def get_today_tick_ave(code, ave=None):
try:
dtick = ts.get_today_ticks(code)
df = dtick
if len(dtick.index) > 0:
p_now = dtick['price'].values[0] * 100
ep = dtick['amount'].sum() / dtick['volume'].sum()
if not ave == None:
if p_now > ave and ep > ave:
print ("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" % (code, ep, p_now, ave, get_now_time()))
elif p_now > ave and ep < ave:
print ("gold:%s ep:%s UP:%s! A:%s %s !" % (code, ep, p_now, ave, get_now_time()))
elif p_now < ave and ep > ave:
print ("down:%s ep:%s Dow:%s? A:%s %s ?" % (code, ep, p_now, ave, get_now_time()))
else:
print ("DOWN:%s ep:%s now:%s??? A:%s %s ???" % (code, ep, p_now, ave, get_now_time()))
else:
if ep > ave:
print ("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" % (code, ep, p_now, ave, get_now_time()))
else:
print ("down:%s ep:%s now:%s??? A:%s %s ?" % (code, ep, p_now, ave, get_now_time()))
else:
df = ts.get_realtime_quotes(code)
print "name:%s op:%s price:%s" % (df['name'].values[0], df['open'].values[0], df['price'].values[0])
# print df
return df
except (IOError, EOFError, KeyboardInterrupt) as e:
print("Except:%s" % (e))
def updateData(self):
self.stk_quotes = ts.get_realtime_quotes(self.stk_code)
self.stk_ticks = ts.get_today_ticks(self.stk_code).sort_values(by="time")
#df = df.sort("time")
#df = ts.get_realtime_quotes(self.stk_code)
self.setWindowTitle(str(self.stk_quotes['date'][0] + ' 分笔明细图'))
self.repaint()
def get_multiday_ave_compare_silent(code, dayl='10'):
dtick = ts.get_today_ticks(code)
d_hist = ema.getdata_ema_trend_silent(code, dayl, 'd')
# print d_hist
day_t = ema.get_today()
if day_t in d_hist.index:
dl = d_hist.drop(day_t).index
else:
dl = d_hist.index
# print dl
# print dl
ep_list = []
for da in dl.values:
# print code,da
td = ts.get_tick_data(code, da)
# print td
if not type(td) == types.NoneType:
ep = td['amount'].sum() / td['volume'].sum()
ep_list.append(ep)
# print ("D: %s P: %s" % (da[-5:], ep))
ave = ema.less_average(ep_list)
if len(dtick.index) > 0:
ep = dtick['amount'].sum() / dtick['volume'].sum()
p_now = dtick['price'].values[0] * 100
if p_now > ave or ep > ave:
print ("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" % (code, ep, p_now, ave, get_now_time()))
# elif p_now > ave and ep < ave:
# print ("gold:%s ep:%s UP:%s! A:%s %s !" % (code, ep, p_now, ave, get_now_time()))
# elif p_now < ave and ep > ave:
# print ("down:%s ep:%s Dow:%s? A:%s %s ?" % (code, ep, p_now, ave, get_now_time()))
return True
else:
if p_now < ave and ep < ave:
print ("DOWN:%s ep:%s now:%s??? A:%s %s ???" % (code, ep, p_now, ave, get_now_time()))
return False
def __init__(self,code):
self.df=ts.get_today_ticks(code)
#转换str为时间格式，便于下面用来比较时间的大小
self.df['time']=self.df['time'].map(lambda x:datetime.datetime.strptime(str(x),'%H:%M:%S'))
print('\n')
self.total= self.df['volume'].sum()
def ticktoday(code):
dftick = ts.get_today_ticks(code)
dftick.insert(0,'uploadtime',nowtime)
dftick.insert(0,'code',code)
dftick.to_sql('mkt_tick',engine,if_exists='append')
counttick = cursor.execute('show columns from mkt_tick like \'tick_id\'')
if counttick == 0:
insertidtick()
def main():
df = ts.get_today_ticks('300104')
total_vol=df['volume'].sum()*100
print('总成交股数')
big_deal=df[df['volume']>=100]['volume'].sum()*100
print('大于100手的总和',big_deal)
percent=float(big_deal)/total_vol*100
print('大单占比',percent)
print(df['volume'].value_count())
def main():
#regression
import tushare as ts
df=ts.get_today_ticks('000030')
xArr,yArr = loadDataSet('ex0.txt')
ws = standRegres(xArr,yArr)
print ws
#plotStandRegres(xArr,yArr,ws)
print calcCorrcoef(xArr,yArr,ws)
#lwlr
lwlrTestPlot(xArr,yArr,k=1)
def get_trading_records(code):
try:
df = ts.get_today_ticks(code)
j_datas = json.loads(df.to_json(orient='records'))
trading_records = []
from models import TradingRecord
for j_data in j_datas:
trading_records.append(TradingRecord(code, j_data))
return trading_records
except IOError:
pass
def realtime(self, id):
# all_stock=ts.get_today_all()
# print(all_stock)
df = ts.get_realtime_quotes(id)
# print(df[['time','name','price','bid','ask','volume']])
# print(df.head())
price_change = ts.get_today_ticks(id)
print(price_change[['time', 'change', 'type', 'volume']])
big_share = ts.get_sina_dd(id, date='2016-04-01')
print(big_share[['time', 'code', 'price', 'preprice', 'volume', 'type']])
def sub_process_ratio(i,q):
print "Start"
try:
df=ts.get_today_ticks(i)
#print 'len of df ',len(df)
#print df
if len(df)==0:
print "Pause of exchange"
return i,'pause'
except Exception,e:
print e
print "ERROR"
return id,'pause'
def _fill_today_bars(self):
today = datetime.date.today().isoformat()
if is_holiday(today): # do nothing if holiday
return
today_bars = {}
for identifier in self.__identifiers:
try:
df = ts.get_today_ticks(identifier)
today_bars[identifier] = get_bar_list(df, self.__frequency, None)
except Exception, e:
logger.error(e)
def get_info(self,id):
print id
try:
df=ts.get_today_ticks(id)
print 'len of df ',len(df)
#print df
if len(df)==0:
print "Pause of exchange"
return id,'pause'
except Exception,e:
print e
print "ERROR"
return id,'pause'
def cmp_top(ticks):
if ticks is None:
print 'no'
return None
try:
today = ts.get_today_ticks(id).sort('amount', ascending=False)
except:
return None
sticks = ticks.sort('amount', ascending=False).head(20)
sticks = sticks.reset_index()
today = today.reset_index()
top = sticks.ix[0]
top_today = today.ix[0]
return [(round((top.price - top_today.price)/top.price, 3)), round(top.price, 3), round(top_today.price, 3), top.amount, top_today.amount]
def get_tick(stockCode=None, date=None):
"""
根据股票列表的股票代码获取当日/指定日期历史分笔
Return
--------
DataFrame
"""
tick_data = ''
if date != None and date != '':
tick_data = ts.get_tick_data(stockCode, date)
else:
tick_data = ts.get_today_ticks(stockCode)
if not tick_data.dropna(axis=0, how='any', thresh=None).empty:
tick_data.insert(0, 'code', stockCode) #插入股票代码字段
return tick_data
def get_all_hot():
try:
gold = {}
goldl = []
# df = ts.get_today_all()
# top = df[df['changepercent'] > 6]
# top = top[top['changepercent'] <11]
# print "top:", len(top['code'])
codes="000893,002351,000705,002451,002515,002279,002679,002761,600895,000532,002113,002438,600235,600210,600857".split(',')
print codes
# for code in top['code']:
for code in codes:
dtick = ts.get_today_ticks(code)
d_hist = ema.getdata_ema_trend(code, '10', 'd')
# print d_hist
day_t = ema.get_today()
# if len(dtick.index) == 0:
# dtick=d_hist[]
if day_t in d_hist.index:
dl = d_hist.drop(day_t).index
else:
dl = d_hist.index
# print dl
# print dl
ep_list = []
for da in dl.values:
# print da
td = ts.get_tick_data(code, da)
# print td
if len(td) > 0:
ep = td['amount'].sum() / td['volume'].sum()
ep_list.append(ep)
# print ("D: %s P: %s"%(da,ep))
total_ave = ema.less_average(ep_list)
all_max=max(ep_list)
if len(dtick.index) > 0:
ep = dtick['amount'].sum() / dtick['volume'].sum()
if ep >= all_max:
gold[code] = d_hist
goldl.append(code)
print ("Gold:%s ep:%s ave:%s max:%s" % (code, ep, total_ave,all_max))
except (IOError, EOFError, KeyboardInterrupt):
# print "key"
# print "break"
print "why"
return
def getBigDeal(self, code,vol):
df = ts.get_today_ticks(code)
t= df[df['volume']>vol]
s=df[df['amount']>100000000]
print('\n')
if t.size!=0:
print("Big volume")
print(self.base[self.base['code']==str(code)]['name'].values[0])
print(t)
if s.size!=0:
print("Big amount: ")
print(self.base[self.base['code']==str(code)]['name'].values[0])
print(s)
r=df[df['volume']>vol*10]
if r.size!=0:
print("Super amount:")
print(self.base[self.base['code']==str(code)]['name'].values[0])
print(r)
def _fill_today_bars(self):
today = datetime.date.today().isoformat()
if is_holiday(today): # do nothing if holiday
return
elif datetime.date.today().weekday() in [5, 0]:
return
# #James:
# if datetime.datetime.now().hour * 60 + 30 < 9*60 + 30:
# return
today_bars = {}
for identifier in self.__identifiers:
try:
df = ts.get_today_ticks(identifier)
today_bars[identifier] = get_bar_list(df, self.__frequency, None)
except Exception, e:
logger.error(e)
def get_multiday_ave_compare(code, dayl='10'):
dtick = ts.get_today_ticks(code)
d_hist = ema.getdata_ema_trend(code, dayl, 'd')
# print d_hist
day_t = ema.get_today()
if d_hist is not None:
if day_t in d_hist.index:
dl = d_hist.drop(day_t).index
else:
dl = d_hist.index
else:
return 0
# print dl
# print dl
ep_list = []
for da in dl.values:
# print da
td = ts.get_tick_data(code, da)
# print td
if not type(td) == types.NoneType:
ep = td['amount'].sum() / td['volume'].sum()
ep_list.append(ep)
print("D: %s P: %s" % (da[-5:], ep))
ave = ema.less_average(ep_list)
if len(dtick.index) > 0:
ep = dtick['amount'].sum() / dtick['volume'].sum()
p_now = dtick['price'].values[0] * 100
if p_now > ave and ep > ave:
print("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" %
(code, ep, p_now, ave, cct.get_now_time()))
elif p_now > ave and ep < ave:
print("gold:%s ep:%s UP:%s! A:%s %s !" %
(code, ep, p_now, ave, cct.get_now_time()))
elif p_now < ave and ep > ave:
print("down:%s ep:%s Dow:%s? A:%s %s ?" %
(code, ep, p_now, ave, cct.get_now_time()))
else:
print("DOWN:%s ep:%s now:%s??? A:%s %s ???" %
(code, ep, p_now, ave, cct.get_now_time()))
return ave
def get_today_tick_data(code, retry_count=RETRY_COUNT, pause=PAUSE):
"""获取个股以往交易历史的分笔数据明细，通过分析分笔数据，可以大致判断资金的进出情况。"""
logger.info('Begin get today tick data, code is: %s, date is: %s.' %
(code, today_line))
try:
data_df = ts.get_today_ticks(code, retry_count, pause)
except Exception as e:
logger.exception(
'Error get today tick data, code is :%s, date is %s.' %
(code, today_line))
else:
if data_df is not None and not data_df.empty:
data = data_df.values
today_tick_data_queue.put((code, today_line, data))
logger.info(
'Success get today tick data, code is :%s, date is %s.' %
(code, today_line))
else:
logger.warn(
'Empty get today tick data, code is :%s, date is %s.' %
(code, today_line))
def _fill_today_bars(self):
today = datetime.date.today().isoformat()
if is_holiday(today): # do nothing if holiday
return
elif datetime.date.today().weekday() in [5, 0]:
return
# #James:
# if datetime.datetime.now().hour * 60 + 30 < 9*60 + 30:
# return
today_bars = {}
for identifier in self.__identifiers:
try:
df = ts.get_today_ticks(identifier)
today_bars[identifier] = get_bar_list(df, self.__frequency,
None)
except Exception, e:
logger.error(e)
def get_trade_data(self):
if(self.trade_data_type == 'hq'):
df = ts.get_hist_data(code=self.stock_code, start=self.start_date, end=self.end_date, ktype=self.stock_K_type, retry_count=self.retry_count, pause=self.pause)
elif(self.trade_data_type == 'rd'):
df = ts.get_h_data(code=self.stock_code, start=self.start_date, end=self.end_date, autype=self.autype, index=self.index, retry_count=self.retry_count, pause=self.pause)
elif(self.trade_data_type == 'rq'):
df = ts.get_today_all()
elif(self.trade_data_type == 'ht'):
df = ts.get_tick_data(code=self.stock_code, date=self.date, retry_count=self.retry_count, pause=self.pause)
elif(self.trade_data_type == 'rt'):
df = ts.get_realtime_quotes(symbols=self.stock_code)
elif(self.trade_data_type == 'cht'):
df = ts.get_today_ticks(code=self.stock_code, retry_count=self.retry_count, pause=self.pause)
elif(self.trade_data_type == 'index'):
df = ts.get_index()
elif(self.trade_data_type == 'btd'):
df = ts.get_sina_dd(code=self.stock_code, date=self.date, vol=self.vol, retry_count=self.retry_count, pause=self.pause)
if(df is not None):
return df
else:
return -1
def volume_calculation(code, start, end):
'''
计算某个股票的某个时间段的累计成交量
:param start: 开始日期
:param end: 结束日期
:return: 成交量，占每天比例
'''
df = ts.get_today_ticks(code)
# 转换str为时间格式，便于下面用来比较时间的大小
df['time'] = df['time'].map(lambda x: datetime.datetime.strptime(str(x), '%H:%M:%S'))
total = df['volume'].sum()
start = datetime.datetime.strptime(start, '%H:%M:%S')
end = datetime.datetime.strptime(end, '%H:%M:%S')
new_df = df[(df['time'] >= start) & (df['time'] < end)]
volume = new_df['volume'].sum()
rate = round(volume * 1.00 / total * 100, 2)
return volume, rate
def get_dangrifenbi_data(self):
'''
参数说明：
code：股票代码，即6位数字代码
retry_count : int, 默认3,如遇网络等问题重复执行的次数
pause : int, 默认 0,重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
返回值说明:
time：时间
price：当前价格
pchange:涨跌幅
change：价格变动
volume：成交手
amount：成交金额(元)
type：买卖类型【买盘、卖盘、中性盘】
'''
drifenbi_data = ts.get_today_ticks(code=self.code,
retry_count=self.retry_count,
pause=self.pause)
print(drifenbi_data)
return drifenbi_data
def xtick_real_downsub(xcod):
''' 中国A股,tick 历史或real实时 tick 分笔数据下载子程序
会自动将中文type，替换成 英文：中性盘：norm；买盘：buy 卖盘：sell
【输入】
xcod,股票代码
xtim，日期字符串，当xtim为空时，下载的是当天 实时 tick数据
【输出】
df,股票 tick 数据
数据列格式：
time,price,change,volume,amount,type
'''
xd = ts.get_today_ticks(xcod)
dn = len(xd) # print('n',dn) # 跳过无数据 日期
if dn > 10:
xd['type'] = xd['type'].str.replace(u'中性盘', 'norm')
xd['type'] = xd['type'].str.replace(u'买盘', 'buy')
xd['type'] = xd['type'].str.replace(u'卖盘', 'sell')
# xd.to_csv('tmp\\'+xcod+'_'+xtim+'.csv',index=False,encoding='utf')
else:
xd = []
#
return xd
def xtick_real_downsub(xcod):
''' 中國A股,tick 歷史或real即時 tick 分筆資料下載副程式
會自動將中文type，替換成 英文：中性盤：norm；買盤：buy 賣盤：sell
【輸入】
xcod,股票代碼
xtim，日期字串，當xtim為空時，下載的是當天 即時 tick資料
【輸出】
df,股票 tick 資料
資料列格式：
time,price,change,volume,amount,type
'''
xd = ts.get_today_ticks(xcod)
dn = len(xd) # print('n',dn) # 跳過無數據 日期
if dn > 10:
xd['type'] = xd['type'].str.replace(u'中性盤', 'norm')
xd['type'] = xd['type'].str.replace(u'買盤', 'buy')
xd['type'] = xd['type'].str.replace(u'賣盤', 'sell')
# xd.to_csv('tmp\\'+xcod+'_'+xtim+'.csv',index=False,encoding='utf')
else:
xd = []
#
return xd
def xtick_real_downsub(xcod):
''' 中国A股,tick 历史或real实时 tick 分笔数据下载子程序
会自动将中文type，替换成 英文：中性盘：norm；买盘：buy 卖盘：sell
【输入】
xcod,股票代码
xtim，日期字符串，当xtim为空时，下载的是当天 实时 tick数据
【输出】
df,股票 tick 数据
数据列格式：
time,price,change,volume,amount,type
'''
xd = ts.get_today_ticks(xcod)
dn = len(xd) # print('n',dn) # 跳过无数据 日期
if dn > 10:
xd['type'] = xd['type'].str.replace(u'中性盘', 'norm')
xd['type'] = xd['type'].str.replace(u'买盘', 'buy')
xd['type'] = xd['type'].str.replace(u'卖盘', 'sell')
# xd.to_csv('tmp\\'+xcod+'_'+xtim+'.csv',index=False,encoding='utf')
else:
xd = []
#
return xd
def get_today_tick_ave(code, ave=None):
try:
dtick = ts.get_today_ticks(code)
df = dtick
if len(dtick.index) > 0:
p_now = dtick['price'].values[0] * 100
ep = dtick['amount'].sum() / dtick['volume'].sum()
if not ave == None:
if p_now > ave and ep > ave:
print("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" %
(code, ep, p_now, ave, get_now_time()))
elif p_now > ave and ep < ave:
print("gold:%s ep:%s UP:%s! A:%s %s !" %
(code, ep, p_now, ave, get_now_time()))
elif p_now < ave and ep > ave:
print("down:%s ep:%s Dow:%s? A:%s %s ?" %
(code, ep, p_now, ave, get_now_time()))
else:
print("DOWN:%s ep:%s now:%s??? A:%s %s ???" %
(code, ep, p_now, ave, get_now_time()))
else:
if ep > ave:
print("GOLD:%s ep:%s UP:%s!!! A:%s %s !!!" %
(code, ep, p_now, ave, get_now_time()))
else:
print("down:%s ep:%s now:%s??? A:%s %s ?" %
(code, ep, p_now, ave, get_now_time()))
else:
df = ts.get_realtime_quotes(code)
print "name:%s op:%s price:%s" % (df['name'].values[0],
df['open'].values[0],
df['price'].values[0])
# print df
return df
except (IOError, EOFError, KeyboardInterrupt) as e:
print("Except:%s" % (e))
def ticks(code, source="spider", date=None, cons=None):
"""返回date日期的分笔数据
:param source:
:param code: str: 股票代码，如 603655
:param date: str: 日期，如 2018-03-15
:param cons: tushare的api连接
:return:
"""
if not date:
date = datetime.now().date().__str__()
TODAY = datetime.now().date().__str__()
# 统一 ticks 的输出结果
def _unify_out(ticks, date):
ticks = ticks[['time', 'price', 'volume', 'type']]
ticks['datetime'] = ticks['time'].apply(
lambda x: datetime.strptime(date + " " + x, "%Y-%m-%d %H:%M:%S"))
ticks['vol'] = ticks['volume']
type_convert = {"买盘": 0, "卖盘": 1, "中性盘": 2, "0": 2}
ticks['type'] = ticks["type"].apply(lambda x: type_convert[str(x)])
ticks.drop(['time', 'volume'], axis=1, inplace=True)
ticks.sort_values('datetime', inplace=True)
ticks.reset_index(drop=True, inplace=True)
return ticks[['datetime', 'price', 'vol', 'type']]
if source == "spider" and date == TODAY:
ticks = ts.get_today_ticks(code=code)
ticks = _unify_out(ticks, date=TODAY)
elif source == "spider" and date != TODAY:
ticks = ts.get_tick_data(code=code, date=date)
ticks = _unify_out(ticks, date=date)
else:
if not cons:
cons = ts.get_apis()
ticks = ts.tick(code=code, conn=cons, date=date)
return ticks
def stat(code):
"""区间成交量统计"""
df = ts.get_today_ticks(code=code, )
df['amount'] = df['price'] * df['vol'] * 100
print(df.head(100))
buy = df[df['type'] == '买入']
sale = df[df['type'] == '卖出']
s = sale.groupby(['price'])['vol'].sum()
b = buy.groupby(['price'])['vol'].sum()
t = df.groupby(['price'])['vol'].sum()
print('买入总成交：', buy['vol'].sum(), '手')
print('卖出总成交：', sale['vol'].sum(), '手')
print('总买入：', buy['amount'].sum())
print('总卖出：', sale['amount'].sum())
print('净买入额：', (buy['amount'].sum() - sale['amount'].sum()) / 10000, '万')
fig = subplots.make_subplots(rows=3, cols=1)
traceS = go.Bar(x=list(s.to_dict().values()),
y=list(s.to_dict().keys()),
name='卖出',
marker=dict(color='green'),
orientation='h')
traceB = go.Bar(x=list(b.to_dict().values()),
y=list(b.to_dict().keys()),
name='买入',
marker=dict(color='red'),
orientation='h')
# traceT
# = go.Bar(x = list(t.to_dict().keys()),y = list(t.to_dict().values()),name='总数',marker=dict(color='blue'))
fig.append_trace(traceS, 1, 1)
fig.append_trace(traceB, 2, 1)
fig.add_trace(traceB, row=3, col=1)
fig.add_trace(traceS, row=3, col=1)
fig.update_layout(barmode='stack')
fig.show()
def update_stock_tick(self,security):
'''
目前仅能更新当日的股票历史分笔数据
:param security:
:return:
'''
stock = self._session.query(Stock).filter(Stock.security == security).one() # 获取股票基本信息
df = ts.get_today_ticks(security)
df = df.set_index('time')
for time in df.index:
temp = datetime.datetime.today().strftime('%Y-%m-%d')+' '+time
date = datetime.datetime.strptime(temp, "%Y-%m-%d %H:%M:%S")
stock_tick_item = Tick_data(id = security+'#'+temp,
time = date,
price = df.loc[time]['price'],
change = df.loc[time]['change'],
volume = df.loc[time]['volume'],
amount = df.loc[time]['amount'],
type = unicode(df.ix[time]['type']))
try:
self._session.query(Tick_data).filter(Tick_data.id == security+'#'+temp).one()
except:
stock.tick_data.append(stock_tick_item)
self._session.commit()
if x < 0.01:
x1 = str(x) + ' ***'
else:
x1 = str(x) + ' ***'
b.append(x1)
a.P值.apply(lambda x: x[:3])
temp['P值'] = b
temp.columns
a = temp.drop('t 值', axis=1)
a = a.set_index('参数')
b = a.stack()
c = pd.DataFrame({'dfdf': b})
c.to_excel('tem.xlsx')
df = ts.get_today_ticks('000852')
df1 = ts.get_tick_data('000852', date='2018-04-09')
z = df.type == '买盘'
df.time[1]
df.sort_values('time', inplace=True)
df.groupby('type').mean()
t = df[df.volume > 400]
junzhi = (df.price * df.volume).sum() / df.volume.sum()
t1 = df[(df.type == '买盘') & (df.volume > 400)]
junzhi1 = (t1.price * t1.volume).sum() / t1.volume.sum()
import time
def generateline(stocknumber, Type, startdate, enddate, interval):
startdata = startdate.encode("ascii").replace("/", "-").replace(
"\n", "") #convert to tushare readable date
enddata = enddate.encode("ascii").replace("/", "-").replace("\n", "")
array = df()
#print startdata
#print enddata
current_time = time.strftime("%Y/%m/%d")
if Type == "分笔".decode("utf-8"):
if startdate != current_time:
array = ts.get_tick_data(stocknumber, date=startdata) #分笔
if array is None:
return
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype): #if卖盘，交易变成负数
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘": #if中性盘，则忽略. Might have a problem with this part??
amount[idx] = 0
returnarray = zip(date, amount, flag, price)
return returnarray
else:
array = ts.get_today_ticks(stocknumber) #Tushare里今日分笔和历史分笔需要分别对待
if array is None:
return
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘".decode("utf-8"):
amount[idx] = -amount[idx]
if val == "中性盘".decode("utf-8"):
amount[idx] = 0
returnarray = zip(date, amount, flag)
return returnarray
if interval != "qfq" and interval != "hfq": #正常历史k线
if Type != "Kline":
array = ts.get_k_data(stocknumber,
start=startdata,
end=enddata,
ktype=interval)
if array is None:
return
Type1 = firstletter(Type).encode("ascii")
target = array[Type1].tolist()
date = array["date"].tolist()
returnarray = zip(date, target)
return returnarray
else:
array = ts.get_k_data(stocknumber,
start=startdata,
end=enddata,
ktype=interval)
if array is None:
return
Date = array["date"].tolist()
Open = array["open"].tolist()
Close = array["close"].tolist()
High = array["high"].tolist()
Low = array["low"].tolist()
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
else:
if Type != "Kline": # 复权
array = ts.get_h_data(stocknumber,
start=startdata,
end=enddata,
autype=interval)
if array is None:
return
Type1 = firstletter(Type).encode("ascii")
array = array.sort_index()
target = array[Type1].tolist()
date = array.index.format()
returnarray = zip(date, target)
return returnarray
else:
array = ts.get_h_data(stocknumber,
start=startdata,
end=enddata,
autype=interval)
if array is None:
return
array = array.sort_index()
Date = array.index.format()
Open = array["open"].tolist()
Close = array["close"].tolist()
High = array["high"].tolist()
Low = array["low"].tolist()
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
# encoding: utf-8
import tushare as ts
import pandas as pd
import numpy as np
if __name__ == '__main__':
#当日历史分笔
#get_today_ticks(code,retry_count,pause)
#返回：时间、当前价格、涨跌幅、价格变动、成交金额（元）、买卖类型
df = ts.get_today_ticks('002410',3,0.001)
print df.head(100)
import tushare as ts
del ts
# 历史分笔
a = ts.get_tick_data('000005', date='2018-12-24')
help(ts)
# 当日历史分
b = ts.get_today_ticks('601333', date='2017-01-09')
# 实时分笔
ts.get_realtime_quotes('000581')
# 历史分笔 和 当日历史分 返回结果
time：时间
price：成交价格
pchange：涨跌幅
change：价格变动
volume：成交手
amount：成交金额(元)
type：买卖类型【买盘、卖盘、中性盘】
import easyquotation
import tf
from urllib.request import urlretrieve
f = open('SHA.csv', 'r')
for line in f:
"""
import tushare as ts
import csv
stockID = '002219'
file_csv = open(stockID + 'TodayTicks.csv', 'wb+')
spamwriter = csv.writer(file_csv,dialect='excel')
spamwriter.writerow(['time', 'price', 'pchange', 'change', 'volume', 'amount', 'type'])
fileHistory_csv = open(stockID + 'HistoryTicks.csv', 'wb+')
spamwriterHistory = csv.writer(fileHistory_csv,dialect='excel')
spamwriterHistory.writerow(['time', 'price', 'change', 'volume', 'amount', 'type'])
df = ts.get_today_ticks('002219')
dfHistory = ts.get_tick_data('002219', date='2016-08-16')
total = 0
neutral = 0
for indexH, rowH in dfHistory.iterrows():
#print(rowH[5])
spamwriterHistory.writerow([rowH[0], rowH[1], rowH[2], rowH[3], rowH[4], rowH[5].decode('utf-8').encode('gb2312')])
for index, row in df.iterrows():
print(row[6])
spamwriter.writerow([row[0], row[1], row[2], row[3], row[4], row[5], row[6].encode('gbk')])
if row[6] == '买盘'.decode('utf-8'):
total += row[5]
if row[6] == '卖盘'.decode('utf-8'):
total -= row[5]
else:
# -*- coding:utf-8 -*-
import tushare as ts
# import sys
# sys.path.append("..")
# from JohhnsonUtil import commonTips as cct
import pandas as pd
import tables
#import pandas.io.pytables
code='601608'
# df=ts.get_hist_data('601608')
df= ts.get_today_ticks('601608')
# .sort_index(ascending=True).dropna()
print df[:1]
store=pd.HDFStore('store.h5',mode='w',format='table', complevel=9, complib='blosc')
# df.index=df.index.astype('datetime64')
# store[code]=df
# df.to_hdf('store.h5','sz'+code,mode='w',format='table',complevel=9, complib='blosc',data_columns=df.columns)
# store=pd.HDFStore('store.h5',mode='r',format='table')
store.put('sz'+code,df)
# h5f = pd.HDFStore('store.h5',mode='r')
dd=store.select('sz'+code)
print dd[:5]
import sys
sys.exit(0)
'''
def today_ticks_tushare(code):
df = tushare.get_today_ticks('%06d' % code)
df['time'] = df['time'].str.slice(start=0, stop=5)
df = df.drop_duplicates('time').iloc[::-1].reset_index()
return df_map(df, {'tick': 'time', 'price': 'price', 'volume': 'amount'})
def getTodayStock(request):
id = request.GET.get('id')
df = ts.get_today_ticks(id)
df = df[::-1]
df = df.to_json(orient='index')
return HttpResponse(simplejson.dumps(df, ensure_ascii=False))
'''
参数说明
code：股票代码，即6位数字代码
retry_count : int, 默认3,如遇网络等问题重复执行的次数
pause : int, 默认 0,重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
返回值说明：
time：时间
price：当前价格
pchange:涨跌幅
change：价格变动
volume：成交手
amount：成交金额(元)
type：买卖类型【买盘、卖盘、中性盘】
'''
df =ts.get_today_ticks('600808')
print(df.head(10))
# 大盘指数行情列表
'''
code:指数代码
name:指数名称
change:涨跌幅
open:开盘点位
preclose:昨日收盘点位
close:收盘点位
high:最高点位
low:最低点位
volume:成交量(手)
amount:成交金额（亿元）
# -*-coding=utf-8-*-
__author__ = 'aqua'
import tushare as ts
import talib as ta
import pandas as pd
from sqlalchemy import create_engine
code = '600153'
df_ticket = ts.get_today_ticks(code)
df_ticket = df_ticket.iloc[::-1]
print(df_ticket)
engine = create_engine('mysql://*****:*****@127.0.0.1/stocking?charset=utf8')
df_ticket.to_sql('tickets', engine, if_exists='append')
def getDataUpdate(stock_selected, date, volume):
#df = ts.get_sina_dd(stock_selected,date,vol=volume)
if is_today(date):
df = ts.get_today_ticks(stock_selected)
else:
df = ts.get_tick_data(stock_selected, date=date,
src='tt') #code,name,time,volume, preprice, type
#df=df_1[df_1['volume']>=volume]
#print(df.head())
_time = ['9:25:0']
_volume = [0]
_price = [0]
all_time = []
all_volume = []
all_price = []
buy_time = []
buy_volume = []
buy_price = []
sell_time = []
sell_volume = []
sell_price = []
if df is None:
length = 0
else:
length = len(df.time)
i = 0
idx = 0
while (idx < length):
if (df.type[idx] == '买盘' and df.volume[idx] >= volume):
i = i + 1
buy_time.append(df.time[idx])
buy_volume.append(df.volume[idx])
buy_price.append(df.price[idx])
_time.append(df.time[idx])
_volume.append(df.volume[idx])
_volume[i] = float(_volume[i]) + float(_volume[i - 1])
_price.append(df.price[idx])
all_time.append(df.time[idx])
all_volume.append(df.volume[idx])
all_price.append(df.price[idx])
#_buy += float(df.volume[idx])*float(df.price[idx])/10000.0
elif (df.type[idx] == '卖盘' and df.volume[idx] >= volume):
i = i + 1
sell_time.append(df.time[idx])
sell_volume.append(0 - df.volume[idx])
sell_price.append(df.price[idx])
_time.append(df.time[idx])
_volume.append(0 - float(df.volume[idx]))
_volume[i] = float(_volume[i]) + float(_volume[i - 1])
_price.append(df.price[idx])
all_time.append(df.time[idx])
all_volume.append(0 - float(df.volume[idx]))
all_price.append(df.price[idx])
idx += 1
df1 = pd.DataFrame({'time': _time, 'volume': _volume, 'price': _price})
df2 = pd.DataFrame({
'time': all_time,
'volume': all_volume,
'price': all_price
})
#print(df2)
df3 = pd.DataFrame({
'time': buy_time,
'volume': buy_volume,
'price': buy_price
})
df4 = pd.DataFrame({
'time': sell_time,
'volume': sell_volume,
'price': sell_price
})
return df1, df2, df3, df4
def getRealTimeInfor(stockCode):
df = ts.get_today_ticks(stockCode)
print df.groupby('type')['volume','amount'].sum()
df = ts.get_realtime_quotes(stockCode)
print df.loc[:,['price','pre_close','open','high','low']]
def getTodayTick(code):
return ts.get_today_ticks()
import tushare
import sys
code = sys.argv[1]
s = tushare.get_today_ticks(code)
s = s.sort("amount")
s.to_csv("%s.csv" % code, encoding="utf-8")
def tick_today(symbol):
return ts.get_today_ticks(symbol)
def generateline(stocknumber,Type,startdate,enddate,interval):
startdata = startdate.encode("ascii").replace("/","-").replace("\n","") #convert to tushare readable date
enddata = enddate.encode("ascii").replace("/","-").replace("\n","")
#print startdata
#print enddata
current_time = time.strftime("%Y/%m/%d")
if Type == "分笔".decode("utf-8"):
if startdate!=current_time:
array = ts.get_tick_data(stocknumber, date = startdata)#分笔
if array is None:
return
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx,val in enumerate(atype):#if卖盘，交易变成负数
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":#if中性盘，则忽略. Might have a problem with this part??
amount[idx] = 0
returnarray = zip(date,amount,flag,price)
return returnarray
else:
array = ts.get_today_ticks(stocknumber)#Tushare里今日分笔和历史分笔需要分别对待
if array is None:
return
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘".decode("utf-8"):
amount[idx] = -amount[idx]
if val == "中性盘".decode("utf-8"):
amount[idx] = 0
returnarray = zip(date, amount, flag)
return returnarray
if Type=="季度饼图".decode("utf-8"):
datestr = startdate.split("/")
thisyear = datestr[0]
df2 = ts.top10_holders(code=stocknumber, gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in xrange(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in xrange(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in xrange(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = zip(namearray,valuearray,quarterarray,flag,num)
return returnarray
if interval!="qfq" and interval!="hfq":
if interval=="1min" or interval=="5min" or interval=="15min" or interval=="30min" or interval=="60min":
df = ts.get_tick_data(stocknumber, date=startdata)
df.sort_values("time")
a = startdata + " " + df["time"]
df["time"] = a
df["time"] = pd.to_datetime(a)
df = df.set_index("time")
price_df = df["price"].resample(interval).ohlc()
price_df = price_df.dropna()
vols = df["volume"].resample(interval).sum() #relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
amounts = df["amount"].resample(interval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
newdf = price_df.merge(vol_df, left_index=True, right_index=True).merge(amount_df, left_index=True,
right_index=True)
if Type != "Kline":
Type1 = firstletter(Type).encode("ascii")
target = newdf[Type1].tolist()
date = newdf.index.format()
returnarray = zip(date, target)
return returnarray
else:
Date = newdf.index.format()
Open = newdf["open"].tolist()
Close = newdf["close"].tolist()
High = newdf["high"].tolist()
Low = newdf["low"].tolist()
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
#正常历史k线
if Type!="Kline":
array = ts.get_k_data(stocknumber, start=startdata, end=enddata, ktype=interval)
if array is None:
return
Type1 = firstletter(Type).encode("ascii")
target = array[Type1].tolist()
date = array["date"].tolist()
returnarray = zip(date,target)
return returnarray
else:
array = ts.get_k_data(stocknumber, start=startdata, end=enddata, ktype=interval)
if array is None:
return
Date = array["date"].tolist()
Open = array["open"].tolist()
Close = array["close"].tolist()
High = array["high"].tolist()
Low = array["low"].tolist()
Candlestick = zip(*[Date,Open,Close,Low,High])
return Candlestick
else:
if Type!="Kline": # 复权
array = ts.get_h_data(stocknumber, start = startdata, end = enddata, autype= interval)
if array is None:
return
Type1 = firstletter(Type).encode("ascii")
array = array.sort_index()
target = array[Type1].tolist()
date = array.index.format()
returnarray = zip(date, target)
return returnarray
else :
array = ts.get_h_data(stocknumber, start=startdata, end=enddata, autype=interval)
if array is None:
return
array = array.sort_index()
Date = array.index.format()
Open = array["open"].tolist()
Close = array["close"].tolist()
High = array["high"].tolist()
Low = array["low"].tolist()
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
def generate_stock_line(stock_id, Type, start_date, end_date, interval):
start_data = start_date.replace("/", "-").replace(
"\n", "") #convert to tushare readable date
end_data = end_date.replace("/", "-").replace("\n", "")
current_time = time.strftime("%Y/%m/%d")
if Type == "分笔":
if start_date != current_time:
array = ts.get_tick_data(stock_id, date=start_data) #分笔
if array is None:
return
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
price = array["price"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype): #if卖盘，交易变成负数
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘": #if中性盘，则忽略. Might have a problem with this part??
amount[idx] = 0
returnarray = zip(date, amount, flag, price)
return returnarray
else:
array = ts.get_today_ticks(stock_id) #Tushare里今日分笔和历史分笔需要分别对待
if array is None:
return
array = array.sort_values("time")
date = array["time"].tolist()
amount = array["amount"].tolist()
atype = array["type"].tolist()
flag = ["bar" for i in date]
for idx, val in enumerate(atype):
if val == "卖盘":
amount[idx] = -amount[idx]
if val == "中性盘":
amount[idx] = 0
returnarray = zip(date, amount, flag)
return returnarray
if Type == "季度饼图":
datestr = start_date.split("/")
thisyear = datestr[0]
df2 = ts.top10_holders(code=stock_id, gdtype="1")
test = df2[1]["quarter"].tolist()
df_ready = df2[1]
idxlist = []
for idx, val in enumerate(test):
a = val.split("-")
if a[0] == thisyear:
# print a[0],idx
idxlist.append(idx)
thing = df_ready.loc[idxlist]
thing = thing.sort_values(["quarter", "name"])
# print a[0],id
name = thing["name"].tolist()
value = thing["hold"].tolist()
quarter = thing["quarter"].tolist()
namearray = [name[i:i + 10] for i in xrange(0, len(name), 10)]
valuearray = [value[j:j + 10] for j in xrange(0, len(value), 10)]
quarterarray = [quarter[k:k + 10] for k in xrange(0, len(quarter), 10)]
flag = ["pie" for i in namearray]
num = [len(value) for k in namearray]
returnarray = zip(namearray, valuearray, quarterarray, flag, num)
return returnarray
if interval != "qfq" and interval != "hfq":
if interval == "1min" or interval == "5min" or interval == "15min" or interval == "30min" or interval == "60min":
df = ts.get_tick_data(stock_id, date=start_data)
df.sort_values("time")
a = start_data + " " + df["time"]
df["time"] = a
df["time"] = pd.to_datetime(a)
df = df.set_index("time")
price_df = df["price"].resample(interval).ohlc()
price_df = price_df.dropna()
vols = df["volume"].resample(interval).sum(
) #relevant data processing algorithm taken from Jimmy, Creator of Tushare
vols = vols.dropna()
vol_df = pd.DataFrame(vols, columns=["volume"])
amounts = df["amount"].resample(interval).sum()
amounts = amounts.dropna()
amount_df = pd.DataFrame(amounts, columns=["amount"])
newdf = price_df.merge(vol_df, left_index=True,
right_index=True).merge(amount_df,
left_index=True,
right_index=True)
if Type != "Kline":
Type1 = firstletter(Type)
target = newdf[Type1].tolist()
date = newdf.index.format()
returnarray = zip(date, target)
return returnarray
else:
Date = newdf.index.format()
Open = newdf["open"].tolist()
Close = newdf["close"].tolist()
High = newdf["high"].tolist()
Low = newdf["low"].tolist()
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
#正常历史k线
if Type != "Kline":
array = ts.get_k_data(stock_id,
start=start_data,
end=end_data,
ktype=interval)
if array is None:
return
Type1 = firstletter(Type)
target = array[Type1].tolist()
date = array["date"].tolist()
returnarray = zip(date, target)
return returnarray
else:
array = ts.get_k_data(stock_id,
start=start_data,
end=end_data,
ktype=interval)
if array is None:
return
Date = array["date"].tolist()
Open = array["open"].tolist()
Close = array["close"].tolist()
High = array["high"].tolist()
Low = array["low"].tolist()
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
else:
if Type != "Kline": # 复权
array = ts.get_h_data(stock_id,
start=start_data,
end=end_data,
autype=interval)
if array is None:
return
Type1 = firstletter(Type)
array = array.sort_index()
target = array[Type1].tolist()
date = array.index.format()
returnarray = zip(date, target)
return returnarray
else:
array = ts.get_h_data(stock_id,
start=start_data,
end=end_data,
autype=15)
if array is None:
return
array = array.sort_index()
Date = array.index.format()
Open = array["open"].tolist()
Close = array["close"].tolist()
High = array["high"].tolist()
Low = array["low"].tolist()
# list_array = [Date, Open, Close, Low, High]
Candlestick = zip(*[Date, Open, Close, Low, High])
return Candlestick
ts.get_hist_data('600848',ktype='60') #获取60分钟k线数据
ts.get_hist_data('sh')#获取上证指数k线数据，其它参数与个股一致，下同
ts.get_hist_data('sz')#获取深圳成指k线数据
ts.get_hist_data('hs300')#获取沪深300指数k线数据
ts.get_hist_data('sz50')#获取上证50指数k线数据
ts.get_hist_data('zxb')#获取中小板指数k线数据
ts.get_hist_data('cyb')#获取创业板指数k线数据
# 2.历史复权数据
# 可提供股票上市以来所有历史数据，默认为前复权
ts.get_h_data('002337') #前复权
ts.get_h_data('002337',autype='hfq') #后复权
ts.get_h_data('002337',autype=None) #不复权
ts.get_h_data('002337',start='2015-01-01',end='2015-03-16') #两个日期之间的前复权数据
ts.get_h_data('399106', index=True) #深圳综合指数
# 3.一次性获取当前交易所有股票的行情数据，如果是节假日，即为上一交易日
ts.get_today_all()
# 4.实时交易数据
ts.get_realtime_quotes('sh')
ts.get_realtime_quotes('000581')
# 5.当日分笔
df = ts.get_today_ticks('601333')
# 6.历史分笔
df = ts.get_tick_data('600848',date='2014-01-09')
# 7.获取所有指数实时行情列表
df = ts.get_index()
import tushare as ts
df = ts.get_index()
print df
df = ts.get_today_ticks('601333')
print df.head(10)
# Load stock real-time data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
import tushare as ts
import datetime as dt
print('Tushare Version ' + ts.__version__)
code_num = '000636'
try:
rdf = ts.get_realtime_quotes(code_num) # Realtime DF
print(rdf)
tdf = ts.get_today_ticks(code_num) # Tick DF
print(tdf)
except:
print('Download exception')
# rdf.index = pd.to_datetime(rdf.date,format="%Y-%m-%d %H:%M")
# stk_today = rdf[rdf.index.date==dt.datetime(2018,8,7).date()].copy()
# tdf.index = pd.to_datetime(tdf.time,format="%H:%M:%S")
tdf = tdf[tdf['time'] > '09:26:00']
tdf.index = pd.to_timedelta(tdf['time'])
mdf = tdf.resample('1Min', closed='left').mean().dropna()
if True:
font_set = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=16)
fig, ax = plt.subplots(figsize=(20, 16))
plt.subplot(211)
mdf['price'].plot(use_index=True,
style='r-',
import matplotlib.pyplot as plt
import pandas as pd
mystock = \
'603063.SH,' \
'000993.SZ,' \
'002797.SH,' \
'002008.SZ,' \
'601519.SH'
trig_volume = 209
dg = ts.get_realtime_quotes('002008') #Single stock symbol
show1 = dg[['code', 'name', 'price', 'bid', 'ask', 'volume', 'amount', 'time']]
# df = ts.get_tick_data('002008', date='2019-09-19',src='tt')
# df = ts.get_hist_data('601519', '2019-09-12','2019-09-17')
df = ts.get_today_ticks('002008')
buy1 = df[(df['volume'] > trig_volume) & (df['change'] > 0)]
buy2 = df[(df['type'] == '买盘') & (df['volume'] > trig_volume) &
(df['change'] == 0)]
buy = pd.concat([buy1, buy2])
sale1 = df[(df['volume'] > trig_volume) & (df['change'] < 0)]
sale2 = df[(df['type'] == '卖盘') & (df['volume'] > trig_volume) &
(df['change'] == 0)]
sale = pd.concat([sale1, sale2])
buy_amount = buy.volume * buy.price
buy_average_price = buy_amount.sum() / buy.volume.sum()
sale_amount = sale.volume * sale.price
draw(fileName, code, name[index])
def tradeOne(fileName, code, name):
df = pd.read_csv(fileName)
test = test1(df, code, name)
test.trade()
test.test()
if __name__ == '__main__':
# df = getTodayAll()
# df.to_excel("today.xls")
code = "601398"
otherStyleTime = datetime.datetime.now().strftime("%Y%m%d")
df = ts.get_today_ticks(code)
df.to_excel("%s-%s.xls" % (code, otherStyleTime), encoding="utf-8")
# todayAll()
# fileName = r"%s\todayAll\20180205154614.csv"%(dir)
# data1= teststd(fileName)
# test20(fileName)
# data2 =ver8(fileName)
# data3= pd.merge(data1, data2, on=["b"])
# print(data3)
# data3.to_csv("%s3.cvs", encoding='utf-8')
# jiaolongmairuList(fileName,'20180119')
fileName = r"getAmt20180205161141ver8.cvs"
# otherStyleTime = datetime.datetime.now().strftime("%Y%m%d")
# otherStyleTime = "20180205"
# 获取个股以往交易历史的分笔数据明细，通过分析分笔数据，可以大致判断资金的进出情况。在使用过程中，对于获取股票某一阶段的历史分笔数据，需要通过加入交易日参数并append到一个DataFrame或者直接append到本地同一个文件里。历史分笔接口只能获取当前交易日之前的数据，当日分笔历史数据请调用get_today_ticks()
# 接口或者在当日18点后通过本接口获取。
# 获取当日历史分笔数据：get_today_ticks()
"""
参数说明：
code：股票代码，即6位数字代码
retry_count : int, 默认3,如遇网络等问题重复执行的次数
pause : int, 默认 0,重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
返回值说明：
time：时间
price：当前价格
pchange:涨跌幅
change：价格变动
volume：成交手
amount：成交金额(元)
type：买卖类型【买盘、卖盘、中性盘】
"""
df = ts.get_today_ticks('300274')
# print(df.head(10))
# 获取当前交易日（交易进行中使用）已经产生的分笔明细数据。
print("999999999")
# 二、 使用finance.yahoo.com这个网站。这个网站常被用来进行金融股票数据的搜索。
import pandas_datareader as pdr
alibaba = pdr.get_data_yahoo('BABA')
print(alibaba.head())
import tushare as ts
import logging
import selenium
fname = '300377'
fdate = '2018-12-13'
#df = ts.get_tick_data(fname,date='2018-12-12',src='tt' )
#df.head(10)
#当日历史分笔
df = ts.get_today_ticks(fname)
df.head(10)
df.to_csv(r'D:\python学习\金融数据\get_today_ticks' + fname + '_' + fdate + '.csv',
sep=',',
header=True,
index=True,
encoding='utf_8_sig')
df = ts.get_realtime_quotes(fname)
df.to_csv(r'D:\python学习\金融数据\get_realtime_quotes' + fname + '_' + fdate +
'.csv',
sep=',',
header=True,
index=True,
encoding='utf_8_sig')
print(df)
def get_today_ticks(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
self.df = ts.get_today_ticks(self.code)
self.base.batchwri(self.df, 'dangrilishifenbi', self.financial_data)
# -*- coding:utf-8 -*-
import tushare as ts
# import sys
# sys.path.append("..")
# from JohnsonUtil import commonTips as cct
import pandas as pd
import tables
#import pandas.io.pytables
code = '601608'
# df=get_kdate_data('601608')
df = ts.get_today_ticks('601608')
# .sort_index(ascending=True).dropna()
print(df[:1])
store = pd.HDFStore('store.h5',
mode='w',
format='table',
complevel=9,
complib='blosc')
# df.index=df.index.astype('datetime64')
# store[code]=df
# df.to_hdf('store.h5','sz'+code,mode='w',format='table',complevel=9, complib='blosc',data_columns=df.columns)
# store=pd.HDFStore('store.h5',mode='r',format='table')
store.put('sz' + code, df)
# h5f = pd.HDFStore('store.h5',mode='r')
dd = store.select('sz' + code)
print(dd[:5])
import sys
sys.exit(0)
import tushare as ts
import stock.emacount as ema
# get now hot 10
date_top = {}
# df = ts.get_today_all()
# top=df[df['changepercent'] > 9.9]
# for code in top['code']:
tick_mean = []
for code in ["601608"]:
dtick = ts.get_today_ticks(code)
d_hist = ema.getdata_ema_trend(code, "10", "d")
# print d_hist
day_t = ema.get_today()
if day_t in d_hist.index:
dl = d_hist.drop(day_t).index
else:
dl = d_hist.index
# print dl
# print dl
for da in dl.values:
# print da
td = ts.get_tick_data(code, da)
# print td
ep = td["amount"].sum() / td["volume"].sum()
print ("D: %s P: %s" % (da, ep))
ep = dtick["amount"].sum() / dtick["volume"].sum()
print "today: ", ep
# print top_count
def getDailyData(stock="", date=""):
if stock[0] == "2" or stock[0] == "9":
print "Pass Stock"
return True
else:
if bool(stock) == False:
return{'errcode':-1,'errmsg':'need stockCode'}
else:
jsonDir = os.path.join(config.dataRootDailyTrade, stock)
if os.path.isdir(jsonDir):
pass
else:
os.mkdir(jsonDir)
if bool(date) == False & bool(stock):
#不带日期的话就检索今天的数据
jsonFile = os.path.join(config.dataRootDailyTrade, stock, dateStr + ".json")
if os.path.isfile(jsonFile):
size = os.path.getsize(jsonFile)
#print 'There are %f K' % (size / 1024.0)
if size >60000 :
print(stock+" Pass")
return True
else:
try:
data = ts.get_today_ticks(stock)
data.to_json(jsonFile, orient='records', force_ascii =False)
print(stock)
print(date)
return True
except:
print("scan error " + stock )
else:
try:
data = ts.get_today_ticks(stock)
data.to_json(jsonFile, orient='records', force_ascii =False)
return True
except:
print("scan error " + stock)
elif bool(date) & bool(stock):
#按日期获取
isWorkDay = isTradeDay(date)
if isWorkDay:
jsonFile = os.path.join(config.dataRootDailyTrade, stock, date + ".json")
### 判断该日期下的文件是否大于100KB 是默认历史数据已获取，反正重新获取
if os.path.isfile(jsonFile):
size = os.path.getsize(jsonFile)
#print 'There are %f K' % (size / 1024.0)
if size >60000 :
print(stock+" Pass")
return True
else:
try:
data = ts.get_tick_data(stock, date=date)
data.to_json(jsonFile, orient='records', force_ascii =False)
print(stock)
print(date)
return True
except:
print("scan error " + stock + "--" + date)
catchError(stock, date)
else:
try:
data = ts.get_tick_data(stock, date=date)
data.to_json(jsonFile, orient='records', force_ascii =False)
print(stock)
print(date)
return True
except:
print("scan error "+stock +"--"+date)
catchError(stock, date)
else:
print ("Weekend Pass")
pass
def get_today_ticks(engine, stock_code):
df = ts.get_today_ticks(stock_code)
df.head(10)
df.to_sql('today_ticks', engine, if_exists='append')
