def getParallelResult(strategyParameter, strategy_class, parasetlist, paranum,
indexcols):
strategyName = strategyParameter['strategy_name']
exchange_id = strategyParameter['exchange_id']
sec_id = strategyParameter['sec_id']
K_MIN = strategyParameter['K_MIN']
startdate = strategyParameter['startdate']
enddate = strategyParameter['enddate']
domain_symbol = '.'.join([exchange_id, sec_id])
result_para_dic = strategyParameter['result_para_dic']
# ======================数据准备==============================================
# 取合约信息
symbolInfo = DI.SymbolInfo(domain_symbol, startdate, enddate)
# 取跨合约数据
# contractswaplist = DC.getContractSwaplist(domain_symbol)
# swaplist = np.array(contractswaplist.swaputc)
# 取K线数据
# rawdata = DC.getBarData(symbol, K_MIN, startdate + ' 00:00:00', enddate + ' 23:59:59').reset_index(drop=True)
rawdataDic = DI.getBarBySymbolList(domain_symbol,
symbolInfo.getSymbolList(), K_MIN,
startdate, enddate)
timestart = time.time()
# 多进程优化，启动一个对应CPU核心数量的进程池
pool = multiprocessing.Pool(multiprocessing.cpu_count() - 1)
l = []
resultlist = pd.DataFrame(columns=['Setname'] + indexcols)
strategy_para_name_list = strategy_class.get_para_name_list()
for i in range(0, paranum):
paraset = {}
setname = parasetlist.ix[i, 'Setname']
paraset['Setname'] = setname
for strategy_para_name in strategy_para_name_list:
paraset[strategy_para_name] = parasetlist.ix[i, strategy_para_name]
#l.append(getResult(strategyName, strategy_class, symbolInfo, K_MIN, rawdataDic, paraset, result_para_dic, indexcols,timestart))
l.append(
pool.apply_async(
getResult,
(strategyName, strategy_class, symbolInfo, K_MIN, rawdataDic,
paraset, result_para_dic, indexcols, timestart)))
pool.close()
pool.join()
timeend = time.time()
print("total time %.2f" % (timeend - timestart))
# 显示结果
i = 0
for res in l:
resultlist.loc[i] = res.get()
i += 1
finalresults = ("%s %s %d finalresult.csv" %
(strategyName, domain_symbol, K_MIN))
resultlist.to_csv(finalresults)
return resultlist
symbol_bar_folder_name = strategy_folder + "%s %s %s %d" % (
strategy_name, exchange_id, sec_id, bar_type)
os.chdir(symbol_bar_folder_name)
paraset_name = "%s %s %s %d Parameter.csv" % (
strategy_name, exchange_id, sec_id, bar_type)
# 读取已有参数表
parasetlist = pd.read_csv(paraset_name)['Setname'].tolist()
cols = [
'open', 'high', 'low', 'close', 'strtime', 'utc_time',
'utc_endtime'
]
#bar1m_dic = DI.getBarBySymbolList(domain_symbol, symbol_info.getSymbolList(), 60, startdate, enddate, cols)
bar1m_dic = DI.getBarDicAfterDomain(symbol_info, 60, cols)
barxm_dic = DI.getBarBySymbolList(domain_symbol,
symbol_info.getSymbolList(),
bar_type, startdate, enddate,
cols)
if 'multi_sl' in stop_loss_dic.keys():
# 混合止损模式
sltlist = []
price_tick = symbol_info.getPriceTick()
for sl_name, stop_loss in stop_loss_dic.items():
if sl_name != 'multi_sl':
stop_loss[
'price_tick'] = price_tick # 传入的止损参数中加入price_tick，部分止损方式定价时要用到
stop_loss_class = StopLoss.strategy_mapping_dic[
sl_name](stop_loss)
sltlist.append({
'name':
sl_name,
