# -*- coding: utf-8 -*-
"""
modules of info class, including cashinfo, indexinfo and fundinfo class
"""
import os
import csv
import datetime as dt
import json
import re
import logging
from functools import lru_cache
import pandas as pd
from bs4 import BeautifulSoup
from sqlalchemy import exc
import xalpha.remain as rm
from xalpha.cons import (
convert_date,
droplist,
myround,
opendate,
yesterday,
yesterdaydash,
yesterdayobj,
today_obj,
rget,
rget_json,
_float,
)
from xalpha.exceptions import FundTypeError, TradeBehaviorError, ParserFailure
from xalpha.indicator import indicator
_warnmess = "Something weird on redem fee, please adjust self.segment by hand"
logger = logging.getLogger(__name__)
[docs]def _shengoucal(sg, sgf, value, label):
"""
Infer the share of buying fund by money input, the rate of fee in the unit of %,
and netvalue of fund
:param sg: positive float, 申购金额
:param sgf: positive float, 申购费，以％为单位，如 0.15 表示 0.15%
:param value: positive float, 对应产品的单位净值
:param label: integer, 1 代表份额正常进行四舍五入， 2 代表份额直接舍去小数点两位之后。金额部分都是四舍五入
:returns: tuple of two positive float, 净申购金额和申购份额
"""
jsg = myround(sg / (1 + sgf * 1e-2))
share = myround(jsg / value, label)
return (jsg, share)
[docs]def _nfloat(string):
"""
deal with comment column in fundinfo price table,
positive value for fenhong and negative value for chaifen,
keep other unrocognized pattern as original string
:param string: string of input from original data
:returns: make fenhong and songpei as float number
"""
result = 0
if string:
try:
result = float(string)
except ValueError:
if re.match(r'"分红\D*(\d*\.\d*)\D*"', string):
result = float(re.match(r'"分红\D*(\d*\.\d*)\D*"', string).group(1))
elif re.match(r".*现金(\d*\.\d*)\D*", string):
result = float(re.match(r".*现金(\d*\.\d*)\D*", string).group(1))
elif re.match(r".*折算(\d*\.\d*)\D*", string):
result = -float(re.match(r".*折算(\d*\.\d*)\D*", string).group(1))
elif re.match(r'"拆分\D*(\d*\.\d*)\D*"', string):
result = -float(re.match(r'"拆分\D*(\d*\.\d*)\D*"', string).group(1))
elif re.match(r"\D*分拆(\d*\.\d*)\D*", string):
result = -float(re.match(r"\D*分拆(\d*\.\d*)\D*", string).group(1))
else:
logger.warning("The comment col cannot be converted: %s" % string)
result = string
return result
[docs]class FundReport:
"""
提供查看各种基金报告的接口
"""
[docs] def __init__(self, code):
self.code = code
r = rget(
"http://api.fund.eastmoney.com/f10/JJGG?callback=&fundcode={code}&pageIndex=1&pageSize=20&type={type_}".format(
code=code, type_="3"
),
headers={
"Referer": "http://fundf10.eastmoney.com/jjgg_{code}_3.html".format(
code=code
)
},
)
self.report_list = r.json()["Data"]
self.report_detail = {}
[docs] def get_report(self, no=0, id_=None):
"""
:param no: int。在type_=3 中的第no个报告。
:param id_: id 可由 :meth:`show_report_list` 中条目的对应 ID 得到
:return:
"""
if id_:
report_url = "https://np-cnotice-fund.eastmoney.com/api/content/ann?client_source=web_fund&show_all=1&art_code={id_}".format(
id_=id_
)
if not self.report_detail.get(no):
report_url = "https://np-cnotice-fund.eastmoney.com/api/content/ann?client_source=web_fund&show_all=1&art_code={id_}".format(
id_=self.report_list[no]["ID"]
)
# report_url = "http://fund.eastmoney.com/gonggao/{code},{id_}.html".format(
# code=self.code, id_=self.report_list[no]["ID"]
# )
# r = rget(report_url)
# b = BeautifulSoup(r.text, "lxml")
# seasonr = b.find("pre")
# sr = [s.string.strip() for s in seasonr.findAll("p") if s.string]
r = rget_json(report_url)
sr = r["data"]["notice_content"]
sr = [s.strip() for s in sr.split("\n") if s.strip()]
self.report_detail[no] = sr
return sr
[docs] def show_report_list(self, type_=3):
"""
:param type_: int。第0栏，第1栏，每栏的含义，请参照天天基金基金报告的页面。
:return:
"""
r = rget(
"http://api.fund.eastmoney.com/f10/JJGG?callback=&fundcode={code}&pageIndex=1&pageSize=20&type={type_}".format(
code=self.code, type_=str(type_)
),
headers={
"Referer": "http://fundf10.eastmoney.com/jjgg_{code}_3.html".format(
code=self.code
)
},
)
return r.json()["Data"]
[docs] def analyse_report(self, no=0):
l = self.get_report(no)
d = {}
d["title"] = ""
for s in l[:5]:
if s.startswith("基金管理"):
break
d["title"] += s + " "
for i, s in enumerate(l):
if s.startswith("业绩比较基准"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
if l[i + 1][0] != "本":
d["benchmark"] = ss[-1] + l[i + 1]
else:
d["benchmark"] = ss[-1]
elif s.startswith("基金管理人"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
d["company"] = ss[-1]
elif s.startswith("基金托管人"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
d["bank"] = ss[-1]
elif s.startswith("场内简称"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
d["shortname"] = ss[-1]
elif s.startswith("基金主代码"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
d["code"] = ss[-1]
elif s.startswith("报告期末基金份额总额"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
d["share"] = ss[-1]
elif s.startswith("基金合同生效日"):
ss = [s for s in s.split(" ") if s.strip()]
if len(ss) == 2:
d["start_date"] = ss[-1]
return d
[docs]@lru_cache()
def get_fund_holdings(code, year="", season="", month="", category="jjcc"):
"""
获取基金详细的底层持仓信息
:param code: str. 6 位基金代码
:param year: int. eg. 2019
:param season: int, 1,2,3,4
:param month: Optional[int]. 指定 season 即可，一般不需理会
:param category: str. stock 股票持仓， bond 债券持仓，天天基金无法自动处理海外基金持仓，暂未兼容 FOF 的国内基金持仓
:return: pd.DataFrame or None. 没有对应持仓时返回 None。
"""
if not month and season:
month = 3 * int(season)
if category in ["stock", "stocks", "jjcc", "", "gp", "s"]:
category = "jjcc"
elif category in ["bond", "bonds", "zq", "zqcc", "b"]:
category = "zqcc"
else:
raise ParserFailure("unrecognized category %s" % category)
if code.startswith("F"):
code = code[1:]
r = rget(
"http://fundf10.eastmoney.com/FundArchivesDatas.aspx?type={category}&code={code}&topline=10&\
year={year}&month={month}".format(
year=str(year), month=str(month), code=code, category=category
),
headers={
"Host": "fundf10.eastmoney.com",
"Referer": "http://fundf10.eastmoney.com/ccmx_{code}.html".format(
code=code
),
},
)
if len(r.text) < 50:
return
# raise ParserFailure(
# "This fund has no holdings on stock or bonds in this period"
# )
s = BeautifulSoup(
re.match("[\s\S]*apidata={ content:(.*),arryear:", r.text).groups()[0], "lxml"
)
if len(s.text) < 30:
return
# raise ParserFailure(
# "This fund has no holdings on stock or bonds in this period"
# )
timeline = [
i.string for i in s.findAll("font", class_="px12") if i.text.startswith("2")
]
ind = 0
if month:
for i, d in enumerate(timeline):
if d.split("-")[1][-1] == str(month)[-1]: # avoid 09 compare to 9
ind = i
break
else:
return # not update to this month
t1 = s.findAll("table")[ind]
main = [[j.text for j in i.contents] for i in t1.findAll("tr")[1:]]
cols = [j.text for j in t1.findAll("tr")[0].contents if j.text.strip()]
icode = 1
iname = 2
iratio = 4
ishare = 5
ivalue = 6
for j, col in enumerate(cols):
if col.endswith("代码"):
icode = j
elif col.endswith("名称"):
iname = j
elif col.endswith("比例"):
iratio = j
elif col.startswith("持股数"):
ishare = j
elif col.startswith("持仓市值"):
ivalue = j
if category == "jjcc":
result = {"code": [], "name": [], "ratio": [], "share": [], "value": []}
for l in main:
result["code"].append(l[icode])
result["name"].append(l[iname])
result["ratio"].append(float(l[iratio][:-1]))
result["share"].append(_float(l[ishare]))
result["value"].append(_float(l[ivalue]))
elif category == "zqcc":
result = {"code": [], "name": [], "ratio": [], "value": []}
for l in main:
result["code"].append(l[1])
result["name"].append(l[2])
result["ratio"].append(float(l[3][:-1]))
result["value"].append(_float(l[4]))
return pd.DataFrame(result)
[docs]class basicinfo(indicator):
"""
Base class for info of fund, index or even cash,
which cannot be directly instantiate, the basic implementation consider
redemption fee as zero when shuhui() function is implemented
:param code: string of code for specific product
:param fetch: boolean, when open the fetch option, the class will try fetching from local files first in the init
:param save: boolean, when open the save option, automatically save the class to files
:param path: string, the file path prefix of IO. Or in sql case, path is the engine from sqlalchemy.
:param form: string, the format of IO, options including: 'csv','sql'
:param round_label: int, default 0 or 1, label to the different round scheme of shares, reserved for fundinfo class. 1 代表全舍而非四舍五入。
:param dividend_label: int, default 0 or 1. 0 代表默认现金分红，1代表红利再投。两者均可通过记账单上的 0.05 来改变单次的默认。
:param value_label: int, default 0 or 1. 1 代表记账单上的赎回数目是按金额而非份额的，只能完美支持货币基金。其他净值型基金本质上无法精确到分支持这一选项，因此不开放支持。
"""
[docs] def __init__(
self,
code,
fetch=False,
save=False,
path="",
form="csv",
round_label=0,
dividend_label=0,
value_label=0,
):
# 增量 IO 的逻辑都由 basicinfo 类来处理，对于具体的子类，只需实现_save_form 和 _fetch_form 以及 update 函数即可
self.code = code
self.round_label = round_label
self.dividend_label = dividend_label
self.value_label = value_label
self.specialdate = []
self.fenhongdate = []
self.zhesuandate = []
# compatible with new ``xa.set_backend()`` API
import xalpha.universal as xu
if (xu.ioconf["backend"] in ["csv", "sql"]) and (not path):
fetch = True
save = True
form = xu.ioconf["backend"]
path = xu.ioconf["path"]
if xu.ioconf["backend"] == "csv":
path = os.path.join(path, xu.ioconf["prefix"] + "INFO-")
self.format = form
if fetch is False:
self._basic_init() # update self. name rate and price table
else:
try:
self.fetch(path, self.format)
df = self.update() # update the price table as well as the file
if (df is not None) and save is True:
self.save(path, self.format, option="a", delta=df)
except (FileNotFoundError, exc.ProgrammingError) as e:
logger.info("no saved copy of %s" % self.code)
fetch = False
self._basic_init()
if (save is True) and (fetch is False):
self.save(path, self.format)
[docs] def _basic_init(self):
"""
set self. name rate and price (dataframe) as well as other necessary attr of info()
"""
# below lines are just showcase, this function must be rewrite by child classes
# self.name = 'unknown'
# self.rate = 0
# self.price = pd.DataFrame(data={'date':[],'netvalue':[],'comment':[]})
raise NotImplementedError
[docs] def shengou(self, value, date, fee=None):
"""
give the realdate deltacash deltashare tuple based on purchase date and purchase amount
if the date is not a trade date, then the purchase would happen on the next trade day, if the date is
in the furture, then the trade date is taken as yesterday.
:param value: the money for purchase
:param date: string or object of date
:param fee: the rate for shengou, default None and info.rate will be used, ok for most cases
:returns: three elements tuple, the first is the actual dateobj of commit
the second is a negative float for cashin,
the third is a positive float for share increase
"""
if fee is None:
fee = self.rate
row = self.price[self.price["date"] >= date].iloc[0]
share = _shengoucal(value, fee, row.netvalue, label=self.round_label + 1)[1]
return (row.date, -myround(value), share)
[docs] def shuhui(self, share, date, rem, value_label=None, fee=None):
"""
give the cashout considering redemption rates as zero.
if the date is not a trade date, then the purchase would happen on the next trade day, if the date is
in the furture, then the trade date is taken as yesterday.
:param share: float or int, number of shares to be sold. if value_label=1, its cash to be sold.
:param date: string or object of date
:param rem: positions with time list
:param value_label: default None, value_label will be chosen by info.value_label, determining
whether shuhui by share 0 or value 1. value_label = 0 will rewrite self.value_label = 1
:param fee: default None, determined automatically, suggested for most of the cases.
Otherwise 0.015 means 1.5% in shuhui
:returns: three elements tuple, the first is dateobj
the second is a positive float for cashout,
the third is a negative float for share decrease
"""
if self.value_label == 0 or value_label == 0:
return self._shuhui_by_share(share, date, rem)
elif self.value_label == 1: # 按金额赎回，仅支持无赎回费的货币基金
partprice = self.price[self.price["date"] >= date]
if len(partprice) == 0:
row = self.price[self.price["date"] < date].iloc[-1]
else:
row = partprice.iloc[0]
share = share / row.netvalue
return self._shuhui_by_share(share, date, rem, fee=fee)
[docs] def _shuhui_by_share(self, share, date, rem, fee=None):
date = convert_date(date)
tots = sum([remitem[1] for remitem in rem if remitem[0] <= date])
if share > tots:
sh = tots
else:
sh = share
partprice = self.price[self.price["date"] >= date]
if len(partprice) == 0:
row = self.price[self.price["date"] < date].iloc[-1]
else:
row = partprice.iloc[0]
value = myround(sh * row.netvalue)
if fee is not None:
value = (1 - fee) * value
return (
row.date,
value,
-myround(sh),
) # TODO: 这里 myround 是否也和 round_label 有关，有待考证
[docs] def info(self):
"""
print basic info on the class
"""
print("fund name: %s" % self.name)
print("fund code: %s" % self.code)
print("fund purchase fee: %s%%" % self.rate)
def __repr__(self):
return self.name
[docs] def save(self, path, form=None, option="r", delta=None):
"""
save info to files, this function is designed to redirect to more specific functions
:param path: string of the folder path prefix! or engine obj from sqlalchemy
:param form: string, option:'csv'
:param option: string, r for replace and a for append output
:param delta: if option is a, you have to specify the delta which is the incremental part of price table
"""
if form is None:
form = self.format
if form == "csv" and option == "r":
self._save_csv(path)
elif form == "csv" and option == "a":
self._save_csv_a(path, delta)
elif form == "sql" and option == "r":
self._save_sql(path)
elif form == "sql" and option == "a":
self._save_sql_a(path, delta)
[docs] def _save_csv_a(self, path, df):
df.sort_index(axis=1).to_csv(
path + self.code + ".csv",
mode="a",
header=None,
index=False,
date_format="%Y-%m-%d",
)
[docs] def _save_sql_a(self, path, df):
df.sort_index(axis=1).to_sql(
"xa" + self.code, path, if_exists="append", index=False
)
[docs] def fetch(self, path, form=None):
"""
fetch info from files
:param path: string of the folder path prefix! end with / in csv case;
engine from sqlalchemy.create_engine() in sql case.
:param form: string, option:'csv' or 'sql
"""
if form is None:
form = self.format
if form == "csv":
self._fetch_csv(path)
elif form == "sql":
self._fetch_sql(path)
[docs] def update(self):
"""
对类的价格表进行增量更新，并进行增量存储，适合 fetch 打开的情形
:returns: the incremental part of price table or None if no incremental part exsits
"""
raise NotImplementedError
[docs]class fundinfo(basicinfo):
"""
class for specific fund with basic info and every day values
所获得的基金净值数据一般截止到昨日。但注意QDII基金的净值数据会截止的更早，因此部分时间默认昨日的函数可能出现问题，
处理QDII基金时，需要额外注意。
:param code: str, 基金六位代码字符
:param round_label: integer 0 or 1, 取1表示基金申购时份额直接舍掉小数点两位之后。当基金处于 cons.droplist 名单中时，
label 总会被自动设置为1。非名单内基金可以显式令 round_label=1.
:param dividend_label: int, default 0 or 1. 0 代表默认现金分红，1代表红利再投。两者均可通过记账单上的 0.05 来改变单次的默认。
:param fetch: boolean, when open the fetch option, the class will try fetching from local files first in the init
:param save: boolean, when open the save option, automatically save the class to files
:param path: string, the file path prefix of IO
:param form: string, the format of IO, options including: 'csv'
"""
[docs] def __init__(
self,
code,
round_label=0,
dividend_label=0,
fetch=False,
save=False,
path="",
form="csv",
priceonly=False,
):
if round_label == 1 or (code in droplist):
label = 1 # the scheme of round down on share purchase
else:
label = 0
if code.startswith("F") and code[1:].isdigit():
code = code[1:]
elif code.startswith("M") and code[1:].isdigit():
raise FundTypeError(
"This code seems to be a mfund, use ``mfundinfo`` instead"
)
code = code.zfill(6) # 1234 is the same as 001234
self._url = (
"http://fund.eastmoney.com/pingzhongdata/" + code + ".js"
) # js url api for info of certain fund
self._feeurl = (
"http://fund.eastmoney.com/f10/jjfl_" + code + ".html"
) # html url for trade fees info of certain fund
self.priceonly = priceonly
super().__init__(
code,
fetch=fetch,
save=save,
path=path,
form=form,
round_label=label,
dividend_label=dividend_label,
)
self.special = self.price[self.price["comment"] != 0]
self.specialdate = list(self.special["date"])
# date with nonvanishing comment, usually fenhong or zhesuan
try:
self.fenhongdate = list(self.price[self.price["comment"] > 0]["date"])
self.zhesuandate = list(self.price[self.price["comment"] < 0]["date"])
except TypeError:
print("There are still string comments for the fund!")
[docs] def _basic_init(self):
if self.code.startswith("96"):
self._hkfund_init() # 中港互认基金处理
return
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()
[docs] def _feepreprocess(self):
"""
Preprocess to add self.feeinfo and self.segment attr according to redemption fee info
"""
feepage = rget(self._feeurl)
soup = BeautifulSoup(
feepage.text, "lxml"
) # parse the redemption fee html page with beautiful soup
somethingwrong = False
if not soup.findAll("a", {"name": "shfl"}):
somethingwrong = True
logger.warning("%s 基金赎回信息为空，可能由于该基金已终止运作" % self.code)
self.feeinfo = []
else:
self.feeinfo = [
item.string
for item in soup.findAll("a", {"name": "shfl"})[
0
].parent.parent.next_sibling.next_sibling.find_all("td")
if item.string != "---"
]
# this could be [], known case 510030
if not self.feeinfo or len(self.feeinfo) % 2 != 0:
somethingwrong = True
else:
for item in self.feeinfo:
if "开放期" in item or "封闭" in item or "开放日期" in item or "运作期" in item:
# 暂时没有完美维护定开基金赎回费处理的计划
somethingwrong = True
if somethingwrong:
logger.warning(
"%s 赎回费信息异常，多是因为定开基金，封闭基金或场内 ETF: %s" % (self.code, self.feeinfo)
)
self.feeinfo = ["小于7天", "1.50%", "大于等于7天", "0.00%"]
# print(self.feeinfo)
try:
self.segment = fundinfo._piecewise(self.feeinfo)
except (ValueError, IndexError) as e:
logger.warning(
"%s 赎回费信息抓取异常，请手动设定 ``self.segment`` 和 ``self.feeinfo``: %s"
% (self.code, self.feeinfo)
)
# below is default one
self.feeinfo = ["小于7天", "1.50%", "大于等于7天", "0.00%"]
self.segment = fundinfo._piecewise(self.feeinfo)
[docs] @staticmethod
def _piecewise(a):
"""
Transform the words list into a pure number segment list for redemption fee, eg. [[0,7],[7,365],[365]]
"""
b = [
(
a[2 * i]
.replace("持有期限", "")
.replace("开放运作期时持有", "")
.replace("不少于", "")
.replace("小于", "")
.replace("大于", "")
.replace("等于", "")
.replace("个", "")
.replace("持有", "")
.replace("以上", "")
.replace("以内", "")
.replace("的", "")
.replace("(含7天)", "")
.replace("份额持有时间", "")
).split("，")
for i in range(int(len(a) / 2))
]
# ['赎回时份额持有7天以内的', '1.50%', '持有7天以上(含7天),30天以内的', '0.10%', '赎回时份额持有满30天以上(含30天)的', '0.00%']
# ['由于本基金最短持有期限为三年,赎回费率设置为零。', '0.00%', '对持续持有期少于7日的投资者收取不低于1.5%的赎回费。', '1.50%']
# ['对持续持有期少于7日的投资者收取1.5%的赎回费并全额计入基金财产', '1.50%', '对于持续持有期大于等于7日的投资者不收取赎回费用。', '0.00%']
# print(b)
for j, tem in enumerate(b):
for i, num in enumerate(tem):
if num[-1] == "天":
num = int(num[:-1])
elif num[-1] == "月":
num = int(num[:-1]) * 30
elif num == ".5年":
num = 183
else:
num = int(float(num[:-1]) * 365)
b[j][i] = num
if len(b[0]) == 1: # 有时赎回费会写大于等于一天
b[0].insert(0, 0)
elif len(b[0]) == 2:
b[0][0] = 0
else:
print(_warnmess)
for i in range(len(b) - 1): # 有时赎回费两区间都是闭区间
if b[i][1] - b[i + 1][0] == -1:
b[i][1] = b[i + 1][0]
elif b[i][1] == b[i + 1][0]:
pass
else:
print(_warnmess)
return b
[docs] def feedecision(self, day):
"""
give the redemption rate in percent unit based on the days difference between purchase and redemption
:param day: integer， 赎回与申购时间之差的自然日数
:returns: float，赎回费率，以％为单位
"""
i = -1
for seg in self.segment:
i += 2
if day - seg[0] >= 0 and (len(seg) == 1 or day - seg[-1] < 0):
return float(self.feeinfo[i].strip("%"))
return 0 # error backup, in case there is sth wrong in segment
[docs] def shuhui(self, share, date, rem, value_label=None, fee=None):
"""
give the cashout based on rem term considering redemption rates
:returns: three elements tuple, the first is dateobj
the second is a positive float for cashout,
the third is a negative float for share decrease
"""
# value = myround(share*self.price[self.price['date']==date].iloc[0].netvalue)
date = convert_date(date)
partprice = self.price[self.price["date"] >= date]
if len(partprice) == 0:
row = self.price[self.price["date"] < date].iloc[-1]
else:
row = partprice.iloc[0]
soldrem, _ = rm.sell(rem, share, row.date)
value = 0
sh = myround(sum([item[1] for item in soldrem]))
for d, s in soldrem:
if fee is None:
tmpfee = self.feedecision((row.date - d).days) * 1e-2
else:
tmpfee = fee
value += myround(
s * row.netvalue * (1 - tmpfee)
) # TODO: round_label whether play a role here?
return (row.date, value, -sh)
[docs] def info(self):
super().info()
print("fund redemption fee info: %s" % self.feeinfo)
[docs] def _save_csv(self, path):
"""
save the information and pricetable into path+code.csv, not recommend to use manually,
just set the save label to be true when init the object
:param path: string of folder path
"""
s = json.dumps(
{
"feeinfo": self.feeinfo,
"name": self.name,
"rate": self.rate,
"segment": self.segment,
}
)
df = pd.DataFrame(
[[s, 0, 0, 0]], columns=["date", "netvalue", "comment", "totvalue"]
)
df = df.append(self.price, ignore_index=True, sort=True)
df.sort_index(axis=1).to_csv(
path + self.code + ".csv", index=False, date_format="%Y-%m-%d"
)
[docs] def _fetch_csv(self, path):
"""
fetch the information and pricetable from path+code.csv, not recommend to use manually,
just set the fetch label to be true when init the object
:param path: string of folder path
"""
try:
content = pd.read_csv(path + self.code + ".csv")
pricetable = content.iloc[1:]
datel = list(pd.to_datetime(pricetable.date))
self.price = pricetable[["netvalue", "totvalue", "comment"]]
self.price["date"] = datel
saveinfo = json.loads(content.iloc[0].date)
if not isinstance(saveinfo, dict):
raise FundTypeError("This csv doesn't looks like from fundinfo")
self.segment = saveinfo["segment"]
self.feeinfo = saveinfo["feeinfo"]
self.name = saveinfo["name"]
self.rate = saveinfo["rate"]
except FileNotFoundError as e:
# print('no saved copy of fund %s' % self.code)
raise e
[docs] def _save_sql(self, path):
"""
save the information and pricetable into sql, not recommend to use manually,
just set the save label to be true when init the object
:param path: engine object from sqlalchemy
"""
s = json.dumps(
{
"feeinfo": self.feeinfo,
"name": self.name,
"rate": self.rate,
"segment": self.segment,
}
)
df = pd.DataFrame(
[[pd.Timestamp("1990-01-01"), 0, s, 0]],
columns=["date", "netvalue", "comment", "totvalue"],
)
df = df.append(self.price, ignore_index=True, sort=True)
df.sort_index(axis=1).to_sql(
"xa" + self.code, con=path, if_exists="replace", index=False
)
[docs] def _fetch_sql(self, path):
"""
fetch the information and pricetable from sql, not recommend to use manually,
just set the fetch label to be true when init the object
:param path: engine object from sqlalchemy
"""
try:
content = pd.read_sql("xa" + self.code, path)
pricetable = content.iloc[1:]
commentl = [float(com) for com in pricetable.comment]
self.price = pricetable[["date", "netvalue", "totvalue"]]
self.price["comment"] = commentl
saveinfo = json.loads(content.iloc[0].comment)
if not isinstance(saveinfo, dict):
raise FundTypeError("This csv doesn't looks like from fundinfo")
self.segment = saveinfo["segment"]
self.feeinfo = saveinfo["feeinfo"]
self.name = saveinfo["name"]
self.rate = saveinfo["rate"]
except exc.ProgrammingError as e:
# print('no saved copy of %s' % self.code)
raise e
[docs] def _hk_update(self):
# 暂时不确定增量更新逻辑无 bug，需时间验证
# 注意增量更新时分红的同步更新
lastdate = self.price.iloc[-1].date
diffdays = (yesterdayobj() - lastdate).days
if diffdays == 0:
return None
import xalpha.universal as xu
df = xu.get_daily("F" + self.code, start=lastdate.strftime("%Y%m%d"))
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
df = df[df["date"] <= yesterdayobj()]
df = df[df["date"] > lastdate]
if len(df) != 0:
r = self._hk_bonus(start=lastdate.strftime("%Y-%m-%d"))
df["comment"] = [0 for _ in range(len(df))]
df["netvalue"] = df["close"]
df = df.drop("close", axis=1)
df = df[df["date"].isin(opendate)] # ? 是否会过滤掉分红日
for d in r:
df.loc[df["date"] == d["EXDDATE"], "comment"] = d["BONUS"]
self.price = self.price.append(df, ignore_index=True, sort=True)
return df
[docs] def update(self):
"""
function to incrementally update the pricetable after fetch the old one
"""
if self.code.startswith("96"):
return self._hk_update()
lastdate = self.price.iloc[-1].date
diffdays = (yesterdayobj() - lastdate).days
if (
diffdays == 0
): ## for some QDII, this value is 1, anyways, trying update is compatible (d+2 update)
return None
self._updateurl = (
"http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code="
+ self.code
+ "&page=1&per=1"
)
con = rget(self._updateurl)
soup = BeautifulSoup(con.text, "lxml")
items = soup.findAll("td")
if dt.datetime.strptime(str(items[0].string), "%Y-%m-%d") == today_obj():
diffdays += 1
if diffdays <= 10:
self._updateurl = (
"http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code="
+ self.code
+ "&page=1&per="
+ str(diffdays)
)
con = rget(self._updateurl)
soup = BeautifulSoup(con.text, "lxml")
items = soup.findAll("td")
elif (
diffdays > 10
): ## there is a 20 item per page limit in the API, so to be safe, we query each page by 10 items only
items = []
for pg in range(1, int(diffdays / 10) + 2):
self._updateurl = (
"http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code="
+ self.code
+ "&page="
+ str(pg)
+ "&per=10"
)
con = rget(self._updateurl)
soup = BeautifulSoup(con.text, "lxml")
items.extend(soup.findAll("td"))
else:
raise TradeBehaviorError(
"Weird incremental update: the saved copy has future records"
)
date = []
netvalue = []
totvalue = []
comment = []
for i in range(int(len(items) / 7)):
ts = pd.Timestamp(str(items[7 * i].string))
if (ts - lastdate).days > 0:
date.append(ts)
netvalue.append(_float(items[7 * i + 1].string))
totvalue.append(_float(items[7 * i + 2].string))
comment.append(_nfloat(items[7 * i + 6].string))
else:
break
df = pd.DataFrame(
{
"date": date,
"netvalue": netvalue,
"totvalue": totvalue,
"comment": comment,
}
)
df = df.iloc[::-1] ## reverse the time order
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
df = df[df["date"] <= yesterdayobj()]
if len(df) != 0:
self.price = self.price.append(df, ignore_index=True, sort=True)
return df
[docs] def get_holdings(self, year="", season="", month="", category="stock"):
return get_fund_holdings(
self.code, year, season=season, month=month, category=category
)
[docs] def get_stock_holdings(self, year="", season="", month=""):
"""
持仓个股细节
:param year:
:param season:
:param month:
:return: pd.DataFrame
"""
return get_fund_holdings(
self.code, year, season=season, month=month, category="stock"
)
[docs] def get_bond_holdings(self, year="", season="", month=""):
"""
持仓债券细节
:param year:
:param season:
:param month:
:return: pd.DataFrame
"""
return get_fund_holdings(
self.code, year, season=season, month=month, category="bond"
)
[docs] def get_portfolio_holdings(self, date=None):
"""
持仓股债现金占比
:param date:
:return: Dict
"""
if date is None:
date = dt.datetime.now().strftime("%Y-%m-%d")
import xalpha.universal as xu
df = xu.get_daily("pt-F" + self.code, end=date)
if df is not None:
d = dict(df.iloc[-1])
del d["assets"], d["date"]
return d
else:
logger.warning("no portfolio information before %s" % date)
return
[docs] def get_industry_holdings(self, year="", season="", month="", threhold=0.5):
"""
持仓行业占比
:param year:
:param season:
:param month:
:param threhold: float, 持仓小于该百分数的个股行业不再统计，加快速度
:return: Dict
"""
# 注意该 API 未直接使用天天基金的行业数据，其数据行业划分比较奇怪，大量行业都划分进了笼统的制造业，
# 用于分析代表性不强，甚至没有消费，医药等行业划分方式
from xalpha.universal import ttjjcode, get_industry_fromxq
df = self.get_stock_holdings(year=year, season=season, month=month)
if df is None:
logger.warning(
"%s has no stock holdings in %s y %s s. (Possible reason: 链接基金，债券基金)"
% (self.code, year, season)
)
return
d = {}
for i, row in df.iterrows():
if row["ratio"] < threhold:
continue
code = ttjjcode(row["code"])
industry = get_industry_fromxq(code)["industryname"]
if not industry.strip():
logger.warning(
"%s has no industry information, cannot be classfied" % code
)
else:
if industry not in d:
d[industry] = 0
d[industry] += row["ratio"]
return d
[docs] def which_industry(self, threhold=1.0):
"""
Experimental API
当单一行业占比较其他行业的 threhold 倍还多时，自动判定为对应的行业基金
注意这里的行业可能比较细分，导致持仓多个行业其实是同一大行业从而误判为宽基基金的可能
:param threhold: float
:return: str
"""
d = self.get_industry_holdings()
l = sorted([(k, v) for k, v in d.items()], key=lambda s: -s[1])
s0 = 0
if l and l[0] and l[0][1]:
s0 = l[0][1]
s1 = sum([l[i][1] for i in range(1, len(l))])
if s0 > threhold * s1:
return "行业基金： " + l[0][0]
else:
return "宽基基金"
[docs] def _hkfund_init(self):
import xalpha.universal as xu
# 互认基金国内休市日也有净值，暂时过滤，不确定是否会引起兼容性问题
self.meta = xu.get_rt("F" + self.code)
self.start = self.meta["startdate"]
self.name = self.meta["name"]
self.price = xu.get_daily("F" + self.code, start=self.start)
self.feeinfo = ["小于7天", "0.00%", "大于等于7天", "0.00%"] # 似乎该类型基金都不收取赎回费
self.segment = fundinfo._piecewise(self.feeinfo)
r = rget("http://overseas.1234567.com.cn/f10/FundSaleInfo/968012#SaleInfo")
b = BeautifulSoup(r.text, "lxml")
self.rate = _float(
[
c.strip()
for c in b.select(".HK_Fund_Table.BigText")[5].text.split("\n")
if c.strip()
][-1]
.split("|")[-1]
.strip()[:-1]
)
r = self._hk_bonus()
df = self.price
df["comment"] = [0 for _ in range(len(df))]
df["netvalue"] = df["close"]
df["date"] = pd.to_datetime(df["date"])
df = df[df["date"].isin(opendate)] # ? 是否会过滤掉分红日
for d in r:
df.loc[df["date"] == d["EXDDATE"], "comment"] = d["BONUS"]
df = df.drop("close", axis=1)
self.price = df
[docs] def _hk_bonus(self, start=None):
"""
[summary]
:param start: "%Y-%m-%d", defaults to None
:type start: [type], optional
"""
import xalpha.universal as xu
todaydash = today_obj().strftime("%Y-%m-%d")
if not start:
start = self.price.iloc[0]["date"].strftime("%Y-%m-%d")
pagesize = int(
(today_obj() - dt.datetime.strptime(start, "%Y-%m-%d")).days / 5
) # 如果存在一周超过一次分红的基金，算我没说
self.hkfcode = xu.get_hkfcode(self.code)
r = rget_json(
"http://overseas.1234567.com.cn/overseasapi/OpenApiHander.ashx?\
api=HKFDApi&m=MethodJZ&hkfcode={hkfcode}&action=3&pageindex=0&pagesize={pagesize}&date1={startdash}&date2={enddash}&callback=".format(
hkfcode=self.hkfcode,
pagesize=pagesize,
startdash=start,
enddash=todaydash,
)
)
return r["Data"]
[docs]class indexinfo(basicinfo):
"""
Get everyday close price of specific index.
In self.price table, totvalue column is the real index
while netvalue comlumn is normalized to 1 for the start date.
In principle, this class can also be used to save stock prices but the price is without adjusted.
:param code: string with seven digitals! note the code here has an extra digit at the beginning,
0 for sh and 1 for sz.
:param value_label: int, default 0 or 1. If set to 1, 记账单数字按金额赎回。
:param fetch: boolean, when open the fetch option, the class will try fetching from local files first in the init
:param save: boolean, when open the save option, automatically save the class to files
:param path: string, the file path prefix of IO
:param form: string, the format of IO, options including: 'csv'
"""
[docs] def __init__(
self, code, value_label=0, fetch=False, save=False, path="", form="csv"
):
date = yesterday()
if code.startswith("SH") and code[2:].isdigit():
code = "0" + code[2:]
elif code.startswith("SZ") and code[2:].isdigit():
code = "1" + code[2:]
self.rate = 0
self._url = (
"http://quotes.money.163.com/service/chddata.html?code="
+ code
+ "&start=19901219&end="
+ date
+ "&fields=TCLOSE"
)
super().__init__(
code, value_label=value_label, fetch=fetch, save=save, path=path, form=form
)
[docs] def _basic_init(self):
raw = rget(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]
[docs] def _save_csv(self, path):
"""
save the information and pricetable into path+code.csv, not recommend to use manually,
just set the save label to be true when init the object
:param path: string of folder path
"""
self.price.sort_index(axis=1).to_csv(
path + self.code + ".csv", index=False, date_format="%Y-%m-%d"
)
[docs] def _fetch_csv(self, path):
"""
fetch the information and pricetable from path+code.csv, not recommend to use manually,
just set the fetch label to be true when init the object
:param path: string of folder path
"""
try:
pricetable = pd.read_csv(path + self.code + ".csv")
datel = list(pd.to_datetime(pricetable.date))
self.price = pricetable[["netvalue", "totvalue", "comment"]]
self.price["date"] = datel
except FileNotFoundError as e:
# print('no saved copy of %s' % self.code)
raise e
[docs] def _save_sql(self, path):
"""
save the information and pricetable into sql, not recommend to use manually,
just set the save label to be true when init the object
:param path: engine object from sqlalchemy
"""
self.price.sort_index(axis=1).to_sql(
"xa" + self.code, con=path, if_exists="replace", index=False
)
[docs] def _fetch_sql(self, path):
"""
fetch the information and pricetable from sql, not recommend to use manually,
just set the fetch label to be true when init the object
:param path: engine object from sqlalchemy
"""
try:
pricetable = pd.read_sql("xa" + self.code, path)
self.price = pricetable
except exc.ProgrammingError as e:
# print('no saved copy of %s' % self.code)
raise e
[docs] def update(self):
lastdate = self.price.iloc[-1].date
lastdatestr = lastdate.strftime("%Y%m%d")
weight = self.price.iloc[1].totvalue
self._updateurl = (
"http://quotes.money.163.com/service/chddata.html?code="
+ self.code
+ "&start="
+ lastdatestr
+ "&end="
+ yesterday()
+ "&fields=TCLOSE"
)
df = pd.read_csv(self._updateurl, encoding="gb2312")
self.name = df.iloc[0].loc["名称"]
if len(df) > 1:
df = df.rename(columns={"收盘价": "totvalue"})
df["date"] = pd.to_datetime(df.日期)
df = df.drop(["股票代码", "名称", "日期"], axis=1)
df["netvalue"] = df.totvalue / weight
df["comment"] = [0 for _ in range(len(df))]
df = df.iloc[::-1].iloc[1:]
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
df = df[df["date"] <= yesterdayobj()]
self.price = self.price.append(df, ignore_index=True, sort=True)
return df
[docs]class cashinfo(basicinfo):
"""
A virtual class for remaining cash manage: behave like monetary fund
:param interest: float, daily rate in the unit of 100%, note this is not a year return rate!
:param start: str of date or dateobj, the virtual starting date of the cash fund
:param value_label: int, default 0 or 1. If set to 1, 记账单数字按金额赎回。
"""
[docs] def __init__(self, interest=0.0001, start="2012-01-01", value_label=0):
self.interest = interest
start = convert_date(start)
self.start = start
super().__init__(
"mf", value_label=value_label, fetch=False, save=False, path="nobackend"
) # 永远不缓存 cashinfo
[docs] 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)]
[docs]class mfundinfo(basicinfo):
"""
真实的货币基金类，可以通过货币基金六位代码，来获取真实的货币基金业绩，并进行交易回测等
:param code: string of six digitals, code of real monetnary fund
:param round_label: int, default 0 or 1, label to the different round scheme of shares, reserved for fundinfo class. 1 代表全舍而非四舍五入。
:param value_label: int, default 0 or 1. 1 代表记账单上的赎回数目是按金额而非份额的，只能完美支持货币基金。
:param fetch: boolean, when open the fetch option, the class will try fetching from local files first in the init
:param save: boolean, when open the save option, automatically save the class to files
:param path: string, the file path prefix of IO
:param form: string, the format of IO, options including: 'csv'
"""
[docs] def __init__(
self,
code,
round_label=0,
value_label=0,
fetch=False,
save=False,
path="",
form="csv",
):
if code.startswith("M") and code[1:].isdigit():
code = code[1:]
code = code.zfill(6)
self._url = "http://fund.eastmoney.com/pingzhongdata/" + code + ".js"
self.rate = 0
super().__init__(
code,
fetch=fetch,
save=save,
path=path,
form=form,
round_label=round_label,
value_label=value_label,
)
[docs] 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"[\s\S]*Data_millionCopiesIncome = ([^;]*);[\s\S]*", self._page.text
).groups()[0]
)
self.name = re.match(
r"[\s\S]*fS_name = \"([^;]*)\";[\s\S]*", 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)]
if len(df) == 0:
raise ParserFailure("no price table for %s" % self.code)
df = df.reset_index(drop=True)
self.price = df[df["date"] <= yesterdaydash()]
[docs] def _save_csv(self, path):
"""
save the information and pricetable into path+code.csv, not recommend to use manually,
just set the save label to be true when init the object
:param path: string of folder path
"""
df = pd.DataFrame(
[[0, 0, self.name, 0]], columns=["date", "netvalue", "comment", "totvalue"]
)
df = df.append(self.price, ignore_index=True, sort=True)
df.sort_index(axis=1).to_csv(
path + self.code + ".csv", index=False, date_format="%Y-%m-%d"
)
[docs] def _fetch_csv(self, path):
"""
fetch the information and pricetable from path+code.csv, not recommend to use manually,
just set the fetch label to be true when init the object
:param path: string of folder path
"""
try:
content = pd.read_csv(path + self.code + ".csv")
pricetable = content.iloc[1:]
datel = list(pd.to_datetime(pricetable.date))
self.price = pricetable[["netvalue", "totvalue", "comment"]]
self.price["date"] = datel
self.name = content.iloc[0].comment
except FileNotFoundError as e:
# print('no saved copy of %s' % self.code)
raise e
[docs] def _save_sql(self, path):
"""
save the information and pricetable into sql, not recommend to use manually,
just set the save label to be true when init the object
:param path: engine object from sqlalchemy
"""
s = json.dumps({"name": self.name})
df = pd.DataFrame(
[[pd.Timestamp("1990-01-01"), 0, s, 0]],
columns=["date", "netvalue", "comment", "totvalue"],
)
df = df.append(self.price, ignore_index=True, sort=True)
df.sort_index(axis=1).to_sql(
"xa" + self.code, con=path, if_exists="replace", index=False
)
[docs] def _fetch_sql(self, path):
"""
fetch the information and pricetable from sql, not recommend to use manually,
just set the fetch label to be true when init the object
:param path: engine object from sqlalchemy
"""
try:
content = pd.read_sql("xa" + self.code, path)
pricetable = content.iloc[1:]
commentl = [float(com) for com in pricetable.comment]
self.price = pricetable[["date", "netvalue", "totvalue"]]
self.price["comment"] = commentl
self.name = json.loads(content.iloc[0].comment)["name"]
except exc.ProgrammingError as e:
# print('no saved copy of %s' % self.code)
raise e
[docs] def update(self):
"""
function to incrementally update the pricetable after fetch the old one
"""
lastdate = self.price.iloc[-1].date
startvalue = self.price.iloc[-1].totvalue
diffdays = (yesterdayobj() - lastdate).days
if diffdays == 0:
return None
self._updateurl = (
"http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code="
+ self.code
+ "&page=1&per=1"
)
con = rget(self._updateurl)
soup = BeautifulSoup(con.text, "lxml")
items = soup.findAll("td")
if dt.datetime.strptime(str(items[0].string), "%Y-%m-%d") == today_obj():
diffdays += 1
if diffdays <= 10:
# caution: there may be today data!! then a day gap will be in table
self._updateurl = (
"http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code="
+ self.code
+ "&page=1&per="
+ str(diffdays)
)
con = rget(self._updateurl)
soup = BeautifulSoup(con.text, "lxml")
items = soup.findAll("td")
elif (
diffdays > 10
): ## there is a 20 item per page limit in the API, so to be safe, we query each page by 10 items only
items = []
for pg in range(1, int(diffdays / 10) + 2):
self._updateurl = (
"http://fund.eastmoney.com/f10/F10DataApi.aspx?type=lsjz&code="
+ self.code
+ "&page="
+ str(pg)
+ "&per=10"
)
con = rget(self._updateurl)
soup = BeautifulSoup(con.text, "lxml")
items.extend(soup.findAll("td"))
else:
raise TradeBehaviorError(
"Weird incremental update: the saved copy has future records"
)
date = []
earnrate = []
comment = []
for i in range(int(len(items) / 6)):
ts = pd.Timestamp(str(items[6 * i].string))
if (ts - lastdate).days > 0:
date.append(ts)
earnrate.append(float(items[6 * i + 1].string) * 1e-4)
comment.append(_nfloat(items[6 * i + 5].string))
date = date[::-1]
earnrate = earnrate[::-1]
comment = comment[::-1]
netvalue = [startvalue]
for earn in earnrate:
netvalue.append(netvalue[-1] * (1 + earn))
netvalue.remove(startvalue)
df = pd.DataFrame(
{
"date": date,
"netvalue": netvalue,
"totvalue": netvalue,
"comment": comment,
}
)
df = df[df["date"].isin(opendate)]
df = df.reset_index(drop=True)
df = df[df["date"] <= yesterdayobj()]
if len(df) != 0:
self.price = self.price.append(df, ignore_index=True, sort=True)
return df
FundInfo = fundinfo
MFundInfo = mfundinfo
CashInfo = cashinfo
IndexInfo = indexinfo
