def __init__(self,startDate,endDate,dailyQuote,signalData,dateList,stockStatDailyDf,stockHoldDailyList,stockTradeList):
self.startDate = startDate
self.endDate = endDate
self.dailyQuote = dailyQuote
self.signalData = signalData
self.dateList = dateList
self.currHoldSet = {}
self.initialMV = 1000000
self.capitalEntity = CapitalEntity.CapitalEntity(self.initialMV)
self.lastMV = self.initialMV
self.stockStatDailyDf = stockStatDailyDf
self.stockHoldDailyList = stockHoldDailyList
self.stockTradeList = stockTradeList
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
def main():
signalData = SourceDataDao.getSignalData()
#dailyQuote = SourceDataDao.getDailyQuote()
dailyQuote = SourceDataDao.getNewDailyQuote()
#columns filter
#df3 = sourceData.loc[(df['Mom'] <= 4) & (df['Mom'] <= 4), ['Mom']]
#index filter
#startDate=DateUtils.str2Datetime('20010105');
#endDate=DateUtils.str2Datetime('20010111');
#df4 = df3.ix[startDate:endDate]
numOfDailyHolding = 5
#select top 5 group by TradingDay order by Mom desc
groupedSignalData = signalData.groupby(level='TradingDay').apply(
SelectUtil.top, numOfDailyHolding, 'Mom', False)
#param
#period = 5
#startDate = '1/8/2001'
startDate = '1/8/2001'
#endDate = '1/1/2017'
#eendDate = '5/18/2001'
endDate = '12/31/2016'
#endDate = '12/31/2002'
#endDate = '1/9/2001'
#time series
#dateList = DateUtil.getDateList2(startDate,endDate)
#当前持仓 key:innerCode value:
currHoldSet = {}
stockHoldDF = {}
stockTradeList = []
#资金情况
#capitalDict = {'usableCach':1000000}
#初始资金
initialMV = 1000000
capitalEntity = CapitalEntity.CapitalEntity(initialMV)
lastMV = initialMV
#lastSignalData = pd.DataFrame()
usableVol = 100
netValue = 1
#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 = []
#从信号表中取得唯一性日期
dateList = SourceDataDao.select_date_from_signal(signalData, startDate,
endDate)
for date in dateList:
dateStr = DateUtil.datetime2_str(date)
#print(dateStr)
#isinstance(date, datetime)
# select by single date
#try:
#print(1)
currSignalData = groupedSignalData.ix[date]
if StockConst.IS_DEBUG:
print("currSignalData:" + str(len(currSignalData)))
#print(2)
buyList = getBuyList(currSignalData, currHoldSet)
#print(3)
sellList = getSellList(currSignalData, currHoldSet)
#print(4)
prevHoldList = getPrevHoldList(currSignalData, currHoldSet)
#print(currSignalData)
"""
if (dateStr >= '2015-01-05') & (dateStr <= '2015-01-05'):
print('-----'+dateStr+'-----')
#print(currSignalData)
#print(buyList)
print(list(currHoldSet.keys()))
for key in currHoldSet.keys():
print(str(key) + ':' + currHoldSet.get(key).openDate)
print('----------------')
"""
#except:
#假期,双休日,原数据问题
#if StockConst.isDebug:
#print(DateUtil.datetime2Str(date) + ': no data')
#continue
dailyChangePCT = 0
actualSellList = []
actualBuyList = []
cannotSellList = []
cannotBuyList = []
#实际买入个数
numOfActualBuy = getNumOfActualBuy(date, buyList, dailyQuote)
#1.sell changePCTSell
handleSellList(date, sellList, dailyQuote, usableVol, stockHoldDF,
stockTradeList, currHoldSet, actualSellList,
cannotSellList, capitalEntity, numOfActualBuy,
signalData, prevHoldList, numOfDailyHolding)
#usableVol = calculateUsableVol(currHoldSet)
#2.buy changePCTBuy
handleBuyList(date, buyList, dailyQuote, usableVol, stockHoldDF,
stockTradeList, currHoldSet, actualBuyList,
cannotBuyList, capitalEntity)
#3.hold
#changePCTHold = handleHoldList(date,prevHoldList,dailyQuote,usableVol,stockHoldDF,stockTradeDF,currHoldSet)
#changePCTHold = 0
currMV = calculateDailyMV(currHoldSet, capitalEntity, dailyQuote, date,
stockHoldDailyList, initialMV)
#handleStockHoldDaily(currHoldSet, stockHoldDailyList, date)
buyCnt = len(actualBuyList)
sellCnt = len(actualSellList)
prevHoldCnt = len(prevHoldList)
currHoldCnt = len(currHoldSet)
cannotSellCnt = len(cannotSellList)
cannotBuyCnt = len(cannotBuyList)
#if StockConst.isDebug:
#print("dateStr:" + dateStr + " changePCTBuy:" + str(changePCTBuy) + " changePCTSell:" + str(changePCTSell) +
#" changePCTHold:" + str(changePCTHold))
#dailyChangePCT = changePCTBuy+changePCTSell+changePCTHold
#print("dailyChangePCT:"+str(dailyChangePCT))
#netValue = netValue * (1 + dailyChangePCT / 100)
#每日净值
netValue = currMV / initialMV
#print("netValue:" + str(netValue))
#每日收益
dailyChangePCT = NumUtil.get_change_pct(lastMV, currMV, 2)
#每日可用现金
usableCach = capitalEntity.get_usable_cash()
stockStatDailyDf.ix[
dateStr] = netValue, dailyChangePCT, buyCnt, sellCnt, prevHoldCnt, currHoldCnt, cannotSellCnt, cannotBuyCnt, usableCach, currMV
lastMV = currMV
#debug 行情数据
debugDailyQuote(groupedSignalData)
# 信号数据
#groupedSignalData.to_csv(StockConst.root + '\export\groupedSignalData.csv')
# 每日交易
# print('每日交易:')
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')
print('每日统计:')
print(stockStatDailyDf)
stockStatDailyDf.to_csv(StockConst.ROOT + '\export\stockStatDaily.csv')
# 每年统计
yearDf = StockYearService.main(stockStatDailyDf)
print('每年统计:')
print(yearDf)
# 最大回撤
maxdrop = StockMaxDropNewService.get_max_drop(stockStatDailyDf)
print('最大回撤:')
print(maxdrop)
#每年的最大回撤
maxdropDf = StockMaxDropNewService.get_max_drop_for_each_year(
stockStatDailyDf)
maxdropDf.sort_values(by=["year"])
print('每年的最大回撤:')
print(maxdropDf)
#夏普比率
sharpRatio = SharpRatioNewService.get_sharp_ratio(stockStatDailyDf)
print('夏普比率:')
print(sharpRatio)
#每年的夏普比率
sharpRatioDf = SharpRatioNewService.get_sharp_ratio_for_each_year(
stockStatDailyDf)
sharpRatioDf.sort_values(by=["year"])
print('每年的夏普比率:')
print(sharpRatioDf)
def main(select_stock, numOfDailySignal):
signalData = SourceDataDao.getSignalData()
#dailyQuote = SourceDataDao.getDailyQuote()
dailyQuote = SourceDataDao.getNewDailyQuote()
#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'
#select top 5 group by TradingDay order by Mom desc
groupedSignalData = select_stock(signalData, numOfDailySignal)
#groupedSignalData = signalData.groupby(level=StockConst.TradingDay).apply(SelectUtil.top,numOfDailySignal,StockConst.Mom,False) #False True
#当前持仓 key:innerCode value:
currHoldSet = {}
stockTradeList = []
#
capitalEntity = CapitalEntity.CapitalEntity(initialMV)
lastMV = initialMV
#netValue = 1;
#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 = []
#从信号表中取得唯一性日期
dateList = SourceDataDao.select_date_from_signal(signalData, startDate,
endDate)
for index, item in enumerate(dateList):
#for date in dateList:
#print('index:'+str(index)+' len:'+str(len(dateList)))
#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]
if StockConst.IS_DEBUG:
print("currSignalData:" + str(len(currSignalData)))
#print(currSignalData)
# 计划买入列表
planBuyList = getPlanBuyList(currSignalData, currHoldSet)
# 计划卖出列表
planSellList = getPlanSellList(currSignalData, currHoldSet)
# 昨日持仓部分（在今日持仓中）
prevHoldList = getPrevHoldList(currSignalData, currHoldSet)
"""
if (dateStr >= '2015-01-05') & (dateStr <= '2015-01-05'):
print('-----'+dateStr+'-----')
#print(currSignalData)
#print(buyList)
print(list(currHoldSet.keys()))
for key in currHoldSet.keys():
print(str(key) + ':' + currHoldSet.get(key).openDate)
print('----------------')
"""
#dailyChangePCT = 0
cannotSellList = []
cannotBuyList = []
# 实际买入列表
actualBuyList = getActualBuyList(planBuyList, dailyQuote, tradingDay,
cannotBuyList)
# "卖出列表"中要保留的股票数
numOfToKeepInSellList = numOfDailySignal - len(actualBuyList) - len(
prevHoldList)
# 实际卖出列表
actualSellList = getActualSellList(planSellList, tradingDay,
signalData, dailyQuote,
numOfToKeepInSellList,
cannotSellList)
#1.处理实际卖出
handleSellList(tradingDay, dailyQuote, stockTradeList, currHoldSet,
capitalEntity, actualSellList)
#2.处理实际买入
handleBuyList(tradingDay, dailyQuote, stockTradeList, currHoldSet,
capitalEntity, actualBuyList)
#3.计算当日市值
currMV = calculateDailyMV(currHoldSet, capitalEntity, dailyQuote,
tradingDay, stockHoldDailyList, initialMV)
#4.个数
buyCnt = len(actualBuyList)
sellCnt = len(actualSellList)
prevHoldCnt = len(prevHoldList)
currHoldCnt = len(currHoldSet)
cannotSellCnt = len(cannotSellList)
cannotBuyCnt = len(cannotBuyList)
#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
# 信号数据
#groupedSignalData.to_csv(StockConst.root + '\export\groupedSignalData.csv')
# 每日交易
# print('每日交易:')
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')
print('每日统计:')
print(stockStatDailyDf)
stockStatDailyDf.to_csv(StockConst.ROOT + '\export\stockStatDaily.csv')
# 每年统计
yearDf = StockYearService.main(stockStatDailyDf)
print('每年统计:')
print(yearDf)
# 最大回撤
maxdrop = StockMaxDropNewService.get_max_drop(stockStatDailyDf)
print('最大回撤:')
print(maxdrop)
#每年的最大回撤
maxdropDf = StockMaxDropNewService.get_max_drop_for_each_year(
stockStatDailyDf)
maxdropDf.sort_values(by=["year"])
print('每年的最大回撤:')
print(maxdropDf)
#夏普比率
sharpRatio = SharpRatioNewService.get_sharp_ratio(stockStatDailyDf)
print('夏普比率:')
print(sharpRatio)
#每年的夏普比率
sharpRatioDf = SharpRatioNewService.get_sharp_ratio_for_each_year(
stockStatDailyDf)
sharpRatioDf.sort_values(by=["year"])
print('每年的夏普比率:')
print(sharpRatioDf)