realtime guard stock

import tushare
import time
import os

while 1:
  df=tushare.get_realtime_quotes('600536')
  print df['price']
  if df['price'].agg('max') > "27.50":
    os.popen('bash ttt.sh')
  time.sleep(2)

url="https://hooks.slack.com/services/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"
curdate="$(date "+%Y_%m_%d %H:%M:%S")"
#curl -v POST -H 'Content-type: application/json' -d '{
curl POST -H 'Content-type: application/json' -d '{
"response_type": "ephemeral",
"channel": "#demo",
"text": "Stock",
"username": "Bug News",
"icon_emoji": "",
"attachments": [{
"color": "#a6364f",
"title": "CI crashs.",
"title_link": "https://gitlab.moonx.cn/Developers/apollo/pipelines",
"text": "'"${curdate}"'",
"fields": [{
  "title": "xxx",
}]
}]
}' $url

上述两脚本能实现实时监控某只stock，并预警通知slack。

https://github.com/waditu/tushare

pip install xxx -i http://pypi.douban.com/simple/

sudo pip install --upgrade tushare   # python package upgrade 

import tushare
import time
import os

while 1:
  #df=tushare.get_realtime_quotes('002571')
  #df=tushare.get_realtime_quotes('603032')
  #print df['a5_p'][0],':',df['a5_v'][0]
  #print df['a4_p'][0],':',df['a4_v'][0]
  #print df['a3_p'][0],':',df['a3_v'][0]
  #print df['a2_p'][0],':',df['a2_v'][0]
  #print df['a1_p'][0],':',df['a1_v'][0]
  #print "------------"
  #print df['b1_p'][0],':',df['b1_v'][0]
  #print df['b2_p'][0],':',df['b2_v'][0]
  #print df['b3_p'][0],':',df['b3_v'][0]
  #print df['b4_p'][0],':',df['b4_v'][0]
  #print df['b5_p'][0],':',df['b5_v'][0]
  #print "------------"
  #print "------------"
  #df=tushare.get_realtime_quotes('603711')
  df=tushare.get_realtime_quotes('000725')
  #df=tushare.get_realtime_quotes('002230')
  print df['a5_p'][0],':',df['a5_v'][0]
  print df['a4_p'][0],':',df['a4_v'][0]
  print df['a3_p'][0],':',df['a3_v'][0]
  print df['a2_p'][0],':',df['a2_v'][0]
  print df['a1_p'][0],':',df['a1_v'][0]
  print "------------"
  print df['b1_p'][0],':',df['b1_v'][0]
  print df['b2_p'][0],':',df['b2_v'][0]
  print df['b3_p'][0],':',df['b3_v'][0]
  print df['b4_p'][0],':',df['b4_v'][0]
  print df['b5_p'][0],':',df['b5_v'][0]
  print df['price'].agg('max')
  time.sleep(1)