def get_tick_data(code=None, date=None, retry_count=3, pause=0.001,
src='sn'):
"""
获取分笔数据
Parameters
------
code:string
股票代码 e.g. 600848
date:string
日期 format: YYYY-MM-DD
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
src : 数据源选择，可输入sn(新浪)、tt(腾讯)、nt(网易)，默认sn
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 or date is None:
return None
if (src.strip() not in ct.TICK_SRCS):
print(ct.TICK_SRC_ERROR)
return None
symbol = ct._code_to_symbol(code)
symbol_dgt = ct._code_to_symbol_dgt(code)
datestr = date.replace('-', '')
url = {
ct.TICK_SRCS[0] : ct.TICK_PRICE_URL % (ct.P_TYPE['http'], ct.DOMAINS['sf'], ct.PAGES['dl'],
date, symbol),
ct.TICK_SRCS[1] : ct.TICK_PRICE_URL_TT % (ct.P_TYPE['http'], ct.DOMAINS['tt'], ct.PAGES['idx'],
symbol, datestr),
ct.TICK_SRCS[2] : ct.TICK_PRICE_URL_NT % (ct.P_TYPE['http'], ct.DOMAINS['163'], date[0:4],
datestr, symbol_dgt)
}
for _ in range(retry_count):
time.sleep(pause)
try:
if src == ct.TICK_SRCS[2]:
df = pd.read_excel(url[src])
df.columns = ct.TICK_COLUMNS
else:
re = Request(url[src])
lines = urlopen(re, timeout=10).read()
lines = lines.decode('GBK')
if len(lines) < 20:
return None
df = pd.read_table(StringIO(lines), names=ct.TICK_COLUMNS,
skiprows=[0])
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def top10_holders(code=None, year=None, quarter=None, gdtype='0',
retry_count=3, pause=0.001):
if code is None:
return None
else:
code = ct._code_to_symbol(code)
gdtype = 'LT' if gdtype == '1' else ''
qdate = ''
if (year is not None) & (quarter is not None):
qdate = du.get_q_date(year, quarter)
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(rv.TOP10_HOLDERS_URL%(ct.P_TYPE['http'], ct.DOMAINS['gw'],
gdtype, code.upper()))
lines = urlopen(request, timeout = 10).read()
lines = lines.decode('utf8') if ct.PY3 else lines
reg = re.compile(r'= \'\[(.*?)\]\';')
lines = reg.findall(lines)[0]
jss = json.loads('[%s]' %lines)
summ = []
data = pd.DataFrame()
for row in jss:
qt = row['jzrq']
hold = row['ljcy']
change = row['ljbh']
props = row['ljzb']
arow = [qt, hold, change ,props]
summ.append(arow)
ls = row['sdgdList']
dlist = []
for inrow in ls:
sharetype = inrow['gbxz']
name = inrow['gdmc']
hold = inrow['cgs']
h_pro = inrow['zzgs']
status = inrow['zjqk']
dlist.append([qt, name, hold, h_pro, sharetype, status])
ddata = pd.DataFrame(dlist, columns=rv.TOP10_PER_COLS)
data = data.append(ddata, ignore_index=True)
df = pd.DataFrame(summ, columns=rv.TOP10_SUMM_COLS)
if qdate != '':
df = df[df.quarter == qdate]
data = data[data.quarter == qdate]
except Exception as e:
print(e)
else:
return df, 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 = 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 = 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_sina_dd(code=None, date=None, vol=400, retry_count=3, pause=0.001):
"""
获取sina大单数据
Parameters
------
code:string
股票代码 e.g. 600848
date:string
日期 format：YYYY-MM-DD
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:股票代码 股票名称 交易时间 价格 成交量 前一笔价格 类型（买、卖、中性盘）
"""
if code is None or len(code) != 6 or date is None:
return None
symbol = ct._code_to_symbol(code)
vol = vol * 100
for _ in range(retry_count):
time.sleep(pause)
try:
headers = {'User-Agent': _get_user_agent()}
re = Request(ct.SINA_DD % (ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['sinadd'], symbol, vol, date),
headers=headers)
lines = urlopen(re, timeout=10).read()
lines = lines.decode('GBK')
if len(lines) < 100:
return None
df = pd.read_csv(StringIO(lines),
names=ct.SINA_DD_COLS,
skiprows=[0])
if df is not None:
df['code'] = df['code'].map(lambda x: x[2:])
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_sina_dd(code=None, date=None, vol=400, retry_count=3, pause=0.001):
"""
获取sina大单数据
Parameters
------
code:string
股票代码 e.g. 600848
date:string
日期 format：YYYY-MM-DD
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:股票代码 股票名称 交易时间 价格 成交量 前一笔价格 类型（买、卖、中性盘）
"""
if code is None or len(code) != 6 or date is None:
return None
symbol = ct._code_to_symbol(code)
vol = vol * 100
for _ in range(retry_count):
time.sleep(pause)
try:
re = Request(ct.SINA_DD % (ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['sinadd'], symbol, vol, date))
lines = urlopen(re, timeout=10).read()
lines = lines.decode('GBK')
lines_j = eval(
lines,
type('Dummy', (dict, ), dict(__getitem__=lambda s, n: n))())
if len(lines) < 100:
return None
df = pd.read_json(json.dumps(lines_j))
if df is not None:
df['symbol'] = df['symbol'].map(lambda x: x[2:])
except Exception as e:
print(e)
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def _parase_fq_factor(code, start, end):
symbol = ct._code_to_symbol(code)
request = Request(ct.HIST_FQ_FACTOR_URL%(ct.P_TYPE['http'],
ct.DOMAINS['vsf'], symbol))
text = urlopen(request, timeout=10).read()
text = text[1:len(text)-1]
text = text.decode('utf-8') if ct.PY3 else text
text = text.replace('{_', '{"')
text = text.replace('total', '"total"')
text = text.replace('data', '"data"')
text = text.replace(':"', '":"')
text = text.replace('",_', '","')
text = text.replace('_', '-')
text = json.loads(text)
df = pd.DataFrame({'date':list(text['data'].keys()), 'factor':list(text['data'].values())})
df['date'] = df['date'].map(_fun_except) # for null case
if df['date'].dtypes == np.object:
df['date'] = pd.to_datetime(df['date'])
df = df.drop_duplicates('date')
df['factor'] = df['factor'].astype(float)
return df
def _parase_fq_factor(code, start, end):
symbol = ct._code_to_symbol(code)
request = Request(ct.HIST_FQ_FACTOR_URL%(ct.P_TYPE['http'],
ct.DOMAINS['vsf'], symbol))
text = urlopen(request, timeout=10).read()
text = text[1:len(text)-1]
text = text.decode('utf-8') if ct.PY3 else text
text = text.replace('{_', '{"')
text = text.replace('total', '"total"')
text = text.replace('data', '"data"')
text = text.replace(':"', '":"')
text = text.replace('",_', '","')
text = text.replace('_', '-')
text = json.loads(text)
df = pd.DataFrame({'date':list(text['data'].keys()), 'factor':list(text['data'].values())})
df['date'] = df['date'].map(_fun_except) # for null case
if df['date'].dtypes == np.object:
df['date'] = pd.to_datetime(df['date'])
df = df.drop_duplicates('date')
df['factor'] = df['factor'].astype(float)
return df
def getDayData(code=None, start="2017-01-01", end="2018-12-31"):
symbol = ct._code_to_symbol(code) #将代码转换成标准格式
url = 'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?_var=kline_dayqfq2017¶m=%s,day,%s,%s,640,qfq' % (
symbol, start, end)
try:
request = Request(url)
lines = urlopen(request, timeout=10).read()
if len(lines) < 100: #no data
return None
except Exception as e:
print(e)
else:
lines = lines.decode('utf-8') if ct.PY3 else lines
lines = lines.split('=')[1]
reg = re.compile(r',{"nd.*?}')
lines = re.subn(reg, '', lines)
reg = re.compile(r',"qt":{.*?}')
lines = re.subn(reg, '', lines[0])
reg = r',"mx_price".*?"version":"4"'
lines = re.subn(reg, '', lines[0])
reg = r',"mx_price".*?"version":"12"'
lines = re.subn(reg, '', lines[0])
#将str格式转换成byte
textByte = bytes(lines[0], encoding='utf-8')
return textByte
raise IOError(ct.NETWORK_URL_ERROR_MSG)
#获取所有股票的日线数据，并Post至java jar中
# def getAllStockData():
# list1=globalFunction.getAllStockCode()
# for code in list1:
# textByte=getDayData(code)
# urlPost='http://localhost:8080/stock/tradeHistory'
# globalFunction.postData(textByte,urlPost,code)
# code="600145"
# textByte = getDayData(code)
# urlPost = 'http://localhost:8080/stock/tradeHistory'
# globalFunction.postData(textByte, urlPost, code)
def get_sina_dd(code=None, date=None, vol=400, retry_count=3, pause=0.001):
"""
获取sina大单数据
Parameters
------
code:string
股票代码 e.g. 600848
date:string
日期 format：YYYY-MM-DD
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:股票代码 股票名称 交易时间 价格 成交量 前一笔价格 类型（买、卖、中性盘）
"""
if code is None or len(code)!=6 or date is None:
return None
symbol = ct._code_to_symbol(code)
vol = vol*100
for _ in range(retry_count):
time.sleep(pause)
try:
re = Request(ct.SINA_DD % (ct.P_TYPE['http'], ct.DOMAINS['vsf'], ct.PAGES['sinadd'],
symbol, vol, date))
lines = urlopen(re, timeout=10).read()
lines = lines.decode('GBK')
if len(lines) < 100:
return None
df = pd.read_csv(StringIO(lines), names=ct.SINA_DD_COLS,
skiprows=[0])
if df is not None:
df['code'] = df['code'].map(lambda x: x[2:])
except Exception as e:
print(e)
else:
return df
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_realtime_quotes(symbols=None):
"""
获取实时交易数据 getting real time quotes data
用于跟踪交易情况（本次执行的结果-上一次执行的数据）
Parameters
------
symbols : string, array-like object (list, tuple, Series).
return
-------
DataFrame 实时交易数据
属性:0：name，股票名字
1：open，今日开盘价
2：pre_close，昨日收盘价
3：price，当前价格
4：high，今日最高价
5：low，今日最低价
6：bid，竞买价，即“买一”报价
7：ask，竞卖价，即“卖一”报价
8：volumn，成交量 maybe you need do volumn/100
9：amount，成交金额（元 CNY）
10：b1_v，委买一（笔数 bid volume）
11：b1_p，委买一（价格 bid price）
12：b2_v，“买二”
13：b2_p，“买二”
14：b3_v，“买三”
15：b3_p，“买三”
16：b4_v，“买四”
17：b4_p，“买四”
18：b5_v，“买五”
19：b5_p，“买五”
20：a1_v，委卖一（笔数 ask volume）
21：a1_p，委卖一（价格 ask price）
...
30：date，日期；
31：time，时间；
"""
symbols_list = ''
if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(
symbols, tuple) or isinstance(symbols, pd.Series):
for code in symbols:
symbols_list += ct._code_to_symbol(code) + ','
else:
symbols_list = ct._code_to_symbol(symbols)
symbols_list = symbols_list[:-1] if len(symbols_list) > 8 else symbols_list
request = Request(
ct.LIVE_DATA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['sinahq'], _random(), symbols_list))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
reg = re.compile(r'\="(.*?)\";')
data = reg.findall(text)
regSym = re.compile(r'(?:sh|sz|gb_)(.*?)\=')
syms = regSym.findall(text)
data_list = []
syms_list = []
for index, row in enumerate(data):
if len(row) > 1:
data_list.append([astr for astr in row.split(',')])
syms_list.append(syms[index])
if len(syms_list) == 0:
return None
if len(data_list[0]) == 28:
df = pd.DataFrame(data_list, columns=ct.US_LIVE_DATA_COLS)
else:
df = pd.DataFrame(data_list, columns=ct.LIVE_DATA_COLS)
df = df.drop('s', axis=1)
df['code'] = syms_list
ls = [cls for cls in df.columns if '_v' in cls]
for txt in ls:
df[txt] = df[txt].map(lambda x: x[:-2])
return df
def get_hist_data(code=None,
start=None,
end=None,
ktype='D',
retry_count=3,
pause=0.001):
"""
获取个股历史交易记录
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取到API所提供的最早日期数据
end:string
结束日期 format：YYYY-MM-DD 为空时取到最近一个交易日数据
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
属性:日期 ，开盘价， 最高价， 收盘价， 最低价， 成交量， 价格变动 ，涨跌幅，5日均价，10日均价，20日均价，5日均量，10日均量，20日均量，换手率
"""
symbol = ct._code_to_symbol(code)
url = ''
if ktype.upper() in ct.K_LABELS:
url = ct.DAY_PRICE_URL % (ct.P_TYPE['http'], ct.DOMAINS['ifeng'],
ct.K_TYPE[ktype.upper()], symbol)
elif ktype in ct.K_MIN_LABELS:
url = ct.DAY_PRICE_MIN_URL % (ct.P_TYPE['http'], ct.DOMAINS['ifeng'],
symbol, ktype)
else:
raise TypeError('ktype input error.')
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(url)
lines = urlopen(request, timeout=10).read()
if len(lines) < 15: #no data
return None
except Exception as e:
print(e)
else:
js = json.loads(lines.decode('utf-8') if ct.PY3 else lines)
cols = []
if (code in ct.INDEX_LABELS) & (ktype.upper() in ct.K_LABELS):
cols = ct.INX_DAY_PRICE_COLUMNS
else:
cols = ct.DAY_PRICE_COLUMNS
if len(js['record'][0]) == 14:
cols = ct.INX_DAY_PRICE_COLUMNS
df = pd.DataFrame(js['record'], columns=cols)
if ktype.upper() in ['D', 'W', 'M']:
df = df.applymap(lambda x: x.replace(u',', u''))
df[df == ''] = 0
for col in cols[1:]:
df[col] = df[col].astype(float)
if start is not None:
df = df[df.date >= start]
if end is not None:
df = df[df.date <= end]
if (code in ct.INDEX_LABELS) & (ktype in ct.K_MIN_LABELS):
df = df.drop('turnover', axis=1)
df = df.set_index('date')
df = df.sort_index(ascending=False)
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_hist_data(code=None, start=None, end=None,
ktype='D', retry_count=3,
pause=0.001):
"""
获取个股历史交易记录
Parameters
------
code:string
股票代码 e.g. 600848
start:string
开始日期 format：YYYY-MM-DD 为空时取到API所提供的最早日期数据
end:string
结束日期 format：YYYY-MM-DD 为空时取到最近一个交易日数据
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
属性:日期 ，开盘价， 最高价， 收盘价， 最低价， 成交量， 价格变动 ，涨跌幅，5日均价，10日均价，20日均价，5日均量，10日均量，20日均量，换手率
"""
symbol = ct._code_to_symbol(code)
url = ''
if ktype.upper() in ct.K_LABELS:
url = ct.DAY_PRICE_URL%(ct.P_TYPE['http'], ct.DOMAINS['ifeng'],
ct.K_TYPE[ktype.upper()], symbol)
elif ktype in ct.K_MIN_LABELS:
url = ct.DAY_PRICE_MIN_URL%(ct.P_TYPE['http'], ct.DOMAINS['ifeng'],
symbol, ktype)
else:
raise TypeError('ktype input error.')
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(url)
lines = urlopen(request, timeout = 10).read()
if len(lines) < 15: #no data
return None
except Exception as e:
print(e)
else:
js = json.loads(lines.decode('utf-8') if ct.PY3 else lines)
cols = []
if (code in ct.INDEX_LABELS) & (ktype.upper() in ct.K_LABELS):
cols = ct.INX_DAY_PRICE_COLUMNS
else:
cols = ct.DAY_PRICE_COLUMNS
if len(js['record'][0]) == 14:
cols = ct.INX_DAY_PRICE_COLUMNS
df = pd.DataFrame(js['record'], columns=cols)
if ktype.upper() in ['D', 'W', 'M']:
df = df.applymap(lambda x: x.replace(u',', u''))
df[df==''] = 0
for col in cols[1:]:
df[col] = df[col].astype(float)
if start is not None:
df = df[df.date >= start]
if end is not None:
df = df[df.date <= end]
if (code in ct.INDEX_LABELS) & (ktype in ct.K_MIN_LABELS):
df = df.drop('turnover', axis=1)
df = df.set_index('date')
df = df.sort_index(ascending = False)
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_realtime_quotes(symbols=None):
"""
获取实时交易数据 getting real time quotes data
用于跟踪交易情况（本次执行的结果-上一次执行的数据）
Parameters
------
symbols : string, array-like object (list, tuple, Series).
return
-------
DataFrame 实时交易数据
属性:0：name，股票名字
1：open，今日开盘价
2：pre_close，昨日收盘价
3：price，当前价格
4：high，今日最高价
5：low，今日最低价
6：bid，竞买价，即“买一”报价
7：ask，竞卖价，即“卖一”报价
8：volumn，成交量 maybe you need do volumn/100
9：amount，成交金额（元 CNY）
10：b1_v，委买一（笔数 bid volume）
11：b1_p，委买一（价格 bid price）
12：b2_v，“买二”
13：b2_p，“买二”
14：b3_v，“买三”
15：b3_p，“买三”
16：b4_v，“买四”
17：b4_p，“买四”
18：b5_v，“买五”
19：b5_p，“买五”
20：a1_v，委卖一（笔数 ask volume）
21：a1_p，委卖一（价格 ask price）
...
30：date，日期；
31：time，时间；
"""
symbols_list = ''
if isinstance(symbols, list) or isinstance(symbols, set) or isinstance(symbols, tuple) or isinstance(symbols, pd.Series):
for code in symbols:
symbols_list += ct._code_to_symbol(code) + ','
else:
symbols_list = ct._code_to_symbol(symbols)
symbols_list = symbols_list[:-1] if len(symbols_list) > 8 else symbols_list
request = Request(ct.LIVE_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['sinahq'],
_random(), symbols_list))
text = urlopen(request,timeout=10).read()
text = text.decode('GBK')
reg = re.compile(r'\="(.*?)\";')
data = reg.findall(text)
regSym = re.compile(r'(?:sh|sz)(.*?)\=')
syms = regSym.findall(text)
data_list = []
syms_list = []
for index, row in enumerate(data):
if len(row)>1:
data_list.append([astr for astr in row.split(',')])
syms_list.append(syms[index])
if len(syms_list) == 0:
return None
df = pd.DataFrame(data_list, columns=ct.LIVE_DATA_COLS)
df = df.drop('s', axis=1)
df['code'] = syms_list
ls = [cls for cls in df.columns if '_v' in cls]
for txt in ls:
df[txt] = df[txt].map(lambda x : x[:-2])
return df
def get_tick_data(code=None, date=None, retry_count=3, pause=0.001,
src='sn',ip=None):
"""
获取分笔数据
Parameters
------
code:string
股票代码 e.g. 600848
date:string
日期 format: YYYY-MM-DD
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
src : 数据源选择，可输入sn(新浪)、tt(腾讯)、nt(网易)，默认sn
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 (src.strip() not in ct.TICK_SRCS):
print(ct.TICK_SRC_ERROR)
return None
symbol = ct._code_to_symbol(code)
symbol_dgt = ct._code_to_symbol_dgt(code)
datestr = date.replace('-', '')
url = {
ct.TICK_SRCS[0] : ct.TICK_PRICE_URL % (ct.P_TYPE['http'], ct.DOMAINS['sf'], ct.PAGES['dl'],
date, symbol),
ct.TICK_SRCS[1] : ct.TICK_PRICE_URL_TT % (ct.P_TYPE['http'], ct.DOMAINS['tt'], ct.PAGES['idx'],
symbol, datestr),
ct.TICK_SRCS[2] : ct.TICK_PRICE_URL_NT % (ct.P_TYPE['http'], ct.DOMAINS['163'], date[0:4],
datestr, symbol_dgt)
}
for _ in range(retry_count):
time.sleep(pause)
try:
if src == ct.TICK_SRCS[2]:
df = pd.read_excel(url[src])
df.columns = ct.TICK_COLUMNS
else:
re = Request(url[src])
print("start..")
#proxies = {'http':'http://'+ct.hq_hosts[ipindex][1]+':'+str(ct.hq_hosts[ipindex][2])}
if ip != None:
proxies = {'http':ip}
proxy_support = urllib.request.ProxyHandler(proxies)
opener = urllib.request.build_opener(proxy_support)
opener.addheaders = [('User-Agent',
'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36')]
urllib.request.install_opener(opener)
print (proxies)
lines = urlopen(re, timeout=10).read()
lines = lines.decode('GBK')
if len(lines) < 20:
return None
df = pd.read_table(StringIO(lines), names=ct.TICK_COLUMNS,
skiprows=[0])
except Exception as e:
print(e)
else:
return df
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)