def set_position(self, value, date=None):
if date is None:
yesterday = last_onday(self.today)
datekey = yesterday.strftime("%Y%m%d")
else:
datekey = date.replace("/", "").replace("-", "")
self.position_cache[datekey] = value
def set_t1(self, value, date=None):
"""
设定 T-1 的基金净值，有时我们只想计算实时净值，这就不需要重复计算 t1，可以先行设定
:param value:
:param date:
:return:
"""
if date is None:
yesterday = last_onday(self.today)
datekey = yesterday.strftime("%Y%m%d")
else:
datekey = date.replace("/", "").replace("-", "")
if datekey in self.t1value_cache:
print("t-1 value already exists, rewriting...")
self.t1value_cache[datekey] = value
self.t1_type = "已计算"
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],
)