def get_today_ticks(code=None, retry_count=3, pause=0.001):
"""
获取当日分笔明细数据
Parameters
------
code:string
股票代码 e.g. 600848
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 当日所有股票交易数据(DataFrame)
属性:成交时间、成交价格、价格变动，成交手、成交金额(元)，买卖类型
"""
if code is None or len(code) != 6:
return None
symbol = ct._code_to_symbol(code)
date = du.today()
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(ct.TODAY_TICKS_PAGE_URL %
(ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['jv'], date, symbol))
data_str = urlopen(request, timeout=10).read()
data_str = data_str.decode('GBK')
data_str = data_str[1:-1]
data_str = eval(
data_str,
type('Dummy', (dict, ), dict(__getitem__=lambda s, n: n))())
data_str = json.dumps(data_str)
data_str = json.loads(data_str)
pages = len(data_str['detailPages'])
data = pd.DataFrame()
ct._write_head()
for pNo in range(1, pages + 1):
data = data.append(_today_ticks(symbol, date, pNo, retry_count,
pause),
ignore_index=True)
except Exception as er:
print(str(er))
else:
return data
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_k_data(code=None, start='', end='',
ktype='D', autype='qfq',
index=False,
retry_count=3,
pause=0.001):
"""
获取k线数据
---------
Parameters:
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取上市首日
end:string
结束日期 format：YYYY-MM-DD 为空时取最近一个交易日
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为qfq
ktype：string
数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D
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
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 成交额
turnoverratio 换手率
code 股票代码
"""
symbol = ct.INDEX_SYMBOL[code] if index else ct._code_to_symbol(code)
url = ''
dataflag = ''
autype = '' if autype is None else autype
if (start is not None) & (start != ''):
end = du.today() if end is None or end == '' else end
if ktype.upper() in ct.K_LABELS:
fq = autype if autype is not None else ''
if code[:1] in ('1', '5') or index:
fq = ''
kline = '' if autype is None else 'fq'
if (start is None or start == '') & (end is None or end == ''):
urls = [ct.KLINE_TT_URL%(ct.P_TYPE['http'], ct.DOMAINS['tt'],
kline, fq, symbol,
ct.TT_K_TYPE[ktype.upper()], start, end,
fq, _random(17))]
else:
years = du.tt_dates(start, end)
urls = []
for year in years:
startdate = str(year) + '-01-01'
enddate = str(year+1) + '-12-31'
url = ct.KLINE_TT_URL%(ct.P_TYPE['http'], ct.DOMAINS['tt'],
kline, fq+str(year), symbol,
ct.TT_K_TYPE[ktype.upper()], startdate, enddate,
fq, _random(17))
urls.append(url)
dataflag = '%s%s'%(fq, ct.TT_K_TYPE[ktype.upper()])
elif ktype in ct.K_MIN_LABELS:
urls = [ct.KLINE_TT_MIN_URL%(ct.P_TYPE['http'], ct.DOMAINS['tt'],
symbol, ktype, ktype,
_random(16))]
dataflag = 'm%s'%ktype
else:
raise TypeError('ktype input error.')
data = pd.DataFrame()
for url in urls:
data = data.append(_get_k_data(url, dataflag,
symbol, code,
index, ktype,
retry_count, pause),
ignore_index=True)
if ktype not in ct.K_MIN_LABELS:
if ((start is not None) & (start != '')) & ((end is not None) & (end != '')):
if data.empty==False:
data = data[(data.date >= start) & (data.date <= end)]
return data
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'])
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