def set_t2(self, value, date=None):
"""
手动设定 t2 净值
:param value:
:return:
"""
if not date:
date = last_onday(last_onday(self.today)).strftime("%Y-%m-%d")
self.t2value_cache = (value, date)
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 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 get_position(self, date=None, refresh=False, return_date=True, **kws):
"""
基于 date 日之前的净值数据，对 date 预估需要的仓位进行计算。
:param date: str. %Y-%m-%d
:param refresh: bool, default False. 若为 True，则刷新缓存，重新计算仓位。
:param return_date: bool, default True. return tuple, the second one is date in the format %Y%m%d
:param kws: 一些预估仓位可能的超参。包括 window，预估所需的时间窗口，decay 加权平均的权重衰减，smooth 每日仓位处理的平滑函数。以上参数均可保持默认即可获得较好效果。
:return: float. 0-100. 100 代表满仓。
"""
if not date:
date = last_onday(self.today).strftime("%Y%m%d")
else:
date = date.replace("/", "").replace("-", "")
if date not in self.position_cache or refresh:
fdict = scale_dict(self.t1dict.copy(), aim=100)
l = kws.get("window", 4)
q = kws.get("decay", 0.8)
s = kws.get("smooth", _smooth_pos)
d = dt.datetime.strptime(date, "%Y%m%d")
posl = [sum([v for _, v in self.t1dict.items()]) / 100]
for _ in range(l):
d = last_onday(d)
for _ in range(l - 1):
d = next_onday(d)
pred = evaluate_fluctuation(
fdict,
d.strftime("%Y-%m-%d"),
lastday=last_onday(d).strftime("%Y-%m-%d"),
)
real = evaluate_fluctuation(
{self.fcode: 100},
d.strftime("%Y-%m-%d"),
lastday=last_onday(d).strftime("%Y-%m-%d"),
)
posl.append(s(real, pred, posl[-1]))
current_pos = sum([q**i * posl[l - i - 1] for i in range(l)
]) / sum([q**i for i in range(l)])
self.position_cache[date] = current_pos
if not return_date:
return self.position_cache[date]
else:
return (
self.position_cache[date],
date[:4] + "-" + date[4:6] + "-" + date[6:8],
)
def _base_value(self, code, shift):
if not shift:
funddf = xu.get_daily(code) ## 获取股指现货日线
return funddf[funddf["date"] <= last_onday(self.today)].iloc[-1][
"close"] # 日期是按当地时间
# TODO: check it is indeed date of last_on(today)
else:
if code not in self.bar_cache:
funddf = get_bar(code, prev=168, interval="3600") ## 获取小时线
## 注意对于国内超长假期，prev 可能还不够
if self.now.hour > 6: # 昨日美国市场收盘才正常，才缓存参考小时线
self.bar_cache[code] = funddf
else:
funddf = self.bar_cache[code]
refdate = last_onday(self.today) + dt.timedelta(days=1) # 按北京时间校准
return funddf[funddf["date"] <= refdate +
dt.timedelta(hours=shift)].iloc[-1][
"close"] # 时间是按北京时间, 小时线只能手动缓存，日线不需要是因为自带透明缓存器
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_bond_rates(rating, date=None):
"""
获取各评级企业债的不同久期的预期利率
:param rating: str. eg AAA, AA-, N for 中国国债
:param date: %Y-%m-%d
:return:
"""
rating_uid = {
"N": "2c9081e50a2f9606010a3068cae70001",
"AAA": "2c9081e50a2f9606010a309f4af50111",
"AAA-": "8a8b2ca045e879bf014607ebef677f8e",
"AA+": "2c908188138b62cd01139a2ee6b51e25",
"AA": "2c90818812b319130112c279222836c3",
"AA-": "8a8b2ca045e879bf014607f9982c7fc0",
"A+": "2c9081e91b55cc84011be40946ca0925",
"A": "2c9081e91e6a3313011e6d438a58000d",
}
def _fetch(date):
r = rpost(
"https://yield.chinabond.com.cn/cbweb-mn/yc/searchYc?\
xyzSelect=txy&&workTimes={date}&&dxbj=0&&qxll=0,&&yqqxN=N&&yqqxK=K&&\
ycDefIds={uid}&&wrjxCBFlag=0&&locale=zh_CN".format(uid=rating_uid[rating],
date=date), )
return r
if not date:
date = dt.datetime.today().strftime("%Y-%m-%d")
r = _fetch(date)
while len(r.text.strip()) < 20: # 当天没有数据，非交易日
date = last_onday(date).strftime("%Y-%m-%d")
r = _fetch(date)
l = r.json()[0]["seriesData"]
l = [t for t in l if t[1]]
df = pd.DataFrame(l, columns=["year", "rate"])
return df
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],
)
