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_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 = _code_to_symbol(code)
date = du.today()
try:
request = Request(ct.TODAY_TICKS_PAGE_URL % ( 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):
data = data.append(_today_ticks(symbol, date, pNo,
retry_count, pause), ignore_index=True)
except Exception as er:
print(str(er))
return data
def get_h_data(code,
start=None,
end=None,
autype='qfq',
index=False,
retry_count=3,
pause=0.001):
'''
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
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
factor 后复权因子
'''
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 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)
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(
data[(data.date >= start) & (data.date <= end)]) == 0:
return None
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':
#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':
#data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort('date', ascending=False)
frow = df.head(1)
rt = get_realtime_quotes(code)
if rt is None:
return None
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']
#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
def get_stock_daily_data(code, start=None, end=None, autype=None,
index=False, retry_count=100000, pause=0.1, drop_factor=True):
'''
获取历史daily数据
Parameters
------
code:string
股票代码 e.g. 600048
start:string
开始日期 format：YYYY-MM-DD 为空时取上市日期
end:string
结束日期 format：YYYY-MM-DD 为空时取当前日期
autype:string
复权类型，qfq-前复权 hfq-后复权 None-不复权，默认为None
retry_count : int, 默认 10000
如遇网络等问题重复执行的次数
pause : int, 默认 0.1
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 = get_stock_timeToMarket(code) if start is None else start
#print str(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 None
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('date', ascending=False)
firstDate = data.head(1)['date']
frow = df[df.date == firstDate[0]]
rt = get_realtime_quotes(code)
if rt is None:
return None
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
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 tick(code, conn=None, date='', asset='E', market='', retry_count=3):
"""
tick数据
Parameters:
------------
code:证券代码，支持股票,ETF/LOF,期货/期权,港股
conn:服务器连接 ，通过ts.api()或者ts.xpi()获得
date:日期
asset:证券品种，E:沪深交易所股票和基金, INDEX:沪深交易所指数， X:其他证券品种，大致如下：
支持的扩展行情包括(asset='X')：
郑州商品期权 OZ 大连商品期权 OD 上海商品期权 OS
上海个股期权 QQ 香港指数 FH 郑州商品 QZ 大连商品 QD 上海期货 QS
香港主板 KH 香港权证 KR 开放式基金 FU 货币型基金 FB
招商理财产品 LC 招商货币产品 LB 国际指数 FW 国内宏观指标 HG 中国概念股 CH
美股知名公司 MG B股转H股 HB 股份转让 SB 股指期货 CZ 香港创业板 KG 香港信托基金 KT
国债预发行 GY 主力期货合约 MA
中证指数 ZZ 港股通 GH
market:市场代码，通过ts.get_markets()获取
Return
----------
DataFrame
date:日期
time:时间
price:成交价
vol:成交量
type:买卖方向，0-买入 1-卖出 2-集合竞价成交
期货 0:开仓 1:多开 -1:空开
期货多一列数据oi_change:增仓数据
"""
code = code.strip().upper()
date = int(date.replace('-', ''))
today = int(str(du.today()).replace('-', ''))
for _ in range(retry_count):
try:
if conn is None:
print(ct.MSG_NOT_CONNECTED)
return None
api, xapi = conn
data = pd.DataFrame()
mkcode = _get_mkcode(code, asset=asset,
xapi=xapi) if market == '' else market
con = api if asset in ['E', 'INDEX'] else xapi
for i in range(200):
if date == today:
ds = con.get_transaction_data(market=mkcode,
code=code,
start=i * 300,
count=300)
else:
ds = con.get_history_transaction_data(market=mkcode,
code=code,
date=date,
start=i * 300,
count=300)
df = api.to_df(ds)
data = data.append(df) if i == 0 else df.append(
data, ignore_index=True)
if len(ds) < 300:
break
if asset in ['E', 'INDEX']:
data['date'] = date
data['date'] = data['date'].map(lambda x: '%s-%s-%s ' % (str(
x)[0:4], str(x)[4:6], str(x)[6:8]))
data['datetime'] = data['date'] + data['time']
data = data[['datetime', 'price', 'vol', 'buyorsell']]
data.columns = ['datetime', 'price', 'vol', 'type']
else:
if mkcode in [31, 71]:
if date == today:
data = data.drop([
'hour', 'minute', 'nature_name', 'zengcang',
'direction', 'second', 'nature_mark',
'nature_value'
],
axis=1)
else:
data = data.drop([
'hour', 'minute', 'nature_name', 'zengcang',
'direction'
],
axis=1)
data.loc[data.nature == 512, 'nature'] = 2
data.loc[data.nature == 256, 'nature'] = 1
data = data.sort_values('date')
data.columns = ['date', 'price', 'vol', 'type']
elif mkcode in [28, 29, 30, 47, 60]:
if date == today:
data = data.drop([
'hour', 'minute', 'nature', 'direction', 'second',
'nature_mark', 'nature_value'
],
axis=1)
else:
data = data.drop(
['hour', 'minute', 'nature', 'direction'], axis=1)
data.columns = [
'date', 'price', 'vol', 'oi_change', 'type'
]
else:
data = data.drop([
'hour', 'minute', 'nature_name', 'zengcang',
'direction', 'nature'
],
axis=1)
except Exception as e:
print(e)
else:
return data
def get_h_data(code, start=None, end=None, autype='qfq',
index=False, retry_count=3, pause=0.001):
'''
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
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
factor 后复权因子
'''
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 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)
data = data.append(df, ignore_index=True)
if len(data) == 0 or len(data[(data.date>=start)&(data.date<=end)]) == 0:
return None
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':
#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':
#data = data.drop('factor', axis=1)
df = _parase_fq_factor(code, start, end)
df = df.drop_duplicates('date')
df = df.sort('date', ascending=False)
frow = df.head(1)
rt = get_realtime_quotes(code)
if rt is None:
return None
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']
#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