def backtest(self):
"""
run the whole backtest
:return:
"""
self.prepare()
dates = pd.bdate_range(self.start, self.end)
for d in dates: # 考虑到暂时只支持基金，只在国内交易日运行
if d not in opendate_set:
d = next_onday(d)
self.run(d)
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],
)