def fetch_realtime(self, codeList = None): if codeList is None: codeList = self.codeList i = 0 while ( self.codeList[i:i+500] != [] ): if (i==0): realtime = ts.get_realtime_quotes( self.codeList[i : i+500] ) else: realtime = realtime.append( ts.get_realtime_quotes( self.codeList[i : i+500] ), ignore_index=True ) i += 500 # Get the datetime data_time = datetime.strptime( realtime.iloc[0]["date"] + " " + realtime.iloc[0]["time"] , '%Y-%m-%d %H:%M:%S') code = realtime["code"] realtime["time"] = data_time # Drop Useless colulmns realtime = realtime.drop( realtime.columns[[0,6,7,30]] ,axis = 1) # Convert string to float realtime = realtime.convert_objects(convert_dates=False,convert_numeric=True,convert_timedeltas=False) # update self.basicInfo & self.outstanding self.self_updated(code) # Compute turn_over_rate realtime["turn_over_ratio"] = realtime["volume"]/self.outstanding/100 realtime["code"] = code return realtime
def getRealData_sk():
klist=cp.get_gplb()
tempnum=0
templist=[]
partdf=[]
alldf=[]
for k in klist:
tempnum=tempnum+1
templist.append(k)
if tempnum==200:
try:
df = ts.get_realtime_quotes(templist)
partdf.append(df)
tempnum=0
templist=[]
except Exception:
print(u'数据读取超时，结束线程')
realDate_stop()
'''
df = ts.get_realtime_quotes(templist)
partdf.append(df)
tempnum=0
templist=[]
'''
#把最后的数据加入
try:
partdf.append(ts.get_realtime_quotes(templist))
alldf=pa.concat(partdf)
except Exception:
print(u'最后数据读取超时，结束线程')
realDate_stop()
return alldf
def sellIt(self, code, percent=0, price=0, t_type=None, am=0, av=0):
'''
Get Amnt and ask price and sell percentage of all
'''
if percent <= 0:
return 0
if av == 0:
amount, avamnt = self.availableSecurity(code)
else:
amount = am
avamnt = av
# 取100之倍數 ceil(autoAmnt(code,fidBroker) * percent)
if amount < 100 and percent < 1: # 少於100股就不賣了
print('{'+'"info":"{code}可用證券{x}, 少於100股"'.format(code=code, x=amount)+'}')
return 0
autoAmnt = min(avamnt, (amount * percent // 100 * 100))
if 101 > autoAmnt > -1:
autoAmnt = 100
# 若未制定賣出價格則自動決定,否則根據制定價格提交
try:
if price == 0:
#dfq = quote126(code)
#bidp = dfq.bid1[code[:-3]]
bidp = float(ts.get_realtime_quotes(code).b1_p[0])
bidv = float(ts.get_realtime_quotes(code).b1_v[0])
if bidv > autoAmnt/100:
bidprice = bidp
else:
# 未解決
bidprice = bidp # bidp - 0.001 # bug here! 股票委託價格只能兩位數!基金只能3位數!
else:
bidprice = price
#print(self.briefOrderInfo().tail(3))#.to_string())
'''
由於經常出現賣不出情況,故降低賣出價位, 最好是採用買一價位,有空時改寫.總之確保賣出
'''
if t_type == 'itc': #如果採用對方限價
result = self.sellAt(code, price=0, amount=autoAmnt)
else:
result = self.sellAt(code, price=bidprice, amount=autoAmnt)
self.db_insert(self.tradings, {'acc_id':self.liveId,'代碼': code, \
'報價': price, '比重': percent,'數量': autoAmnt, '操作': '賣出'})
return result
except Exception as e:
#dfCancel()
print('WebTrader sellAt:')
print(e)
def main_7():
# main_4(year=2015,
# month=12,
# day=10,
# date_diff=0,
# max_shrink_period=10,
# min_shrink_period=3,
# code_list_file='all_stocks',
# result_file='result_file_4')
code_list = read_shrink_code_list('result_file_4')
# print "enter here"
# 上证指数涨幅：
pd = ts.get_realtime_quotes('sh')
p_shangzheng = round((float(pd.iloc[0, 1]) - float(pd.iloc[0, 2])) / float(pd.iloc[0, 2]) * 100, 2)
# 深圳成指涨幅：
pd = ts.get_realtime_quotes('sz')
p_shenzhen = round((float(pd.iloc[0, 1]) - float(pd.iloc[0, 2])) / float(pd.iloc[0, 2]) * 100, 2)
# 中小板：
pd = ts.get_realtime_quotes('zxb')
p_zhongxiaoban = round((float(pd.iloc[0, 1]) - float(pd.iloc[0, 2])) / float(pd.iloc[0, 2]) * 100, 2)
# print "p_shangzheng", p_shangzheng
# print "p_shenzhen", p_shenzhen
# print "p_zhongxiaoban", p_zhongxiaoban
for key in code_list:
for code in code_list[key]:
if code[0] == '3':
continue
df = ts.get_realtime_quotes(code)
open = df.iloc[0, 1]
pre_close = df.iloc[0, 2]
price = df.iloc[0, 3]
low = df.iloc[0, 5]
p_change = round((float(price) - float(pre_close)) / float(pre_close), 2)
if open > pre_close or low > pre_close:
# print "p_change", p_change
print_string = ""
if open > price:
print_string += "高开绿柱，"
else:
print_string += "高开红柱，"
if code[0] == '6' and p_change > p_shangzheng:
print_string += "强于大盘，"
print print_string, str(key), code
elif code[:3] == '000' and p_change > p_shenzhen:
print_string += "强于大盘，"
print print_string, str(key), code
elif code[:3] == '002' and p_change > p_zhongxiaoban:
print_string += "强于大盘，"
print print_string, str(key), code
else:
pass
def get_realtime_all_st(symbols=[], retry=60):
start = time.time()
if len(symbols) < 1:
riclist = getcodelist(True)
symbols = riclist['code'].values
full_df = pd.DataFrame()
length = len(symbols)
loops = int(length / 300 + 1)
for idx in range(0, loops, 1):
sublist = symbols[idx * 300:(idx + 1) * 300]
for _ in range(retry):
try:
df = ts.get_realtime_quotes(sublist.tolist())
except Exception as e:
err = 'Error %s' % e
print('Error %s' % e)
time.sleep(1)
else:
# print('get daily data for %s successfully' % row.code.encode("utf-8"))
break
full_df = full_df.append(df)
finish = time.time()
print (finish - start)
full_df.date = pd.to_datetime(full_df.date)
full_df.open = full_df.open.astype(np.float64)
full_df.pre_close = full_df.pre_close.astype(np.float64)
full_df.price = full_df.price.astype(np.float64)
full_df.high = full_df.high.astype(np.float64)
full_df.low = full_df.low.astype(np.float64)
full_df.volume = full_df.volume.astype(np.int64)
full_df.amount = full_df.amount.astype(np.float64)
return full_df
def set_realtime_quotes(code=['sh'],pause = 10):
"""
获取当日所选股票代码的实时数据，code为股票代码列表，pause为每隔多少秒请求一次.从当前时间开始,未测试
将数据存储到数据库中，若当前时间在9:00--15:00之间则实时获取并存入dic{code:dataFrame}中，否则进入睡眠状态
目前睡眠，未考虑是否为交易日
Parameters
------
return list[DataFrame]
"""
engine = create_engine(ct._ENGINE_)
curTime = datetime.now()
startTime = curTime.replace(hour=9, minute=0, second=0, microsecond=0)
endTime = curTime.replace(hour=15, minute=0, second=0, microsecond=0)
delta_s = startTime - curTime
delta_e = endTime - startTime
if delta_s > timedelta(0, 0, 0):
time.sleep(delta_s.total_seconds())
elif delta_e <timedelta(0, 0, 0):
time.sleep(delta_s.total_seconds()+86400)
_data_ = {}
for items in code:
_data_[items] = DataFrame()
while(curTime<endTime):
for item in code:
df = ts.get_realtime_quotes(item) #Single stock symbol
_data_[item].append(df)
time.sleep(pause)
curTime = datetime.now()
for ite in code:
_data_[ite].to_sql('realtime_data',engine,if_exists='append')
return _data_
def calc():
all_vest = 0
for stock in all_stocks:
code = stock['code']
dg = ts.get_realtime_quotes(stock['code'])
df = None
d = date.today()
count = 0
while (df is None or df.empty) and count < 10:
count += 1
d = d - timedelta(days=1)
datestr = d.strftime('%Y-%m-%d')
log_status('Getting hist data for %s at %s' % (code, datestr))
df = ts.get_hist_data(stock['code'], datestr, datestr)
log_status('Done hist data for %s at %s' % (code, datestr))
stock['name'] = dg['name'][0]
if (df is None or df.empty) or df.get('turnover') is None:
stock['marketvalue'] = 0
else:
stock['marketvalue'] = float(df['close'][0]) * float(df['volume'][0]) * 100 / (float(df['turnover'][0] / 100)) / 100000000
group = stock['group'] = group_stock(stock)
vest = stock['vest'] = calc_stock(stock)
if not group_data.has_key(group):
group_data[group] = vest
else:
group_data[group] += vest
all_vest += vest
for data in group_data.keys():
print '%s:\t\t%4.1f%%\t\t%s' % (data, group_data[data] * 100 / all_vest, str(group_data[data]))
display_stock_group(data, all_vest)
print 'all: ' + str(all_vest)
def run(self):
print('running ...')
i = 0
update = False
df_list = []
while(True):
#now = datetime.now().strftime('%H:%M:%S')
now = datetime.now()
pull = (now > active_time[0] and now < active_time[1]) or \
(now > active_time[2] and now < active_time[3])
pull = True
if(i == 0 and pull):
try:
list_temp = []
for ss in slist:
df = ts.get_realtime_quotes(ss)
list_temp.append(df)
df_list = list_temp
update = True
print('get_realtime_quotes update')
except Exception as e:
print('get_realtime_quotes: %s' % e)
s = ''
for x in range(i):
s = s + '.'
wx.CallAfter(self.context.updateUI, s, df_list, update)
ostm.sleep(1)
if(i > 0):
i = i - 1
else:
i = 19
update = False
def run(self):
print "Starting " + self.name
f_out=open("result/live_hit_line_%s" % (datetime.today().date().strftime('%Y%m%d')), 'a')
while True:
code = get_code()
if not code:
return
try:
current_data = ts.get_realtime_quotes('%s' % (code))
peak_data = pd.read_csv('pd_5days/%s.csv' % (code)).head(1)
if current_data.empty or peak_data.empty:
continue
ph = float(current_data['high'].values[0])
cl = float(current_data['low'].values[0])
price = float(current_data['price'].values[0])
#ma5 = float(peak_data['ma5'].values[0])
#ma10 = float(peak_data['ma10'].values[0])
#ma20 = float(peak_data['ma20'].values[0])
#ma30 = get_ma30(code)
ma5, ma10, ma20, ma30 = get_mas_live(code, price)
if (ma5 > ma10) and (ma10 > ma20) and (ma20 > ma30) and (ph < 20):
if ((cl<=ma10) or (cl<=ma20) or (abs(cl-ma10)<(ma10*0.01)) or (abs(cl-ma20)<(ma20*0.01))):
# print code
print code, ma5, ma10, ma20, ma30, ph, cl
except Exception as e:
print 'something wrong with code: %s, %s' % (code, str(e))
f_out.close()
def stock_today_ditail(request, code):
"""
name，股票名字
open，今日开盘价
pre_close，昨日收盘价
price，当前价格
high，今日最高价
low，今日最低价，竞买价，即“买一”报价
ask，竞卖价，即“卖一”报价
volume，成交量 maybe you need do volume/100
amount，成交金额（元 CNY）
date，日期；
time，时间；
"""
context = {}
df = ts.get_realtime_quotes(code)
context['name'] = df.iloc[0]['name']
context['open'] = df.iloc[0]['open']
context['pre_close'] = df.iloc[0]['pre_close']
context['price'] = df.iloc[0]['price']
context['high'] = df.iloc[0]['high']
context['low'] = df.iloc[0]['low']
context['ask'] = df.iloc[0]['ask']
context['volume'] = df.iloc[0]['volume']
context['amount'] = df.iloc[0]['amount']
context['time'] = df.iloc[0]['amount']
context['date'] = df.iloc[0]['amount']
return JsonResponse(context)
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 monitortrade(self):
conn = pymongo.MongoClient('192.168.222.188', port=27017)
# for item in conn.mystock.trade.find({'buytime':re.compile('2016-05-27')}):
# for item in conn.mystock.yjbtrade.find({'tradestatus':0}).sort('buytime',pymongo.ASCENDING):
# for item in conn.mystock.yjbtrade.find().sort('buytime', pymongo.DESCENDING):
# print item['buytime']
# df = ts.get_realtime_quotes(item['code'])
# df1 = ts.get_hist_data(item['code']).head(1)
#
# status = item['detailtype']
#
# open = df1['open'][0]
# nowprice = df['price'][0]
# profit = (float(nowprice) - float(item['buyprice'])) / float(item['buyprice']) * 100
# nowprofit = (float(nowprice) - float(item['buyprice'])) / float(item['buyprice']) * 100
# maxprofit = (float(item['maxprice']) - float(item['buyprice'])) / float(item['buyprice']) * 100
# maxprofit = 0
#已经卖出的股票的收益
# if item['tradestatus']==1:
# profit = (float(item['sellprice'])-float(item['buyprice']))/float(item['buyprice'])*100
#
# # if profit < 8:
# # continue
# print '[',status,'] ', item['code'], item['name'], item['buytime'], ' buy price ', item['buyprice'], 'and now price ', nowprice, '最大收益', round(maxprofit, 2), '%', '当前收益:', round(nowprofit,2), '%', '总收益:', round(profit, 2), '%', '持股状态:', item['tradestatus']
df = ts.get_realtime_quotes('603159')
nowprice = df['price'][0]
profit = (float(nowprice) - 57) / 57 * 100
print '[hand] ', '603159', '上海亚虹', ' buy price ', 57, 'and now price ', nowprice, '当前收益:', round(profit, 2), '%',
print '\n'
def getLowestGrowth(startDate, endDate,stockList):
result = {}
while len(stockList) > 0:
try:
stockCode = stockList[-1]
print stockCode,'is started'
#取当天有交易的股票
if float(ts.get_realtime_quotes(stockCode).price) > 0:
df_tran = ts.get_h_data(stockCode, start=startDate, end=endDate)
#将收盘价转化为数值
df_tran['close'] = df_tran['close'].convert_objects(convert_numeric=True)
#按日期由远及近进行排序
df_tran = df_tran.sort_index()
stock = {}
stock['maxPxAll'] = max(df_tran.close)
stock['minPxAll'] = min(df_tran.close)
stock['maxGrowthRate'] = (stock['maxPxAll'] - stock['minPxAll'])/stock['minPxAll']
result[stockCode] = stock
print stockCode,'is finished'
stockList.pop()
else:
stockList.pop()
except URLError,e:
print 'Error',stockCode,str(e)
continue
except BaseException, e:
print 'Error',stockCode,str(e)
stockList.pop()
continue
def k_day_sina_one_stock(_stock_id, _db):
# get from web(by tushare)
begin = get_micro_second()
try:
df = ts.get_realtime_quotes(_stock_id)
except Exception:
log_error("warn:error: %s get_k_data exception!", _stock_id)
return -4
# calc cost time
log_info("get_k_data [%s] costs %d us", _stock_id, get_micro_second()-begin)
if df is None :
log_error("warn: stock %s is None, next", _stock_id)
return -1
if df.empty:
log_error("warn: stock %s is empty, next", _stock_id)
return -2
begin = get_micro_second()
k_day_sina_one_to_db(_stock_id, df,_db)
log_info("one_to_db costs %d us", get_micro_second() - begin)
return
def run(self):
print "Starting " + self.name
f_out=open("result/follower_%s" % (datetime.today().date().strftime('%Y%m%d')), 'a')
while True:
code = get_code()
if not code:
return
try:
current_data = ts.get_realtime_quotes('%s' % (code))
csv_file = 'qfq_data/%s.csv' % (code)
if not os.path.exists(csv_file):
continue
peak_data = pd.read_csv(csv_file)
if current_data.empty or peak_data.empty:
continue
ph_today = float(current_data['high'].values[0])
price = float(current_data['price'].values[0])
if price > 15:
continue
pre_close = float(current_data['pre_close'].values[0])
pl_today = float(current_data['low'].values[0])
po_today = float(current_data['open'].values[0])
ma5, ma10, ma20, ma30 = get_mas_live_qfq(code, price)
if (ma5 < ma10) or (ma10 < ma20):
continue
if pl_today > ( po_today * 0.96 ) or pl_today > (ma5 * 1.3):
continue
if hist_zt_qfq(peak_data.head(3), code):
print code, ma5, ma10, ma20, ma30
f_out.write('%s\n' % (code))
except Exception as e:
print 'something wrong with code: %s, %s' % (code, str(e))
f_out.close()
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 puttl():
""""
策略1：认沽期权定价错误套利 k-s-p>3% 认沽期权定价不合理，导致K-S的空间大过权利金，导致可以套利
操作：
买入1张行权价为K，到期日为T的认沽期权，其价格为p，取买二价b2_p
买入1张合约对应数量的标的股票，其价格为s，取当卖二价
平仓：认沽期权行权，卖出股票
市场利率 r=0.057
套利空间 pl=3%
"""
s = ts.get_realtime_quotes('510050').iloc[0]['price']
r = 0.057
pl = 0.03
fee = 30
do = update_contracts()
now =dt.datetime.today()
grouped = do.groupby(['strikePrice','expDate'])
for name,group in grouped:
k, expDate = name
p = group[group['contractType']=='PO'].iloc[0]['a1_p'] #买入认沽，采用卖二价
p_id = group[group['contractType']=='PO'].iloc[0]['optID']
T = dt.datetime.strptime(expDate,'%Y-%m-%d')-now
gain = (k-float(s)-float(p))*10000
gain_per = gain /(float(s)*10000+float(p)*10000)
if gain_per>pl :
print('=======================================================================================\n')
print('认沽套利：对手价买入',p_id,"对手价买入510050","行权价：",k,"到期日期：",expDate,"剩余天数：",T.days)
print('认沽套利：strike=',k,'50etf=',s,'买入认沽=',p,'gain=',gain,'gain%=',gain_per,'expDate=',expDate[:-3],'还剩天数',T.days)
return
def meet_price(code, price_up, price_down,type):
try:
df = ts.get_realtime_quotes(code)
except Exception, e:
print e
time.sleep(5)
return 0
def get_stock_data(stock_code):
stock_data = []
df = ts.get_realtime_quotes(stock_code)
# print(df)
stock_data.append(df['name'][0])
stock_data.append(float(df['price'][0]))
return stock_data
def realtimetick(code):
df = ts.get_realtime_quotes(code)
df.insert(0,'uploadtime',nowtime)
#df.insert(0,'code',code)
df.to_sql('mkt_tickrealtime',engine,if_exists='append')
def insertid():
sql = """ALTER TABLE mkt_tickrealtime
ADD realtimetick_id int(20) not null auto_increment ,
ADD primary key (realtimetick_id)"""
cursor.execute(sql)
countfq = cursor.execute('show columns from mkt_tickrealtime like \'realtimetick_id\'')
if countfq == 0:
insertid()
sql = """create table temp_id as (select realtimetick_id, code,date,time,count(distinct code,date,time) from mkt_tickrealtime group by code,date,time)
"""
cursor.execute(sql)
sql = """
delete from mkt_tickrealtime where realtimetick_id not in(select realtimetick_id from temp_id)
"""
cursor.execute(sql)
sql = """
drop table temp_id
"""
cursor.execute(sql)
'''sql = """
def main():
strategyDir = os.path.join(sys.path[0], 'strategy')
codes=[]
strategies = []
for child in os.listdir(strategyDir):
path = os.path.join(strategyDir, child)
if os.path.isdir(path):
if not os.path.exists(os.path.join(path, 'strategy.py')):
continue
strategyScript = '.'.join(['strategy', child, 'strategy'])
module = __import__(strategyScript, {}, {}, ['any'])
if 'getStockList' in dir(module):
codes.extend(module.getStockList())
strategies.append(module)
codes = ['%06d' %(int(code)) for code in set(codes)]
global price_df
price_df = ts.get_realtime_quotes(codes).set_index('code')
price_df = price_df.rename_axis(mapper= lambda a: int(a), axis=0)
#merge stock info to stock price DataFrame. Drop column 'name' of sotck basics before merge, because it's duplicated in price_df
price_df = pd.concat([price_df, stockBasics_df.drop('name', 1).loc[np.intersect1d(stockBasics_df.index, price_df.index)]], axis=1)
printStockPrice(price_df)
for s in strategies:
print utils.getSeperateLine()
s.run(price_df)
def monitortrade(self):
conn = pymongo.MongoClient('192.168.222.188', port=27017)
# for item in conn.mystock.trade.find({'buytime':re.compile('2016-05-27')}):
for item in conn.mystock.yjbtrade.find():
# print item['buytime']
df = ts.get_realtime_quotes(item['code'])
df1 = ts.get_hist_data(item['code']).head(1)
status = item['detailtype']
open = df1['open'][0]
nowprice = df['price'][0]
profit = (float(nowprice) - float(item['buyprice'])) / float(item['buyprice']) * 100
nowprofit = (float(nowprice) - float(item['buyprice'])) / float(item['buyprice']) * 100
#已经卖出的股票的收益
if item['tradestatus']==1:
continue
profit = (float(item['sellprice'])-float(item['buyprice']))/float(item['buyprice'])*100
if(status=='lowopencross0'):
print '[lowopencross0] ',item['code'],item['buytime'],'buy price ',item['buyprice'],'and now price ',nowprice ,'总收益:',round(profit,2),'%','当前收益:',round(nowprofit,2),'%','持股状态:',item['tradestatus']
if (status == 'highopenlowhigh'):
print '[highopenlowhigh] ',item['code'],item['buytime'],' buy price ', item['buyprice'],'and now price ', nowprice,'总收益:',round(profit,2),'%','当前收益:',round(nowprofit,2),'%''持股状态:',item['tradestatus']
def order_place(self):
table = self.tableWidget
count = table.rowCount()
orders = []
for x in xrange(count):
ticker = unicode(table.item(x, 0).text())
side = unicode(table.item(x, 2).text())
amount = unicode(table.item(x, 3).text())
price = float(ts.get_realtime_quotes(ticker).price)
percent = self.percent
price = round(price*(side==u'买' and 1.+percent or 0.99-percent), 2)
amount = int(round((int(amount)/100)*100, -2))
orders.append((side, ticker, str(amount), str(price)))
message = '\r\n'.join([u'%s %s: %s@%s'%order for order in orders])
reply = QtGui.QMessageBox.question(self, u'下单确认',
message, QtGui.QMessageBox.Yes, QtGui.QMessageBox.No)
if reply != QtGui.QMessageBox.Yes:
return
for side, ticker, amount, price in orders:
if side == u'买':
self.account_instance.buy(ticker, price, amount)
else:
self.account_instance.sell(ticker, price, amount)
def meet_price(code, price_up, price_down,type):
try:
df = ts.get_realtime_quotes(code)
except Exception as e:
print(e)
time.sleep(5)
return 0
real_price = df['price'].values[0]
name = df['name'].values[0]
real_price = float(real_price)
pre_close = float(df['pre_close'].values[0])
percent = (real_price - pre_close) / pre_close * 100
# print(percent)
# percent=df['']
# print(type(real_price))
if real_price >= price_up:
print('%s price higher than %.2f , %.2f' % (name, real_price, percent),)
print('%')
if type=='msn':
push_msg(name, real_price, percent, 'up')
return 1
elif type=='wechat':
push_wechat(name, real_price, percent, 'up')
if real_price <= price_down:
print('%s price lower than %.2f , %.2f' % (name, real_price, percent),)
print('%')
if type=='msn':
push_msg(name, real_price, percent, 'down')
return 1
elif type=='wechat':
push_wechat(name, real_price, percent, 'down')
def start(self):
size = len(self.feed)
try:
balance = self.user.balance
err_log = self.LOGPATH + 'err.log'
trade_log = self.LOGPATH + 'trade.log'
start_time = time.strftime("%Y-%m-%d %H:%M",time.localtime())
reserve_amount = balance[0]['enable_balance']
ERR_LOG = open(err_log, 'a')
TRADE_LOG = open(trade_log, 'a')
if reserve_amount < size * 10000:
MASSAGE = '"%s": There is no enough to start the trade\n' %(start_time)
ERR_LOG.write(MASSAGE)
ERR_LOG.close()
else:
share = reserve_amount / size
for stock in self.feed.load():
quotes = ts.get_realtime_quotes(stock)
price = float(quotes.loc[[0], 'price']) * 100 # the price for one hand, 100 share
last_price = price / 100
quantity = int(share // price * 100)
self.user.buy(stock, price=last_price, amount=quantity)
MASSAGE = '%s,%s,%f,%d,buy\n' %(start_time, stock, last_price, quantity)
TRADE_LOG.write(MASSAGE)
TRADE_LOG.close()
except:
pass
def stop(self):
try:
position = self.user.position
err_log = self.LOGPATH + 'err.log'
trade_log = self.LOGPATH + 'trade.log'
ERR_LOG = open(err_log, 'a')
TRADE_LOG = open(trade_log, 'a')
balance = self.user.balance
trade_log = self.LOGPATH + 'trade.log'
stop_time = time.strftime("%Y-%m-%d %H:%M",time.localtime())
account_amount = balance[0]['asset_balance']
market_value = balance[0]['market_value']
TRADE_LOG = open(trade_log, 'a')
if market_value / account_amount * 100 < 3:
MASSAGE = '"%s": stocks have been clearred\n' %(stop_time)
ERR_LOG.write(MASSAGE)
ERR_LOG.close()
else:
for term in position:
if term['stock_code'] in self.feed.load():
quotes = ts.get_realtime_quotes(term['stock_code'])
price = float(quotes.loc[[0], 'price']) * 100 # the price for one hand, 100 share
last_price = price / 100
quantity = int(term['enable_amount'])
self.user.sell(term['stock_code'], price=last_price, amount=quantity)
MASSAGE = '%s,%s,%f,%d,sell\n' %(stop_time, term['stock_code'], last_price, quantity)
TRADE_LOG.write(MASSAGE)
TRADE_LOG.close()
except:
pass
def deal(self):
conn = self.conn
for item in conn.mystock.yjbtrade.find({'tradestatus':0}):
try:
df = ts.get_realtime_quotes(item['code'])
# 取得开始时间开始的最大值
starttime = item['buytime'].split(' ')[0]
# 最大值需要替换前面程序
maxprice = round(float(item['maxprice']), 2)
nowprice = round(float(df['price'][0]), 2)
if nowprice == 0.0:
continue
preclose = round(float(df['pre_close'][0]), 2)
lossprice = round(float(df['low'][0]), 2)
#如果当天，更新最大价格
if starttime == self.today:
# 更新最大收益价格值
if (nowprice > maxprice):
conn.mystock.yjbtrade.update({'code': item['code'],'buytime':item['buytime']}, {'$set': {'maxprice': nowprice}})
# 更新最低价格
if (nowprice < lossprice):
conn.mystock.yjbtrade.update({'code': item['code'],'buytime':item['buytime']}, {'$set': {'lossprice': lossprice}})
continue
# 更新最大收益价格值
if (nowprice > maxprice):
conn.mystock.yjbtrade.update({'code': item['code'],'buytime':item['buytime']}, {'$set': {'maxprice': nowprice}})
# 当前收益
profit = round((float(nowprice) - float(item['buyprice'])) / float(item['buyprice']) * 100, 2)
# 最大收益
maxprofit = round((float(maxprice) - float(item['buyprice'])) / float(item['buyprice']) * 100, 2)
# 当天收益
todayprofit = round((float(nowprice) - float(preclose)) / float(preclose) * 100, 2)
# 坑爹的卖出价格计算
sellprice = round(float(nowprice) * 0.98, 2)
#可以卖出标识
if item['tradestatus']==0:
#止损卖出
sellcount = item['stockcount']
# if nowprice < item['lossprice']:
# print "nowprice",nowprice," item['lossprice']",item['lossprice']
# self.sellStock(item['code'].encode("utf-8"), sellprice, sellcount, 'zhisun', item['buytime'])
#止盈卖出=
if self.ifSell(profit, maxprofit, todayprofit, 0):
self.sellStock(item['code'].encode("utf-8"), sellprice, sellcount, 'zhiying', item['buytime'])
except Exception as e:
print e
continue
def get_realtime_detail(code):
try:
df = ts.get_realtime_quotes(code)
field_dict = json.loads(df.to_json(orient='records'))[0]
except IOError:
pass
from models import StockDetail
return StockDetail(field_dict)
def download(self):
codes = self.stockBasics.index.values
fqFactorDF = DataFrame()
codeDF = DataFrame()
for code in codes:
descStr = " (%s, %s) "%(code, self.date)
_intervalFactor = 2
_interval = self.interval
_retryCount = 0
while _retryCount < self.retryTimes:
_retryCount += 1
logging.info("Downloading daily %s trying %d times."%(descStr, _retryCount))
_interval *= _intervalFactor
try:
# a brand new code into market may cause '_parase_fq_factor' raise exceptions
_df = ts.get_realtime_quotes(code)
if _df is None: # if the code is off the market, this could happen
break
_df = _df[['code','open','high','pre_close','price','low','volume','amount','date']].set_index('date')
_df.rename(columns={'price':'close'},inplace=True)
# a brand new code into market, could also like this, the get_realtime_quotes may return something
if ((float(_df['high']) == 0) & (float(_df['low'])==0)):
break # no need to store
_fqDF = ts.stock.trading._parase_fq_factor(code,'','')
_fqDF.insert(0,"code",code,True)
_fqDF = _fqDF.drop_duplicates('date').set_index('date').sort_index(ascending=False)
#_fqDF = _fqDF.ix[self.date]
_fqDF = _fqDF.head(1)
# stock may exit the market or just pause
if ((float(_df['high']) == 0) & (float(_df['low'])==0)):
break # no need to store
#_rate = float(_fqDF['factor'])/float(_df['pre_close'])
else:
_rate = float(_fqDF['factor'])/float(_df['close'])
_df = _df.drop('pre_close',axis=1)
for label in ['open', 'high', 'close', 'low']:
_df[label] = float(_df[label]) * _rate
#_df[label] = _df[label].map(lambda x:'%.2f'%x)
_df[label] = _df[label].astype(float)
except Exception, e:
if _retryCount + 1 == self.retryTimes or conf.DEBUG:
raise e
logging.info("Download error, waiting for %d secs."%_interval)
time.sleep(_interval)
continue
fqFactorDF = pd.concat([fqFactorDF,_fqDF])
codeDF = pd.concat([codeDF, _df])
break
if conf.DEBUG:
break
def realtime_write():
con=sql.connect(workdir+'concept1.db')
concept_all_stock=sql2.read_sql('select * from concept',con)
con.close()
con=sql.connect(workdir+'realtime_'+datetime.now().date().strftime('%Y%m%d')+'.db')
con.execute('PRAGMA journal_mode=WAL')
#con.execute('PRAGMA cache_size =80000')
#con.execute('PRAGMA synchronous = off')
#con.execute('PRAGMA page_size = 8192')
#con.execute('PRAGMA temp_store = MEMORY')
#con.execute('PRAGMA wal_autocheckpoint=10000000')
creat_table_command='create table if not exists realtime(open,pre_close,price,high,low,volume,b1_v,b1_p,b2_v,b2_p,b3_v,b3_p,b4_v,b4_p,b5_v,b5_p,a1_v,a1_p,a2_v,a2_p,a3_v,a3_p,a4_v,a4_p,a5_v,a5_p,time,code)'
insert_table_command='INSERT INTO realtime VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)'
con.execute(creat_table_command)
#con.execute('create index if not exists code on realtime(code)')
concept_name=list(set(concept_all_stock.c_name))
stock_code=list(set(concept_all_stock.code))
#all_time=pd.DataFrame()
last_time=pd.DataFrame()
n=0
#time1=time.clock()
while True :
try:
one_time=pd.DataFrame()
for i in range(5):
temp=ts.get_realtime_quotes(stock_code[i*500:(i+1)*500])
temp=temp.drop(['name','date','bid','ask','amount'],axis=1)
one_time=one_time.append(temp)
except:
continue
new_one_time=last_time.append(one_time).drop_duplicates()[len(last_time.index):]
if len(new_one_time)==0:
print 'no new data'
continue
#sql2.to_sql(new_one_time,'realtime',con,if_exists='append',index=True)
time1=time.clock()
con.executemany(insert_table_command,new_one_time.values)
con.commit()
time2=time.clock()
#new_one_time=new_one_time.set_index([pd.to_datetime(new_one_time.time),new_one_time.code])
#all_time=all_time.append(new_one_time)
last_time=one_time
#print len(all_time.index)
n+=1
print n,len(new_one_time.index),time2-time1
def getAllChinaStock2():
df_list = pd.read_csv(cm.DownloadDir + cm.TABLE_STOCKS_BASIC + '.csv')
stockList = df_list['code'].values
stockList_group = util.group_list(stockList, 20)
print len(stockList_group)
print stockList_group[1]
stockList = []
for group in stockList_group:
df = ts.get_realtime_quotes(group)
for se in df.get_values():
stock = st.Stock('')
stock.code = se[0]
stock.name = se[1]
stock.current = se[3]
stock.open = se[4]
stock.high = se[5]
stock.low = se[6]
stock.close = se[7]
stock.dealAmount = se[8] / 100
stock.time = time.localtime(time.time()) #时间
#print stock
stockList.append(stock)
return stockList
def stock():
time = datetime.datetime.now() # 获取当前时间
now = time.strftime('%H:%M:%S')
data = ts.get_realtime_quotes(stock_symbol) # 获取股票信息
r1 = float(data['price'])
name = data['name']
r2 = name + ' ' + str(stock_symbol) + ' 的当前价格为 ' + str(r1)
content = now + '\n' + r2
itchat.send(content, toUserName='******')
# print itchat.get_contact()
# flist = itchat.get_friends()
# for item in flist:
# print item
# print(content)
# 设置预警价格并发送预警信息
if r1 <= float(price_low):
itchat.send('低于最低预警价格', toUserName='******')
print('低于最低预警价格')
elif r1 >= float(price_high):
itchat.send('高于最高预警价格', toUserName='******')
print('高于最高预警价格')
else:
itchat.send('价格正常', toUserName='******')
print('价格正常')
def get_predict_data(file_name):
f = open(file_name)
(filepath, temp_file_name) = os.path.split(file_name)
(stock_code, extension) = os.path.splitext(temp_file_name)
f = pd.read_csv(f)
data_now = time.strftime('%Y-%m-%d', time.localtime(time.time()))
hist_data = f[-args.time_step + 1:]
real_data = ts.get_realtime_quotes(stock_code)
real_data = real_data[['open', 'high', 'price', 'low', 'volume']]
real_data.insert(0, 'date', data_now)
p_change = (float(real_data.iloc[-1, 3]) -
float(hist_data.iloc[-1, 3])) / float(hist_data.iloc[-1, 3])
real_data['p_change'] = p_change
real_data.rename(index=str, columns={"price": "close"}, inplace=True)
real_data[['open', 'high', 'close', 'low', 'volume', 'p_change']] = \
real_data[['open', 'high', 'close', 'low', 'volume', 'p_change']].astype('float')
hist_data = hist_data.append(real_data)
print("---------数据更新完成-----------")
pre_data = hist_data.iloc[:, 1:].values
x = (pre_data - np.mean(pre_data, axis=0)) / np.std(pre_data,
axis=0) # 标准化
x = [x.tolist()]
print(np.shape(x))
return x, stock_code
def getStockData():
"""
获取股票实时数据
:return:股票实时数据
"""
global stock_codes
code_name_price = []
try:
df = ts.get_realtime_quotes(stock_codes)
df_len = len(df)
for stock_code in stock_codes:
is_found = False
for i in range(df_len):
actual_code = df['code'][i]
if stock_code == actual_code:
code_name_price.append(
(actual_code, df['name'][i], float(df['price'][i])))
is_found = True
break
if is_found is False:
code_name_price.append(('', '', 0))
except:
code_name_price = [('', '', 0)] * NUM_OF_STOCKS # 网络不行，返回空
return code_name_price
def continuous(ticket, growthRate=0.02, opentVSclosey=0.98):
# dateToday = datetime.datetime.today().strftime('%Y-%m-%d')
# Notice: the value of two function is not same, get_hist_data, get_realtime_quotes
Point80 = ''
Point90 = ''
Point95 = ''
history = ts.get_hist_data(ticket)
op = history['open']
close = history['close']
low = history['low']
thisYesterday = ts.get_hist_data(ticket)
closeYesterday = thisYesterday['close'][0]
openYesterday = thisYesterday['open'][0]
highYesterday = thisYesterday['high'][0]
closeBeforeY = thisYesterday['close'][1]
openBeforeY = thisYesterday['open'][1]
thisToday = ts.get_realtime_quotes(ticket)
openToday = thisToday['open'][0]
priceToday = thisToday['price'][0]
lowToday = thisToday['low'][0]
openTodayValue = float(openToday)
priceTodayValue = float(priceToday)
lowTodayValue = float(lowToday)
if (close[0] - op[0]) / op[0] >= growthRate or (low[0] > close[1]
and low[0] > op[1]):
if openTodayValue / closeYesterday >= opentVSclosey and lowTodayValue >= openYesterday:
Point80 = ticket
if lowTodayValue >= closeYesterday and (
closeBeforeY - openBeforeY) / openBeforeY >= 0.01:
Point90 = ticket
if lowTodayValue > highYesterday:
Point95 = ticket
return Point80, Point90, Point95
def flushStockInformation():
dbo = DataBaseOperator()
# dbo.delete(StockInformation, StockInformation.stock_name, "API缺少该股信息")
stock_list = dbo.searchAll(StockInformation)
for stock in stock_list:
try:
df = ts.get_realtime_quotes(stock.stock_id)
stock.stock_name = df['name'][0]
stock.now_price = float(df['price'])
stock.flush_time = datetime.strptime(
df['date'][0] + " " + df['time'][0], '%Y-%m-%d %H:%M:%S')
try:
stock.up_down_rate = 100 * (float(df['price'][0]) - float(
df['pre_close'][0])) / float(df['pre_close'][0])
except:
stock.up_down_rate = 0
except:
pass
dbo.update()
time.sleep(3)
flushAliveOrders()
global timer
timer = threading.Timer(global_flush_time * 60, flushStockInformation)
timer.start()
def get_real_time_info():
all_info = None
stock_infos = get_classified_stocks()
stock_nums = len(stock_infos)
i = 0
start_index = 0
while start_index < stock_nums:
if start_index + 800 > stock_nums:
end_index = stock_nums - 1
else:
end_index = start_index + 800 - 1
stock_codes = stock_infos['code'][start_index:end_index]
_info = ts.get_realtime_quotes(stock_codes)
if start_index == 0:
all_info = _info
else:
frames = [all_info, _info]
all_info = pd.concat(frames, ignore_index=True)
start_index = end_index + 1
outstandings = []
for index, code_id in all_info['code'].iteritems():
outstanding = stock_infos.query(
'code=="%s"' % code_id).outstanding.values[0] * 1000000
outstandings.append(outstanding)
all_info['limit-up-time'] = 0
all_info['limit-dowm-time'] = 0
all_info['date'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
all_info['outstanding'] = outstandings
all_info = all_info[(all_info['volume'].astype(float) > 0)
& (all_info['outstanding'] > 0)]
all_info['turnover'] = all_info['volume'].astype(float).divide(
all_info['outstanding'])
all_info['p_change'] = 100 * (all_info['price'].astype(float) -
all_info['pre_close'].astype(float)).divide(
all_info['pre_close'].astype(float))
return all_info
def get_data_ts(self): #获取开始数据
try:
self.code_data = ts.get_k_data(
self.code, self.startDate, end=self.endDate)
except ValueError:
print('Input code is wrong/输入代码错误.')
else:
self.code_data = self.code_data.sort_index(ascending=True) # 从后倒序
self.code_data.date = self.code_data.date.apply(
lambda x: datetime.datetime.strptime(x, "%Y-%m-%d"))
#self.code_data.date=self.code_data.date.apply(lambda x:matplotlib.dates.date2num(x))
self.code_data = self.code_data.set_index('date')
if self.endDate == '%s' % self.today:
todat_realtime = ts.get_realtime_quotes(self.code)
realtime_price = float(todat_realtime.price)
realtime_high = float(todat_realtime.high)
realtime_low = float(todat_realtime.low)
realtime_open = float(todat_realtime.open)
realtime_volume = float(todat_realtime.volume)
self.code_data.loc['%s' % self.today,
'code'] = '%s' % self.code
self.code_data.loc['%s' % self.today,
'volume'] = '%s' % realtime_volume
self.code_data.loc['%s' % self.today,
'open'] = '%s' % realtime_open
self.code_data.loc['%s' % self.today,
'high'] = '%s' % realtime_high
self.code_data.loc['%s' % self.today,
'low'] = '%s' % realtime_low
self.code_data.loc['%s' % self.today, 'close'] = realtime_price
self.df = self.code_data
return self.code_data
def updateMarketlist():
sh = ts.get_realtime_quotes('sh')
sz = ts.get_realtime_quotes('sz')
hs300 = ts.get_realtime_quotes('hs300')
sz50 = ts.get_realtime_quotes('sz50')
zxb = ts.get_realtime_quotes('zxb')
cyb = ts.get_realtime_quotes('cyb')
conn = ms.connect(host='localhost',
port=3306,
user='******',
passwd='123456',
db='websql',
charset="utf8")
cur = conn.cursor()
s = [sh, sz, hs300, sz50, zxb, cyb]
values = []
for x in s:
data = pd.DataFrame(x)
data1 = Series(list(data['code']))
data2 = Series(list(data['name']))
data3 = Series(list(data['open']))
data4 = Series(list(data['pre_close']))
data5 = Series(list(data['price']))
data6 = Series(list(data['high']))
data7 = Series(list(data['low']))
data8 = Series(list(data['volume']))
data9 = Series(list(data['amount']))
data10 = Series(list(data['time']))
values.append((data1[0].encode("utf-8"), data2[0].encode('utf-8'),
data3[0].encode("utf-8"), data4[0].encode("utf-8"),
data5[0].encode("utf-8"), data6[0].encode("utf-8"),
data7[0].encode("utf-8"), data8[0].encode("utf-8"),
data9[0].encode("utf-8"), data10[0].encode("utf-8")))
cur.execute('delete from marketlist')
cur.executemany(
'insert into marketlist (code,name,open,pre_close,price,high,low,volume,amount,time) values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',
values)
conn.commit()
cur.close()
conn.close()
def greet(name):
print("--- hello,%s ---" % name)
data = tushare.get_realtime_quotes('600000')
print(data)
def __init__(self, code):
self.df = ts.get_realtime_quotes(code)
def get_stock_name(stocknumber):
realtimeData = ts.get_realtime_quotes(stocknumber)
realtimeData = realtimeData.to_dict('record')
stock_name = realtimeData[0]['name']
return stock_name
#最高价的比率
high_price_rate = 0.97
#涨幅最大比率
up_price_high = 1.07
#上影线比率
up_line_rate = 0.3
#读取所有股票代码
df_stock = pd.DataFrame(pd.read_csv('C:/stock_data/all_code.csv', index_col=None))
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')) #日期格式化
for stockCode in df_stock.code:
# print('code-------------'+"%06d"%stockCode)
try:
df_today = ts.get_realtime_quotes("%06d"%stockCode) # 获取股票实时数据
# print(df_today)
# 实时成交量
volume_today = int(df_today.iloc[0].volume)/100
# print('volume_today-----------'+str(volume_today))
# 今日最高价
high_today = df_today.iloc[0].high
# 今日时价
price_today = df_today.iloc[0].price
# print('high_today-----------'+str(high_today))
#从csv文件中获取历史的股票数据
df_history = pd.DataFrame(pd.read_csv('C:/stock_data/' + var_date + '/' + "%06d" % stockCode + '.csv', index_col=None))
# 历史成交量
volume_yestoday_1 = df_history.iloc[0].volume
def get_price(self):
df = ts.get_realtime_quotes(self.stock_code)
price = float(df['b1_p'].tolist()[0])
return price
ts.get_report_data(2017, 2).to_sql('report', engine, if_exists='append')
print('basic information over ....')
basic_information()
@retry(tries=5, delay=2)
def get_today():
today_all = ts.get_today_all()
print(today_all[:5])
get_today()
realtime = ts.get_realtime_quotes("603618")
realtime2 = ts.get_realtime_quotes(["sh", "zs", "hs300", "sz50", "zxb", "cyb"])
# get news
# ts.guba_sina().to_sql('guba_sina',engine,if_exists='append')
print('guba_sina')
# ts.get_notices().to_sql('notices',engine,if_exists='append')
print('notices')
ts.get_latest_news().to_sql('latest_news', engine, if_exists='append')
print('latest_news')
# save all stock to mysql
# open high close low volume p_change
# conn = pymysql.connect('localhost','root','caicai520','quantist')
# cursor = conn.cursor()
# cursor.execute("select distinct good1 from t_good limit 5")
def bars(codes):
"""获取codes的实时quotes"""
return ts.get_realtime_quotes(codes)
def getAmt():
df = ts.get_realtime_quotes(ss)
print(df[['name', 'price']])
# -*-coding=utf-8-*- import tushare as ts pingan_code='601318' shangqi_code='600104' icbc_code='601398' huada_genic='300676' real_time=ts.get_realtime_quotes([pingan_code,shangqi_code,icbc_code,huada_genic]) need_data=real_time[['name','open','price','high','low','bid','volume','date','time']] print(need_data)
# -×- coding: utf-8 -×- __author__ = 'xlyang0211' import tushare as ts df = ts.get_realtime_quotes(['000581', '601299']) print df print df.iloc[0, 1], df.iloc[0, 2], df.iloc[0, 5]
def test3():
ts_pro = ls.get_ts_pro()
df = ts.get_realtime_quotes('601318')
print(df)
# 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 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:
#读取所有股票代码
# df_all_code = pd.DataFrame(pd.read_csv(stock_data_path + df_all_code_file, index_col=None))
df_all_code = pd.DataFrame(pd.read_csv(stock_data_path + var_date +'_T_1.csv', index_col=None))
#历史数据日期yyyymmdd文件夹
index_stock = 0
# ,code
#直接保存
out = open(stock_data_path + var_date + '_T_1_PM2.csv','a', newline='')
csv_write = csv.writer(out,dialect='excel')
csv_write.writerow(['',"code","price"])
for stock_code in df_all_code.code:
# print('>>>>>>>>>>>'+ "%06d"%stock_code +'>>>>>>>>>')
try:
# 获取股PM2点的实时数据
df_today = ts.get_realtime_quotes("%06d"%stock_code)
# 今日实时（PM2点）价
price_today = df_today.iloc[0].price
print("%06d"%stock_code)
csv_write.writerow([index_stock,"%06d"%stock_code,price_today])
index_stock += 1
except IndexError:
# print("%06d" % stock_code + 'IndexError')
continue
except FileNotFoundError:
# print("%06d" % stock_code + 'FileNotFoundError')
continue
except urllib.error.URLError:
continue
except socket.timeout:
continue
def get_all_price(code_list):
'''按列表内容获取stk实时数据'''
df = ts.get_realtime_quotes(STOCK)
print(df)
def get_price():
return float(ts.get_realtime_quotes('hs300')['price'])
def jiance(gpdm, pb=1, ps=1, vb=1, vs=1):
jl = junliang(gpdm)
with open(f"{gpdm}-{datetime.now().date()}.txt", "a") as f:
print(f"{gpdm}最近三天均量为{jl}")
f.write(f"{gpdm}最近三交易日大单均值{jl}\n")
while datetime.now().hour < 15:
r = ts.get_realtime_quotes(gpdm)
pn = float(r.price[0])
# print(r.b5_v[0])
zheng = 0
fu = 0
# print(r.b1_v[0],r.b2_v[0],r.b3_v[0],r.b5_v[0],r.b1_v[0],)
if r.b1_v[0].isdigit():
zheng += float(r.b1_v[0])
# print("买一",r.b1_v[0])
if r.b2_v[0].isdigit():
zheng += float(r.b2_v[0])
# print("买2", r.b2_v[0])
if r.b3_v[0].isdigit():
zheng += float(r.b3_v[0])
if r.b4_v[0].isdigit():
zheng += float(r.b4_v[0])
if r.b5_v[0].isdigit():
zheng += float(r.b5_v[0])
if r.a1_v[0].isdigit():
fu += float(r.a1_v[0])
if r.a2_v[0].isdigit():
fu += float(r.a2_v[0])
if r.a3_v[0].isdigit():
fu += float(r.a3_v[0])
if r.a4_v[0].isdigit():
fu += float(r.a4_v[0])
if r.a5_v[0].isdigit():
fu += float(r.a5_v[0])
# liangbi = (float(r.b1_v[0]) + float(r.b2_v[0]) + float(r.b3_v[0]) + float(r.b4_v[0]) + float(r.b5_v[0])) / (float(r.a1_v[0]) + float(r.a2_v[0]) + float(r.a3_v[0]) + float(r.a4_v[0]) + float(r.a5_v[0]))
if zheng == 0 or fu == 0:
continue
liangbi = zheng / fu
print(datetime.now(), gpdm, liangbi, zheng, fu)
if liangbi > 10 and (abs(pb - pn) / pb > 0.01
or liangbi > vb * 1.5) and max(zheng, fu) > jl:
with open(f"{gpdm}-{datetime.now().date()}.txt", "a") as f:
line = f"{datetime.now()}\t买入\t{pn}\t{round(liangbi,2)}\t{zheng}\t{fu}\n"
print(line)
f.write(line)
pb = pn
vb = liangbi
if liangbi < 0.1 and (abs(ps - pn) / ps > 0.01
or liangbi < vs / 1.5) and max(zheng, fu) > jl:
with open(f"{gpdm}-{datetime.now().date()}.txt", "a") as f:
line = f"{datetime.now()}\t卖出\t{pn}\t{round(1/liangbi,2)}\t{zheng}\t{fu}\n"
print(line)
f.write(line)
ps = pn
vs = liangbi
sleep(1)
import tushare as ts
import pandas as pd
import datetime
f = open('../data/stocks.txt')
time1 = datetime.datetime.now()
stocks = [line.strip() for line in f.readlines()]
dw = 880
data1 = ts.get_realtime_quotes(stocks[0:dw])
data2 = ts.get_realtime_quotes(stocks[dw:2 * dw])
data3 = ts.get_realtime_quotes(stocks[2 * dw, 3 * dw])
data4 = ts.get_realtime_quotes(stocks[3 * dw, -1])
time2 = datetime.datetime.now()
print(str(time1))
print(str(time2))
print(data1)
print(data2)
print(data3)
print(data4)
def deal(self):
conn = self.conn
for item in conn.mystock.yjbtrade.find({'tradestatus': 0}):
try:
df = ts.get_realtime_quotes(item['code'])
# 取得开始时间开始的最大值
starttime = item['buytime'].split(' ')[0]
# 最大值需要替换前面程序
maxprice = round(float(item['maxprice']), 2)
nowprice = round(float(df['price'][0]), 2)
if nowprice == 0.0:
continue
preclose = round(float(df['pre_close'][0]), 2)
lossprice = round(float(df['low'][0]), 2)
#如果当天，更新最大价格
if starttime == self.today:
# 更新最大收益价格值
if (nowprice > maxprice):
conn.mystock.yjbtrade.update(
{
'code': item['code'],
'buytime': item['buytime']
}, {'$set': {
'maxprice': nowprice
}})
# 更新最低价格
if (nowprice < lossprice):
conn.mystock.yjbtrade.update(
{
'code': item['code'],
'buytime': item['buytime']
}, {'$set': {
'lossprice': lossprice
}})
continue
# 更新最大收益价格值
if (nowprice > maxprice):
conn.mystock.yjbtrade.update(
{
'code': item['code'],
'buytime': item['buytime']
}, {'$set': {
'maxprice': nowprice
}})
# 当前收益
profit = round((float(nowprice) - float(item['buyprice'])) /
float(item['buyprice']) * 100, 2)
# 最大收益
maxprofit = round((float(maxprice) - float(item['buyprice'])) /
float(item['buyprice']) * 100, 2)
# 当天收益
todayprofit = round((float(nowprice) - float(preclose)) /
float(preclose) * 100, 2)
# 坑爹的卖出价格计算
sellprice = round(float(nowprice) * 0.98, 2)
#可以卖出标识
if item['tradestatus'] == 0:
#止损卖出
sellcount = item['stockcount']
# if nowprice < item['lossprice']:
# print "nowprice",nowprice," item['lossprice']",item['lossprice']
# self.sellStock(item['code'].encode("utf-8"), sellprice, sellcount, 'zhisun', item['buytime'])
#止盈卖出=
if self.ifSell(profit, maxprofit, todayprofit, 0):
self.sellStock(item['code'].encode("utf-8"), sellprice,
sellcount, 'zhiying', item['buytime'])
except Exception as e:
print e
continue
def query_stock_read_price(self):
df = ts.get_realtime_quotes(self.stock_num)
df = df[['price', 'time']]
price = df['price'][0] # df['price'][0] == df['price'].values[0]
time = df['time'][0]
return price, time
def get_now_price(self):
return float(ts.get_realtime_quotes(self.code)['price'][0])