def test_remote_data_service_industry_df():
from jaqs.data.dataservice import Calendar
cal = Calendar()
ds = RemoteDataService()
ds.init_from_config()
arr = ds.get_index_comp(index='000300.SH',
start_date=20130101,
end_date=20170505)
symbol_arr = ','.join(arr)
sec = '000008.SZ'
type_ = 'ZZ'
df_raw = ds.get_industry_raw(symbol=sec, type_=type_)
df = ds.get_industry_daily(symbol=symbol_arr,
start_date=df_raw['in_date'].min(),
end_date=20170505,
type_=type_,
level=1)
for idx, row in df_raw.iterrows():
in_date = row['in_date']
value = row['industry1_code']
if in_date in df.index:
assert df.loc[in_date, sec] == value
else:
idx = cal.get_next_trade_date(in_date)
assert df.loc[idx, sec] == value
def test_remote_data_service_components():
ds = RemoteDataService()
res = ds.get_index_comp_df(index='000300.SH', start_date=20140101, end_date=20170505)
assert res.shape == (814, 430)
arr = ds.get_index_comp(index='000300.SH', start_date=20140101, end_date=20170505)
assert len(arr) == 430
def test_remote_data_service_industry():
from jaqs.data.align import align
import pandas as pd
ds = RemoteDataService()
arr = ds.get_index_comp(index='000300.SH', start_date=20130101, end_date=20170505)
df = ds.get_industry_raw(symbol=','.join(arr), type_='ZZ')
df = df.astype(dtype={'in_date': int})
# df_ann = df.loc[:, ['in_date', 'symbol']]
# df_ann = df_ann.set_index(['symbol', 'in_date'])
# df_ann = df_ann.unstack(level='symbol')
from jaqs.data.dataview import DataView
dic_sec = DataView._group_df_to_dict(df, by='symbol')
dic_sec = {sec: df.reset_index() for sec, df in dic_sec.viewitems()}
df_ann = pd.concat([df.loc[:, 'in_date'].rename(sec) for sec, df in dic_sec.viewitems()], axis=1)
df_value = pd.concat([df.loc[:, 'industry1_code'].rename(sec) for sec, df in dic_sec.viewitems()], axis=1)
dates_arr = ds.get_trade_date(20140101, 20170505)
res = align(df_value, df_ann, dates_arr)
# df_ann = df.pivot(index='in_date', columns='symbol', values='in_date')
# df_value = df.pivot(index=None, columns='symbol', values='industry1_code')
def align_single_df(df_one_sec):
df_value = df_one_sec.loc[:, ['industry1_code']]
df_ann = df_one_sec.loc[:, ['in_date']]
res = align(df_value, df_ann, dates_arr)
return res
# res_list = [align_single_df(df) for sec, df in dic_sec.viewitems()]
res_list = [align_single_df(df) for sec, df in dic_sec.items()[:10]]
res = pd.concat(res_list, axis=1)
def test_remote_data_service_adj_factor():
ds = RemoteDataService()
arr = ds.get_index_comp(index='000300.SH', start_date=20130101, end_date=20170505)
symbol_arr = ','.join(arr)
res = ds.get_adj_factor_daily(symbol_arr, start_date=20130101, end_date=20170101, div=False)
assert abs(res.loc[20160408, '300024.SZ'] - 10.735) < 1e-3
assert abs(res.loc[20160412, '300024.SZ'] - 23.658) < 1e-3
assert res.isnull().sum().sum() == 0
def test_remote_data_service_daily_ind_performance():
ds = RemoteDataService()
hs300 = ds.get_index_comp('000300.SH', 20140101, 20170101)
hs300_str = ','.join(hs300)
fields = "pb,pe,share_float_free,net_assets,limit_status"
res, msg = ds.query("lb.secDailyIndicator", fields=fields,
filter=("symbol=" + hs300_str
+ "&start_date=20160907&end_date=20170907"),
orderby="trade_date")
assert msg == '0,'
def run_strategy():
start_date = 20150501
end_date = 20171030
index = '399975.SZ'
ds = RemoteDataService()
ds.init_from_config()
symbol_list = ds.get_index_comp(index, start_date, start_date)
# add the benchmark index to the last position of symbol_list
symbol_list.append(index)
props = {
"symbol": ','.join(symbol_list),
"start_date": start_date,
"end_date": end_date,
"bar_type": "1d",
"init_balance": 1e7,
"std multiplier": 1.5,
"m": 10,
"n": 60,
"commission_rate": 2E-4
}
tapi = BacktestTradeApi()
ins = EventBacktestInstance()
strat = SectorRolling()
pm = PortfolioManager()
context = model.Context(data_api=ds,
trade_api=tapi,
instance=ins,
strategy=strat,
pm=pm)
ins.init_from_config(props)
ins.run()
ins.save_results(folder_path=result_dir_path)
ta = ana.EventAnalyzer()
ta.initialize(data_server_=ds, file_folder=result_dir_path)
df_bench, _ = ds.daily(index, start_date=start_date, end_date=end_date)
ta.data_benchmark = df_bench.set_index('trade_date').loc[:, ['close']]
ta.do_analyze(result_dir=result_dir_path,
selected_sec=props['symbol'].split(',')[:2])