def classification(class_types):
if class_types == 'industry':
industry_classified = ts.get_industry_classified()
industry_classified.to_csv('D:\\ts\\classification\\industry_classified.csv', encoding='gbk')
elif class_types == 'concept':
concept_classified = ts.get_concept_classified()
concept_classified.to_csv('D:\\ts\\classification\\concept_classified.csv', encoding='gbk')
elif class_types == 'area':
area_classified = ts.get_area_classified()
area_classified.to_csv('D:\\ts\\classification\\area_classified.csv', encoding='gbk')
elif class_types == 'sme':
sme_classified = ts.get_sme_classified()
sme_classified.to_csv('D:\\ts\\classification\\sme_classified.csv', encoding='gbk')
elif class_types == 'gem':
gem_classified = ts.get_gem_classified()
gem_classified.to_csv('D:\\ts\\classification\\gem_classified.csv', encoding='gbk')
elif class_types == 'st':
st_classified = ts.get_st_classified()
st_classified.to_csv('D:\\ts\\classification\\st_classified.csv', encoding='gbk')
elif class_types == 'hs300':
hs300s = ts.get_hs300s()
hs300s.to_csv('D:\\ts\\classification\\hs300s.csv', encoding='gbk')
elif class_types == 'sz50':
sz50s = ts.get_sz50s()
sz50s.to_csv('D:\\ts\\classification\\sz50s.csv', encoding='gbk')
elif class_types == 'zz500':
zz500s = ts.get_zz500s()
zz500s.to_csv('D:\\ts\\classification\\zz500s.csv', encoding='gbk')
elif class_types == 'terminated':
terminated = ts.get_terminated()
terminated.to_csv('D:\\ts\\classification\\terminated.csv', encoding='gbk')
elif class_types == 'suspended':
suspended = ts.get_suspended()
suspended.to_csv('D:\\ts\\classification\\suspended.csv', encoding='gbk')
def get_zz500s():
try:
df = ts.get_zz500s();
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
# df.insert(0,'code','600848')
df.to_sql('zz500s', engine, if_exists='append')
except Exception, e:
e.message
def gen_zz800_stock_list():
'''
:return: 生成中证800股票代码，[code, name, weight, date]
'''
zz500 = ts.get_zz500s()
hs300 = ts.get_hs300s()
zz800 = zz500.append(hs300)
zz800 = zz800[['code', 'name', 'weight', 'date']]
zz800.columns = zz800.columns.str.upper()
zz800.to_csv(config.rootPath + '/data/gen_data/zz800_codes.csv', index=False)
def storagepool(self):
#storage zz500
df=ts.get_zz500s()
self.pool['zz500'].insert_many(json.loads(df.to_json(orient='records')))
#hs300
df=ts.get_hs300s()
self.pool['hz300'].insert_many(json.loads(df.to_json(orient='records')))
#zh50
df=ts.get_sz50s()
self.pool['sz'].insert_many(json.loads(df.to_json(orient='records')))
#st
df=ts.get_st_classified()
self.pool['st'].insert_many(json.loads(df.to_json(orient='records')))
def list(self, stock_block_type):
stock_block = None
if stock_block_type == self.industry:
stock_block = db.get(STOCK_BLOCK_INDUSTRY)
if stock_block is None:
stock_block = ts.get_industry_classified()
db.save(STOCK_BLOCK_INDUSTRY, stock_block)
elif stock_block_type == self.concept:
stock_block = db.get(STOCK_BLOCK_CONCEPT)
if stock_block is None:
stock_block = ts.get_concept_classified()
db.save(STOCK_BLOCK_CONCEPT, stock_block)
elif stock_block_type == self.area:
stock_block = db.get(STOCK_BLOCK_AREA)
if stock_block is None:
stock_block = ts.get_area_classified()
db.save(STOCK_BLOCK_AREA, stock_block)
elif stock_block_type == self.sme:
stock_block = db.get(STOCK_BLOCK_SME)
if stock_block is None:
stock_block = ts.get_sme_classified()
db.save(STOCK_BLOCK_SME, stock_block)
elif stock_block_type == self.gem:
stock_block = db.get(STOCK_BLOCK_GEM)
if stock_block is None:
stock_block = ts.get_gem_classified()
db.save(STOCK_BLOCK_GEM, stock_block)
elif stock_block_type == self.st:
stock_block = db.get(STOCK_BLOCK_ST)
if stock_block is None:
stock_block = ts.get_st_classified()
db.save(STOCK_BLOCK_ST, stock_block)
elif stock_block_type == self.hs300s:
stock_block = db.get(STOCK_BLOCK_HS300S)
if stock_block is None:
stock_block = ts.get_hs300s()
db.save(STOCK_BLOCK_HS300S, stock_block)
elif stock_block_type == self.sz50s:
stock_block = db.get(STOCK_BLOCK_SZ50S)
if stock_block is None:
stock_block = ts.get_sz50s()
db.save(STOCK_BLOCK_SZ50S, stock_block)
elif stock_block_type == self.zz500s:
stock_block = db.get(STOCK_BLOCK_ZZ500S)
if stock_block is None:
stock_block = ts.get_zz500s()
db.save(STOCK_BLOCK_ZZ500S, stock_block)
else:
return None
return stock_block
def preload():
stock_block = ts.get_industry_classified()
db.save(STOCK_BLOCK_INDUSTRY, stock_block)
stock_block = ts.get_concept_classified()
db.save(STOCK_BLOCK_CONCEPT, stock_block)
stock_block = ts.get_area_classified()
db.save(STOCK_BLOCK_AREA, stock_block)
stock_block = ts.get_sme_classified()
db.save(STOCK_BLOCK_SME, stock_block)
stock_block = ts.get_gem_classified()
db.save(STOCK_BLOCK_GEM, stock_block)
stock_block = ts.get_st_classified()
db.save(STOCK_BLOCK_ST, stock_block)
stock_block = ts.get_hs300s()
db.save(STOCK_BLOCK_HS300S, stock_block)
stock_block = ts.get_sz50s()
db.save(STOCK_BLOCK_SZ50S, stock_block)
stock_block = ts.get_zz500s()
db.save(STOCK_BLOCK_ZZ500S, stock_block)
def spr():
"""
Main interface
:return: a filtered stock list
"""
global count
today = date.today().strftime('%Y-%m-%d').encode()
trx_date = ts.get_hist_data('sh').index
if today in trx_date:
# four days included today
begin = trx_date[2].encode()
else:
# four days excluded today
begin = trx_date[3].encode()
print 'Starting from %s ...' % str(begin)
# get full stock list
print 'Getting stock list ...',
stock_list = ts.get_zz500s().code
print 'Done: %s stocks' % len(stock_list)
# filtering process
count = 0
return filter(lambda x: stock_filter(x, begin), stock_list)
end_day = now
else:
end_day = now - datetime.timedelta(days=1)
try:
stock_basics = ts.get_stock_basics()
except:
print('get_stock_basics timeout!')
sys.exit(1)
try:
list_code = sys.argv[1]
if int(list_code) == 500:
try:
stock_500 = ts.get_zz500s()
except:
print('get_zz500s timeout!')
sys.exit(1)
stock_list = stock_500.code.values
else:
sys.exit(1)
except:
stock_list = stock_basics.index.values
pool = multiprocessing.Pool(processes=4)
for stock_code in sorted(stock_list):
pool.apply_async(job2weight,(stock_code,end_day,stock_basics))
pool.close()
pool.join()
def get_zz500s_history():
zz500s = ts.get_zz500s()
return get_data_by_column(zz500s)
# ST Enterprise
ST = ts.get_st_classified()
ST.to_csv('./ASHR/DATA/ST.csv', index = False)
ts.get_h_data()
# HS 300
HS300S = ts.get_hs300s()
HS300S.to_csv('./ASHR/DATA/HS300S.csv', index = False)
# SZ 50
SZ50S = ts.get_sz50s()
SZ50S.to_csv('./ASHR/DATA/SZ50S.csv', index = False)
# ZZ 500
ZZ500S = ts.get_zz500s()
ZZ500S.to_csv('./ASHR/DATA/ZZ500S.csv', index = False)
#################
# Fund Holdings #
#################
# TODO Data is available quarterly
FundHolding = ts.fund_holdings(CURRENT.year, np.floor((CURRENT.month+2)/3))
####################
# Financial Report #
####################
# FinancialReport: EPS, EPS_YOY, ROE, net_profits, profits_yoy
#-*- coding: utf-8 -*-
import tushare as ts
import pandas as pd
import numpy as np
import datetime
import uniout
from sqlalchemy import create_engine
stock_concept = ts.get_concept_classified()
stock_hs300s = ts.get_hs300s()
stock_sz50s = ts.get_sz50s()
stock_zz500s = ts.get_zz500s()
stock_list = ts.get_stock_basics()
stock_list['concept_name'] = ''
stock_list['ishs300s'] = 0
stock_list['issz50s'] = 0
stock_list['iszz500s'] = 0
for code in stock_list.index:
temp_con = stock_concept[stock_concept['code'] == code]
temp_hs300s = stock_hs300s[stock_hs300s['code'] == code]
temp_sz50s = stock_sz50s[stock_sz50s['code'] == code]
temp_zz500s = stock_zz500s[stock_zz500s['code'] == code]
if len(temp_con) > 0:
stock_list.loc[code]['concept_name'] = temp_con['c_name']
else:
stock_list.loc[code]['concept_name'] = ''
print 'concept_name insert'
if len(temp_hs300s) == 0:
返回值说明：
code：股票代码
name：股票名称'''
print('上证50成份股')
print(ts.get_sz50s())
'''获取中证500成份股
返回值说明：
code：股票代码
name：股票名称'''
print('中证500成份股')
print(ts.get_zz500s())
'''获取已经被终止上市的股票列表，数据从上交所获取，目前只有在上海证券交易所交易被终止的股票。
返回值说明：
code：股票代码
name：股票名称
oDate:上市日期
tDate:终止上市日期'''
print('终止上市股票列表')
print(ts.get_terminated())
''''获取被暂停上市的股票列表，数据从上交所获取，目前只有在上海证券交易所交易被终止的股票。
返回值说明：
print("#0", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23')
print("#1", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23', ktype='W')
print("#2", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23', ktype='M')
print("#3", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23', ktype='5')
print("#4", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23', ktype='15')
print("#5", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23', ktype='30')
print("#6", df)
df = ts.get_hist_data('600848',start='2016-08-05',end='2016-08-23', ktype='60')
print("#7", df)
df = ts.get_index()
printD("ts.get_index", df)
df = ts.get_suspended()
printD("ts.get_suspended()", df)
df = ts.get_sz50s()
printD("ts.get_sz50s", df)
df = ts.get_terminated()
printD("ts.get_terminated", df)
df = ts.get_zz500s()
printD("ts.get_zz500s", df)
def stat_all(tmp_datetime):
# 存款利率
# data = ts.get_deposit_rate()
# common.insert_db(data, "ts_deposit_rate", False, "`date`,`deposit_type`")
#
# # 贷款利率
# data = ts.get_loan_rate()
# common.insert_db(data, "ts_loan_rate", False, "`date`,`loan_type`")
#
# # 存款准备金率
# data = ts.get_rrr()
# common.insert_db(data, "ts_rrr", False, "`date`")
#
# # 货币供应量
# data = ts.get_money_supply()
# common.insert_db(data, "ts_money_supply", False, "`month`")
#
# # 货币供应量(年底余额)
# data = ts.get_money_supply_bal()
# common.insert_db(data, "ts_money_supply_bal", False, "`year`")
#
# # 国内生产总值(年度)
# data = ts.get_gdp_year()
# common.insert_db(data, "ts_gdp_year", False, "`year`")
#
# # 国内生产总值(季度)
# data = ts.get_gdp_quarter()
# common.insert_db(data, "ts_get_gdp_quarter", False, "`quarter`")
#
# # 三大需求对GDP贡献
# data = ts.get_gdp_for()
# common.insert_db(data, "ts_gdp_for", False, "`year`")
#
# # 三大产业对GDP拉动
# data = ts.get_gdp_pull()
# common.insert_db(data, "ts_gdp_pull", False, "`year`")
#
# # 三大产业贡献率
# data = ts.get_gdp_contrib()
# common.insert_db(data, "ts_gdp_contrib", False, "`year`")
#
# # 居民消费价格指数
# data = ts.get_cpi()
# common.insert_db(data, "ts_cpi", False, "`month`")
#
# # 工业品出厂价格指数
# data = ts.get_ppi()
# common.insert_db(data, "ts_ppi", False, "`month`")
#############################基本面数据 http://tushare.org/fundamental.html
# 股票列表
data = ts.get_stock_basics()
# print(data.index)
# 解决ESP 小数问题。
data["esp"] = data["esp"].round(2) # 数据保留2位小数
common.insert_db(data, "ts_stock_basics", True, "`code`")
# http://tushare.org/classifying.html#id9
# # 行业分类 必须使用 PRO 接口查询。
# data = ts.get_industry_classified()
# common.insert_db(data, "ts_industry_classified", True, "`code`")
#
# # 概念分类 必须使用 PRO 接口查询。
# data = ts.get_concept_classified()
# common.insert_db(data, "ts_concept_classified", True, "`code`")
# 沪深300成份股及权重
data = ts.get_hs300s()
data = data.drop(columns=["date"]) # 删除日期
data = data.set_index("code") # 替换索引
common.insert_db(data, "ts_stock_hs300s", True, "`code`")
# # 上证50成份股 没有数据？？
# data = ts.get_sz50s()
# common.insert_db(data, "ts_sz50s", True, "`code`")
# 中证500成份股
data = ts.get_zz500s()
data = data.drop(columns=["date"]) # 删除日期
data = data.set_index("code") # 替换索引
common.insert_db(data, "ts_stock_zz500s", True, "`code`")
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 13 21:39:24 2016
@author: Administrator
"""
import tushare as ts
import pandas as pd
import numpy as np
from pandas import DataFrame,Series
zz500s = ts.get_zz500s()
#==============================================================================
# for stock in zz500s['code']:
# hisdata = ts.get_hist_data(stock,start ='20140101' )
# hisdata.to_csv('D:\\pythonTest\\stockfile\\'+ stock + '.csv' )
#==============================================================================
stocklist = []
for stock in zz500s['code']:
hisdata = ts.get_hist_data(stock,start ='2016-04-01')
if (abs(hisdata['close']-hisdata['ma5'])/hisdata['ma5']<0.02).all() \
and (hisdata['ma5'] > hisdata['ma10']).all() \
and (hisdata['ma10'] > hisdata['ma20']).all() \
and (abs(hisdata['high']-hisdata['close'])/hisdata['close']<0.02).all():
gc = ts.get_gem_classified() print(gc) # 风险警示板分类 # 获取风险警示板股票数据，即查找所有st股票 st = ts.get_st_classified() print(st) # 沪深300成份及权重 # 获取沪深300当前成份股及所占权重 # FIXME 有bug h3 = ts.get_hs300s() print(h3) # 上证50成份股 # 获取上证50成份股 # FIXME 有bug ts.get_sz50s() # 中证500成份股 # FIXME 有bug ts.get_zz500s() # 终止上市股票列表 t = ts.get_terminated() print(t) # 暂停上市股票列表 # 获取被暂停上市的股票列表，数据从上交所获取，目前只有在上海证券交易所交易被终止的股票。 s = ts.get_suspended() print(s)
import tushare as ts # 中证500成份股 # 获取中证500成份股 # # 返回值说明： # # code：股票代码 # name：股票名称 df = ts.get_zz500s() print(df) # date code name weight # 0 2019-09-30 600006 东风汽车 0.10 # 1 2019-09-30 600008 首创股份 0.20 # 2 2019-09-30 600017 日照港 0.14 # 3 2019-09-30 600021 上海电力 0.22 # 4 2019-09-30 600022 山东钢铁 0.25 # .. ... ... ... ... # 495 2019-09-30 300376 易事特 0.11 # 496 2019-09-30 300383 光环新网 0.45 # 497 2019-09-30 300418 昆仑万维 0.19 # 498 2019-09-30 300450 先导智能 0.47 # 499 2019-09-30 300459 金科文化 0.08 # # [500 rows x 4 columns]
def get_stock_zz500():
"""
获取中证500成份股
"""
return ts.get_zz500s()
db.commit()
else:
sql8 = "update stock_classify w set w.sz50 = '%s' where w.stock_code = '%s'" % (
str(1), str(resu8[0]))
cursor.execute(sql8)
db.commit()
except Exception as ex:
print(ex)
continue
con_sql8 = "select distinct stock_code from stock_classify"
cursor.execute(con_sql8)
done_set8 = cursor.fetchall()
db.commit()
con8 = [x[0] for x in done_set8]
temp9 = ts.get_zz500s()
temp9mid = np.array(temp9)
print('Init_Classify ===>> 9 of 9. ZZ500')
for q in range(len(temp9mid)):
resu9 = temp9mid[q]
try:
if resu9[0] not in con8:
sql9 = "insert into stock_classify(stock_code,zz500)values('%s','%s')" % (
str(resu9[0]), str(1))
cursor.execute(sql9)
db.commit()
else:
sql9 = "update stock_classify w set w.zz500 = '%s' where w.stock_code = '%s'" % (
str(1), str(resu9[0]))
cursor.execute(sql9)
db.commit()
def classified():
#在现实交易中，经常会按行业统计股票的涨跌幅或资金进出，本接口按照sina财经对沪深股票进行的行业分类，返回所有股票所属行业的信息。
#注意：有的股票属于两个行业：如000587
industry = ts.get_industry_classified()
industry = industry.drop_duplicates()
industry_new = data_pro(industry)
industry_new.rename(columns={'c_name': 'industry'}, inplace=True)
#返回股票概念的分类数据，现实的二级市场交易中，经常会以”概念”来炒作，在数据分析过程中，可根据概念分类监测资金等信息的变动情况。
concept = ts.get_concept_classified()
concept = concept.drop_duplicates()
concept_new = data_pro(concept)
concept_new.rename(columns={'c_name': 'concept'}, inplace=True)
#按地域对股票进行分类，即查找出哪些股票属于哪个省份。
area = ts.get_area_classified()
area = area.drop_duplicates()
area_new = data_pro(area)
#获取中小板股票数据，即查找所有002开头的股票
sme = ts.get_sme_classified()
sme['belong_bk'] = '中小板'
#获取创业板股票数据，即查找所有300开头的股票
gem = ts.get_gem_classified()
gem['belong_bk'] = '创业板'
#获取风险警示板股票数据，即查找所有st股票
st = ts.get_st_classified()
st['belong_bk'] = '风险警示板'
bk = pd.concat([sme, gem, st], join='outer', axis=0)
bk = bk.drop_duplicates()
bk = data_pro(bk)
# 获取沪深300当前成份股及所占权重
hs300s = ts.get_hs300s()
hs300s = hs300s.iloc[:, 1:-1]
hs300s['belong_cfg'] = '沪深300成份股'
# 获取上证50成份股
sz50s = ts.get_sz50s()
sz50s = sz50s.iloc[:, 1:]
sz50s['belong_cfg'] = '上证50成份股'
# 获取中证500成份股
zz500s = ts.get_zz500s()
zz500s = zz500s.iloc[:, 1:-1]
zz500s['belong_cfg'] = '中证500成份股'
cfg = pd.concat([hs300s, sz50s, zz500s], join='outer', axis=0)
cfg = cfg.drop_duplicates()
cfg = data_pro(cfg)
# 获取已经被终止上市的股票列表，数据从上交所获取，目前只有在上海证券交易所交易被终止的股票。
terminated = ts.get_terminated()
if terminated != None:
terminated['终止上市'] = '终止上市'
# 获取被暂停上市的股票列表，数据从上交所获取，目前只有在上海证券交易所交易被终止的股票。
suspended = ts.get_suspended()
if suspended != None:
suspended['暂停上市'] = '暂停上市'
# 数据拼接
table_list = [industry_new, concept_new, area_new, bk, cfg]
for i in range(len(table_list)):
if i == 0:
classified_data = table_list[i]
else:
classified_data = pd.merge(classified_data,
table_list[i],
how='outer',
on=['code', 'name'])
classified_data = classified_data.fillna('未知')
#一个代码对应多个名称的数据整理
classified_temp = classified_data.groupby(['code'])
classified_new = classified_temp.agg(lambda x: '|'.join(x))
classified_dict = {
'industry': '所属行业',
'name': '名称',
'concept': '概念',
'area': '区域',
'belong_bk': '所属板块',
'belong_cfg': '所属成分股'
}
classified_new.columns = classified_new.columns.map(classified_dict)
industry_list = classified_new['所属行业']
industry_list = [i.split('|')[0] for i in industry_list]
classified_new['所属行业'] = industry_list
return classified_new
def init_zz500s_class(self):
df = ts.get_zz500s()
df.to_csv(self.zz500_class_file)
def update_stock_info():
trade_cal(True)
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_stock_basics()
fle = os.path.join(CURDIR, 'stock.basics.csv')
df.to_csv(fle, encoding="utf-8")
df = df.set_index(df.index.astype(int))
df.to_sql('stock_list',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update 股票列表 save to sql')
time.sleep(0.1)
df = ts.get_today_all()
fle = os.path.join(CURDIR, 'stock.today.csv')
df.to_csv(fle, encoding="utf-8")
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_today',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update stock_today')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_industry_classified()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_industry',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update 股票行业 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
# ytrack.info('成功 股票基本信息 from network..')
# ynotice.send(ytrack.get_logs(), style='stock', title='更新股票基础数据和交易日期')
# return
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_concept_classified()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_concept',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update 股票概念 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_area_classified()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_area',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update 股票地域 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_sme_classified()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_sme',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update 中小板股票 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_gem_classified()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_gem',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update 创业板股票 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_st_classified()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_st',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update st股票 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_hs300s()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_hs300',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update hs300股票 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_sz50s()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_sz50s',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update sz50s股票 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
for _ in range(5):
try:
time.sleep(0.1)
df = ts.get_zz500s()
df = df.set_index('code')
df = df.set_index(df.index.astype(int))
df.to_sql('stock_zz500s',
engine,
if_exists='replace',
index=True,
index_label='code')
ytrack.info('update zz500s股票 save to sql')
break
except Exception as e:
ytrack.error(traceback.format_exc())
ytrack.info('成功 股票基本信息 from network..')
ynotice.send(ytrack.get_logs(), style='stock', title='更新股票基础数据和交易日期')
def classification_type(class_types):
if class_types == 'classification_industry':
industry_classified = ts.get_industry_classified('sw')
industry_classified.to_sql('classification_industry',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'concept':
concept_classified = ts.get_concept_classified()
concept_classified.to_sql('classification_concept',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'area':
area_classified = ts.get_area_classified()
area_classified.to_sql('classification_area',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'sme':
sme_classified = ts.get_sme_classified()
sme_classified.to_sql('classification_sme',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'gem':
gem_classified = ts.get_gem_classified()
gem_classified.to_sql('classification_gem',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'st':
st_classified = ts.get_st_classified()
st_classified.to_sql('classification_st',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'hs300':
hs300s = ts.get_hs300s()
hs300s.to_sql('classification_hs300s',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'sz50':
sz50s = ts.get_sz50s()
sz50s.to_sql('classification_sz50s',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'zz500':
zz500s = ts.get_zz500s()
zz500s.to_sql('classification_zz500s',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'terminated':
terminated = ts.get_terminated()
terminated.to_sql('classification_terminated',
engine,
flavor='mysql',
if_exists='replace')
elif class_types == 'suspended':
suspended = ts.get_suspended()
suspended.to_sql('classification_suspended',
engine,
flavor='mysql',
if_exists='replace')
time.localtime(time.time() - 24 * 3600 * 30))
endDate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
# print(startDate)
# print(endDate)
df = ts.get_hist_data(code, startDate, endDate)
closeList = df['close'].to_list()
def add(x, y):
return x + y
sum = reduce(add, closeList)
return round(sum / len(closeList), 2)
stockList = ts.get_zz500s()
startDate = time.strftime("%Y-%m-%d",
time.localtime(time.time() - 24 * 3600 * 5))
endDate = time.strftime("%Y-%m-%d", time.localtime(time.time()))
goodStock1 = goodStock2 = {}
for index, row in stockList.iterrows():
code = row["code"]
name = row["name"]
print(name)
print(code)
m30 = m30(code)
print(m30)
df = ts.get_hist_data(code, startDate, endDate)
closeList = df['close'].to_list()
result = list(filter(lambda x: x > m30, closeList))
if len(result) == len(closeList):
import pymongo
import datetime
import tushare as ts
code_list = ts.get_zz500s().code.tolist()
print('unasync-mongo')
time2 = datetime.datetime.now()
clientx = pymongo.MongoClient('192.168.4.253', 27017).quantaxis.stock_day
data = []
def get_data(codelist):
for code in codelist:
for message in clientx.find({'code': code}):
data.append(message)
get_data(code_list)
print(datetime.datetime.now() - time2)
def get_zz500s_history():
zz500s = ts.get_zz500s()
return get_data_by_column(zz500s)
'''最近周k线'''
D_recent_k_data = ts.get_k_data(target_stock_code, ktype='D')
'''最近日k线'''
recent_k60_data = ts.get_k_data(target_stock_code, ktype='60')
'''最近小时线'''
recent_k30_data = ts.get_k_data(target_stock_code, ktype='30')
'''最近30min线'''
recent_k15_data = ts.get_k_data(target_stock_code, ktype='15')
'''最近15min线'''
recent_k5_data = ts.get_k_data(target_stock_code, ktype='5')
'''最近5min线'''
#==============================================================================指数成分==============================================================================
'''获取指数成分'''
df_hs300 = ts.get_hs300s() #获取沪深300当前成份股及所占权重
df_sz50 = ts.get_sz50s() #获取上证50成份股
df_zz500 = ts.get_zz500s() #获取中证500成份股
#==============================================================================投资参考==============================================================================
df_dividend = ts.profit_data(top=60)
df_dividend.sort('shares',ascending=False)
'''分配预案'''
df_forecast = ts.forecast_data(2017,4)
'''业绩预告'''
df_restrict = ts.xsg_data()
'''限售股解禁'''
df_fund = ts.fund_holdings(2017, 2)
'''基金持股'''
df_sh = ts.sh_margin_details(start='2015-01-01', end='2015-04-19', symbol='601989')
'''上交所融资融券明细'''
df_sz = ts.sz_margins(start='2015-01-01', end='2015-04-19')
df_sz_detail = ts.sz_margin_details('2015-04-20')
'''深交所融资融券总额'''
def capture_stock_data():
capture_date = datetime.datetime.now().strftime("%Y%m%d")
current_dir = os.getcwd()
save_dir = os.path.join(current_dir, capture_date)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
print("The save directory is created successfully!\n", save_dir)
else:
print("The save directory is already exist!\n", save_dir)
# ======================Daily Command================================================================
# get the boxoffcie data of the last day and save as csvfile named as the capture command
ts.day_boxoffice().to_csv(
save_dir + "/" + capture_date + "_day_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("day_boxoffice data capture completed!")
# get the cinema data of the last day and save as csvfile named as the capture command
ts.day_cinema().to_csv(
save_dir + "/" + capture_date + "_day_cinema.csv",
header=True,
sep=",",
index=False,
)
print("day_cinema data capture completed!")
'''ts.month_boxoffice().to_csv(
save_dir + "/" + capture_date + "_month_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("month_boxoffice data capture completed!")'''
ts.realtime_boxoffice().to_csv(
save_dir + "/" + capture_date + "_realtime_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("realtime_boxoffice data capture completed!")
# get the stock data index of the last day and save as csvfile named as the capture command
ts.get_index().to_csv(
save_dir + "/" + capture_date + "_get_index.csv",
header=True,
sep=",",
index=False,
)
print("get_index data capture completed!")
# get the history cpi data and save as csvfile named as the capture command
ts.get_cpi().to_csv(
save_dir + "/" + capture_date + "_get_cpi.csv",
header=True,
sep=",",
index=False,
)
print("get_cpi data capture completed!")
# get the history gdp data by month and save as csvfile named as the capture command
ts.get_gdp_year().to_csv(
save_dir + "/" + capture_date + "_get_gdp_year.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_year data capture completed!")
# get today all stock data and save as csvfile named as the capture command
# ts.get_today_all().to_csv(save_dir+'/'+capture_date+'_get_today_all.csv',header=True,sep=',',index=False)
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.broker_tops().to_csv(
save_dir + "/" + capture_date + "_broker_tops.csv",
header=True,
sep=",",
index=False,
)
print("broker_tops data capture completed!")
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.cap_tops().to_csv(
save_dir + "/" + capture_date + "_cap_tops.csv",
header=True,
sep=",",
index=False,
)
print("cap_tops data capture completed!")
ts.get_area_classified().to_csv(
save_dir + "/" + capture_date + "_get_area_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_area_classified data capture completed!")
# ts.get_balance_sheet(code='').to_csv(save_dir+'/'+capture_date+'_get_balance_sheet.csv',header=True,sep=',',index=False)
# print('get_balance_sheet data capture completed!')
# ts.get_cash_flow(code='').to_csv(save_dir+'/'+capture_date+'_get_cash_flow.csv',header=True,sep=',',index=False)
# print('get_cash_flow data capture completed!')
ts.get_day_all().to_csv(
save_dir + "/" + capture_date + "_get_day_all.csv",
header=True,
sep=",",
index=False,
)
print("get_day_all data capture completed!")
ts.get_cashflow_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_cashflow_data.csv",
header=True,
sep=",",
index=False,
)
print("get_cashflow_data data capture completed!")
ts.get_concept_classified().to_csv(
save_dir + "/" + capture_date + "_get_concept_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_concept_classified data capture completed!")
ts.get_debtpaying_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_debtpaying_data.csv",
header=True,
sep=",",
index=False,
)
print("get_debtpaying_data data capture completed!")
ts.get_deposit_rate().to_csv(
save_dir + "/" + capture_date + "_get_deposit_rate.csv",
header=True,
sep=",",
index=False,
)
print("get_deposit_rate data capture completed!")
ts.get_gdp_contrib().to_csv(
save_dir + "/" + capture_date + "_get_gdp_contrib.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_for().to_csv(
save_dir + "/" + capture_date + "_get_gdp_for.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_pull().to_csv(
save_dir + "/" + capture_date + "_get_gdp_pull.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_quarter().to_csv(
save_dir + "/" + capture_date + "_get_gdp_quarter.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_ data capture completed!")
# ts.get_gdp_year().to_csv(save_dir+'/'+capture_date+'_get_gdp_year.csv',header=True,sep=',',index=False)
ts.get_gem_classified().to_csv(
save_dir + "/" + capture_date + "_get_gem_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_gold_and_foreign_reserves().to_csv(
save_dir + "/" + capture_date + "_get_gold_and_foreign_reserves.csv",
header=True,
sep=",",
index=False,
)
ts.get_growth_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_growth_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_industry_classified().to_csv(
save_dir + "/" + capture_date + "_get_industry_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_hs300s().to_csv(
save_dir + "/" + capture_date + "_get_hs300s.csv",
header=True,
sep=",",
index=False,
)
# =============================================================================
# ts.get_sz50s().to_csv(
# save_dir + "/" + capture_date + "_get_sz50s.csv",
# header=True,
# sep=",",
# index=False,
# )
# =============================================================================
ts.get_zz500s().to_csv(
save_dir + "/" + capture_date + "_get_zz500s.csv",
header=True,
sep=",",
index=False,
)
ts.get_operation_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_operation_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_stock_basics().to_csv(
save_dir + "/" + capture_date + "_get_stock_basics.csv",
header=True,
sep=",",
index=False,
)
ts.get_report_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_report_data.csv",
header=True,
sep=",",
index=False,
)
ts.inst_detail().to_csv(
save_dir + "/" + capture_date + "_inst_detail.csv",
header=True,
sep=",",
index=False,
)
ts.inst_tops().to_csv(
save_dir + "/" + capture_date + "_inst_tops.csv",
header=True,
sep=",",
index=False,
)
print("inst_tops data capture completed!")
# =============================================================================
# ts.new_stocks().to_csv(
# save_dir + "/" + capture_date + "_new_stocks.csv",
# header=True,
# sep=",",
# index=False,
# )
# print("new_stocks data capture completed!")
# =============================================================================
ts.top_list().to_csv(
save_dir + "/" + capture_date + "_top_list.csv",
header=True,
sep=",",
index=False,
)
print("top_list data capture completed!")
ts.get_k_data('sh', start='1990-01-01').to_csv(
save_dir + "/" + capture_date + "_get_k_data.csv",
header=True,
sep=",",
index=False,
)
print("get_k_data capture completed!")
from sqlalchemy import create_engine
import tushare as ts
stock_basics = ts.get_stock_basics() #所有股票列表 stock_basic
industry = ts.get_industry_classified() # 行业分类 industry
concept = ts.get_concept_classified() # 概念分类 concept
area = ts.get_area_classified() # 地域分类 area
#sme = ts.get_sme_classified() # 中小板 sme
#gem = ts.get_gem_classified() # 创业板 gem
#st = ts.get_st_classified() # ST风险警示板 st
hs300s = ts.get_hs300s() # 沪深300 hs300s
sz50s = ts.get_sz50s() # 上证50 sz50s
zz500s = ts.get_zz500s() # 中证500 zz500s
terminated = ts.get_terminated() # 终止上市 terminated
suspended = ts.get_suspended() # 暂停上市 suspended
engine = create_engine('mysql://*****:*****@10.20.116.107/CN_BASSOON?charset=utf8')
#sme.to_sql('sme', engine, if_exists='append')
#gem.to_sql('gem', engine, if_exists='append')
#st.to_sql('st', engine, if_exists='append')
hs300s.to_sql('hs300s', engine, if_exists='append')
sz50s.to_sql('sz50s', engine, if_exists='append')
zz500s.to_sql('zz500s', engine, if_exists='append')
terminated.to_sql('terminated', engine, if_exists='append')
suspended.to_sql('suspended', engine, if_exists='append')
stock_basics.to_sql('stock_basics', engine, if_exists='append')
industry.to_sql('industry', engine, if_exists='append')
concept.to_sql('concept', engine, if_exists='append')
area.to_sql('area', engine, if_exists='append')
def get_zz500s_info(file_path):
zz500s_info = ts.get_zz500s()
zz500s_info.to_csv(file_path, encoding='utf-8')
print '\ndownload zz500s info finished\n'
import tushare as ts import storage as storage storage.save_sql(ts.get_industry_classified(), "basics/industry_classified") storage.save_sql(ts.get_concept_classified(), "basics/concept_classified") storage.save_sql(ts.get_area_classified(), "basics/area_classified") storage.save_sql(ts.get_st_classified(), "basics/st_classified") storage.save_sql(ts.get_hs300s(), "basics/hs300s") storage.save_sql(ts.get_sz50s(), "basics/sz50s") storage.save_sql(ts.get_zz500s(), "basics/zz500s") storage.save_sql(ts.get_terminated(), "basics/terminated") storage.save_sql(ts.get_suspended(), "basics/suspended") storage.save_sql(ts.get_sme_classified(), "basics/sme_classified") storage.save_sql(ts.get_gem_classified(), "basics/gem_classified")
@author: Administrator ''' import tushare as ts # 行业分类 ts.get_industry_classified() # 概念分类 ts.get_concept_classified() # 地域分类 ts.get_area_classified() # 获取沪深300当前成份股及所占权重 ts.get_hs300s() # 获取中小板股票数据，即查找所有002开头的股票 ts.get_sme_classified() # 获取创业板股票数据，即查找所有300开头的股票 ts.get_gem_classified() # 获取风险警示板股票数据，即查找所有st股票 ts.get_st_classified() # 获取上证50成份股 ts.get_sz50s() # 获取中证500成份股 ts.get_zz500s()
def syncZZ500S():
zz500s_df = ts.get_zz500s()
zz500s_df.to_csv(config.ZZ500_CodePath, encoding="utf-8")
saveDataFileByCode(zz500s_df)
print("sync and save ZZ500 done!")
def load_company_industry_info():
#下载加载行业分类数据
try:
rs=ts.get_industry_classified()
sql.write_frame(rs, "company_industry_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载公司行业分类信息ok")
except:
print("下载公司行业分类信息出错")
#下载加载概念分类数据
try:
rs=ts.get_concept_classified()
sql.write_frame(rs, "company_concept_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("载公司概念分类信息ok")
except:
print("下载公司概念分类信息出错")
#下载加载地域分类数据
try:
rs=ts.get_area_classified()
sql.write_frame(rs, "company_area_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载公司区域分类信息ok")
except:
print("下载公司区域分类信息出错")
#下载加载中小板分类数据
try:
rs=ts.get_sme_classified()
sql.write_frame(rs, "company_sme_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载中小板分类数据ok")
except:
print("下载中小板分类数据出错")
#下载加载创业板分类数据
try:
rs=ts.get_gem_classified()
sql.write_frame(rs, "company_gem_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载创业板分类数据ok")
except:
print("下载创业板分类数据出错")
#下载加载st板分类数据
try:
rs=ts.get_st_classified()
sql.write_frame(rs, "company_st_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载st板分类数据ok")
except:
print("下载st板分类数据出错")
#下载加载沪深300板分类数据
try:
rs=ts.get_hs300s()
sql.write_frame(rs, "company_hs300_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载加载沪深300板分类数据ok")
except:
print("下载加载沪深300板分类数据出错")
#下载加载上证50板分类数据
try:
rs=ts.get_sz50s()
sql.write_frame(rs, "company_sz50_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载加载上证50板分类数据ok")
except:
print("下载加载上证50板分类数据出错")
#下载加载中证500板分类数据
try:
rs=ts.get_zz500s()
sql.write_frame(rs, "company_zz500_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载加载中证500板分类数据ok")
except:
print("下载加载中证500板分类数据出错")
#下载加载终止上市分类数据
try:
rs=ts.get_terminated()
sql.write_frame(rs, "company_terminated_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载加载终止上市分类数据ok")
except:
print("下载加载终止上市分类数据出错")
#下载加载暂停上市分类数据
try:
rs=ts.get_suspended()
sql.write_frame(rs, "company_suspended_classified", con=conn_company_classified , flavor='mysql', if_exists='replace',index=True)
print("下载加载暂停上市分类数据ok")
except:
print("下载加载暂停上市分类数据出错")
cur.execute('show tables') # 罗列所有当前库里面的所有表格
tables = cur.fetchall()
selectab = re.compile(r'\w*\w*')
tabnames = selectab.findall(str(tables))
res = tab_name in tabnames
return res
stk_concept_class = ts.get_concept_classified()
stk_area_class = ts.get_area_classified()
stk_sme_class = ts.get_sme_classified()
stk_gem_class = ts.get_gem_classified()
stk_st_class = ts.get_st_classified()
stk_hs300_class = ts.get_hs300s()
stk_sz50_class = ts.get_sz50s()
stk_zz500_class = ts.get_zz500s()
stk_terminated_class = ts.get_terminated()
stk_sus_class = ts.get_suspended()
while True:
#hostname = input('Please enter the hostname: ')
pw = input('Please enter the password: '******'Please enter the number of datebase: ')
try:
conn = mysql.connector.connect(host='10.23.0.2',
port=3306,
user='******',
password=pw,
database=dbname)
cur = conn.cursor()
break
# 参见 https://github.com/sherlockhoatszx/Stock_Pickup_and_Reminding/issues/7
#filter_funcs = [stock_filter, ver_hammer,feature3]'''
filter_funcs = [feature3]
def spr(filter_func, begin):
"""
Main interface
:param filter_func: 股票筛选函数,返回 True or False
:param begin: beginning date
:return: a filtered stock list
"""
global count
# get full stock list
# print 'Getting stock list ...',
stock_list = ts.get_zz500s().code
# print 'Done: %s stocks' % len(stock_list)
# filtering process
count = 0
print "筛选方式 %s" % filter_func.__name__
return filter(lambda x: filter_func(x, begin), stock_list)
if __name__ == '__main__':
today = date.today().strftime('%Y-%m-%d').encode()
trx_date = ts.get_hist_data('sh').index
if today in trx_date:
# four days included today
_begin = trx_date[3].encode()
else:
def stock_list_update(self,type='all'):
'''
股票基本面板更新
:param type:
:return:
'''
type_list =['all',
'industry',
'concept',
'area',
'sme',
'gem',
'st',
'hs300s',
'ss50s',
'zz500s',
'terminated',
'suspended']
#输入类型检查
if not isinstance(type,str):
raise TypeError,'stock_type must be string'
elif isinstance(type,str) and type not in type_list:
raise ValueError,'%s is not a valid type' % (type)
#更新数据
if type == 'all':
temp = ts.get_stock_basics()
path = os.path.join(StockDataControler().stock_basis_init(),
'global.csv')
temp.to_csv(path)
return temp
if type == 'industry':
temp = ts.get_industry_classified()
#temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'industry.csv')
temp.to_csv(path)
if type == 'concept':
temp = ts.get_concept_classified()
#temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'concept.csv')
temp.to_csv(path)
if type == 'area':
temp = ts.get_area_classified()
#temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'area.csv')
temp.to_csv(path)
if type == 'sme':
temp = ts.get_sme_classified()
#temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'sme.csv')
temp.to_csv(path)
if type == 'gem':
temp = ts.get_gem_classified()
#temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'gem.csv')
temp.to_csv(path)
if type == 'st':
temp = ts.get_st_classified()
# temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'st.csv')
temp.to_csv(path)
if type == 'hs300s':
temp = ts.get_hs300s()
temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'hs300s.csv')
temp.to_csv(path)
if type == 'sz50s':
temp = ts.get_sz50s()
temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'sz50s.csv')
temp.to_csv(path)
if type == 'zz500s':
temp = ts.get_zz500s()
temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'zz500s.csv')
temp.to_csv(path)
if type == 'terminated':
temp = ts.get_terminated()
temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'terminated.csv')
temp.to_csv(path)
if type == 'suspended':
temp = ts.get_suspended()
temp['name'] = temp['name'].map(lambda x: x.encode('utf-8'))
path = os.path.join(StockDataControler().stock_basis_init(),
'suspended.csv')
temp.to_csv(path)
this file help to fill the database table, such as Classified
hs300 sz50 zz500
"""
import sys
sys.path.append('../publicstuff')
import tushare as ts
import pandas as pd
import config
from mongoModel import *
industry = ts.get_industry_classified()
concept = ts.get_concept_classified()
area = ts.get_area_classified()
hs300 = ts.get_hs300s()
sz50 = ts.get_sz50s()
zz500 = ts.get_zz500s()
res = pd.merge(area, industry, on=['code', 'name'])
res = pd.merge(res, concept, on=['code', 'name'])
# print(res)
# exit()
for stock in config.stolist:
current = res[res.code == stock]
print(current)
name = config.StocksList[stock]
area_ = list(set(list(current.area)))
if len(area_) > 0:
area_ = area_[0]
else:
area_ = 'None'
def zz500db():
zz500 = ts.get_zz500s()
print(zz500, sep=' ', end='\n', file=sys.stdout, flush=False)
zz500.to_sql('zz500_data', ENGINE)
