def get_market(code):
"""
非常粗糙的通过代码获取交易市场的函数
:param code:
:return:
"""
trans = {
"USD": "US",
"GBP": "UK",
"HKD": "HK",
"CNY": "CN",
"CHF": "CH",
"JPY": "JP",
"EUR": "DE",
"AUD": "AU",
"INR": "IN",
"SGD": "SG",
}
try:
if code in market_info:
return market_info[code]
elif code.startswith("CNY/") or code.endswith("/CNY"):
return "CM" # china money 中间价市场标记
elif code.startswith("HK") and code[2:].isdigit():
return "HK"
market = get_rt(code)["market"]
if market is None:
market = get_currency(code)
market = trans.get(market, market)
except (TypeError, AttributeError, ValueError, IndexError):
market = "CN"
return market
def get_currency(code):
"""
通过代码获取计价货币的函数
:param code:
:return:
"""
# 强制需要自带 cache，否则在回测 table 是，info 里没有的代码将很灾难。。。
# only works for HKD JPY USD GBP CNY EUR, not very general when data source gets diverse more
try:
if code in currency_info:
return currency_info[code]
elif (code.startswith("F")
or code.startswith("M")) and code[1:].isdigit():
return "CNY"
currency = get_rt(code)["currency"]
if currency is None:
currency = "CNY"
elif currency == "JPY":
currency = "100JPY"
except (TypeError, AttributeError, ValueError):
if code.startswith("FT-") and len(code.split(":")) > 2:
currency = code.split(":")[-1]
else:
currency = "CNY"
return currency
def __init__(self, code, start=None, end=None):
"""
:param code: str. 形式可以是 399971.XSHE 或者 SH000931
:param start: Optional[str]. %Y-%m-%d, 估值历史计算的起始日。
:param end: Dont use, only for debug
"""
yesterday_str = (dt.datetime.now() -
dt.timedelta(days=1)).strftime("%Y-%m-%d")
if len(code.split(".")) == 2:
self.code = code
self.scode = _convert_code(code)
else:
self.scode = code
self.code = _inverse_convert_code(self.scode)
if self.code in self.indexs:
self.name = self.indexs[self.code][0]
if not start:
start = self.indexs[self.code][1]
else:
try:
self.name = get_rt(self.scode)["name"]
except:
self.name = self.scode
if not start:
start = "2012-01-01" # 可能会出问题，对应指数还未有数据
self.start = start
if not end:
end = yesterday_str
self.df = xu.get_daily("peb-" + self.scode, start=self.start, end=end)
self.ratio = None
self.title = "指数"
self._gen_percentile()
def get_currency(code):
"""
通过代码获取计价货币的函数
:param code:
:return:
"""
# 强制需要自带 cache，否则在回测 table 时，info 里没有的代码将很灾难。。。
# only works for HKD JPY USD GBP CNY EUR, not very general when data source gets diverse more
try:
if code in currency_info:
return currency_info[code]
elif (code.startswith("F")
or code.startswith("M")) and code[1:].isdigit():
return "CNY"
elif code.startswith("FT-") and len(code.split(":")) > 2:
# be careful! FT-ABC:IOM has no currency information!
return code.split(":")[-1]
elif code.startswith("HK") and code[2:].isdigit():
return "HKD"
currency = get_rt(code)["currency"]
if currency is None:
currency = "CNY"
elif currency == "JPY":
currency = "100JPY"
except (TypeError, AttributeError, ValueError):
logger.warning("set currency of %s as default CNY" % code)
currency = "CNY"
return currency
def __init__(self, code, status, name=None):
"""
:param code: str. 代码格式与 :func:`xalpha.universal.get_daily` 要求相同
:param status: 记账单或 irecord 类。
:param name: Optional[str]. 可提供标的名称。
"""
self.code = code
if isinstance(status, irecord):
self.status = status.filter(code)
else:
self.status = status[status.code == code]
# self.cftable = pd.DataFrame([], columns=["date", "cash", "share"])
try:
self.price = xu.get_daily(
self.code,
start=self.status.iloc[0]["date"].strftime("%Y-%m-%d"))
self.price["netvalue"] = self.price["close"]
except Exception as e:
logger.warning("%s when trade trying to get daily price of %s" %
(e, self.code))
self.price = None
self._arrange()
if not name:
try:
self.name = get_rt(code)["name"]
except:
self.name = code
self.type_ = None
def get_market(code):
"""
非常粗糙的通过代码获取交易市场的函数
:param code:
:return:
"""
trans = {
"USD": "US",
"GBP": "UK",
"HKD": "HK",
"CNY": "CN",
"CHF": "CH",
"JPY": "JP",
"EUR": "DE",
"AUD": "AU",
"INR": "IN",
"SGD": "SG",
}
try:
if code in market_info:
return market_info[code]
market = get_rt(code)["market"]
if market is None:
market = get_currency(code)
market = trans.get(market, market)
except (TypeError, AttributeError, ValueError, IndexError):
market = "CN"
return market
def get_stock_holdings(
self, year=None, season=None, date=yesterdayobj(), threhold=100
):
"""
获取整个基金组合的底层股票持仓总和和细节，组合穿透
:param year: 基于的基金季报年份
:param season: 基于的基金季报季度
:param date: 默认昨天
:param threhold: 默认100。小于100元的底层股票将不在最后的结果中展示
:return: pd.DataFrame column: name, code, value, ratio
"""
d = {}
if year is None or season is None:
rd = convert_date(date) - pd.Timedelta(days=120)
if not year:
year = rd.year
if not season:
season = int((rd.month - 0.1) / 3) + 1
logger.debug("use %s, %s for fund report" % (year, season))
for f in self.fundtradeobj:
if isinstance(f, itrade):
if f.get_type() == "股票":
code = f.code
elif f.get_type() == "场内基金":
code = f.code[2:]
else:
continue
else:
code = f.code
value = f.briefdailyreport(date).get("currentvalue", 0)
if value > 0:
if code.startswith("SH") or code.startswith("SZ"):
stock = code
d[stock] = d.get(stock, 0) + value
elif code == "mf":
continue
else:
df = get_fund_holdings(code, year, season)
if df is None:
continue
for _, row in df.iterrows():
stock = row["code"]
stock = ttjjcode(stock)
d[stock] = d.get(stock, 0) + row["ratio"] / 100 * value
# print("%s has %s contribution from %s" %(stock, row["ratio"] / 100 * value, f.name))
l = []
for code, value in sorted(d.items(), key=lambda item: -item[1]):
if value >= threhold:
try:
name = get_rt(code)["name"]
except:
name = code
l.append([name, code, value])
fdf = pd.DataFrame(l, columns=["name", "code", "value"])
fdf["ratio"] = fdf["value"] / fdf["value"].sum()
return fdf
def get_t1_rate(self, date=None, return_date=True):
t1v, d = self.get_t1(date=date, return_date=True)
cp = get_rt(self.code)["current"]
r = (cp / t1v - 1) * 100
if return_date:
return r, d
else:
return r
def get_t0_rate(self, percent=False, return_date=True):
t0v, d = self.get_t0(percent=percent, return_date=True)
cp = get_rt(self.code)["current"]
r = (cp / t0v - 1) * 100
if return_date:
return r, d
else:
return r
def get_t0_rate(self, percent=False, return_date=True):
iopv = self.get_t0(percent=False, return_date=False)
rtv = get_rt(self.code)["current"]
r = (rtv / iopv - 1) * 100
if return_date:
return r, self.today.strftime("%Y-%m-%d")
else:
return r
def get_t0(self, percent=False, return_date=True):
"""
获取当日实时净值估计, 该接口每日凌晨到美股收盘（早晨），不保证自洽和可用
:param percent: bool， default False。现在有两种实时的预测处理逻辑。若 percent 是 True，则将 t0dict 的
每个持仓标的的今日涨跌幅进行估算，若为 False，则将标的现价和标的对应指数昨日收盘价的比例作为涨跌幅估算。不推荐使用 percent=True.
:param return_date: bool, default True. return tuple, the second one is date in the format %Y%m%d
:return: float
"""
if not self.t0dict:
raise ValueError("Please provide t0dict for prediction")
t1value = self.get_t1(date=None, return_date=False)
t = 0
n = 0
today_str = self.today.strftime("%Y%m%d")
for k, v in self.t0dict.items():
if not isinstance(v, dict):
v = {"weight": v}
if len(k.split("~")) > 1 and k.split("~")[-1].isdigit():
# 为了持仓中可以同标的多次出现的 workaround
k = k.split("~")[0]
w = v["weight"]
shift = v.get("time", None)
base = v.get("base", None)
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:
if k in futures_info and not base:
kf = futures_info[k]
elif not base:
kf = k[:-8] # k + "-futures"
else:
kf = base
try:
basev = self._base_value(kf, shift)
except Exception as e:
kf = get_alt(kf)
if not kf:
raise e
else:
basev = self._base_value(kf, shift)
c = w / 100 * r["current"] / basev
currency_code = get_currency_code(k)
if currency_code:
c = c * daily_increment(currency_code, today_str)
# TODO: 中间价未更新，但实时数据不检查问题也不大
n += c
n += (100 - t) / 100
t0value = n * t1value
self.t0_delta = n
if not return_date:
return t0value
else:
return t0value, self.today.strftime("%Y-%m-%d")
def fluctuation(self):
if not self.ratio:
d = self.df.iloc[-1]["date"]
oprice = xu.get_daily(code=self.scode,
end=d.strftime("%Y%m%d"),
prev=20).iloc[-1]["close"]
nprice = get_rt(self.scode)["current"]
self.ratio = nprice / oprice
return self.ratio
def __init__(self, code, start=None, end=None):
self.code = code
self.scode = code
if not end:
end = (dt.datetime.now() -
dt.timedelta(days=1)).strftime("%Y-%m-%d")
if not start:
start = "2016-01-01" # 基金历史通常比较短
self.start = start
self.df = xu.get_daily("peb-" + code, start=start, end=end)
self.name = get_rt(code)["name"]
self.title = "基金"
self.ratio = None
self._gen_percentile()
def get_t1(self, return_date=True):
"""
获取昨日基金净值
:return:
"""
if not self.t1value_cache:
last_r = get_rt(self.fcode)
last_value, last_date = last_r["current"], last_r["time"]
self.t1value_cache = (last_value, last_date)
if return_date:
return self.t1value_cache
else:
return self.t1value_cache[0]
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 get_t2(self, return_date=True):
"""
返回最新的已公布基金净值，注意这里严格按照最新公布，不一定是前两个交易日，可以更新，但更老会报错 DateMismatch
:param return_date:
:return: if return_date is True, tuple (value, %Y-%m-%d)
"""
if not self.t2value_cache:
last_r = get_rt(self.fcode)
last_value, last_date = last_r["current"], last_r["time"]
self.t2value_cache = (last_value, last_date)
if return_date:
return self.t2value_cache
else:
return self.t2value_cache[0]
def __init__(self, code, status, name=None):
"""
:param code: str. 代码格式与 :func:`xalpha.universal.get_daily` 要求相同
:param status: 记账单或 irecord 类。
:param name: Optional[str]. 可提供标的名称。
"""
self.code = code
if isinstance(status, irecord):
self.status = status.filter(code)
else:
self.status = status[status.code == code]
# self.cftable = pd.DataFrame([], columns=["date", "cash", "share"])
self._arrange()
if not name:
try:
self.name = get_rt(code)["name"]
except:
self.name = code
def __init__(self, code, start=None, end=None):
"""
:param code: 801180 申万行业指数
:param start:
:param end:
"""
self.code = code
self.scode = code
if not end:
end = (dt.datetime.now() -
dt.timedelta(days=1)).strftime("%Y-%m-%d")
if not start:
start = "2012-01-01"
self.start = start
self.df = xu.get_daily("peb-" + code, start=start, end=end)
self.name = get_rt(code)["name"]
self.ratio = 1
self.title = "个股"
self._gen_percentile()
def process_byday(self, date):
if not date:
self.date_obj = dt.datetime.today()
else:
self.date_obj = dt.datetime.strptime(
date.replace("-", "").replace("/", ""), "%Y%m%d")
if not date:
rt = get_rt(self.code)
self.name = rt["name"]
self.cbp = rt["current"] # 转债价
self.stockp = get_rt(self.scode)["current"] # 股票价
else:
try:
if not self.name:
rt = get_rt(self.code)
self.name = rt["name"]
except:
self.name = "unknown"
df = xu.get_daily(self.code,
prev=100,
end=self.date_obj.strftime("%Y%m%d"))
self.cbp = df.iloc[-1]["close"]
df = xu.get_daily(self.scode,
prev=100,
end=self.date_obj.strftime("%Y%m%d"))
self.stockp = df.iloc[-1]["close"]
df = xu.get_daily(self.scode,
prev=360,
end=self.date_obj.strftime("%Y%m%d"))
self.history_volatility = np.std(
np.log(df["close"] / df.shift(1)["close"])) * np.sqrt(244)
if not self.refvolatility:
self.volatility = 0.17
if self.rating in ["A", "A+", "AA-"]:
self.volatility = 0.19
elif self.rating in ["AA"]:
self.volatility = 0.18
if self.history_volatility < 0.25:
self.volatility -= 0.01
elif self.history_volatility > 0.65:
self.volatility += 0.02
elif self.history_volatility > 0.45:
self.volatility += 0.01
self.years = len(self.rlist) - 1
syear = int(self.enddate.split("-")[0]) - self.years
self.issuedate = str(syear) + self.enddate[4:]
self.days = (dt.datetime.strptime(self.enddate, "%Y-%m-%d") -
self.date_obj).days
if not self.refbondrate:
ratestable = get_bond_rates(self.rating,
self.date_obj.strftime("%Y-%m-%d"))
if self.rating in ["A", "A+", "AA-"]:
## AA 到 AA- 似乎是利率跳高的一个坎
cutoff = 2
else:
cutoff = 4
if self.days / 365 > cutoff:
# 过长久期的到期收益率，容易造成估值偏离，虽然理论上是对的
# 考虑到国内可转债市场信用风险较低，不应过分低估低信用债的债券价值
self.bondrate = (
ratestable[ratestable["year"] <= cutoff].iloc[-1]["rate"] /
100)
else:
self.bondrate = (ratestable[ratestable["year"] >= self.days /
365].iloc[0]["rate"] / 100)
if not self.refriskfreerate:
ratestable = get_bond_rates("N",
self.date_obj.strftime("%Y-%m-%d"))
if self.days / 365 > 5:
self.riskfreerate = (
ratestable[ratestable["year"] <= 5].iloc[-1]["rate"] / 100)
else:
self.riskfreerate = (
ratestable[ratestable["year"] >= self.days /
365].iloc[0]["rate"] / 100)
