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
def get_sw_industry():
logging_joinquant()
df=get_industries(name='sw_l1')
df=df.append(get_industries(name='sw_l2'))
df=df.append(get_industries(name='sw_l3'))
df.index.name='industry_code'
df.reset_index(inplace=True)
# 插入数据库
m=MongoDB_io()
m.set_db('stock_daily_data')
m.set_collection('sw_industry_code')
m.remove_all_documents_from_mongodb()
m.insert_dataframe_to_mongodb(df)
def insert_index_data():
m = MongoDB_io()
m.set_db('index_daily_data')
m.set_collection('index_info')
m.delete_document_include_condition()
logging_joinquant()
df = get_all_securities(types='index', date=None)
df.index.name = 'index'
df.reset_index(inplace=True)
df.start_date = pd.to_datetime(df.start_date)
df.end_date = pd.to_datetime(df.end_date)
# 插入数据库
m.insert_huge_dataframe_by_block_to_mongodb(df)
pass
def insert_zz500_data():
m = MongoDB_io()
m.set_db('index_daily_data')
m.set_collection('000905_XSHG')
m.delete_document_include_condition()
logging_joinquant()
df = get_price('000905.XSHG',
start_date='2005-01-01',
end_date='2019-09-25',
fq=None,
frequency='daily')
df.dropna(inplace=True)
## 指数没有复权一说
# df2=get_price('000905.XSHG',fq='pre')
df.index.name = 'date'
df.reset_index(inplace=True)
df.date = pd.to_datetime(df.date)
# 插入数据库
m.insert_huge_dataframe_by_block_to_mongodb(df)
pass
def __init__(self):
self.m = MongoDB_io()
self.nothing = ''
self.start_trade_date = '1990-01-01'
pass
def __init__(self):
self.m = MongoDB_io()
self.nothing = ''
pass
from data_base.mongodb import MongoDB_io
import pandas as pd
from decorate_func.decorate_function import typing_func_name
start_date = '2016-01-01'
end_date = '2019-10-05'
m = MongoDB_io()
@typing_func_name
def get_capital_data():
"""
获得流通市值
:return:
"""
m.set_db('stock_daily_data')
m.set_collection('stock_capital_data')
df = m.read_data_to_get_dataframe_include_condition(start_date=start_date,
end_date=end_date)
return df[['circulating_market_cap', 'code',
'date']].set_index(['date',
'code']).circulating_market_cap.unstack()
pass
@typing_func_name
def get_stock_industry():
"""
获得股票行业分类
:return:
def __init__(self):
self.m = MongoDB_io()
self.m.set_db('stock_min_data')
self.nothing = ''
self.collection_list = self.m.list_collection_names()
pass