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 bs_k_data_stock(code_val='sz.000651', start_val='2009-01-01', end_val='2019-06-01',
freq_val='d', adjust_val='3'):
# 登陆系统
lg = bs.login()
# 获取历史行情数据
fields= "date,open,high,low,close,volume,turn,peTTM,pbMRQ"
df_bs = bs.query_history_k_data_plus(code_val, fields, start_date=start_val, end_date=end_val,
frequency=freq_val, adjustflag=adjust_val) # <class 'baostock.data.resultset.ResultData'>
# frequency="d"取日k线，adjustflag="3"默认不复权，1：后复权；2：前复权
data_list = []
while (df_bs.error_code == '0') & df_bs.next():
# 获取一条记录，将记录合并在一起
data_list.append(df_bs.get_row_data())
result = pd.DataFrame(data_list, columns=df_bs.fields)
for i, v in result.turn.items():
if v.find('.') == -1:
result.iloc[i, result.columns.get_loc('turn')] = np.nan
result = result.astype({'turn': 'float64', 'pbMRQ': 'float64', 'peTTM': 'float64','volume':'uint64',
'close': 'float64', 'open': 'float64', 'low': 'float64', 'high': 'float64'})
result.volume = result.volume / 100 # 单位转换：股-手
result.volume = result.volume.astype('uint64')
result.date = pd.DatetimeIndex(result.date)
result.set_index("date", drop=True, inplace=True)
result.index = result.index.set_names('Date')
result['OpenInterest'] = 0
result.turn.fillna(method='ffill', inplace=True)
recon_data = {'high': result.high, 'low': result.low, 'open': result.open, 'close': result.close,\
'volume': result.volume,
'turn': result.turn,
'peTTM': result.peTTM,
'pbMRQ': result.pbMRQ}
df_recon = pd.DataFrame(recon_data)
# 登出系统
bs.logout()
return df_recon
def get_trade_dates(start_date, end_date):
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_trade_dates(start_date, end_date)
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 inportIndustryData(name='tb_today_industry', code=None, date=None):
configger.init()
# 显示登陆返回信息
# 结果集输出到csv文件
con = configger.engine
try:
sql = configger.lastOperateTimeSql.format(name)
lastTime = pd.read_sql(sql=sql, con=con).iloc[0, 0]
print(lastTime)
except:
print('没有操作数据')
if (lastTime == None):
lastTime = pd.to_datetime('1990-1-1 00:00:00')
if (needUpdate(lastTime, datetime.now(), True) == True):
lg = bs.login()
# 获取行业分类数据
rs = bs.query_stock_industry()
# rs = bs.query_stock_basic(code_name="浦发银行")
# 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())
result = pd.DataFrame(industry_list, columns=rs.fields)
result['date'] = datetime.now().date()
result.to_sql(name=name, con=con, if_exists='append', index=False)
bs.logout()
operation = pd.DataFrame()
operation.loc[0, 'name'] = name
operation.loc[0, 'updateTime'] = datetime.now().date()
operation.to_sql(name='tb_operation_time',
con=con,
if_exists='append',
index=False)
print('industry_data_import_ok')
else:
print('无需插入')
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 generate_stock_details(stock_price_df, quarter_date_list, saved=False):
code_list = stock_price_df['code'].unique()
result = pd.DataFrame()
lg = bs.login()
for i in range(len(quarter_date_list) - 1):
print(quarter_date_list[i], end=',')
left_date = datetime.datetime.strptime(
quarter_date_list[i], "%Y-%m-%d") - datetime.timedelta(1)
left_date_str = datetime.datetime(left_date.year, left_date.month,
left_date.day, 0,
0).strftime("%Y-%m-%d")
right_date_str = quarter_date_list[i + 1]
profit_list = []
for code in code_list:
rs_profit = bs.query_profit_data(code=code,
year=left_date.year,
quarter=left_date.month // 4 + 1)
while (rs_profit.error_code == '0') & rs_profit.next():
temp = rs_profit.get_row_data()
profit_list.append(temp)
profit_df = pd.DataFrame(profit_list, columns=rs_profit.fields)
result = pd.concat([
result,
pd.merge(
stock_price_df[(stock_price_df['date'] > left_date_str)
& (stock_price_df['date'] < right_date_str)],
profit_df[['code', 'totalShare']],
on='code')
])
result[['totalShare']] = result[['totalShare']].astype('float')
result['marketCapitalization'] = result['totalShare'] * result['ave_close']
bs.logout()
if saved is True:
result.to_csv("TMT_stock_details.csv",
index=False,
encoding='utf_8_sig')
else:
result.reset_index(drop=True)
return result
def getdata(searchKey, startdate, enddate, frequency, adjustflag):
#登录系统
lg = bs.login()
#打印返回信息看登陆是否成功
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
#获取历史K线数据
#根据“历史行情指标参数”章节设置的指标参数，必填
if (frequency == 'w' or frequency == 'm'):
queryString = "date,code,open,high,low,close,volume,amount,adjustflag,turn,pctChg"
elif frequency == 'd':
queryString = "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,psTTM,pcfNcfTTM,pbMRQ,isST"
else:
queryString = "date,time,code,open,high,low,close,volume,amount,adjustflag"
rs = bs.query_history_k_data_plus(searchKey,
queryString,
start_date=startdate,
end_date=enddate,
frequency=frequency,
adjustflag=adjustflag)
print('query_history_k_data_plus respond error_code:' + rs.error_code)
print('query_history_k_data_plus 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 get_all_stock_names():
# 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code + ', error_msg:' +
lg.error_msg)
dt = stock_utils.get_recently_trade_date()
dt = '2020-08-03'
k_rs = bs.query_all_stock(day=dt)
print(k_rs)
data_list = []
while (k_rs.error_code == '0') & k_rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(k_rs.get_row_data())
result = pd.DataFrame(data_list, columns=k_rs.fields)
print(result.tail())
result.to_csv(ConfigUtils.get_stock("STOCK_NAME"), index=False)
print("init all stock names")
bs.logout()
def computeVOL(code, startdate, enddate, period):
# 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)
# 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(code,
"date,volume,amount",
start_date=startdate, end_date=enddate, 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())
result = pd.DataFrame(data_list, columns=rs.fields)
result.index = result['date']
result = result[['volume']]
result['volume'] = result['volume'].astype(float)
# print(result)
#print(result)
loc_list = range(len(result))[::period]
result = result.iloc[loc_list, :]
result.plot(title=f'fb_vol_{period}day')
result=result.volume.tolist()
# if is_show:
# plt.show()
# 结果集输出到csv文件
# if is_refresh:
# result.to_csv(os.path.join(root, 'fb_vol.csv'), encoding='gbk')
# # 登出系统
bs.logout()
#print(result.iloc[loc_list,:0])
#print(result)
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 query_profit():
# 接口提供的数据最早从2007年开始
lg = bs.login()
shobjs = AStocksHeader.objects.all()
count = 0
start = time()
for obj in shobjs:
profit_list = []
count += 1
print(count)
for year in range(2007, 2020):
for quarter in range(1, 5):
if obj.stock_code.startswith('6'):
rs_profit = bs.query_profit_data(code="sh.{}".format(
obj.stock_code),
year=year,
quarter=quarter)
else:
rs_profit = bs.query_profit_data(code="sz.{}".format(
obj.stock_code),
year=year,
quarter=quarter)
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).values
for item in result_profit:
ap = AStocksProfit()
ap.stock = obj
ap.pub_date = item[1]
ap.stat_date = item[2]
ap.roe_avg = float(item[3]) if item[3] else 0.0
ap.np_margin = float(item[4]) if item[4] else 0.0
ap.gp_margin = float(item[5]) if item[5] else 0.0
ap.net_profit = float(item[6]) if item[6] else 0.0
ap.epsttm = float(item[7]) if item[7] else 0.0
ap.mb_revenue = float(item[8]) if item[8] else 0.0
ap.total_share = float(item[9]) if item[9] else 0.0
ap.liqa_share = float(item[10]) if item[10] else 0.0
ap.save()
print('import finish, need time: ', time() - start)
bs.logout()
def get_dailiy_info(cls, stock: str, start_date, end_date):
"""获取每日收盘数据"""
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
logging.info('login respond error_msg:' + lg.error_msg)
rs = bs.query_history_k_data_plus(
stock,
"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="3")
# 登出系统
bs.logout()
return rs
def __init__(self, jb='d'):
'''
根据周期初始化 开始时间，结束时间，股票列表
'''
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
if int(lg.error_code) == 0:
self.status = '远程登录成功'
else:
self.status = '远程登录失败'
print('baostock 远程登录失败:', lg.error_msg)
return
# df = stock_base.get_stock_code('sz')
self.jb = jb
date = stock_base.get_start_time(jb)
self.begin = date[0]
self.end = date[1]
print('k线级别:', self.jb, '\t开始时间:', self.begin, '\t结束时间:', self.end)
def main():
#### 登陆系统 ####
lg = bs.login()
#### 分析数据 ####
for att_stock in Bank:
# 获取数据
daily_data = get_stock_daily(bs, att_stock, "high", "2020-01-01",
"2020-08-29")
#print(daily_data)
point_value, break_succ = brust_point_monitor(daily_data)
if break_succ:
print(att_stock + " 画出颈线数据:" + str(point_value / 1000.0) +
" , 并突破颈线成功！")
else:
print(att_stock + " 画出颈线数据:" + str(point_value / 1000.0) +
" , 突破失败")
#### 登出系统 ####
bs.logout()
def download(stockCode, yearList: list, fileName):
#### 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)
# 查询季频估值指标盈利能力
profit_list = []
for year in yearList:
for quarter in [1, 2, 3, 4]:
rs_profit = bs.query_profit_data(code=stockCode, year=year, quarter=quarter)
while (rs_profit.error_code == '0') & rs_profit.next():
profit_list.append(rs_profit.get_row_data())
saveToCsv(profit_list, fileName)
#### 登出系统 ####
bs.logout()
def get_china_share(code,quota="date,open,high,low,close,preclose,volume,amount,turn,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM",start_date=19970101,end_date=20301231):
try:
login = baostock.login()
if login.error_code != "0":raise Exception(login.error_msg)
rs = baostock.query_history_k_data_plus(
code,quota,
start_date = datetime.datetime.strptime(str(start_date), '%Y%m%d').strftime("%Y-%m-%d"),
end_date = datetime.datetime.strptime(str(end_date), '%Y%m%d').strftime("%Y-%m-%d"),
frequency = "d",
adjustflag = "2",#复权状态(1：后复权， 2：前复权，3：不复权）
)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
return result
except Exception as e:
print(str(e))
finally:
baostock.logout()
def generate_stock_price(stock_basic_df,
start_date_str,
end_date_str,
shift_size,
columns_str="date,code,close,pbMRQ,isST0",
saved=False):
lg = bs.login()
result = pd.DataFrame()
for code in stock_basic_df['code']:
#print(code,end = ',')
rs = bs.query_history_k_data_plus(code,
columns_str,
start_date_str,
end_date_str,
frequency="d",
adjustflag="3")
temp = rs.get_data()
for i in range(shift_size, len(temp), shift_size):
temp.loc[[i - 1],
'ave_close'] = temp['close'][i - shift_size:i].astype(
'float').mean()
temp.loc[[i - 1],
'ave_pbMRQ'] = temp['pbMRQ'][i - shift_size:i].astype(
'float').mean()
temp.dropna(inplace=True)
result = pd.concat([result, temp], ignore_index=True)
bs.logout()
result = pd.merge(result, stock_basic_df[['code', 'code_name']], on='code')
if saved is True:
result.to_csv("TMT_stock_price.csv", index=False, encoding='utf_8_sig')
else:
result.reset_index(drop=True)
return result
def save_stock_k_data_from_baostock(startdate,
enddate,
code,
datafilename,
market='sh'):
lg = bs.login()
if lg.error_code != '0':
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
exit('登陆baostock失败')
#### 获取历史K线数据 ####
# 详细指标参数,参见“历史行情指标参数”章节
# frequency="d"取日k线,adjustflag="3"默认不复权
rs = bs.query_history_k_data_plus(
market + '.' + code,
"date,code,open,high,low,close,preclose,volume,amount,pbMRQ",
start_date=startdate.strftime('%Y-%m-%d'),
end_date=enddate.strftime('%Y-%m-%d'),
frequency="d",
adjustflag="2")
if rs.error_code != '0':
print('query_history_k_data_plus respond error_code:' + rs.error_code)
print('query_history_k_data_plus respond error_msg:' + rs.error_msg)
exit('从获取K线数据失败')
#### 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())
#### 登出系统 ####
bs.logout()
#### 结果集输出到csv文件 ####
result = pd.DataFrame(data_list, columns=rs.fields)
result.to_csv(datafilename, encoding="gbk", index=False)
def get_name():
###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)
### 获取证券信息 ###
nowdate = datetime.datetime.now()
delta = datetime.timedelta(days=-2)
today = nowdate + delta
today = today.strftime('%Y-%m-%d')
rs = bs.query_all_stock(day = today)
print('query_all_stock respond error_code:' + rs.error_code)
print('query_all_stock 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:\\all_stock.csv", encoding="gbk", index=False)
### 登出系统 ###
bs.logout()
conn = pymysql.connect("localhost", "root", "zjxtest", "stock",
use_unicode=True, charset="utf8")
cursorl = conn.cursor()
ls = []
for i in range(len(result['code'])):
ls.append([result['code'][i].split('.')[1], result['tradeStatus'][i],
result['code_name'][i]])
#print(len(ls))
for i in range(len(ls)):
#sql = 'insert into stock_code_data values(%s,%s,%s,%s)'
commit_data = [ls[i][0], ls[i][1].encode('utf8'), ls[i][2].encode('utf8'), today]
#print(commit_data)
cursorl.execute('insert into stock_code_data values(%s,%s,%s,%s)',commit_data)
#cursorl.execute(sql, commit_data)
conn.commit()
conn.close()
return ls
def init_data(code_name):
stock = code_name[0]
name = code_name[1]
file_name = stock + '_' + name + '.csv'
# data = ts.pro_bar(ts_code=stock, adj='qfq', api=ts.pro_api(token=settings.TOKEN, timeout=30))
# data = ts.get_k_data(stock, autype='qfq')
# 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code + ' , error_msg:' +
lg.error_msg)
# 获取历史K线数据 ####
# frequency="d"取日k线，adjustflag="3"默认不复权
rs = bs.query_history_k_data_plus(stock,
fields=settings.STOCK_FIELDS,
start_date=settings.START_DATE,
end_date=settings.END_DATE,
frequency="d",
adjustflag="3")
print('query_history_k_data_plus respond error_code:' + rs.error_code +
' , error_msg:' + rs.error_msg + ' , code:' + stock + "/" + name)
# 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())
if data_list:
result = pd.DataFrame(data_list, columns=rs.fields)
result['p_change'] = tl.ROC(result['close'], 1)
result.to_csv(settings.DATA_DIR + "/" + file_name,
encoding="utf-8",
index=False,
header=True)
# print(result.head(3))
else:
logging.debug("股票：" + stock + " 没有数据，略过...")
# 登出系统 ####
bs.logout()
def computeMA(self, code, startdate, enddate, is_show, is_refresh, period):
name_ = ['MA8', 'MA13', 'MA21', 'MA34', 'MA55']
fb_list = [8, 13, 21, 34, 55]
# 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)
# 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(code,
"date,close,volume,amount",
start_date=startdate, end_date=enddate, 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())
result = pd.DataFrame(data_list, columns=rs.fields)
# 结果集输出到csv文件
result.to_csv(r"/Users/zou/PycharmProjects/weiyl_919/history_Index_k_data.csv", index=False)
close = [float(x) for x in result['close']]
for item in zip(name_, fb_list):
result[item[0]] = ta.MA(np.array(close), timeperiod=item[1])
result.index = result['date']
result = result[name_]
loc_list = range(len(result))[::period]
result = result.iloc[loc_list, :]
# print(result)
result.plot(title=f'fb_ma_{period}day')
if is_show:
plt.show()
if is_refresh:
result.to_csv(os.path.join(root, 'fb_ma.csv'), encoding='gbk')
# 登出系统
bs.logout()
return result
def get_month_k_data(stock_code, start_date, end_date):
csv_file_name = "m.{stock_code}-{start_date}-{end_date}.csv"
result = None
try:
result = pd.read_csv(csv_file_name.format_map(vars()), parse_dates=[1])
except Exception as e:
print(e)
if result is None:
#### 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(stock_code, "date,open,high,low,close,volume,amount", start_date=start_date, end_date=end_date, frequency="m", adjustflag="2")
result = rs2result(rs)
result.to_csv(csv_file_name.format_map(vars()), index=False)
#### 登出系统 ####
bs.logout()
return result
def get_growth_data_year(stock_number,stock_name,start_year,end_year):
print('==========================================================')
print("开始进行: "+stock_name+"("+stock_number+")"+"的数据处理")
print("尝试登陆baostock")
data_list=[]
lg=bs.login(user_id="anonymous",password="******")
if(lg.error_code == '0'):
print("登陆成功")
else:
print("登录失败")
for y in range(int(start_year),int(end_year)+1):
for q in range(1,5):
#####get stock data#####
rs=bs.query_growth_data(code=stock_number,year=y,quarter=q)
while(rs.error_code=='0')&rs.next():
data_list.append(rs.get_row_data())
result=pd.DataFrame(data_list,columns=rs.fields)
bs.logout()
print(stock_name+"("+stock_number+")"+"的数据处理完成")
print('==========================================================')
return result
def get_stock_basic_message(stock_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=stock_code)
# 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)
bs.logout()
return result
def download(stockCode, yearList: list, fileName):
#### 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)
# 查询季频估值指标盈利能力
operation_list = []
for year in yearList:
for quarter in [1, 2, 3, 4]:
rs_operation = bs.query_operation_data(code=stockCode, year=year, quarter=quarter)
# rs_operation = bs.query_operation_data(code="sh.600000", year=2017, quarter=2)
while (rs_operation.error_code == '0') & rs_operation.next():
operation_list.append(rs_operation.get_row_data())
saveToCsv(operation_list, fileName)
#### 登出系统 ####
bs.logout()
def query_growth():
# 接口提供的数据最早从2007年开始
lg = bs.login()
shobjs = AStocksHeader.objects.all()
count = 0
start = time()
for obj in shobjs:
growth_list = []
count += 1
print(count)
for year in range(2007, 2020):
for quarter in range(1, 5):
if obj.stock_code.startswith('6'):
rs_profit = bs.query_growth_data(code="sh.{}".format(
obj.stock_code),
year=year,
quarter=quarter)
else:
rs_profit = bs.query_growth_data(code="sz.{}".format(
obj.stock_code),
year=year,
quarter=quarter)
while (rs_profit.error_code == '0') & rs_profit.next():
growth_list.append(rs_profit.get_row_data())
result_profit = pd.DataFrame(growth_list,
columns=rs_profit.fields).values
for item in result_profit:
if any([item[3], item[4], item[5], item[6], item[7]]):
ag = AStocksGrowth()
ag.stock = obj
ag.pub_date = item[1]
ag.stat_date = item[2]
ag.yoy_equity = float(item[3]) if item[3] else 0.0
ag.yoy_asset = float(item[4]) if item[4] else 0.0
ag.yoy_ni = float(item[5]) if item[5] else 0.0
ag.yoy_eps_basic = float(item[6]) if item[6] else 0.0
ag.yoy_pni = float(item[7]) if item[7] else 0.0
ag.save()
print('import finish, need time: ', time() - start)
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 get_shibor_data(startDate,endDate):
"""获取历史shibor数据"""
# 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)
# 获取银行间同业拆放利率
rs = bs.query_shibor_data(start_date=startDate, end_date=endDate)
print('query_shibor_data respond error_code:'+rs.error_code)
print('query_shibor_data 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('shiborON_list:', data_list)
# 登出系统
bs.logout()
return result
def future_calendar_collector(qlib_dir: [str, Path], freq: str = "day"):
"""get future calendar
Parameters
----------
qlib_dir: str or Path
qlib data directory
freq: str
value from ["day", "1min"], by default day
"""
qlib_dir = Path(qlib_dir).expanduser().resolve()
if not qlib_dir.exists():
raise FileNotFoundError(str(qlib_dir))
lg = bs.login()
if lg.error_code != "0":
logger.error(f"login error: {lg.error_msg}")
return
# read daily calendar
daily_calendar = read_calendar_from_qlib(qlib_dir)
end_year = pd.Timestamp.now().year
if daily_calendar.empty:
start_year = pd.Timestamp.now().year
else:
start_year = pd.Timestamp(daily_calendar.iloc[-1, 0]).year
rs = bs.query_trade_dates(start_date=pd.Timestamp(f"{start_year}-01-01"),
end_date=f"{end_year}-12-31")
data_list = []
while (rs.error_code == "0") & rs.next():
_row_data = rs.get_row_data()
if int(_row_data[1]) == 1:
data_list.append(_row_data[0])
data_list = sorted(data_list)
date_list = generate_qlib_calendar(data_list, freq=freq)
date_list = sorted(
set(daily_calendar.loc[:, 0].values.tolist() + date_list))
write_calendar_to_qlib(qlib_dir, date_list, freq=freq)
bs.logout()
logger.info(
f"get trading dates success: {start_year}-01-01 to {end_year}-12-31")
def Lilv():
# 登陆系统
lg = bs.login()
# 获取贷款利率
rs6 = bs.query_loan_rate_data(start_date="2010-01-01",
end_date="2020-12-31")
# 打印结果集
data_list = []
while (rs6.error_code == '0') & rs6.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs6.get_row_data())
result2 = pd.DataFrame(data_list, columns=rs6.fields)
# 登出系统
bs.logout()
lilv = {
'loanRate6Month': [],
'loanRate6MonthTo1Year': [],
'loanRate1YearTo3Year': [],
'loanRate3YearTo5Year': [],
'loanRateAbove5Year': []
}
lilv['loanRate6Month'] = np.array(result2['loanRate6Month']).tolist()
lilv['loanRate6MonthTo1Year'] = np.array(
result2['loanRate6MonthTo1Year']).tolist()
lilv['loanRate1YearTo3Year'] = np.array(
result2['loanRate1YearTo3Year']).tolist()
lilv['loanRate3YearTo5Year'] = np.array(
result2['loanRate3YearTo5Year']).tolist()
lilv['loanRateAbove5Year'] = np.array(
result2['loanRateAbove5Year']).tolist()
lilv['loanRate6Month'] = list(map(eval, lilv['loanRate6Month']))
lilv['loanRate6MonthTo1Year'] = list(
map(eval, lilv['loanRate6MonthTo1Year']))
lilv['loanRate1YearTo3Year'] = list(map(eval,
lilv['loanRate1YearTo3Year']))
lilv['loanRate3YearTo5Year'] = list(map(eval,
lilv['loanRate3YearTo5Year']))
lilv['loanRateAbove5Year'] = list(map(eval, lilv['loanRateAbove5Year']))
return json.dumps(lilv, ensure_ascii=False)
