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 query_basic(code='', code_name=''):
assert(isinstance(code, str))
rs = bs.query_stock_basic(code, code_name)
while Stock.retried_num < Stock.RETRY_MAX_NUM and rs.error_code != '0':
sleep(Stock.RETRY_DELAY_S)
rs = bs.query_stock_basic(code, code_name)
Stock.retried_num += 1
if '0' == rs.error_code:
Stock.retried_num = 0
return Stock.rs_to_list(rs)
def compute_Avg_EarningRate():
# 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)
# 获取全部证券基本资料
rs = bs.query_stock_basic()
result = pd.DataFrame()
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
code = rs.get_row_data()[0]
df = get_closeprice(code)
if result.empty:
result = df
else:
result = result.append(df)
result = result[result['open'] != '']
result['open'] = result['open'].astype(float)
result['close'] = result['close'].astype(float)
result['avgEarningRate'] = (
result['close'] /
result['open']).apply(lambda x: math.pow(x, 1 / 3) - 1)
result = result.sort_values(by=['avgEarningRate'], ascending=False)
result.to_csv("D:\\Avg_Earning_Rate_data.csv", encoding="gbk", index=False)
result[:10]['avgEarningRate'].plot(title='Avg Earning Rate', kind='bar')
plt.show()
# 登出系统
bs.logout()
def getStockInfo():
# 登陆系统
lg = bstk.login()
# 显示登陆返回信息
print('login respond error_msg:' + lg.error_msg)
# 获取证券基本资料
data_list = []
# stock_list = getCode()
# rs = bstk.query_stock_basic(code="sh.{}".format(stock_code))
rs = bstk.query_stock_basic()
# 打印结果集
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)
# 登出系统
bstk.logout()
df = result
df.columns = [
'stock_code', 'stock_name', 'ipo_date', 'out_date', 'stock_type',
'stock_status'
]
df['out_date'] = result['out_date'].apply(lambda x: np.NaN
if x == '' else x)
return df
def getBasicInfoByName(name):
# 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)
# 获取证券基本资料
rs = bs.query_stock_basic(code_name=name) # 支持模糊查询
print('query_stock_basic respond error_code:' + rs.error_code)
print('query_stock_basic respond error_msg:' + rs.error_msg)
obj = {}
if (rs.error_code != '0'):
obj['error_code'] = rs.error_code
obj['error_msg'] = rs.error_msg
return obj
#print(type(rs))#<class 'baostock.data.resultset.ResultData'>
#打印结果集
data_list = []
while (rs.error_code == '0' and rs.next()):
#获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
obj['error_code'] = '0'
obj['data_list'] = data_list
#登出系统
bs.logout()
return obj
def compute_avg_earning_rate():
# 登录系统
lg = bs.login()
# 显示登录返回信息
print("login response error_code:{},error_msg:{}".format(
lg.error_code, lg.error_msg))
# 获取全部证券基本资料
rs = bs.query_stock_basic()
result = pd.DataFrame()
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并
code = rs.get_row_data()[0]
df = get_close_price(code)
if df is None:
continue
if df.empty:
result = df
else:
result = result.append(df)
result = result[result['open'] != '']
result['open'] = result['open'].astype(float)
result['close'] = result['close'].astype(float)
result['avgEarningRate'] = (
result['close'] /
result['open']).apply(lambda x: math.pow(x, 1 / 3) - 1)
result = result.sort_values(by=['avgEarningRate'], ascending=False)
result.to_csv("Avg_Earning_Rate_data.csv", encoding="utf-8", index=False)
result[:10]['avgEarningRate'].plot(title='Avg Earning Rate', kind='bar')
plt.show()
# 登出系统
bs.logout()
def getIPO(code):
re = 0
try:
re = bs.query_stock_basic(code)
re = re.get_row_data()[2]
except:
pass
return re
def compute_total_ROE():
# 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)
# 获取全部证券基本资料
rs = bs.query_stock_basic()
# rs = bs.query_stock_basic(code_name="浦发银行") # 支持模糊查询
print('query_stock_basic respond error_code:' + rs.error_code)
print('query_stock_basic respond error_msg:' + rs.error_msg)
result_profit = pd.DataFrame()
jingduCount = 0
startt = time.time()
yearSE = (2015, 2020)
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
code = rs.get_row_data()[0]
jingduCount += 1
newt = time.time()
lg = bs.login()
print('\r',
"进度:%.2f%% | Code:%s | End@:%s | year:%d~%d" %
(100 * jingduCount / len(rs.data), code,
time.asctime(
time.localtime(newt + (newt - startt) / jingduCount *
(len(rs.data) - jingduCount))), yearSE[0],
yearSE[1]),
end='')
for year in range(yearSE[0], yearSE[1]):
for season in range(1, 5):
df = computeROE(code, year, season)
if df.empty:
continue
else:
if result_profit.empty:
result_profit = df
else:
result_profit = result_profit.append(df)
# 原始数据存储
result_profit.to_csv("./data3/dupont_data_ROE_" + str(yearSE) + ".csv",
encoding="utf-8",
index=False)
# 筛选有用数据
# result = result_profit[['code', 'dupontROE']]
# result = result[result['dupontROE'] != '']
# result['dupontROE'] = result['dupontROE'].astype(float)
# series_mean = result.groupby(by=['code'])['dupontROE'].mean()
# series_std = result.groupby(by=['code'])['dupontROE'].std()
# df2 = pd.DataFrame({'mean': series_mean.data, 'std':series_std.data},\
# columns=['mean', 'std'], index=series_mean.index)
# df2 = df2.sort_values(['mean'])
# df2.to_csv("./data3/dupont_data_sorted_by_roe.csv", encoding="gbk",index=True)
# 登出系统
bs.logout()
def appendCompanyDetail(self, company):
rs = bs.query_stock_basic(code_name=company.short_name)
print('query_stock_industry {} {} :', rs.error_code, rs.error_msg)
if rs.error_code == '0':
company.ipo_date = rs.get_row_data()[2]
return company
def getStockBasic(code):
# 获取证券基本资料
rs = bs.query_stock_basic(code=code)
# 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())
return pd.DataFrame(data_list, columns=rs.fields)
def query_stock_basic(self,code = None, code_name = None):
'''
code：A股股票代码，sh或sz.+6位数字代码，或者指数代码，如：sh.601398。sh：上海；sz：深圳。可以为空；
code_name：股票名称，支持模糊查询，可以为空。
'''
if code:
rs = bs.query_stock_basic(code=code)
else:
rs = bs.query_stock_basic(code_name=code_name) # 支持模糊查询
self.log('query_stock_basic respond error_msg:', rs)
# 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)
return result
def download_data(date):
data_df = download_data_by_day(date)
data_df = filter_data(data_df)
# print(data_df)
# rs_df = pd.DataFrame()
# begin_date = datetime.datetime.strptime(date, "%Y-%m-%d").date()
# end_date = begin_date + datetime.timedelta(days=1)
# if end_date <= datetime.date.today():
# for row_index, row in data_df.iterrows():
# k_rs = get_history_k_data(row.code, end_date.strftime("%Y-%m-%d"))
# #print(k_rs.get_data().loc[0].low,row.low,row.code)
# #print(float(k_rs.get_data().loc[0].low)-row.low,row.code)
# rs_df = rs_df.append(row,ignore_index=False)
# rs_df = rs_df.append(k_rs.get_data())
#
print("当日过滤数据", data_df)
write_to_excel(
data_df, 'd:\\output-' + datetime.datetime.now().strftime('%Y-%m') +
'-filter.xlsx', date)
# 过滤上个交易日相关指标
begin_date = datetime.datetime.strptime(date, "%Y-%m-%d").date()
basic_df = pd.DataFrame()
for row_index, row in data_df.iterrows():
if begin_date.isoweekday() == 1:
day_step = -3
else:
day_step = -1
# 上一个工作日
last_work_day = begin_date + datetime.timedelta(days=day_step)
last_rs = get_history_k_data(row.code,
last_work_day.strftime("%Y-%m-%d"))
last_st = last_rs.get_data().loc[0]
# 当日最低价小于等于上日最低价，当日收盘价高于上日最高价,上一日涨幅或跌幅<10%
if row.low <= float(last_st.low) and row.close > float(
last_st.high) and abs(
float(last_st.close) - float(last_st.open)) < 10:
# 名称
basic_rs = bs.query_stock_basic(code=row.code)
basic_df = basic_df.append(basic_rs.get_data())
data_df = pd.merge(data_df, basic_df, how='inner', on=['code'])
# 改变数据列的顺序
data_df = data_df[[
'date', 'code', 'code_name', 'pctChg', 'amplitude', 'turn', 'close',
'open', 'high', 'low', 'amount', 'peTTM'
]]
# 输出
print(data_df)
write_to_excel(
data_df,
'd:\\output-' + datetime.datetime.now().strftime('%Y-%m') + '.xlsx',
date)
def baseinfo(code):
filename = "data/baseinfo_{}.csv".format(code)
if os.path.exists(filename):
result = pd.read_csv(filename)
else:
rs = bs.query_stock_basic(code="sh.601012")
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.to_csv(filename, encoding="utf-8", index=False)
return result
def get_single_stock_ipo_date(code):
#### 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)
# 获取证券基本资料
rs = bs.query_stock_basic(code)
res_df = rs.get_data()
# 登出系统
bs.logout()
# print(res_df['ipoDate'][0])
return res_df['ipoDate'][0]
def queryStockName(stockCode):
customLogin()
#查股票名字
#返回示例数据
#code code_name ipoDate outDate type status
#sh.600000 浦发银行 1999-11-10 1 1
rs = bs.query_stock_basic(code=stockCode)
stockName = None
if (rs.error_code == '0') & rs.next():
stockName = rs.get_row_data()[1]
if stockName == None:
raise RuntimeError("无此股票代码：", stockCode)
return stockName
def getCode():
lg = bs.login()
rs = bs.query_stock_basic()
# rs = bs.query_all_stock()
print('query_all_stock respond error_code:'+rs.error_code)
print('query_all_stock respond error_msg:'+rs.error_msg)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
bs.logout()
return data_list
def get_bao_PE(yearSE = '2019-03-24'):
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股估值指标(日频)数据 ####
# peTTM 滚动市盈率
# psTTM 滚动市销率
# pcfNcfTTM 滚动市现率
# pbMRQ 市净率
rsAll = bs.query_stock_basic ()
jingduCount = 0
startt = time.time()
result_list = []
while (rsAll.error_code == '0') & rsAll.next():
# 获取一条记录，将记录合并在一起
code = rsAll.get_row_data()[0]
jingduCount += 1
newt = time.time()
print('\r',
"进度:%.2f%% | Code:%s | End@:%s | date:%s" % (100*jingduCount/len(rsAll.data), code,
time.asctime( time.localtime(newt+(newt - startt)/jingduCount*(len(rsAll.data) - jingduCount))),yearSE),end='')
rs = bs.query_history_k_data_plus(str(code),
"date,code,close,peTTM,pbMRQ,psTTM,pcfNcfTTM",
start_date=yearSE, end_date=yearSE,
frequency="d", adjustflag="1")
#### 打印结果集 ####
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
lrs = rs.get_row_data()
if(len(lrs) > 3 and lrs[3] != ''
and float(lrs[3]) > 0.000001):
result_list.append(lrs)
break
if len(result_list) == 0:
print('no PE data on ', yearSE)
return 0
result = pd.DataFrame(result_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("./data3/history_PE_"+yearSE+".csv", encoding="utf-8", index=False)
#### 登出系统 ####
bs.logout()
return 1
def bs_save_stock_basic():
'''
获取股票和指数
:return:
'''
bs.login()
rs = bs.query_stock_basic()
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_stock_basic', engine, index=True)
bs.logout()
def get_stock_basic(type, save=False):
'''
获取所有股票基本信息，并且返回type类型的数据
:param type: 选择希望返回的数据
:param save: 选择是否保存股票基本信息
:return: 返回type类型的数据
'''
stock_basic = bs.query_stock_basic()
res = pd.DataFrame(stock_basic.data, columns=stock_basic.fields)
if save:
res.to_csv("D:\quant\BaoStock\data\stock_basic.csv", index=False)
if type == 'code-ipoDate':
return res['code'].values, res['ipoDate'].values
else:
return res
def get_stock_basic():
bs.login()
rs = bs.query_stock_basic()
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
data = pd.DataFrame(data_list, columns=rs.fields)
data.rename(columns={
'code_name': 'name',
'ipoDate': 'ipo_date',
'outDate': 'out_date'
},
inplace=True)
bs.logout()
return data
def get_stock_basic():
"""
获取最新BS-A股股票列表
"""
#清空原有数据
BS_Stock_Basic.del_all_date()
# 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)
# 获取证券基本资料
rs = bs.query_stock_basic()
# rs = bs.query_stock_basic(code_name="浦发银行") # 支持模糊查询
print('query_stock_basic respond error_code:' + rs.error_code)
print('query_stock_basic 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)
result['updated_on'] = datetime.now()
result['status'] = [
None if x == "" else bool(get_int_from_str(x))
for x in result["status"]
]
result['ipoDate'] = [
convert_to_date(x, '%Y-%m-%d') for x in result.ipoDate
]
result['outDate'] = [
convert_to_date(x, '%Y-%m-%d') for x in result.outDate
]
result['ts_code'] = [convert_to_tscode(x) for x in result.code]
# 输出结果集
result.to_sql('odl_bs_stock_basic',
engine,
schema=CQ_Config.DB_SCHEMA,
if_exists='append',
index=False)
# 登出系统
bs.logout()
def get_stock_basic(_code):
'''
code: 证券代码
code_name: 证券名称
ipoDate: 上市日期
outDate: 退市日期
type: 证券类型（1，股票；2，指数；3，其他）
status: 上市状态（1，上市；0，退市）
'''
rs = BS.query_stock_basic(code=_code)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = PD.DataFrame(data_list, columns=rs.fields)
print(result)
return result
def create(self, code):
rsbasic = bs.query_stock_basic(code)
if len(rsbasic.data) > 0:
data_basic_list = []
while (rsbasic.error_code == '0') & rsbasic.next():
data_basic_list.append(rsbasic.get_row_data())
result_basic = pd.DataFrame(data_basic_list,
columns=rsbasic.fields)
# print(result_basic.code)
# print(result_basic.to_dict()['code_name'][0])
fields = "date,code,open,high,low,close"
rs = bs.query_history_k_data_plus(code,
fields,
start_date=self.start_date,
end_date=self.end_date,
frequency="d",
adjustflag="2")
# frequency="d"取日k线，adjustflag="3"默认不复权，
# 1：后复权；2：前复权
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)
result.index = pd.to_datetime(result.date)
y = list(result.loc[:, ['open', 'close', 'low', 'high']].values)
x = list(result.index.strftime('%Y%m%d'))
kline = Kline(result_basic.to_dict()['code_name'][0],
title_text_size=15)
kline.add("",
x,
y,
is_datazoom_show=False,
mark_line=["average"],
mark_point=["max", "min"],
mark_point_symbolsize=60,
mark_line_valuedim=['highest', 'lowest'])
kline.render("baostock/%s.gif" % (code))
kline
else:
print("empty ----> " + rsbasic.code)
def get_basic_code_info(self, kechuang=False):
rs = bs.query_stock_basic()
print('query_stock_basic 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)
# 结果集输出到csv文件
# result.to_csv("D:/stock_basic.csv", encoding="gbk", index=False)
result['only_code'] = result['code'].str.lower().str.split('.').str[1]
result_stock = result[result['type'] == '1']
if kechuang:
pass
else:
result_stock = result_stock[(result_stock['only_code'].str[:2] !=
'30')]
# result_stock[('st' not in result_stock['code_name'].str.lower() )]
# result_stock['code_name']=result_stock['code_name'].str.lower()
# result_stock['st股'] = result_stock['code_name'].apply(lambda x: True if 'st' in str(x) else False )
# print(result_stock[result_stock['st股'] == False])
# result[((result['code'].str[:2] == 'sh')&(result['only_code'].str[:2] == '60'))|((result['code'].str[:2] == 'sz')&(result['only_code'].str[:2] == '00'))])
result_stock.reset_index(drop=True, inplace=True)
# 获取行业分类数据
rs = bs.query_stock_industry()
print('query_stock_industry error_code:' + rs.error_code)
print('query_stock_industry respond error_msg:' + rs.error_msg)
# 打印结果集
industry_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
industry_list.append(rs.get_row_data())
result2 = pd.DataFrame(industry_list, columns=rs.fields)
result = pd.merge(
result_stock,
result2[['code', 'industry', 'industryClassification']],
how='left',
on=['code'])
# print(result.keys())
# exit()
return result
def download_k_line_csv_data(code):
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”不包含指数。
# 分钟线指标：date,time,code,open,high,low,close,volume,amount,adjustflag
# 周月线指标：date,code,open,high,low,close,volume,amount,adjustflag,turn,pctChg
yesterday = date.today() + timedelta(days=-1)
d = yesterday.strftime("%Y-%m-%d")
rs = bs.query_history_k_data_plus(
code,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date='2017-07-01',
end_date=d,
frequency="d",
adjustflag="3")
# print('query_history_k_data_plus respond error_code:' + rs.error_code)
# print('query_history_k_data_plus 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)
rsb = bs.query_stock_basic(code="")
# data_list1 = []
# while (rsb.error_code == '0') & rsb.next():
# # 获取一条记录，将记录合并在一起
# data_list1.append(rsb.get_row_data())
#
# filename=""
# for data in data_list1:
# if data[0] == code:
# print(data)
filename = "D:\\stock\\astock\\" + code + '-' + time.strftime(
"%Y-%m-%d-%H%M%S", time.localtime()) + '.csv'
#### 结果集输出到csv文件 ####
result.to_csv(filename, index=False)
print('Stock coed：' + code + ' data download done ,file path :' + filename)
#### 登出系统 ####
bs.logout()
def downloadAllStocks(tradeDate):
customLogin()
# set_trace()
# stock_rs = bs.query_all_stock(tradeDate)
stock_rs = bs.query_stock_basic()
stock_df = stock_rs.get_data()
dataList = []
for index,stock in stock_df.iterrows():
stockObj = Stock(stock["code"], stock["code_name"])
stockObj.stockType = stock["type"]
dataList.append(stockObj.__dict__)
mydb = dbutil.connectDB()
mydb["Stock"].delete_many({})
mydb["Stock"].insert_many(dataList)
customLogout()
return True
def getAllStock(self):
BsUtils.login()
rs = bs.query_all_stock(self.date)
rs_detail = bs.query_stock_basic()
print('query_all_stock respond error_code:' + rs.error_code)
print('query_all_stock respond error_msg:' + rs.error_msg)
print('query_stock_basic respond error_code:' + rs_detail.error_code)
print('query_stock_basic respond error_msg:' + rs_detail.error_msg)
data, data_detail = [], []
while (rs.error_code == '0') & rs.next():
data.append(rs.get_row_data())
while (rs_detail.error_code == '0') & rs_detail.next():
data_detail.append(rs_detail.get_row_data())
bs.logout()
self.saveToDB('all_stock_code', rs.fields, data, self.date)
self.saveToDB('all_stock_basic', rs_detail.fields, data_detail,
self.date)
return data, rs.fields
def dispatch_request(self):
post_data = request.get_json()
code = post_data.get('code', '')
code_name = post_data.get('code_name', '')
attr_fields = post_data.get('attr_fields', None)
rs = bs.query_stock_basic(code=code, code_name=code_name)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
fields = ['code', 'code_name', 'ipoDate', 'outDate', 'type', 'status']
data = jsonWrapper(data_list, 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 update_ipodate():
data_list = []
lg = bs.login()
shobj = AStocksHeader.objects.all()
for obj in shobj:
rs = bs.query_stock_basic(code_name=obj.stock_name)
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
for index, item in enumerate(data_list):
sb = AStocksHeader.objects.filter(stock_name=item[1]).first()
if sb:
print(index, item[1])
sb.ipodate = item[2]
if item[3] != '':
sb.outdate = item[3]
sb.isdelisted = True
sb.save()
print('update finished...')
bs.logout()
def update_stock_all(self):
bs.login()
rs = bs.query_stock_basic()
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())
data = pd.DataFrame(data_list, columns=rs.fields)
# 创建会话
session = sqlUtils.get_sqlalchemy_session()
d_len = data.shape[0]
for i in range(d_len):
row = data.iloc[i]
stock_basic = StockBasic.tranfer(row)
session.merge(stock_basic)
session.commit()
session.close()
bs.logout()
print("股票列表已更新")
