def get_area_classified():
"""
获取地域分类数据
Return
--------
DataFrame
code :股票代码
name :股票名称
area :地域名称
"""
df = fd.get_stock_basics()
df = df[['name', 'area']]
df.reset_index(inplace=True)
df = df.sort_values('area').reset_index(drop=True)
return df
def get_sme_classified():
"""
获取中小板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.code.str[0:3] == "002"]
df = df.sort("code").reset_index(drop=True)
return df
def get_gem_classified(file_path=None):
"""
获取创业板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics(file_path)
df.reset_index(level=0, inplace=True)
df = df[['code', 'name']]
df = df.ix[df.code.str[0] == '3']
df = df.sort('code').reset_index(drop=True)
return df
def get_st_classified():
"""
获取风险警示板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(inplace=True)
df = df[ct.FOR_CLASSIFY_COLS]
df = df.loc[df.name.str.contains('ST')]
df = df.sort_values('code').reset_index(drop=True)
return df
def get_sme_classified():
"""
获取中小板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(inplace=True)
df = df[ct.FOR_CLASSIFY_COLS]
df = df.loc[df.code.str[0:3] == '002']
df = df.sort_values('code').reset_index(drop=True)
return df
def get_gem_classified():
"""
获取创业板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.code.str[0] == '3']
df = df.sort('code').reset_index(drop=True)
return df
def get_sme_classified(file_path=None):
"""
获取中小板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics(file_path)
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.code.str[0:3] == '002']
df = df.sort('code').reset_index(drop=True)
return df
def get_st_classified(file_path=None):
"""
获取风险警示板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics(file_path)
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.name.str.contains('ST')]
df = df.sort('code').reset_index(drop=True)
return df
def get_gem_classified():
"""
获取创业板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.code.str[0] == '3']
df = df.sort('code').reset_index(drop=True)
return df
def get_st_classified():
"""
获取风险警示板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.name.str.contains('ST')]
df = df.sort('code').reset_index(drop=True)
return df
def get_area_classified():
"""
获取地域分类数据
Return
--------
DataFrame
code :股票代码
name :股票名称
area :地域名称
"""
df = fd.get_stock_basics()
df = df[['name', 'area']]
df.reset_index(level=0, inplace=True)
df = df.sort('area').reset_index(drop=True)
return df
def get_sme_classified(file_path=None):
"""
获取中小板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics(file_path)
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.code.str[0:3] == '002']
df = df.sort('code').reset_index(drop=True)
return df
def get_area_classified(file_path=None):
"""
获取地域分类数据
Return
--------
DataFrame
code :股票代码
name :股票名称
area :地域名称
"""
df = fd.get_stock_basics(file_path)
df = df[['name', 'area']]
df.reset_index(level=0, inplace=True)
df = df.sort('area').reset_index(drop=True)
return df
def get_st_classified():
"""
获取风险警示板股票
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
df = fd.get_stock_basics()
df.reset_index(level=0, inplace=True)
df = df[ct.FOR_CLASSIFY_B_COLS]
df = df.ix[df.name.str.contains('ST')]
# replace with new sort method
df = df.sort_values('code').reset_index(drop=True)
return df
def get_stock_cn_name(code=None):
base_file = '%s/base.csv' % csv_dir
df = pd.DataFrame
try:
if os.path.exists(base_file) is True:
df = pd.read_csv(base_file, encoding='GBK')
else:
df = get_stock_basics()
df.to_csv(base_file, encoding='GBK')
except Exception as er:
print(str(er))
else:
names = df.loc[df['code'] == code]['name'].values
if (len(names) > 0):
return names[0]
else:
return ''
def get_hs300s():
"""
获取沪深300当前成份股及所占权重
Return
--------
DataFrame
code :股票代码
name :股票名称
date :日期
weight:权重
"""
from tushare.stock.fundamental import get_stock_basics
try:
wt = pd.read_excel(ct.HS300_CLASSIFY_URL_FTP%(ct.P_TYPE['ftp'], ct.DOMAINS['idxip'],
ct.PAGES['hs300w']), parse_cols=[0, 3, 6])
wt.columns = ct.FOR_CLASSIFY_W_COLS
wt['code'] = wt['code'].map(lambda x :str(x).zfill(6))
df = get_stock_basics()[['name']]
df = df.reset_index()
return pd.merge(df,wt)
except Exception as er:
print(str(er))
def get_hs300s():
"""
获取沪深300当前成份股及所占权重
Return
--------
DataFrame
code :股票代码
name :股票名称
date :日期
weight:权重
"""
from tushare.stock.fundamental import get_stock_basics
try:
wt = pd.read_excel(ct.HS300_CLASSIFY_URL_FTP%(ct.P_TYPE['ftp'], ct.DOMAINS['idxip'],
ct.PAGES['hs300w']), parse_cols=[0, 3, 6])
wt.columns = ct.FOR_CLASSIFY_W_COLS
wt['code'] = wt['code'].map(lambda x :str(x).zfill(6))
df = get_stock_basics()[['name']]
df = df.reset_index()
return pd.merge(df,wt)
except Exception as er:
print(str(er))
def get_zz500s():
"""
获取中证500成份股
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
from tushare.stock.fundamental import get_stock_basics
try:
# df = pd.read_excel(ct.HS300_CLASSIFY_URL_FTP%(ct.P_TYPE['ftp'], ct.DOMAINS['idxip'],
# ct.PAGES['zz500b']), parse_cols=[0,1])
# df.columns = ct.FOR_CLASSIFY_B_COLS
# df['code'] = df['code'].map(lambda x :str(x).zfill(6))
wt = pd.read_excel(ct.HS300_CLASSIFY_URL_FTP%(ct.P_TYPE['ftp'], ct.DOMAINS['idxip'],
ct.PAGES['zz500wt']), parse_cols=[0, 3, 6])
wt.columns = ct.FOR_CLASSIFY_W_COLS
wt['code'] = wt['code'].map(lambda x :str(x).zfill(6))
df = get_stock_basics()[['name']]
df = df.reset_index()
return pd.merge(df,wt)
except Exception as er:
print(str(er))
def get_zz500s():
"""
获取中证500成份股
Return
--------
DataFrame
code :股票代码
name :股票名称
"""
from tushare.stock.fundamental import get_stock_basics
try:
# df = pd.read_excel(ct.HS300_CLASSIFY_URL_FTP%(ct.P_TYPE['ftp'], ct.DOMAINS['idxip'],
# ct.PAGES['zz500b']), parse_cols=[0,1])
# df.columns = ct.FOR_CLASSIFY_B_COLS
# df['code'] = df['code'].map(lambda x :str(x).zfill(6))
wt = pd.read_excel(ct.HS300_CLASSIFY_URL_FTP%(ct.P_TYPE['ftp'], ct.DOMAINS['idxip'],
ct.PAGES['zz500wt']), parse_cols=[0, 3, 6])
wt.columns = ct.FOR_CLASSIFY_W_COLS
wt['code'] = wt['code'].map(lambda x :str(x).zfill(6))
df = get_stock_basics()[['name']]
df = df.reset_index()
return pd.merge(df,wt)
except Exception as er:
print(str(er))
def test_get_stock_basics(self):
print(fd.get_stock_basics())
def bar2h5(market='', date='', freq='D', asset='E', filepath=''):
cons = get_apis()
stks = get_stock_basics()
fname = "%s%s%sbar%s.h5" % (filepath, market, date, freq)
store = pd.HDFStore(fname, "a")
if market in ['SH', 'SZ']:
if market == 'SH':
stks = stks.ix[stks.index.str[0] == '6', :]
elif market == 'SZ':
stks = stks.ix[stks.index.str[0] != '6', :]
else:
stks = ''
market = 1 if market == 'SH' else 0
for stk in stks.index:
symbol = '%s.SH' % stk
if 'min' in freq:
df = bar(stk,
conn=cons,
start_date=date,
end_date=date,
freq=freq,
market=market,
asset=asset)
df['Time'] = df.index
df['Time'] = df['Time'].apply(get_dt_time)
df.index = df['Time']
df.drop(['code', 'Time'], axis=1, inplace=True)
df.rename(columns={'open': 'OPEN'}, inplace=True)
df.rename(columns={'close': 'CLOSE'}, inplace=True)
df.rename(columns={'low': 'LOW'}, inplace=True)
df.rename(columns={'high': 'HIGH'}, inplace=True)
df.rename(columns={'vol': 'VOLUME'}, inplace=True)
df.rename(columns={'amount': 'TURNOVER'}, inplace=True)
df.loc[:, 'HIGH'] = df.loc[:, 'HIGH'].astype("int64")
df.loc[:, 'LOW'] = df.loc[:, 'LOW'].astype("int64")
df.loc[:, 'OPEN'] = df.loc[:, 'OPEN'].astype("int64")
df.loc[:, 'CLOSE'] = df.loc[:, 'CLOSE'].astype("int64")
df.loc[:, 'VOLUME'] = df.loc[:, 'VOLUME'].astype("int64")
df.loc[:, 'TURNOVER'] = df.loc[:, 'TURNOVER'].astype("int64")
df.loc[:, 'OPEN'] *= 10000
df.loc[:, 'CLOSE'] *= 10000
df.loc[:, 'HIGH'] *= 10000
df.loc[:, 'LOW'] *= 10000
df.loc[:, 'ASKPRICE1'] = 0
df.loc[:, 'ASKPRICE2'] = 0
df.loc[:, 'ASKPRICE3'] = 0
df.loc[:, 'ASKPRICE4'] = 0
df.loc[:, 'ASKPRICE5'] = 0
df.loc[:, 'ASKPRICE6'] = 0
df.loc[:, 'ASKPRICE7'] = 0
df.loc[:, 'ASKPRICE8'] = 0
df.loc[:, 'ASKPRICE9'] = 0
df.loc[:, 'ASKPRICE10'] = 0
df.loc[:, 'BIDPRICE1'] = 0
df.loc[:, 'BIDPRICE2'] = 0
df.loc[:, 'BIDPRICE3'] = 0
df.loc[:, 'BIDPRICE4'] = 0
df.loc[:, 'BIDPRICE5'] = 0
df.loc[:, 'BIDPRICE6'] = 0
df.loc[:, 'BIDPRICE7'] = 0
df.loc[:, 'BIDPRICE8'] = 0
df.loc[:, 'BIDPRICE9'] = 0
df.loc[:, 'BIDPRICE10'] = 0
df.loc[:, 'ASKVOL1'] = 0
df.loc[:, 'ASKVOL2'] = 0
df.loc[:, 'ASKVOL3'] = 0
df.loc[:, 'ASKVOL4'] = 0
df.loc[:, 'ASKVOL5'] = 0
df.loc[:, 'ASKVOL6'] = 0
df.loc[:, 'ASKVOL7'] = 0
df.loc[:, 'ASKVOL8'] = 0
df.loc[:, 'ASKVOL9'] = 0
df.loc[:, 'ASKVOL10'] = 0
df.loc[:, 'BIDVOL1'] = 0
df.loc[:, 'BIDVOL2'] = 0
df.loc[:, 'BIDVOL3'] = 0
df.loc[:, 'BIDVOL4'] = 0
df.loc[:, 'BIDVOL5'] = 0
df.loc[:, 'BIDVOL6'] = 0
df.loc[:, 'BIDVOL7'] = 0
df.loc[:, 'BIDVOL8'] = 0
df.loc[:, 'BIDVOL9'] = 0
df.loc[:, 'BIDVOL10'] = 0
df.loc[:, 'VWAP'] = 0.0
df.loc[:, 'VOL30'] = 0.0
df.loc[:, 'TOTAL_VOLUME'] = 0.0
df.loc[:, 'TOTAL_TURNOVER'] = 0.0
df.loc[:, 'INTEREST'] = 0.0
print(df)
# if market == 1 and stk[0] == '6':
# df = bar(stk, conn=cons, start_date=date, end_date=date, freq=freq, market=market, asset=asset)
store[symbol] = df
store.close()
close_apis(cons)
def test_get_stock_basics(self):
print(fd.get_stock_basics())
def bar2h5(market='', date='', freq='D', asset='E', filepath=''):
cons = get_apis()
stks = get_stock_basics()
fname = "%s%s%sbar%s.h5"%(filepath, market, date, freq)
store = pd.HDFStore(fname, "a")
if market in ['SH', 'SZ']:
if market == 'SH':
stks = stks.ix[stks.index.str[0]=='6', :]
elif market == 'SZ':
stks = stks.ix[stks.index.str[0]!='6', :]
else:
stks = ''
market = 1 if market == 'SH' else 0
for stk in stks.index:
symbol = '%s.SH'%stk
if 'min' in freq:
df = bar(stk, conn=cons, start_date=date, end_date=date, freq=freq,
market=market, asset=asset)
df['Time'] = df.index
df['Time'] = df['Time'].apply(get_dt_time)
df.index = df['Time']
df.drop(['code','Time'], axis = 1, inplace=True)
df.rename(columns={'open':'OPEN'}, inplace=True)
df.rename(columns={'close':'CLOSE'}, inplace=True)
df.rename(columns={'low':'LOW'}, inplace=True)
df.rename(columns={'high':'HIGH'}, inplace=True)
df.rename(columns={'vol':'VOLUME'}, inplace=True)
df.rename(columns={'amount':'TURNOVER'}, inplace=True)
df.loc[:,'HIGH'] = df.loc[:,'HIGH'].astype("int64")
df.loc[:,'LOW'] = df.loc[:,'LOW'].astype("int64")
df.loc[:,'OPEN'] = df.loc[:,'OPEN'].astype("int64")
df.loc[:,'CLOSE'] = df.loc[:,'CLOSE'].astype("int64")
df.loc[:,'VOLUME'] = df.loc[:,'VOLUME'].astype("int64")
df.loc[:,'TURNOVER'] = df.loc[:,'TURNOVER'].astype("int64")
df.loc[:,'OPEN'] *= 10000
df.loc[:,'CLOSE'] *= 10000
df.loc[:,'HIGH'] *= 10000
df.loc[:,'LOW'] *= 10000
df.loc[:,'ASKPRICE1'] = 0
df.loc[:,'ASKPRICE2'] = 0
df.loc[:,'ASKPRICE3'] = 0
df.loc[:,'ASKPRICE4'] = 0
df.loc[:,'ASKPRICE5'] = 0
df.loc[:,'ASKPRICE6'] = 0
df.loc[:,'ASKPRICE7'] = 0
df.loc[:,'ASKPRICE8'] = 0
df.loc[:,'ASKPRICE9'] = 0
df.loc[:,'ASKPRICE10'] = 0
df.loc[:,'BIDPRICE1'] = 0
df.loc[:,'BIDPRICE2'] = 0
df.loc[:,'BIDPRICE3'] = 0
df.loc[:,'BIDPRICE4'] = 0
df.loc[:,'BIDPRICE5'] = 0
df.loc[:,'BIDPRICE6'] = 0
df.loc[:,'BIDPRICE7'] = 0
df.loc[:,'BIDPRICE8'] = 0
df.loc[:,'BIDPRICE9'] = 0
df.loc[:,'BIDPRICE10'] = 0
df.loc[:,'ASKVOL1'] = 0
df.loc[:,'ASKVOL2'] = 0
df.loc[:,'ASKVOL3'] = 0
df.loc[:,'ASKVOL4'] = 0
df.loc[:,'ASKVOL5'] = 0
df.loc[:,'ASKVOL6'] = 0
df.loc[:,'ASKVOL7'] = 0
df.loc[:,'ASKVOL8'] = 0
df.loc[:,'ASKVOL9'] = 0
df.loc[:,'ASKVOL10'] = 0
df.loc[:,'BIDVOL1'] = 0
df.loc[:,'BIDVOL2'] = 0
df.loc[:,'BIDVOL3'] = 0
df.loc[:,'BIDVOL4'] = 0
df.loc[:,'BIDVOL5'] = 0
df.loc[:,'BIDVOL6'] = 0
df.loc[:,'BIDVOL7'] = 0
df.loc[:,'BIDVOL8'] = 0
df.loc[:,'BIDVOL9'] = 0
df.loc[:,'BIDVOL10'] = 0
df.loc[:,'VWAP'] = 0.0
df.loc[:,'VOL30']=0.0
df.loc[:,'TOTAL_VOLUME']=0.0
df.loc[:,'TOTAL_TURNOVER']=0.0
df.loc[:,'INTEREST']=0.0
print(df)
# if market == 1 and stk[0] == '6':
# df = bar(stk, conn=cons, start_date=date, end_date=date, freq=freq, market=market, asset=asset)
store[symbol] = df
store.close()
close_apis(cons)
