def __init__(self, code, t1dict=None, t0dict=None, positions=False):
"""
:param code: str, 场内基金代码，eg SH501018
:param t1dict: Dict[str, float]. 用来预测 T-1 净值的基金组合持仓，若为空自动去 holdings 中寻找。
:param t0ict: Dict[str, float]. 用来预测 T 实时净值的基金组合持仓，若为空自动去 holdings 中寻找。
:param positions: bool. 仓位是否浮动，默认固定仓位。
"""
self.code = code
self.fcode = "F" + code[2:]
if not t1dict:
self.t1dict = holdings.get(code[2:], None)
if not self.t1dict:
raise ValueError("Please provide t1dict for prediction")
else:
self.t1dict = t1dict
if not t0dict:
self.t0dict = holdings.get(code[2:] + "rt", None)
else:
self.t0dict = t0dict
self.position_cache = {}
self.t1value_cache = {}
self.t2value_cache = None
# t0 实时净值自然不 cache
self.positions = positions
self.position_zero = sum([v for _, v in self.t1dict.items()])
self.now = dt.datetime.now(tz=tz_bj).replace(tzinfo=None)
self.today = self.now.replace(hour=0,
minute=0,
second=0,
microsecond=0)
self.t1_type = "未计算"
self.bar_cache = {}
self.t0_delta = None
self.t1_delta = None
def __init__(self,
code,
t1dict=None,
t0dict=None,
positions=False,
fetch=False,
save=False):
"""
:param code: str, 场内基金代码，eg SH501018
:param t1dict: Dict[str, float]. 用来预测 T-1 净值的基金组合持仓，若为空自动去 holdings 中寻找。
:param t0ict: Dict[str, float]. 用来预测 T 实时净值的基金组合持仓，若为空自动去 holdings 中寻找。
:param positions: bool. 仓位是否浮动，默认固定仓位。
:param fetch: bool, default True. 优先从 backend fetch t1。
:param save: bool, default True. 将 t1 缓存到 backend。
"""
self.code = code
self.fcode = "F" + code[2:]
self.fetch = fetch
self.save = save
if not t1dict:
self.t1dict = holdings.get(code[2:], None)
if not self.t1dict:
raise ValueError("Please provide t1dict for prediction")
else:
self.t1dict = t1dict
if not t0dict:
self.t0dict = holdings.get(code[2:] + "rt", None)
else:
self.t0dict = t0dict
self.position_cache = {}
self.t1value_cache = {}
self.t2value_cache = None
# t0 实时净值自然不 cache
self.positions = positions
self.position_zero = sum([v for _, v in self.t1dict.items()])
self.now = dt.datetime.now(tz=tz_bj).replace(tzinfo=None)
self.today = self.now.replace(hour=0,
minute=0,
second=0,
microsecond=0)
self.t1_type = "未计算"
self.bar_cache = {}
self.t0_delta = None
self.t1_delta = None
# 不建议直接使用以上两者看变化量，在手动 set 后，以上两者可能继续为 None
if fetch:
df = fetch_backend("t1-" + code)
if df is not None:
df["date"] = pd.to_datetime(df["date"])
for i, r in df.iterrows():
self.set_t1(float(r["t1"]), r["date"].strftime("%Y-%m-%d"))
self.set_position(float(r["pos"]),
r["date"].strftime("%Y-%m-%d"))
else: # nodf
emptydf = pd.DataFrame({"date": [], "t1": [], "pos": []})
save_backend("t1-" + code, emptydf, header=True)
def __init__(self, code, t0dict=None):
"""
:param code:
:param t0dict:
"""
self.code = code
self.fcode = "F" + code[2:]
if not t0dict:
t0dict = holdings.get(code[2:], None)
if not t0dict:
raise ValueError("Please provide t0dict for prediction")
if isinstance(t0dict, str):
t0dict = {t0dict: 100}
self.t0dict = t0dict
self.t1value_cache = None
self.now = dt.datetime.now(tz=tz_bj).replace(tzinfo=None)
self.today = self.now.replace(hour=0,
minute=0,
second=0,
microsecond=0)
