def detect(self, df0, period=14, uid=''):
docs = self.stageApply(df0, period=period, uid=uid)
res = pd.DataFrame(docs)
if len(res) > 0:
if self.saveDetail:
self.Train.insertAll(docs)
diff = res[res['diff'] > 0]['diff'].mean()
self.testDays = public.dayDiff(self.endDate, self.startDate)
# 计算最大回测
sum = res['income'].cumsum() + self.iniAmount
inr = res['income'] / self.iniAmount
# 计算夏普指数
sha = (res['income'].sum() / self.iniAmount - 0.02 * self.testDays / 252) / inr.std()
doc = {
"count": int(len(docs) / 2),
"amount": self.iniAmount,
"price": res['price'].mean(),
"income": res["income"].sum(),
# "delta": res['delta'].mean(),
"maxdown": ((sum.shift(1) - sum) / sum.shift(1)).max(),
"sharprate": sha,
"timediff": int(0 if np.isnan(diff) else diff)
}
#print(self.code, doc['count'], doc['income'])
return doc
else:
return None
def record(self):
# 510050 行情
stock = self.Int.get510050()
stock["datetime"] = public.getDatetime()
self.Owner.insert(stock)
# 期权行情
etfs = self.Int.get_price()
spys = []
self.price_owner = float(stock["price"])
for i in range(0, len(etfs), 2):
# 计算剩余天数
r = public.dayDiff(self.Int.expireMap[etfs[i]["code"]],
public.getDate())
rec0 = self.calc(etfs[i], etfs[i + 1], r, mode=0)
spys.append(rec0)
rec1 = self.calc(etfs[i + 1], etfs[i], r, mode=1)
spys.append(rec1)
# inform 通知
try:
self.inform(spys)
except:
logger.error("ws disconnect")
# 写入数据库
self.sPrice.insertAll(etfs)
self.Spy.insertAll(spys)
def main():
action = {
"kline": 1,
"area":0
}
if action["kline"] == 1:
obj = train_future_jump_sg()
obj.Pool()
if action["area"] == 1:
obj = train_future_jump_sg()
Rice = interface_Rice()
for t in [1, 2]:
lists = obj.getList(t)
df = pd.DataFrame(lists)
if t==1:
df['diff'] = df.apply(lambda r: public.timeDiff(r['enddate'], r['startdate'])//86400, axis=1)
else:
df['diff'] = df.apply(lambda r: public.dayDiff(r['enddate'], r['startdate']), axis=1)
file = Rice.basePath + 'area_%s_sg.csv' % (t)
df.to_csv(file, index=0, columns=['code', 'name', 'startdate', 'enddate', 'diff'])