def scenarioHistF(self, st, en, scenarioName, num):
''' 情景分布生成 '''
# if self.TradeTime == '2017-06-08':
# pass
scenarioSummary = {u'mean': {}, u'std': {}, u'IR': {}, u'sortino': {}, u'sharpe': {}}
distDict = {u'mean': {}, u'std': {}, u'IR': {}, u'sortino': {}, u'sharpe': {}}
scenarioCond = copy.deepcopy(self.scenarioCond)
scenarioIndex = scenarioCond[scenarioCond == scenarioName].index
# scenarioPeriod = pd.Series(index=freqTransfer(scenarioIndex, freq='W').to_series().astype(str).values)
scenarioSeries = pd.Series(scenarioIndex, index=scenarioIndex)
targePeriodIndex = scenarioSeries.loc[st:en].index
pct_Data = self.pct_fund.loc[targePeriodIndex]
colLen = pct_Data.count(axis=0)
lenFilter = colLen[colLen >= num].index
rptFilter = self.fundLatestRptDate[self.fundLatestRptDate >= self.TradeTime].index
filterData = pct_Data[lenFilter & rptFilter]
scenarioSummary[u'mean'][scenarioName] = filterData.mean(axis=0) # 默认skipna=True
scenarioSummary[u'std'][scenarioName] = filterData.std(axis=0)
tempMean = copy.deepcopy(scenarioSummary[u'mean'][scenarioName])
tempStd = copy.deepcopy(scenarioSummary[u'std'][scenarioName])
rf = 0.1/252.
# timeDecayWeight = pd.DataFrame(np.transpose([(dateGap/dateGap.sum()).values] * len(filterData.columns)), index=targePeriodIndex, columns=filterData.columns)
# timeDecayData = pd.eval('filterData * timeDecayWeight')
# weightedMean = timeDecayData.mean(axis=0)
# weightedStd = np.sqrt((np.power(filterData - weightedMean, 2) * np.power(timeDecayWeight/len(targePeriodIndex), 2)).sum(axis=0))
# scenarioSummary[u'sharpe'][scenarioName] = (weightedMean - rf)/weightedStd
tempIR = copy.deepcopy(tempMean)
# # 下行标准差
# filterDf = pct_Data[lenFilter & rptFilter]
# downsideStd = filterDf[pd.eval('filterDf < tempMean')]
# downsideStd = np.sqrt(np.power(downsideStd, 2).sum() / float(num))
# tempSortino = copy.deepcopy(tempMean)
# tempIR[pd.eval('tempMean > rf')] = ((tempMean - rf)/tempStd)[pd.eval('tempMean > rf')].values
# tempIR[pd.eval('tempMean < -rf')] = ((tempMean + rf)/tempStd)[pd.eval('tempMean < -rf')].values
# tempIR[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# scenarioSummary[u'IR'][scenarioName] = tempIR # 不包含变动小的基金
# scenarioSummary[u'IR'][scenarioName] = tempMean/tempStd # 含变动小的基金
scenarioSummary[u'sharpe'][scenarioName] = (tempMean - rf)/tempStd
# tempSortino[pd.eval('tempMean > rf')] = ((tempMean - rf) / downsideStd)[pd.eval('tempMean > rf')].values
# tempSortino[pd.eval('tempMean < -rf')] = ((tempMean + rf) / downsideStd)[pd.eval('tempMean < -rf')].values
# tempSortino[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# scenarioSummary[u'sortino'][scenarioName] = tempSortino # 不包含变动小的基金
distDict[u'sharpe'][scenarioName] = seriesTop(scenarioSummary[u'sharpe'][scenarioName], 10, ascending=False)[0]
# distDict[u'IR'][scenarioName] = seriesTop(scenarioSummary[u'IR'][scenarioName], 5, ascending=False)[0]
# rank = distDict[u'IR'][scenarioName].to_csv('rank.csv')
# distDict[u'IR'][scenarioName] = seriesPercentage(scenarioSummary[u'IR'][scenarioName], 0.01, u'right')[0] # 历史分布--百分比
return distDict
def scenarioHistF(self, st, en, scenarioName, num):
''' 情景分布生成 '''
scenarioSummary = {
u'mean': {},
u'std': {},
u'IR': {},
u'sortino': {},
u'sharp': {}
}
distDict = {
u'mean': {},
u'std': {},
u'IR': {},
u'sortino': {},
u'sharp': {}
}
scenarioCond = copy.deepcopy(self.scenarioCond)
scenarioIndex = scenarioCond[scenarioCond == scenarioName].index
scenarioSeries = pd.Series(scenarioIndex, index=scenarioIndex)
targePeriodIndex = scenarioSeries.loc[st:en].index
pct_Data = self.pct_fund.loc[targePeriodIndex]
colLen = pct_Data.count(axis=0)
lenFilter = colLen[colLen >= num].index
# tempLen = pct_Data.index.size
# tempGap = math.e
# a1 = 1./sum([pow(tempGap, i) for i in range(tempLen)])
# dataGap = [a1] + [a1*pow(tempGap, i)for i in range(1, tempLen)]
rf = 0.1 / 252.
filterData = pct_Data[lenFilter]
scenarioSummary[u'mean'][scenarioName] = filterData.mean(
axis=0) # 默认skipna=True
scenarioSummary[u'std'][scenarioName] = filterData.std(axis=0)
tempMean = copy.deepcopy(scenarioSummary[u'mean'][scenarioName])
tempStd = copy.deepcopy(scenarioSummary[u'std'][scenarioName])
# 时间衰减IR -- 测试效果不好
# timeDecayWeight = pd.DataFrame(np.transpose([dataGap] * len(filterData.columns)), index=targePeriodIndex, columns=filterData.columns)
# timeDecayData = pd.eval('filterData * timeDecayWeight')
# weightedMean = timeDecayData.mean(axis=0)
# weightedStd = np.sqrt((np.power(filterData - weightedMean, 2) * np.power(timeDecayWeight/tempLen, 2)).sum(axis=0))
# scenarioSummary[u'sharp'][scenarioName] = (weightedMean - rf)/weightedStd
# # 下行标准差
# filterDf = pct_Data[lenFilter & rptFilter]
# downsideStd = filterDf[pd.eval('filterDf < tempMean')]
# downsideStd = np.sqrt(np.power(downsideStd, 2).sum() / float(num))
# tempSortino = copy.deepcopy(tempMean)
tempIR = copy.deepcopy(tempMean)
# tempIR[pd.eval('tempMean > rf')] = ((tempMean - rf)/tempStd)[pd.eval('tempMean > rf')].values
# tempIR[pd.eval('tempMean < -rf')] = ((tempMean + rf)/tempStd)[pd.eval('tempMean < -rf')].values
# tempIR[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# scenarioSummary[u'IR'][scenarioName] = tempIR # 不包含变动小的基金
# scenarioSummary[u'IR'][scenarioName] = tempMean/tempStd # 含变动小的基金
# 夏普率
scenarioSummary[u'sharp'][scenarioName] = (tempMean - rf) / tempStd
# tempSortino[pd.eval('tempMean > rf')] = ((tempMean - rf) / downsideStd)[pd.eval('tempMean > rf')].values
# tempSortino[pd.eval('tempMean < -rf')] = ((tempMean + rf) / downsideStd)[pd.eval('tempMean < -rf')].values
# tempSortino[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# scenarioSummary[u'sortino'][scenarioName] = tempSortino # 不包含变动小的基金
# tempIR.to_csv('IR.csv')
t = seriesTop(scenarioSummary[u'sharp'][scenarioName],
5,
ascending=False)[0]
distDict[u'sharp'][scenarioName] = seriesTop(
scenarioSummary[u'sharp'][scenarioName], 5, ascending=False)[0]
# distDict[u'IR'][scenarioName] = seriesTop(scenarioSummary[u'IR'][scenarioName], 10, ascending=False)[0]
# rank = distDict[u'IR'][scenarioName].to_csv('rank.csv')
# distDict[u'IR'][scenarioName] = seriesPercentage(scenarioSummary[u'IR'][scenarioName], 0.01, u'right')[0] # 历史分布--百分比
return distDict
def scenarioHistF2(self, longRef, shortRef, scenarioName, num):
''' 情景分布函数：长短维度'''
scenarioSummary = {
u'mean': {},
u'std': {},
u'IR': {},
u'sortino': {},
u'sharpe': {}
}
distDict = {
u'mean': {},
u'std': {},
u'IR': {},
u'sortino': {},
u'sharpe': {}
}
rf = 0.07 / 252
en = self.getTime(ref=1) # 上一天
longSt = self.getTime(ref=longRef)
shortSt = self.getTime(ref=shortRef)
if scenarioName:
scenarioCond = copy.deepcopy(self.scenarioCond)
scenarioIndex = scenarioCond[scenarioCond == scenarioName].index
scenarioSeries = pd.Series(scenarioIndex, index=scenarioIndex)
targePeriodIndex = scenarioSeries.loc[longSt:en].index
pct_Data = self.pct_fund.loc[targePeriodIndex]
else:
pct_Data = self.pct_fund.loc[longSt:en]
pct_Data_short = self.pct_fund.loc[shortSt:en]
colLen = pct_Data.count(axis=0)
lenFilter = colLen[colLen >= num].index
filterData = pct_Data[lenFilter]
filterData_short = pct_Data_short[lenFilter]
scenarioSummary[u'mean'][scenarioName] = filterData.mean(
axis=0) # 默认skipna=True
scenarioSummary[u'std'][scenarioName] = filterData.std(axis=0)
tempMean = scenarioSummary[u'mean'][scenarioName]
tempStd = scenarioSummary[u'std'][scenarioName]
shortMean = filterData_short[lenFilter].mean(axis=0)
shortStd = filterData_short[lenFilter].std(axis=0)
# 时间衰减IR -- 测试效果不好
# timeDecayWeight = pd.DataFrame(np.transpose([dataGap] * len(filterData.columns)), index=targePeriodIndex, columns=filterData.columns)
# timeDecayData = pd.eval('filterData * timeDecayWeight')
# weightedMean = timeDecayData.mean(axis=0)
# weightedStd = np.sqrt((np.power(filterData - weightedMean, 2) * np.power(timeDecayWeight/tempLen, 2)).sum(axis=0))
# scenarioSummary[u'sharpe'][scenarioName] = (weightedMean - rf)/weightedStd
# 下行标准差
# downsideStd = filterData[pd.eval('filterData < tempMean')]
# downsideStd = np.sqrt(np.power(downsideStd, 2).sum() / float(num))
# tempSortino = copy.deepcopy(tempMean)
# tempSortino[pd.eval('tempMean > rf')] = ((tempMean - rf) / downsideStd)[pd.eval('tempMean > rf')].values
# tempSortino[pd.eval('tempMean < -rf')] = ((tempMean + rf) / downsideStd)[pd.eval('tempMean < -rf')].values
# tempSortino[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# scenarioSummary[u'sortino'][scenarioName] = tempSortino # 不包含变动小的基金
# downsideStd_short = filterData_short[pd.eval('filterData_short < shortMean')]
# downsideStd_short = np.sqrt(np.power(downsideStd_short, 2).sum() / float(num))
# tempSortino_short = (shortMean - rf)/downsideStd_short
# tempSortino_short[pd.eval('shortMean > rf')] = ((shortMean - rf) / downsideStd_short)[pd.eval('shortMean > rf')].values
# tempSortino_short[pd.eval('shortMean < -rf')] = ((shortMean + rf) / downsideStd_short)[pd.eval('shortMean < -rf')].values
# tempSortino_short[pd.eval('shortMean >= -rf and shortMean <= rf')] = 0.
# tempIR = copy.deepcopy(tempMean)
# tempIR[pd.eval('tempMean > rf')] = ((tempMean - rf)/tempStd)[pd.eval('tempMean > rf')].values
# tempIR[pd.eval('tempMean < -rf')] = ((tempMean + rf)/tempStd)[pd.eval('tempMean < -rf')].values
# tempIR[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# scenarioSummary[u'IR'][scenarioName] = tempIR # 不包含变动小的基金
# scenarioSummary[u'IR'][scenarioName] = tempMean/tempStd # 含变动小的基金
# # 夏普率
scenarioSummary[u'sharpe'][scenarioName] = (tempMean - rf) / tempStd
# tempSharpe_short = (shortMean - 0.06/252)/shortStd
# tempIR.to_csv('IR.csv')
# distDict[u'IR'][scenarioName] = seriesTop(scenarioSummary[u'IR'][scenarioName], 10, ascending=False)[0]
# rank = distDict[u'IR'][scenarioName].to_csv('rank.csv')
# distDict[u'IR'][scenarioName] = seriesPercentage(scenarioSummary[u'IR'][scenarioName], 0.01, u'right')[0] # 历史分布--百分比
longSort = seriesTop(scenarioSummary[u'sharpe'][scenarioName],
5,
ascending=False)[0] # 长期
# shortSort = seriesTop(shortMean.loc[longSort.index], 5, ascending=False)[0] # 短期
# longSort = seriesTop(tempMean, 5, ascending=False)[0] # 长期
distDict[u'sharpe'][scenarioName] = longSort
return distDict
def scenarioHistF2(self, st, en, scenarioName, num, dataType):
''' 情景分布生成 '''
scenarioCond = copy.deepcopy(self.scenarioCond)
scenarioIndex = scenarioCond[scenarioCond == scenarioName].index
scenarioSeries = pd.Series(scenarioIndex, index=scenarioIndex)
targePeriodIndex = scenarioSeries.loc[st:en].index
# pct_Data = self.pct_fund.loc[targePeriodIndex]
pct_Data = self.pct_fund.loc[targePeriodIndex]
colLen = pct_Data.count(axis=0)
lenFilter = colLen[colLen >= num].index
pct_Data = pct_Data[lenFilter]
if dataType == u'IR':
# 超额收益，带相关性
tempDf = pd.concat(
[pct_Data, self.tempBenchPct.loc[targePeriodIndex]], axis=1)
corrSeries = tempDf.corr().loc[u'close'].loc[lenFilter].dropna()
corrFilter = corrSeries.index
tempBenchPct = self.tempBenchPctDf[
lenFilter & corrFilter].loc[targePeriodIndex] * corrSeries
exPct = pct_Data[corrFilter] - tempBenchPct
# 时间衰减IR
# tempLen = pct_Data.index.size
# tempGap = math.e
# a1 = 1./sum([pow(tempGap, i) for i in range(tempLen)])
# dataGap = [a1] + [a1*pow(tempGap, i)for i in range(1, tempLen)]
fundType = [u'股票型基金', u'债券型基金', u'混合型基金']
typeDict = {}
for typeName in fundType:
scenarioSummary = {
u'mean': {},
u'std': {},
u'IR': {},
u'sortino': {},
u'sharpe': {}
}
rf = 0.1 / 252.
if typeName == u'股票型基金':
timeFilter = self.stockLatest.index[
self.stockLatest['最新净值日期'] > en]
elif typeName == u'债券型基金':
rf = 0.05 / 252.
timeFilter = self.fixedincomeLatest.index[
self.fixedincomeLatest['最新净值日期'] > en]
elif typeName == u'混合型基金':
timeFilter = self.hybridLatest.index[
self.hybridLatest['最新净值日期'] > en]
if dataType == u'IR':
filterData = exPct[exPct.columns & timeFilter]
elif dataType == u'sharpe':
filterData = pct_Data[pct_Data.columns & timeFilter]
tempMean = filterData.mean(axis=0) # 默认skipna=True
tempStd = filterData.std(axis=0)
# 时间衰减IR -- 测试效果不好
# timeDecayWeight = pd.DataFrame(np.transpose([dataGap] * len(filterData.columns)), index=targePeriodIndex, columns=filterData.columns)
# timeDecayData = pd.eval('filterData * timeDecayWeight')
# weightedMean = timeDecayData.mean(axis=0)
# weightedStd = np.sqrt((np.power(filterData - weightedMean, 2) * np.power(timeDecayWeight/tempLen, 2)).sum(axis=0))
# weightedIR = (weightedMean - rf)/weightedStd
# # 下行标准差
# downsideStd = filterDf[pd.eval('filterDf < tempMean')]
# downsideStd = np.sqrt(np.power(downsideStd, 2).sum() / float(num))
# tempSortino = copy.deepcopy(tempMean)
# tempSortino[pd.eval('tempMean > rf')] = ((tempMean - rf) / downsideStd)[pd.eval('tempMean > rf')].values
# tempSortino[pd.eval('tempMean < -rf')] = ((tempMean + rf) / downsideStd)[pd.eval('tempMean < -rf')].values
# tempSortino[pd.eval('tempMean >= -rf and tempMean <= rf')] = 0.
# if scenarioName == u'显著上升':
# sortData = tempMean
# elif scenarioName == u'震荡':
# sortData = (tempMean - rf) / tempStd # 不含变动小的基金
# else:
# sortData = (tempMean - rf) / tempStd # 不含变动小的基金
sortData = (tempMean - rf) / tempStd # 不含变动小的基金
sortResult = seriesTop(sortData, 10, ascending=False)[0]
# sortResult = seriesPercentage(scenarioSummary[dataType][scenarioName], 0.01, u'right')[0] # 历史分布--百分比
typeDict[typeName] = {dataType: {scenarioName: sortResult}}
return typeDict
