def edit_index_and_fill_na(df: pd.DataFrame):
# region Description:edit columns and index
m = MongoDB_io()
m.set_db('stock_daily_data')
m.set_collection('stock_ipo_date')
ipo_df = m.read_data_to_get_dataframe()
ipo_df['stock_short_name'] = ipo_df.stock.apply(lambda x: x[:6])
map_series = ipo_df.set_index('stock_short_name').stock
df = df.reindex(map_series.index, axis=1)
df.columns = df.columns.map(lambda x: map_series[x])
df.loc['1990-01-01 00:00:00', :] = 1
df.sort_index(inplace=True)
df.index = pd.to_datetime(df.index)
# endregion
# region Description:
df.fillna(method='ffill', inplace=True)
m.set_collection('stock_trade_date')
trade_date = m.read_data_to_get_dataframe()
trade_date_list = trade_date.date.tolist()
df = df.reindex(trade_date_list, axis=0)
df.fillna(method='ffill', inplace=True)
# endregion
return df
pass
from jqdatasdk import *
import pandas as pd
from data_base.mongodb import MongoDB_io
auth('15915765128','87662638qjf')
m=MongoDB_io()
m.set_db('stock_daily_data')
m.set_collection('stock_sw_industry_code')
sw_indus=m.read_data_to_get_dataframe()
start_date='2010-01-01'
trade_date_list=get_trade_days(start_date=start_date, end_date=None, count=None)
group_day_num=1000
group_num=1
trade_date_list=get_trade_days(start_date=start_date, end_date=None, count=None)
weight_df=pd.DataFrame()
m=MongoDB_io()
m.set_db('stock_daily_data')
m.set_collection('stock_ipo_date')
ipo_df=m.read_data_to_get_dataframe()
stock_list=ipo_df.stock.tolist()
for date in trade_date_list:
print(date)
panel = get_price(stock_list, start_date=date, end_date=date, frequency='daily', fields=None, skip_paused=False, fq='none', count=None)
df=panel.iloc[:,0,:]
df.reset_index(inplace=True)
df.rename(columns={'index':'stock'},inplace=True)
from jqdatasdk import *
import pandas as pd
from data_base.mongodb import MongoDB_io
auth('15915765128', '87662638qjf')
m = MongoDB_io()
m.set_db('stock_daily_data')
m.set_collection('stock_sw_industry_code')
sw_indus = m.read_data_to_get_dataframe()
m.set_collection('stock_trade_date')
trade_day_df = m.read_data_to_get_dataframe()
trade_day_df = trade_day_df[
trade_day_df.trade_date > pd.to_datetime('2010-01-01')]
trade_day_list = trade_day_df.trade_date.astype(str)
industry_code_list = sw_indus.industry_code.iloc[:34].tolist()
industry_stock_grouping = pd.DataFrame()
for date in trade_day_list:
print(date)
daily_industry_series = pd.Series()
for industry_code in industry_code_list[:]:
stock_list = get_industry_stocks(industry_code, date=date)
daily_industry_series = daily_industry_series.append(
pd.Series(industry_code, index=stock_list))
pass
daily_industry_series.name = pd.to_datetime(date)
daily_industry_df = daily_industry_series.to_frame()
daily_industry_df_stack_up = daily_industry_df.stack().reset_index()
daily_industry_df_stack_up.columns = ['stock', 'date', 'industry_category']
