def get_random_price(price,
code='rb1905',
tradingDay='20181119',
mu=0,
sigma=0.2,
theta=0.15,
dt=1e-2,
ifprint=False,
weight=0.1,
callback=None):
ou_noise = OrnsteinUhlenbeckActionNoise(mu=np.array(mu))
data = []
tick_pickle = {
'InstrumentID':
str(code),
'UpdateTime':
'',
'UpdateMillisec':
'',
'LastPrice':
'',
'Volume':
0,
'TradingDay':
tradingDay,
'ActionDay':
QA_util_date_str2int(
QA_util_get_last_day(QA_util_date_int2str(tradingDay)))
}
for item in time_index:
x = str(item).split()[2]
if '.' in x:
tick_pickle['UpdateMillisec'] = 500
else:
tick_pickle['UpdateMillisec'] = 0
tick_pickle['UpdateTime'] = x.split('.')[0]
if item.seconds >= 75600:
tick_pickle['ActionDay'] = QA_util_date_str2int(
QA_util_get_last_day(
QA_util_date_int2str(tick_pickle['TradingDay'])))
else:
tick_pickle['ActionDay'] = tick_pickle['TradingDay']
tick_pickle['Volume'] += random.randint(50, 5000)
tick_pickle['LastPrice'] = (ou_noise() + 1) * \
weight * price + (1 - weight) * price
data.append(copy.deepcopy(tick_pickle))
if ifprint:
print(tick_pickle)
if callback is not None:
callback(tick_pickle)
return pd.DataFrame(data)
def get(self):
"""[summary]
/fetcher
http://localhost:8010/marketdata/fetcher?code=RB1905&market=future_cn&end=2018-12-01&gap=20&frequence=15min
http://localhost:8010/marketdata/fetcher?code=000001&market=stock_cn&end=2018-12-01&gap=20&frequence=15min
http://localhost:8010/marketdata/fetcher?code=000001,000002&market=stock_cn&end=2018-12-01&gap=20&frequence=realtime&source=tdx
一个统一了多市场的多周期数据接口
param:
code
market
end
gap
frequence
TODO:
source
"""
code = self.get_argument('code', '000001')
market = self.get_argument('market', MARKET_TYPE.STOCK_CN)
end = self.get_argument('end', str(datetime.date.today))
gap = self.get_argument('gap', 50)
frequence = self.get_argument('frequence', FREQUENCE.FIFTEEN_MIN)
start = self.get_argument(
'start', QA_util_get_last_day(QA_util_get_real_date(end),
int(gap)))
source = self.get_argument('source', DATASOURCE.MONGO)
if len(code) > 6:
try:
code = code.split(',')
print(code)
except:
code = code
#print(code, start, end, frequence, market)
res = QA_quotation(code,
start,
end,
frequence,
market,
source=source,
output=OUTPUT_FORMAT.DATASTRUCT)
return self.write({'status': 200, 'result': res.to_json()})
def __init__(self, code, start, end):
super().__init__()
self.frequence = FREQUENCE.DAY
self.market_type = MARKET_TYPE.STOCK_CN
self.backtest_stock_code = code
start = QA_util_get_last_day(
QA_util_get_real_date(start, trade_date_sse, -1), 100)
self.stock_day_data = QAQuery_Advance.QA_fetch_stock_day_adv(
self.backtest_stock_code, start, end)
self.stock_day_data_qfq = self.stock_day_data.to_qfq()
# print(self.stock_day_data)
self.stock_bollinger_bands = QA_indicator_BOLL(
self.stock_day_data_qfq())
# print(len(self.stock_bollinger_bands))
# print(self.stock_bollinger_bands)
self.current_state = 0
def get_random_price(price,
code='rb1905',
tradingDay='20181119',
mu=0,
sigma=0.2,
theta=0.15,
dt=1e-2,
ifprint=False,
weight=0.1,
market_type=None):
ou_noise = OrnsteinUhlenbeckActionNoise(mu=np.array(mu))
data = []
tick_pickle = {
'InstrumentID':
str(code),
'UpdateTime':
'',
'UpdateMillisec':
'',
'LastPrice':
'',
'Volume':
0,
'TradingDay':
tradingDay,
'ActionDay':
QA_util_date_str2int(
QA_util_get_last_day(QA_util_date_int2str(tradingDay)))
}
if market_type is None:
market_type = MARKET_TYPE.FUTURE_CN if re.search(
r'[a-zA-z]+', code) else MARKET_TYPE.STOCK_CN
if market_type == MARKET_TYPE.FUTURE_CN:
time_index = time_index_future
else:
time_index = time_index_stock
for item in time_index:
x = str(item).split()[2]
if '.' in x:
tick_pickle['UpdateMillisec'] = 500
else:
tick_pickle['UpdateMillisec'] = 0
tick_pickle['UpdateTime'] = x.split('.')[0]
if (item.seconds >= 75600) & (market_type == MARKET_TYPE.FUTURE_CN):
tick_pickle['ActionDay'] = QA_util_date_str2int(
QA_util_get_last_day(
QA_util_date_int2str(tick_pickle['TradingDay'])))
else:
tick_pickle['ActionDay'] = tick_pickle['TradingDay']
tick_pickle['Volume'] += random.randint(50, 5000)
tick_pickle['LastPrice'] = (ou_noise()+1) * \
weight*price + (1-weight)*price
data.append(copy.deepcopy(tick_pickle))
if ifprint:
print(tick_pickle)
return pd.DataFrame(data)