def _inner(universe, start_time, end_time):
'''
上证综指
'''
sql = sql_tmplate.replace('code', index_code).replace('price_type', price_type)
data = fdgetter.get_db_data(sql, cols=('close', 'time'), start_time=start_time,
end_time=end_time, add_stockcode=False)
# pdb.set_trace()
data = pd.DataFrame(np.repeat([data.close.values], len(universe), axis=0).T,
index=data.time, columns=sorted(universe))
checkdata_completeness(data, start_time, end_time)
check_indexorder(data)
return data
def get_adjfactor(universe, start_time, end_time):
'''
股票的复权因子
'''
sql = '''
SELECT A.ExDiviDate, A.RatioAdjustingFactor, M.SecuCode
FROM QT_AdjustingFactor A, SecuMain M
WHERE
A.InnerCode = M.InnerCode AND
M.secuMarket in (83, 90) AND
M.SECUCATEGORY = 1
ORDER BY M.SecuCode ASC, A.ExDiviDate ASC
'''
data = fdgetter.get_db_data(sql, cols=('time', 'data', 'code'), add_stockcode=False)
data['code'] = data.code.apply(datatoolkits.add_suffix)
by_code = data.groupby('code')
tds = dateshandle.get_tds(start_time, end_time)
data = by_code.apply(datatoolkits.map_data, days=tds, fromNowOn=True,
fillna={'code': lambda x: x.code.iloc[0], 'data': lambda x: 1})
data = data.reset_index(drop=True)
data = data.pivot_table('data', index='time', columns='code')
data = data.loc[:, sorted(universe)].fillna(1) # 因为新股大多数情况下没有分红记录
assert check_indexorder(data), 'Error, data order is mixed!'
assert checkdata_completeness(data, start_time, end_time), "Error, data missed!"
return data
def get_ctargetprice(universe, start_time, end_time):
'''
获取一致预期目标价数据
'''
sql = '''
SELECT TARGET_PRICE, CON_DATE, STOCK_CODE
FROM CON_FORECAST_SCHEDULE
WHERE
CON_DATE >= CAST(\'{start_time}\' AS datetime) AND
CON_DATE <= CAST(\'{end_time}\' AS datetime)
'''
data = fdgetter.get_db_data(sql,
start_time=start_time,
end_time=end_time,
cols=('data', 'time', 'code'),
db=fdgetter.zyyx,
add_stockcode=False)
# pdb.set_trace()
data['code'] = data.code.apply(datatoolkits.add_suffix)
data = data.pivot_table('data', index='time', columns='code')
data = data.loc[:, sorted(universe)]
assert check_indexorder(data), 'Error, data order is mixed!'
assert checkdata_completeness(data, start_time,
end_time), "Error, data missed!"
return data
def _inner(universe, start_time, end_time):
data = fdgetter.get_db_data(transed_sql, cols=cols, start_time=start_time,
end_time=end_time, add_stockcode=False)
data['code'] = data.code.apply(datatoolkits.add_suffix)
if len(data.columns) == 4:
data.loc[data.data == 0, 'data'] = data['prevclose']
data.drop('prevclose', inplace=True, axis=1)
data = data.pivot_table('data', index='time', columns='code')
data = data.loc[:, sorted(universe)]
assert check_indexorder(data), 'Error, data order is mixed!'
assert checkdata_completeness(data, start_time, end_time), "Error, data missed!"
return data
def inner(universe, start_time, end_time):
new_start = tds_shift(start_time, 130)
data = fdgetter.get_db_data(sql, code=index_code, cols=('code', 'weight', 'time'),
add_stockcode=False, start_time=new_start, end_time=end_time)
data.code = data.code.apply(datatoolkits.add_suffix)
# pdb.set_trace()
data = data.pivot_table('weight', index='time', columns='code')
tds = dateshandle.get_tds(new_start, end_time)
data = datatoolkits.map_data(data.reset_index(), days=tds, fromNowOn=True)
data = data.set_index('time')
data_tds = dateshandle.get_tds(start_time, end_time)
data = data.reindex(data_tds)
data = data.loc[:, sorted(universe)] / 100
assert check_indexorder(data), "Mixed index order"
return data
def _inner(universe, start_time, end_time):
data = fdgetter.get_db_data(transed_sql, cols=('data', 'time', 'code'),
add_stockcode=False)
data['code'] = data.code.apply(datatoolkits.add_suffix)
data = data.drop_duplicates().sort_values(['code', 'time']) # 此处假设若时间相同则股本数量相同
by_code = data.groupby('code')
tds = dateshandle.get_tds(start_time, end_time)
data = by_code.apply(datatoolkits.map_data, days=tds, fromNowOn=True,
fillna={'code': lambda x: x.code.iloc[0]})
data = data.reset_index(drop=True)
data = data.pivot_table('data', index='time', columns='code')
data = data.loc[:, sorted(universe)]
assert check_indexorder(data), 'Error, data order is mixed!'
assert checkdata_completeness(data, start_time, end_time), "Error, data missed!"
return data
def _inner(universe, start_time, end_time):
offset = 366 * n + 180
# pdb.set_trace()
new_start = pd.to_datetime(start_time) - pd.Timedelta('%d day' % offset)
data = fdgetter.get_db_data(sql, start_time=new_start, end_time=end_time,
cols=('update_time', 'rpt_date', 'code', 'data'),
add_stockcode=False)
data['code'] = data.code.apply(datatoolkits.add_suffix)
data = _handle_dbdata(data, start_time, end_time, fdmutils.cal_yr, col_name='data',
offset=n)
# pdb.set_trace()
data = data.loc[:, sorted(universe)]
assert check_indexorder(data), 'Error, data order is mixed!'
assert checkdata_completeness(data, start_time, end_time), "Error, data missed!"
return data
def _inner(universe, start_time, end_time):
new_start = pd.to_datetime(start_time) - pd.Timedelta('540 day')
data = fdgetter.get_db_data(sql_template, start_time=new_start, end_time=end_time,
cols=('update_time', 'rpt_date', 'code', 'data'),
add_stockcode=False)
data['code'] = data.code.apply(datatoolkits.add_suffix)
by_code = data.groupby('code')
data = by_code.apply(fdmutils.get_observabel_data).reset_index(drop=True)
by_cno = data.groupby(['code', 'obs_time'])
data = by_cno.apply(fdmutils.cal_ttm, col_name='data').reset_index()\
.rename(columns={'obs_time': 'time'})
tds = dateshandle.get_tds(start_time, end_time)
# pdb.set_trace()
data = data.groupby('code').apply(datatoolkits.map_data, days=tds,
fillna={'code': lambda x: x.code.iloc[0]})
data = data.reset_index(drop=True)
data = data.pivot_table('data', index='time', columns='code')
data = data.loc[:, sorted(universe)]
assert check_indexorder(data), 'Error, data order is mixed!'
assert checkdata_completeness(data, start_time, end_time), "Error, data missed!"
return data