def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20150101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'volume,turnover,float_mv,pb,total_mv', 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() trade_status = dv.get_ts('trade_status') mask_sus = trade_status == '停牌' dv.append_df(mask_sus, 'suspended', is_quarterly=False) dv.add_formula('not_index_member', '!index_member', is_quarterly=False) dv.add_formula('limit_reached', 'Abs((open - Delay(close, 1)) / Delay(close, 1)) > 0.095', is_quarterly=False) dv.save_dataview(dataview_folder)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20150101, 'end_date': 20170930, 'universe': '000905.SH', 'fields': ('turnover,float_mv,close_adj,pe,pb'), 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'Cutoff(Standardize(turnover / 10000 / float_mv), 2)' dv.add_formula('TO', factor_formula, is_quarterly=False) factor_formula = 'Cutoff(Standardize(1/pb), 2)' dv.add_formula('BP', factor_formula, is_quarterly=False) factor_formula = 'Cutoff(Standardize(Return(close_adj, 20)), 2)' dv.add_formula('REVS20', factor_formula, is_quarterly=False) factor_formula = 'Cutoff(Standardize(Log(float_mv)), 2)' dv.add_formula('float_mv_factor', factor_formula, is_quarterly=False) factor_formula = 'Delay(Return(close_adj, 1), -1)' dv.add_formula('NextRet', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def save_data(): """ This function fetches data from remote server and stores them locally. Then we can use local data to do back-test. """ dataview_props = {# Start and end date of back-test 'start_date': 20170101, 'end_date': 20171030, # Investment universe and performance benchmark 'universe': UNIVERSE, 'benchmark': '000300.SH', # Data fields that we need 'fields': 'total_mv,turnover', # freq = 1 means we use daily data. Please do not change this. 'freq': 1} # RemoteDataService communicates with a remote server to fetch data ds = RemoteDataService() # Use username and password in data_config to login ds.init_from_config(data_config) # DataView utilizes RemoteDataService to get various data and store them dv = DataView() dv.init_from_config(dataview_props, ds) dv.prepare_data() dv.save_dataview(folder_path=dataview_store_folder)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20160101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'volume,turnover', 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() # for convenience to check limit reachers dv.add_formula('limit_reached', 'Abs((open - Delay(close, 1)) / Delay(close, 1)) > 0.095', is_quarterly=False) dv.add_formula('mask_limit_reached', 'limit_reached > 0', is_quarterly=False) dv.add_formula('mask_index_member', '!(index_member > 0)', is_quarterly=False) trade_status = dv.get_ts('trade_status') mask_sus = trade_status == u'停牌' dv.append_df(mask_sus, 'mask_sus', is_quarterly=False) # dv.add_formula('size', '', is_quarterly=False) dv.save_dataview(dataview_folder)
def test_write(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() secs = '600030.SH,000063.SZ,000001.SZ' props = { 'start_date': 20160601, 'end_date': 20170601, 'symbol': secs, 'fields': 'open,close,high,low,volume,pb,net_assets,pcf_ncf', 'freq': 1 } dv.init_from_config(props, data_api=ds) dv.prepare_data() assert dv.data_d.shape == (281, 48) assert dv.dates.shape == (281, ) # TODO """ PerformanceWarning: your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->mixed,key->block1_values] [items->[('000001.SZ', 'int_income'), ('000001.SZ', 'less_handling_chrg_comm_exp'), ('000001.SZ', 'net_int_income'), ('000001.SZ', 'oper_exp'), ('000001.SZ', 'symbol'), ('000063.SZ', 'int_income'), ('000063.SZ', 'less_handling_chrg_comm_exp'), ('000063.SZ', 'net_int_income'), ('000063.SZ', 'oper_exp'), ('000063.SZ', 'symbol'), ('600030.SH', 'int_income'), ('600030.SH', 'less_handling_chrg_comm_exp'), ('600030.SH', 'net_int_income'), ('600030.SH', 'oper_exp'), ('600030.SH', 'symbol')]] """ dv.save_dataview(folder_path=daily_path)
def save_data(): """ This function fetches data from remote server and stores them locally. Then we can use local data to do back-test. """ dataview_props = {'start_date': 20170101, # Start and end date of back-test 'end_date': 20171030, 'universe': UNIVERSE, # Investment universe and performance benchmark 'benchmark': '000905.SH', 'fields': 'high,low,close', # Data fields that we need 'freq': 1 # freq = 1 means we use daily data. Please do not change this. } # RemoteDataService communicates with a remote server to fetch data ds = RemoteDataService() # Use username and password in data_config to login ds.init_from_config(data_config) # DataView utilizes RemoteDataService to get various data and store them dv = DataView() dv.init_from_config(dataview_props, ds) dv.prepare_data() # 以9日为周期的KD线为例。首先须计算出最近9日的RSV值,即未成熟随机值, # 计算公式为 # 9日RSV=(C-L9)÷(H9-L9)×100 # 式中,C为第9日的收盘价;L9为9日内的最低价;H9为9日内的最高价。 # K值=2/3×前一日K值+1/3×当日RSV # D值=2/3×前一日K值+1/3×当日RSV # J指标的计算公式为: J=3D—2K factor_formula = '2/3*50 + (close-Ts_Min(low,5))/(Ts_Max(high,5)-Ts_Min(low,5)*100)' dv.add_formula() dv.save_dataview(folder_path=dataview_store_folder)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20170101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': ('float_mv,pb,pe_ttm,sw2'), 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'GroupQuantile(-float_mv, sw2, 10)' dv.add_formula('rank_mv', factor_formula, is_quarterly=False) factor_formula = 'GroupQuantile(If(pb >= 0.2, pb, 100), sw2, 10)' dv.add_formula('rank_pb', factor_formula, is_quarterly=False) factor_formula = 'GroupQuantile(If(pe_ttm >= 3, pe_ttm, 9999.0), sw2, 10)' dv.add_formula('rank_pe', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20150101, 'end_date': 20170930, 'universe': '000905.SH', 'fields': ('tot_cur_assets,tot_cur_liab,inventories,pre_pay,deferred_exp,' 'eps_basic,ebit,pe,pb,float_mv,sw1'), 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'pe < 30' dv.add_formula('pe_condition', factor_formula, is_quarterly=False) factor_formula = 'pb < 3' dv.add_formula('pb_condition', factor_formula, is_quarterly=False) factor_formula = 'Return(eps_basic, 4) > 0' dv.add_formula('eps_condition', factor_formula, is_quarterly=True) factor_formula = 'Return(ebit, 4) > 0' dv.add_formula('ebit_condition', factor_formula, is_quarterly=True) factor_formula = 'tot_cur_assets/tot_cur_liab > 2' dv.add_formula('current_condition', factor_formula, is_quarterly=True) factor_formula = '(tot_cur_assets - inventories - pre_pay - deferred_exp)/tot_cur_liab > 1' dv.add_formula('quick_condition', factor_formula, is_quarterly=True) dv.add_formula('mv_rank', 'Rank(float_mv)', is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20150101, 'end_date': 20170930, 'universe': '000905.SH', 'fields': ('turnover,float_mv,close_adj,pe,pb'), 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'Cutoff(Standardize(turnover / 10000 / float_mv), 2)' dv.add_formula('TO', factor_formula, is_quarterly=False) factor_formula = 'Cutoff(Standardize(1/pb), 2)' dv.add_formula('BP', factor_formula, is_quarterly=False) factor_formula = 'Cutoff(Standardize(Return(close_adj, 20)), 2)' dv.add_formula('REVS20', factor_formula, is_quarterly=False) factor_formula = 'Cutoff(Standardize(Log(float_mv)), 2)' dv.add_formula('float_mv_factor', factor_formula, is_quarterly=False) factor_formula = 'Delay(Return(close_adj, 1), -1)' dv.add_formula('NextRet', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def save_data(): """ This function fetches data from remote server and stores them locally. Then we can use local data to do back-test. """ dataview_props = { 'start_date': 20170101, # Start and end date of back-test 'end_date': 20171030, 'universe': UNIVERSE, # Investment universe and performance benchmark 'benchmark': '000300.SH', 'fields': 'total_mv,turnover', # Data fields that we need 'freq': 1 # freq = 1 means we use daily data. Please do not change this. } # RemoteDataService communicates with a remote server to fetch data ds = RemoteDataService() # Use username and password in data_config to login ds.init_from_config(data_config) # DataView utilizes RemoteDataService to get various data and store them dv = DataView() dv.init_from_config(dataview_props, ds) dv.prepare_data() dv.save_dataview(folder_path=dataview_store_folder)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20150101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'volume,turnover,float_mv,pb,total_mv', 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() # for convenience to check limit reachers dv.add_formula('limit_reached', 'Abs((open - Delay(close, 1)) / Delay(close, 1)) > 0.095', is_quarterly=False) dv.add_formula('random', 'StdDev(volume, 20)', is_quarterly=False) dv.add_formula('momentum', 'Return(close_adj, 20)', is_quarterly=False) # dv.add_formula('size', '', is_quarterly=False) dv.save_dataview(dataview_folder)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20170101, 'end_date': 20171030, 'universe': '000300.SH', 'fields': ( 'open,high,low,close,vwap,volume,turnover,sw1,' # + 'pb,net_assets,' + 'eps_basic,total_mv,tot_profit,int_income'), 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'close >= Delay(Ts_Max(close, 20), 1)' # 20 days new high factor_name = 'new_high' dv.add_formula(factor_name, factor_formula, is_quarterly=False) dv.add_formula('total_profit_growth', formula='Return(tot_profit, 4)', is_quarterly=True) dv.save_dataview(folder_path=dataview_dir_path)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20160101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'volume,turnover', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() # for convenience to check limit reachers dv.add_formula('limit_reached', 'Abs((open - Delay(close, 1)) / Delay(close, 1)) > 0.095', is_quarterly=False) dv.add_formula('mask_limit_reached', 'limit_reached > 0', is_quarterly=False) dv.add_formula('mask_index_member', '!(index_member > 0)', is_quarterly=False) trade_status = dv.get_ts('trade_status') mask_sus = trade_status == u'停牌' dv.append_df(mask_sus, 'mask_sus', is_quarterly=False) # dv.add_formula('size', '', is_quarterly=False) dv.save_dataview(dataview_folder)
def add(formula, name): dv = DataView() dv.load_dataview(folder_path=dataview_dir_path) dv.add_formula(name, formula, is_quarterly=False, formula_func_name_style='lower') dv.save_dataview(folder_path=dataview_dir_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() print(DataView) props = {'start_date': 20170501, 'end_date': 20171001, 'universe': '000016.SH', 'fields': 'volume,pb,pe,ps,float_mv,sw1', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() dv.save_dataview(dataview_folder)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20160101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'volume,turnover', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() dv.save_dataview(dataview_folder)
def store_ic_weight(): """ Calculate IC weight and save it to file """ dv = DataView() dv.load_dataview(folder_path=dataview_dir_path) factorList = ['TO', 'BP', 'REVS20', 'float_mv_factor'] orthFactor_dic = {} for factor in factorList: orthFactor_dic[factor] = {} # add the orthogonalized factor to dataview for trade_date in dv.dates: snapshot = dv.get_snapshot(trade_date) factorPanel = snapshot[factorList] factorPanel = factorPanel.dropna() if len(factorPanel) != 0: orthfactorPanel = Schmidt(factorPanel) orthfactorPanel.columns = [x + '_adj' for x in factorList] snapshot = pd.merge(left=snapshot, right=orthfactorPanel, left_index=True, right_index=True, how='left') for factor in factorList: orthFactor_dic[factor][trade_date] = snapshot[factor] for factor in factorList: dv.append_df(pd.DataFrame(orthFactor_dic[factor]).T, field_name=factor + '_adj', is_quarterly=False) dv.save_dataview(dataview_dir_path) factorList_adj = [x + '_adj' for x in factorList] jutil.save_json(factorList_adj, custom_data_path) w = get_ic_weight(dv) store = pd.HDFStore(ic_weight_hd5_path) store['ic_weight'] = w store.close()
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20170201, 'end_date': 20171001, 'universe': '000300.SH', 'fields': ('float_mv,sw2,sw1'), 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'GroupQuantile(float_mv, sw1, 10)' dv.add_formula('gq30', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20170901, 'end_date': 20171129, 'universe': BENCHMARK, 'fields': 'close,volume,sw1', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() dv.add_formula('ret', 'Return(close_adj, 20)', is_quarterly=False) dv.add_formula('rank_ret', 'Rank(ret)', is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def download_data(): dataview_props = { 'start_date': 20080101, 'end_date': 20180731, 'universe': '000016.SH', 'fields': 'open,close,high,low', 'freq': 1 } ds = RemoteDataService() ds.init_from_config(data_config) # DataView utilizes RemoteDataService to get various data and store them dv = DataView() dv.init_from_config(dataview_props, ds) dv.prepare_data() dv.save_dataview(folder_path=dataview_store_folder)
def save_data(self): """ This function fetches data from remote server and stores them locally. Then we can use local data to do back-test. """ # RemoteDataService communicates with a remote server to fetch data ds = RemoteDataService() # Use username and password in data_config to login ds.init_from_config(self.data_config) # DataView utilizes RemoteDataService to get various data and store them dv = DataView() dv.init_from_config(self.dataview_props, ds) dv.prepare_data() dv.save_dataview(folder_path=self.dataview_store_folder)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': start_date, 'end_date': end_date, 'universe': universe, 'fields': 'roe_yearly,roa_yearly', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() dv.add_formula('roe_cond', 'roe_yearly >= 20', is_quarterly=True) dv.add_formula('roa_cond', 'roa_yearly >= 5', is_quarterly=True) dv.add_formula('cond', 'roe_cond && roa_cond', is_quarterly=True) dv.save_dataview(folder_path=dataview_folder)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20170101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'pe_ttm,net_profit_incl_min_int_inc', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'Return(net_profit_incl_min_int_inc, 4)' factor_name = 'net_profit_growth' dv.add_formula(factor_name, factor_formula, is_quarterly=True) dv.save_dataview(folder_path=dataview_dir_path)
def test_q(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() secs = '600030.SH,000063.SZ,000001.SZ' props = {'start_date': 20160609, 'end_date': 20170601, 'symbol': secs, 'fields': ('open,close,' + 'pb,net_assets,' + 'total_oper_rev,oper_exp,' + 'cash_paid_invest,' + 'capital_stk,' + 'roe'), 'freq': 1} dv.init_from_config(props, data_api=ds) dv.prepare_data() dv.save_dataview(folder_path=quarterly_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': start_date, 'end_date': end_date, 'fields': 'sw1', 'symbol': my_symbols, 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() # set the benchmark res, _ = ds.daily(benchmark, start_date=dv.start_date, end_date=dv.end_date) dv._data_benchmark = res.set_index('trade_date').loc[:, ['close']] dv.save_dataview(folder_path=dataview_dir_path)
def store_ic_weight(): """ Calculate IC weight and save it to file """ dv = DataView() dv.load_dataview(folder_path=dataview_dir_path) factorList = ['TO', 'BP', 'REVS20', 'float_mv_factor'] orthFactor_dic = {} for factor in factorList: orthFactor_dic[factor] = {} # add the orthogonalized factor to dataview for trade_date in dv.dates: snapshot = dv.get_snapshot(trade_date) factorPanel = snapshot[factorList] factorPanel = factorPanel.dropna() if len(factorPanel) != 0: orthfactorPanel = Schmidt(factorPanel) orthfactorPanel.columns = [x + '_adj' for x in factorList] snapshot = pd.merge(left=snapshot, right=orthfactorPanel, left_index=True, right_index=True, how='left') for factor in factorList: orthFactor_dic[factor][trade_date] = snapshot[factor] for factor in factorList: dv.append_df(pd.DataFrame(orthFactor_dic[factor]).T, field_name=factor + '_adj', is_quarterly=False) dv.save_dataview(dataview_dir_path) factorList_adj = [x + '_adj' for x in factorList] jutil.save_json(factorList_adj, custom_data_path) w = get_ic_weight(dv) store = pd.HDFStore(ic_weight_hd5_path) store['ic_weight'] = w store.close()
def test_save_dataview(sub_folder='test_dataview'): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20150101, 'end_date': 20170930, 'universe': '000905.SH', 'fields': ('float_mv,tot_shrhldr_eqy_excl_min_int,deferred_tax_assets,sw2'), 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'Quantile(-float_mv,5)' dv.add_formula('rank_mv', factor_formula, is_quarterly=False) factor_formula = 'Quantile(float_mv/(tot_shrhldr_eqy_excl_min_int+deferred_tax_assets), 5)' dv.add_formula('rank_pb', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20170901, 'end_date': 20171129, 'universe': BENCHMARK, 'fields': 'close,volume,sw1', 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() dv.add_formula('ret', 'Return(close_adj, 20)', is_quarterly=False) dv.add_formula('rank_ret', 'Rank(ret)', is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20150101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': 'volume,turnover,float_mv,pb,total_mv', 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() trade_status = dv.get_ts('trade_status') mask_sus = trade_status == '停牌' dv.append_df(mask_sus, 'suspended', is_quarterly=False) dv.add_formula('not_index_member', '!index_member', is_quarterly=False) dv.add_formula('limit_reached', 'Abs((open - Delay(close, 1)) / Delay(close, 1)) > 0.095', is_quarterly=False) dv.save_dataview(dataview_folder)
def download_data(): dataview_props = { 'start_date': 20120101, 'end_date': 20181231, 'universe': '000905.SH', # 'symbol':'600030.SH,600104.SH', 'fields': 'open,close,high,low,close_adj,volume', 'freq': 1 } ds = RemoteDataService() ds.init_from_config(data_config) # DataView utilizes RemoteDataService to get various data and store them dv = DataView() dv.init_from_config(dataview_props, ds) dv.prepare_data() factor_formula = 'Delay(Return(close_adj, 2, 0), -2)' dv.add_formula('future_return_2', factor_formula, is_quarterly=False, is_factor=False) factor_formula = 'Delay(Return(close_adj, 3, 0), -3)' dv.add_formula('future_return_3', factor_formula, is_quarterly=False, is_factor=False) factor_formula = 'Delay(Return(close_adj, 4, 0), -4)' dv.add_formula('future_return_4', factor_formula, is_quarterly=False, is_factor=False) factor_formula = 'Delay(Return(close_adj, 5, 0), -5)' dv.add_formula('future_return_5', factor_formula, is_quarterly=False, is_factor=False) dv.save_dataview(folder_path=dataview_store_folder)
def test_write(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() secs = '600030.SH,000063.SZ,000001.SZ' props = {'start_date': 20160601, 'end_date': 20170601, 'symbol': secs, 'fields': 'open,close,high,low,volume,pb,net_assets,pcf_ncf', 'freq': 1} dv.init_from_config(props, data_api=ds) dv.prepare_data() assert dv.data_d.shape == (281, 48) assert dv.dates.shape == (281, ) # TODO """ PerformanceWarning: your performance may suffer as PyTables will pickle object types that it cannot map directly to c-types [inferred_type->mixed,key->block1_values] [items->[('000001.SZ', 'int_income'), ('000001.SZ', 'less_handling_chrg_comm_exp'), ('000001.SZ', 'net_int_income'), ('000001.SZ', 'oper_exp'), ('000001.SZ', 'symbol'), ('000063.SZ', 'int_income'), ('000063.SZ', 'less_handling_chrg_comm_exp'), ('000063.SZ', 'net_int_income'), ('000063.SZ', 'oper_exp'), ('000063.SZ', 'symbol'), ('600030.SH', 'int_income'), ('600030.SH', 'less_handling_chrg_comm_exp'), ('600030.SH', 'net_int_income'), ('600030.SH', 'oper_exp'), ('600030.SH', 'symbol')]] """ dv.save_dataview(folder_path=daily_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = {'start_date': 20170101, 'end_date': 20171001, 'universe': '000300.SH', 'fields': ('float_mv,pb,pe_ttm,sw2'), 'freq': 1} dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'GroupQuantile(-float_mv, sw2, 10)' dv.add_formula('rank_mv', factor_formula, is_quarterly=False) factor_formula = 'GroupQuantile(If(pb >= 0.2, pb, 100), sw2, 10)' dv.add_formula('rank_pb', factor_formula, is_quarterly=False) factor_formula = 'GroupQuantile(If(pe_ttm >= 3, pe_ttm, 9999.0), sw2, 10)' dv.add_formula('rank_pe', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def test_q(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() secs = '600030.SH,000063.SZ,000001.SZ' props = { 'start_date': 20160609, 'end_date': 20170601, 'symbol': secs, 'fields': ('open,close,' + 'pb,net_assets,' + 'total_oper_rev,oper_exp,' + 'cash_paid_invest,' + 'capital_stk,' + 'roe'), 'freq': 1 } dv.init_from_config(props, data_api=ds) dv.prepare_data() dv.save_dataview(folder_path=quarterly_path)
def test_save_dataview(sub_folder='test_dataview'): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() props = { 'start_date': 20150101, 'end_date': 20170930, 'universe': '000905.SH', 'fields': ('float_mv,tot_shrhldr_eqy_excl_min_int,deferred_tax_assets,sw2'), 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() factor_formula = 'Quantile(-float_mv,5)' dv.add_formula('rank_mv', factor_formula, is_quarterly=False) factor_formula = 'Quantile(float_mv/(tot_shrhldr_eqy_excl_min_int+deferred_tax_assets), 5)' dv.add_formula('rank_pb', factor_formula, is_quarterly=False) dv.save_dataview(folder_path=dataview_dir_path)
def test_save_dataview(): ds = RemoteDataService() ds.init_from_config(data_config) dv = DataView() #dataview参数选择 props = { 'start_date': 20080527, 'end_date': 20180807, 'universe': '000002.SH,399107.SZ', "benchmark": "000905.SH,000905.SH", 'fields': ('open,close,volume,vwap,high,low,turnover'), 'freq': 1 } dv.init_from_config(props, ds) dv.prepare_data() #因子 factor_formula = 'rank(volume)*(ts_sum(close, 5)/5)*(vwap-close)/(high-low)' dv.add_formula('alpha', factor_formula, is_quarterly=False, formula_func_name_style='lower') dv.save_dataview(folder_path=dataview_dir_path)
print(dv.get_ts('roe_pb').head()) #这里用get_ts的方法输入新的名称即可 #5_从数据服务添加新数据至本地 #先设置Config data_config = { "remote.data.address": "tcp://data.tushare.org:8910", #地址统一,暂不做修改 "remote.data.username": "******", #quantos账号(手机号码) #quantos账号的API令牌号码 "remote.data.password": "******" } ds = RemoteDataService() #DataService启动 ds.init_from_config(data_config) #data_config启动 dv.add_field('eps_basic', ds) #添加新数据(eps_basic)至本地(ds) print(dv.get_ts('eps_basic').head()) A = dv.get_ts('eps_basic').head() #get_ts为数据获取 dv.remove_field('eps_basic') #删除数据(eps_basic) dv.add_field('volume', ds) #添加新数据至本地(ds) A = dv.get_ts('volume').head() dv.save_dataview('G:/data/hs300') #保存 dv.save_dataview('G:/data/hs300_1') #这相当于另存为 print(dv.fields) #查看dv中取得的数据 dv.add_field('roe', ds) #添加新数据至本地(ds)
"remote.trade.username": "******", "remote.trade.password": "******" } dataview_dir_path = '***' #dataview存储路径 backtest_result_dir_path = '***' #回测结果存储路径 api = DataApi(addr='tcp://data.quantos.org:8910') api.login("phone", "token") dv = DataView() dv.load_dataview(folder_path=dataview_dir_path) dv.add_field('turnover_ratio', ds) dv.save_dataview(folder_path=dataview_dir_path) dv.update_snapshot() #在已经保存dataview之后,需要添加某项指标(dataview.py里面可以找这些指标,不是因子!),如turnover_ratio,使用这段代码,添加完之后下一次运行就可以注释掉 def my_selector(context, user_options=None): #筛选僵尸股和ST股 df = context.dataview.data_inst['name'] result = pd.DataFrame(df, columns=['ST'], index=df.index, dtype='bool') selector_volume = context.snapshot[ 'mean_turnover'] > 50000000 #这里要先add('ts_mean(turnover, 5)', 'mean_turnover') names = list(df) for i in range(len(df)): if 'ST' in names[i]: result['ST'][i] = False
dv.append_df(alpha195_.run_formula(dv),'alpha195_') dv.append_df(alpha42_.run_formula(dv),'alpha42_') dv.append_df(alpha62_.run_formula(dv),'alpha62_') dv.append_df(alpha64_.run_formula(dv),'alpha64_') dv.append_df(alpha197_.run_formula(dv),'alpha197_') dv.append_df(alpha211_.run_formula(dv),'alpha211_') dv.append_df(alpha56_.run_formula(dv),'alpha56_') factor_lis = ['alpha32_','alpha42_','alpha56_','alpha62_','alpha64_','alpha194','alpha195','alpha197','alpha211','pb','pe','roa','roe','pm','ETOP'] for each in factor_lis: assert(each in dv.fields) factors = {name:dv.get_ts(name) for name in factor_lis} dv.save_dataview('muti_factor/') # In[87]: import pandas as pd id_zz500 = dp.daily_index_cons(api, "000300.SH", start, end) id_hs300 = dp.daily_index_cons(api, "000905.SH", start, end) columns_500 = list(set(id_zz500.columns)-set(id_hs300.columns)) def limit_up_down(): trade_status = dv.get_ts('trade_status').fillna(0) mask_sus = trade_status == 0 # 涨停