🛠️ **refactor**(data_downloader.py): 改进save_stock_data和update_metadata函数以增强灵活性与健壮性
✨ **feat**(data_downloader.py): 新增`perform_full_update`与`perform_incremental_update`函数以支持全量与增量更新 ♻️ **style**(data_downloader.py): 添加完整函数文档与空值处理，提升代码可读性与容错性
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@ -281,8 +281,19 @@ def download_stock_data(pro, ts_code, start_date, end_date):
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return result_df
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def save_stock_data(df, engine, ts_code):
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"""保存股票数据到对应的表中"""
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def save_stock_data(df, engine, ts_code, if_exists='replace'):
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"""
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保存股票数据到对应的表中
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Args:
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df: 股票数据DataFrame
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engine: 数据库引擎
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ts_code: 股票代码
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if_exists: 如果表已存在，处理方式。'replace': 替换现有表，'append': 附加到现有表
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Returns:
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int: 保存的记录数量
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"""
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if df.empty:
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print(f"警告：{ts_code}没有数据可保存")
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return 0
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@ -291,12 +302,12 @@ def save_stock_data(df, engine, ts_code):
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if 'ts_code' in df.columns:
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df = df.drop(columns=['ts_code'])
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# 使用replace模式，确保表中只包含最新数据
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# 使用指定的模式保存数据
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try:
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symbol = ts_code.split('.')[0]
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result = df.to_sql(f'{symbol}', engine, index=False,
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if_exists='replace', chunksize=1000)
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print(f"成功保存{result}条{ts_code}股票数据")
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if_exists=if_exists, chunksize=1000)
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print(f"成功保存{result}条{ts_code}股票数据 (模式: {if_exists})")
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return result
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except Exception as e:
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print(f"保存{ts_code}数据时出错: {str(e)}")
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@ -304,7 +315,17 @@ def save_stock_data(df, engine, ts_code):
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def update_metadata(engine, ts_code):
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"""更新股票元数据中的统计信息，从股票表中直接获取数据"""
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"""
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更新股票元数据中的统计信息，从股票表中直接获取数据
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Args:
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engine: 数据库连接引擎
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ts_code: 股票代码(带后缀，如000001.SZ)
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Returns:
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bool: 更新成功返回True，否则返回False
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"""
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try:
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# 提取股票代码（不含后缀）
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symbol = ts_code.split('.')[0]
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@ -334,44 +355,89 @@ def update_metadata(engine, ts_code):
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# 获取统计信息
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stats_result = conn.execute(text(stats_query)).fetchone()
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if not stats_result:
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print(f"警告：{ts_code}没有数据，无法更新元数据")
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return
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print(f"警告：{ts_code}没有数据，将更新元数据状态为异常")
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# 更新状态为异常
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update_empty_sql = f"""
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UPDATE stock_metadata
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SET last_full_update = NOW(),
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record_count = 0,
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status = 2
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WHERE ts_code = '{ts_code}'
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"""
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conn.execute(text(update_empty_sql))
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conn.commit()
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return False
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# 处理日期字段
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try:
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data_start_date = stats_result[0].strftime('%Y-%m-%d') if stats_result[0] else 'NULL'
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data_end_date = stats_result[1].strftime('%Y-%m-%d') if stats_result[1] else 'NULL'
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record_count = stats_result[2]
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except AttributeError:
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# 处理日期可能是字符串而不是datetime对象的情况
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data_start_date = stats_result[0] if stats_result[0] else 'NULL'
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data_end_date = stats_result[1] if stats_result[1] else 'NULL'
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record_count = stats_result[2] or 0
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# 获取最新交易日数据
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latest_data_result = conn.execute(text(latest_data_query)).fetchone()
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latest_price = f"{latest_data_result[0]}" if latest_data_result and latest_data_result[
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0] is not None else 'NULL'
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latest_pe_ttm = f"{latest_data_result[1]}" if latest_data_result and latest_data_result[
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1] is not None else 'NULL'
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latest_pb = f"{latest_data_result[2]}" if latest_data_result and latest_data_result[2] is not None else 'NULL'
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latest_total_mv = f"{latest_data_result[3]}" if latest_data_result and latest_data_result[
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3] is not None else 'NULL'
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# 加载更新SQL模板
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with open('sql/update_metadata.sql', 'r', encoding='utf-8') as f:
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update_sql_template = f.read()
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# 设置默认值并处理NULL
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default_value = 'NULL'
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latest_price = default_value
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latest_pe_ttm = default_value
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latest_pb = default_value
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latest_total_mv = default_value
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# 填充模板
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update_sql = update_sql_template.format(
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data_start_date=data_start_date,
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data_end_date=data_end_date,
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record_count=record_count,
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latest_price=latest_price,
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latest_pe_ttm=latest_pe_ttm,
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latest_pb=latest_pb,
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latest_total_mv=latest_total_mv,
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ts_code=ts_code
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)
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# 如果有最新数据，则更新相应字段
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if latest_data_result:
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latest_price = str(latest_data_result[0]) if latest_data_result[0] is not None else default_value
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latest_pe_ttm = str(latest_data_result[1]) if latest_data_result[1] is not None else default_value
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latest_pb = str(latest_data_result[2]) if latest_data_result[2] is not None else default_value
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latest_total_mv = str(latest_data_result[3]) if latest_data_result[3] is not None else default_value
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# 构建更新SQL
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update_sql = f"""
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UPDATE stock_metadata
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SET
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last_full_update = NOW(),
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data_start_date = CASE WHEN '{data_start_date}' = 'NULL' THEN NULL ELSE '{data_start_date}' END,
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data_end_date = CASE WHEN '{data_end_date}' = 'NULL' THEN NULL ELSE '{data_end_date}' END,
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record_count = {record_count},
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status = CASE WHEN {record_count} > 0 THEN 1 ELSE 2 END,
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latest_price = CASE WHEN '{latest_price}' = 'NULL' THEN NULL ELSE {latest_price} END,
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latest_date = CASE WHEN '{data_end_date}' = 'NULL' THEN NULL ELSE '{data_end_date}' END,
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latest_pe_ttm = CASE WHEN '{latest_pe_ttm}' = 'NULL' THEN NULL ELSE {latest_pe_ttm} END,
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latest_pb = CASE WHEN '{latest_pb}' = 'NULL' THEN NULL ELSE {latest_pb} END,
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latest_total_mv = CASE WHEN '{latest_total_mv}' = 'NULL' THEN NULL ELSE {latest_total_mv} END
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WHERE ts_code = '{ts_code}'
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"""
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# 执行更新
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conn.execute(text(update_sql))
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conn.commit()
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print(f"已更新({ts_code})的元数据信息")
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return True
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except Exception as e:
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print(f"更新({ts_code})元数据时出错: {str(e)}")
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# 尝试将状态更新为异常
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try:
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with engine.connect() as conn:
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error_update_sql = f"""
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UPDATE stock_metadata
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SET status = 2,
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last_full_update = NOW()
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WHERE ts_code = '{ts_code}'
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"""
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conn.execute(text(error_update_sql))
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conn.commit()
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except Exception as inner_e:
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print(f"更新({ts_code})状态为异常时出错: {str(inner_e)}")
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return False
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def process_stock(engine, pro, ts_code, start_date, end_date):
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@ -391,7 +457,16 @@ def process_stock(engine, pro, ts_code, start_date, end_date):
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print(f"警告：({ts_code})没有获取到数据")
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def main():
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def perform_full_update(start_year=2020):
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"""
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执行全量更新：从指定年份开始到今天的所有数据
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Args:
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start_year: 开始年份，默认为2020年
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"""
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print(f"开始执行全量更新 (从{start_year}年至今)...")
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start_time = datetime.now()
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# 加载配置
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config = load_config()
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@ -402,22 +477,328 @@ def main():
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ts.set_token(config['tushare_token'])
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pro = ts.pro_api()
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# 设置数据获取的日期范围
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = f"{start_year}0101" # 从指定年份的1月1日开始
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print(f"数据范围: {start_date} 至 {end_date}")
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# 从元数据表获取所有股票代码
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with engine.connect() as conn:
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# 查询所有需要更新的股票代码
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query = """
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SELECT ts_code
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FROM stock_metadata
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WHERE list_status = 'L' -- 只选择已上市的股票
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"""
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result = conn.execute(text(query))
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stock_codes = [row[0] for row in result]
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if not stock_codes:
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print("没有找到需要更新的股票，请先确保元数据表已初始化")
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return
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print(f"共找到 {len(stock_codes)} 只股票需要更新")
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# 记录成功和失败的股票数量
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success_count = 0
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failed_count = 0
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skipped_count = 0
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# 当前更新时间
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update_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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# 逐一处理每只股票
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total_stocks = len(stock_codes)
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for index, ts_code in enumerate(stock_codes):
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try:
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print(f"[{index + 1}/{total_stocks}] 正在全量更新股票: {ts_code}")
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# 检查股票表是否存在，不存在则创建
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symbol = ts_code.split('.')[0]
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with engine.connect() as conn:
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table_exists_query = f"""
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SELECT COUNT(*)
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FROM information_schema.tables
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WHERE table_schema = '{config['mysql']['database']}'
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AND table_name = '{symbol}'
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"""
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table_exists = conn.execute(text(table_exists_query)).scalar() > 0
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if not table_exists:
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create_stock_table(engine, ts_code)
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# 下载股票数据
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stock_data = download_stock_data(pro, ts_code, start_date, end_date)
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# 如果下载成功，保存数据并更新元数据
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if not stock_data.empty:
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# 使用replace模式保存数据（全量替换）
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records_saved = save_stock_data(stock_data, engine, ts_code)
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# 更新元数据统计信息
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update_metadata(engine, ts_code)
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# 只更新全量更新时间戳
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with engine.connect() as conn:
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update_status_sql = f"""
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UPDATE stock_metadata
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SET last_full_update = '{update_time}'
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WHERE ts_code = '{ts_code}'
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"""
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conn.execute(text(update_status_sql))
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conn.commit()
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success_count += 1
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print(f"成功更新 {ts_code}，保存了 {records_saved} 条记录")
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else:
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failed_count += 1
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print(f"警告：{ts_code} 没有获取到数据")
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# 防止API限流，每次请求后短暂休息
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import time
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time.sleep(0.5)
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except Exception as e:
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failed_count += 1
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print(f"处理股票 {ts_code} 时出错: {str(e)}")
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end_time = datetime.now()
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duration = (end_time - start_time).total_seconds() / 60 # 转换为分钟
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print("\n全量更新完成!")
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print(f"总耗时: {duration:.2f} 分钟")
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print(f"成功更新: {success_count} 只股票")
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print(f"更新失败: {failed_count} 只股票")
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print(f"总计: {total_stocks} 只股票")
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def perform_incremental_update():
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"""
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执行增量更新：从上次增量或全量更新日期到今天的数据
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- 使用最近的更新日期（增量或全量）作为起点
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- 如果表不存在，跳过该股票
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- 只更新增量更新时间字段
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"""
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print("开始执行增量更新...")
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start_time = datetime.now()
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# 加载配置
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config = load_config()
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# 创建数据库引擎
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engine = create_engine_from_config(config)
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# 设置Tushare API
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ts.set_token(config['tushare_token'])
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pro = ts.pro_api()
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# 当前日期作为结束日期
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end_date = datetime.now().strftime('%Y%m%d')
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# 获取需要更新的股票及其上次更新日期
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stocks_to_update = []
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with engine.connect() as conn:
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query = """
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SELECT
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ts_code,
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last_incremental_update,
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last_full_update
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FROM
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stock_metadata
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WHERE
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list_status = 'L' AND
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status != 2 -- 排除状态异常的股票
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"""
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result = conn.execute(text(query))
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for row in result:
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ts_code = row[0]
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last_incr_update = row[1] # 上次增量更新日期
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last_full_update = row[2] # 上次全量更新日期
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# 确定起始日期：使用最近的更新日期
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latest_update = None
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# 检查增量更新日期
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if last_incr_update:
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latest_update = last_incr_update
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# 检查全量更新日期
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if last_full_update:
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if not latest_update or last_full_update > latest_update:
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latest_update = last_full_update
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# 如果有更新日期，使用其后一天作为起始日期
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if latest_update:
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start_date = (latest_update + timedelta(days=1)).strftime('%Y%m%d')
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else:
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# 如果没有任何更新记录，默认从30天前开始
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start_date = (datetime.now() - timedelta(days=30)).strftime('%Y%m%d')
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# 如果起始日期大于等于结束日期，则跳过此股票
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if start_date >= end_date:
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print(f"股票 {ts_code} 数据已是最新，无需更新")
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continue
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stocks_to_update.append({
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'ts_code': ts_code,
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'start_date': start_date
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})
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if not stocks_to_update:
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print("没有找到需要增量更新的股票")
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return
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print(f"共找到 {len(stocks_to_update)} 只股票需要增量更新")
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# 记录成功和失败的股票数量
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success_count = 0
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failed_count = 0
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no_new_data_count = 0
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skipped_count = 0
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# 当前更新时间
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update_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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# 逐一处理每只股票
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total_stocks = len(stocks_to_update)
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for index, stock in enumerate(stocks_to_update):
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ts_code = stock['ts_code']
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start_date = stock['start_date']
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try:
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print(f"[{index + 1}/{total_stocks}] 正在增量更新股票: {ts_code} (从 {start_date} 到 {end_date})")
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# 确保股票表存在
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symbol = ts_code.split('.')[0]
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with engine.connect() as conn:
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table_exists_query = f"""
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SELECT COUNT(*)
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FROM information_schema.tables
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WHERE table_schema = '{config['mysql']['database']}'
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AND table_name = '{symbol}'
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"""
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table_exists = conn.execute(text(table_exists_query)).scalar() > 0
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if not table_exists:
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print(f"股票 {ts_code} 数据表不存在，跳过此股票")
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skipped_count += 1
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continue # 表不存在则跳过
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# 下载增量数据
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new_data = download_stock_data(pro, ts_code, start_date, end_date)
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if not new_data.empty:
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# 获取现有数据
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try:
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with engine.connect() as conn:
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existing_data = pd.read_sql(f"SELECT * FROM `{symbol}`", conn)
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except Exception as e:
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print(f"读取现有数据失败: {str(e)}，跳过此股票")
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skipped_count += 1
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continue
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if not existing_data.empty:
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# 转换日期列为相同的格式以便合并
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existing_data['trade_date'] = pd.to_datetime(existing_data['trade_date'])
|
||||
new_data['trade_date'] = pd.to_datetime(new_data['trade_date'])
|
||||
|
||||
# 删除可能重复的日期记录
|
||||
existing_dates = set(existing_data['trade_date'])
|
||||
new_data = new_data[~new_data['trade_date'].isin(existing_dates)]
|
||||
|
||||
if new_data.empty:
|
||||
print(f"股票 {ts_code} 没有新数据需要更新")
|
||||
no_new_data_count += 1
|
||||
continue
|
||||
|
||||
# 合并数据
|
||||
combined_data = pd.concat([existing_data, new_data], ignore_index=True)
|
||||
|
||||
# 按日期排序
|
||||
combined_data = combined_data.sort_values('trade_date')
|
||||
|
||||
# 保存合并后的数据
|
||||
records_saved = save_stock_data(combined_data, engine, ts_code)
|
||||
else:
|
||||
# 如果表存在但为空，直接保存新数据
|
||||
records_saved = save_stock_data(new_data, engine, ts_code)
|
||||
|
||||
# 更新元数据统计信息
|
||||
update_metadata(engine, ts_code)
|
||||
|
||||
# 只更新增量更新时间戳
|
||||
with engine.connect() as conn:
|
||||
update_status_sql = f"""
|
||||
UPDATE stock_metadata
|
||||
SET last_incremental_update = '{update_time}'
|
||||
WHERE ts_code = '{ts_code}'
|
||||
"""
|
||||
conn.execute(text(update_status_sql))
|
||||
conn.commit()
|
||||
|
||||
success_count += 1
|
||||
print(f"成功增量更新 {ts_code}，新增 {len(new_data)} 条记录")
|
||||
else:
|
||||
print(f"股票 {ts_code} 在指定时间范围内没有新数据")
|
||||
no_new_data_count += 1
|
||||
|
||||
# 防止API限流，每次请求后短暂休息
|
||||
import time
|
||||
time.sleep(0.5)
|
||||
|
||||
except Exception as e:
|
||||
failed_count += 1
|
||||
print(f"增量更新股票 {ts_code} 时出错: {str(e)}")
|
||||
|
||||
end_time = datetime.now()
|
||||
duration = (end_time - start_time).total_seconds() / 60 # 转换为分钟
|
||||
|
||||
print("\n增量更新完成!")
|
||||
print(f"总耗时: {duration:.2f} 分钟")
|
||||
print(f"成功更新: {success_count} 只股票")
|
||||
print(f"无新数据: {no_new_data_count} 只股票")
|
||||
print(f"跳过股票: {skipped_count} 只股票")
|
||||
print(f"更新失败: {failed_count} 只股票")
|
||||
print(f"总计: {total_stocks} 只股票")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数，允许用户选择更新方式"""
|
||||
import argparse
|
||||
|
||||
# 命令行参数解析
|
||||
parser = argparse.ArgumentParser(description='股票数据更新工具')
|
||||
parser.add_argument('--init', action='store_true', help='初始化元数据表')
|
||||
parser.add_argument('--mode', choices=['full', 'incremental', 'both'],
|
||||
default='full', help='更新模式: full=全量更新, incremental=增量更新, both=两者都执行')
|
||||
parser.add_argument('--year', type=int, default=2020, help='全量更新的起始年份')
|
||||
args = parser.parse_args()
|
||||
|
||||
# 如果需要初始化元数据
|
||||
if args.init:
|
||||
# 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)
|
||||
print("元数据初始化完成")
|
||||
|
||||
# 示例：处理000001股票
|
||||
ts_code = '000001.SZ' # 平安银行的ts_code
|
||||
# 根据选择的模式执行更新
|
||||
if args.mode == 'full' or args.mode == 'both':
|
||||
perform_full_update(args.year)
|
||||
|
||||
# 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 args.mode == 'incremental' or args.mode == 'both':
|
||||
perform_incremental_update()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -2,13 +2,16 @@
|
||||
UPDATE stock_metadata
|
||||
SET
|
||||
last_full_update = NOW(),
|
||||
data_start_date = '{data_start_date}',
|
||||
data_end_date = '{data_end_date}',
|
||||
data_start_date = CASE WHEN '{data_start_date}' = 'NULL' THEN NULL ELSE '{data_start_date}' END,
|
||||
data_end_date = CASE WHEN '{data_end_date}' = 'NULL' THEN NULL ELSE '{data_end_date}' END,
|
||||
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}
|
||||
status = CASE
|
||||
WHEN {record_count} > 0 THEN 1 -- 有数据，状态为正常
|
||||
ELSE 2 -- 无数据，状态为异常
|
||||
END,
|
||||
latest_price = CASE WHEN '{latest_price}' = 'NULL' THEN NULL ELSE {latest_price} END,
|
||||
latest_date = CASE WHEN '{data_end_date}' = 'NULL' THEN NULL ELSE '{data_end_date}' END,
|
||||
latest_pe_ttm = CASE WHEN '{latest_pe_ttm}' = 'NULL' THEN NULL ELSE {latest_pe_ttm} END,
|
||||
latest_pb = CASE WHEN '{latest_pb}' = 'NULL' THEN NULL ELSE {latest_pb} END,
|
||||
latest_total_mv = CASE WHEN '{latest_total_mv}' = 'NULL' THEN NULL ELSE {latest_total_mv} END
|
||||
WHERE ts_code = '{ts_code}'
|
||||
Loading…
Reference in New Issue
Block a user