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def format(x):
    if x ==1:
	    template = '{} song'
	else:
	    template = '{} songs'
	return template.format(x)

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create view lowest1 as 
select * from stockprice where low in (select  min(low) from stockprice group by secid)
group by secid;

select * from lowest1
where tradedate>'20170501';

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from WindPy import *
w.start()
today=dt.datetime.now().strftime('%Y%m%d')
a=w.wset("sectorconstituent","date=%s;sectorid=%s" % (today,'a001010100000000'))
all_A_code=a.Data[1]
#获取当天的股票收盘价
ohlc=w.wss(all_A_code, "open,high,low,close,volume,amt","tradeDate=%s;priceAdj=F;cycle=D" % today)
ohlc
a=DataFrame(np.matrix(ohlc.Data))

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# -*- coding:utf-8 -*-
####################################################################################################################
'''
 程序：Wind股票数据下载
 功能：从Wind终端或者Wind资讯量化接口个人免费版中下载股票相关数据，保存至本地MySQL数据库，以进一步加工处理和分析
 创建时间：2016/01/15  V1.01 创建版本，Python2.7
 更新历史：2017/01/06  V1.02 从本地文件读取股票代码列表;升级到Python3.5版本
           2017/01/07  V1.03 封装为函数，便于调试和代码管理
           2017/01/08  V1.04 封装为类，为后续完善功能准备。自动从Wind中获取股票列表，独立运行；增加日志和参数处理

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#连接数据库
conn = pymysql.connect(host='localhost',user='root',password='1235', db='wind',charset="utf8")

#从数据库查询数据，构建dataframe
cur = conn.cursor()
cur.execute("select tradedate,secid,closeprice,volume from stockprice;") #调整sql字段顺序即可获得不同顺序的【】
cur.scroll(0,"absolute")
ohlc=[]

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from WindPy import w   
w.start()
from datetime import datetime
dt=datetime.now()

#连接WIND数据库  execute内要用三个引号
import pymysql #python2要使用其他接口
conn = pymysql.connect(host='localhost',user='root',password='1235', db='wind',charset="utf8")
cursor = conn.cursor()
cursor.execute("""

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import tushare as ts
a=ts.get_today_all()
x=(a.changepercent*a.nmc)/a.nmc.sum()
a['weight']=x
a.sort(['weight'],ascending=False)

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
from pandas import Series,DataFrame
from datetime import datetime
from dateutil.parser import parse
import time
from pandas.tseries.offsets import Hour,Minute,Day,MonthEnd
import pytz

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###sql文件信息读写
import tushare as ts
import pymysql
import pandas as pd
from pandas import DataFrame as df
#下载数据并写入
a=ts.get_today_all()
from sqlalchemy import create_engine
e=create_engine('mysql+pymysql://'+'root:1235@localhost/yaotest?charset=utf8')
a.to_sql('a',e,if_exists='replace')

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from WindPy import *
import pandas as pd
from pandas import DataFrame

import ffn
w.start()
all_data={}
a=w.weqs('tp')#筛选出自由流通市值在50-200亿元之间，成交量增加50%，涨幅大于4%，且不为今年上市的新股
for ticker in a.Data[0]:
    all_data[ticker]=w.wsd(ticker,'close','20170101')