def update_divi_data( self ):
def download_divi_data( self, year, top ):
try:
df_tmp = ts.profit_data( year, top )
except:
ERROR( 'exception occurs when update divi data of year {0}'.format( year ) )
return pd.DataFrame()
else:
LOG( 'update divi data of year {0}'.format( year ) )
thread_queue.put( df_tmp )
if not os.path.exists( self.__divi_file_name ):
cur_year = time.strftime( '%Y',time.localtime( time.time() ) )
df_tmp_stock_basics = Utils.read_data( self.__basics_file_name )
num_stock = df_tmp_stock_basics.index.size
df_divi_data = pd.DataFrame()
thread_queue = Queue()
list_threads = []
for year in range( 2002, int( Utils.cur_date().split('_')[ 0 ] ) + 1 ):
list_threads.append( threading.Thread( target = download_divi_data, \
args=( self, year, num_stock ) ) )
for thread in list_threads:
thread.start()
for thread in list_threads:
thread.join()
while not thread_queue.empty():
df_divi_data = df_divi_data.append( thread_queue.get() )
Utils.save_data( df_divi_data, self.__divi_file_name, 'divi data' )
# 18点之前，采用前一个交易日的分析结果
analyse_class = model.spill_wave.Analyse()
list_process = []
list_process.append(
Process(target=Notify().notify_investment_opportunity))
# list_process.append( Process( target = Notify().notify_realtime_earnings ) )
list_process.append(Process(target=Notify().serve_query_request))
for process in list_process:
process.start()
for process in list_process:
process.join()
else:
# 当日18点之后方可更新数据
file_date = Utils.cur_date()
Data(file_date).update_all()
analyse_class = model.spill_wave.Analyse(file_date)
# analyse_class.statistics()
list_process = []
list_process.append(
Process(target=analyse_class.find_spill_wave_stock))
list_process.append(Process(target=Profit().calc_profit_grow))
list_process.append(Process(target=Pe().calc_pe))
for process in list_process:
process.start()
for process in list_process:
process.join()
df_growth_data = self.get_growth_data() df_growth_data = df_growth_data.set_index( 'code' ) df_debtpaying_data = self.get_debtpaying_data() df_debtpaying_data = df_debtpaying_data.set_index( 'code' ) df_cashflow_data = self.get_cashflow_data() df_cashflow_data = df_cashflow_data.set_index( 'code' ) df_divi_data = self.get_divi_data() df_divi_data = df_divi_data.set_index( 'code' ) df_forcast_quarter_report_data = self.get_forcast_quarter_report_data() df_forcast_quarter_report_data = df_forcast_quarter_report_data.set_index( 'code' ) df_restrict_stock_data = self.get_restrict_stock_data() df_restrict_stock_data = df_restrict_stock_data.set_index( 'code' ) df_concept_classified = self.get_concept_classified_data() df_concept_classified = df_concept_classified.set_index( 'code' ) LOG( 'finish loading.' ) return [ df_stock_basics, df_quarter_report_data, df_profit_data, df_operation_data, df_growth_data, df_debtpaying_data, \ df_cashflow_data, df_divi_data, df_forcast_quarter_report_data, df_restrict_stock_data, df_concept_classified ] '''--------------- run ---------------''' if __name__ == '__main__': Data( Utils.cur_date() ).update_all()
def do_realtime_trade( self ):
pd.options.mode.chained_assignment = None # 不显示warn信息 default='warn'
df_model_basics = Basics().get_basics().set_index( 'code' )
if os.path.exists( self.__file_path_trade ):
self.df_trade = Utils.read_data( self.__file_path_trade )
if self.df_trade.index.size >= 1:
self.remain_money = self.df_trade.loc[ self.df_trade.index.size - 1 ][ 'remain' ]
if os.path.exists( self.__file_path_position ):
self.df_position = Utils.read_data( self.__file_path_position )
while True:
cur_time = Utils.cur_time()
hour = int( cur_time.split( ':' )[ 0 ] )
minute = int( cur_time.split( ':' )[ 1 ] )
if hour < 9 or ( hour == 9 and minute < 30 ):
LOG( 'Trade_Simulator: morning\n{0} hours {1} minutes later market open'\
.format( int( Utils.now2market_morning_time() / 3600 ), int( Utils.now2market_morning_time() % 3600 / 60 ) ) )
sleep( Utils.now2market_morning_time() )
elif ( hour == 11 and minute >= 30 ) or hour == 12:
LOG( 'Trade_Simulator: nooning\n{0} hours {1} minutes later market open'
.format( int( Utils.now2market_nooning_time() / 3600 ), int( Utils.now2market_nooning_time() % 3600 / 60 ) ) )
sleep( Utils.now2market_nooning_time() )
elif hour >= 15:
LOG( 'Trade_Simulator: market close' )
break
# sell
for index in self.df_position.index:
code = '%06d' % int( self.df_position.loc[ index ][ 'code' ] )
df_realtime_quotes = Data().get_realtime_quotes( code )
# T + 1 交易
if self.df_position.loc[ index ][ 'buy_date' ] < Utils.cur_date():
name = self.df_position.loc[ index ][ 'name' ]
if float( df_realtime_quotes[ 'price' ] ) >= self.df_position.loc[ index ][ 'sell_price' ]:
self.remain_money += float( df_realtime_quotes[ 'price' ] ) * float( self.df_position.loc[ index ][ 'position' ] )
total_earn = '{0:.2f}'.format( ( float( df_realtime_quotes[ 'price' ] ) - float( self.df_position.loc[ index ][ 'cost_price' ] ) ) \
* float( self.df_position.loc[ index ][ 'position' ] ) + float( self.df_trade.loc[ self.df_trade.index.size - 1 ][ 'total_earn' ] ) )
self.df_trade.loc[ self.df_trade.index.size ] = [ ' '.join( [ Utils.cur_date(), Utils.cur_time() ] ), code, name, 'sell', \
self.df_position.loc[ index ][ 'position' ], total_earn, self.remain_money ]
self.df_position = self.df_position.drop( index )
continue
self.df_position[ 'earn' ][ index ] = '{0:.2f}'.format( ( float( df_realtime_quotes[ 'price' ] ) - float( self.df_position.loc[ index ][ 'cost_price' ] ) ) \
* float( self.df_position.loc[ index ][ 'position' ] ) )
sleep( 1 )
# buy
for index in self.df_model_stock.index:
code = '%06d' % int( self.df_model_stock.loc[ index ][ 'code' ] )
name = self.df_model_stock.loc[ index ][ 'name' ]
# 每只在仓股不再买入
if int( code ) in self.df_position[ 'code' ].to_dict().values() or \
code in self.df_position[ 'code' ].to_dict().values():
continue
try:
df_realtime_quotes = Data().get_realtime_quotes( code )
if float( df_realtime_quotes[ 'price' ] ) >= ( float( self.df_model_stock.loc[ index ][ 'buy_price' ] ) * 0.99 ) and \
float( df_realtime_quotes[ 'price' ] ) <= ( float( self.df_model_stock.loc[ index ][ 'buy_price' ] ) * 1.002 ) and \
float( self.df_model_stock.loc[ index ][ 'expect_earn_rate' ] ) >= 0.2:
try:
if float( df_model_basics[ 'rank_profit_grow' ][ int( code ) ].split( '/' )[ 0 ] ) / \
float( df_model_basics[ 'rank_profit_grow' ][ int( code ) ].split( '/' )[ 1 ] ) >= 0.5:
continue
# LOG( '{0} {1}'.format( code, df_model_basics[ 'rank_profit_grow' ][ int( code ) ].split( '/' ) ) )
except:pass
buy_hand_num = 0
# 至少剩余买一手的资金
if 100 * float( df_realtime_quotes[ 'price' ] ) <= self.remain_money:
buy_hand_num = int( min( self.initial_fund * float( self.df_model_stock.loc[ index ][ 'expect_earn_rate' ] ) * 0.5 / float( df_realtime_quotes[ 'price' ] ) / 100, \
self.max_buy_each_stock / float( df_realtime_quotes[ 'price' ] ) / 100, self.remain_money / float( df_realtime_quotes[ 'price' ] ) / 100 ) )
if self.df_trade.index.size >= 1:
total_earn = self.df_trade.loc[ self.df_trade.index.size - 1 ][ 'total_earn' ]
else:
total_earn = 0
self.remain_money -= buy_hand_num * 100 * float( df_realtime_quotes[ 'price' ] )
self.df_trade.loc[ self.df_trade.index.size ] = [ ' '.join( [ Utils.cur_date(), Utils.cur_time() ] ), code, name, 'buy', \
buy_hand_num * 100, total_earn, self.remain_money ]
self.df_position.loc[ self.df_position.index.size ] = [ code, name, Utils.cur_date(), float( df_realtime_quotes[ 'price' ] ), \
float( df_realtime_quotes[ 'price' ] ) * ( 0.2 * float( self.df_model_stock.loc[ index ][ 'expect_earn_rate' ] ) + 1.0 ), \
buy_hand_num * 100, 0 ]
sleep( 1 )
except:
pass
self.save_data( self.df_trade, self.__file_path_trade )
self.save_data( self.df_position, self.__file_path_position )
