def download_dd_data(start=None):
'''
获取大单数据
'''
conn = db.get_dd_data_db()
start = start
if start is None:
start = utils.today_last_year(1)
for code in get_all_stock_code():
end = datetime.today().date()
while start < end:
date = end.strftime('%Y-%m-%d')
df = ts.get_sina_dd(code=code, date=date, vol=500)
if df is not None:
df.insert(0, 'code', code)
try:
sql_df = df.loc[:, :]
sql.to_sql(sql_df,
name='dd_data',
con=conn,
index=True,
if_exists='append')
log.info('%s,%s dd data download ok.' % (code, start))
except Exception as e:
log.error('download error:%s,%s' % (code, date))
pass
start = start + timedelta(days=1)
def download_dd_data(start=None):
'''
获取大单数据
'''
conn = db.get_dd_data_db()
start = start
if start is None:
start = utils.today_last_year(1)
for code in get_all_stock_code():
end = datetime.today().date()
while start < end:
date = end.strftime('%Y-%m-%d')
df = ts.get_sina_dd(code=code, date=date, vol=500)
if df is not None:
df.insert(0, 'code', code)
try:
sql_df = df.loc[:, :]
sql.to_sql(sql_df, name='dd_data', con=conn,
index=True, if_exists='append')
log.info('%s,%s dd data download ok.' % (code, start))
except Exception as e:
log.error('download error:%s,%s' % (code, date))
pass
start = start + timedelta(days=1)
def run(self):
'''
strategy goes here
Returns:
'''
# dataFrames = self.datas.values()
for tick in self.btData.ticks:
try:
self.curr_tick = tick
self.curr_tick_pos += 1
for df in self.btData.datas.values():
row = df.ix[tick]
if len(row) > 3 and tick in df.index:
order = self.handle_tick(tick=tick, data=df.ix[:tick], row=row)
self.acount.update_price_of_position(code=row.code, price=row.close, date=tick)
# if self.begin is None:
# self.begin = tick
self.end = tick
except Exception as e:
if isinstance(e, KeyError):
pass
else:
logger.info('error %s' % e)
traceback.print_exc()
# ignore
pass
def download_money_flow_data(num=1000):
'''
get money flow from sina finance
:param num:
:return:
'''
conn = db.get_money_flow_db()
cur_time = datetime.now()
for code in get_all_stock_code():
code = append_stock_perfix(code)
cost = datetime.now()
for i in range(retry):
try:
url = sina_money_flow_api % (num, code)
text = request.urlopen(url, timeout=10).read()
text = text.decode('GBK')
log.info('url=%s,size=%d, try=%d' % (url, len(text), i))
if len(text) < 10:
continue
# j = demjson.decode(text, 'utf-8') #json很大的时候效率非常查
text = text[2:-2]
j = text.replace('"', '').split('},{')
head = ['date', 'close', 'chg_r', 'turnover', 'netamount', 'ratio', 'zl_netamount', 'zl_ratio',
'cat_ratio']
# 日期 收盘价 涨跌幅 换手率 净流入 净流入率 主力净流入 主力净流入率 行业净流入率
data = []
for x in j:
m = {}
for s in x.split(','):
k, v = s.split(':')
if '-' == v or 'null' == v:
v = '0.0'
m[k] = v
date = m['opendate']
close = float(m['trade'])
chg_r = float(m['changeratio'])
turnover = float(m['turnover']) / 10000
netamount = float(m['netamount']) / 10000
ratio = float(m['ratioamount'])
zl_netamount = float(m['r0_net']) / 10000
zl_ratio = float(m['r0_ratio'])
cat_ratio = float(m['cate_ra'])
data.append([date, close, chg_r, turnover, netamount, ratio, zl_netamount, zl_ratio, cat_ratio])
df = pd.DataFrame(data, columns=head)
log.info('data ok')
if not df.empty:
df.insert(1, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df, name='money_flow', con=conn, index=False, if_exists='append')
log.info('%s,%s,%d money flow data download ok.' % (code, str(start), len(sql_df)))
break
except Exception as e:
log.error('error:code=%s,start=%s,msg=%s' % (code, start, e))
log.debug('%s,costs:%d s' % (code, (datetime.now() - cost).seconds))
conn.close()
log.info('money flow data download complete. cost %d s' % (datetime.now() - cur_time).seconds)
pass
def bench_mark(codes, all_data, strategy=None, **kwargs):
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
matplotlib.style.use('ggplot')
start = kwargs.get('start', '2016-01-01')
end = kwargs.get('end', '2016-08-01')
for code in codes:
try:
a = AbstrictStrategy(stock_pool=[code], start=start, end=end, data=all_data)
t = pd.DataFrame(index=a.btData.datas[code].index)
# print a.btData.datas[code].head()
# t['c'] = a.btData.datas[code].close
# t['ma5'] = a.btData.datas[code].ma5
# t['ma10'] = a.btData.datas[code].ma10
t['beta'] = a.btData.datas[code].beta
bs = []
a.run()
t['bs'] = 0
t_times = 0
if a.acount.history_orders.get_history(code):
print len(a.acount.history_orders.get_history(code)), 'trade'
t_times = len(a.acount.history_orders.get_history(code))
for oh in a.acount.history_orders.get_history(code):
logger.info('order#%s' % oh)
if oh.date in t.index:
if 'buy' == oh.cmd:
# t.ix[oh.date]['bs'] = 5
t['bs'][oh.date] = 1
elif 'sell' == oh.cmd:
t['bs'][oh.date] = -1
# t.ix[oh.date]['bs']= oh.cmd
# t.ix['2015-08-07']['bs'] = 10
# print t.ix['2015-08-07']['bs']
# print '############################# history assets #####################'
# for x in a.acount.history_assets:
# print x
# print 'total_profit=', a.acount.history_orders.get_total_profit(code)
# print a.acount.cash, a.acount.get_market_value(), a.acount.get_assets(), a.acount.get_position_profit()
logger.info( '~ '+ code +'\t'+ str(a.baseline()) +'\t'+ str((a.acount.get_assets() - a.acount.initial_cash)/a.acount.initial_cash))
X.append((code, a.baseline(), (a.acount.get_assets() - a.acount.initial_cash)/a.acount.initial_cash))
# print t.describe()
# t.plot()
# plt.show()
except Exception, e:
traceback.print_exc()
pass
def __init__(self, stock_pool=['000001'], **kwargs):
self.hd = HistoryData()
self.stock_pool = stock_pool
start_date = kwargs.get('start')
end_date = kwargs.get('end')
if start_date is None or end_date is None:
raise Exception('no start,end time set')
logger.info('load data...')
self.btData = BtData(stock_pool=stock_pool, **kwargs)
self.btData.indicator()
logger.info('load data completed')
self.curr_tick = 0
self.curr_tick_pos = 0
self.begin = None
self.end = None
if 'acount' in kwargs:
self.acount = kwargs.get('acount')
else:
self.acount = Account(max_position=1., min_position=0., max_position_per_stock=1.)
def download_history_data_fq(autype='qfq', startTime=None):
'''
获取前复权的历史k线数据
'''
conn = db.get_history_data_db('D')
start = startTime
if startTime is None:
start = utils.today_last_year(6)
for code in get_all_stock_code():
df = ts.get_h_data(code, start=start, drop_factor=False)
if df is not None:
try:
df.insert(0, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df, name='history_data_%s' %
autype, con=conn, index=True, if_exists='append')
log.info('%s,%s history qfq data download ok.' % (code, start))
except Exception as e:
log.error('error:code:%s,start:%s' % (code, start))
def updata_all_stock_basics():
'''
更新所有股票的基本数据
return
DataFrame
code,代码
name,名称
industry,细分行业
area,地区
pe,市盈率
outstanding,流通股本
totals,总股本(万)
totalAssets,总资产(万)
liquidAssets,流动资产
fixedAssets,固定资产
reserved,公积金
reservedPerShare,每股公积金
eps,每股收益
bvps,每股净资
pb,市净率
timeToMarket,上市日期
'''
conn = db.get_history_data_db()
conn.text_factory = str
retry = 3
for i in range(retry):
try:
df = ts.get_stock_basics()
if not df.empty:
sql_df = df.loc[:, :]
sql.to_sql(sql_df,
name='stock_basics',
con=conn,
index=True,
if_exists='replace')
log.info('all stock basics updated, total size=%d' % len(df))
break
except Exception as e:
log.error(e)
conn.close()
def updata_all_stock_basics():
'''
更新所有股票的基本数据
return
DataFrame
code,代码
name,名称
industry,细分行业
area,地区
pe,市盈率
outstanding,流通股本
totals,总股本(万)
totalAssets,总资产(万)
liquidAssets,流动资产
fixedAssets,固定资产
reserved,公积金
reservedPerShare,每股公积金
eps,每股收益
bvps,每股净资
pb,市净率
timeToMarket,上市日期
'''
conn = db.get_history_data_db()
conn.text_factory = str
retry = 3
for i in range(retry):
try:
df = ts.get_stock_basics()
if not df.empty:
sql_df = df.loc[:, :]
sql.to_sql(sql_df, name='stock_basics', con=conn,
index=True, if_exists='replace')
log.info('all stock basics updated, total size=%d' % len(df))
break
except Exception as e:
log.error(e)
conn.close()
def download_history_data_fq(autype='qfq', startTime=None):
'''
获取前复权的历史k线数据
'''
conn = db.get_history_data_db('D')
start = startTime
if startTime is None:
start = utils.today_last_year(6)
for code in get_all_stock_code():
df = ts.get_h_data(code, start=start, drop_factor=False)
if df is not None:
try:
df.insert(0, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df,
name='history_data_%s' % autype,
con=conn,
index=True,
if_exists='append')
log.info('%s,%s history qfq data download ok.' % (code, start))
except Exception as e:
log.error('error:code:%s,start:%s' % (code, start))
def baseline(self, sync=False):
'''
等权买入并持有
'''
b = 0.
e = 0.
for data in self.btData.datas.values():
if sync:
b += data.ix[self.begin].close
e += data.ix[self.end].close
logger.info('#SYNC\t%s=>%f,%s=>%f' % (self.begin, data.ix[self.begin].close, self.end, data.ix[self.end].close))
else:
b += data.ix[0].close
e += data.ix[-1].close
logger.info('#!SYNC\t%s=>%f,%s=>%f' % (data.ix[0].date, data.ix[0].close, data.ix[-1].date, data.ix[-1].close))
n = len(self.btData.datas)
logger.info('avg_b=%f, avg_e=%f' % (b/n, e/n))
return (e/n - b/n) / b/n
def download_money_flow_data(num=1000):
'''
get money flow from sina finance
:param num:
:return:
'''
conn = db.get_money_flow_db()
cur_time = datetime.now()
for code in get_all_stock_code():
code = append_stock_perfix(code)
cost = datetime.now()
for i in range(retry):
try:
url = sina_money_flow_api % (num, code)
text = request.urlopen(url, timeout=10).read()
text = text.decode('GBK')
log.info('url=%s,size=%d, try=%d' % (url, len(text), i))
if len(text) < 10:
continue
# j = demjson.decode(text, 'utf-8') #json很大的时候效率非常查
text = text[2:-2]
j = text.replace('"', '').split('},{')
head = [
'date', 'close', 'chg_r', 'turnover', 'netamount', 'ratio',
'zl_netamount', 'zl_ratio', 'cat_ratio'
]
# 日期 收盘价 涨跌幅 换手率 净流入 净流入率 主力净流入 主力净流入率 行业净流入率
data = []
for x in j:
m = {}
for s in x.split(','):
k, v = s.split(':')
if '-' == v or 'null' == v:
v = '0.0'
m[k] = v
date = m['opendate']
close = float(m['trade'])
chg_r = float(m['changeratio'])
turnover = float(m['turnover']) / 10000
netamount = float(m['netamount']) / 10000
ratio = float(m['ratioamount'])
zl_netamount = float(m['r0_net']) / 10000
zl_ratio = float(m['r0_ratio'])
cat_ratio = float(m['cate_ra'])
data.append([
date, close, chg_r, turnover, netamount, ratio,
zl_netamount, zl_ratio, cat_ratio
])
df = pd.DataFrame(data, columns=head)
log.info('data ok')
if not df.empty:
df.insert(1, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df,
name='money_flow',
con=conn,
index=False,
if_exists='append')
log.info('%s,%s,%d money flow data download ok.' %
(code, str(start), len(sql_df)))
break
except Exception as e:
log.error('error:code=%s,start=%s,msg=%s' % (code, start, e))
log.debug('%s,costs:%d s' % (code, (datetime.now() - cost).seconds))
conn.close()
log.info('money flow data download complete. cost %d s' %
(datetime.now() - cur_time).seconds)
pass
def download_index_history_data(start=None, end=None, init_run=False):
'''
start:开始时间 yyyyMMdd，第一次调用空则取20100101，之后以数据表中最近时间为准
end:结束时间 yyyyMMdd，空则取当前日期
'''
cur_time = datetime.now()
conn = db.get_history_data_db()
if init_run:
start = default_start_time
if start is None:
try:
onerow = conn.execute(config.sql_last_date_index_all).fetchone()
if onerow is not None:
start = onerow[0]
dt = datetime.strptime(start, '%Y-%m-%d') + timedelta(days=1)
start = datetime.strftime(dt, '%Y%m%d')
else:
start = default_start_time
except Exception as e:
start = default_start_time
if end is None:
end = datetime.today().date().strftime('%Y%m%d')
print(start, end)
if int(end) <= int(start):
return None
for code in indices.keys():
for i in range(retry):
try:
url = sohu_history_api % (code, start, end)
text = request.urlopen(url, timeout=10).read()
text = text.decode('GBK')
log.info('url=%s,size=%d, try=%d' % (url, len(text), i))
if len(text) < 20:
continue
j = demjson.decode(text, 'utf-8')
head = [
'date', 'open', 'close', 'chg', 'chg_r', 'low', 'high',
'vibration', 'volume', 'amount'
] # 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万)
# 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万) 换手率
data = []
for x in j[0].get('hq'):
date, open, close, change, _, low, high, valume, amount, _ = x
chg_r = '%.4f' % (
(float(close) - float(open)) / float(open))
vibration = '%.4f' % (float(
(float(high) - float(low)) / float(open)))
# print date, vibration, str(float(vibration))
data.append([
date,
float(open),
float(close),
float(change),
float(chg_r),
float(low),
float(high),
float(vibration),
float(valume),
float(amount)
])
# sql_str = 'insert OR IGNORE into all_index values(?,?,?,?,?,?,?,?,?,?,?)'
# print len(data[0])
# conn.executemany(sql_str, data)
df = pd.DataFrame(data, columns=head)
if not df.empty:
df.insert(1, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df,
name='all_index',
con=conn,
index=False,
if_exists='append')
log.info('%s,%s index history download ok.' %
(code, start))
break
except Exception as e:
log.error(e)
conn.close()
log.info('index history data download complete. cost %d s' %
(datetime.now() - cur_time).seconds)
def download_history_data(ktype='D', start=None, end=None, init_run=False):
'''
获取近不复权的历史k线数据
'''
if init_run:
start = default_start_time
if end == None:
end = datetime.today().date().strftime('%Y%m%d')
conn = db.get_history_data_db(ktype)
cost = 0
cur_time = datetime.now()
for code in get_all_stock_code():
cost = datetime.now()
if start is None:
_start = get_laste_update_dt(code)
if _start is not None:
dt = datetime.strptime(_start, '%Y-%m-%d') + timedelta(days=1)
start = datetime.strftime(dt, '%Y%m%d')
else:
row = conn.execute(config.sql_last_date_history_data_by_code %
code).fetchone()
if row is not None:
start = row[0]
dt = datetime.strptime(start,
'%Y-%m-%d') + timedelta(days=1)
start = datetime.strftime(dt, '%Y%m%d')
else:
start = default_start_time
for i in range(retry):
try:
url = sohu_history_api % ('cn_' + code, start, end)
text = request.urlopen(url, timeout=10).read()
text = text.decode('GBK')
log.info('url=%s,size=%d, try=%d' % (url, len(text), i))
if len(text) < 20:
continue
j = demjson.decode(text, 'utf-8')
head = [
'date', 'open', 'close', 'chg', 'chg_r', 'low', 'high',
'vibration', 'volume', 'amount', 'turnover'
] # 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万)
# 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万) 换手率
data = []
for x in j[0].get('hq'):
date, open, close, change, _, low, high, valume, amount, turnover = x
if '-' == turnover:
turnover = '0.0%'
chg_r = '%.4f' % (
(float(close) - float(open)) / float(open))
vibration = '%.4f' % float(
(float(high) - float(low)) / float(open))
chg_r = float(chg_r)
vibration = float(vibration)
data.append([
date,
float(open),
float(close),
float(change),
float(chg_r),
float(low),
float(high),
float(vibration),
float(valume),
float(amount),
float(turnover[:-1])
])
df = pd.DataFrame(data, columns=head)
if not df.empty:
df.insert(1, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df,
name='history_data',
con=conn,
index=False,
if_exists='append')
log.info('%s,%s,%d history data download ok.' %
(code, str(start), len(sql_df)))
slog.info('%s,%s' % (code, data[0][0]))
break
except Exception as e:
log.error('error:code=%s,start=%s,msg=%s' % (code, start, e))
if str(e).find('UNIQUE constraint') > -1:
break
log.debug('%s,costs:%d s' % (code, (datetime.now() - cost).seconds))
conn.close()
log.info('history data download complete. cost %d s' %
(datetime.now() - cur_time).seconds)
def download_index_history_data(start=None, end=None, init_run=False):
'''
start:开始时间 yyyyMMdd，第一次调用空则取20100101，之后以数据表中最近时间为准
end:结束时间 yyyyMMdd，空则取当前日期
'''
cur_time = datetime.now()
conn = db.get_history_data_db()
if init_run:
start = default_start_time
if start is None:
try:
onerow = conn.execute(config.sql_last_date_index_all).fetchone()
if onerow is not None:
start = onerow[0]
dt = datetime.strptime(start, '%Y-%m-%d') + timedelta(days=1)
start = datetime.strftime(dt, '%Y%m%d')
else:
start = default_start_time
except Exception as e:
start = default_start_time
if end is None:
end = datetime.today().date().strftime('%Y%m%d')
print(start, end)
if int(end) <= int(start):
return None
for code in indices.keys():
for i in range(retry):
try:
url = sohu_history_api % (code, start, end)
text = request.urlopen(url, timeout=10).read()
text = text.decode('GBK')
log.info('url=%s,size=%d, try=%d' % (url, len(text), i))
if len(text) < 20:
continue
j = demjson.decode(text, 'utf-8')
head = ['date', 'open', 'close', 'chg', 'chg_r', 'low', 'high', 'vibration', 'volume',
'amount'] # 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万)
# 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万) 换手率
data = []
for x in j[0].get('hq'):
date, open, close, change, _, low, high, valume, amount, _ = x
chg_r = '%.4f' % ((float(close) - float(open)) / float(open))
vibration = '%.4f' % (float((float(high) - float(low)) / float(open)))
# print date, vibration, str(float(vibration))
data.append([date, float(open), float(close), float(change), float(chg_r), float(low), float(high),
float(vibration), float(valume), float(amount)])
# sql_str = 'insert OR IGNORE into all_index values(?,?,?,?,?,?,?,?,?,?,?)'
# print len(data[0])
# conn.executemany(sql_str, data)
df = pd.DataFrame(data, columns=head)
if not df.empty:
df.insert(1, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df, name='all_index', con=conn, index=False, if_exists='append')
log.info('%s,%s index history download ok.' % (code, start))
break
except Exception as e:
log.error(e)
conn.close()
log.info('index history data download complete. cost %d s' % (datetime.now() - cur_time).seconds)
# t['ma10'] = a.btData.datas[code].ma10
# t['beta'] = a.btData.datas[code].beta
# t['l_angle'] = a.btData.datas[code].l_angle
# t['l_intercept'] = a.btData.datas[code].l_intercept
# t['l_slope'] = a.btData.datas[code].l_slope
# t['sar'] = a.btData.datas[code].sar
bs = []
t['bs'] = 0
# print t['sar']
# print t.tail()
# plt.figure()
if a.acount.history_orders.get_history(code):
print len(a.acount.history_orders.get_history(code)), 'trade'
t_times = len(a.acount.history_orders.get_history(code))
for oh in a.acount.history_orders.get_history(code):
logger.info('order#%s' % oh)
if oh.date in t.index:
if 'buy' == oh.cmd:
# t.ix[oh.date]['bs'] = 5
t['bs'][oh.date] = 1
elif 'sell' == oh.cmd:
t['bs'][oh.date] = -1
# t.ix[oh.date]['bs']= oh.cmd
# t.ix['2015-08-07']['bs'] = 10
# print t.ix['2015-08-07']['bs']
# print '############################# history assets #####################'
# for x in a.acount.history_assets:
# print x
# print 'total_profit=', a.acount.history_orders.get_total_profit(code)
# print a.acount.cash, a.acount.get_market_value(), a.acount.get_assets(), a.acount.get_position_profit()
def download_history_data(ktype='D', start=None, end=None, init_run=False):
'''
获取近不复权的历史k线数据
'''
if init_run:
start = default_start_time
if end == None:
end = datetime.today().date().strftime('%Y%m%d')
conn = db.get_history_data_db(ktype)
cost = 0
cur_time = datetime.now()
for code in get_all_stock_code():
cost = datetime.now()
if start is None:
_start = get_laste_update_dt(code)
if _start is not None:
dt = datetime.strptime(_start, '%Y-%m-%d') + timedelta(days=1)
start = datetime.strftime(dt, '%Y%m%d')
else:
row = conn.execute(config.sql_last_date_history_data_by_code % code).fetchone()
if row is not None:
start = row[0]
dt = datetime.strptime(start, '%Y-%m-%d') + timedelta(days=1)
start = datetime.strftime(dt, '%Y%m%d')
else:
start = default_start_time
for i in range(retry):
try:
url = sohu_history_api % ('cn_' + code, start, end)
text = request.urlopen(url, timeout=10).read()
text = text.decode('GBK')
log.info('url=%s,size=%d, try=%d' % (url, len(text), i))
if len(text) < 20:
continue
j = demjson.decode(text, 'utf-8')
head = ['date', 'open', 'close', 'chg', 'chg_r', 'low', 'high', 'vibration', 'volume',
'amount', 'turnover'] # 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万)
# 日期 开盘 收盘 涨跌额 涨跌幅 最低 最高 成交量(手) 成交金额(万) 换手率
data = []
for x in j[0].get('hq'):
date, open, close, change, _, low, high, valume, amount, turnover = x
if '-' == turnover:
turnover = '0.0%'
chg_r = '%.4f' % ((float(close) - float(open)) / float(open))
vibration = '%.4f' % float((float(high) - float(low)) / float(open))
chg_r = float(chg_r)
vibration = float(vibration)
data.append(
[date, float(open), float(close), float(change), float(chg_r), float(low), float(high),
float(vibration), float(valume), float(amount), float(turnover[:-1])])
df = pd.DataFrame(data, columns=head)
if not df.empty:
df.insert(1, 'code', code)
sql_df = df.loc[:, :]
sql.to_sql(sql_df, name='history_data', con=conn, index=False, if_exists='append')
log.info('%s,%s,%d history data download ok.' % (code, str(start), len(sql_df)))
slog.info('%s,%s' % (code, data[0][0]))
break
except Exception as e:
log.error('error:code=%s,start=%s,msg=%s' % (code, start, e))
if str(e).find('UNIQUE constraint') > -1:
break
log.debug('%s,costs:%d s' % (code, (datetime.now() - cost).seconds))
conn.close()
log.info('history data download complete. cost %d s' % (datetime.now() - cur_time).seconds)