def query_hs300_stocks():
rs = bs.query_hs300_stocks()
while Stock.retried_num < Stock.RETRY_MAX_NUM and rs.error_code != '0':
sleep(Stock.RETRY_DELAY_S)
rs = bs.query_hs300_stocks()
Stock.retried_num += 1
if '0' == rs.error_code:
Stock.retried_num = 0
return Stock.rs_to_list(rs)
def query_hs300_stocks():
rs = bs.query_hs300_stocks()
while BaoStock.retried_num < BaoStock.RETRY_MAX_NUM and rs.error_code != '0':
sleep(BaoStock.RETRY_DELAY_S)
rs = bs.query_hs300_stocks()
BaoStock.retried_num += 1
assert('0' == rs.error_code)
BaoStock.retried_num = 0
return BaoStock.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 querHS300Stocks(date=''):
rs = bs.query_hs300_stocks(date=date)
# 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())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
def get_hs300_stocks():
rs = BS.query_hs300_stocks()
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
hs300_stocks.append(rs.get_row_data())
result = PD.DataFrame(hs300_stocks, columns=rs.fields)
print(result)
return result
def get_code(file_path):
rs = bs.query_hs300_stocks()
print('query_hs300 error_code:' + rs.error_code)
print('query_hs300 error_msg:' + rs.error_msg)
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
hs300_stocks.append(rs.get_row_data())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
result.to_csv(file_path, encoding="utf-8", index=False)
return None
def bao_hs300_stocks() -> pd.DataFrame:
"""
获取沪深300成分股信息
:return:
"""
df = bao.query_hs300_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 + '获取沪深300成分股信息')
return df
def calculate_hs300_spec():
lg = bs.login()
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
rs = bs.query_hs300_stocks()
print('query_hs300 error_code:'+rs.error_code)
print('query_hs300 error_msg:'+rs.error_msg)
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
hs300_stocks.append(rs.get_row_data())
bs.logout()
db = create_engine(common.db_path_sqlalchemy)
hs300code = 'sh.000300'
sql_cmd = "SELECT * FROM stock_day_k where code='" + hs300code + "' order by date desc limit 0,251"
datash = pd.read_sql(sql=sql_cmd, con=db)
price = DataFrame({'date': datash['date'], hs300code:datash['close']})
result = []
currentdate = datash['date'][0]
for stockinfo in hs300_stocks:
stockcode = stockinfo[1]
code_name = stockinfo[2]
sql_cmd = "SELECT * FROM stock_day_k where code='" + stockcode+"' order by date desc limit 0,251"
daily = pd.read_sql(sql=sql_cmd, con=db)
pp = DataFrame({stockcode:daily['close']})
price2 = pd.concat([price, pp], axis=1)
price2 = price2.drop(['date'], axis=1)
price2 = price2.reset_index(drop = True)
tickerresult = []
tickerresult.append(currentdate)
tickerresult.append(stockcode)
tickerresult.append(code_name)
trendgap_y = calculate_trendgap(price2, pp.shape[0])
trendgap_hy = calculate_trendgap(price2, pp.shape[0], 120)
trendgap_qy = calculate_trendgap(price2, pp.shape[0], 60)
trendgap_m = calculate_trendgap(price2, pp.shape[0], 20)
trendgap_10 = calculate_trendgap(price2, pp.shape[0], 10)
trendgap_5 = calculate_trendgap(price2, pp.shape[0], 5)
tickerresult.append(trendgap_y)
tickerresult.append(trendgap_hy)
tickerresult.append(trendgap_qy)
tickerresult.append(trendgap_m)
tickerresult.append(trendgap_10)
tickerresult.append(trendgap_5)
result.append(tickerresult)
print(code_name)
if len(result) != 0:
db.execute(r'''
INSERT OR REPLACE INTO stock_hs300_spec VALUES (?,?,?,?,?,?,?,?,?)
''', result)
def get_300_codes(self, date='2017-01-01'):
# 获取沪深300成分股
rs = bs.query_hs300_stocks(date)
# 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())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
return result
def hs300():
filename = "data/hs300.csv"
if os.path.exists(filename):
result = pd.read_csv(filename)
else:
rs = bs.query_hs300_stocks()
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
hs300_stocks.append(rs.get_row_data())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
result.to_csv(filename, encoding="utf-8", index=False)
return result
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 bs_save_hs300_stocks():
'''
获取沪深300成分股信息
:return:
'''
bs.login()
rs = bs.query_hs300_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_hs300_stocks', engine, index=True)
bs.logout()
def query_hs300_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_hs300_stocks(date = date)
self.log('query_hs300 error_msg:', rs)
# 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())
result = pd.DataFrame(hs300_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 get_hs300_stocks(date=today):
# 获取沪深300成分股
rs = bs.query_hs300_stocks(date=date)
if rs.error_code != '0':
print('query_hs300 respond error_code::' + rs.error_msg)
login()
return get_hs300_stocks(date)
# 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())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
return result
def getNormalDataForBaoStock(start_date, end_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
hs300_stocks = []
rs = bs.query_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())
for stock_info in hs300_stocks:
code = stock_info[1]
rs = bs.query_history_k_data_plus(
code,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date=start_date,
end_date=end_date,
frequency="d",
adjustflag="1")
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 ####
file_directory = os.path.join(save_path, code)
if not os.path.exists(file_directory):
os.makedirs(file_directory)
result.to_csv(os.path.join(save_path, code,
"history_A_stock_k_data.csv"),
index=False)
print(result)
#### 登出系统 ####
bs.logout()
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_hs300_stocks(date=date)
hs50_stocks = []
while (rs.error_code == '0') & rs.next():
hs50_stocks.append(rs.get_row_data())
fields = ['updateDate', 'code', 'code_name']
data = jsonWrapper(hs50_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_hs300_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_hs300_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 compute_avg_earning():
lg = bs.login()
rs = bs.query_hs300_stocks()
result = pd.DataFrame()
while(rs.error_code == '0') & rs.next():
row = rs.get_row_data()
code = row[1]
df = get_closeprice(code)
df['code_name'] = row[2]
if result.empty:
result = df
else:
result = result.append(df)
# zz = bs.query_zz500_stocks()
# while(zz.error_code == '0') & zz.next():
# row = zz.get_row_data()
# code = row[1]
# df = get_closeprice(code)
# df['code_name'] = row[2]
# result = result.append(df)
result = result[result['netProfit_x'] != '']
result['netProfit_x'] = result['netProfit_x'].astype(float)
result = result[result['netProfit_x'] > 0]
result['netProfit_y'] = result['netProfit_y'].astype(float)
result = result[result['netProfit_y'] > 0]
result['avgEarningRate'] = (result['netProfit_y']/result['netProfit_x']).apply(lambda x: math.pow(x,1/5)-1)
result = result[result['avgEarningRate'] > 0]
result['roeAvg_y'] = result['roeAvg_y'].astype(float)
result = result[result['roeAvg_y'] > 0.1]
result['npMargin_y'] = result['npMargin_y'].astype(float)
result = result[result['npMargin_y'] > 0.1]
result['pe'] = result['pe'].astype(float)
result = result[result['pe'] > 0]
result = result[result['pe'] < 50]
result['peg'] = result['pe'] / result['avgEarningRate']
result = result.sort_values(by=['peg'], ascending=True)
result.to_csv("avg_earning_rate.csv", encoding="utf-8", index=False)
result[:10].plot.bar(title='peg', x='code_name', y='peg')
plt.show()
bs.logout()
def get_hs300_stocks():
"""
获取沪深300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)
# 获取沪深300成分股
rs = bs.query_hs300_stocks()
print('query_hs300 error_code:' + rs.error_code)
print('query_hs300 error_msg:' + rs.error_msg)
# 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())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
# # 结果集输出到csv文件
# result.to_csv("hs300_stocks.csv", encoding="utf-8", index=False)
dtype = {
'updateDate': String(10),
'code': String(9),
'code_name': String(10)
}
result.to_sql('odl_bs_hs300_stocks',
engine,
schema=CQ_Config.DB_SCHEMA,
if_exists='replace',
index=False,
dtype=dtype)
# 登出系统
bs.logout()
def hs300_stocks():
# 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_hs300_stocks()
print('query_hs300 error_code:' + rs.error_code)
print('query_hs300 error_msg:' + rs.error_msg)
# 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())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
# 结果集输出到csv文件
# result.to_csv("D:/hs300_stocks.csv", encoding="gbk", index=False)
result.to_csv(ConfigUtils.get_stock("HS_300_STOCK_NAME"), index=False)
print(result.tail())
print("init all hs300 stock names")
def hs300_index_component(self):
file_path = './raw_data/hs300_stocks.csv'
if os.path.exists(file_path):
is_outdate = None
else:
is_outdate = True
# 获取沪深300成分股
rs = bs.query_hs300_stocks()
print(f'hs300_index_component, error_code:{rs.error_code}')
# 打印结果集
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
row = rs.get_row_data()
if is_outdate is None:
is_outdate = self.check_index_component_outdate(
row[0], file_path)
# is_outdate = True
hs300_stocks.append(row)
elif is_outdate == True:
# 获取一条记录，将记录合并在一起
hs300_stocks.append(row)
else:
return
result = pd.DataFrame(hs300_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_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
def query_hs300_stocks(date=None):
""""
沪深300成分股
方法说明：通过API接口获取沪深300成分股信息，更新频率：每周一更新。
返回类型：pandas的DataFrame类型。
参数含义：
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_hs300_stocks(date)
if rs.error_code != '0':
logger.error('query_hs300_stocks respond error_msg:' +
rs.error_msg)
hs300_stocks = []
while (rs.error_code == '0') & rs.next():
hs300_stocks.append(rs.get_row_data())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
bs.logout()
return result
def his_hs300(date, ifcache = True):
dict = get_cache()
if ifcache and ("hs300", date) in dict.keys():
#print("hit")
return dict["hs300", 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)
# 获取沪深300成分股
rs = bs.query_hs300_stocks(str(date))
#print('query_hs300 error_code:'+rs.error_code)
#print('query_hs300 error_msg:'+rs.error_msg)
# 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())
result = pd.DataFrame(hs300_stocks, columns=rs.fields)
# 结果集输出到csv文件
#print(result)
datas = []
for data in hs300_stocks:
datas.append(data[1])
if ifcache:
dict["hs300", date] = datas
save_cache(dict)
# 登出系统
bs.logout()
return datas
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 hs300(self):
'''沪深300'''
return get_data(bs.query_hs300_stocks())
def get_hs300_stocks(self):
result = baostock.query_hs300_stocks().get_data()
result.to_excel(r'''E:\桌面\PycharmProjects\getstock\test.xlsx''',
index=False)
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)
all_stocks = []
while (rs.error_code == '0') & rs.next():
all_stocks.append(rs.get_row_data())
# 获取沪深300
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():
all_stocks.append(rs.get_row_data())
result = pd.DataFrame(all_stocks, columns=rs.fields)
# 去除创业板
result = result[result['code'].str.contains("300") == False]
# 结果集输出到csv文件
result.to_csv("./base_stocks.csv", encoding="utf_8_sig", index=False)
print(result)
