def QA_util_make_min_index(day, type_='1min'):
"""
explanation:
创建股票分钟线的index
params:
* day ->
含义:天数
类型:int
参数支持: []
* type_ ->
含义: 周期索引
类型: str
参数支持: ["1min"]
"""
if QA_util_if_trade(day) is True:
return pd.date_range(
str(day) + ' 09:30:00',
str(day) + ' 11:30:00',
freq=type_,
closed='right'
).append(
pd.date_range(
str(day) + ' 13:00:00',
str(day) + ' 15:00:00',
freq=type_,
closed='right'
)
)
else:
return pd.DataFrame(['No trade'])
def job111(trading_date):
if datetime.strptime(trading_date, "%Y-%m-%d").weekday() == 4:
train(trading_date, working_dir=working_dir)
else:
pass
if QA_util_if_trade(trading_date):
predict(trading_date)
else:
predict(QA_util_get_real_date(trading_date))
def QA_util_make_hour_index(day, type_='1h'):
if QA_util_if_trade(day) is True:
return pd.date_range(str(day) + ' 09:30:00',
str(day) + ' 11:30:00',
freq=type_,
closed='right').append(
pd.date_range(str(day) + ' 13:00:00',
str(day) + ' 15:00:00',
freq=type_,
closed='right'))
else:
return pd.DataFrame(['No trade'])
def QA_util_make_hour_index(day, type_='1h'):
"""创建股票的小时线的index
Arguments:
day {[type]} -- [description]
Returns:
[type] -- [description]
"""
if QA_util_if_trade(day) is True:
return pd.date_range(str(day) + ' 09:30:00', str(day) + ' 11:30:00', freq=type_, closed='right').append(
pd.date_range(str(day) + ' 13:00:00', str(day) + ' 15:00:00', freq=type_, closed='right'))
else:
return pd.DataFrame(['No trade'])
def QA_util_make_future_min_index(day, type_='1min'):
"""创建期货分钟线的index
Arguments:
day {[type]} -- [description]
Returns:
[type] -- [description]
"""
if QA_util_if_trade(day) is True:
return pd.date_range(str(day) + '21:00:00',
str(day) + '',
freq=type_,
closed='right').append(
pd.date_range(str(day) + ' 13:00:00',
str(day) + ' 15:00:00',
freq=type_,
closed='right'))
else:
return pd.DataFrame(['No trade'])
def QA_fetch_stock_target(codes, start_date, end_date, type='close'):
if QA_util_if_trade(end_date):
pass
else:
end_date = QA_util_get_real_date(end_date)
end = QA_util_get_next_datetime(end_date, 5)
rng1 = pd.Series(pd.date_range(start_date, end_date,
freq='D')).apply(lambda x: str(x)[0:10])
data = QA.QA_fetch_stock_day_adv(codes, start_date, end)
market = QA.QA_fetch_index_day(['000001'], start_date, end,
format='pd')['close'].reset_index()
market = index_pct(market)[[
'date', 'INDEX_TARGET', 'INDEX_TARGET3', 'INDEX_TARGET4',
'INDEX_TARGET5', 'INDEX_TARGET10'
]]
res1 = data.to_qfq().data
res1.columns = [x + '_qfq' for x in res1.columns]
data = data.data.join(res1).fillna(0).reset_index()
res = data.groupby('code').apply(pct, type=type)[[
'date', 'code', 'PRE_DATE', 'OPEN_MARK', 'PASS_MARK', 'TARGET',
'TARGET3', 'TARGET4', 'TARGET5', 'TARGET10', 'AVG_TARGET'
]]
res = pd.merge(res, market, on='date')
res['date'] = res['date'].apply(lambda x: str(x)[0:10])
res['next_date'] = res['date'].apply(
lambda x: QA_util_get_pre_trade_date(x, -2))
res['PRE_DATE'] = res['PRE_DATE'].apply(lambda x: str(x)[0:10])
res = res.set_index(['date', 'code']).loc[rng1]
res['INDEX_TARGET'] = res['TARGET'] - res['INDEX_TARGET']
res['INDEX_TARGET3'] = res['TARGET3'] - res['INDEX_TARGET3']
res['INDEX_TARGET4'] = res['TARGET4'] - res['INDEX_TARGET4']
res['INDEX_TARGET5'] = res['TARGET5'] - res['INDEX_TARGET5']
res['INDEX_TARGET10'] = res['TARGET10'] - res['INDEX_TARGET10']
for columnname in res.columns:
if res[columnname].dtype == 'float64':
res[columnname] = res[columnname].astype('float16')
if res[columnname].dtype == 'int64':
res[columnname] = res[columnname].astype('int8')
return (res)
def QA_fetch_index_target(codes, start_date, end_date):
if QA_util_if_trade(end_date):
pass
else:
end_date = QA_util_get_real_date(end_date)
end = QA_util_get_next_datetime(end_date, 5)
rng1 = pd.Series(pd.date_range(start_date, end_date,
freq='D')).apply(lambda x: str(x)[0:10])
data = QA.QA_fetch_index_day_adv(codes, start_date,
end).data.fillna(0).reset_index()
res = data.groupby('code').apply(index_pct)[[
'date', 'code', 'INDEX_TARGET', 'INDEX_TARGET3', 'INDEX_TARGET4',
'INDEX_TARGET5', 'INDEX_TARGET10'
]]
res['date'] = res['date'].apply(lambda x: str(x)[0:10])
res = res.set_index(['date', 'code']).loc[rng1]
for columnname in res.columns:
if res[columnname].dtype == 'float64':
res[columnname] = res[columnname].astype('float16')
if res[columnname].dtype == 'int64':
res[columnname] = res[columnname].astype('int8')
return (res)
def util_is_trade_time(
_time=datetime.datetime.now(), market=MARKET_TYPE.STOCK_CN, code=None):
"""判断当前是否为交易时间"""
date_today = _time.isoformat()[0:10]
if market is MARKET_TYPE.STOCK_CN:
if QA_util_if_trade(date_today):
if _time.hour in [10, 13, 14]:
return True
elif _time.hour == 9 and _time.minute >= 15: # 修改成9:15 加入 9:15-9:30的盘前竞价时间
return True
elif _time.hour == 11 and _time.minute <= 32: # 11:30 -> 11:31 也刷新数据
return True
# elif _time.hour == 12 and _time.minute >= 58: # 12:58 - 13:00 也刷新数据
# return True
elif _time.hour == 15 and _time.minute <= 2: # 15:00 - 15:02 也刷新数据
return True
else:
return False
else:
return False
elif market is MARKET_TYPE.FUTURE_CN:
date_yesterday = str((_time - datetime.timedelta(days=1)).date())
is_today_open = QA_util_if_trade(date_today)
is_yesterday_open = QA_util_if_trade(date_yesterday)
# 考虑周六日的期货夜盘情况
if is_today_open == False: # 可能是周六或者周日
if is_yesterday_open == False or (_time.hour > 2 or _time.hour == 2
and _time.minute > 30):
return False
shortName = "" # i , p
for i in range(len(code)):
ch = code[i]
if ch.isdigit(): # ch >= 48 and ch <= 57:
break
shortName += code[i].upper()
period = [[9, 0, 10, 15], [10, 30, 11, 30], [13, 30, 15, 0]]
if (shortName in ["IH", 'IF', 'IC']):
period = [[9, 30, 11, 30], [13, 0, 15, 0]]
elif (shortName in ["T", "TF"]):
period = [[9, 15, 11, 30], [13, 0, 15, 15]]
if 0 <= _time.weekday <= 4:
for i in range(len(period)):
p = period[i]
if ((_time.hour > p[0] or
(_time.hour == p[0] and _time.minute >= p[1]))
and (_time.hour < p[2] or
(_time.hour == p[2] and _time.minute < p[3]))):
return True
# 最新夜盘时间表_2019.03.29
nperiod = [
[['AU', 'AG', 'SC'], [21, 0, 2, 30]],
[['CU', 'AL', 'ZN', 'PB', 'SN', 'NI'], [21, 0, 1, 0]],
[['RU', 'RB', 'HC', 'BU', 'FU', 'SP'], [21, 0, 23, 0]],
[[
'A', 'B', 'Y', 'M', 'JM', 'J', 'P', 'I', 'L', 'V', 'PP', 'EG',
'C', 'CS'
], [21, 0, 23, 0]],
[['SR', 'CF', 'RM', 'MA', 'TA', 'ZC', 'FG', 'IO', 'CY'],
[21, 0, 23, 30]],
]
for i in range(len(nperiod)):
for j in range(len(nperiod[i][0])):
if nperiod[i][0][j] == shortName:
p = nperiod[i][1]
condA = _time.hour > p[0] or (_time.hour == p[0]
and _time.minute >= p[1])
condB = _time.hour < p[2] or (_time.hour == p[2]
and _time.minute < p[3])
# in one day
if p[2] >= p[0]:
if ((_time.weekday >= 0 and _time.weekday <= 4)
and condA and condB):
return True
else:
if (((_time.weekday >= 0 and _time.weekday <= 4)
and condA)
or ((_time.weekday >= 1 and _time.weekday <= 5)
and condB)):
return True
return False
return False
