def xirrcal(cftable, trades, date, guess=0.1):
"""
calculate the xirr rate
:param cftable: cftable (pd.Dateframe) with date and cash column
:param trades: list [trade1, ...], every item is an trade object,
whose shares would be sold out virtually
:param date: string of date or datetime object,
the date when virtually all holding positions being sold
:param guess: floating number, a guess at the xirr rate solution to be used
as a starting point for the numerical solution
:returns: the IRR as a single floating number
"""
date = convert_date(date)
partcftb = cftable[cftable["date"] <= date]
if len(partcftb) == 0:
return 0
cashflow = [(row["date"], row["cash"]) for i, row in partcftb.iterrows()]
rede = 0
for fund in trades:
if not isinstance(fund, itrade):
rede += fund.aim.shuhui(
fund.briefdailyreport(date).get("currentshare", 0),
date,
fund.remtable[fund.remtable["date"] <= date].iloc[-1].rem,
)[1]
else: # 场内交易
pricedf = get_daily(fund.code, end=date.strftime("%Y%m%d"), prev=20)
price = pricedf[pricedf.date <= date].iloc[-1]["close"]
rede += fund.cftable.share.sum() * price
cashflow.append((date, rede))
return xirr(cashflow, guess)
def xirrcal(cftable, trades, date, guess):
'''
calculate the xirr rate
:param cftable: cftable (pd.Dateframe) with date and cash column
:param trades: list [trade1, ...], every item is an trade object,
whose shares would be sold out virtually
:param date: string of date or datetime object,
the date when virtually all holding positions being sold
:param guess: floating number, a guess at the xirr rate solution to be used
as a starting point for the numerical solution
:returns: the IRR as a single floating number
'''
date = convert_date(date)
partcftb = cftable[cftable['date'] <= date]
if len(partcftb) == 0:
return 0
cashflow = [(row['date'], row['cash']) for i, row in partcftb.iterrows()]
rede = 0
for fund in trades:
rede += fund.aim.shuhui(
fund.briefdailyreport(date).get('currentshare', 0), date,
fund.remtable[fund.remtable['date'] <= date].iloc[-1].rem)[1]
cashflow.append((date, rede))
return xirr(cashflow, guess)
def xirrcal(cftable, trades, date, startdate=None, guess=0.01):
"""
calculate the xirr rate
:param cftable: cftable (pd.Dateframe) with date and cash column
:param trades: list [trade1, ...], every item is an trade object,
whose shares would be sold out virtually
:param date: string of date or datetime object,
the date when virtually all holding positions being sold
:param guess: floating number, a guess at the xirr rate solution to be used
as a starting point for the numerical solution
:returns: the IRR as a single floating number
"""
date = convert_date(date)
partcftb = cftable[cftable["date"] <= date]
if len(partcftb) == 0:
return 0
if not startdate:
cashflow = [(row["date"], row["cash"])
for i, row in partcftb.iterrows()]
else:
if not isinstance(startdate, dt.datetime):
startdate = dt.datetime.strptime(
startdate.replace("-", "").replace("/", ""), "%Y%m%d")
start_cash = 0
for fund in trades:
start_cash += fund.briefdailyreport(startdate).get(
"currentvalue", 0)
cashflow = [(startdate, -start_cash)]
partcftb = partcftb[partcftb["date"] > startdate]
cashflow.extend([(row["date"], row["cash"])
for i, row in partcftb.iterrows()])
rede = 0
for fund in trades:
if not isinstance(fund, itrade):
partremtb = fund.remtable[fund.remtable["date"] <= date]
if len(partremtb) > 0:
rem = partremtb.iloc[-1]["rem"]
else:
rem = []
rede += fund.aim.shuhui(
fund.briefdailyreport(date).get("currentshare", 0), date,
rem)[1]
else: # 场内交易
rede += fund.briefdailyreport(date).get("currentvalue", 0)
cashflow.append((date, rede))
return xirr(cashflow, guess)
def cb_ytm(issue_date, rlist, cp, date=None, tax=1.0, guess=0.01):
"""
可转债到期收益率计算器
:param issue_date: 发行日期
:param rlist: 计息及赎回列表
:param cp: 可转债现价
:param date: 参考日期
:param tax: 计税 1 vs 0.8 税后 YTM
:param guess: YTM 估计初始值
:return:
"""
if rlist[-1] < 100:
logger.warning(
"the format of rlist must contain the final return more than 100 without interest of that year"
)
issue_date = issue_date.replace("-", "").replace("/", "")
issue_date_obj = dt.datetime.strptime(issue_date, "%Y%m%d")
if date is None:
date_obj = dt.datetime.today()
else:
date = date.replace("-", "").replace("/", "")
date_obj = dt.datetime.strptime(date, "%Y%m%d")
cf = [(date_obj, -cp)]
passed = (date_obj - issue_date_obj).days // 365
for i, r in enumerate(rlist[:-1]):
if i >= passed:
cf.append(
(issue_date_obj + dt.timedelta(days=(i + 1) * 365), r * tax))
# 关于赎回利息计算： https://www.jisilu.cn/?/question/339
# https://www.jisilu.cn/question/5807
# 富投网的算法：将最后一年超出100的部分，全部按照20%计税，
cf.append((issue_date_obj + dt.timedelta(days=(len(rlist) - 1) * 365),
rlist[-1]))
# print(cf)
return xirr(cf, guess=guess)