def get_t0(self, return_date=True, percent=False):
last_value, last_date = self.get_t1()
last_date_obj = dt.datetime.strptime(last_date, "%Y-%m-%d")
cday = last_onday(self.today)
while last_date_obj < cday: # 昨天净值数据还没更新
# 是否存在部分部分基金可能有 gap？
if cday.strftime("%Y-%m-%d") not in gap_info[self.fcode]:
self.t1_type = "昨日未出"
raise DateMismatch(
self.code,
reason="%s netvalue has not been updated to yesterday" %
self.code,
)
else:
cday = last_onday(cday)
# 经过这个没报错，就表示数据源是最新的
if last_date_obj >= self.today: # 今天数据已出，不需要再预测了
print("no need to predict net value since it has been out for %s" %
self.code)
self.t1_type = "今日已出"
if not return_date:
return last_value
else:
return last_value, last_date
t = 0
n = 0
today_str = self.today.strftime("%Y%m%d")
for k, v in self.t0dict.items():
w = v
t += w
r = get_rt(k)
# k should support get_rt, investing pid doesn't support this!
if percent:
c = w / 100 * (1 + r["percent"] / 100) # 直接取标的当日涨跌幅
else:
df = xu.get_daily(k)
basev = df[df["date"] <= last_date].iloc[-1]["close"]
c = w / 100 * r["current"] / basev
currency_code = get_currency_code(k)
if currency_code:
c = c * daily_increment(currency_code, today_str)
n += c
n += (100 - t) / 100
t0value = n * last_value
self.t0_delta = n
if not return_date:
return t0value
else:
return t0value, self.today.strftime("%Y-%m-%d")
def daily_increment(code, date, lastday=None, _check=False):
"""
单一标的 date 日（若 date 日无数据则取之前的最晚有数据日，但该日必须大于 _check 对应的日期）较上一日或 lastday 的倍数，
lastday 支持不完整，且不能离 date 太远
:param code:
:param date:
:param lastday: 如果用默认 None，则表示和前一日的涨跌
:param _check: 数据必须已更新到 date 日，除非之前每天都是节假日
:return:
"""
try:
tds = xu.get_daily(code=code, end=date, prev=30)
except Exception as e: # 只能笼统 catch 了，因为抓取失败的异常是什么都能遇到。。。
code = get_alt(code)
if code:
tds = xu.get_daily(code=code, end=date, prev=30)
else:
raise e
tds = tds[tds["date"] <= date]
if _check:
date = date.replace("-", "").replace("/", "")
date_obj = dt.datetime.strptime(date, "%Y%m%d")
while tds.iloc[-1]["date"] < date_obj:
# in case data is not up to date
# 但是存在日本市场休市时间不一致的情况，估计美股也存在
if not is_on(
date_obj.strftime("%Y%m%d"),
get_market(code),
no_trading_days=no_trading_days,
) or (date_obj.strftime("%Y-%m-%d") in gap_info.get(code, [])):
print("%s is closed on %s" % (code, date))
if not lastday:
return 1 # 当日没有涨跌，这里暂时为考虑 _check 和 lastday 相同的的情形
date_obj -= dt.timedelta(days=1)
else:
raise DateMismatch(
code,
reason="%s has no data newer than %s" %
(code, date_obj.strftime("%Y-%m-%d")),
)
if not lastday:
ratio = tds.iloc[-1]["close"] / tds.iloc[-2]["close"]
else:
tds2 = tds[tds["date"] <= lastday]
# 未考虑连 lastday 的数据数据源都没更新的情形，这种可能极小
ratio = tds.iloc[-1]["close"] / tds2.iloc[-1]["close"]
return ratio
def get_t1(self, date=None, return_date=True):
"""
预测 date 日的净值，基于 date-1 日的净值和 date 日的外盘数据，数据自动缓存，不会重复计算
:param date: str. %Y-%m-%d. 注意若是 date 日为昨天，即今日预测昨日的净值，date 取默认值 None。
:param return_date: bool, default True. return tuple, the second one is date in the format %Y%m%d
:return: float, (str).
:raises NonAccurate: 由于外盘数据还未及时更新，而 raise，可在调用程序中用 except 捕获再处理。
"""
if date is None:
yesterday = last_onday(self.today)
datekey = yesterday.strftime("%Y%m%d")
else:
datekey = date.replace("/", "").replace("-", "")
if datekey not in self.t1value_cache:
if self.positions:
current_pos = self.get_position(datekey, return_date=False)
hdict = scale_dict(self.t1dict.copy(), aim=current_pos * 100)
else:
hdict = self.t1dict.copy()
if date is None: # 此时预测上个交易日净值
yesterday_str = datekey
last_value, last_date = self.get_t2()
last_date_obj = dt.datetime.strptime(last_date, "%Y-%m-%d")
cday = last_onday(last_onday(self.today))
while last_date_obj < cday: # 前天净值数据还没更新
# 是否存在部分 QDII 在 A 股交易日，美股休市日不更新净值的情形？
if (cday.strftime("%Y-%m-%d")
not in gap_info[self.fcode]) and is_on(
cday, "US", no_trading_days):
# 这里检查比较宽松，只要当天美股休市，就可以认为确实基金数据不存在而非未更新
self.t1_type = "前日未出"
raise DateMismatch(
self.code,
reason=
"%s netvalue has not been updated to the day before yesterday"
% self.code,
)
else:
cday = last_onday(cday)
# 经过这个没报错，就表示数据源是最新的
if last_date_obj >= last_onday(self.today): # 昨天数据已出，不需要再预测了
print(
"no need to predict t-1 value since it has been out for %s"
% self.code)
self.t1_type = "昨日已出"
self.t1value_cache = {
last_date.replace("-", ""): last_value
}
if not return_date:
return last_value
else:
return last_value, last_date
else:
yesterday_str = datekey
fund_price = xu.get_daily(self.fcode) # 获取国内基金净值
fund_last = fund_price[fund_price["date"] < date].iloc[-1]
# 注意实时更新应用 date=None 传入，否则此处无法保证此数据是前天的而不是大前天的，因为没做校验
# 事实上这里计算的预测是针对 date 之前的最晚数据和之前一日的预测
last_value = fund_last["close"]
last_date = fund_last["date"].strftime("%Y-%m-%d")
self.t1_delta = (1 + evaluate_fluctuation(
hdict, yesterday_str, lastday=last_date, _check=True) / 100)
net = last_value * self.t1_delta
self.t1value_cache[datekey] = net
self.t1_type = "已计算"
if not return_date:
return self.t1value_cache[datekey]
else:
return (
self.t1value_cache[datekey],
datekey[:4] + "-" + datekey[4:6] + "-" + datekey[6:8],
)