def query_zz500_stocks():
rs = bs.query_zz500_stocks()
while BaoStock.retried_num < BaoStock.RETRY_MAX_NUM and rs.error_code != '0':
sleep(BaoStock.RETRY_DELAY_S)
rs = bs.query_zz500_stocks()
BaoStock.retried_num += 1
assert('0' == rs.error_code)
BaoStock.retried_num = 0
return BaoStock.rs_to_list(rs)
def query_zz500_stocks():
rs = bs.query_zz500_stocks()
while Stock.retried_num < Stock.RETRY_MAX_NUM and rs.error_code != '0':
sleep(Stock.RETRY_DELAY_S)
rs = bs.query_zz500_stocks()
Stock.retried_num += 1
if '0' == rs.error_code:
Stock.retried_num = 0
return Stock.rs_to_list(rs)
def getStockList(type: str = "default", date: str = ""):
result = []
data: DataFrame
if type == "sz50":
data = BaoStock.query_sz50_stocks(date).get_data()
elif type == "hs300":
data = BaoStock.query_hs300_stocks(date).get_data()
elif type == "zz500":
data = BaoStock.query_zz500_stocks(date).get_data()
elif type == "default":
path = Path(__file__).parent.absolute()/"list.json"
file = open(str(path), "r", encoding="utf8")
return Json.loads(file.read())
else:
data = BaoStock.query_stock_basic().get_data()
keys = data.keys()
data = [x[1]
for x in BaoStock.query_stock_basic().get_data().iterrows()]
if type == "index":
data = [x for x in data if x["type"] == "2"]
elif type == "stock":
data = [x for x in data if x["type"] == "1"]
for row in data:
result.append({getStockListMap[key][0]: row[key]
for key in keys if key in getStockListMap})
return result
for row in data.iterrows():
result.append({getStockListMap[key][0]: row[1][key]
for key in data.keys() if key in getStockListMap})
return result
def getList(self):
stocks = []
# 获取中证500成分股
rs = bs.query_zz500_stocks()
print('query_zz500 error_code:' + rs.error_code)
print('query_zz500 error_msg:' + rs.error_msg)
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
stocks.append(rs.get_row_data())
# 获取中证500成分股
rs = bs.query_hs300_stocks()
print('query_hs300 error_code:' + rs.error_code)
print('query_hs300 error_msg:' + rs.error_msg)
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
stocks.append(rs.get_row_data())
result = pd.DataFrame(stocks, columns=rs.fields)
# 结果集输出到csv文件
self.makeDIR('./data')
result.to_csv('data/' + 'stockList.csv', index=False)
def get_history_companies(self) -> pd.DataFrame:
"""
Returns
-------
pd.DataFrame:
symbol date type
SH600000 2019-11-11 add
SH600000 2020-11-10 remove
dtypes:
symbol: str
date: pd.Timestamp
type: str, value from ["add", "remove"]
"""
bs.login()
today = pd.datetime.now()
date_range = pd.DataFrame(
pd.date_range(start="2007-01-15", end=today, freq="7D"))[0].dt.date
ret_list = []
col = ["date", "symbol", "code_name"]
for date in tqdm(date_range, desc="Download CSI500"):
rs = bs.query_zz500_stocks(date=str(date))
zz500_stocks = []
while (rs.error_code == "0") & rs.next():
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=col)
result["symbol"] = result["symbol"].apply(
lambda x: x.replace(".", "").upper())
result = self.get_data_from_baostock(date)
ret_list.append(result[["date", "symbol"]])
bs.logout()
return pd.concat(ret_list, sort=False)
def get_data_from_baostock(self, date) -> pd.DataFrame:
"""
Data source: http://baostock.com/baostock/index.php/%E4%B8%AD%E8%AF%81500%E6%88%90%E5%88%86%E8%82%A1
Avoid a large number of parallel data acquisition,
such as 1000 times of concurrent data acquisition, because IP will be blocked
Returns
-------
pd.DataFrame:
date symbol code_name
SH600039 2007-01-15 四川路桥
SH600051 2020-01-15 宁波联合
dtypes:
date: pd.Timestamp
symbol: str
code_name: str
"""
col = ["date", "symbol", "code_name"]
rs = bs.query_zz500_stocks(date=str(date))
zz500_stocks = []
while (rs.error_code == "0") & rs.next():
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=col)
result["symbol"] = result["symbol"].apply(
lambda x: x.replace(".", "").upper())
return result
def get_zz500_stocks():
rs = BS.query_zz500_stocks()
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
zz500_stocks.append(rs.get_row_data())
result = PD.DataFrame(zz500_stocks, columns=rs.fields)
print(result)
return result
def queryZZ500Stocks(date):
rs = bs.query_zz500_stocks(date=date)
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
return result
def bao_zz500_stocks() -> pd.DataFrame:
"""
获取沪深300成分股信息
:return:
"""
df = bao.query_zz500_stocks().get_data()
df['code'] = df['code'].apply(lambda x: ts_code(x))
df.set_index(keys='code', drop=False, inplace=True)
logging.info(colorama.Fore.YELLOW + '获取中证500成分股信息')
return df
def get_constituent_stock(i):
basepath = os.path.dirname(__file__)
basicpath = 'static/morestock/' + i + '-stock'
show_time = today_time()
param = request.form
show_code = ""
if param and (param['code'] or param['code'] == ""):
code = param['code']
show_code = stock_com(code)
if show_code == "":
# baostock相关设置
lg = bs.login()
if i == "hs300":
rs = bs.query_hs300_stocks()
elif i == "sz":
rs = bs.query_sz50_stocks()
elif i == "zz":
rs = bs.query_zz500_stocks()
result = res_data(rs)
# 创建个股文件夹
create_file(basicpath)
# 存储csv、excel、josn文件
parsed = save_all(basicpath, i, show_time, result, 'data')
# 从json获取数据(all)
all_len = len(parsed)
type_name = i + "Stock"
show_obj = {"data": parsed, "total": all_len, "type": type_name}
# baostock登出
bs.logout()
return show_obj
else:
jsonpath = basicpath + '/json/' + i + '_' + show_time + '.json'
joson_file = os.path.join(basepath, jsonpath)
jsonFlag = "0"
search_arr = []
with open(joson_file, 'r+', encoding="utf-8") as load_f:
load_file = load_f.read()
home_file = json.loads(load_file, strict=False)
if show_code == "-1":
search_arr = home_file['data']
elif show_code == "-2":
search_arr = []
else:
for item in home_file['data']:
if item['code'] == show_code or item[
'code_name'] == show_code:
jsonFlag = "1"
search_arr.append(item)
all_len = len(search_arr)
type_name = i + "Stock"
show_obj = {"data": search_arr, "total": all_len, "type": type_name}
return show_obj
def get_500_codes(self, date='2017-01-01'):
# 获取中证500成分股
rs = bs.query_zz500_stocks(date)
print('query_zz500 error_code:' + rs.error_code)
print('query_zz500 error_msg:' + rs.error_msg)
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
return result
def query_zz500_stocks(self, date = None):
'''
date：查询日期，格式XXXX-XX-XX，为空时默认最新日期。
'''
rs = bs.query_zz500_stocks(date = date)
self.log('query_zz500 error_msg:', rs)
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
return result
def main():
# 沪深300
rs = bs.query_hs300_stocks()
# 获取上证50成分股
rs1 = bs.query_sz50_stocks()
# 获取中证500成分股
rs2 = bs.query_zz500_stocks()
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
re = Combine(rs.get_row_data()[1])
if re:
datalist.append(re)
result = pd.DataFrame(datalist)
result.to_csv("/tmp/a.csv", index=False)
def bs_save_zz500_stocks():
'''
获取中证500成分股信息
:return:
'''
bs.login()
rs = bs.query_zz500_stocks()
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
engine = create_engine(
'mysql+pymysql://root:root@localhost:3306/stock_quant?charset=utf8')
result.to_sql('bs_zz500_stocks', engine, index=True)
bs.logout()
def get_zz500_stocks(date=today):
# 获取中证500成分股
rs = bs.query_zz500_stocks(date=date)
if rs.error_code != '0':
print('query_zz500 respond error_code::' + rs.error_msg)
login()
return get_zz500_stocks(date)
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
# 结果集输出到csv文件
return result
def dispatch_request(self):
post_data = request.get_json()
date = post_data.get('date', '')
attr_fields = post_data.get('attr_fields', None)
rs = bs.query_zz500_stocks(date=date)
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
zz500_stocks.append(rs.get_row_data())
fields = ['updateDate', 'code', 'code_name']
data = jsonWrapper(zz500_stocks, fields)
result = {
'code': 200 if rs.error_code == '0' else rs.error_code,
'data': list(map(lambda x: { attr: x.get(attr, '') for attr in attr_fields }, data)) if attr_fields else data,
'msg': rs.error_msg
}
self.logout()
return jsonify(result)
def get_zz500_stocks():
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#### 获取交易日信息 ####
rs = bs.query_zz500_stocks()
print('query_trade_dates respond error_code:' + rs.error_code)
print('query_trade_dates respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
bs.logout()
return result
def zz500_index_component(self):
file_path = './raw_data/zz500_stocks.csv'
if os.path.exists(file_path):
is_outdate = None
else:
is_outdate = True
# 获取中证500成分股
rs = bs.query_zz500_stocks()
print(f'zz500_index_component, error_code:{rs.error_code}')
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
row = rs.get_row_data()
if is_outdate is None:
is_outdate = self.check_index_component_outdate(
row[0], file_path)
# is_outdate = True
zz500_stocks.append(row)
elif is_outdate == True:
# 获取一条记录，将记录合并在一起
zz500_stocks.append(row)
else:
return
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
result = result.set_index("code")
# result = self.get_stock_industry_from_dongcai(result)
for code in result.index.values.tolist():
stock_info = self.get_stock_detail(code)
result.loc[code, 'url'] = stock_info['url']
result.loc[code, 'industry'] = stock_info['industry']
result.loc[code, 'pe_max'] = stock_info['pe_max']
result.loc[code, 'pe_mean'] = stock_info['pe_mean']
result.loc[code, 'pe_min'] = stock_info['pe_min']
# 结果集输出到csv文件
result.to_csv(file_path, encoding="gbk")
def get_zz500_stocks():
"""
获取中证500成分股
"""
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
# 获取中证500成分股
rs = bs.query_zz500_stocks()
print('query_zz500 error_code:' + rs.error_code)
print('query_zz500 error_msg:' + rs.error_msg)
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
dtype = {
'updateDate': String(10),
'code': String(9),
'code_name': String(10)
}
result.to_sql('odl_bs_zz500_stocks',
engine,
schema=CQ_Config.DB_SCHEMA,
if_exists='replace',
index=False,
dtype=dtype)
# 登出系统
bs.logout()
def query_zz500_stocks(date=None):
"""
中证500成分股
方法说明：通过API接口获取中证500成分股信息，更新频率：每周一更新。
返回类型：pandas的DataFrame类型。
date：查询日期，格式XXXX-XX-XX，为空时默认最新日期。
"""
lg = bs.login()
if lg.error_code != '0':
logger.error('login respond error_msg:' + lg.error_msg)
rs = bs.query_zz500_stocks(date)
if rs.error_code != '0':
logger.error('query_zz500_stocks respond error_msg:' +
rs.error_msg)
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
zz500_stocks.append(rs.get_row_data())
result = pd.DataFrame(zz500_stocks, columns=rs.fields)
bs.logout()
return result
def download_sample_stocks(sample_name='sz50'):
if sample_name == 'sz50':
filename = FILE_PATH + "sz50_stocks.csv"
rs = bs.query_sz50_stocks()
elif sample_name == 'hs300':
filename = FILE_PATH + "hs300_stocks.csv"
rs = bs.query_hs300_stocks()
elif sample_name == 'zz500':
filename = FILE_PATH + "zz500_stocks.csv"
rs = bs.query_zz500_stocks()
elif sample_name == 'all':
filename = FILE_PATH + "all_stocks.csv"
# 取前七天的成分股信息，如果取当前天的，可能当天没有更新
trade_cal = get_trade_cal()
trade_day = trade_cal[(
trade_cal['is_trading_day'] == 1)]['calendar_date'].values
rs = bs.query_all_stock()
stocks = []
while (rs.error_code == '0') & rs.next():
stocks.append(rs.get_row_data())
result = pd.DataFrame(stocks, columns=rs.fields)
result.to_csv(filename, encoding="gbk", index=False)
logger.debug("将成分股%s保存至%s" % (sample_name, filename))
def get_hs300_zz500_data():
# 获取沪深300和中证500成分股
rs_hs300 = bs.query_hs300_stocks("2020-07-30")
rs_zz500 = bs.query_zz500_stocks("2020-07-30")
print('query_hs300 error_code:' + rs_hs300.error_code)
print('query_hs300 error_msg:' + rs_hs300.error_msg)
print('query_zz500 error_code:' + rs_zz500.error_code)
print('query_zz500 error_msg:' + rs_zz500.error_msg)
# 打印结果集
hs300_stocks = []
zz500_stocks = []
while (rs_hs300.error_code == '0') & rs_hs300.next():
# 获取一条记录，将记录合并在一起
hs300_stocks.append(rs_hs300.get_row_data())
while (rs_zz500.error_code == '0') & rs_zz500.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs_zz500.get_row_data())
stocks_result = pd.DataFrame(hs300_stocks + zz500_stocks,
columns=rs_hs300.fields)
# stocks_result.to_csv("./stocks-pool/hs300_zz500_stocks.csv", encoding="gbk", index=False)
return stocks_result
import baostock as bs
import pandas as pd
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#### 获取沪深A股历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节；“分钟线”参数与“日线”参数不同。
# 分钟线指标：date,time,code,open,high,low,close,volume,amount,adjustflag
rs = bs.query_zz500_stocks()
print('query_sz50 error_code:' + rs.error_code)
print('query_sz50 error_msg:' + rs.error_msg)
# 打印结果集
sz50_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
sz50_stocks.append(rs.get_row_data())
list_50 = []
for i in range(len(sz50_stocks)):
list_50.append(sz50_stocks[i][1])
for i in list_50:
rs = bs.query_history_k_data_plus(
i,
"date,time,code,open,high,low,close,volume,amount,adjustflag",
start_date='2015-01-01',
import baostock as bs
import pandas as pd
import datetime
startdate = (datetime.datetime.now()-datetime.timedelta(days=50)).strftime("%Y-%m-%d")
enddate = datetime.datetime.now().strftime("%Y-%m-%d")
# baostock login
lg = bs.login()
# print login error msg
if lg.error_code != '0':
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
zz500_data = bs.query_zz500_stocks()
# 打印结果集
zz500_stocks = []
while (zz500_data.error_code == '0') & zz500_data.next():
# 获取一条记录，将记录合并在一起
row_data = zz500_data.get_row_data()
code = row_data[1]
# query history k data
rs = bs.query_history_k_data_plus(code,
"date,code,close,tradeStatus,open,volume",
start_date=startdate, end_date=enddate,
frequency="d", adjustflag="3")
# handle k data
result_list = []
while (rs.error_code == '0') & rs.next():
def getA50StockList(self):
try:
self.initMysqlConn()
# 查询当前所有正常上市交易的股票列表
df = ts.get_sz50s() # 不能用了？返回值是none
# 登陆系统
lg = bs.login()
rs = bs.query_sz50_stocks()
# 打印结果集
sz50_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
sz50_stocks.append(rs.get_row_data())
result = pd.DataFrame(sz50_stocks, columns=rs.fields)
# result['code']='\t'+result['code'].str[3:]
# ex=os.path.isfile('sz50s')
# if ex==False:
result.to_csv('G:\\stockData\\sz50s.csv',
encoding='utf_8_sig') #指数直接覆盖掉
rs = bs.query_hs300_stocks()
# 打印结果集
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
hs300_stocks.append(rs.get_row_data())
result1 = pd.DataFrame(hs300_stocks, columns=rs.fields)
# result1['code']=result1['code'].str[3:]
# ex=os.path.isfile('sz50s')
# if ex==False:
result1.to_csv('G:\\stockData\\hs300s.csv',
encoding='utf_8_sig') #指数直接覆盖掉
# result1.to_csv('G:\\stockData\\hs300s.csv',encoding='gbk')#指数直接覆盖掉
rs = bs.query_zz500_stocks()
# 打印结果集
zz500_stocks = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
zz500_stocks.append(rs.get_row_data())
result2 = pd.DataFrame(zz500_stocks, columns=rs.fields)
# result2['code']=result2['code'].str[3:]#没有t的话会丢掉前面的0
# ex=os.path.isfile('sz50s')
# if ex==False:
result2.to_csv('G:\\stockData\\zz500s.csv',
encoding='utf_8_sig') #指数直接覆盖掉
# 登出系统
bs.logout()
print('getsz50StockList & hs300s & zz500s Finished!')
return
c_len = 0
if (df == None):
df = result
c_len = df.shape[0] # 行数
except Exception as aa:
print(aa)
print('get error')
for j in range(c_len): # 按行读取股票列表
resu0 = list(df.loc[c_len - 1 - j])
resu = []
for k in range(len(resu0)): # 读取股票的字段
if str(resu0[k]) == 'nan':
resu.append(-1)
else:
resu.append(resu0[k])
try:
sql_insert = "INSERT INTO sz50StockList(date,code,name) VALUES ('%s', '%s', '%s')" % (
str(resu[0]), str(resu[1]), str(resu[2]))
self.cursor.execute(sql_insert)
self.db.commit()
except Exception as err:
continue
self.cursor.close()
self.db.close()
# 登出系统
bs.logout()
print('getsz50StockList Finished!')
def zz500(self):
'''中证500'''
return get_data(bs.query_zz500_stocks())
def zz500_stocks(self):
self.getStockInstance()
rs = bs.query_zz500_stocks()
self._test_rs(rs)
return rs
