def get_tops(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 默认为空''
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:
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))
text = urlopen(request, timeout=10).read()
text = text.decode("GBK")
html = lxml.html.parse(StringIO(text))
res = html.xpath('//table[@id="dt_1"]')
if ct.PY3:
sarr = [etree.tostring(node).decode("utf-8") for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = "".join(sarr)
df = pd.read_html(sarr)[0]
df.columns = [i for i in range(1, 12)]
df = df.apply(_f_rows, axis=1)
df = df.fillna(method="ffill")
df = df.drop([1, 4], axis=1)
df.columns = rv.LHB_COLS
df = df.drop_duplicates()
df["code"] = df["code"].astype(int)
df["code"] = df["code"].map(lambda x: str(x).zfill(6))
df["date"] = date
except:
pass
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def get_tops(date = None):
if date is None:
if du.get_hour() < 18:
date = du.last_tddate()
else:
if(du.is_holiday(date)):
return None
request = Request(rv.LHB_URL%(ct.P_TYPE['http'], ct.DOMAINS['em'], date))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@id=\"dt_1\"]")
if ct.PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = ''.join(sarr)
df = pd.read_html(sarr)[0]
df.columns = [i for i in range(1,12)]
df = df.apply(_f_rows, axis=1)
df = df.fillna(method='ffill')
df = df.drop([1, 4], axis=1)
df.columns = rv.LHB_COLS
df = df.drop_duplicates()
df['code'] = df['code'].astype(int)
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df['date'] = date
return df
def top_detail(code, date=None, retry_count= 3, pause= 0.001):
"""
根据指定日期，指定股票的龙虎榜交易细节
Parameters
--------
code: string
指定股票代码
date: string
指定日期
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
--------
code:代码
name:名称
bamount:累积买入额(万)
bcount:买入次数
samount:累积卖出额(万)
scount:卖出次数
net:净额(万)
"""
if code is None or code == "":
return None
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
time.sleep(pause)
try:
request = Request(rv.LHB_SINA_URL2%(ct.P_TYPE['http'], ct.DOMAINS['vsf'], code, date))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
text = text.split('details=')[1][2:-2]
text = eval(text, type('Dummy', (dict,),
dict(__getitem__ = lambda s, n:n))())
text = json.dumps(text)
text = json.loads(text)
buy_df = pd.DataFrame(text['buy'], columns=rv.LHB_TMP_DETAIL_COLS)
buy_df['type'] = 'buy'
sell_df = pd.DataFrame(text['sell'], columns=rv.LHB_TMP_DETAIL_COLS)
sell_df['type'] = 'sell'
#LHB_DETAIL_COLS = ['code', 'type', 'insCode', 'insName', 'bamount', 'samount', 'net']
df = sell_df.append(buy_df)
df.columns = rv.LHB_DETAIL_COLS
df['bamount'] = df['bamount'].astype(float)
df['samount'] = df['samount'].astype(float)
df['net'] = df['net'].astype(float)
df['date'] = date
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):
'''
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
'''
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
# arguments can be passed through command line.
# usage: python parser.py "qqq spy xme xop" > output.txt
if len(sys.argv) > 1:
ticker_list = sys.argv[1].split()
for t in ticker_list:
parser = MyHTMLParser(t)
filehandle = urllib.urlopen("http://finance.yahoo.com/q?s=%s" % t)
html_string = filehandle.read()
parser.feed(html_string)
dowj_string = (parser.close())
dowj_string = dowj_string[:-1]
dowj = float(dowj_string) * 0.01
print dowj
if (du.get_hour() > 15):
dowj = 0
import tushare as ts
df = ts.get_stock_basics()
myselect = []
for i in range(0, len(df)):
if (df.irow(i).totalAssets > 1000000 and df.irow(i).totalAssets < 9000000
and df.irow(i).name[0] != '3' and df.irow(i).esp > 1.0):
myselect.insert(i, df.irow(i).name)
#print df.irow(i).name
print len(myselect)
print myselect
'''
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
'''
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 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['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:
pass
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
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))
text = urlopen(request, timeout=10).read()
text = text.decode('GBK')
html = lxml.html.parse(StringIO(text))
res = html.xpath("//table[@id=\"dt_1\"]")
if ct.PY3:
sarr = [etree.tostring(node).decode('utf-8') for node in res]
else:
sarr = [etree.tostring(node) for node in res]
sarr = ''.join(sarr)
df = pd.read_html(sarr)[0]
df.columns = [i for i in range(1,12)]
df = df.apply(_f_rows, axis=1)
df = df.fillna(method='ffill')
df = df.drop([1, 4], axis=1)
df.columns = rv.LHB_COLS
df = df.drop_duplicates()
df['code'] = df['code'].astype(int)
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df['date'] = date
except:
pass
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
'''
print("本接口即将停止更新，请尽快使用Pro版接口：https://waditu.com/document/2")
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
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['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:
pass
else:
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
import tushare as ts
from sqlalchemy import create_engine
from tushare.util import dateu as du
import threading
import time
import TsExt as te
from util.util import last_tddate_delta
if __name__ == '__main__':
engine = create_engine('mysql+pymysql://root:[email protected]/tushare?charset=utf8')
td = du.today()
if(du.is_holiday(td) | int(du.get_hour()) < 16):
dt = du.last_tddate()
else:
dt = td
print(dt)
"""数据表
stk_lhb_broker_detail 龙虎榜股票营业部买入详细情况
stk_k_line_data 股票K线数据
"""
"""
获取股票基本信息
"""
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 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":
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)
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"]
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 lhb_details(retry_count=3, pause=0.001, code=None, date=None):
"""
获取最近一个交易日龙虎榜上榜个股的交易详情. 包括买1-5和卖1-5的买入及卖出量,营业部名称.
可选参数:
证券代码(code): 指定代码用于只获取该证券的交易详情.省略的话会返回当日所有上榜证券的交易详情
日期(date): 获取指定日期的交易详情.默认为最近一个交易日
返回值:
----------
code:股票代码
name:股票名称
date:交易日期
买1-5营业部名称及买入和卖出额及净额(万)
卖1-5营业部名称及买入和卖出额净额(万)
"""
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
if code is None:
print('请输入股票代码,比如： lhb_details(000001)')
raise SystemExit(5)
lhb_details_url = 'http://vip.stock.finance.sina.com.cn/q/api/jsonp.php/var%20details=/InvestConsultService.getLHBComBSData?symbol=%s&tradedate=%s&type=01'
text = urlopen(lhb_details_url % (code, date), timeout=10).read()
text = text.decode('GBK')
text = text.split('details=')[1]
text = eval(text,
type('Dummy', (dict, ), dict(__getitem__=lambda s, n: n))())
text = json.dumps(text)
text = json.loads(text)
## text['buy'] = list of dicts
## text['sell'] = list of dicts
#for i in range(len(text['buy'])):
# for key in text['buy'][i].keys():
# print(text['buy'][i][key],end=' ')
# print()
#SYMBOL sellAmount buyAmount comCode type netAmount comName
#000973 7.6802 2101.4900 80291363 01 2093.8098 浙商证券股份有限公司临安万马路证券营业部
#000973 0.0000 1777.2700 80348499 01 1777.27 华泰证券股份有限公司浙江分公司
#000973 317.3780 2069.5400 80127954 01 1752.162 安信证券股份有限公司南昌胜利路证券营业部
#000973 0.9050 1389.3300 80564863 01 1388.425 华鑫证券有限责任公司郑州普惠路证券营业部
#000973 39.0354 1239.5100 80034811 01 1200.4746 财通证券股份有限公司绍兴人民中路证券营业部
print('%4s %6s %10s %10s %10s %s' %
('买入榜', '股票代码', '卖出额(万)', '买入额(万)', '买入净额(万)', '营业部名称'))
for i in range(len(text['buy'])):
print('%-4d %-8s %-10s %-10s %-12s %s' %(i+1,text['buy'][i]['SYMBOL'],text['buy'][i]['sellAmount'],\
text['buy'][i]['buyAmount'],text['buy'][i]['netAmount'],text['buy'][i]['comName']))
#1 000973 7.6802 2101.4900 2093.8098 浙商证券股份有限公司临安万马路证券营业部
#2 000973 0.0000 1777.2700 1777.27 华泰证券股份有限公司浙江分公司
#3 000973 317.3780 2069.5400 1752.162 安信证券股份有限公司南昌胜利路证券营业部
#4 000973 0.9050 1389.3300 1388.425 华鑫证券有限责任公司郑州普惠路证券营业部
#5 000973 39.0354 1239.5100 1200.4746 财通证券股份有限公司绍兴人民中路证券营业部
print('%4s %6s %10s %10s %10s %s' %
('卖出榜', '股票代码', '卖出额(万)', '买入额(万)', '买入净额(万)', '营业部名称'))
for i in range(len(text['sell'])):
print('%-4d %-8s %-10s %-10s %-12s %s' %(i+1,text['sell'][i]['SYMBOL'],text['sell'][i]['sellAmount'],\
text['sell'][i]['buyAmount'],text['sell'][i]['netAmount'],text['sell'][i]['comName']))
