def __init__(
self,
infoobj,
start,
col,
buy,
sell=None,
buylow=True,
end=yesterdaydash(),
totmoney=100000,
):
self.pos = 0
self.col = col
self.buylow = buylow
self.selllevel = 0
bdivision = sum([it[1] for it in buy])
self.buy = []
for item in buy:
self.buy.append((item[0], item[1] / bdivision))
if sell is not None:
self.sell = []
sdivision = sum([it[1] for it in sell])
for item in sell:
self.sell.append((item[0], item[1] / sdivision))
else:
self.sell = sell
super().__init__(infoobj, start, end, totmoney)
def v_positions_history(self, end=yesterdaydash(), rendered=True):
"""
river chart visulization of positions ratio history
use text size to avoid legend overlap in some sense, eg. legend_text_size=8
"""
start = self.totcftable.iloc[0].date
times = pd.date_range(start, end)
tdata = []
for date in times:
sdata = sorted(
[(
date,
fob.briefdailyreport(date).get("currentvalue", 0),
fob.name,
) for fob in self.fundtradeobj],
key=lambda x: x[1],
reverse=True,
)
tdata.extend(sdata)
tr = ThemeRiver()
tr.add(
series_name=[foj.name for foj in self.fundtradeobj],
data=tdata,
label_opts=opts.LabelOpts(is_show=False),
singleaxis_opts=opts.SingleAxisOpts(type_="time",
pos_bottom="10%"),
)
if rendered:
return tr.render_notebook()
else:
return tr
def __init__(
self,
infoobj,
buypercent,
sellpercent,
start,
end=yesterdaydash(),
totmoney=100000,
):
assert len(buypercent) == len(sellpercent)
self.division = len(buypercent)
self.pos = 0
self.zero = (infoobj.price[
infoobj.price["date"] >= start].iloc[0].loc["netvalue"])
buypts = [self.zero]
sellpts = []
for term in buypercent:
buypts.append(buypts[-1] * (1 - term / 100.0))
for i, term in enumerate(sellpercent):
sellpts.append(buypts[i + 1] * (1 + term / 100.0))
self.buypts = buypts[1:]
self.sellpts = sellpts
self.buypercent = buypercent
self.sellpercent = sellpercent
super().__init__(infoobj, start, end, totmoney)
def _basic_init(self):
self._page = rget(self._url)
if self._page.text[:800].find("Data_fundSharesPositions") >= 0:
raise FundTypeError(
"This code seems to be a fund, use fundinfo instead")
l = eval(
re.match(r".*Data_millionCopiesIncome = ([^;]*);.*",
self._page.text).groups()[0])
self.name = re.match(r".*fS_name = \"([^;]*)\";.*",
self._page.text).groups()[0]
tz_bj = dt.timezone(dt.timedelta(hours=8))
datel = [
dt.datetime.fromtimestamp(int(d[0]) / 1e3,
tz=tz_bj).replace(tzinfo=None) for d in l
]
ratel = [float(d[1]) for d in l]
netvalue = [1]
for dailyrate in ratel:
netvalue.append(netvalue[-1] * (1 + dailyrate * 1e-4))
netvalue.remove(1)
df = pd.DataFrame(
data={
"date": datel,
"netvalue": netvalue,
"totvalue": netvalue,
"comment": [0 for _ in datel],
})
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
self.price = df[df["date"] <= yesterdaydash()]
def __init__(self, infoobj, start, end=yesterdaydash(), totmoney=100000):
self.aim = infoobj
self.totmoney = totmoney
self.price = infoobj.price[(infoobj.price["date"] >= start)
& (infoobj.price["date"] <= end)]
if len(self.price) == 0:
self.start = convert_date(start)
self.end = convert_date(end)
self.status = pd.DataFrame(data={"date": [], self.aim.code: []})
else:
self.start = self.price.iloc[0].date
self.end = self.price.iloc[-1].date
datel = []
actionl = []
times = pd.date_range(self.start, self.end)
for date in times:
action = self.status_gen(date)
if action > 0:
datel.append(date)
actionl.append(action)
elif action < 0:
datel.append(date)
actionl.append(action * 0.005)
df = pd.DataFrame(data={"date": datel, self.aim.code: actionl})
self.status = df
def _basic_init(self):
self._page = rget(self._url)
if self._page.status_code == 404:
raise ParserFailure(
"Unrecognized fund, please check fund code you input.")
if self._page.text[:800].find("Data_millionCopiesIncome") >= 0:
raise FundTypeError(
"This code seems to be a mfund, use mfundinfo instead")
l = re.match(r"[\s\S]*Data_netWorthTrend = ([^;]*);[\s\S]*",
self._page.text).groups()[0]
l = l.replace("null", "None") # 暂未发现基金净值有 null 的基金，若有，其他地方也很可能出问题！
l = eval(l)
ltot = re.match(r"[\s\S]*Data_ACWorthTrend = ([^;]*);[\s\S]*",
self._page.text).groups()[0] # .* doesn't match \n
ltot = ltot.replace("null", "None") ## 096001 总值数据中有 null！
ltot = eval(ltot)
## timestamp transform tzinfo must be taken into consideration
tz_bj = dt.timezone(dt.timedelta(hours=8))
infodict = {
"date": [
dt.datetime.fromtimestamp(int(d["x"]) / 1e3,
tz=tz_bj).replace(tzinfo=None)
for d in l
],
"netvalue": [float(d["y"]) for d in l],
"comment": [_nfloat(d["unitMoney"]) for d in l],
}
if len(l) == len(ltot): # 防止总值和净值数据量不匹配，已知有该问题的基金：502010
infodict["totvalue"] = [d[1] for d in ltot]
try:
rate = float(
eval(
re.match(r"[\s\S]*fund_Rate=([^;]*);[\s\S]*",
self._page.text).groups()[0]))
except ValueError:
rate = 0
logger.info(
"warning: this fund has no data for rate") # know cases: ETF
name = eval(
re.match(r"[\s\S]*fS_name = ([^;]*);[\s\S]*",
self._page.text).groups()[0])
self.rate = rate
# shengou rate in tiantianjijin, daeshengou rate discount is not considered
self.name = name # the name of the fund
df = pd.DataFrame(data=infodict)
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
if len(df) == 0:
raise ParserFailure("no price table found for this fund %s" %
self.code)
self.price = df[df["date"] <= yesterdaydash()]
# deal with the redemption fee attrs finally
if not self.priceonly:
self._feepreprocess()
def __init__(self,
infoobj,
col,
start,
end=yesterdaydash(),
totmoney=100000):
self.col = col
self.pos = 0
super().__init__(infoobj, start, end, totmoney)
def _basic_init(self):
self._page = _download(self._url)
if self._page.text[:800].find("Data_millionCopiesIncome") >= 0:
raise FundTypeError(
"This code seems to be a mfund, use mfundinfo instead")
l = eval(
re.match(r".*Data_netWorthTrend = ([^;]*);.*",
self._page.text).groups()[0])
ltot = eval(
re.match(r".*Data_ACWorthTrend = ([^;]*);.*",
self._page.text).groups()[0])
## timestamp transform tzinfo must be taken into consideration
tz_bj = dt.timezone(dt.timedelta(hours=8))
infodict = {
"date": [
dt.datetime.fromtimestamp(int(d["x"]) / 1e3,
tz=tz_bj).replace(tzinfo=None)
for d in l
],
"netvalue": [float(d["y"]) for d in l],
"comment": [_nfloat(d["unitMoney"]) for d in l],
}
if len(l) == len(ltot): # 防止总值和净值数据量不匹配，已知有该问题的基金：502010
infodict["totvalue"] = [d[1] for d in ltot]
try:
rate = float(
eval(
re.match(r".*fund_Rate=([^;]*);.*",
self._page.text).groups()[0]))
except ValueError:
rate = 0
if _do_print_warning:
print("warning: this fund has no data for rate"
) # know cases: 510030
name = eval(
re.match(r".*fS_name = ([^;]*);.*", self._page.text).groups()[0])
self.rate = rate
# shengou rate in tiantianjijin, daeshengou rate discount is not considered
self.name = name # the name of the fund
df = pd.DataFrame(data=infodict)
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
self.price = df[df["date"] <= yesterdaydash()]
# deal with the redemption fee attrs finally
self._feepreprocess()
def v_positions_history(self, end=yesterdaydash(), **vkwds):
'''
river chart visulization of positions ratio history
use text size to avoid legend overlap in some sense, eg. legend_text_size=8
'''
start = self.totcftable.iloc[0].date
times = pd.date_range(start, end)
tdata = []
for date in times:
sdata = sorted([(date, fob.briefdailyreport(date).get('currentvalue', 0), fob.aim.name)
for fob in self.fundtradeobj], key=lambda x: x[1], reverse=True)
tdata.extend(sdata)
tr = ThemeRiver()
tr.add([foj.aim.name for foj in self.fundtradeobj], tdata, is_datazoom_show=True,
is_label_show=False, legend_top="0%", legend_orient='horizontal', **vkwds)
return tr
def _basic_init(self):
self._page = _download(self._url)
if self._page.text[:800].find("Data_fundSharesPositions") >= 0:
raise FundTypeError("This code seems to be a fund, use fundinfo instead")
parser = Parser()
tree = parser.parse(self._page.text)
nodenet = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement)
and node.children()[0].children()[0].value == "Data_millionCopiesIncome"
][0]
name = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement)
and (node.children()[0].children()[0].value == "fS_name")
][0]
self.name = name.value.strip('"')
tz_bj = dt.timezone(dt.timedelta(hours=8))
datel = [
dt.datetime.fromtimestamp(
int(nodenet.children()[i].children()[0].value) / 1e3, tz=tz_bj
).replace(tzinfo=None)
for i in range(len(nodenet.children()))
]
ratel = [
float(nodenet.children()[i].children()[1].value)
for i in range(len(nodenet.children()))
]
netvalue = [1]
for dailyrate in ratel:
netvalue.append(netvalue[-1] * (1 + dailyrate * 1e-4))
netvalue.remove(1)
df = pd.DataFrame(
data={
"date": datel,
"netvalue": netvalue,
"totvalue": netvalue,
"comment": [0 for _ in datel],
}
)
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
self.price = df[df["date"] <= yesterdaydash()]
def _basic_init(self):
self.name = "货币基金"
self.rate = 0
datel = list(
pd.date_range(dt.datetime.strftime(self.start, "%Y-%m-%d"), yesterdaydash())
)
valuel = []
for i, date in enumerate(datel):
valuel.append((1 + self.interest) ** i)
dfdict = {
"date": datel,
"netvalue": valuel,
"totvalue": valuel,
"comment": [0 for _ in datel],
}
df = pd.DataFrame(data=dfdict)
self.price = df[df["date"].isin(opendate)]
def _basic_init(self):
self.name = "货币基金"
self.rate = 0
datel = list(
pd.date_range(dt.datetime.strftime(self.start, '%Y-%m-%d'),
yesterdaydash()))
valuel = []
for i, date in enumerate(datel):
valuel.append((1 + self.interest)**i)
dfdict = {
'date': datel,
'netvalue': valuel,
'totvalue': valuel,
'comment': [0 for _ in datel]
}
df = pd.DataFrame(data=dfdict)
self.price = df[df['date'].isin(opendate)]
def _basic_init(self):
self._page = _download(self._url)
parser = Parser()
tree = parser.parse(self._page.text)
nodenet = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement) and node.children()
[0].children()[0].value == 'Data_millionCopiesIncome'
][0]
name = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement) and (
node.children()[0].children()[0].value == 'fS_name')
][0]
self.name = name.value.strip('"')
tz_bj = dt.timezone(dt.timedelta(hours=8))
datel = [
dt.datetime.fromtimestamp(
int(nodenet.children()[i].children()[0].value) / 1e3,
tz=tz_bj).replace(tzinfo=None)
for i in range(len(nodenet.children()))
]
ratel = [
float(nodenet.children()[i].children()[1].value)
for i in range(len(nodenet.children()))
]
netvalue = [1]
for dailyrate in ratel:
netvalue.append(netvalue[-1] * (1 + dailyrate * 1e-4))
netvalue.remove(1)
df = pd.DataFrame(
data={
'date': datel,
'netvalue': netvalue,
'totvalue': netvalue,
'comment': [0 for _ in datel]
})
df = df[df['date'].isin(opendate)]
df = df.reset_index(drop=True)
self.price = df[df['date'] <= yesterdaydash()]
def _basic_init(self):
raw = _download(self._url)
cr = csv.reader(raw.text.splitlines(), delimiter=",")
my_list = list(cr)
factor = float(my_list[-1][3])
dd = {
"date": [
dt.datetime.strptime(my_list[i + 1][0], "%Y-%m-%d")
for i in range(len(my_list) - 1)
],
"netvalue": [
float(my_list[i + 1][3]) / factor for i in range(len(my_list) - 1)
],
"totvalue": [float(my_list[i + 1][3]) for i in range(len(my_list) - 1)],
"comment": [0 for _ in range(len(my_list) - 1)],
}
index = pd.DataFrame(data=dd)
index = index.iloc[::-1]
index = index.reset_index(drop=True)
self.price = index[index["date"].isin(opendate)]
self.price = self.price[self.price["date"] <= yesterdaydash()]
self.name = my_list[-1][2]
def _basic_init(self):
self._page = _download(self._url)
if self._page.text[:800].find("Data_millionCopiesIncome") >= 0:
raise FundTypeError("This code seems to be a mfund, use mfundinfo instead")
parser = Parser() # parse the js text of API page using slimit module
tree = parser.parse(self._page.text)
nodenet = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement)
and node.children()[0].children()[0].value == "Data_netWorthTrend"
][0]
nodetot = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement)
and node.children()[0].children()[0].value == "Data_ACWorthTrend"
][0]
## timestamp transform tzinfo must be taken into consideration
tz_bj = dt.timezone(dt.timedelta(hours=8))
infodict = {
"date": [
dt.datetime.fromtimestamp(
int(nodenet.children()[i].children()[0].right.value) / 1e3, tz=tz_bj
).replace(tzinfo=None)
for i in range(len(nodenet.children()))
],
"netvalue": [
float(nodenet.children()[i].children()[1].right.value)
for i in range(len(nodenet.children()))
],
"comment": [
_nfloat(nodenet.children()[i].children()[3].right.value)
for i in range(len(nodenet.children()))
],
}
if len(nodenet.children()) == len(
nodetot.children()
): # 防止总值和净值数据量不匹配，已知有该问题的基金：502010
infodict["totvalue"] = [
float(nodetot.children()[i].children()[1].value)
for i in range(len(nodenet.children()))
]
rate = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement)
and (node.children()[0].children()[0].value == "fund_Rate")
][0]
name = [
node.children()[0].children()[1]
for node in nodevisitor.visit(tree)
if isinstance(node, ast.VarStatement)
and (node.children()[0].children()[0].value == "fS_name")
][0]
self.rate = float(
rate.value.strip('"')
) # shengou rate in tiantianjijin, daeshengou rate discount is not considered
self.name = name.value.strip('"') # the name of the fund
df = pd.DataFrame(data=infodict)
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
self.price = df[df["date"] <= yesterdaydash()]
# deal with the redemption fee attrs finally
self._feepreprocess()
