f = open("stock_code_sz.txt")
sz = f.read()
f.close()
sh_code = re.findall(r'[(](.*?)[)]', sh)
sz_code = re.findall(r'[(](.*?)[)]', sz)
print(sh_code)
print(sz_code)
for i in range(0, int(len(sz_code))):
rs = bs.query_history_k_data_plus(
"sz." + sz_code[i],
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date='2019-12-01',
end_date='2020-04-07',
frequency="d",
adjustflag="3") # frequency="d"取日k线，adjustflag="3"默认不复权
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)
# 结果集输出到csv文件
def stock_analysis():
bs.login()
with open(
os.path.join("..\\data\\industry_insight",
"_".join(keywords) + ".csv"), "r") as source_f:
with open(
os.path.join("..\\data\\industry_insight",
"_".join(keywords) + "_result.csv"),
"w") as result_f:
first_line = True
for source_line in source_f:
if first_line:
result_f.write(
source_line[:-2] +
",peTTM(滚动市盈率),psTTM(滚动市销率),pcfNcfTTM(滚动市现率),pbMRQ(市净率),2017MBRevenue, 2017netProfit, 2018MBRevenue, 2018netProfit\n"
)
first_line = False
continue
stock_number = get_full_stock_number(source_line.split(",")[0])
# source info
result_f.write(source_line.replace("\n", ""))
# pe and ps
rs = bs.query_history_k_data_plus(
stock_number,
"date,code,peTTM,pbMRQ,psTTM,pcfNcfTTM",
start_date='2019-04-04',
end_date='2019-04-04',
frequency="d",
adjustflag="3")
result_list = []
while (rs.error_code == '0') & rs.next():
result_list.append(rs.get_row_data())
result = pd.DataFrame(result_list, columns=rs.fields)
result_f.write("," + ",".join(result.loc[0][-4:]))
# 2018 and 2019 finance
try:
rs_profit = bs.query_profit_data(code=stock_number,
year=2017,
quarter=4)
profit_list = []
while (rs_profit.error_code == '0') & rs_profit.next():
profit_list.append(rs_profit.get_row_data())
result_profit = pd.DataFrame(profit_list,
columns=rs_profit.fields)
result_f.write("," +
str(result_profit.iloc[0]["MBRevenue"]) +
"," +
str(result_profit.iloc[0]["netProfit"]))
rs_profit = bs.query_profit_data(code=stock_number,
year=2018,
quarter=4)
profit_list = []
while (rs_profit.error_code == '0') & rs_profit.next():
profit_list.append(rs_profit.get_row_data())
result_profit = pd.DataFrame(profit_list,
columns=rs_profit.fields)
result_f.write("," +
str(result_profit.iloc[0]["MBRevenue"]) +
"," +
str(result_profit.iloc[0]["netProfit"]))
except IndexError: # 有些18年报还没出
pass
result_f.write("\n")
# break # debuging
bs.logout()
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_history_k_data_plus(
"sz.300001",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date='2019-01-01',
end_date='2020-01-12',
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)
# print(type(result))
#### 结果集输出到csv文件 ####
def download_data(stock_index, start_date, end_date):
data_dir = data_dict.get("baostock")
lg = bs.login(user_id="anonymous", password="******")
try:
if stock_index[0:2] == "60":
stock_index_in = "sh." + stock_index
else:
stock_index_in = "sz." + stock_index
df2 = bs.query_history_k_data_plus(
stock_index_in,
"date,code,open,high,low,close,volume,amount,turn,tradestatus,pctChg,peTTM,psTTM,pcfNcfTTM,pbMRQ,isST",
start_date=start_date,
end_date=end_date,
frequency="d",
adjustflag="1")
save_file = os.path.join(data_dir, stock_index + ".csv")
df3 = df2.get_data()
#print(df3)
df3["open"] = [
np.round(float(x), 2) if x != '' else -999
for x in df3["open"].tolist()
]
df3["high"] = [
np.round(float(x), 2) if x != '' else -999
for x in df3["high"].tolist()
]
df3["close"] = [
np.round(float(x), 2) if x != '' else -999
for x in df3["close"].tolist()
]
df3["low"] = [
np.round(float(x), 2) if x != '' else -999
for x in df3["low"].tolist()
]
df3["amount"] = [
int(float(x)) if x != '' else -999 for x in df3["amount"].tolist()
]
df3["turn"] = [
np.round(float(x), 3) if x != '' else -999
for x in df3["turn"].tolist()
]
df3["pctChg"] = [
np.round(float(x), 3) if x != '' else -999
for x in df3["pctChg"].tolist()
]
df3["peTTM"] = [
np.round(float(x), 3) if x != '' else -999
for x in df3["peTTM"].tolist()
]
df3["psTTM"] = [
np.round(float(x), 3) if x != '' else -999
for x in df3["psTTM"].tolist()
]
df3["pbMRQ"] = [
np.round(float(x), 3) if x != '' else -999
for x in df3["pbMRQ"].tolist()
]
df3["stock_index"] = [
x.split(".")[1] for x in df3["code"].values.tolist()
]
df3["dt"] = df3["date"]
df3["volume"] = [
int(x) if x != '' else -999 for x in df3["volume"].tolist()
]
#df3 = df3[["stock_index","dt","open","high","close","low","volume"]]
df3.to_csv(save_file, index=0)
except Exception as e:
pass
print("error")
print(e)
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。
rs = bs.query_history_k_data_plus(
"sh.600000",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date='2017-07-01',
end_date='2017-12-31',
frequency="d",
adjustflag="2")
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)
#### 结果集输出到csv文件 ####
result.to_csv("history_A_stock_k_data.csv", index=False)
print(result)
import baostock as bs
import pandas as pd
import pickle
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
rs = bs.query_history_k_data_plus('sh.000016',
"date,code,open,high,low,close,volume,amount,adjustflag",
start_date='2010-01-01', end_date='2020-01-31',
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)
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)
#file=open('benchmark_sz50.pickle','wb')
#pickle.dump(result,file)
#file.close()
df.to_csv(保存路径, encoding='utf_8_sig', index=False)
#### 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”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
rs = bs.query_history_k_data_plus("sz.000778",
"date,code,time,open,close",
start_date='2021-03-01',
end_date='2021-03-03',
frequency="5",
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())
df = pd.DataFrame(data_list, columns=rs.fields)
print(df)
保存路径 = 'C:/Users/YcAllenEffy/Desktop/222.csv'
保存csv文件(保存路径, df)
import com.dateUtils as du
import numpy as np
#### 登陆系统 ####
print("-----登陆系统：")
lg = bs.login(user_id="anonymous", password="******")
while True:
print("-----请输入要查询的股票代码：")
code = input()
#### 登出系统 ####
if (code == 'exit'):
break
#### 获取沪深A股估值指标(日频)数据 ##### peTTM 动态市盈率# psTTM 市销率# pcfNcfTTM 市现率# pbMRQ 市净率
rs = bs.query_history_k_data_plus(code,
"date,pbMRQ,peTTM",
start_date=du.getTenYearsAgoTime(),
end_date=du.getYesterDayTime(),
frequency="d",
adjustflag="3")
#### 打印结果集 ####
result_list = []
while (rs.error_code == '0') & rs.next():
result_list.append(rs.get_row_data())
result2 = pd.DataFrame(result_list, columns=rs.fields, dtype=np.float)
print(result2.dtypes)
result2 = result2.sort_values(by='pbMRQ', ascending=False)
result2 = result2.reset_index(drop=True)
#### 结果集输出到csv文件 ####
my_file = "/Users/mfhj-dz-001-068/pythonData/pe_" + code + "_data.csv"
if os._exists(my_file):
#删除文件
os.remove(my_file)
import baostock as bs
import pandas as pd
lg = bs.login()
#显示登录信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#获取历史k线数据
rs = bs.query_history_k_data_plus(
"sh.600000",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date='2017-06-01',
end_date='2019-2-28',
frequency="d",
adjustflag="3")
#显示信息
print('login respond error_code:' + rs.error_code)
print('login 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，将记录合并到一起
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:\gupiaoshuju\history_k_data.csv", encoding="gbk", index=False)
print(result)
#登出
import matplotlib.pyplot as plt
#get all the ticks
pro = ts.pro_api()
data = pro.stock_basic(exchange='',
list_status='L',
fields='ts_code,symbol,name,area,industry,list_date')
data.to_csv('result.csv', encoding='gbk')
# login
lg = bs.login()
# 获取沪深A股历史K线数据
rs = bs.query_history_k_data_plus("sz.300313",
"date,code,open,high,low,close",
start_date='2020-06-01',
end_date='2020-09-29',
frequency="d",
adjustflag="3")
# get result
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("szzz.csv", index=False)
# 退出系统
bs.logout()
def getAllIndexHistoryData(self, days=1):
self.initMysqlConn() # 连接数据库
# 设定获取日线行情的初始日期和终止日期，其中终止日期设定为昨天。
# start_dt = '19901219'
time_temp = datetime.datetime.now()
end_dt = time_temp.strftime('%Y%m%d')
end_dt1 = (datetime.datetime.strptime(end_dt,
"%Y%m%d")).strftime('%Y-%m-%d')
start_dt = (time_temp -
datetime.timedelta(days=days)).strftime('%Y%m%d')
start_dt = (datetime.datetime.strptime(start_dt,
"%Y%m%d")).strftime('%Y-%m-%d')
# 获取开始结束之间所有有交易的日期
# df1 = self.pro.trade_cal(exchange='SSE', is_open='1',
# start_date=start_dt,
# end_date=end_dt,
# fields='cal_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)
# 设定需要获取数据的股票池
stock_pool = ['sh.000001', 'sz.399001', 'sz.399006']
ss = ['000001_sh', '399001_sz', '399006_cyb']
name_pool = ['上证指数', '深证成指', '创业板']
# startDate = ['1990-12-19', '1990-12-01', '2009-10-30']
startDate = [start_dt, start_dt, start_dt] #追加一天的数据
total = len(stock_pool)
for i in range(len(stock_pool)):
# for date in df1['cal_date'].values:
try:
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data_plus(
stock_pool[i],
"date,code,open,high,low,close,preclose,volume,amount,pctChg",
start_date=startDate[i],
end_date=end_dt1,
frequency="d")
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())
df = pd.DataFrame(data_list, columns=rs.fields)
filename = 'G:\\stockData\\originData\\' + ss[i] + '.csv'
ex = os.path.isfile(filename) #当前文件是否存在，存在即添加，不存在新建
if ex == False:
df.to_csv(filename, encoding='utf_8_sig')
else:
df.to_csv(filename,
mode='a',
header=False,
encoding='utf_8_sig')
df1 = pd.read_csv(filename, encoding='utf_8_sig')
df1 = df1.dropna() #删除所有包含空值的行
# df=df.drop(df.tail(4).index)
df1 = df1.drop_duplicates(['date'])
df1 = df1.sort_values('date') #降序
df1.to_csv(filename, index=None, encoding='utf_8_sig')
continue
# 查询当天所有正常上市交易的股票数据
# df = ts.get_h_data(stock_pool[i],start=start_dt1,end=end_dt1,index=True)
# print(df.index[i])
# df = ts.get_h_data('399006', index=True)
c_len = df.shape[0]
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.iloc[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])
# state_dt = (datetime.datetime.strptime(str(df.index[i]), "%Y%m%d")).strftime('%Y-%m-%d')
# state_dt = str(df.index[c_len-1-j])
state_dt = str(resu[0])
try:
pass
# sql_insert = "INSERT INTO index_data(date,code,name,open,high,low,close,preclose,volume,amount,p_change,price_change) VALUES ('%s','%s','%s','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f')" % (state_dt,str(stock_pool[i]),str(name_pool[i]),float(resu[2]),float(resu[3]),float(resu[4]),float(resu[5]),float(resu[6]),float(resu[7]),float(resu[8],float(resu[9]))
sql_insert = "INSERT INTO index_data_bs(date,code,name,open,high,low,close,preclose,volume,amount,p_change) VALUES ('%s','%s', '%s','%.2f', '%.2f','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f')" % (
state_dt, str(stock_pool[i]), str(name_pool[i]),
float(resu[2]), float(resu[3]), float(resu[4]),
float(resu[5]), float(resu[6]), float(
resu[7]), float(resu[8]), float(resu[9]))
# sql_insert = "INSERT INTO index_data_bs(date,code,name,open,high,low,close,preclose,volume,amount,p_change) VALUES ('%s','%s','%s','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f','%.2f')" % (state_dt, str(stock_pool[i]), str(name_pool[i]), float(resu[2]), float(resu[3]), float(resu[4]), float(resu[5]), float(resu[6]), float(resu[7]), float(resu[8], float(resu[9]))
self.cursor.execute(sql_insert)
self.db.commit()
# pass
except Exception as err:
print('insert err', err)
continue
self.cursor.close()
self.db.close()
# 登出系统
bs.logout()
print('getAllIndexHistoryData Finished!')
#### 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)
stock_code = "sz.000507"
#### 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
# adjustflag：复权类型，默认不复权：3；1：后复权；2：前复权。已支持分钟线、日线、周线、月线前后复权。
rs = bs.query_history_k_data_plus(stock_code,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,isST,pctChg,pbMRQ,peTTM,psTTM,pcfNcfTTM",
start_date='1960-01-01',
end_date='2020-02-14',
frequency="d", adjustflag="2")
# rs = bs.query_history_k_data_plus(stock_code,
# "date,code,open,high,low,close,volume,amount,adjustflag,turn,pctChg",
# start_date='1960-01-01',
# end_date='2020-02-14',
# frequency="m", adjustflag="2")
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 = []
import baostock as bs
import pandas as pd
####登录系统####
lg = bs.login()
#显示登录返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#####获取股票历史K线数据####
#详细指标参数
rs = bs.query_history_k_data_plus(
"sz.002739",
"date,open,high,low,close,volume,amount,preclose,pctChg",
start_date='2020-01-01',
end_date='2020-08-10',
frequency="d")
print('query_history_k_data_plus respond error_code:' + rs.error_code)
print('query_history_k_data_plus 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)
####结果输出到csv文件####
result.to_csv("C:/Users/大可/Desktop/sz.002739_k_data.csv", index=False)
print(result)
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
filename="E:\\historystock\\getostock.log",
filemode='a')
if __name__ == '__main__':
#### 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)
rs = bs.query_history_k_data_plus("sh.000015",
"date,code,close,pctChg",
start_date='2017-07-11', end_date='2020-04-30',
frequency="m", adjustflag="2")
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)
#### 结果集输出到csv文件 ####
result.to_csv("E:\\historystock\\zhongxiaoban.csv", index=False)
print(result)
def getOnlyKline(stockCodeArray,toFile=1,start_date='2018-01-06',end_date='2023-10-31'):
#如果toFile=0就直接读取本地csv文件
if toFile == 0:
df = pd.read_csv('/Users/miketam/Downloads/getOnlyKline_300.csv',sep=',')
# df = pd.read_csv('/Users/miketam/Downloads/getOnlyKline.csv', header=None, sep=',')
# 得先把数据按股票拆分为一个个array，每个股票的k线是一个array
dfArray = dfDivide(stockCodeArray,df)
kLineArray = []
for i in dfArray:
j = i.values
kLineArray.append(j)
return kLineArray
arrayMerage = []
klineArray = []
for i in stockCodeArray:
data_list = []
code = codeFormat(i)
rs = bs.query_history_k_data_plus(code,
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
# "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
"date,code,open,high,low,close,volume",
start_date= start_date, end_date= end_date,
frequency="d", adjustflag="2")
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())
#去掉停牌的日期的数据
temp_list = []
for i in range(len(data_list)):
if data_list[i][5] != data_list[i-1][5] or data_list[i][3] != data_list[i-1][3] and i > 0:
temp_list.append(data_list[i])
data_list = temp_list
arrayMerage.extend(data_list)
klineArray.append(data_list) #用在二次处理
result: DataFrame = pd.DataFrame(arrayMerage)
result.columns = ["date","code","open","high","low","close","volume"]
# 获取周K线数据
# klineMonthArray = getMonthKline(stockCodeArray, start_date, end_date)
klineWeekArray = getWeeklyKline(stockCodeArray, start_date, end_date)
# KlineHourArray = getHourKline(stockCodeArray, start_date, end_date)
# 用日线数据给周线数据补充最后一周数据
for x in range(len(klineWeekArray)):
week = klineWeekArray[x] #一个股票的周k线
week.reverse()
day = klineArray[x] #一个股票的日k线
day.reverse() #倒序，最后一天在前面
dateDayLast = day[0][0]
dateWeekLast = week[0][0]
temp = []
#通过日数据计算最后一周数据
weekDate = getYearWeekFromDate(dateDayLast) #计算是本年的第几个周
closeLastWeek = day[0][5] #这周最后一天收盘价就是周收盘价
openLastWeek = day[0][2]
highLastWeek = day[0][3]
lowLastWeek = day[0][4]
for d in day:
if getYearWeekFromDate(d[0]) == weekDate:# 从后向前查询这周每天数据
openLastWeek = d[2] #这个周的第一天开盘价就是周开盘价
if d[3] > highLastWeek: #取最大值
highLastWeek = d[3]
if d[4] < lowLastWeek:
lowLastWeek = d[4]
else:
break
weekLast = [day[0][0], day[0][1], openLastWeek, highLastWeek, lowLastWeek, closeLastWeek ]
if dateDayLast == dateWeekLast: #若果最后一日和最后一周是在相同周，就覆盖，否则就新建
week[0] = weekLast
else:
week.insert(0,weekLast)
week.reverse()
day.reverse()
if toFile == 1:
result.to_excel('/Users/miketam/Downloads/getOnlyKline.xlsx', float_format='%.5f', index=False)
result.to_csv("/Users/miketam/Downloads/getOnlyKline.csv", encoding="gbk", index=False)
# print(result)
return [klineArray, klineWeekArray]
code=result["code"]
# In[ ]:
#获取对应股票近三年价格数据
# In[8]:
history_data = pd.DataFrame(columns = ["date", "code", "close"])
for i in range(0,50):
data = bs.query_history_k_data_plus(code[i],
"date,code,close",
start_date='2018-03-01', end_date='2021-3-01',adjustflag="2")#adjustflag="2"该参数为历史数据前复权
print('query_history_k_data_plus respond error_code:'+data.error_code)
print('query_history_k_data_plus respond error_msg:'+data.error_msg)
data_list = []
while (data.error_code == '0') & data.next():
data_list.append(data.get_row_data())
result = pd.DataFrame(data_list, columns=data.fields)
x=result.shape
print(x)
if (x[0]==729):
result.to_csv(code[i], index=0,header=1)
else:
code=code.drop([i])#删除近三年价格数据有缺失的股票
i=i+1
def getMaLineTrend(stockCodeArray,stockNameArray):
array = []
temp = ''
maMultiStockPd = ''
x = 0
for i in stockCodeArray:
stockName = stockNameArray[x]
x = x + 1
data_list = []
code = codeFormat(i)
rs = bs.query_history_k_data_plus(code,
# 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
# "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
"date,code,open,high,low,close",
start_date='2020-01-01', end_date='2021-12-31',
frequency="d", adjustflag="2")
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)
#添加均线
result[6] = result[5].rolling(window=5).mean() # 5日线
result[7] = result[5].rolling(window=10).mean() # 10日线
#result[8] = result[5].rolling(window=20).mean() # 10日线
#result.columns = ["date","code","open","high","low","close","ma5","ma10","ma20"]
#判断均线向上向下
array = result.values #转为数组
ma5 = ""
ma10 = ""
maArrayOneStock = []
# maArray = []
m = 0 # 双向下个数
n = 0 # ma5向上
for j in range(0, len(array)): #获取上一个，下一个
Current = array[j]
Prev = array[j - 1]
if Current[6] > 0 and Prev[6] > 0:
if Current[6] > Prev[6]:
ma5 = "向上_5"
else:
ma5 = ""
if Current[7] > 0 and Prev[7] > 0:
if Current[7] > Prev[7]:
ma10 = "向上_10"
else:
ma10 = ""
# 这里预留位置，调用函数取"双均线连续3天向下，5日线今天向上"
# 在近4天，有1个或2个"5日向上"，其余为双向下
if j >= (len(array) - 4):
if ma5 == '' and ma10 == '':
m = m + 1
if ma5 == '向上_5' and ma10 == '':
n = n + 1
if j == (len(array) - 1):
if (n == 1 and m == 3) or (n == 2 and m == 2):
ma5 = ma5 + '快来看我'
maArrayOneDay = [Current[0], ma5, ma10] #后续日期做索引，df merge时不重复
maArrayOneStock.append(maArrayOneDay)
temp = pd.DataFrame(maArrayOneStock)
temp.columns = ["date", stockName + "_ma5",str(i) + "_ma10"]
# temp.set_index('date',inplace=True, drop=True)
if x == 1:
maMultiStockPd = temp
else:
maMultiStockPd = pd.merge(maMultiStockPd,temp,on='date')
### 结果集输出到csv文件 ####
print(maMultiStockPd)
# maMultiStockPd.to_csv("/Users/miketam/Downloads/getMaline.csv", encoding="gbk", index=False)
maMultiStockPd.to_excel('/Users/miketam/Downloads/getMaline.xlsx', float_format='%.5f', index=False)
return
df = mytech.get_index(df)
df["code"] = code
df_last = df_last.append(df)
if i % 10 == 0:
print(i, code)
df_last.reset_index(drop=True, inplace=True)
'''
SH300 data
'''
lg = bs.login()
rs = bs.query_history_k_data_plus("sh.000300",
"date,code,open,high,low,close,volume",
start_date="2000-01-01",
frequency="d")
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())
df = pd.DataFrame(data_list, columns=rs.fields)
df.rename({"volume": "vol", "date": "trade_date"}, inplace=True, axis=1)
for col in ["open", "high", "low", "close", "vol"]:
df[col] = df[col].astype(float)
mytech = common.TechnicalIndicatorPriceVol4Model()
df = mytech.get_index(df)
del df['pct_chg']
df.columns = ["trade_date"
] + [c + "_sh" for c in df.columns if c != "trade_date"]
df["trade_date"] = pd.to_datetime(df["trade_date"])
import baostock as bs
import pandas as pd
####登录系统####
lg = bs.login()
#显示登录返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
#####获取股票历史K线数据####
#详细指标参数
rs = bs.query_history_k_data_plus("sz.002104", "date,open,high,low,close,volume,amount", start_date='2015-01-01', end_date='2020-08-10', frequency="d")
print('query_history_k_data_plus respond error_code:'+rs.error_code)
print('query_history_k_data_plus 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)
####结果输出到csv文件####
result.to_csv("C:/Users/大可/Desktop/sz.002104_k_data.csv", index=False)
print(result)
####登出系统####
bs.logout
import baostock as bs
import pandas as pd
lg = bs.login()
fields = 'date,open,high,low,close,volume'
df_bs = bs.query_history_k_data_plus("sh.000001",
fields,
start_date='2009-01-01',
end_date='2019-06-01',
frequency="d",
adjustflag="2")
data_list = []
while (df_bs.error_code == '0') & df_bs.next():
data_list.append(df_bs.get_row_data())
result = pd.DataFrame(data_list, columns=df_bs.fields)
result.close = result.close.astype('float64')
result.open = result.open.astype('float64')
result.low = result.low.astype('float64')
result.high = result.high.astype('float64')
result.volume = result.volume.astype('int')
result.index = pd.to_datetime(result.date)
print(result.head())
print(result.tail())
print(result.info())
print(result.axes)
bs.logout()
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():
rs_data = rs.get_row_data()
if rs_data[3] == '1':
result_list.append(rs_data)
pd_result = pd.DataFrame(result_list, columns=rs.fields)
# MA13
ma=13
pd_result['ma_'+str(ma)] = pd_result['close'].rolling(ma).mean()
+ symbol + "', " + str(last_index) + ")"
conn.execute(macd_sql)
truncate_tables()
for stock_code in stock_df["code"]:
symbol_string = stock_code[0:2] + stock_code[3:9]
startDay = (datetime.datetime.now() - datetime.timedelta(days=9)).strftime("%Y-%m-%d")
interval_type_string = "Min30"
min_30_result = bs.query_history_k_data_plus(stock_code, "date,code,open,high,low,close,"
"volume,amount,adjustflag",
start_date=startDay, end_date='',
frequency="30", adjustflag="2")
day_data_list = []
while (min_30_result.error_code == '0') & min_30_result.next():
day_data_list.append(min_30_result.get_row_data())
day_data_frame = pd.DataFrame(day_data_list, columns=min_30_result.fields)
if day_data_frame.size == 0:
continue
volume_array = day_data_frame['volume'].astype(float).values
open_array = day_data_frame['open'].astype(float).values
close_array = day_data_frame['close'].astype(float).values
day_array = day_data_frame['date'].values
high_array = day_data_frame['high'].astype(float).values
def run(self):
stock_df = self.get_codes_by_date(self.date_end)
#return
for index, row in stock_df.iterrows():
print(f'processing {index} {row["code"]} {row["code_name"]}')
break
start_time = time.time()
#code = "sh.600037"
#code = "sz.002007"
#df_code = bs.query_history_k_data_plus(code, self.fields,
# adjustflag：复权类型，默认不复权：3；1：后复权；2：前复权。
df_code = bs.query_history_k_data_plus(row["code"],
self.fields,
start_date=self.date_start,
end_date=self.date_end,
frequency="d",
adjustflag="2").get_data()
#frequency="d", adjustflag="3").get_data()
#print('query_history_k_data_plus respond error_code:'+rs.error_code)
#print('query_history_k_data_plus respond error_msg :'+rs.error_msg)
#df_code = rs.get_data()
# code_name = row["code_name"].replace('*', '')
code = row["code"].replace('.', '').upper()
#print(code)
#code = code[0:2].upper()+code[3:9]
df_code.columns = [
'RQ', 'CODE', 'OPEN', 'HIGH', 'LOW', 'CLOSE', 'VOLUME',
'AMOUNT', 'ADJUSTFLAG', 'TURN', 'TRADESTATUS', 'PCTCHG',
'PETTM', 'PBMRQ', 'PSTTM', 'PCFNCFTTM', 'ISST'
]
#print(df_code.columns)
df_code['RQ'] = pd.to_datetime(df_code['RQ'], format='%Y-%m-%d')
df_code['CODE'] = code
# df_code['CODE'].apply(
# lambda x: str(x[0:2]).upper()+x[3:9])
df_code['VOLUME'].replace('', '0', inplace=True)
df_code['AMOUNT'].replace('', '0', inplace=True)
df_code['TURN'].replace('', '0', inplace=True)
df_code['PCTCHG'].replace('', '0', inplace=True)
df_code['PETTM'].replace('', '0', inplace=True)
df_code['PBMRQ'].replace('', '0', inplace=True)
df_code['PSTTM'].replace('', '0', inplace=True)
df_code['PCFNCFTTM'].replace('', '0', inplace=True)
df_code['ISST'].replace('', '0', inplace=True)
convert_dict = {
'CODE': str,
'OPEN': float,
'HIGH': float,
'LOW': float,
'CLOSE': float,
'VOLUME': int,
'AMOUNT': float,
'ADJUSTFLAG': int,
'TURN': float,
'TRADESTATUS': int,
'PCTCHG': float,
'PETTM': float,
'PBMRQ': float,
'PSTTM': float,
'PCFNCFTTM': float,
'ISST': int
}
#print(df_code.head())
df_code = df_code.astype(convert_dict)
print(df_code)
#print(df_code.dtypes)
print(df_code.shape)
df_code.to_sql('hq_baostock',
engine,
index=False,
if_exists='append',
dtype={
'CODE': types.VARCHAR(length=8),
'ISST': types.INTEGER()
})
end_time = time.time()
print('elapsed ' + str(end_time - start_time))
#break
'''
def bs_daily_mysql(pro, cur, engine):
sql = 'select ts_code from stock_basic'
cur.execute(sql)
ts_codes = str(cur.fetchall())
# 处理从数据库提取的关于股票代码的数据
ts_code_list = re.findall("(\'.*?\')", ts_codes)
ts_code_list = [re.sub("'", '', each) for each in ts_code_list]
print(ts_code_list)
bs_code_list = []
for i in range(len(ts_code_list)):
stock_code = ts_code_list[i][0:6]
# print(stock_code)
ex = (ts_code_list[i][-2:]).lower()
# print(ex)
bs_code = ex + '.' + stock_code
bs_code_list.append(bs_code)
# print(bs_code_list)
sql = "show tables;"
cur.execute(sql)
tables = [cur.fetchall()]
table_list = re.findall('(\'.*?\')', str(tables))
table_list = [re.sub("'", '', each) for each in table_list]
# print(table_list)
sql = "select table_name, table_rows from information_schema.tables where table_schema = 'baostock' and table_rows <1;"
cur.execute(sql)
empty_tables = [cur.fetchall()]
empty_table_list = re.findall('(\'.*?\')', str(empty_tables))
empty_table_list = [re.sub("'", '', each) for each in empty_table_list]
# print(empty_table_list)
for bs_code in bs_code_list:
# print(bs_code)
name = (bs_code.replace('.', '_') + "_daily").lower()
if name in table_list and name not in empty_table_list:
print(bs_code + "对应的日线表已存在且不为空")
continue
sql = "CREATE TABLE IF NOT EXISTS "+bs_code.replace('.','_') +"_daily(date CHAR(11)," \
"code CHAR(10) PRIMARY KEY,open float(4,2),high float(4,2),low float(4,2),close float(4,2), preclose float(4,2)," \
"volume float(11,2),amount float(11,2),turn float(11,2),tradestatus float(11,2), pctChg float(11,2),peTTM float(11,2),psTTM float(11,2),pcfNcfTTM float(11,2)," \
"pbMRQ float(11,2),isST INT UNSIGNED) ENGINE=innodb DEFAULT CHARSET=utf8"
# print(bs_code.replace('.','_') +"_daily")
cur.execute(sql)
rs = bs.query_history_k_data_plus(
bs_code, "date,code,open,high,low,close,preclose,volume,amount,"
"turn,tradestatus,pctChg,peTTM,psTTM,pcfNcfTTM,pbMRQ,isST",
start_date='2010-01-01',
end_date='2020-11-15',
frequency="d",
adjustflag="1")
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)
date = result['date']
for index in range(0, len(date)):
new_date = str(date[index]).replace("-", "")
date[index] = new_date
if (result is None) or (result.empty):
print(bs_code + "返回数据是空的，不导入数据")
else:
print(bs_code + "返回数据不为空，导入数据")
result.to_sql(str(bs_code.replace('.', '_')) + '_daily',
engine,
if_exists='replace')
for ts_date in ts_date_series:
date = str(ts_date)
date_series.append(date[:4] + '-' + date[4:6] + '-' + date[6:])
return date_series
if download_stocks:
rs = bs.query_hs300_stocks()
# 获取股票名称与代码
result = data_load(rs)
result.to_csv(base_data_path+'hs300_stocks.csv')
for code in tqdm(result['code']):
for start_date, end_date, path in ((data_start_date, data_end_date, data_path),
(train_data_start_date, train_data_end_date, train_data_path)):
rs = bs.query_history_k_data_plus(code, fields,
start_date=start_date, end_date=end_date,
frequency="d", adjustflag="3")
status(rs)
# baostock日线数据
df1 = data_load(rs)
df1.set_index('date', inplace=True)
# tushare指标数据
df2 = pro.daily_basic(ts_code=ts_c(code), start_date=ts_d(start_date),
end_date=ts_d(end_date), fields=ts_basic_fields)
df2['trade_date'] = bs_d(df2['trade_date'])
df2.set_index('trade_date', inplace=True)
# 按index横向拼接
df1 = df1.join(df2)
def downBarBySymbol(self,
symbol,
start_date=None,
end_date=None,
freq='d'):
"""下载某一合约的分钟线数据"""
start = time.time()
# 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)
db_freq = freq
req_colume = "date,code,open,high,low,close,volume,amount,adjustflag"
if (freq[0] in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']):
if (freq[-1] not in ['N']):
db_freq = freq + 'MIN'
else:
freq = freq[0:-3]
req_colume = "date,time,code,open,high,low,close,volume,amount,adjustflag"
if (freq.startswith('d')):
db_freq = 'D'
self.dbop.dbEnsureIndex(DB_NAME_DICT[db_freq], symbol, 'datetime')
# 时间字符串
if isinstance(start_date, datetime):
start_date = start_date.strftime('%Y-%m-%d')
if isinstance(end_date, datetime):
end_date = end_date.strftime('%Y-%m-%d')
rs = bs.query_history_k_data_plus(symbol,
req_colume,
start_date,
end_date,
frequency=freq,
adjustflag="2")
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())
df = pd.DataFrame(data_list, columns=rs.fields)
for ix, row in df.iterrows():
bar = self.generateVtBar(row)
d = bar.__dict__
flt = {'datetime': bar.datetime}
self.dbop.dbUpdate(DB_NAME_DICT[db_freq],
symbol,
d,
flt,
upsert=True)
end = time.time()
cost = (end - start) * 1000
print(u'合约%s 周期%s数据下载完成%s - %s，耗时%s毫秒' %
(symbol, freq, df.index[0], df.index[-1], cost))
data_dir = tmp_data_dict.get("baostock")
lg = bs.login(user_id="anonymous", password="******")
stock_index = sys.argv[1]
#stock_index = "601398"
#stock_index = "601398"
if stock_index[0:2] == "60":
stock_index_in = "sh." + stock_index
else:
stock_index_in = "sz." + stock_index
df2 = bs.query_history_k_data_plus(
stock_index_in,
"date,code,open,high,low,close,volume,turn,amount,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date='2006-10-27',
end_date='2020-08-11',
frequency="d",
adjustflag="1")
save_file = os.path.join(data_dir, stock_index + ".csv")
df3 = df2.get_data()
df3.to_csv(save_file, index=0)
'''
参数名称 参数描述
date 交易所行情日期
code 证券代码
open 开盘价
high 最高价
low 最低价
close 收盘价
preclose 昨日收盘价
def test_1():
#### 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_history_k_data_plus(
"sz.000001",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date='2018-04-01',
end_date='2020-04-18',
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)
#### 结果集输出到csv文件 ####
# result.to_csv("D:\\history_A_stock_k_data.csv", index=False)
# print(result)
#### 登出系统 ####
bs.logout()
ohlc = result.rename(
columns={
'date': 'Date',
'open': 'O',
'high': 'H',
'low': 'L',
'close': 'C', #'Adj Close': 'AC',
'volume': 'V'
})
ohlc['Date'] = pd.to_datetime(ohlc['Date'])
ohlc.set_index("Date", inplace=True)
ohlc["O"] = ohlc["O"].astype("float")
ohlc["H"] = ohlc["H"].astype("float")
ohlc["L"] = ohlc["L"].astype("float")
ohlc["C"] = ohlc["C"].astype("float")
ohlc["V"] = ohlc["V"].astype("int")
ohlc.info()
# ohlc = ohlc.set_index('Date', drop=False)
# ohlc = pybacktest.load_from_yahoo('GOOG')
ohlc.tail()
short_ma = 10
long_ma = 20
ms = ohlc.C.rolling(short_ma).mean()
ml = ohlc.C.rolling(long_ma).mean()
buy = cover = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
sell = short = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
print('> Short MA\n%s\n' % ms.tail())
print('> Long MA\n%s\n' % ml.tail())
print('> Buy/Cover signals\n%s\n' % buy.tail())
print('> Short/Sell signals\n%s\n' % sell.tail())
bt = pybacktest.Backtest(locals(), 'ma_cross')
print(list(filter(lambda x: not x.startswith('_'), dir(bt))))
print('\n> bt.signals\n%s' % bt.signals.tail())
print('\n> bt.trades\n%s' % bt.trades.tail())
print('\n> bt.positions\n%s' % bt.positions.tail())
print('\n> bt.equity\n%s' % bt.equity.tail())
print('\n> bt.trade_price\n%s' % bt.trade_price.tail())
bt.summary()
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.figsize'] = (15.0, 8.0)
bt.plot_equity()
bt.plot_trades()
ohlc.C.rolling(short_ma).mean().plot(c='green')
ohlc.C.rolling(long_ma).mean().plot(c='blue')
plt.legend(loc='upper left')
# bt.trdplot['2018':'2020']
# ohlc.C['2018':'2020'].rolling(short_ma).mean().plot(c='green')
# ohlc.C['2018':'2020'].rolling(long_ma).mean().plot(c='blue')
#
plt.show()
while (cd.error_code == '0') & cd.next():
# 获取一条记录，将记录合并在一起
cd_list.append(cd.get_row_data())
result1 = pd.DataFrame(cd_list, columns=cd.fields)
print(result1)
# 结果切片
result1.drop([i for i in range(0,4700)],inplace=True)
# dataframe columns to list
rlist = result1.code.values.tolist()
# 求数组长度 rnum = len(rlist)
data_list = []
for r in rlist:
rs = bs.query_history_k_data_plus(r,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date='2021-03-04', end_date='2021-03-05',
frequency="d", adjustflag="3") #frequency="d"取日k线，adjustflag="3"默认不复权
print('query_history_k_data_plus respond error_code:{}, error_msg:{}'.format(rs.error_code, rs.error_msg))
while (cd.error_code == '0') & cd.next():
# 获取一条记录，将记录合并在一起
cd_list.append(cd.get_row_data())
result1 = pd.DataFrame(cd_list, columns=cd.fields)
print(result1)
result1.drop([i for i in range(0,4700)],inplace=True)
print(result1)
#result1 = result1.drop(’code_name‘,axis=1)
del result1['code_name']
print(result1)
#### 转换数据型 ####
result1 = result1['tradeStatus'].astype(float)
def query_history_k_data_plus(
taskEnum,
frequency,
adjustflag,
load_data_func,
load_data_func_params: dict = None,
):
"""
按照任务表获取历史A股K线数据
"""
fields = _get_fields(frequency)
#### 登陆系统 ####
lg = bs.login()
with concurrent.futures.ThreadPoolExecutor() as executor:
with session_scope() as sm:
rp = sm.query(TaskTable).filter(TaskTable.task == taskEnum.value,
TaskTable.finished == False).all()
for task in tqdm(rp):
if task.finished:
continue
start_date = task.begin_date.strftime("%Y-%m-%d")
end_date = task.end_date.strftime("%Y-%m-%d")
max_try = 8 # 失败重连的最大次数
for i in range(max_try):
rs = bs.query_history_k_data_plus(task.bs_code,
fields,
start_date=start_date,
end_date=end_date,
frequency=frequency,
adjustflag=adjustflag)
if rs.error_code == '0':
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())
#_logger.info('{}下载成功,数据{}条'.format(task.ts_code, len(data_list)))
result = pd.DataFrame(data_list, columns=rs.fields)
if load_data_func_params:
params = copy.deepcopy(load_data_func_params)
else:
params = {}
params['result'] = result
params['bs_code'] = task.bs_code
params['frequency'] = frequency
params['adjustflag'] = adjustflag
executor.submit(load_data_func, params)
task.finished = True
break
elif i < (max_try - 1):
time.sleep(2)
continue
else:
_logger.error(
'获取历史A股K线数据失败/query_history_k_data_plus respond error_code:'
+ rs.error_code)
_logger.error(
'获取历史A股K线数据失败/query_history_k_data_plus respond error_msg:'
+ rs.error_msg)
sm.commit()
#### 登出系统 ####
bs.logout()
