def __init__(self,
*fundtradeobj,
status=None,
property=None,
fetch=False,
save=False,
path="",
form="csv",
totmoney=100000,
cashobj=None):
super().__init__(*fundtradeobj,
status=status,
property=property,
fetch=fetch,
save=save,
path=path,
form=form)
if cashobj is None:
cashobj = cashinfo()
self.totmoney = totmoney
nst = mulfix._vcash(totmoney, self.totcftable, cashobj)
cashtrade = trade(cashobj, nst)
# super().__init__(*self.fundtradeobj, cashtrade)
self.fundtradeobj = list(self.fundtradeobj)
self.fundtradeobj.append(cashtrade)
self.fundtradeobj = tuple(self.fundtradeobj)
btnk = bottleneck(self.totcftable)
if btnk > totmoney:
raise TradeBehaviorError("the initial total cash is too low")
self.totcftable = pd.DataFrame(data={
"date": [nst.iloc[0].date],
"cash": [-totmoney]
})
def combsummary(self, date=yesterdayobj()):
'''
brief report table of every funds and the combination investment
:param date: string or obj of date, show info of the date given
:returns: empty dict if nothing is remaining that date
dict of various data on the trade positions
'''
date = convert_date(date)
columns = ['基金名称', '基金代码', '当日净值', '单位成本', '持有份额', '基金现值', '基金总申购', '历史最大占用',
'基金持有成本', '基金分红与赎回', '换手率', '基金收益总额', '投资收益率']
summarydf = pd.DataFrame([], columns=columns)
for fund in self.fundtradeobj:
summarydf = summarydf.append(fund.dailyreport(date), ignore_index=True, sort=True)
tname = '总计'
tcode = 'total'
tunitvalue = float('NaN')
tunitcost = float('NaN')
tholdshare = float('NaN')
tcurrentvalue = summarydf['基金现值'].sum()
tpurchase = summarydf['基金总申购'].sum()
tbtnk = bottleneck(self.totcftable[self.totcftable['date'] <= date])
tcost = summarydf['基金持有成本'].sum()
toutput = summarydf['基金分红与赎回'].sum()
tturnover = turnoverrate(self.totcftable[self.totcftable['date'] <= date], date)
# 计算的是总系统作为整体和外界的换手率，而非系统各成分之间的换手率
tearn = summarydf['基金收益总额'].sum()
trate = round(tearn / tbtnk * 100, 4)
trow = pd.DataFrame([[tname, tcode, tunitvalue, tunitcost, tholdshare,
tcurrentvalue, tpurchase, tbtnk, tcost, toutput, tturnover, tearn, trate]],
columns=columns)
summarydf = summarydf.append(trow, ignore_index=True, sort=True)
return summarydf[columns].sort_values(by="基金现值", ascending=False)
def __init__(self, *fundtradeobj, status=None, fetch=False, save=False, path='', form='csv', totmoney=100000,
cashobj=None):
super().__init__(*fundtradeobj, status=status, fetch=fetch, save=save, path=path, form=form)
if cashobj is None:
cashobj = cashinfo()
self.totmoney = totmoney
nst = mulfix._vcash(totmoney, self.totcftable, cashobj)
cashtrade = trade(cashobj, nst)
# super().__init__(*self.fundtradeobj, cashtrade)
self.fundtradeobj = list(self.fundtradeobj)
self.fundtradeobj.append(cashtrade)
self.fundtradeobj = tuple(self.fundtradeobj)
btnk = bottleneck(self.totcftable)
if btnk > totmoney:
raise Exception('the initial total cash is too low')
self.totcftable = pd.DataFrame(data={'date': [nst.iloc[0].date], 'cash': [-totmoney]})
def combsummary(self, date=yesterdayobj()):
"""
brief report table of every funds and the combination investment
:param date: string or obj of date, show info of the date given
:returns: empty dict if nothing is remaining that date
dict of various data on the trade positions
"""
date = convert_date(date)
columns = [
"基金名称",
"基金代码",
"当日净值",
"单位成本",
"持有份额",
"基金现值",
"基金总申购",
"历史最大占用",
"基金持有成本",
"基金分红与赎回",
"换手率",
"基金收益总额",
"投资收益率",
]
summarydf = pd.DataFrame([], columns=columns)
for fund in self.fundtradeobj:
summarydf = summarydf.append(fund.dailyreport(date),
ignore_index=True,
sort=True)
tname = "总计"
tcode = "total"
tunitvalue = float("NaN")
tunitcost = float("NaN")
tholdshare = float("NaN")
tcurrentvalue = summarydf["基金现值"].sum()
tpurchase = summarydf["基金总申购"].sum()
tbtnk = bottleneck(self.totcftable[self.totcftable["date"] <= date])
tcost = summarydf["基金持有成本"].sum()
toutput = summarydf["基金分红与赎回"].sum()
tturnover = turnoverrate(
self.totcftable[self.totcftable["date"] <= date], date)
# 计算的是总系统作为整体和外界的换手率，而非系统各成分之间的换手率
tearn = summarydf["基金收益总额"].sum()
trate = round(tearn / tbtnk * 100, 4)
trow = pd.DataFrame(
[[
tname,
tcode,
tunitvalue,
tunitcost,
tholdshare,
tcurrentvalue,
tpurchase,
tbtnk,
tcost,
toutput,
tturnover,
tearn,
trate,
]],
columns=columns,
)
summarydf = summarydf.append(trow, ignore_index=True, sort=True)
return summarydf[columns].sort_values(by="基金现值", ascending=False)
def v_xirrate(self,start_date, end_date=yesterdayobj()):
"""
graph of the xirate of the over all investment
:param date: string or obj of date, show info of the date given
:returns:
"""
start_date = convert_date(start_date)
end_date = convert_date(end_date)
columns = [
"基金名称",
"基金代码",
"当日净值",
"单位成本",
"持有份额",#changed to date
"基金现值",
"基金总申购",
"历史最大占用",
"基金持有成本",
"基金分红与赎回",
"换手率",
"基金收益总额",
"投资收益率",
]
columns_tot = [
"基金名称",
"基金代码",
"当日净值",
"单位成本",
"持有份额",#changed to date
"基金现值",
"基金总申购",
"历史最大占用",
"基金持有成本",
"基金分红与赎回",
"换手率",
"基金收益总额",
"投资收益率",
]
tsummarydf = pd.DataFrame([],columns=columns)
for date in pd.date_range(start_date,end_date):
summarydf = pd.DataFrame([], columns=columns)
for fund in self.fundtradeobj:
summarydf = summarydf.append(
fund.dailyreport(date), ignore_index=True, sort=True
)
tname = "总计"
tcode = "total"
tunitvalue = float("NaN")
tunitcost = float("NaN")
tholdshare = date
tcurrentvalue = summarydf["基金现值"].sum()
tpurchase = summarydf["基金总申购"].sum()
tbtnk = bottleneck(self.totcftable[self.totcftable["date"] <= date])
tcost = summarydf["基金持有成本"].sum()
toutput = summarydf["基金分红与赎回"].sum()
tturnover = turnoverrate(self.totcftable[self.totcftable["date"] <= date], date)
# 计算的是总系统作为整体和外界的换手率，而非系统各成分之间的换手率
tearn = summarydf["基金收益总额"].sum()
trate = round(tearn / tbtnk * 100, 4)
trow = pd.DataFrame(
[
[
tname,
tcode,
tunitvalue,
tunitcost,
tholdshare,
tcurrentvalue,
tpurchase,
tbtnk,
tcost,
toutput,
tturnover,
tearn,
trate,
]
],
columns=columns
)
# summarydf = summarydf.append(trow, ignore_index=True, sort=True)
tsummarydf = tsummarydf.append(trow, ignore_index=True, sort=True)
return tsummarydf[columns].sort_values(by="持有份额", ascending=False)
