def test_TA(date):
if not get_calendar('stock.sse').is_tradingday(date):
date = get_calendar('stock.sse').shift_tradingdays(date, -1)
sample = get_sample(date, 500)
# sample = ['300167.SZ']
sql_result = pd.Series({
symbol: bs_shift_db(symbol, date, 'TotalAssets', 1)
for symbol in sample
}).fillna(-1000)
db_result = query('TA', date).reindex(sql_result.index).fillna(-1000)
if not np.all(np.isclose(sql_result, db_result)):
diff = ~np.isclose(sql_result, db_result)
print(sql_result[diff])
print(db_result[diff])
raise "Error in TA test!"
print('All test passed!')
def test_NI5S(date):
if not get_calendar('stock.sse').is_tradingday(date):
date = get_calendar('stock.sse').shift_tradingdays(date, -1)
sample = get_sample(date, 500)
# sample = ['300167.SZ']
sql_result = pd.Series({
symbol: cshift_db(symbol, date, 'NPParentCompanyOwners',
'LC_IncomeStatementAll', 5)
for symbol in sample
}).fillna(-1000)
db_result = query('NI5S', date).reindex(sql_result.index).fillna(-1000)
if not np.all(np.isclose(sql_result, db_result)):
diff = ~np.isclose(sql_result, db_result)
print(sql_result[diff])
print(db_result[diff])
raise "Error in NI5S test!"
print('All test passed!')
def test_TA2Y(date):
if not get_calendar('stock.sse').is_tradingday(date):
date = get_calendar('stock.sse').shift_tradingdays(date, -1)
last_date = get_calendar('stock.sse').shift_tradingdays(date, -3)
sample = get_sample(date, 500)
# sample = ['300167.SZ']
sql_result = pd.Series({
symbol: bs_shift_db_year(symbol, date, 2, 'TotalAssets')
for symbol in sample
}).fillna(-1000)
db_result = query('TA2Y', date).reindex(sql_result.index).fillna(-1000)
# db_result = data_simu_calculation('FIEXP_TTM', last_date, date).iloc[-1].reindex(sql_result.index).fillna(-1000)
if not np.all(np.isclose(sql_result, db_result)):
diff = ~np.isclose(sql_result, db_result)
print(sql_result[diff])
print(db_result[diff])
raise "Error in TA test!"
print('All test passed!')
def test_SPS3Y(date):
if not get_calendar('stock.sse').is_tradingday(date):
date = get_calendar('stock.sse').shift_tradingdays(date, -1)
last_date = get_calendar('stock.sse').shift_tradingdays(date, -3)
sample = get_sample(date, 500)
# sample = ['600571.SH']
sql_result = pd.Series({
symbol: xps_shift_year(symbol, date, 'TotalOperatingRevenue',
'LC_IncomeStatementAll', 3)
for symbol in sample
}).fillna(-1000)
db_result = query('SPS3Y', date).reindex(sql_result.index).fillna(-1000)
# db_result = data_simu_calculation('FIEXP_TTM', last_date, date).iloc[-1].reindex(sql_result.index).fillna(-1000)
if not np.all(np.isclose(sql_result, db_result)):
diff = ~np.isclose(sql_result, db_result)
print(sql_result[diff])
print(db_result[diff])
raise "Error in TA test!"
print('All test passed!')
def test_NI3Y(date):
if not get_calendar('stock.sse').is_tradingday(date):
date = get_calendar('stock.sse').shift_tradingdays(date, -1)
last_date = get_calendar('stock.sse').shift_tradingdays(date, -3)
sample = get_sample(date, 500)
# sample = ['300167.SZ']
sql_result = pd.Series({
symbol: cshift_year_db(symbol, date, 'NPParentCompanyOwners',
'LC_IncomeStatementAll', 3)
for symbol in sample
}).fillna(-1000)
db_result = query('NI3Y', date).reindex(sql_result.index).fillna(-1000)
# db_result = data_simu_calculation('FIEXP_TTM', last_date, date).iloc[-1].reindex(sql_result.index).fillna(-1000)
if not np.all(np.isclose(sql_result, db_result)):
diff = ~np.isclose(sql_result, db_result)
print(sql_result[diff])
print(db_result[diff])
raise "Error in TA test!"
print('All test passed!')
#!/usr/bin/env python # -*- coding:utf-8 """ Author: Hao Li Email: [email protected] Github: https://github.com/SAmmer0 Created: 2018/5/2 """ from pitdata import update_all from pitdata import query, query_group update_all() # query single ts data tmp1 = query('universe', '2018-01-01', '2018-03-01') print(tmp1.head()) print(tmp1.tail()) # query single cs data tmp2 = query('universe', '2018-01-01') print(tmp2) tmp3 = query('universe', '2018-01-02') print(tmp3.head()) # query_group ts data tmp4 = query_group(['universe', 'close'], '2018-01-01', '2018-03-01') print(tmp4.head()) # query_group cs data tmp5 = query_group(['universe', 'close'], '2018-01-01') print(tmp5)
def get_sample(date, sample_size):
# date: 测试数据的时间
# sample_size: 样本量大小
universe = query('UNIVERSE', date).index.tolist()
sample_res = sample(universe, sample_size)
return sample_res