def GetStockIndustry(self, inputTradingDay, strStock):
dtTradingDay = dateTime.ToDateTime(inputTradingDay)
strSi = '-'
if strStock in self.__dfData[0].index:
dfStock = self.__dfData[0].ix[strStock]
if isinstance(dfStock, pd.Series):
dfLoop = dfStock
dtIn = dateTime.ToDateTime(dfLoop[self.__colIn])
if dtTradingDay >= dtIn:
if dfLoop[self.__colCurSign] == '1' or dtTradingDay <= dateTime.ToDateTime(dfLoop[self.__colOut]):
strSi = dfLoop[self.__colIndCode]
else:
nIdxLen = len(dfStock.index)
nIndex = 0
while nIndex < nIdxLen:
dfLoop = dfStock.ix[nIndex]
nIndex += 1
dtIn = dateTime.ToDateTime(dfLoop[self.__colIn])
if dtTradingDay < dtIn:
continue
else:
if dfLoop[self.__colCurSign] == '1' or dtTradingDay <= dateTime.ToDateTime(dfLoop[self.__colOut]):
strSi = dfLoop[self.__colIndCode]
break
return strSi
def GenerateTdIndex(self):
print('CStockPoolManager.GenerateTdIndex ...')
nLoopIndex = 0
nShowPercent = 0
for nStockId, listElems in self.__dictSpElems.items():
nLoopPercent = int(nLoopIndex * 100 / len(self.__dictSpElems))
if (nShowPercent < nLoopPercent and nLoopPercent % 10 == 0):
nShowPercent = int(nLoopIndex * 100 / len(self.__dictSpElems))
print(str(nShowPercent) + '%')
# print(nLoopIndex, '/', len(self.__dictSpElems))
nLoopIndex += 1
for elem in listElems:
nInDate = elem.GetInDate()
nOutDate = elem.GetOutDate()
nLoopDate = nInDate
while (nLoopDate <= nOutDate):
if ((nLoopDate in self.__dictSpElemsByTd) == False):
self.__dictSpElemsByTd[nLoopDate] = list()
self.__dictSpElemsByTd[nLoopDate].append(nStockId)
# Next Date
dateLoopDate = dateTime.ToDateTime(nLoopDate)
dateTomorrow = dateLoopDate + datetime.timedelta(days=1)
nLoopDate = int(dateTime.ToIso(dateTomorrow))
def GetTradingDayList(self, strExchange, dtFrom, dtTo, bDateTime=False):
listRtn = []
nTradingDay = int(dateTime.ToIso(dtFrom))
nTdTo = int(dateTime.ToIso(dtTo))
if self.IsTradingDay(strExchange, nTradingDay) == True:
if bDateTime == True:
listRtn.append(dateTime.ToDateTime(dtFrom))
else:
listRtn.append(nTradingDay)
while True:
bSuccess, nTradingDay = self.GetNextTradingDay(
strExchange, nTradingDay)
if bSuccess == False or nTradingDay > nTdTo:
break
if bDateTime == True:
dtTradingDay = dateTime.ToDateTime(nTradingDay)
listRtn.append(dtTradingDay)
else:
listRtn.append(nTradingDay)
return listRtn
def GetPreTradingDay(self, strExchange, tradingDay):
dtTradingDay = dateTime.ToDateTime(tradingDay)
nTradingDay = int(dateTime.ToIso(tradingDay))
if nTradingday <= self.__dictTc[strExchange][0]:
return False, 0
while True:
dtTradingDay = Yestoday(dtTradingDay)
nTradingDay = int(dateTime.ToIso(dtTradingDay))
if self.IsTradingDay(nTradingDay) == True:
return True, nTradingDay
def GetStocksByDatePeriod(self, dtFrom, dtTo):
if (isinstance(self.__dfData, DataFrame) == False
or self.__dfData.empty):
return None
dtFromFix = dateTime.ToDateTime(dtFrom)
dtToFix = dateTime.ToDateTime(dtTo)
if (dtFromFix == None or dtToFix == None):
return None
setRtn = set()
# datePeriod = dateTime.CDatePeriod(dtFromFix, dtToFix)
for index, row in self.__dfData.iterrows():
dtSpBegin = index[0]
dtSpEnd = index[1]
if (dtSpEnd < dtFromFix or dtSpBegin > dtToFix):
continue
setRtn.add(row['STOCK_WINDCODE'])
return setRtn
def GetStocksByTd(self, inputDate):
if (isinstance(self.__dfData, DataFrame) == False
or self.__dfData.empty):
return None
dtTd = dateTime.ToDateTime(inputDate)
if (dtTd == None):
return None
setRtn = set()
for index, row in self.__dfData.iterrows():
if (dtTd >= index[0] and dtTd <= index[1]):
setRtn.add(row['STOCK_WINDCODE'])
return setRtn
def DBReqStockEODPrice(strHost, strUser, strPwd, strDb, listStocks, strDateFrom = '', strDateTo = ''):
dbInst = windDb.CWindDb(strHost, strUser, strPwd, strDb)
listStockEODPrice = dbInst.DBReqStockEODPrice(listStocks, strDateFrom, strDateTo)
# print('DBReqStockEODPrice() return [', len(listStockEODPrice), '] records')
mdbarMgr = mdBar.CMdBarDataManager()
for row in listStockEODPrice:
if (len(row) != 9):
continue
# print(row)
dtTd = dateTime.ToDateTime(row[1])
if (dtTd == None):
print('DBReqStockEODPrice.tradingDay error: ', row)
mdbarData = mdBar.CMdBarData(mdBar.EU_MdBarInterval.mdbi_1d, float(row[2]), float(row[3]), float(row[4]), float(row[5]), float(row[6]), float(row[7]), float(row[8]), dtTd)
mdbarMgr.Add(row[0], mdbarData)
# mdbarMgr.Print()
return True
def Add(self, strStockWindCode, mdBarData):
nStockId = stockCn.StockWindCode2Int(strStockWindCode)
if (nStockId == None):
return False
if (isinstance(mdBarData, CMdBarData) == False):
return False
euMdBi = mdBarData.GetInterval()
if (euMdBi not in self.__dictMdBarData.keys()):
self.__dictMdBarData[euMdBi] = {}
if (nStockId not in self.__dictMdBarData[euMdBi]):
self.__dictMdBarData[euMdBi][nStockId] = {}
dtDateTime = dateTime.ToDateTime(mdBarData.GetDateTime())
if (dtDateTime == None):
return False
self.__dictMdBarData[euMdBi][nStockId][dtDateTime] = mdBarData
return True
def RemoveBefore(self, inputBefore):
dtBefore = inputBefore
print(1)
if (isinstance(inputBefore, datetime.datetime) == False):
print(2)
dtBefore = dateTime.ToDateTime(inputBefore)
if (dtBefore == None):
print(3, dtBefore, inputBefore)
return False
print(4)
for keyMdbi in list(self.__dictMdBarData.keys()):
for keyStockId in list(self.__dictMdBarData[keyMdbi].keys()):
for keyTd in list(
self.__dictMdBarData[keyMdbi][keyStockId].keys()):
if (keyTd < dtBefore):
del self.__dictMdBarData[keyMdbi][keyStockId][keyTd]
print(5)
# self.Print()
pass
def __IsIn(self, euMdbi, strStockWindCode, strTradingDay):
nStockId = stockCn.StockWindCode2Int(strStockWindCode)
if (nStockId == None):
print('nStockId is None')
return False
dtDateTime = dateTime.ToDateTime(strTradingDay)
if (dtDateTime == None):
print('dtDateTime is None')
return False
if (euMdbi not in self.__dictMdBarData.keys()):
print('euMdbi is not in: ', euMdi, self.__dictMdBarData.keys())
return False
if (nStockId not in self.__dictMdBarData[euMdbi].keys()):
# print('nStockId is not in: ', nStockId, self.__dictMdBarData[euMdbi].keys())
return False
if (dtDateTime not in self.__dictMdBarData[euMdbi][nStockId].keys()):
# print('dtDateTime is not in: ', dtDateTime, strStockWindCode, self.__dictMdBarData[euMdbi][nStockId].keys())
return False
return True
def GetStockReportPeriod(inputTradingDay):
dtTd = dateTime.ToDateTime(inputTradingDay)
dtInput = dtTd.date()
nYearInput = dtInput.year
dt0 = datetime.date(nYearInput - 1, 11, 1)
dt1 = datetime.date(nYearInput, 4, 1)
dt2 = datetime.date(nYearInput, 5, 1)
dt3 = datetime.date(nYearInput, 9, 1)
dt4 = datetime.date(nYearInput, 11, 1)
dt5 = datetime.date(nYearInput + 1, 4, 1)
dtRtn = None
if (dtInput >= dt0 and dtInput < dt1):
dtRtn = datetime.date(nYearInput - 1, 9, 30)
elif (dtInput >= dt1 and dtInput < dt2):
dtRtn = datetime.date(nYearInput - 1, 12, 31)
elif (dtInput >= dt2 and dtInput < dt3):
dtRtn = datetime.date(nYearInput, 3, 31)
elif (dtInput >= dt3 and dtInput < dt4):
dtRtn = datetime.date(nYearInput, 6, 30)
elif (dtInput >= dt4 and dtInput < dt5):
dtRtn = datetime.date(nYearInput, 9, 30)
return dtRtn
def GetVolume(self, euMdbi, strStockWindCode, strTradingDay):
dtDateTime = dateTime.ToDateTime(strTradingDay)
nStockId = stockCn.StockWindCode2Int(strStockWindCode)
if (self.__IsIn(euMdbi, nStockId, dtDateTime) == False):
return None
return self.__dictMdBarData[euMdbi][nStockId][dtDateTime].GetVolume()
def CalcIndex(strDbHost, strDbUserName, strDbPasswd, strDbDatabase,
strDateFrom, strDateTo, strCsvFile, euCFI):
# 初始化数据
tcInst = tCal.CTradingCalendar()
## 资金总额，分配到不同的成分中
dFundTotal = BaseIndexValue() * IndexMulti()
## 读取成分比重
dfCFIndexWeight_NanHua = pd.read_csv(strCsvFile)
nCFRowLen = len(dfCFIndexWeight_NanHua)
nCFColLen = len(dfCFIndexWeight_NanHua.columns)
nCFRowIndex = 0
## 行情数据；连续合约实际合约对应表
### 循环处理成分比重表示，第一循环从数据库读取数据
dfCFPrice = pd.DataFrame()
dfCFMapping = pd.DataFrame()
dt1stDate = None
dtLastDate = dateTime.ToDateTime(strDateTo)
dictDailyIndexValue = {}
dtDailyConstituent = pd.DataFrame()
# 每次循环做一次一次指数成分调整
while nCFRowIndex < nCFRowLen:
dictFundByProduct = {} # 各个品种分配的资金
nColIndex = 0
dtLoopFrom = None
dtLoopTo = None
# 处理起始日期 和 各个品种分配的初始资金
while nColIndex < nCFColLen:
strCol = dfCFIndexWeight_NanHua.columns[nColIndex]
dValue = dfCFIndexWeight_NanHua[strCol][nCFRowIndex]
if (strCol == 'TradingDay'):
dtLoopFrom = dateTime.ToDateTime(
int(dfCFIndexWeight_NanHua[strCol][nCFRowIndex]))
else:
if dValue != 0:
dictFundByProduct[strCol] = dfCFIndexWeight_NanHua[strCol][
nCFRowIndex] * dFundTotal
nColIndex += 1
# 查询数据库， 初始化数据
if (nCFRowIndex == 0):
dt1stDate = dtLoopFrom
dfCFPrice, dfCFMapping = InitData_Common(strDbHost, strDbUserName,
strDbPasswd,
strDbDatabase,
dateTime.ToIso(dt1stDate),
strDateTo)
# 处理结束日期
if nCFRowIndex + 1 == nCFRowLen:
dtLoopTo = dtLastDate
else:
dtLoopTo = dateTime.ToDateTime(
int(dfCFIndexWeight_NanHua['TradingDay'][nCFRowIndex + 1]))
if tcInst.IsTradingDay('SHFE', dateTime.ToIso(dtLoopFrom)) == False:
bSuccess, nRtn = tcInst.GetNextTradingDay('SHFE', dtLoopFrom)
if bSuccess == True:
dtLoopFrom = dateTime.ToDateTime(nRtn)
else:
print('tcInst.GetNextTradingDay failed', 'SHFE', dtLoopFrom)
break
if tcInst.IsTradingDay('SHFE', dateTime.ToIso(dtLoopTo)) == False:
bSuccess, nRtn = tcInst.GetNextTradingDay('SHFE', dtLoopTo)
if bSuccess == True:
dtLoopTo = dateTime.ToDateTime(nRtn)
else:
print('tcInst.GetNextTradingDay failed', 'SHFE', dtLoopTo)
break
print(
'--------------------------------------------------------------------'
)
print('Processing From ' + dateTime.ToIso(dtLoopFrom) + ' To ' +
dateTime.ToIso(dtLoopTo))
# 处理每个成分
dfLoopAll = pd.DataFrame()
for strProduct in dictFundByProduct.keys():
# 连续合约与现实合约进行对应
for idxProd in dfCFMapping.ix[strProduct].index:
# 期货合约
strInstrument = dfCFMapping['FS_MAPPING_WINDCODE'][strProduct][
idxProd]
if strInstrument not in dfCFPrice.index.levels[0]:
continue
strExchange = GetExchange(strProduct)
if strExchange == '':
print('xxxxxx Exchange Is NULL: ', strProduct)
continue
# 日期处理, 只处理本次循环的
bIncludeTailRow = True
dtProductFrom = idxProd[0]
dtProductTo = idxProd[1]
if dtProductFrom > dtLoopTo:
break
if dtProductTo < dtLoopFrom:
continue
if dtProductFrom < dtLoopFrom:
dtProductFrom = dtLoopFrom
if dtProductTo >= dtLoopTo:
dtProductTo = dtLoopTo
else:
bSuccess, nProductTo = tcInst.GetNextTradingDay(
strExchange, dtProductTo)
if bSuccess == False:
continue
dtProductTo = dateTime.ToDateTime(nProductTo)
bIncludeTailRow = False
# 获取时间区间的行情数据
listDates = tcInst.GetTradingDayList(strExchange,
dtProductFrom,
dtProductTo, True)
dfLoopMd = dfCFPrice.ix[strInstrument].ix[listDates].dropna()
# 处理缺失的行情数据，使用昨收盘和昨结算
if len(list(set(listDates).difference(dfLoopMd.index))) >= 1:
dateLastTd = None
for keyDate in listDates:
if keyDate not in dfLoopMd.index:
if dateLastTd == None:
bSuccess, nLastTd = tcInst.GetPreTradingDay(
strExchange, keyDate)
if (bSuccess == None):
print(
'xxxxxx Last TradingDay Md Is NULL: ' +
strInstrument + ' ' + str(keyDate))
continue
dateLastTd = dateTime.ToDateTime(nLastTd)
dClose = dfLoopMd['S_DQ_CLOSE'][dateLastTd]
dSettle = dfLoopMd['S_DQ_SETTLE'][dateLastTd]
dfToday = pd.DataFrame(
{
'S_DQ_CLOSE': [dClose],
'S_DQ_SETTLE': [dSettle]
},
index=[keyDate])
dfToday.index.names = ['TRADE_DT']
dfLoopMd = pd.concat([dfLoopMd, dfToday],
axis=0,
join='outer')
dateLastTd = keyDate
dfLoopMd['InstrumentId'] = strInstrument
dfLoopMd['DominantContract'] = strProduct
dfLoopMd['Volume'] = np.nan
dfLoopMd['Value'] = np.nan
dfLoopMd.reset_index(inplace=True)
dfLoopMd = dfLoopMd.set_index(['DominantContract', 'TRADE_DT'])
dfLoopMd = dfLoopMd.sortlevel(1)
dPrice = dfLoopMd['S_DQ_CLOSE'][strProduct][dtProductFrom]
dVolume = dictFundByProduct[strProduct] / dPrice
dfLoopMd['Volume'][strProduct][
dtProductFrom:dtProductTo] = dVolume
dfLoopMd['Value'][strProduct][dtProductFrom:dtProductTo] = \
dfLoopMd['S_DQ_CLOSE'][strProduct][dtProductFrom:dtProductTo] * \
dfLoopMd['Volume'][strProduct][dtProductFrom:dtProductTo]
dfLoopMd['Value'][strProduct][
dtProductFrom] = dictFundByProduct[strProduct]
dictFundByProduct[strProduct] = dfLoopMd['Value'][strProduct][
dtProductTo]
dfLoopAll = pd.concat([dfLoopAll, dfLoopMd[:-1]],
axis=0,
join='outer')
dfSwap = dfLoopAll.swaplevel(0, 1)
dfSwap['Weight'] = np.nan
for dtKey in dfSwap['Value'].index.levels[0]:
total = dfSwap['Value'][dtKey].sum()
dictDailyIndexValue[dtKey] = total
print(str(dtKey) + ' ' + str(dfSwap['Value'][dtKey].sum()))
dfSwap['Weight'][dtKey] = dfSwap['Value'][dtKey] / total
dFundTotal = total
nCFRowIndex += 1
dtDailyConstituent = pd.concat([dtDailyConstituent, dfSwap],
axis=0,
join='outer')
# 处理每日每个品种的权重
dtDailyConstituent = dtDailyConstituent.swaplevel(0, 1)
print('Dump Index Value into index_daily.csv')
fIndex = open('index_daily.csv', 'w')
for key in sorted(dictDailyIndexValue.keys()):
strOutput = dateTime.ToIso(key) + ',' + str(
dictDailyIndexValue[key] / IndexMulti()) + ',' + str(
euCFI.value) + '\n'
fIndex.write(strOutput)
fIndex.close()
del dtDailyConstituent['Volume']
del dtDailyConstituent['Value']
print('Dump Index Daily Constituent into index_daily_constituent.csv')
dtDailyConstituent['IndexType'] = euCFI.value
dtDailyConstituent.to_csv('index_daily_constituent.csv', header=False)