def refresh_all_stock_adjust(start_date="2020-03-27",
current_date="2020-03-30"):
bs.login()
db_conn = create_engine(common.db_path_sqlalchemy)
stock_rs = bs.query_all_stock(day=current_date)
stock_df = stock_rs.get_data()
rs_list = []
for code in stock_df["code"]:
if code.startswith("sh.6") | code.startswith(
"sz.00") | code.startswith("sz.300"):
print('query_stock_factor code:' + code)
rs_factor = bs.query_adjust_factor(code=code,
start_date=start_date,
end_date=current_date)
while (rs_factor.error_code == '0') & rs_factor.next():
rs_list.append(rs_factor.get_row_data())
factor_list = []
for d in rs_list:
code = d[0]
print("refresh all factor for " + code)
rs_factor = bs.query_adjust_factor(code=code,
start_date='2006-01-01',
end_date=current_date)
while (rs_factor.error_code == '0') & rs_factor.next():
factor_list.append(rs_factor.get_row_data())
if len(factor_list) > 0:
db_conn.execute(
r'''
INSERT OR REPLACE INTO stock_adjustfactor VALUES (?, ?, ?, ?, ?)
''', factor_list)
bs.logout()
def downLoadAdjustFactorByStockCode(stockCode):
'''下载指定股票的复权因子
下载指定股票所有的复权因子
Args:
stockCode: 6位的股票代码，字符串格式
Returns:
返回复权因子的列表 [['sz.300634', '2018-03-23', '0.996584', '1.000000', '1.000000'], ['sz.300634', '2019-06-03', '1.000000', '1.003428', '1.003428']]
[股票代码，除权除息日期 ，前复权，后复权,最后一个不要]
'''
if stockCode[0] == '6':
baoStockCode = 'sh.' + stockCode
else:
baoStockCode = 'sz.' + stockCode
rs = bs.query_adjust_factor(code=baoStockCode,
start_date="1990-01-01",
end_date=getTodayDate())
if rs.error_code != '0':
print('下载复权因子失败 :%s %s' % (baoStockCode, rs.error_msg))
return
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
return data_list
def load_adjfactor(symbol, data_path):
adj_file = os.path.join(data_path, symbol, 'adj.csv')
if os.path.exists(adj_file):
return load_adjfactor_from_file(adj_file)
lg = bs.login('anonymous', '123456')
if lg.error_code != '0':
raise ValueError('baostock login failed:{}, {}'.format(lg.error_code, lg.error_msg))
rs_list = []
rs_factor = bs.query_adjust_factor(convert_symbol(symbol), start_date="1990-01-01", end_date="2199-12-31")
while (rs_factor.error_code == '0') & rs_factor.next():
rs_list.append(rs_factor.get_row_data())
if rs_factor.error_code != '0':
bs.logout()
raise ValueError('baostock read data failed:{}'.format(rs_factor.error_msg))
bs.logout()
result_factor = pd.DataFrame(rs_list, columns=rs_factor.fields)
result_factor.to_csv(adj_file, encoding="utf-8", index=False)
return load_adjfactor_from_file(adj_file)
def testGetFactor():
bs.login()
data_list = []
k_rs = bs.query_adjust_factor(code="sh.600161",
start_date="2010-01-01",
end_date="")
while (k_rs.error_code == '0') & k_rs.next():
data_list.append(k_rs.get_row_data())
print(data_list)
bs.logout()
def updateAllAdjustFactor():
stockList = getStockListFromBaoStock()
for i in stockList:
rs = bs.query_adjust_factor(code=i, start_date="1990-01-01", end_date=getTodayDate())
if rs.error_code != '0':
print('下载复权因子失败 :%s %s' % (i, rs.error_msg))
return
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
updateAdjustFactorInDB(data_list)
print('更新复权因子 %s' %i)
def query_adjust_factor(self, code, start_date = None, end_date = None):
'''
code：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。此参数不可为空；
start_date：开始日期，为空时默认为2015-01-01，包含此日期；
end_date：结束日期，为空时默认当前日期，包含此日期。
'''
rs_list = []
rs = bs.query_adjust_factor(code=code, start_date=start_date, end_date=end_date)
self.log('query_adjust_factor respond error_msg:', rs)
while (rs.error_code == '0') & rs.next():
rs_list.append(rs.get_row_data())
result = pd.DataFrame(rs_list, columns=rs.fields)
return result
def query_adjust_factor(code, start_date=None, end_date=None):
"""
查询复权因子信息
BaoStock提供的是涨跌幅复权算法复权因子
:param code: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。此参数不可为空；
:param start_date：开始日期，为空时默认为2015-01-01，包含此日期；
:param end_date：结束日期，为空时默认当前日期，包含此日期。
"""
lg = bs.login()
if lg.error_code != '0':
logger.error('login respond error_msg:' + lg.error_msg)
rs_list = []
rs_factor = bs.query_adjust_factor(code=code,
start_date=start_date,
end_date=end_date)
while (rs_factor.error_code == '0') & rs_factor.next():
rs_list.append(rs_factor.get_row_data())
result_factor = pd.DataFrame(rs_list, columns=rs_factor.fields)
bs.logout()
return result_factor
def dmpAdjFactorsToFile(self, codes:list, filename:str):
codes = transCodes(codes)
stocks = {
"SSE":{},
"SZSE":{}
}
count = 0
length = len(codes)
for code in codes:
exchg = code[:2]
if exchg == 'sh':
exchg = 'SSE'
else:
exchg = 'SZSE'
count += 1
stocks[exchg][code[3:]] = list()
print("Fetching adjust factors of %s(%d/%s)..." % (code, count, length))
rs = bs.query_adjust_factor(code=code, start_date="1990-01-01")
if rs.error_code == '0':
print("Error occured: %s" % (rs.error_msg))
continue
while rs.next():
items = rs.get_row_data()
date = int(items[1].replace("-",""))
factor = float(items[4])
stocks[exchg][code[3:]].append({
"date": date,
"factor": factor
})
print("Writing adjust factors into file %s..." % (filename))
f = open(filename, 'w+')
f.write(json.dumps(stocks, sort_keys=True, indent=4, ensure_ascii=False))
f.close()
def get_adj_factor():
# baostock登录
lg = bs.login()
param = request.form
if param and param['code']:
# sh.600000(600000.SH)
code = param['code']
if param and param['begin']:
# 2015-01-01
begin = param['begin']
if param and param['end']:
# 2015-01-01
end = param['end']
rs_list = []
show_code = stock_com(code)
rs_factor = bs.query_adjust_factor(code=show_code,
start_date=begin,
end_date=end)
while (rs_factor.error_code == '0') & rs_factor.next():
rs_list.append(rs_factor.get_row_data())
result_factor = pd.DataFrame(rs_list, columns=rs_factor.fields)
# 保存路径
basepath = os.path.dirname(__file__)
basicpath = 'static/perstock/' + code
show_time = today_time()
fileFlag = get_file_exist(basicpath, 'main')
factorFlag = get_file_exist(basicpath, 'factor')
csv_name = basicpath + '/factor/' + code + '_' + show_time + '.csv'
save_csv(csv_name, result_factor)
# 转为json存储
json_name = basicpath + '/factor/' + code + "_" + show_time + ".json"
parsed = save_json(json_name, result_factor, 'com')
all_len = len(parsed)
show_obj = {"data": parsed, "total": all_len, "type": "factorList"}
# baostock登出
bs.logout()
return show_obj
def dmpAdjFactorsToDB(self, dbHelper:DBHelper, codes:list):
codes = transCodes(codes)
stocks = {
"SSE":{},
"SZSE":{}
}
count = 0
length = len(codes)
for code in codes:
exchg = code[:2]
if exchg == 'sh':
exchg = 'SSE'
else:
exchg = 'SZSE'
count += 1
print("Fetching adjust factors of %s(%d/%s)..." % (code, count, length))
stocks[exchg][code[3:]] = list()
rs = bs.query_adjust_factor(code=code, start_date="1990-01-01")
if rs.error_code == '0':
print("Error occured: %s" % (rs.error_msg))
continue
while rs.next():
items = rs.get_row_data()
date = int(items[1].replace("-",""))
factor = float(items[4])
stocks[exchg][code[3:]].append({
"date": date,
"factor": factor
})
print("Writing adjust factors into database...")
dbHelper.writeFactors(stocks)
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)
# 查询2015至2017年复权因子
rs_list = []
rs_factor = bs.query_adjust_factor(code="sh.600000",
start_date="2015-01-01",
end_date="2017-12-31")
while (rs_factor.error_code == '0') & rs_factor.next():
rs_list.append(rs_factor.get_row_data())
result_factor = pd.DataFrame(rs_list, columns=rs_factor.fields)
# 打印输出
print(result_factor)
# 结果集输出到csv文件
result_factor.to_csv("D:\\adjust_factor_data.csv", encoding="gbk", index=False)
# 登出系统
bs.logout()
def adjust_factor(self, code, start_date, end_date):
self.getStockInstance()
rs = bs.query_adjust_factor(code, start_date, end_date)
self._test_rs(rs)
return rs
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)
# 查询2017至2019年复权因子
rs_list = []
rs_factor = bs.query_adjust_factor(code="sz.000001", start_date="2017-01-01", end_date="2019-12-31")
while (rs_factor.error_code == '0') & rs_factor.next():
rs_list.append(rs_factor.get_row_data())
result_factor = pd.DataFrame(rs_list, columns=rs_factor.fields)
# 打印输出
print(result_factor)
# 结果集输出到csv文件
result_factor.to_csv("D:\\adjust_factor_data.csv", encoding="gbk", index=False)
# 登出系统
bs.logout()
