def big_deal_loader(self, code, vol=400):
today = date.today()
today_str = datetime.strftime(today, '%Y-%m-%d')
try:
df = ts.get_sina_dd(code, date=today_str, vol=vol)
assert df is not None
result_set = df.to_json(orient='records')
result_set = json.loads(result_set)
for record in result_set:
volume = record['volume']
price = record['price']
amount = volume * price
if amount < 300000:
continue
deal_time = record.pop('time')
time_str = '{} {}'.format(today_str, deal_time)
date_time = datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S')
record['date'] = date_time
self.db.deal_record.insert(record)
self.logger.info('{} has been inserted successfully at {}'.format(
code, today_str))
except Exception, e:
self.logger.error(
'{} has failed to insert beacuse of {} at {}'.format(
code, e, today_str))
def dd(stock_code, date_val=date.today(), vol=0):
if vol == 0:
vol = get_default_vol(stock_code)
result = ts.get_sina_dd(stock_code, date_val, vol)
return result
def getBigVol(stockCode='000748', vols=400):
today = time.strftime('%Y-%m-%d') #.decode('utf-8')
dtype = [u'中性盘', u'买盘', u'卖盘']
color = ['b', 'r', 'g']
df = ts.get_sina_dd(stockCode, date=today, vol=vols) #默认400手
df = df.sort_values(by='time')
df['amount'] = df.volume * df.price
df.index = range(df.shape[0])
gp = df.groupby(['type'])
sumt = gp.sum()
sumt['avgPrice'] = sumt.amount / sumt.volume
print(sumt)
plt.figure(figsize=(15, 4))
# plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] #用来正常显示中文标签
# plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签
# plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号
pdf = df.tail(100)
for i in range(3):
plt.bar(pdf[pdf['type'] == dtype[i]].index,
pdf[pdf['type'] == dtype[i]].volume,
alpha=0.7,
color=color[i])
plt.grid(True)
plt.title(stockCode + u"大单设置为：" + str(vols))
#plt.xticks(range(df.shape[0]),range(df.shape[0]))
plt.margins(0)
plt.show()
return df, sumt #
def dd_buy(code, trade_days_dict):
result = True
for day in trade_days_dict.keys():
dd = ts.get_sina_dd(code, date=day, vol=500)
if dd is None:
result = False
break
buy_vol = dd[dd.type == '买盘']['volume'].to_dict()
sel_vol = dd[dd.type == '卖盘']['volume'].to_dict()
total_buy = 0
total_sel = 0
for k in buy_vol.keys():
total_buy += buy_vol[k]
for k in sel_vol.keys():
total_sel += sel_vol[k]
if total_buy - total_sel < 0:
result = False
break
return result
def ddjy(index, date=getYesterday(), vol=2000):
df = ts.get_sina_dd(index, date=str(getYesterday()), vol=2000)
# =============================================================================
# df['date'] = datetime.date.today()
# =============================================================================
return df
def trade_block_worker(engine, codes, sdate, edate):
pid = os.getpid()
tsl.log("pid %i start with %i codes..." % (pid, len(codes)))
df = pd.DataFrame()
cdate = sdate
temp = {}
while cdate <= edate:
if not tsu.is_holiday(cdate):
for code in codes:
try:
newdf = ts.get_sina_dd(code, cdate, vol=10000)
if newdf is not None:
newdf['date'] = cdate
df = df.append(newdf, ignore_index=True)
except BaseException, e:
if 'timed out' in str(e) or 'urlopen error' in str(e):
temp.setdefault(cdate, [])
temp[cdate].append(code)
pass
else:
print e
tsl.log("pid %i error for %s on %s" %
(pid, code, str(cdate)))
if len(df) != 0:
df = df.set_index('code', drop='true')
df.to_sql('trade_block', engine, if_exists='append')
cdate += datetime.timedelta(days=1)
def getSinaDd(self, code, date, vol=400, bseq=True):
data = tushare.get_sina_dd(code, date, vol, 60, 0.5)
it = data.iterrows()
l = []
for row in it:
ll = [date]
# 如果数据是NaN或者是'--'则改为0
ll.extend([
e if
(isinstance(e, str) or not math.isnan(e)) and e != '--' else 0
for e in row[1].values
])
l.append(ll)
sql = 'insert into sina_dd values('
sql += 'null' if bseq else ''
# date参数位置
sql += ',%s'
for e in data.columns.values:
sql += ',%s'
sql += ')'
conn = self.getDBConn()
cursor = conn.cursor()
cursor.executemany(sql, l)
conn.commit()
conn.close()
print(code + ' ' + date + ' inserted')
def get_dadan_data(self):
'''
参数说明：
code：股票代码，即6位数字代码
date:日期，格式YYYY-MM-DD
vol:手数，默认为400手，输入数值型参数
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：
code：代码
name：名称
time：时间
price：当前价格
volume：成交手
preprice ：上一笔价格
type：买卖类型【买盘、卖盘、中性盘】
'''
dadan_data = ts.get_sina_dd(code=self.code,
date='2018-08-08',
vol=400,
retry_count=self.retry_count,
pause=self.pause)
print(dadan_data)
return dadan_data
def get_tick_sina(_stock_id, _trade_date):
# sina base
base_vol = 200 # should >= 400
df = None
try:
df = ts.get_sina_dd(_stock_id, date=_trade_date, vol=base_vol)
except Exception:
log_error("warn: %s get sina exception!", _stock_id)
time.sleep(5)
return None
if df is None :
log_error("warn: stock %s, %s is None, next", _stock_id, _trade_date)
return None
if df.empty:
log_error("warn: stock %s, %s is empty, next", _stock_id, _trade_date)
return None
if len(df) <= 5:
log_error("warn: stock %s, %s is short %d, next", _stock_id, _trade_date, len(df))
return None
# sina data: convert to 手
df['volume'] = df['volume'] / 100
df = df.set_index('time').sort_index()
return df
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 get_bid(index):
if index in codes:
return
hist = ts.get_sina_dd(index, date=date, vol=500)
if hist is not None:
# print(index, hist['volume'].sum())
hist = hist.iloc[::-1]
r = redis.Redis(connection_pool=redis_pool)
rise_stop = r.get('quant.{}.rise_stop'.format(index))
rise_stop = float(rise_stop)
super_dd = False
for i in range(len(hist)):
if hist['volume'][i] > 2000 * 100:
super_dd = True
if hist['price'][i] == rise_stop: # 涨停
if hist['preprice'][i] != 0: # 开盘涨停
# if hist['preprice'][i] != hist['price'][i] or super_dd: # 大单打到涨停
if hist['preprice'][i] != hist['price'][i]: # 大单打到涨停
mu.acquire()
codes.append(index)
mu.release()
print(
'time: {}, rise stop: {}, preprice: {}, price: {}'.
format(hist['time'][i], rise_stop,
hist['preprice'][i], hist['price'][i]))
print('https://xueqiu.com/S/{}{}'.format(
get_stock_type(index).upper(), index))
break
def get_index_data(code):
stock_basic = get_basic(code).ix[0]
subtitle = [stock_basic[1],code]
max_date = get_max_date(code)#dt.datetime.now()
max_date = dt.datetime.strptime(max_date,"%Y-%m-%d")
start = max_date + dt.timedelta(days=-90)
stock_90 = get_one_stock_all(code,start=start.strftime('%Y-%m-%d'),end=max_date.strftime('%Y-%m-%d'))
stock_data = stock_90.tail(2).sort_values(by="date",ascending=False)
sotck_sh_90 = get_one_stock_all('sh',start=start.strftime('%Y-%m-%d'),end=max_date.strftime('%Y-%m-%d'))#ts.get_hist_data('sh',start=start.strftime('%Y-%m-%d'),end=max_date.strftime('%Y-%m-%d'))
x_data = stock_90.date.values.tolist()
y_data0 = stock_90.p_change.values.tolist()
y_data1 = sotck_sh_90.p_change.values.tolist()
#大手买卖
tf = ts.get_sina_dd(code, stock_data.iloc[0]['date'])
if isinstance(tf,pd.DataFrame):
gf = tf.groupby('type')
s = gf.volume.sum()
pie_l = {x:y for x,y in s.items()}
pie_data = generate_pie_data(pie_l)
category = list(pie_l.keys())
else:
category = ["没有数据",]
pie_data = "[{name:'没有数据',value:1}]"
return stock_basic,subtitle,stock_data,x_data,y_data0,y_data1,category,pie_data#,cloud_text
def updatestockbigchange():
conn = ms.connect(host='localhost',
port=3306,
user='******',
passwd='123456',
db='investment',
charset="utf8")
cur = conn.cursor()
cur.execute('select code from mainlist')
stus = cur.fetchall()
now = time.strftime("%Y-%m-%d", time.localtime(time.time()))
code = ''
for stu in stus:
code = stu[0]
stockbigchangelist = ts.get_sina_dd(code, now)
stockbigchange = pd.DataFrame(stockbigchangelist)
if stockbigchange.size != 0:
cur.execute("delete from stockbigchange_" + code)
values = []
for index, row in stockbigchange.iterrows():
values.append((row['time'], row['price'], row['volume'],
row['preprice'], row['type']))
insertstr = 'insert into stockbigchange_' + code + ' (time,price,volume,preprice,type) values(%s,%s,%s,%s,%s)'
cur.executemany(insertstr, values)
conn.commit()
cur.close()
conn.close()
def get_sina_dd(self, code, date='', vol=400):
"""
大单交易数据
"""
if date == '':
date = date_time.get_today_str()
d = ts.get_sina_dd(code, date=date, vol=vol)
return d
def exportDadan(fromDate, toDate, code):
df = ts.get_sina_dd(code, toDate)
engine = create_engine(sql_str)
table_name = "dadan_" + code + "_" + toDate.replace("-", "", 3)
# 存入数据库
df.to_sql('dadan', engine, if_exists='append')
Log.logger.info("dadan数据导入完成。")
return True
def get_sina_dd(stock_code, **params):
try:
logger.info('get sina DD data starting...')
df = ts.get_sina_dd(stock_code, **params)
logger.info('get sina DD data end...')
except:
logger.exception('some errors between get sina DD data end...')
return df
def GetStockData(self, code=None, startTime=None):
if code is None:
return None
try:
return ts.get_sina_dd(code=code, date=startTime)
except:
print(sys.exc_info()[0])
return None
def get_large_order(code=None,
date=None,
vol=400,
retry_count=3,
pause=0.001,
data_source='tushare'):
if is_tushare(data_source):
return ts.get_sina_dd(code, date, vol, retry_count, pause)
def get_dd_df_thru_code(code, vol=400, date=None):
'''
获得某个股票的大单df
'''
if date is None:
date = datetime.datetime.now().strftime("%Y-%m-%d")
df = ts.get_sina_dd(code, date=date, vol=vol)
return df
def get_sina_dd(self, code, timeStr):
df = ts.get_sina_dd(code, date=timeStr)
ndf = pandas.DataFrame()
if df is not None:
ndf = df.groupby('type').sum().reset_index()
if len(ndf) > 2:
return ndf[1:].reset_index()
return ndf
def big_deal(self, codes, vol, check_time):
'''
for code in codes:
df=ts.get_sina_dd(code,vol,check_time)
print df
'''
df = ts.get_sina_dd('603918', '2017-04-22')
print df
def get_dd(index):
hist = ts.get_sina_dd(index, date=date, vol=500)
print(hist)
print(hist['volume'].sum())
buy = hist[hist['type'].str.contains('买盘')]['volume'].sum()
sell = hist[hist['type'].str.contains('卖盘')]['volume'].sum()
print('买盘', buy)
print('卖盘', sell)
print(round(buy / sell, 2))
def download_Large_amount(time,code,vols):
data_L=ts.get_sina_dd(code , date=time ,vol=vols)
files_path = '../report/Daily_database/big_amu'+code
if os.path.exists(files_path) == False: # 判断文件是不是存在
os.mkdir(files_path) # 创建目录5
data_L.to_csv(files_path+'/%s_LGamount%s_csv.csv'%(time,vols),encoding='gbk')
with pd.ExcelWriter(files_path+'/%s_LGamount%s_xlx.xlsx'%(time,vols)) as writer:
data_L.to_excel(writer, sheet_name='Sheet1')
print('\n%s 大单 have been saved'%time)
def bigs():
"""大于400手的大单
参数说明：
code：股票代码，即6位数字代码
date:日期，格式YYYY-MM-DD
vol:手数，默认为400手，输入数值型参数
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"""
df = ts.get_sina_dd('600848', date='2020-10-09', vol=500) # 默认400手
print(df)
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 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 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=self.date)
print(big_share[['time', 'code', 'price', 'preprice', 'volume', 'type']])
print(big_share.sort(columns='volume'))
def get_sina_dd():
df = ts.get_sina_dd('600848', date='2016-12-09')
grouped = df.groupby(df['type'])
groupedSum = grouped.sum()
a = groupedSum.iloc[0:1,:]
buy = groupedSum[1,2] if groupedSum[1,1]=='买盘' else groupedSum[2,2]
print df
print grouped
print groupedSum
print a
print buy
return a
def anyl_dadan_zhanbi(self):
matchs = []
j = 0
for code in self._codes:
# if code.startswith('300'):
# continue
# code_zhanbi=()
# code_zhanbi['code']=code
j += 1
df = ts.get_realtime_quotes(code)
# keys=numpy.array(df.volume.keys).tolist()
# if not days[i] in keys:
# continue
if 0 not in df.volume:
continue
chengjiaoliang = 0
if self._maxval <= 0:
chengjiaoliang = float(df.volume[0])
self._maxval = int(chengjiaoliang * 0.00005)
print self._maxval
print '-----------------------'
# print ('%d/%d current=%s' %(j,len(self._codes),code))
ds = ts.get_sina_dd(code=code,
date=self._time_start,
vol=self._maxval)
# ds['total']=ds.price*ds.volume
# maipantol=ds[ds.type=='买盘'].total.sum()
# maipan1tol=ds[ds.type=='卖盘'].total.sum()
if ds is None:
continue
maipantol = ds[ds.type == '买盘'].volume.sum()
maipan1tol = ds[ds.type == '卖盘'].volume.sum()
jinliuru = maipantol - maipan1tol
# df= ts.get_hist_data(code=code,start=self._time_start,end=self._time_start)
time.sleep(1)
if jinliuru == 0.0 or chengjiaoliang == 0.0:
continue
liuruzhanbi = jinliuru / chengjiaoliang
# p_change=(df.price-df.open[0])/df.open[0]
# if liuruzhanbi<self._zhanbi:
# print liuruzhanbi
# continue
# code_zhanbi['zhanbi']=liuruzhanbi
matchs.append(
(code, Decimal.from_float(liuruzhanbi), self._time_start))
# print ('date=%s,code=%s,liuruzhanbi=%s' %(str(self._time_start),str(code),str(liuruzhanbi)))
# print ('date=%s,code=%s,zhangfu=%s,jinliuru=%s' %(str(days[i]),str(self._codes[0]),str(secdayzhangfu),str(jinliuru)))
# print matchs
return matchs
def get_plenty_of_order():
# 笔数阀值
vol = 1000
for index, date in enumerate(dates):
for i in range(len(stock_list)):
target_stock = stock_list.loc[i]
if i <= 2:
target_date_stock = ts.get_sina_dd(stock_code_format(
target_stock['ts_code']),
date=date,
vol=vol)
if target_date_stock is not None:
print(target_date_stock, date)
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=self.date)
print big_share[[
'time', 'code', 'price', 'preprice', 'volume', 'type'
]]
print big_share.sort(columns='volume')
def rt_dadan_rank_one(_stock_id, _dd_date, _db):
buy = 0
sell = 0
mid = 0
# 100手起
base_vol = 100
df = None
try:
df = ts.get_sina_dd(_stock_id, date=_dd_date, vol=base_vol)
except Exception:
log_error("warn: %s get_sina_dd exception!", _stock_id)
time.sleep(5)
return -4,-4,-4
if df is None :
# log_error("warn: stock %s is None, next", _stock_id)
return -1,-1,-1
if df.empty:
# log_error("warn: stock %s is empty, next", _stock_id)
return -2,-2,-2
if len(df) <= 5:
# log_error("warn: stock %s is short, next", _stock_id)
return -3,-3,-3
# convert to 手
df['volume'] = df['volume'] / 100
log_debug("ranking: %s, size: %d", _stock_id, len(df))
# 保持 升序
df = df.sort_index(ascending=False)
# rank 2016/8/28
rank, content = get_df_rank(df)
# if rank >= 300 or (rank >= 209 and rank % 100 == 9):
# if rank >= 100 or (rank >= 209 and rank % 100 == 9):
if rank >= 309:
subject = "rank: %d 净流入 %s (%.2f%%)" % (rank, _stock_id, get_chg_rate(_stock_id))
content += get_basic_info_all(_stock_id, _db)
log_info("nice: %s, %s", subject, content)
# saimail_dev(subject, content)
buy, sell, mid = get_buy_sell_sum2(df)
return buy, sell, mid
def get_fangliang_time(self, code, timeStr=None):
if not timeStr:
timeStr = time.strftime("%Y-%m-%d", time.localtime())
df = ts.get_sina_dd(code, date=timeStr)
if len(df) < 20:
return None
index = len(df) - 20
while index > 0:
ndf = df[index:].groupby(['type']).sum().reset_index()
if (ndf['volume'][1] > ndf['volume'][2] * 1.5):
print(ndf)
return df['time'][index]
index -= 1
return None
def getBigVol(code):
# 获取当天的分笔
# today_vol=ts.get_today_ticks(code)
# hist_vol=ts.get_tick_data(code,date='2016-11-28')
# print(today_vol.head(10))
# print(hist_vol)
hist_big_deal = ts.get_sina_dd(code, date='2016-12-01', vol=500)
if hist_big_deal is None:
print("No Big Deal")
else:
print(hist_big_deal)
def getBigVol(code):
# 获取当天的分笔
# today_vol=ts.get_today_ticks(code)
# hist_vol=ts.get_tick_data(code,date='2016-11-28')
# print(today_vol.head(10))
# print(hist_vol)
hist_big_deal = ts.get_sina_dd(code, date='2016-12-01', vol=500)
if hist_big_deal is None:
print("No Big Deal")
else:
print(hist_big_deal)
def bigTrade(code, dat=str(date.today())):
df = ts.get_sina_dd(code=code, date=dat, vol=600) # 默认400手
conn = connMysql().createconn()
conn.autocommit(True)
cur = conn.cursor()
sql = 'delete from stk_dadan_detail where code=' + '\'' + code + '\'' + ' and date=' + '\'' + dat + '\';'
cur.execute(sql)
try:
if (type(df) == pandas.core.frame.DataFrame):
df['date'] = dat
#print()
df.to_sql('stk_dadan_detail', engine, if_exists='append')
except:
print(code + ': 获取大单数据失败')
def updatestockbigchange():
conn= ms.connect(host='localhost',port = 3306,user='******', passwd='123456',db ='investment',charset="utf8")
cur = conn.cursor()
cur.execute('select code from mainlist')
stus = cur.fetchall()
now=time.strftime("%Y-%m-%d",time.localtime(time.time()))
code=''
for stu in stus:
code=stu[0]
stockbigchangelist=ts.get_sina_dd(code,now)
stockbigchange=pd.DataFrame(stockbigchangelist)
if stockbigchange.size!=0:
cur.execute("delete from stockbigchange_"+code)
values=[]
for index,row in stockbigchange.iterrows():
values.append((row['time'],row['price'],row['volume'],row['preprice'],row['type']))
insertstr='insert into stockbigchange_'+code+' (time,price,volume,preprice,type) values(%s,%s,%s,%s,%s)'
cur.executemany(insertstr,values)
conn.commit()
cur.close()
conn.close()
def get_sina_big_deal(engine, stock_code):
df = ts.get_sina_dd(stock_code, date='2016-03-09') # 默认400手
df.to_sql('sina_bigdeal', engine, if_exists='append')
def getSinaDD(code, date, vol, retry_count, pause):
return ts.get_sina_dd(code, date, vol, retry_count, pause)
if __name__=="__main__":
sailog_set("sairank.log")
base_vol = 100
stock_id = "000736"
dd_date = "2016-08-17"
stock_id = "002694"
stock_id = "600187"
stock_id = "600084"
stock_id = "000002"
dd_date = "2016-08-19"
dd_date = "2016-08-17"
stock_id = "002154"
stock_id = "002694"
# df = ts.get_tick_data(stock_id, date=dd_date)
df = ts.get_sina_dd(stock_id, date=dd_date, vol=base_vol)
if df is None:
log_debug("None")
else:
df['volume'] = df['volume'] / 100 # sina
df = df.sort_values(by='time')
# df = df[df.volume >= 200]
log_debug("size: %d", len(df))
# df = df.head(580)
log_debug("data:\n%s", df)
rank, rs, ns, mess = check_df_rates(df)
log_debug("rank: %d", rank)
# end
def rt_dadan_one(_stock_id, _dd_date, _db):
global g_has_noticed
global g_has_noticed2
global g_has_noticed3
global g_good_list
global g_good_list2
global g_good_list3
# today data
base_vol = 200
df = None
try:
df = ts.get_sina_dd(_stock_id, date=_dd_date, vol=base_vol)
except Exception:
log_error("warn: %s get_sina_dd exception!", _stock_id)
time.sleep(5)
return -4
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
log_debug("processing: %s", _stock_id)
# convert to 手
df['volume'] = df['volume'] / 100
global g_check_count
g_check_count += 1
df = df.sort_index(ascending=False)
# buy
vol_base = 3000
df_buy = df[df.volume >= vol_base]
# check df
vol = 3000
cnt = 20
pri = 7.00
if g_has_noticed.has_key(_stock_id):
pass
else:
good = rt_dadan_check_buy(_stock_id, df_buy, _db, vol, cnt, g_has_noticed, g_good_list, pri)
vol = 10000
cnt = 12
pri = 6.00
if g_has_noticed2.has_key(_stock_id):
pass
else:
good = rt_dadan_check_buy(_stock_id, df_buy, _db, vol, cnt, g_has_noticed2, g_good_list2, pri)
vol = 120000
cnt = 1
pri = 6.00
g_good_list3 = [] # 2017-4-3
if g_has_noticed3.has_key(_stock_id):
pass
else:
good = rt_dadan_check_buy(_stock_id, df_buy, _db, vol, cnt, g_has_noticed3, g_good_list3, pri)
return
# download_dir = setup_download_dir()
# links = [l for l in get_links(client_id) if l.endswith('.jpg')]
# Create a queue to communicate with the worker threads
queue = Queue.Queue()
if len(codes) >=8:
thread_n=8
else:
thread_n=len(codes)
# Create 8 worker threads
print "thread is ::::",thread_n
print ""
for x in range(thread_n):
worker = DownloadWorker(queue)
# Setting daemon to True will let the main thread exit even though the workers are blocking
worker.daemon = True
worker.start()
# Put the tasks into the queue as a tuple
for code in codes:
logger.info('Queueing {}'.format(code))
queue.put((code))
# Causes the main thread to wait for the queue to finish processing all the tasks
queue.join()
logging.info('Took %s', time() - ts)
# print('Took %s', time() - ts)
if __name__ == '__main__':
# codes=mp.get_all_top()
# main(codes)
df = ts.get_sina_dd('601198','2015-11-20')
print df