def get_TobefreeTrade_qq(code=None, start=today, end='20191231'):
"""大小非解禁时间表
------------------
code : 查询股票的代码,code is None,查询该时间段所有的股票解禁时间表
start：开始查询的时间，20170101
end :截止查询的时间，20170630
"""
if code is None:
code = ''
url = 'http://stock.finance.qq.com//sstock/list/view/dxf.php?c={0}&b={1}&e={2}'.format(
code, start, end)
r = requests.get(url, headers=hds())
text = r.text.split('=[[')[1].replace("]];", '')
text = text.replace('],[', '\n')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop(6, axis=1)
df.columns = [
'code', 'name', 'Free.date', 'Free.S(10K)', 'Free.MV(10K)',
'Curr.S(10K)', 'Source', 'F.S/Curr.S.%'
]
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_reportlistjson_qq(code):
url = 'http://message.finance.qq.com/report/get_hq_report_jgyc.php?n=5000&zqdm={0}&seq=0&format=json&r=0.38097072788514197'.format(
code)
r = requests.get(url)
text = r.text.split('=')[1]
data = json.loads(text)
df = pd.DataFrame(data['data']['report'])
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_forcast_qq(code=None, year=2017, qu=3, mtype=0):
"""获取企业的业绩预告信息
code:股票代码6个字符
report：报告期,like 20170630
mtype: 类型,0-全部的业绩预告,1-预增的业绩预告,2-预减的业绩预告,
3-预盈的业绩预告,4-预亏的业绩预告,5-大幅上升的业绩预告,
6-大幅下降的业绩预告,7-扭亏的业绩预告,8-减亏的业绩预告,
9-无大变的业绩预告,10-不确定的业绩预告
"""
report = quarter[qu] % year
if code is None:
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm=&type={1}&bgq={0}&p=1&lmt=50&sort_name=ggrq&order=desc'.format(
report, mtype)
elif isinstance(code, str):
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm={0}&type={2}&bgq={1}&p=1&lmt=50&sort_name=ggrq&order=desc'.format(
code, report, mtype)
r = requests.get(url, headers=hds())
#print(url)
text = r.text.split("yjyg=")[1]
data = json.loads(text)
df = pd.DataFrame(data['data']['data'])
tpage = data['data']['totalPages']
if tpage > 1:
tpage = tpage + 1
for i in range(2, tpage):
if code is None:
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm=&type={1}&bgq={0}&p={2}&lmt=50&sort_name=ggrq&order=desc'.format(
report, mtype, i)
elif isinstance(code, str):
url = 'http://message.finance.qq.com/stock/jbnb/get_yjyg.php?gpdm={0}&type={3}&bgq={1}&p={2}&lmt=50&sort_name=ggrq&order=desc'.format(
code, report, i, mtype)
r = requests.get(url, headers=hds())
text = r.text.split("yjyg=")[1]
data = json.loads(text)
df = df.append(pd.DataFrame(data['data']['data']))
try:
df.columns = [
'report.D', 'publish.D', 'code', 'name', 'eps_last', 'type',
'describe'
]
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
except:
pass
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_drogan_tiger_qq(code=None, start=None, end=None):
"""查看龙虎榜的信息
------------------------
code : 查询股票的代码
start：开始查询的时间，20170101
end :截止查询的时间，20170630
"""
if (code is None) and (start is None) and (end is None):
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=0&c=&b=&e=&p=1&l=&ol=6&o=desc'
if (start is not None) and (end is not None):
if code is None:
code = ''
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=2&c={0}&b={1}&e={2}&p=1&l=&ol=6&o=desc'.format(
code, start, end)
else:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=1&c={0}&b={1}&e={2}&p=1&l=&ol=6&o=desc'.format(
code, start, end)
#print(url)
r = requests.get(url, headers=hds())
text = r.text
df = _text2pd(text)
tps = int(r.text.split(",_pages:")[1].split(",_num:")[0])
tpss = tps + 1
if tps > 1:
for i in range(2, tpss):
if (code is None) and (start is None) and (end is None):
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=0&c=&b=&e=&p={0}&l=&ol=6&o=desc'.format(
i)
if (start is not None) and (end is not None):
if code is None:
code = ''
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=2&c={0}&b={1}&e={2}&p={3}&l=&ol=6&o=desc'.format(
code, start, end, i)
else:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_lhb_js?t=1&c={0}&b={1}&e={2}&p={3}&l=&ol=6&o=desc'.format(
code, start, end, i)
r = requests.get(url, headers=hds())
text = r.text
df = df.append(_text2pd(text))
df = df.drop(4, axis=1)
df.columns = ['date', 'code', 'name', 'descrise', 'price', 'chg%']
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('date')
df = df.sort_index()
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_cashfl_industry_qq(mtype=1):
"""获取交易日当天的板块资金流动情况
-------------------------------
mtype:整数
1-按行业主力资金流动情况
3-按概念主力资金流动情况
2-按个股的增仓资金流动情况
4-价跌主力增仓排名
5-价涨主力减仓排名
6-主力资金放量股
----------------------
url_cashfl_industry={1'http://stock.gtimg.cn/data/view/flow.php?t=2',
2:'http://stock.gtimg.cn/data/view/flow.php?t=4',
3:'http://stock.gtimg.cn/data/view/flow.php?t=5',
4:'http://stock.gtimg.cn/data/view/flow.php?t=7&d=1',
5:'http://stock.gtimg.cn/data/view/flow.php?t=8&d=1',
6:'http://stock.gtimg.cn/data/view/flow.php?t=9&d=1'}
"""
url = wt.url_cashfl_industry[mtype]
r = requests.get(url, headers=hds())
text = r.content.decode('gbk')
if mtype in [1, 3]:
text = text.split("boardzhuli='")[1]
text = text.split("';var v_s_boardfunds='")
text = '\n'.join(text)
text = text.replace("'", '')
if mtype == 2:
text = text.split("stock_fund_netin_10='")[1]
text = text.split("; var stock_fund_netout_10='")
text = '\n'.join(text)
text = text.replace("'", '')
if mtype == 4:
text = text.split("PrDwn='")[1].replace("';", '')
if mtype == 5:
text = text.split("v_s_PrUp='")[1].replace("';", '')
if mtype == 6:
text = text.split("v_s_zlfl_rank='")[1].replace("';", '')
text = text.replace("~", ',').replace("^", '\n').replace(";", '')
df = pd.read_csv(StringIO(text), header=None)
df = df.drop_duplicates()
#df=df.replace('--',np.nan)
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_finance_index_qq(code=None, year=2017, qu=2):
"""获取年报季报的基本指标数据，按年获取
"""
rpday = quarter[qu] % year
if code is None:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_yjgg_js?c=&d={0}&b=&p=1&l=&o='.format(
rpday)
else:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_yjgg_js?c={0}&d={1}&b=&p=2&l=&o='.format(
code, rpday)
r = requests.get(url, headers=hds())
text = r.text.split("_datas:[[")[1].replace("]],_o:0};\n", "")
text = text.replace("],[", '\n')
df = pd.read_csv(StringIO(text), header=None)
tps = int(r.text.split(",_pages:")[1].split(",_num:")[0])
tpss = tps + 1
if tps > 1:
for i in range(2, tpss):
if code is None:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_yjgg_js?c=&d={0}&b=&p={1}&l=&o='.format(
rpday, i)
else:
url = 'http://stock.finance.qq.com/cgi-bin/sstock/q_yjgg_js?c={0}&d={1}&b=&p={2}&l=&o='.format(
code, rpday, i)
r = requests.get(url, headers=hds())
text = r.text.split("_datas:[[")[1].replace("]],_o:0};\n", "")
text = text.replace("],[", '\n')
df = df.append(pd.read_csv(StringIO(text), header=None))
df = df.drop([0, 11], axis=1)
df.columns = [
'code', 'name', 'report.d', 'publish.d', 'eps', 'nav.ps', 'cf.ps',
'roe', 'cost.pro.rate', 'Rev.p.yoy%', 'dispatch'
]
df = df.replace('--', np.nan)
df = df.applymap(lambda x: wt._tofl(x))
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
return df
def get_income_composition_qq(code, mtype='cp'):
"""
获得公司收入构成情况表
----------------
mtype: 查询的分类数据
cp:按产品对收入进行归类
hy:按行业对收入进行归类
dy:按地域对收入进行归类
return :
---------------------
DataFrame()
"""
url = 'http://stock.finance.qq.com/corp1/income.php?zqdm={0}'.format(code)
r = requests.get(url, headers=hds())
text = r.text
#print(text)
soup = BeautifulSoup(text, 'lxml')
table = soup.find_all('table')
#print(len(table))
if mtype == 'cp':
df = pd.read_html(str(table[1]), header=None, skiprows=5)[0]
elif mtype == 'hy':
df = pd.read_html(str(table[4]), header=None, skiprows=5)[0]
elif mtype == 'dy':
df = pd.read_html(str(table[7]), header=None, skiprows=5)[0]
df = df.drop([0], axis=0)
name = [
'name', 'Rev(10k)', 'Rev.Rate', 'Cost', 'Rev.Profit', 'Rev.Pro.Rate',
'Margin.Rate'
]
df = df.iloc[:, :7]
df.columns = name
df = df.applymap(lambda x: wt._tofl(x))
df = df.replace('', np.nan)
df = df.dropna(how='all', axis=1)
df['code'] = code
#print(df)
return df
def get_inst_hypeizhi_qq(mtype='ins', date='2017-06-30'):
"""mtype='inc' or 'dec';
"""
url = 'http://web.ifzq.gtimg.cn/fund/hyconf/hyconf/hypm?type={0}&colum=bdsz&order=desc&rd={1}'.format(
mtype, date)
r = requests.get(url, headers=hds())
print(url)
data = json.loads(r.text)
df = pd.DataFrame(data["data"]["data"])
df.rename(columns={
'bdsz': 'Chang',
'ccjj': 'inst_num',
'ccsz': 'hold_MarketV',
'hymc': "industry_name",
'rd': 'date'
},
inplace=True)
df = df[['industry_name', 'inst_num', 'Chang', 'hold_MarketV', 'date']]
df = df.applymap(lambda x: wt._tofl(x))
df = df.sort_values(by='Chang')
return df
def get_bigtradeinfo_qq(code=None, start='20010120', end=today):
"""大宗交易
"""
if code is None:
code = ''
url = 'http://stock.finance.qq.com/sstock/list/view/dzjy.php?b={1}&e={2}&p=1&o=0&c={0}'.format(
code, start, end)
r = requests.get(url, headers=hds())
text = r.text
#print(text)
df = _text2pd(text)
tps = int(r.text.split(",_pages:")[1].split(",_num:")[0])
tpss = tps + 1
if tps > 1:
for i in range(2, tpss):
if code is None:
code = ''
url = 'http://stock.finance.qq.com/sstock/list/view/dzjy.php?b={1}&e={2}&p={3}&o=0&c={0}'.format(
code, start, end, i)
r = requests.get(url, headers=hds())
text = r.text
df = df.append(_text2pd(text))
df.columns = [
'date', 'code', 'name', 'price', 'amount(10K)', 'volume(10K)',
'buy_inst', 'sell_inst'
]
df['code'] = df['code'].map(lambda x: str(x).zfill(6))
df = df.set_index('date')
df = df.sort_index()
df = df.applymap(lambda x: wt._tofl(x))
return df
def get_cwfx_qq(code, mtype='ylnl'):
"""
从腾讯财务获得相应的 指标
---------------------------------------
code:上海、深圳交易所的股票代码
mtype: mgzb--每股指标
ylnl--盈利能力
yynl--营运能力
cznl--成长能力
djcw--单季财务
czzb--偿债及资本结构
--------------------------------------------
return:
DataFrame:
ylnl:
cbfylrl:成本费用利润率(%)
fjcxsybl:非经常性损益比率(%)
jlrkc:扣除非经常性损益后的净利润(元)
jzcsyljq:净资产收益率(加权)(%)
sxfyl:三项费用率(%)
xsjll:销售净利率(%)
xsmll:销售毛利率(%)
xsqlr:息税前利润(%)
xsqlrl:息税前利润率(%)
yylrl:营业利润率(%)
zzclrl:总资产利润率(%)
ylnlname=['Cost.Profit.R','Net.oper.R','Profit_d','ROE','Three.Cost.R','Profit.R','Gross.Margin.R','Profit_bTax','Profit_rate_bTax','O.Profit.Rate','ROA']
mgzb:
jbmgsy:基本每股收益(元)
jqmgsy: 每股收益(加权)(元)
jqmgsykc: 加权每股收益(扣除)(元)
mgjzc: 每股净资产(元)
mgxjll: 每股现金流量(元)
mgxssr: 每股销售收入(元)
tbmgsy:摊薄每股收益(元)
tbmgsykc: 摊薄每股收益(扣除)(元)
tzhmgjzc: 调整后每股净资产(元)
xsmgsy:稀释每股收益(元)
mgzbname=['EPS','W.EPS','W.EPS.D','Net.A.PS','CF_PS','Rev.PS','EPS.Dilute','EPS.Dilute.D','Net.A.PS.Adj','D.EPS']
yynl:
chzcgcl: 存货资产构成率(%)
chzzl: 存货周转率(%)
chzzts: 存货周转天数(天)
gdqyzzl: 股东权益周转率(%)
gdzczzl: 固定资产周转率(%)
ldzczzl:流动资产周转率(%)
ldzczzts: 流动资产周转天数(天)
yszkzzl: 应收账款周转率(%)
yszkzzts: 应收账款周转天数(天)
zzczzl: 总资产周转率(%)
zzczzts:总资产周转天数(天)
yynlname=['Invt_Asset_rate','Invt_turnover_rate','Invt_turnover_day','Shareholder_turnover_rate','Fixed_turnover_rate','Curr_Asset_turnover_rate','Curr_Asset_turnover_day','Receivable_turnover_ratio','Receivable_turnover_day','Asset_turnover_ratio,'Asset_turnover_day']
cznl:
jlr: 净利润增长率(%)
lrze: 利润总额增长率(%)
mgsy: 每股收益增长率(%)
mgxj: 每股现金流增长率(%)
xsqlr: 息税前利润增长率(%)
yylr: 营业利润增长率(%)
zysr: 主营收入增长率(%)
zzc:总资产增长率(%)
cznlname=['Net.profit_yoy','T.profit_yoy','Rev.PS_yoy','CF_PS_yoy',Profit.bTax_yoy','Rev.Profit_yoy','Rev_yoy',Asset_yoy']
djcw:
jzcsyl: 净资产收益率(%)
jzcsylkc: 净资产收益率(扣除)(%)
mgsykctb: 每股收益（扣除后摊薄）(元)
mgsytb: 每股收益(摊薄)(元)
mgxssr: 每股销售收入(元)
sqzzcsyl: 税前总资产收益率(%)
xsjll: 销售净利率(%)
xsmll: 销售毛利率(%)
xsqlr: 息税前利润(元)
xsqlrl:息税前利润率(%)
zzcsyl:总资产收益率(%)
djcwname=['ROE','ROE.D','EPS.DD','EPS.D','Rev.PS','ROA.bTax',Rev.NetProfit.R','Rev.Gr.Profit.R','Profit.bTax','Profit.bTax.R','ROA']
czzb:
cqbl: 产权比率(%)
czyzb: 长期债务与营运资金比率(%)
gdzcbl: 固定资产比率(%)
gzjzl: 固定资产净值率(%
ldbl: 流动比率(%)
qsjzbl: 清算价值比率(%)
sdbl: 速动比率(%)
xjbl:现金比率(%)
zbgbbl: 资本固定化比率(%)
zbhbl: 资本化比率(%)
zcfzl: 资产负债率(%)
zzc:总资产(元)
czzbname=['Debt_Equit_R','L.Debt_Work.Capital_R','Fix.Asset.R','Net.Asset.R','Curr_Rate','Liquidation_ratio','Quick_Rate','Cash_Rate','Capital_fixed_ratio','Capitalization_R','Assets_Debt_R','Asset']
"""
if code[0] in ['0', '2', '3']:
code = 'sz' + code
elif code[0] in ['6', '9']:
code = 'sh' + code
else:
print("Input the right code, like 600000")
url = 'http://comdata.finance.gtimg.cn/data/{1}/{0}'.format(code, mtype)
r = requests.get(url, headers=hds())
text = r.text
text = text.split('=')[1].replace("]}};", "]}}")
data = json.loads(text)
yearlist = data['data']['nflb']
df = pd.DataFrame()
for y in yearlist:
url = 'http://comdata.finance.gtimg.cn/data/{1}/{0}/{2}'.format(
code, mtype, y)
r = requests.get(url, headers=hds())
#print(url)
text = r.text
text = text.split('=')[1].replace("]}};", "]}}")
data = json.loads(text)
dd = pd.DataFrame(data['data'][mtype])
df = df.append(dd)
df = df.set_index('bgrq')
df = df.sort_index()
df = df.replace('--', np.nan)
df = df.applymap(lambda x: wt._tofl(x))
#df=df.applymap(lambda x:np.where(x=='--',np.nan,float(x)))
df.columns = wt.named[mtype]
df = df.applymap(lambda x: wt._tofl(x))
return df
