424 lines
15 KiB
Python
424 lines
15 KiB
Python
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() |