e:
+            logger.error(f"选择行业/概念板块时出错: {e}")
+            return [], []
+    
+    def _build_heatmap_data(self, names: List[str], is_concept: bool, days: int = 120) -> pd.DataFrame:
+        """
+        构建热力图数据矩阵
+        
+        Args:
+            names: 行业/概念板块名称列表
+            is_concept: 是否为概念板块
+            days: 需要分析的天数
+            
+        Returns:
+            包含热力图数据的DataFrame，行为行业/概念，列为日期，值为拥挤度百分位
+        """
+        try:
+            # 计算日期范围
+            end_date = datetime.datetime.now()
+            start_date = end_date - datetime.timedelta(days=days)
+            
+            # 获取所有交易日期（从第一个有效数据中获取）
+            all_dates = set()
+            data_dict = {}
+            
+            prefix = 'concept_crowding:' if is_concept else 'industry_crowding:'
+            
+            for name in names:
+                cache_key = f"{prefix}{name}"
+                cached_data = redis_client.get(cache_key)
+                
+                if not cached_data:
+                    continue
+                
+                try:
+                    df = pd.DataFrame(json.loads(cached_data))
+                    if df.empty:
+                        continue
+                    
+                    # 确保trade_date是日期类型
+                    if 'trade_date' in df.columns:
+                        df['trade_date'] = pd.to_datetime(df['trade_date'])
+                    
+                    # 筛选日期范围
+                    df = df[(df['trade_date'] >= start_date) & (df['trade_date'] <= end_date)]
+                    df = df.sort_values('trade_date')
+                    
+                    if df.empty:
+                        continue
+                    
+                    # 收集所有日期
+                    all_dates.update(df['trade_date'].tolist())
+                    
+                    # 存储数据
+                    data_dict[name] = df.set_index('trade_date')['percentile'].to_dict()
+                    
+                except Exception as e:
+                    logger.warning(f"处理 {name} 数据时出错: {e}")
+                    continue
+            
+            if not all_dates:
+                logger.warning("没有找到有效数据")
+                return pd.DataFrame()
+            
+            # 创建完整的日期序列（只包含交易日）
+            all_dates = sorted(list(all_dates))
+            
+            # 构建矩阵
+            matrix_data = []
+            for name in names:
+                row = []
+                for date in all_dates:
+                    if name in data_dict and date in data_dict[name]:
+                        row.append(float(data_dict[name][date]))
+                    else:
+                        row.append(np.nan)
+                matrix_data.append(row)
+            
+            # 创建DataFrame
+            heatmap_df = pd.DataFrame(matrix_data, index=names, columns=all_dates)
+            
+            logger.info(f"构建热力图数据完成: {heatmap_df.shape}")
+            return heatmap_df
+            
+        except Exception as e:
+            logger.error(f"构建热力图数据失败: {e}")
+            return pd.DataFrame()
+    
+    def plot_crowding_heatmap(self, days: int = 120, items_per_page: int = 40, output_dir: str = None):
+        """
+        绘制行业和概念板块拥挤度热力图
+        每40个行业/概念生成一张图
+        
+        Args:
+            days: 分析的天数，默认120天
+            items_per_page: 每张图显示的行业/概念数量，默认40个
+            output_dir: 输出目录，默认为src/data目录
+        """
+        try:
+            # 设置输出目录
+            if output_dir is None:
+                # 获取项目根目录
+                current_file = Path(__file__)
+                project_root = current_file.parent.parent.parent
+                output_dir = project_root / "src" / "data"
+            else:
+                output_dir = Path(output_dir)
+            
+            os.makedirs(output_dir, exist_ok=True)
+            
+            # 获取所有行业和概念
+            all_industries, all_concepts = self._get_all_industries_concepts(days=days)
+            
+            if not all_industries and not all_concepts:
+                logger.warning("没有找到有效的行业或概念板块数据")
+                return
+            
+            # 设置中文字体（使用setup_fonts工具）
+            self._setup_chinese_font()
+            
+            # 设置seaborn样式
+            sns.set_style("whitegrid")
+            
+            # 生成时间戳用于文件名
+            timestamp = datetime.datetime.now().strftime('%Y%m%d')
+            
+            # 绘制行业热力图（按每items_per_page个分组）
+            if all_industries:
+                total_industries = len(all_industries)
+                total_pages = (total_industries + items_per_page - 1) // items_per_page  # 向上取整
+                logger.info(f"开始绘制行业热力图，共 {total_industries} 个行业，将生成 {total_pages} 张图")
+                
+                for page in range(total_pages):
+                    start_idx = page * items_per_page
+                    end_idx = min(start_idx + items_per_page, total_industries)
+                    page_industries = all_industries[start_idx:end_idx]
+                    
+                    logger.info(f"绘制行业热力图第 {page + 1}/{total_pages} 张（行业 {start_idx + 1}-{end_idx}）")
+                    industry_heatmap = self._build_heatmap_data(page_industries, is_concept=False, days=days)
+                    
+                    if not industry_heatmap.empty:
+                        self._plot_single_heatmap(
+                            industry_heatmap, 
+                            title=f"行业拥挤度热力图（近{days}天，第{page + 1}/{total_pages}页，{len(page_industries)}个行业）",
+                            output_path=output_dir / f"industry_crowding_heatmap_{timestamp}_page{page + 1:02d}.png",
+                            is_concept=False
+                        )
+            
+            # 绘制概念板块热力图（按每items_per_page个分组）
+            if all_concepts:
+                total_concepts = len(all_concepts)
+                total_pages = (total_concepts + items_per_page - 1) // items_per_page  # 向上取整
+                logger.info(f"开始绘制概念板块热力图，共 {total_concepts} 个概念，将生成 {total_pages} 张图")
+                
+                for page in range(total_pages):
+                    start_idx = page * items_per_page
+                    end_idx = min(start_idx + items_per_page, total_concepts)
+                    page_concepts = all_concepts[start_idx:end_idx]
+                    
+                    logger.info(f"绘制概念板块热力图第 {page + 1}/{total_pages} 张（概念 {start_idx + 1}-{end_idx}）")
+                    concept_heatmap = self._build_heatmap_data(page_concepts, is_concept=True, days=days)
+                    
+                    if not concept_heatmap.empty:
+                        self._plot_single_heatmap(
+                            concept_heatmap,
+                            title=f"概念板块拥挤度热力图（近{days}天，第{page + 1}/{total_pages}页，{len(page_concepts)}个概念）",
+                            output_path=output_dir / f"concept_crowding_heatmap_{timestamp}_page{page + 1:02d}.png",
+                            is_concept=True
+                        )
+            
+            logger.info("热力图绘制完成")
+            
+        except Exception as e:
+            logger.error(f"绘制热力图失败: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+    
+    def _plot_single_heatmap(self, heatmap_df: pd.DataFrame, title: str, output_path: Path, is_concept: bool):
+        """
+        绘制单个热力图
+        
+        Args:
+            heatmap_df: 热力图数据DataFrame
+            title: 图表标题
+            output_path: 输出路径
+            is_concept: 是否为概念板块
+        """
+        try:
+            # 计算图表尺寸（根据数据量动态调整）
+            n_rows = len(heatmap_df)
+            n_cols = len(heatmap_df.columns)
+            
+            # 基础尺寸，根据数据量调整
+            fig_width = max(20, n_cols * 0.15)
+            fig_height = max(12, n_rows * 0.4)
+            
+            fig, ax = plt.subplots(figsize=(fig_width, fig_height))
+            
+            # 获取当前设置的字体
+            from matplotlib.font_manager import FontProperties
+            current_font = plt.rcParams['font.sans-serif'][0] if plt.rcParams['font.sans-serif'] else 'SimHei'
+            font_prop = FontProperties(fname=str(Path(__file__).parent.parent / "fundamentals_llm" / "fonts" / "simhei.ttf")) if (Path(__file__).parent.parent / "fundamentals_llm" / "fonts" / "simhei.ttf").exists() else None
+            
+            # heatmap_df: 行为行业/概念，列为日期
+            # seaborn heatmap: 行在y轴，列在x轴
+            # 所以：y轴是行业/概念，x轴是日期（符合要求）
+            plot_data = heatmap_df
+            
+            # 使用自定义颜色映射：绿色（不拥挤）-> 黄色（中性）-> 红色（拥挤）
+            # 创建从绿色到红色渐变的colormap
+            colors = ['#2ecc71', '#f1c40f', '#e74c3c']  # 绿色、黄色、红色
+            from matplotlib.colors import LinearSegmentedColormap
+            n_bins = 100
+            cmap = LinearSegmentedColormap.from_list('crowding', colors, N=n_bins)
+            
+            # 绘制热力图
+            heatmap = sns.heatmap(
+                plot_data,
+                cmap=cmap,
+                vmin=0,
+                vmax=100,
+                annot=False,  # 不显示数值（数据太多）
+                fmt='.1f',
+                cbar_kws={
+                    'label': '拥挤度百分位 (%)', 
+                    'shrink': 0.8,
+                    'format': '%.0f'
+                },
+                linewidths=0.05,
+                linecolor='white',
+                ax=ax,
+                xticklabels=False,  # 先不显示x轴标签，后面自定义
+                yticklabels=True   # 显示y轴标签（行业/概念名称）
+            )
+            
+            # 设置colorbar标签字体
+            try:
+                cbar = heatmap.collections[0].colorbar
+                if cbar is not None:
+                    if font_prop:
+                        cbar.set_label('拥挤度百分位 (%)', fontproperties=font_prop, fontsize=10)
+                    else:
+                        cbar.set_label('拥挤度百分位 (%)', fontfamily=current_font, fontsize=10)
+                    # 设置colorbar刻度标签字体
+                    for label in cbar.ax.get_yticklabels():
+                        if font_prop:
+                            label.set_fontproperties(font_prop)
+                        else:
+                            label.set_fontfamily(current_font)
+            except Exception as e:
+                logger.warning(f"设置colorbar字体失败: {e}")
+            
+            # 设置标题和标签，显式指定字体
+            if font_prop:
+                ax.set_title(title, fontsize=16, fontweight='bold', pad=20, fontproperties=font_prop)
+                ax.set_xlabel('日期（时间）', fontsize=12, fontproperties=font_prop)
+                ax.set_ylabel('行业/概念板块', fontsize=12, fontproperties=font_prop)
+            else:
+                ax.set_title(title, fontsize=16, fontweight='bold', pad=20, fontfamily=current_font)
+                ax.set_xlabel('日期（时间）', fontsize=12, fontfamily=current_font)
+                ax.set_ylabel('行业/概念板块', fontsize=12, fontfamily=current_font)
+            
+            # 调整y轴标签（行业/概念名称）- 显示完整名称，但可以旋转
+            y_labels = plot_data.index.tolist()
+            if len(y_labels) > 0:
+                # 如果名称超过15个字符，截断
+                y_labels_display = [label[:15] + '...' if len(str(label)) > 15 else str(label) for label in y_labels]
+                if font_prop:
+                    ax.set_yticklabels(y_labels_display, fontsize=9, rotation=0, ha='right', fontproperties=font_prop)
+                else:
+                    ax.set_yticklabels(y_labels_display, fontsize=9, rotation=0, ha='right', fontfamily=current_font)
+            
+            # 调整x轴标签（日期）- 只显示部分日期，避免过于密集
+            x_labels = plot_data.columns.tolist()
+            if len(x_labels) > 0:
+                # 如果日期太多，只显示部分
+                if len(x_labels) > 30:
+                    step = max(1, len(x_labels) // 15)  # 最多显示15个日期标签
+                    x_positions = list(range(0, len(x_labels), step))
+                    x_labels_display = []
+                    for pos in x_positions:
+                        if pos < len(x_labels):
+                            date_str = str(x_labels[pos])
+                            # 只显示月-日
+                            if isinstance(x_labels[pos], pd.Timestamp):
+                                x_labels_display.append(x_labels[pos].strftime('%m-%d'))
+                            else:
+                                x_labels_display.append(date_str[:10] if len(date_str) > 10 else date_str)
+                    ax.set_xticks(x_positions)
+                    if font_prop:
+                        ax.set_xticklabels(x_labels_display, fontsize=8, rotation=45, ha='right', fontproperties=font_prop)
+                    else:
+                        ax.set_xticklabels(x_labels_display, fontsize=8, rotation=45, ha='right', fontfamily=current_font)
+                else:
+                    # 日期不多，全部显示
+                    x_labels_display = []
+                    for date in x_labels:
+                        if isinstance(date, pd.Timestamp):
+                            x_labels_display.append(date.strftime('%m-%d'))
+                        else:
+                            date_str = str(date)
+                            x_labels_display.append(date_str[:10] if len(date_str) > 10 else date_str)
+                    if font_prop:
+                        ax.set_xticklabels(x_labels_display, fontsize=8, rotation=45, ha='right', fontproperties=font_prop)
+                    else:
+                        ax.set_xticklabels(x_labels_display, fontsize=8, rotation=45, ha='right', fontfamily=current_font)
+            
+            plt.tight_layout()
+            
+            # 保存图片
+            plt.savefig(output_path, dpi=300, bbox_inches='tight')
+            plt.close()
+            
+            logger.info(f"热力图已保存到: {output_path}")
+            
+        except Exception as e:
+            logger.error(f"绘制热力图失败: {e}")
+            import traceback
+            logger.error(traceback.format_exc())
+            if 'fig' in locals():
+                plt.close(fig)
+
+
+def main():
+    """主函数：生成行业和概念板块拥挤度热力图"""
+    try:
+        logger.info("=" * 60)
+        logger.info("开始生成行业和概念板块拥挤度热力图")
+        logger.info("=" * 60)
+        
+        # 创建分析器实例
+        analyzer = IndustryAnalyzer()
+        
+        # 绘制热力图
+        # 参数说明：
+        # days: 分析的天数，默认120天
+        # items_per_page: 每张图显示的行业/概念数量，默认40个
+        # output_dir: 输出目录，默认为src/data目录
+        analyzer.plot_crowding_heatmap(days=120, items_per_page=40)
+        
+        logger.info("=" * 60)
+        logger.info("热力图生成完成！")
+        logger.info("=" * 60)
+        
+    except Exception as e:
+        logger.error(f"执行主函数失败: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+
+
+if __name__ == '__main__':
+    main()