From 66116caf94cb76783dff4ad1a593ac876cd8d5c8 Mon Sep 17 00:00:00 2001
From: Qihang Zhang <zqh6200@qq.com>
Date: Sat, 5 Apr 2025 01:04:54 +0800
Subject: [PATCH] =?UTF-8?q?=F0=9F=86=95=20feat(data=5Fdownloader):=20?=
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---
 data_downloader.py           | 424 +++++++++++++++++++++++++++++++++++
 sql/meta.sql                 |  41 ++++
 sql/stock_table_template.sql |  55 +++++
 sql/update_metadata.sql      |  14 ++
 4 files changed, 534 insertions(+)
 create mode 100644 data_downloader.py
 create mode 100644 sql/meta.sql
 create mode 100644 sql/stock_table_template.sql
 create mode 100644 sql/update_metadata.sql

diff --git a/data_downloader.py b/data_downloader.py
new file mode 100644
index 0000000..08e702c
--- /dev/null
+++ b/data_downloader.py
@@ -0,0 +1,424 @@
+import pandas as pd
+import tushare as ts
+import numpy as np
+import os
+from sqlalchemy import create_engine, text
+from datetime import datetime, timedelta
+
+from utils import load_config
+
+
+def create_engine_from_config(config):
+    mysql = config['mysql']
+    connection_string = f"mysql+pymysql://{mysql['user']}:{mysql['password']}@{mysql['host']}:{mysql['port']}/{mysql['database']}?charset={mysql['charset']}&use_unicode=1"
+    return create_engine(connection_string)
+
+
+def create_metadata_table(engine):
+    """创建股票元数据表"""
+    # 从sql文件读取创建表的SQL
+    with open('sql/meta.sql', 'r', encoding='utf-8') as f:
+        create_table_sql = f.read()
+
+    with engine.connect() as conn:
+        conn.execute(text(create_table_sql))
+        conn.commit()
+    print("股票元数据表创建成功")
+
+
+def create_stock_table(engine, ts_code):
+    """为指定股票创建数据表"""
+    # 确保sql目录存在
+    os.makedirs('sql', exist_ok=True)
+
+    # 读取模板SQL
+    with open('sql/stock_table_template.sql', 'r', encoding='utf-8') as f:
+        template_sql = f.read()
+
+    # 替换模板中的symbol
+    create_table_sql = template_sql.format(symbol=ts_code.split('.')[0])
+
+    # 执行创建表的SQL
+    with engine.connect() as conn:
+        conn.execute(text(create_table_sql))
+        conn.commit()
+
+    print(f"股票({ts_code})数据表创建成功")
+
+
+def download_stock_metadata(pro):
+    """下载股票基础信息"""
+    df = pro.stock_basic(exchange='', list_status='L',
+                         fields='ts_code,symbol,name,area,industry,fullname,enname,cnspell,market,exchange,curr_type,list_status,list_date,delist_date,is_hs,act_name,act_ent_type')
+    # 添加状态字段
+    df['status'] = 0  # 未初始化
+    df['remark'] = '自动导入'
+    df['last_full_update'] = None
+    df['last_incremental_update'] = None
+    df['data_start_date'] = None
+    df['data_end_date'] = None
+    df['record_count'] = 0
+    df['latest_price'] = None
+    df['latest_date'] = None
+    df['latest_pe_ttm'] = None
+    df['latest_pb'] = None
+    df['latest_total_mv'] = None
+
+    return df
+
+
+def save_metadata(df, engine):
+    """保存元数据到数据库，只更新指定字段发生变化的记录，处理日期格式问题"""
+    # 需要监控变化的基础元数据字段
+    metadata_fields = [
+        'ts_code', 'symbol', 'name', 'area', 'industry', 'fullname',
+        'enname', 'cnspell', 'market', 'exchange', 'curr_type',
+        'list_status', 'list_date', 'delist_date', 'is_hs',
+        'act_name', 'act_ent_type'
+    ]
+
+    # 日期字段列表
+    date_fields = ['list_date', 'delist_date']
+
+    # 从数据库中读取现有的元数据
+    try:
+        with engine.connect() as conn:
+            # 只查询我们关心的字段
+            query = f"SELECT {', '.join(metadata_fields)} FROM stock_metadata"
+            existing_df = pd.read_sql(query, conn)
+    except Exception as e:
+        print(f"读取现有元数据时出错: {str(e)}")
+        existing_df = pd.DataFrame()  # 表不存在时创建空DataFrame
+
+    if existing_df.empty:
+        # 如果表是空的，直接插入所有数据
+        result = df.to_sql('stock_metadata', engine, index=False,
+                           if_exists='append', chunksize=1000)
+        print(f"成功保存{result}条股票元数据（新建）")
+        return result
+    else:
+        # 数据预处理：处理日期格式
+        df_processed = df.copy()
+        existing_processed = existing_df.copy()
+
+        # 标准化日期格式进行比较
+        def normalize_date(date_str):
+            if pd.isna(date_str) or date_str == '' or date_str == 'None' or date_str is None:
+                return ''
+
+            # 去除所有非数字字符
+            date_str = str(date_str).strip()
+            digits_only = ''.join(c for c in date_str if c.isdigit())
+
+            # 如果是8位数字形式的日期，返回标准格式
+            if len(digits_only) == 8:
+                return digits_only
+            return ''
+
+        # 对两个DataFrame应用日期标准化
+        for field in date_fields:
+            if field in df_processed.columns and field in existing_processed.columns:
+                df_processed[field] = df_processed[field].apply(normalize_date)
+                existing_processed[field] = existing_processed[field].apply(normalize_date)
+
+        # 对其他非日期字段进行标准化处理
+        for col in [f for f in metadata_fields if f not in date_fields and f != 'ts_code']:
+            if col in df_processed.columns and col in existing_processed.columns:
+                # 将两个DataFrame的相同列转换为相同的数据类型（字符串类型）
+                df_processed[col] = df_processed[col].astype(str)
+                existing_processed[col] = existing_processed[col].astype(str)
+
+                # 处理NaN值
+                df_processed[col] = df_processed[col].fillna('').str.strip()
+                existing_processed[col] = existing_processed[col].fillna('').str.strip()
+
+        # 找出新增的记录
+        existing_ts_codes = set(existing_processed['ts_code'])
+        new_records = df[~df['ts_code'].isin(existing_ts_codes)].copy()
+
+        # 找出需要更新的记录
+        changed_records = []
+        unchanged_count = 0
+
+        # 对于每个已存在的ts_code，检查是否有变化
+        for ts_code in df_processed[df_processed['ts_code'].isin(existing_ts_codes)]['ts_code']:
+            # 获取新旧记录（已经标准化后的）
+            new_record = df_processed[df_processed['ts_code'] == ts_code].iloc[0]
+            old_record = existing_processed[existing_processed['ts_code'] == ts_code].iloc[0]
+
+            # 同时取原始记录用于更新操作
+            original_record = df[df['ts_code'] == ts_code].iloc[0]
+
+            # 检查是否有变化
+            has_change = False
+            changed_fields = []
+
+            for field in metadata_fields:
+                if field == 'ts_code':  # 跳过主键
+                    continue
+
+                new_val = new_record[field]
+                old_val = old_record[field]
+
+                if new_val != old_val:
+                    has_change = True
+                    changed_fields.append(field)
+                    print(f"发现变化 - {ts_code} 的 {field}: '{old_val}' -> '{new_val}'")
+
+            if has_change:
+                changed_records.append({
+                    'record': original_record,  # 使用原始记录，保持原始格式
+                    'changed_fields': changed_fields
+                })
+            else:
+                unchanged_count += 1
+
+        # 插入新记录
+        new_count = 0
+        if not new_records.empty:
+            new_count = new_records.to_sql('stock_metadata', engine, index=False,
+                                           if_exists='append', chunksize=1000)
+
+        # 更新变化的记录
+        updated_count = 0
+        for change_info in changed_records:
+            record = change_info['record']
+            fields = change_info['changed_fields']
+
+            # 构建更新语句，只更新变化的字段
+            fields_to_update = []
+            params = {'ts_code': record['ts_code']}
+
+            for field in fields:
+                fields_to_update.append(f"{field} = :{field}")
+                params[field] = record[field]
+
+            if fields_to_update:
+                update_stmt = text(f"""
+                    UPDATE stock_metadata 
+                    SET {', '.join(fields_to_update)}
+                    WHERE ts_code = :ts_code
+                """)
+
+                with engine.connect() as conn:
+                    conn.execute(update_stmt, params)
+                    updated_count += 1
+                    conn.commit()
+
+        print(f"元数据更新统计:")
+        print(f"  • 新增记录: {new_count}条")
+        print(f"  • 更新记录: {updated_count}条")
+        print(f"  • 无变化记录: {unchanged_count}条")
+        print(f"  • 总处理记录: {new_count + updated_count + unchanged_count}条")
+
+        return new_count + updated_count
+
+
+def download_stock_data(pro, ts_code, start_date, end_date):
+    """下载股票的所有类型数据并合并"""
+    # 下载daily价格数据
+    daily_df = pro.daily(ts_code=ts_code, start_date=start_date, end_date=end_date)
+    if daily_df.empty:
+        print(f"警告：{ts_code}没有找到daily数据")
+        return pd.DataFrame()
+
+    # 下载daily_basic数据
+    daily_basic_df = pro.daily_basic(ts_code=ts_code, start_date=start_date, end_date=end_date)
+
+    # 下载moneyflow数据
+    moneyflow_df = pro.moneyflow(ts_code=ts_code, start_date=start_date, end_date=end_date)
+
+    # 确保每个DataFrame都有trade_date列作为合并键
+    if 'trade_date' not in daily_df.columns:
+        print(f"错误：{ts_code}的daily数据缺少trade_date列")
+        return pd.DataFrame()
+
+    # 为方便处理，确保所有日期列是字符串类型
+    daily_df['trade_date'] = daily_df['trade_date'].astype(str)
+
+    # 通过merge而不是join合并数据，这样可以更好地控制列
+    result_df = daily_df
+
+    # 合并daily_basic数据
+    if not daily_basic_df.empty:
+        daily_basic_df['trade_date'] = daily_basic_df['trade_date'].astype(str)
+
+        # 识别重叠的列（除了ts_code和trade_date）
+        overlap_cols = list(set(result_df.columns) & set(daily_basic_df.columns) - {'ts_code', 'trade_date'})
+
+        # 从daily_basic中排除这些重叠列
+        daily_basic_df_filtered = daily_basic_df.drop(columns=overlap_cols)
+
+        # 合并数据
+        result_df = pd.merge(
+            result_df,
+            daily_basic_df_filtered,
+            on=['ts_code', 'trade_date'],
+            how='left'
+        )
+
+    # 合并moneyflow数据
+    if not moneyflow_df.empty:
+        moneyflow_df['trade_date'] = moneyflow_df['trade_date'].astype(str)
+
+        # 识别重叠的列（除了ts_code和trade_date）
+        overlap_cols = list(set(result_df.columns) & set(moneyflow_df.columns) - {'ts_code', 'trade_date'})
+
+        # 从moneyflow中排除这些重叠列
+        moneyflow_df_filtered = moneyflow_df.drop(columns=overlap_cols)
+
+        # 合并数据
+        result_df = pd.merge(
+            result_df,
+            moneyflow_df_filtered,
+            on=['ts_code', 'trade_date'],
+            how='left'
+        )
+
+    # 将trade_date转换为datetime格式
+    result_df['trade_date'] = pd.to_datetime(result_df['trade_date'])
+
+    return result_df
+
+
+def save_stock_data(df, engine, ts_code):
+    """保存股票数据到对应的表中"""
+    if df.empty:
+        print(f"警告：{ts_code}没有数据可保存")
+        return 0
+
+    # 删除ts_code列，因为表中不需要
+    if 'ts_code' in df.columns:
+        df = df.drop(columns=['ts_code'])
+
+    # 使用replace模式，确保表中只包含最新数据
+    try:
+        symbol = ts_code.split('.')[0]
+        result = df.to_sql(f'{symbol}', engine, index=False,
+                           if_exists='replace', chunksize=1000)
+        print(f"成功保存{result}条{ts_code}股票数据")
+        return result
+    except Exception as e:
+        print(f"保存{ts_code}数据时出错: {str(e)}")
+        return 0
+
+
+def update_metadata(engine, ts_code):
+    """更新股票元数据中的统计信息，从股票表中直接获取数据"""
+    # 提取股票代码（不含后缀）
+    symbol = ts_code.split('.')[0]
+
+    # 查询股票数据统计信息
+    stats_query = f"""
+    SELECT 
+        MIN(trade_date) as min_date,
+        MAX(trade_date) as max_date,
+        COUNT(*) as record_count
+    FROM `{symbol}`
+    """
+
+    # 查询最新交易日数据
+    latest_data_query = f"""
+    SELECT 
+        close,
+        pe_ttm,
+        pb,
+        total_mv
+    FROM `{symbol}`
+    WHERE trade_date = (SELECT MAX(trade_date) FROM `{symbol}`)
+    LIMIT 1
+    """
+
+    # 执行查询
+    with engine.connect() as conn:
+        # 获取统计信息
+        stats_result = conn.execute(text(stats_query)).fetchone()
+        if not stats_result:
+            print(f"警告：{ts_code}没有数据，无法更新元数据")
+            return
+
+        data_start_date = stats_result[0].strftime('%Y-%m-%d') if stats_result[0] else 'NULL'
+        data_end_date = stats_result[1].strftime('%Y-%m-%d') if stats_result[1] else 'NULL'
+        record_count = stats_result[2]
+
+        # 获取最新交易日数据
+        latest_data_result = conn.execute(text(latest_data_query)).fetchone()
+        latest_price = f"{latest_data_result[0]}" if latest_data_result and latest_data_result[
+            0] is not None else 'NULL'
+        latest_pe_ttm = f"{latest_data_result[1]}" if latest_data_result and latest_data_result[
+            1] is not None else 'NULL'
+        latest_pb = f"{latest_data_result[2]}" if latest_data_result and latest_data_result[2] is not None else 'NULL'
+        latest_total_mv = f"{latest_data_result[3]}" if latest_data_result and latest_data_result[
+            3] is not None else 'NULL'
+
+        # 加载更新SQL模板
+        with open('sql/update_metadata.sql', 'r', encoding='utf-8') as f:
+            update_sql_template = f.read()
+
+        # 填充模板
+        update_sql = update_sql_template.format(
+            data_start_date=data_start_date,
+            data_end_date=data_end_date,
+            record_count=record_count,
+            latest_price=latest_price,
+            latest_pe_ttm=latest_pe_ttm,
+            latest_pb=latest_pb,
+            latest_total_mv=latest_total_mv,
+            ts_code=ts_code
+        )
+
+        # 执行更新
+        conn.execute(text(update_sql))
+        conn.commit()
+
+    print(f"已更新({ts_code})的元数据信息")
+
+
+def process_stock(engine, pro, ts_code, start_date, end_date):
+    """处理单个股票的完整流程"""
+    # 创建该股票的表
+    create_stock_table(engine, ts_code)
+
+    # 下载该股票的数据
+    stock_data = download_stock_data(pro, ts_code, start_date, end_date)
+
+    # 保存股票数据
+    if not stock_data.empty:
+        save_stock_data(stock_data, engine, ts_code)
+
+        update_metadata(engine, ts_code)
+    else:
+        print(f"警告：({ts_code})没有获取到数据")
+
+
+def main():
+    # 加载配置
+    config = load_config()
+
+    # 创建数据库引擎
+    engine = create_engine_from_config(config)
+
+    # 设置Tushare API
+    ts.set_token(config['tushare_token'])
+    pro = ts.pro_api()
+
+    # 创建元数据表
+    create_metadata_table(engine)
+
+    # 下载并保存股票元数据
+    metadata_df = download_stock_metadata(pro)
+    save_metadata(metadata_df, engine)
+
+    # 示例：处理000001股票
+    ts_code = '000001.SZ'  # 平安银行的ts_code
+
+    # 设置数据获取的日期范围（示例：过去5年的数据）
+    end_date = datetime.now().strftime('%Y%m%d')
+    start_date = (datetime.now() - timedelta(days=15 * 365)).strftime('%Y%m%d')
+
+    # 处理该股票
+    process_stock(engine, pro, ts_code, start_date, end_date)
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/sql/meta.sql b/sql/meta.sql
new file mode 100644
index 0000000..5b46433
--- /dev/null
+++ b/sql/meta.sql
@@ -0,0 +1,41 @@
+-- 股票元数据表定义
+CREATE TABLE IF NOT EXISTS stock_metadata (
+    ts_code VARCHAR(10) PRIMARY KEY COMMENT '股票代码',
+    symbol VARCHAR(10) COMMENT '股票代码（不含交易所）',
+    name VARCHAR(50) COMMENT '股票名称',
+    area VARCHAR(20) COMMENT '地域',
+    industry VARCHAR(20) COMMENT '所属行业',
+    fullname VARCHAR(100) COMMENT '股票全称',
+    enname VARCHAR(200) COMMENT '英文全称',
+    cnspell VARCHAR(20) COMMENT '拼音缩写',
+    market VARCHAR(10) COMMENT '市场类型',
+    exchange VARCHAR(10) COMMENT '交易所代码 SSE/SZSE/BSE',
+    curr_type VARCHAR(10) COMMENT '交易货币',
+    list_status VARCHAR(2) COMMENT '上市状态 L上市 D退市 P暂停上市',
+    list_date DATE COMMENT '上市日期',
+    delist_date DATE COMMENT '退市日期',
+    is_hs VARCHAR(1) COMMENT '是否沪深港通标的，N否 H沪股通 S深股通',
+    act_name VARCHAR(50) COMMENT '实际控制人名称',
+    act_ent_type VARCHAR(30) COMMENT '实际控制人企业性质',
+    -- 数据管理相关字段
+    last_full_update TIMESTAMP NULL DEFAULT NULL COMMENT '最后一次全量更新时间',
+    last_incremental_update TIMESTAMP NULL DEFAULT NULL COMMENT '最后一次增量更新时间',
+    data_start_date DATE DEFAULT NULL COMMENT 'daily数据开始日期',
+    data_end_date DATE DEFAULT NULL COMMENT 'daily数据结束日期',
+    record_count INT DEFAULT 0 COMMENT '记录总数',
+    status TINYINT DEFAULT 0 COMMENT '数据状态: 0-未初始化, 1-正常, 2-异常',
+    -- 最新交易日基本信息（便于快速访问）
+    latest_price DECIMAL(10,2) DEFAULT 0.00 COMMENT '最新收盘价',
+    latest_date DATE DEFAULT NULL COMMENT '最新交易日期',
+    latest_pe_ttm DECIMAL(10,4) DEFAULT 0.0000 COMMENT '最新市盈率(TTM)',
+    latest_pb DECIMAL(10,4) DEFAULT 0.0000 COMMENT '最新市净率',
+    latest_total_mv DECIMAL(20,4) DEFAULT 0.0000 COMMENT '最新总市值(万元)',
+    remark VARCHAR(500) DEFAULT '自动导入' COMMENT '备注信息',
+    create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
+    update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
+    INDEX idx_status (status),
+    INDEX idx_industry (industry),
+    INDEX idx_market (market),
+    INDEX idx_exchange (exchange),
+    INDEX idx_latest_date (latest_date)
+) ENGINE=InnoDB COMMENT '股票数据元信息表';
\ No newline at end of file
diff --git a/sql/stock_table_template.sql b/sql/stock_table_template.sql
new file mode 100644
index 0000000..bebf8ae
--- /dev/null
+++ b/sql/stock_table_template.sql
@@ -0,0 +1,55 @@
+CREATE TABLE IF NOT EXISTS `{symbol}` (
+    trade_date DATE PRIMARY KEY COMMENT '交易日期',
+
+    -- daily价格数据
+    open DECIMAL(10,2) COMMENT '开盘价',
+    high DECIMAL(10,2) COMMENT '最高价',
+    low DECIMAL(10,2) COMMENT '最低价',
+    close DECIMAL(10,2) COMMENT '收盘价',
+    pre_close DECIMAL(10,2) COMMENT '昨收价',
+    change_val DECIMAL(10,2) COMMENT '涨跌额',
+    pct_chg DECIMAL(10,4) COMMENT '涨跌幅',
+    vol DECIMAL(20,2) COMMENT '成交量',
+    amount DECIMAL(20,2) COMMENT '成交额',
+
+    -- daily_basic基础指标
+    turnover_rate DECIMAL(10,4) COMMENT '换手率',
+    turnover_rate_f DECIMAL(10,4) COMMENT '换手率(自由流通股本)',
+    volume_ratio DECIMAL(10,2) COMMENT '量比',
+    pe DECIMAL(10,4) COMMENT '市盈率',
+    pe_ttm DECIMAL(10,4) COMMENT '市盈率(TTM)',
+    pb DECIMAL(10,4) COMMENT '市净率',
+    ps DECIMAL(10,4) COMMENT '市销率',
+    ps_ttm DECIMAL(10,4) COMMENT '市销率(TTM)',
+    dv_ratio DECIMAL(10,4) COMMENT '股息率',
+    dv_ttm DECIMAL(10,4) COMMENT '股息率(TTM)',
+    total_share DECIMAL(20,4) COMMENT '总股本(万股)',
+    float_share DECIMAL(20,4) COMMENT '流通股本(万股)',
+    free_share DECIMAL(20,4) COMMENT '自由流通股本(万股)',
+    total_mv DECIMAL(20,4) COMMENT '总市值(万元)',
+    circ_mv DECIMAL(20,4) COMMENT '流通市值(万元)',
+
+    -- moneyflow资金流向
+    buy_sm_vol DECIMAL(20,2) COMMENT '小单买入量(手)',
+    buy_sm_amount DECIMAL(20,2) COMMENT '小单买入金额(万元)',
+    sell_sm_vol DECIMAL(20,2) COMMENT '小单卖出量(手)',
+    sell_sm_amount DECIMAL(20,2) COMMENT '小单卖出金额(万元)',
+    buy_md_vol DECIMAL(20,2) COMMENT '中单买入量(手)',
+    buy_md_amount DECIMAL(20,2) COMMENT '中单买入金额(万元)',
+    sell_md_vol DECIMAL(20,2) COMMENT '中单卖出量(手)',
+    sell_md_amount DECIMAL(20,2) COMMENT '中单卖出金额(万元)',
+    buy_lg_vol DECIMAL(20,2) COMMENT '大单买入量(手)',
+    buy_lg_amount DECIMAL(20,2) COMMENT '大单买入金额(万元)',
+    sell_lg_vol DECIMAL(20,2) COMMENT '大单卖出量(手)',
+    sell_lg_amount DECIMAL(20,2) COMMENT '大单卖出金额(万元)',
+    buy_elg_vol DECIMAL(20,2) COMMENT '特大单买入量(手)',
+    buy_elg_amount DECIMAL(20,2) COMMENT '特大单买入金额(万元)',
+    sell_elg_vol DECIMAL(20,2) COMMENT '特大单卖出量(手)',
+    sell_elg_amount DECIMAL(20,2) COMMENT '特大单卖出金额(万元)',
+    net_mf_vol DECIMAL(20,2) COMMENT '净流入量(手)',
+    net_mf_amount DECIMAL(20,2) COMMENT '净流入额(万元)',
+
+    update_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间',
+
+    INDEX idx_date (trade_date)
+) ENGINE=InnoDB COMMENT '{symbol}股票所有交易数据';
\ No newline at end of file
diff --git a/sql/update_metadata.sql b/sql/update_metadata.sql
new file mode 100644
index 0000000..4cde69c
--- /dev/null
+++ b/sql/update_metadata.sql
@@ -0,0 +1,14 @@
+-- 更新股票元数据信息
+UPDATE stock_metadata
+SET
+    last_full_update = NOW(),
+    data_start_date = '{data_start_date}',
+    data_end_date = '{data_end_date}',
+    record_count = {record_count},
+    status = 1,
+    latest_price = {latest_price},
+    latest_date = '{data_end_date}',
+    latest_pe_ttm = {latest_pe_ttm},
+    latest_pb = {latest_pb},
+    latest_total_mv = {latest_total_mv}
+WHERE ts_code = '{ts_code}'
\ No newline at end of file