def TrimData(self):
'''
清洗和预处理数据
:return: 处理好的 pandas.Dataframe
'''
# 数据导入
fundNav = read_csvEx(self.dataFile, 0)
benchName = self.benchmarkName.strip(u'_stocks')
bench = self.index_DF(benchName,
u'stocks')[u'close'].to_frame(benchName)
fundNav = fundNav.replace({0: np.nan})
pct_fund = fundNav.pct_change()
pct_fund.iloc[0, :] = 0.
# 直接给出市道
scenarioCond = self.marketPoint
scenarioCond = scenarioCond.replace({
u'下行': u'显著下降',
u'上行': u'显著上升'
}).loc[bench.index]
self.fund_predfund_manager = pd.read_csv(
u'../Data/Main/fund_predfund_manager.csv', encoding='gbk')
managerIndex = self.fund_predfund_manager.index
# 基金经理管理基金时间处理
stCol = []
enCol = []
lastDate = self.time_List()[-1]
for i in range(self.fund_predfund_manager.index.size):
tempStr = self.fund_predfund_manager[u'fund_manage_time'].iloc[i]
try:
if isinstance(tempStr, unicode):
if u'至今' in tempStr:
tempSt, tempEn = [tempStr.strip(u'至今'), lastDate]
else:
tempSt, tempEn = tempStr.split(u'-')
else:
tempSt, tempEn = [None, None]
except:
tempSt, tempEn = [None, None]
stCol.append(tempSt)
enCol.append(tempEn)
self.fund_predfund_manager[u'fund_manage_time_st'] = pd.Series(
pd.DatetimeIndex(stCol, index=managerIndex))
self.fund_predfund_manager[u'fund_manage_time_en'] = pd.Series(
pd.DatetimeIndex(enCol, index=managerIndex))
# self.stockLatest = pd.read_csv(u'../Data/Main/股票型基金日期.csv', index_col=0)
# self.hybridLatest = pd.read_csv(u'../Data/Main/混合型基金日期.csv', index_col=0)
# self.fixedincomeLatest = pd.read_csv(u'../Data/Main/债券型基金日期.csv', index_col=0)
self.actualNetasset = read_csvEx(u'../Data/Main/基金规模.csv', 0)
self.fundNavFirst = read_csvEx(u'../Data/Main/基金净值.csv', 0)
self.pct_fund = pct_fund
self.scenarioCond = scenarioCond
self.firstTriger = True
return super(MyStrategy, self).TrimData()
def TrimData(self):
'''
清洗和预处理数据
:return: 处理好的 pandas.Dataframe
'''
# 数据导入
fundNav = read_csvEx(self.dataFile, 0)
fundNav = fundNav.replace({0: np.nan})
pct_fund = fundNav.pct_change()
pct_fund.iloc[0, :] = 0.
# 基金经理代码
self.managerIndex = []
# 直接给出市道
self.fund_predfund_manager = pd.read_csv(
u'../Data/Main/fund_predfund_manager.csv', encoding='gbk')
managerIndex = self.fund_predfund_manager.index
# 基金经理管理基金时间处理
stCol = []
enCol = []
lastDate = self.time_List()[-1]
for i in range(self.fund_predfund_manager.index.size):
tempStr = self.fund_predfund_manager[u'fund_manage_time'].iloc[i]
try:
if isinstance(tempStr, unicode):
if u'至今' in tempStr:
tempSt, tempEn = [tempStr.strip(u'至今'), lastDate]
else:
tempSt, tempEn = tempStr.split(u'-')
else:
tempSt, tempEn = [None, None]
except:
tempSt, tempEn = [None, None]
stCol.append(tempSt)
enCol.append(tempEn)
self.fund_predfund_manager[u'fund_manage_time_st'] = pd.Series(
pd.DatetimeIndex(stCol, index=managerIndex))
self.fund_predfund_manager[u'fund_manage_time_en'] = pd.Series(
pd.DatetimeIndex(enCol, index=managerIndex))
self.actualNetasset = read_csvEx(u'../Data/Main/基金规模.csv', 0)
self.fundNavFirst = read_csvEx(u'../Data/Main/基金净值.csv', 0)
self.fund_list = pd.read_csv(u'../Data/Main/fund_list.csv',
index_col=0,
encoding='utf-8')
self.firstTriger = True
self.pct_fund = pct_fund
return super(MyStrategy, self).TrimData()
def TrimData(self):
'''
清洗和预处理数据
:return: 处理好的 pandas.Dataframe
'''
# 数据导入
fundNav = read_csvEx(self.dataFile, 0)
benchName = self.benchmarkName.strip(u'_stocks')
bench = self.index_DF(benchName,
u'stocks')[u'close'].to_frame(benchName)
fundNav = fundNav.replace({0: np.nan})
pct_fund = fundNav.pct_change()
pct_fund.iloc[0, :] = 0.
# 直接给出市道
scenarioCond = self.marketPoint
scenarioCond = scenarioCond.replace({
u'下行': u'显著下降',
u'上行': u'显著上升'
}).loc[bench.index]
fundLatestRptDate = self.fundLatestRptDate[pct_fund.columns].dropna()
self.fundNavFirst = fundNav
self.pct_fund = pct_fund
self.scenarioCond = scenarioCond
self.fundLatestRptDate = fundLatestRptDate
self.firstTriger = True
return super(MyStrategy, self).TrimData()
def TrimData(self):
'''
清洗和预处理数据
:return: 处理好的 pandas.Dataframe
'''
# 数据导入
fundNav = read_csvEx(self.dataFile, 0)
benchName = self.benchmarkName.strip(u'_stocks')
bench = self.index_DF(benchName,
u'stocks')[u'close'].to_frame(benchName)
fundNav = fundNav.replace({0: np.nan})
pct_fund = fundNav.pct_change()
pct_fund.iloc[0, :] = 0.
# 直接给出市道
scenarioCond = self.marketPoint
scenarioCond = scenarioCond.replace({
u'下行': u'显著下降',
u'上行': u'显著上升'
}).loc[bench.index]
self.stockLatest = pd.read_csv(u'../Data/Main/股票型基金日期.csv',
index_col=0)
self.hybridLatest = pd.read_csv(u'../Data/Main/混合型基金日期.csv',
index_col=0)
self.fixedincomeLatest = pd.read_csv(u'../Data/Main/债券型基金日期.csv',
index_col=0)
self.actualNetasset = read_csvEx(u'../Data/Main/基金规模.csv', 0)
self.fundNavFirst = fundNav
self.pct_fund = pct_fund
self.scenarioCond = scenarioCond
self.firstTriger = True
self.typeDict = {u'显著上升': u'股票型基金', u'震荡': u'混合型基金', u'显著下降': u'债券型基金'}
# 超额收益
self.tempBenchPct = self.benchmark.values()[0].pct_change().loc[
pct_fund.index]
self.tempBenchPctDf = pd.DataFrame(
{i: self.tempBenchPct.iloc[:, 0]
for i in self.pct_fund})
# self.exPct = pct_fund - tempDf
return super(MyStrategy, self).TrimData()