def getIntersection(currSourceData, lastSourceData):
currList = []
lastList = []
for index, row in currSourceData.iterrows():
currList.append(index[1])
for index, row in lastSourceData.iterrows():
lastList.append(index[1])
returnList = SetUtil.intersection(currList, lastList)
return returnList
def getDifference(currSourceData, currHoldSet, typ):
currList = []
lastList = []
if len(currSourceData) != 0:
for index, row in currSourceData.iterrows():
currList.append(index[1])
if len(currHoldSet) != 0:
for innerCode in currHoldSet:
lastList.append(innerCode)
#currList - lastList
if typ == 1:
returnList = SetUtil.difference(currList, lastList)
#lastList - currList
else:
returnList = SetUtil.difference(lastList, currList)
return returnList
def getIntersection(currSourceData, currHoldSet):
currList = []
lastList = []
for index, row in currSourceData.iterrows():
currList.append(index[1])
for innerCode in currHoldSet:
lastList.append(innerCode)
returnList = SetUtil.intersection(currList, lastList)
return returnList
def getDifference(currSourceData, currHoldSet, typ):
currList = []
lastList = []
if len(currSourceData) != 0:
#for index, row in currSourceData.iterrows():
#currList.append(index[1])
currList = list(currSourceData.index.get_level_values(1))
if len(currHoldSet) != 0:
#for innerCode in currHoldSet:
#lastList.append(innerCode)
lastList = list(currHoldSet.keys())
lastList.sort()
#currList - lastList
if typ == 1:
returnList = SetUtil.difference(currList, lastList)
#lastList - currList
else:
returnList = SetUtil.difference(lastList, currList)
return returnList
def getDifference(currSourceData, lastSourceData):
currList = []
lastList = []
if len(currSourceData) != 0:
for index, row in currSourceData.iterrows():
currList.append(index[1])
if len(lastSourceData) != 0:
for index, row in lastSourceData.iterrows():
lastList.append(index[1])
returnList = SetUtil.difference(currList, lastList)
return returnList
def getIntersection(currSourceData, currHoldSet):
currList = []
lastList = []
if len(currSourceData) != 0:
#for index, row in currSourceData.iterrows():
#currList.append(index[1])
currList = list(currSourceData.index.get_level_values(1))
if len(currHoldSet) != 0:
#for innerCode in currHoldSet:
#lastList.append(innerCode)
lastList = list(currHoldSet.keys())
lastList.sort()
returnList = SetUtil.intersection(currList, lastList)
return returnList
def main(select_stock_1day_func,trade_1day_func,techList,signalDataAddr = '', dailyQuoteAddr = '', startDate = '2004-01-01', endDate = '2005-12-31'):
start = time.clock()
signalData = SourceDataDao.load_signal_data(signalDataAddr) #ByDate
#dailyQuote = SourceDataDao.loadDailyQuote()
dailyQuote = SourceDataDao.load_new_daily_quote(dailyQuoteAddr) #ByDate
start_1 = time.clock()
signalData = signalData[(signalData.index.get_level_values(0) >= startDate) & (signalData.index.get_level_values(0) <= endDate) ]
end_1 = time.clock()
print("signalData花费时间：%f s" % (end_1 - start_1))
# print(startDate)
# print(endDate)
dateList = SourceDataDao.select_date_from_signal(signalData, startDate, endDate)
# print(dateList)
# 统计结果
stockStatDailyDf = pd.DataFrame(index=pd.date_range(startDate, endDate),
columns=['netValue', 'changePCT', 'buyCnt', 'sellCnt', 'prevHoldCnt',
'currHoldCnt', 'cannotSellCnt', 'cannotBuyCnt',
'usableCach', 'mv'])
# 每日持仓
stockHoldDailyList = []
# 交易结果
stockTradeList = []
start_trade = time.clock()
# 交易
t1d = process1day(startDate, endDate, dailyQuote, signalData, dateList, stockStatDailyDf, stockHoldDailyList, stockTradeList)
res = signalData.groupby(level=StockConst.TRADINGDAY).apply(t1d.deal, techList, select_stock_1day_func, trade_1day_func)
end_trade = time.clock()
print("交易花费时间：%f s" % (end_trade - start_trade))
result = ''
start_stat = time.clock()
# 信号数据
#groupedSelectStock.to_csv(StockConst.root + '\export\groupedSelectStock.csv')
# 每日交易
# print('每日交易:')
# print(stockTradeList)
stockTradeDailyDf = pd.DataFrame(stockTradeList)
stockTradeDailyDf.sort_values(by=['tradingDate'], ascending=True)
# stockTradeDailyDf.to_csv(StockConst.root + '\export\stockTradeDaily.csv')
# 每日持仓
#print('每日持仓:')
stockHoldDailyDf = pd.DataFrame(stockHoldDailyList)
stockHoldDailyDf.sort_values(by=['tradingDate'],ascending=True)
# stockHoldDailyDf.to_csv(StockConst.root + '\export\stockHoldDaily.csv')
#每日统计(收益，净值，买入数，卖出数，持有数)
stockStatDailyDf = stockStatDailyDf.dropna(how='all')
result += '--' * 70 + '\n'
result += '每日统计:' + '\n'
result += stockStatDailyDf.to_string() + '\n'
# stockStatDailyDf.to_csv(StockConst.root + '\export\stockStatDaily.csv')
# 每年统计
result += '--' * 70 + '\n'
yearDf = StockYearService.main(stockStatDailyDf)
result += '每年统计:' + '\n'
result += yearDf.to_string() + '\n'
# 最大回撤
result += '--' * 70 + '\n'
maxdrop = StockMaxDropNewService.get_max_drop(stockStatDailyDf)
result += '最大回撤:' + '\n'
result += SetUtil.dict_to_string(maxdrop) + '\n'
#每年的最大回撤
maxdropDf = StockMaxDropNewService.get_max_drop_for_each_year(stockStatDailyDf)
maxdropDf.sort_values(by=["year"])
result += '每年的最大回撤:' + '\n'
result += maxdropDf.to_string() + '\n'
#夏普比率
result += '--' * 70 + '\n'
sharpRatio = SharpRatioNewService.get_sharp_ratio(stockStatDailyDf)
result += '夏普比率:' + '\n'
result += str(sharpRatio) + '\n'
#每年的夏普比率
sharpRatioDf = SharpRatioNewService.get_sharp_ratio_for_each_year(stockStatDailyDf)
sharpRatioDf.sort_values(by=["year"])
result += '每年的夏普比率:' + '\n'
result += sharpRatioDf.to_string() + '\n'
end_stat = time.clock()
print("统计花费时间：%f s" % (end_stat - start_stat))
# result += '回测完成'
end = time.clock()
print("main花费时间：%f s" % (end - start))
# 净值曲线图
stockStatDailyDf['netValue'].plot()
plt.show()
resultDic={'stockStatDailyDf':stockStatDailyDf,'result':result}
print(result)
return resultDic
def main(read_func,select_stock_func,trade_func,techList,sliceDict,signalDataAddr = '', dailyQuoteAddr = '', indexQuoteAddr='', benchmark='',
startDate = '2004-01-01', endDate = '2005-12-31', doPlot=True, table_name=None):
start = time.clock()
# signalData = SourceDataDao.loadSignalData(signalDataAddr)
# dailyQuote = SourceDataDao.loadNewDailyQuote(dailyQuoteAddr)
# indexQuote = SourceDataDao.loadH5(indexQuoteAddr) #StockConst.root + StockConst.fakeIndexQuoteH5
# signalData = SourceDataDao.read_file_set_index(signalDataAddr)
signalData = read_func(signalDataAddr, table_name)
dailyQuote = SourceDataDao.read_file_set_index(dailyQuoteAddr)
indexQuote = SourceDataDao.read_file_set_index(indexQuoteAddr)
if signalDataAddr is not None:
maxSignalDate = SourceDataDao.get_max_date(signalDataAddr)
minSignalDate = SourceDataDao.get_min_date(signalDataAddr)
else:
maxSignalDate = SignalDataMSSQLDao.select_max_data(table_name)
minSignalDate = SignalDataMSSQLDao.select_min_data(table_name)
# print('print')
# print(maxSignalDate)
# print(minSignalDate)
maxDailyQuoteDate = SourceDataDao.get_max_date(dailyQuoteAddr)
minDailyQuoteDate = SourceDataDao.get_min_date(dailyQuoteAddr)
maxIndexQuoteDate = SourceDataDao.get_max_date(indexQuoteAddr)
minIndexQuoteDate = SourceDataDao.get_min_date(indexQuoteAddr)
# print('minSignalDate:' + minSignalDate)
# print('maxSignalDate:' + maxSignalDate)
#
# print('minDailyQuoteDate:' + minDailyQuoteDate)
# print('maxDailyQuoteDate:' + maxDailyQuoteDate)
#
# print('minIndexQuoteDate:' + minIndexQuoteDate)
# print('maxIndexQuoteDate:' + maxIndexQuoteDate)
#
if minSignalDate < minDailyQuoteDate:
startDate = minDailyQuoteDate
else:
startDate = minSignalDate
if maxSignalDate > maxDailyQuoteDate:
endDate = maxDailyQuoteDate
else:
endDate = maxSignalDate
#
if startDate < minIndexQuoteDate:
startDate = minIndexQuoteDate
else:
pass
if endDate > maxIndexQuoteDate:
endDate = maxIndexQuoteDate
else:
pass
print('startDate:'+startDate)
print('endDate:' + endDate)
#TODO 暂时不需要
# dailyQuotePn = SourceDataDao.loadNewDailyQuoteToPn(dailyQuoteAddr)
dailyQuotePn = None
#参数
#初始资金
initialCash = 1000000 #1000000
#选股结果（外部传入）
#select top 5 group by TradingDay order by Mom desc
allSelectStock = select_stock_func(signalData,techList,sliceDict)
print('allSelectStock:'+str(len(allSelectStock)))
# print(allSelectStock)
if len(allSelectStock) == 0:
return
#当前持仓
currHoldSet = {}
#资金情况
capitalEntity = CapitalEntity.CapitalEntity(initialCash, 0, initialCash)
lastCapitalEntity = copy.copy(capitalEntity)
#初始化每日统计表
stockStatDailyDf = pd.DataFrame(index=pd.date_range(startDate, endDate),
columns=['netValue','changePCT','buyCnt','sellCnt','prevHoldCnt','currHoldCnt','cannotSellCnt','cannotBuyCnt',
'usableCash','totalAsset','indexChangePCT','relativeChangePCT','relativeNetValue'])
#初始化每日持仓表
stockHoldDailyList = []
stockTradeList = []
#从信号表中取得唯一性日期
dateList = SourceDataDao.select_date_from_signal(dailyQuote, startDate, endDate) #signalData dailyQuote
# print(dateList)
# lastTradingDay = None
# lastTradingDayStr = None
lastRelativeNetValue = 1 #昨日相对净值
start_loop = time.clock()
for index, item in enumerate(dateList):
#for date in dateList:
#print('index:'+str(index)+' len:'+str(len(dateList)))
# start_inloop0 = time.clock()
#信号日
signalDay = dateList[index]
# 非最后一天
if index < (len(dateList) - 1):
#交易日
tradingDay = dateList[index + 1]
tradingDayStr = DateUtil.datetime2_str(tradingDay)
# tradingDayTsp = pd.Timestamp(tradingDayStr)
# print(tradingDay)
# print(tradingDayStr)
# print(tradingDayTsp)
# print(type(tradingDay))
# print(type(tradingDayStr))
# print(type(tradingDayTsp))
#print('dateStr:'+dateStr)
# 最后一天
else:
break
# dailyQuote1day = dailyQuote[dailyQuote.index.get_level_values(0) == tradingDay]
#TODO
# start_slice = time.clock()
# print(str(len(dailyQuote)))
# print(type(tradingDay))
#交易日行情
dailyQuote1day = dailyQuote.xs(tradingDay)
# print('dailyQuote1day:'+str(len(dailyQuote1day)))
# end_slice = time.clock()
# print("切片花费时间：%f s" % (end_slice - start_slice))
# dailyQuote1day = None
#print(dateStr)
#信号日选股
currSelectStock = allSelectStock.xs(signalDay) #.xs(signalDay)
# print('currSelectStock:'+str(len(currSelectStock)))
# if StockConst.isDebug:
# print('tradingDayStr:'+tradingDayStr+' currSignalData:'+str(len(currSignalData)))
#print(currSignalData)
# end_inloop0 = time.clock()
# print("inloop0花费时间：%f s" % (end_inloop0 - start_inloop0))
#交易方法（外部传入）
# start_inloop1 = time.clock()
dict = trade_func(currSelectStock, currHoldSet, dailyQuote, tradingDay, signalData, capitalEntity, dailyQuotePn, dailyQuote1day, lastCapitalEntity)
# end_inloop1 = time.clock()
# print("trade_func花费时间：%f s" % (end_inloop1 - start_inloop1))
actualBuyList = dict['actualBuyList']
actualSellList = dict['actualSellList']
prevHoldList = dict['prevHoldList']
cannotBuyList = dict['cannotBuyList']
cannotSellList = dict['cannotSellList']
stockTradeListDaily = dict['stockTradeList']
if len(stockTradeListDaily) > 0 :
stockTradeList.extend(stockTradeListDaily)
# print(currHoldSet)
#3.计算当日市值
# start_inloop2 = time.clock()
calculate_daily_mv(currHoldSet, capitalEntity, dailyQuote, tradingDay, stockHoldDailyList, initialCash)
totalAsset = capitalEntity.get_total_asset()
# end_inloop2 = time.clock()
# print("calculateDailyMV花费时间：%f s" % (end_inloop2 - start_inloop2))
# start_inloop3 = time.clock()
#4.个数
buyCnt = len(actualBuyList)
sellCnt = len(actualSellList)
prevHoldCnt = len(prevHoldList)
currHoldCnt = len(currHoldSet)
cannotSellCnt = len(cannotSellList)
cannotBuyCnt = len(cannotBuyList)
# print(currMV)
#if StockConst.isDebug:
#print("dateStr:" + dateStr + " changePCTBuy:" + str(changePCTBuy) + " changePCTSell:" + str(changePCTSell) +
#" changePCTHold:" + str(changePCTHold))
#当日净值
netValue = totalAsset / initialCash
#print("netValue:" + str(netValue))
#当日收益(%)
dailyChangePCT = NumUtil.get_change_pct(lastCapitalEntity.get_total_asset(), totalAsset, 5)
#当日可用现金
usableCash = capitalEntity.get_usable_cash()
# 当日的指数
indexQuote1day = None
indexChangePCT = 0
if indexQuote is not None:
indexQuote1day = indexQuote.ix[(tradingDay, benchmark)]
indexChangePCT = indexQuote1day['ChangePCT']
#相对收益=每日收益-指数收益 (%)
relativeChangePCT = dailyChangePCT - indexChangePCT
#相对净值=昨日相对净值*（1+相对收益/100）
relativeNetValue = lastRelativeNetValue*(1+relativeChangePCT/100)
#统计表
stockStatDailyDf.ix[tradingDayStr] = netValue,dailyChangePCT,buyCnt,sellCnt,prevHoldCnt,currHoldCnt,cannotSellCnt,cannotBuyCnt,usableCash,totalAsset,indexChangePCT,relativeChangePCT,relativeNetValue
#昨日资产
lastCapitalEntity = copy.copy(capitalEntity)
#昨日相对净值
lastRelativeNetValue = relativeNetValue
# lastTradingDay = tradingDay
# lastTradingDayStr = tradingDayStr
# end_inloop3 = time.clock()
# print("inloop3花费时间：%f s" % (end_inloop3 - start_inloop3))
end_loop = time.clock()
print("for循环花费时间：%f s" % (end_loop - start_loop))
result = ''
start_stat = time.clock()
# 信号数据
#allSelectStock.to_csv(StockConst.root + '\export\allSelectStock.csv')
# # 每日交易
# # print('每日交易:')
# # print(stockTradeList)
# stockTradeDailyDf = pd.DataFrame(stockTradeList)
# stockTradeDailyDf.sort_values(by=['tradingDay'], ascending=True)
# # stockTradeDailyDf.to_csv(StockConst.root + '\export\stockTradeDaily.csv')
# # 每日持仓
# #print('每日持仓:')
# stockHoldDailyDf = pd.DataFrame(stockHoldDailyList)
# stockHoldDailyDf.sort_values(by=['tradingDay'],ascending=True)
# # stockHoldDailyDf.to_csv(StockConst.root + '\export\stockHoldDaily.csv')
start_stat_1 = time.clock()
#每日统计(收益，净值，买入数，卖出数，持有数)
stockStatDailyDf = stockStatDailyDf.dropna(how='all')
result += '--' * 70 + '\n'
result += '每日统计:' + '\n'
result += stockStatDailyDf.to_string() + '\n'
stockStatDailyDf.to_csv(StockConst.ROOT + '\export\stockStatDaily.csv')
end_stat_1 = time.clock()
print("统计1花费时间：%f s" % (end_stat_1 - start_stat_1))
start_stat_2 = time.clock()
# 每年统计
result += '--' * 70 + '\n'
yearDf = StockYearService.main(stockStatDailyDf)
result += '每年统计:' + '\n'
result += yearDf.to_string() + '\n'
end_stat_2 = time.clock()
print("统计2花费时间：%f s" % (end_stat_2 - start_stat_2))
start_stat_3 = time.clock()
# 最大回撤
result += '--' * 70 + '\n'
maxdrop = StockMaxDropNewService.get_max_drop(stockStatDailyDf)
result += '最大回撤:' + '\n'
result += SetUtil.dict_to_string(maxdrop) + '\n'
# #每年的最大回撤
# maxdropDf = StockMaxDropNewService.getMaxDropForEachYear(stockStatDailyDf)
# maxdropDf.sort_values(by=["year"])
# result += '每年的最大回撤:' + '\n'
# result += maxdropDf.to_string() + '\n'
end_stat_3 = time.clock()
print("统计3花费时间：%f s" % (end_stat_3 - start_stat_3))
start_stat_4 = time.clock()
#夏普比率
result += '--' * 70 + '\n'
sharpRatio = SharpRatioNewService.get_sharp_ratio(stockStatDailyDf)
result += '夏普比率:' + '\n'
result += str(sharpRatio) + '\n'
#每年的夏普比率
sharpRatioDf = SharpRatioNewService.get_sharp_ratio_for_each_year(stockStatDailyDf)
sharpRatioDf.sort_values(by=["year"])
result += '每年的夏普比率:' + '\n'
result += sharpRatioDf.to_string() + '\n'
end_stat_4 = time.clock()
print("统计4花费时间：%f s" % (end_stat_4 - start_stat_4))
end_stat = time.clock()
print("统计花费时间：%f s" % (end_stat - start_stat))
# result += '回测完成'
end = time.clock()
print("main花费时间：%f s" % (end - start))
if doPlot:
# # # 净值曲线图
# stockStatDailyDf['netValue'].plot()
# plt.show()
#
# # # 相对净值曲线图
# stockStatDailyDf['relativeNetValue'].plot()
# plt.show()
pass
#
resultDic={
'stockStatDailyDf': stockStatDailyDf,
'result': result,
'sharpRatio': sharpRatio
}
print(result)
return resultDic
def main(select_stock_func,
trade_func,
techList,
signalDataAddr='',
dailyQuoteAddr='',
startDate='1/1/2004',
endDate='12/31/2005'):
start = time.clock()
signalData = SourceDataDao.load_signal_data(signalDataAddr)
#dailyQuote = SourceDataDao.loadDailyQuote()
dailyQuote = SourceDataDao.load_new_daily_quote(dailyQuoteAddr)
#参数
#每天信号数
#numOfDailySignal = 5
#初始资金
initialMV = 1000000 #1000000
#开始结束日期
#startDate = '1/8/2001'
#startDate = '1/1/2004'
#endDate = '1/1/2017'
#eendDate = '5/18/2001'
#endDate = '12/31/2005'
#endDate = '12/31/2002'
#endDate = '1/9/2001'
#选股结果（外部传入）
#select top 5 group by TradingDay order by Mom desc
groupedSignalData = select_stock_func(signalData, techList)
#当前持仓 key:innerCode value:
currHoldSet = {}
#资金情况
capitalEntity = CapitalEntity.CapitalEntity(initialMV)
lastMV = initialMV
#初始化每日统计表
stockStatDailyDf = pd.DataFrame(index=pd.date_range(startDate, endDate),
columns=[
'netValue', 'changePCT', 'buyCnt',
'sellCnt', 'prevHoldCnt',
'currHoldCnt', 'cannotSellCnt',
'cannotBuyCnt', 'usableCach', 'mv'
])
#初始化每日持仓表
stockHoldDailyList = []
stockTradeList = []
#从信号表中取得唯一性日期
dateList = SourceDataDao.select_date_from_signal(signalData, startDate,
endDate)
# print(dateList)
start_loop = time.clock()
for index, item in enumerate(dateList):
#for date in dateList:
#print('index:'+str(index)+' len:'+str(len(dateList)))
start_inloop0 = time.clock()
#信号日
signalDay = dateList[index]
# 非最后一天
if index < (len(dateList) - 1):
#交易日
tradingDay = dateList[index + 1]
tradingDayStr = DateUtil.datetime2_str(tradingDay)
#print('dateStr:'+dateStr)
# 最后一天
else:
break
#print(dateStr)
currSignalData = groupedSignalData.ix[signalDay]
# print(currSignalData)
if StockConst.IS_DEBUG:
print("currSignalData:" + str(len(currSignalData)))
#print(currSignalData)
end_inloop0 = time.clock()
print("inloop0花费时间：%f s" % (end_inloop0 - start_inloop0))
#交易方法（外部传入）
start_inloop1 = time.clock()
dict = trade_func(currSignalData, currHoldSet, dailyQuote, tradingDay,
signalData, capitalEntity)
end_inloop1 = time.clock()
print("trade_func花费时间：%f s" % (end_inloop1 - start_inloop1))
actualBuyList = dict['actualBuyList']
actualSellList = dict['actualSellList']
prevHoldList = dict['prevHoldList']
cannotBuyList = dict['cannotBuyList']
cannotSellList = dict['cannotSellList']
stockTradeListDaily = dict['stockTradeList']
if len(stockTradeListDaily) > 0:
stockTradeList.extend(stockTradeListDaily)
# print(currHoldSet)
#3.计算当日市值
start_inloop2 = time.clock()
currMV = calculateDailyMV(currHoldSet, capitalEntity, dailyQuote,
tradingDay, stockHoldDailyList, initialMV)
end_inloop2 = time.clock()
print("calculateDailyMV花费时间：%f s" % (end_inloop2 - start_inloop2))
start_inloop3 = time.clock()
#4.个数
buyCnt = len(actualBuyList)
sellCnt = len(actualSellList)
prevHoldCnt = len(prevHoldList)
currHoldCnt = len(currHoldSet)
cannotSellCnt = len(cannotSellList)
cannotBuyCnt = len(cannotBuyList)
# print(currMV)
#if StockConst.isDebug:
#print("dateStr:" + dateStr + " changePCTBuy:" + str(changePCTBuy) + " changePCTSell:" + str(changePCTSell) +
#" changePCTHold:" + str(changePCTHold))
#当日净值
netValue = currMV / initialMV
#print("netValue:" + str(netValue))
#当日收益
dailyChangePCT = NumUtil.get_change_pct(lastMV, currMV, 2)
#当日可用现金
usableCach = capitalEntity.get_usable_cash()
#
stockStatDailyDf.ix[
tradingDayStr] = netValue, dailyChangePCT, buyCnt, sellCnt, prevHoldCnt, currHoldCnt, cannotSellCnt, cannotBuyCnt, usableCach, currMV
#
lastMV = currMV
end_inloop3 = time.clock()
print("inloop3花费时间：%f s" % (end_inloop3 - start_inloop3))
pass
end_loop = time.clock()
print("for循环花费时间：%f s" % (end_loop - start_loop))
result = ''
start_stat = time.clock()
# 信号数据
#groupedSignalData.to_csv(StockConst.root + '\export\groupedSignalData.csv')
# 每日交易
# print('每日交易:')
# print(stockTradeList)
stockTradeDailyDf = pd.DataFrame(stockTradeList)
stockTradeDailyDf.sort_values(by=['tradingDate'], ascending=True)
# stockTradeDailyDf.to_csv(StockConst.root + '\export\stockTradeDaily.csv')
# 每日持仓
#print('每日持仓:')
stockHoldDailyDf = pd.DataFrame(stockHoldDailyList)
stockHoldDailyDf.sort_values(by=['tradingDate'], ascending=True)
# stockHoldDailyDf.to_csv(StockConst.root + '\export\stockHoldDaily.csv')
#每日统计(收益，净值，买入数，卖出数，持有数)
stockStatDailyDf = stockStatDailyDf.dropna(how='all')
result += '--' * 70 + '\n'
result += '每日统计:' + '\n'
result += stockStatDailyDf.to_string() + '\n'
# stockStatDailyDf.to_csv(StockConst.root + '\export\stockStatDaily.csv')
# 每年统计
result += '--' * 70 + '\n'
yearDf = StockYearService.main(stockStatDailyDf)
result += '每年统计:' + '\n'
result += yearDf.to_string() + '\n'
# 最大回撤
result += '--' * 70 + '\n'
maxdrop = StockMaxDropNewService.get_max_drop(stockStatDailyDf)
result += '最大回撤:' + '\n'
result += SetUtil.dict_to_string(maxdrop) + '\n'
#每年的最大回撤
maxdropDf = StockMaxDropNewService.get_max_drop_for_each_year(
stockStatDailyDf)
maxdropDf.sort_values(by=["year"])
result += '每年的最大回撤:' + '\n'
result += maxdropDf.to_string() + '\n'
#夏普比率
result += '--' * 70 + '\n'
sharpRatio = SharpRatioNewService.get_sharp_ratio(stockStatDailyDf)
result += '夏普比率:' + '\n'
result += str(sharpRatio) + '\n'
#每年的夏普比率
sharpRatioDf = SharpRatioNewService.get_sharp_ratio_for_each_year(
stockStatDailyDf)
sharpRatioDf.sort_values(by=["year"])
result += '每年的夏普比率:' + '\n'
result += sharpRatioDf.to_string() + '\n'
end_stat = time.clock()
print("统计花费时间：%f s" % (end_stat - start_stat))
# result += '回测完成'
end = time.clock()
print("main花费时间：%f s" % (end - start))
# 净值曲线图
stockStatDailyDf['netValue'].plot()
plt.show()
resultDic = {'stockStatDailyDf': stockStatDailyDf, 'result': result}
print(result)
return resultDic
