🆕 feat(data_downloader): 添加股票数据下载与存储功能
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data_downloader.py
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424
data_downloader.py
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import pandas as pd
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import tushare as ts
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import numpy as np
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import os
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from sqlalchemy import create_engine, text
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from datetime import datetime, timedelta
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from utils import load_config
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def create_engine_from_config(config):
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mysql = config['mysql']
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connection_string = f"mysql+pymysql://{mysql['user']}:{mysql['password']}@{mysql['host']}:{mysql['port']}/{mysql['database']}?charset={mysql['charset']}&use_unicode=1"
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return create_engine(connection_string)
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def create_metadata_table(engine):
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"""创建股票元数据表"""
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# 从sql文件读取创建表的SQL
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with open('sql/meta.sql', 'r', encoding='utf-8') as f:
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create_table_sql = f.read()
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with engine.connect() as conn:
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conn.execute(text(create_table_sql))
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conn.commit()
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print("股票元数据表创建成功")
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def create_stock_table(engine, ts_code):
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"""为指定股票创建数据表"""
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# 确保sql目录存在
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os.makedirs('sql', exist_ok=True)
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# 读取模板SQL
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with open('sql/stock_table_template.sql', 'r', encoding='utf-8') as f:
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template_sql = f.read()
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# 替换模板中的symbol
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create_table_sql = template_sql.format(symbol=ts_code.split('.')[0])
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# 执行创建表的SQL
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with engine.connect() as conn:
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conn.execute(text(create_table_sql))
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conn.commit()
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print(f"股票({ts_code})数据表创建成功")
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def download_stock_metadata(pro):
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"""下载股票基础信息"""
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df = pro.stock_basic(exchange='', list_status='L',
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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')
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# 添加状态字段
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df['status'] = 0 # 未初始化
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df['remark'] = '自动导入'
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df['last_full_update'] = None
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df['last_incremental_update'] = None
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df['data_start_date'] = None
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df['data_end_date'] = None
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df['record_count'] = 0
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df['latest_price'] = None
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df['latest_date'] = None
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df['latest_pe_ttm'] = None
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df['latest_pb'] = None
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df['latest_total_mv'] = None
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return df
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def save_metadata(df, engine):
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"""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"""
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# 需要监控变化的基础元数据字段
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metadata_fields = [
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'ts_code', 'symbol', 'name', 'area', 'industry', 'fullname',
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'enname', 'cnspell', 'market', 'exchange', 'curr_type',
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'list_status', 'list_date', 'delist_date', 'is_hs',
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'act_name', 'act_ent_type'
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]
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# 日期字段列表
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date_fields = ['list_date', 'delist_date']
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# 从数据库中读取现有的元数据
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try:
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with engine.connect() as conn:
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# 只查询我们关心的字段
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query = f"SELECT {', '.join(metadata_fields)} FROM stock_metadata"
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existing_df = pd.read_sql(query, conn)
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except Exception as e:
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print(f"读取现有元数据时出错: {str(e)}")
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existing_df = pd.DataFrame() # 表不存在时创建空DataFrame
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if existing_df.empty:
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# 如果表是空的，直接插入所有数据
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result = df.to_sql('stock_metadata', engine, index=False,
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if_exists='append', chunksize=1000)
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print(f"成功保存{result}条股票元数据（新建）")
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return result
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else:
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# 数据预处理：处理日期格式
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df_processed = df.copy()
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existing_processed = existing_df.copy()
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# 标准化日期格式进行比较
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def normalize_date(date_str):
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if pd.isna(date_str) or date_str == '' or date_str == 'None' or date_str is None:
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return ''
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# 去除所有非数字字符
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date_str = str(date_str).strip()
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digits_only = ''.join(c for c in date_str if c.isdigit())
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# 如果是8位数字形式的日期，返回标准格式
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if len(digits_only) == 8:
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return digits_only
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return ''
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# 对两个DataFrame应用日期标准化
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for field in date_fields:
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if field in df_processed.columns and field in existing_processed.columns:
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df_processed[field] = df_processed[field].apply(normalize_date)
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existing_processed[field] = existing_processed[field].apply(normalize_date)
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# 对其他非日期字段进行标准化处理
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for col in [f for f in metadata_fields if f not in date_fields and f != 'ts_code']:
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if col in df_processed.columns and col in existing_processed.columns:
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# 将两个DataFrame的相同列转换为相同的数据类型（字符串类型）
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df_processed[col] = df_processed[col].astype(str)
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existing_processed[col] = existing_processed[col].astype(str)
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# 处理NaN值
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df_processed[col] = df_processed[col].fillna('').str.strip()
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existing_processed[col] = existing_processed[col].fillna('').str.strip()
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# 找出新增的记录
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existing_ts_codes = set(existing_processed['ts_code'])
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new_records = df[~df['ts_code'].isin(existing_ts_codes)].copy()
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# 找出需要更新的记录
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changed_records = []
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unchanged_count = 0
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# 对于每个已存在的ts_code，检查是否有变化
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for ts_code in df_processed[df_processed['ts_code'].isin(existing_ts_codes)]['ts_code']:
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# 获取新旧记录（已经标准化后的）
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new_record = df_processed[df_processed['ts_code'] == ts_code].iloc[0]
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old_record = existing_processed[existing_processed['ts_code'] == ts_code].iloc[0]
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# 同时取原始记录用于更新操作
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original_record = df[df['ts_code'] == ts_code].iloc[0]
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# 检查是否有变化
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has_change = False
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changed_fields = []
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for field in metadata_fields:
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if field == 'ts_code': # 跳过主键
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continue
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new_val = new_record[field]
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old_val = old_record[field]
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if new_val != old_val:
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has_change = True
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changed_fields.append(field)
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print(f"发现变化 - {ts_code} 的 {field}: '{old_val}' -> '{new_val}'")
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if has_change:
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changed_records.append({
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'record': original_record, # 使用原始记录，保持原始格式
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'changed_fields': changed_fields
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})
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else:
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unchanged_count += 1
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# 插入新记录
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new_count = 0
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if not new_records.empty:
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new_count = new_records.to_sql('stock_metadata', engine, index=False,
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if_exists='append', chunksize=1000)
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# 更新变化的记录
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updated_count = 0
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for change_info in changed_records:
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record = change_info['record']
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fields = change_info['changed_fields']
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# 构建更新语句，只更新变化的字段
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fields_to_update = []
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params = {'ts_code': record['ts_code']}
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for field in fields:
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fields_to_update.append(f"{field} = :{field}")
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params[field] = record[field]
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if fields_to_update:
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update_stmt = text(f"""
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UPDATE stock_metadata
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SET {', '.join(fields_to_update)}
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WHERE ts_code = :ts_code
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""")
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with engine.connect() as conn:
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conn.execute(update_stmt, params)
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updated_count += 1
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conn.commit()
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print(f"元数据更新统计:")
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print(f" • 新增记录: {new_count}条")
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print(f" • 更新记录: {updated_count}条")
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print(f" • 无变化记录: {unchanged_count}条")
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print(f" • 总处理记录: {new_count + updated_count + unchanged_count}条")
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return new_count + updated_count
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def download_stock_data(pro, ts_code, start_date, end_date):
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"""下载股票的所有类型数据并合并"""
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# 下载daily价格数据
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daily_df = pro.daily(ts_code=ts_code, start_date=start_date, end_date=end_date)
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if daily_df.empty:
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print(f"警告：{ts_code}没有找到daily数据")
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return pd.DataFrame()
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# 下载daily_basic数据
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daily_basic_df = pro.daily_basic(ts_code=ts_code, start_date=start_date, end_date=end_date)
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# 下载moneyflow数据
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moneyflow_df = pro.moneyflow(ts_code=ts_code, start_date=start_date, end_date=end_date)
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# 确保每个DataFrame都有trade_date列作为合并键
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if 'trade_date' not in daily_df.columns:
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print(f"错误：{ts_code}的daily数据缺少trade_date列")
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return pd.DataFrame()
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# 为方便处理，确保所有日期列是字符串类型
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daily_df['trade_date'] = daily_df['trade_date'].astype(str)
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# 通过merge而不是join合并数据，这样可以更好地控制列
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result_df = daily_df
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# 合并daily_basic数据
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if not daily_basic_df.empty:
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daily_basic_df['trade_date'] = daily_basic_df['trade_date'].astype(str)
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# 识别重叠的列（除了ts_code和trade_date）
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overlap_cols = list(set(result_df.columns) & set(daily_basic_df.columns) - {'ts_code', 'trade_date'})
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# 从daily_basic中排除这些重叠列
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daily_basic_df_filtered = daily_basic_df.drop(columns=overlap_cols)
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# 合并数据
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result_df = pd.merge(
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result_df,
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daily_basic_df_filtered,
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on=['ts_code', 'trade_date'],
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how='left'
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)
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# 合并moneyflow数据
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if not moneyflow_df.empty:
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moneyflow_df['trade_date'] = moneyflow_df['trade_date'].astype(str)
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# 识别重叠的列（除了ts_code和trade_date）
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overlap_cols = list(set(result_df.columns) & set(moneyflow_df.columns) - {'ts_code', 'trade_date'})
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# 从moneyflow中排除这些重叠列
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moneyflow_df_filtered = moneyflow_df.drop(columns=overlap_cols)
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# 合并数据
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result_df = pd.merge(
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result_df,
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moneyflow_df_filtered,
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on=['ts_code', 'trade_date'],
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how='left'
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)
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# 将trade_date转换为datetime格式
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result_df['trade_date'] = pd.to_datetime(result_df['trade_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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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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# 删除ts_code列，因为表中不需要
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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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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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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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return 0
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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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# 提取股票代码（不含后缀）
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symbol = ts_code.split('.')[0]
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# 查询股票数据统计信息
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stats_query = f"""
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SELECT
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MIN(trade_date) as min_date,
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MAX(trade_date) as max_date,
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COUNT(*) as record_count
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FROM `{symbol}`
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"""
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# 查询最新交易日数据
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latest_data_query = f"""
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SELECT
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close,
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pe_ttm,
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pb,
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total_mv
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FROM `{symbol}`
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WHERE trade_date = (SELECT MAX(trade_date) FROM `{symbol}`)
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LIMIT 1
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"""
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# 执行查询
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with engine.connect() as conn:
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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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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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# 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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# 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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# 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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def process_stock(engine, pro, ts_code, start_date, end_date):
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"""处理单个股票的完整流程"""
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# 创建该股票的表
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create_stock_table(engine, ts_code)
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# 下载该股票的数据
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stock_data = download_stock_data(pro, ts_code, start_date, end_date)
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# 保存股票数据
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if not stock_data.empty:
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save_stock_data(stock_data, engine, ts_code)
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update_metadata(engine, ts_code)
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else:
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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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# 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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# 创建元数据表
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create_metadata_table(engine)
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# 下载并保存股票元数据
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metadata_df = download_stock_metadata(pro)
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save_metadata(metadata_df, engine)
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# 示例：处理000001股票
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ts_code = '000001.SZ' # 平安银行的ts_code
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# 设置数据获取的日期范围（示例：过去5年的数据）
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = (datetime.now() - timedelta(days=15 * 365)).strftime('%Y%m%d')
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# 处理该股票
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process_stock(engine, pro, ts_code, start_date, end_date)
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if __name__ == '__main__':
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main()
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41
sql/meta.sql
Normal file
41
sql/meta.sql
Normal file
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-- 股票元数据表定义
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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';
|
||||
55
sql/stock_table_template.sql
Normal file
55
sql/stock_table_template.sql
Normal file
@ -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';
|
||||
14
sql/update_metadata.sql
Normal file
14
sql/update_metadata.sql
Normal file
@ -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}'
|
||||
Loading…
Reference in New Issue
Block a user