def job_once_global_tick(ts):
"""
获取全部商品分笔数据
:param ts:
:return:
"""
global is_closing
df = ts.get_stock_basics()
if df is None:
return
cont = False
#lastsymbol = '300598'
for symbol in df['name'].index.values:
# if symbol == lastsymbol:
# cont = True
# if not cont:
# continue
for ndays in range(1, 2, 1):
if not is_closing:
day = DateUtil.getDatetimePastStr(DateUtil.getDatetimeToday(),
ndays)
if not is_holiday(day):
ts.get_tick_data(symbol, day)
time.sleep(1)
else:
logging.info('is holiday')
if not is_closing:
time.sleep(2)
def job_realtime_global(ts):
"""
交易日里读取指数信息和全盘Snap(20秒左右)
:param ts:
:return:
"""
global is_closing
today = DateUtil.getDatetimeToday()
while not is_closing and not is_holiday(DateUtil.date_toString(today)):
ts.get_index()
time.sleep(3)
ts.get_today_all()
time.sleep(5)
def job_global_remedy_task(ts):
"""
定时作业，收盘数据：分笔数据，5日数据和日线数据
:param ts:
:return:
"""
global is_closing
df = ts.get_stock_basics()
if df is None:
return
ndays = 1
startDate = '2018-07-09'
endDate = DateUtil.getDatetimeFutureStr(DateUtil.string_toDate(startDate),
ndays)
days = []
days.append(startDate)
for day in range(1, ndays, 1):
continueDate = DateUtil.getDatetimeFutureStr(
DateUtil.string_toDate(startDate), day)
days.append(continueDate)
cont = False
#lastsymbol = '603895'
for symbol in df['name'].index.values:
# if symbol == lastsymbol:
# cont = True
# if not cont:
# continue
for day in days:
if not is_closing:
if not is_holiday(day):
ts.get_tick_data(symbol, day)
time.sleep(1)
else:
logging.info('is holiday')
ts.get_hist_data(symbol, startDate, endDate, '5')
time.sleep(2)
ts.get_hist_data(symbol, startDate, endDate)
time.sleep(2)
def top_list(date=None, retry_count=3, pause=0.001):
"""
获取每日龙虎榜列表
Parameters
--------
date:string
明细数据日期 format：YYYY-MM-DD 如果为空，返回最近一个交易日的数据
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
------
DataFrame
code：代码
name ：名称
pchange：涨跌幅
amount：龙虎榜成交额(万)
buy：买入额(万)
bratio：占总成交比例
sell：卖出额(万)
sratio ：占总成交比例
reason：上榜原因
date ：日期
"""
if date is None:
if du.get_hour() < 18:
date = du.last_tddate()
else:
date = du.today()
else:
if (du.is_holiday(date)):
return None
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(
rv.LHB_URL % (ct.P_TYPE['http'], ct.DOMAINS['em'], date, date))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
text = text.split('_1=')[1]
text = eval(
text,
type('Dummy', (dict, ), dict(__getitem__=lambda s, n: n))())
text = json.dumps(text)
text = json.loads(text)
df = pd.DataFrame(text['data'], columns=rv.LHB_TMP_COLS)
df.columns = rv.LHB_COLS
df = df.fillna(0)
df = df.replace('', 0)
df['buy'] = df['buy'].astype(float)
df['sell'] = df['sell'].astype(float)
df['amount'] = df['amount'].astype(float)
df['Turnover'] = df['Turnover'].astype(float)
df['bratio'] = df['buy'] / df['Turnover']
df['sratio'] = df['sell'] / df['Turnover']
df['bratio'] = df['bratio'].map(ct.FORMAT)
df['sratio'] = df['sratio'].map(ct.FORMAT)
df['date'] = date
for col in ['amount', 'buy', 'sell']:
df[col] = df[col].astype(float)
df[col] = df[col] / 10000
df[col] = df[col].map(ct.FORMAT)
df = df.drop('Turnover', axis=1)
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_h_data(code,
start=None,
end=None,
autype='qfq',
index=False,
retry_count=3,
pause=0.001,
drop_factor=True):
'''
获取历史复权数据
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取当前日期
end:string
结束日期 format：YYYY-MM-DD 为空时取去年今日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
drop_factor : bool, 默认 True
是否移除复权因子，在分析过程中可能复权因子意义不大，但是如需要先储存到数据库之后再分析的话，有该项目会更加灵活
return
-------
DataFrame
date 交易日期 (index)
open 开盘价
high 最高价
close 收盘价
low 最低价
volume 成交量
amount 成交金额
'''
start = du.today_last_year() if start is None else start
end = du.today() if end is None else end
qs = du.get_quarts(start, end)
qt = qs[0]
ct._write_head()
data = _parse_fq_data(_get_index_url(index, code, qt), index, retry_count,
pause)
if data is None:
data = pd.DataFrame()
if len(qs) > 1:
for d in range(1, len(qs)):
qt = qs[d]
ct._write_console()
df = _parse_fq_data(_get_index_url(index, code, qt), index,
retry_count, pause)
if df is None: # 可能df为空，退出循环
break
else:
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(
data[(data.date >= start) & (data.date <= end)]) == 0:
return pd.DataFrame()
data = data.drop_duplicates('date')
if index:
data = data[(data.date >= start) & (data.date <= end)]
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
if autype == 'hfq':
if drop_factor:
data = data.drop('factor', axis=1)
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
else:
if autype == 'qfq':
if drop_factor:
data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort_values('date', ascending=False)
firstDate = data.head(1)['date']
frow = df[df.date == firstDate[0]]
rt = get_realtime_quotes(code)
if rt is None:
return pd.DataFrame()
if ((float(rt['high']) == 0) & (float(rt['low']) == 0)):
preClose = float(rt['pre_close'])
else:
if du.is_holiday(du.today()):
preClose = float(rt['price'])
else:
if (du.get_hour() > 9) & (du.get_hour() < 18):
preClose = float(rt['pre_close'])
else:
preClose = float(rt['price'])
rate = float(frow['factor']) / preClose
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'low', 'close']:
data[label] = data[label] / rate
data[label] = data[label].map(ct.FORMAT)
data[label] = data[label].astype(float)
data = data.set_index('date')
data = data.sort_index(ascending=False)
return data
else:
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label] / data['factor']
if drop_factor:
data = data.drop('factor', axis=1)
data = data[(data.date >= start) & (data.date <= end)]
for label in ['open', 'high', 'close', 'low']:
data[label] = data[label].map(ct.FORMAT)
data = data.set_index('date')
data = data.sort_index(ascending=False)
data = data.astype(float)
return data