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 QA_save_stock_day_pg(start_date='19901219'):
t = time.localtime(time.time())
if int(time.strftime('%H%M%S', t)) < 190000: #晚上七点之后在更新当天数据，以免不及时
t = time.localtime(time.time() - 3600 * 24)
tS = time.strftime("%Y-%m-%d", t)
else:
tS = time.strftime("%Y-%m-%d", t)
end_date = tS
try:
mes = 'select distinct trade_date from stock_day;'
trade_data_pg = load_data_from_postgresql(mes=mes).trade_date.tolist()
for i in range(len(trade_data_pg)):
trade_data_pg[i] = trade_data_pg[i].strftime("%Y-%m-%d")
except: #第一次运行
trade_data_pg = list()
if isinstance(start_date, int):
start_date = QA_util_date_int2str(start_date)
elif len(start_date) == 8:
start_date = start_date[0:4] + '-' + start_date[
4:6] + '-' + start_date[6:8]
trade_date = QA_util_get_trade_range(start_date, end_date)
trade_date2 = list(set(trade_date) ^ set(trade_data_pg))
trade_date2.sort()
if len(trade_date2) == 0:
QA_util_log_info(
'Stock day is up to date and does not need to be updated')
for i in trade_date2:
pass
try:
t = time.time()
df = download_day_data_from_tushare(i)
#i=i[7:10].lower()+i[0:6]
save_data_to_postgresql('stock_day', df, 'append')
t1 = time.time()
QA_util_log_info('save ' + i + ' stock day success,take ' +
str(round(t1 - t, 2)) + ' S')
except Exception as e:
print(e)
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)