import tushare as ts

#获取2017年第3季度的盈利能力数据

ts.get_profit_data(2017,3)

def profit(code,start,end):

    try:

        df=ts.get_k_data(code,start=start,end=end)

    except Exception,e:

        print e

        return None

    try:

        p=(df['close'].iloc[-1]-df['close'].iloc[0])/df['close'].iloc[0]*100.00

    except Exception,e:

        print e

        return None 

    return round(p,2)

profit('300678','2016-12-31','2017-12-31')

def price_change():

    basic=ts.get_stock_basics()

    pro=



    for code in basic.index.values:

        print code

        p=profit(code,'2016-12-31','2017-12-31')

        pro.append(p)

    basic['price_change']=pro

    basic.to_csv('2017_all_price_change.xls',encoding='gbk')

    df=pd.read_csv('2017_all_price_change.xls',encoding='gbk')

    df.to_excel('2017_all_price_change.xls',encoding='gbk')

def analysis():

    df=pd.read_excel('2017_all_price_change.xls',encoding='gbk')

    df=df.sort_values(by='price_change',ascending=False)

    df.to_excel('2017-year.xls',encoding='gbk')

engine=get_engine('stock')

df.to_sql('2017years',engine)

def get_engine(db):

    engine = create_engine('mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8'.format(MYSQL_USER, MYSQL_PASSWORD, MYSQL_HOST, MYSQL_PORT, db))

    return engine

• 2017-year.zip

    df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]})

    df2 = pd.DataFrame({'A': [6], 'B': [60]})

    print('df', df)

    print('df2', df2)



    df_x = [df, df2]

    result = pd.concat(df_x)

    print('first result ', result)

result2= result.reset_index()

result2 = result.reset_index(drop=True)

result3 = result.reindex(columns=['A','C'])

    df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]})

    df2 = pd.DataFrame({'A': [6], 'B': [60]})

    print 'df\n', df

    print 'df2\n', df2

    df_x = [df, df2]

    result = pd.concat(df_x)

    print 'first result\n', result

    basic=ts.get_stock_basics()

    

    print basic.info()

    print basic.head(10)



    basic.to_excel('basic2017.xls',encoding='gbk')

    basic.to_csv('2017_all_price_change.xls',encoding='gbk')

    df=pd.read_csv('2017_all_price_change.xls',encoding='gbk')

    df.to_excel('2017_all_price_change.xls',encoding='gbk')

	df=pd.read_excel('600050.xls',dtype={'datetime':np.datetime64})

	df=df.set_index('datetime')

	df['close'].plot(use_index=True,grid=True)

	plt.show()

这个策略是我做虚拟货币以来的主要策略，后面经过不断完善和修改，复杂了很多，但主要思想并没有改变，分享的这个版本是无明显bug的 最初版本，最为简单清晰，没有仓位管理，每次交易都是满仓，没有卡死后重启等等，但也足够说明问题。

function GetPrice(Type) {

   //_C()是平台的容错函数

    var depth=_C(exchange.GetDepth);

    var amountBids=0;

    var amountAsks=0;

    //计算买价，获取累计深度达到预设的价格

    if(Type=="Buy"){

       for(var i=0;i<20;i++){

           amountBids+=depth.Bids[i].Amount;

           //参数floatamountbuy是预设的累计深度

           if (amountBids>floatamountbuy){

               //稍微加0.01，使得订单排在前面

              return depth.Bids[i].Price+0.01;}

        }

    }

    //同理计算卖价

    if(Type=="Sell"){

       for(var j=0; j<20; j++){

    	   amountAsks+=depth.Asks[j].Amount;

            if (amountAsks>floatamountsell){

            return depth.Asks[j].Price-0.01;}

        }

    }

    //遍历了全部深度仍未满足需求，就返回一个价格，以免出现bug

    return depth.Asks[0].Price

}



// 每个循环的主函数onTick(),这里定的循环时间3.5s，每次循环都会把原来的单子撤销，重新挂单，越简单越不会遇到bug.



function onTick() {

    var buyPrice = GetPrice("Buy");

    var sellPrice= GetPrice("Sell");

    //diffprice是预设差价，买卖价差如果小于预设差价，就会挂一个相对更深的价格

    if ((sellPrice - buyPrice) <= diffprice){

            buyPrice-=10;

            sellPrice+=10;}

    //把原有的单子全部撤销，实际上经常出现新的价格和已挂单价格相同的情况，此时不需要撤销

    CancelPendingOrders() 

    //获取账户信息，确定目前账户存在多少钱和多少币

    var account=_C(exchange.GetAccount);

    //可买的比特币量，_N()是平台的精度函数

    var amountBuy = _N((account.Balance / buyPrice-0.1),2); 

    //可卖的比特币量，注意到没有仓位的限制，有多少就买卖多少，因为我当时的钱很少

    var amountSell = _N((account.Stocks),2); 

    if (amountSell > 0.02) {

        exchange.Sell(sellPrice,amountSell);}

    if (amountBuy > 0.02) {

        exchange.Buy(buyPrice, amountBuy);}

    //休眠，进入下一轮循环

    Sleep(sleeptime);

}

date = date + '-01-01'

        cmd = 'select * from `{}` where datetime > \'{}\''.format(code, date)



        try:

            df = pd.read_sql(cmd, history_engine,index_col='index')

        except Exception,e:

            print e

            return None

        

        # 不知道为啥，这里的类型发生改变

        idx= df['low'].idxmin()

datetime    datetime64[ns]

code                object

name                object

open                object

close               object

high                object

low                 object

vol                float64

amount             float64

dtype: object

df['low']=df['low'].astype('float64')

# -*-coding=utf-8-*-

__author__ = 'Rocky'

'''

http://30daydo.com

Contact: weigesysu@qq.com

'''

import random

import hashlib

import hmac,time

import smtplib

from email.mime.text import MIMEText

from email import Utils

import threading

import requests,datetime,itchat



from toolkit import Toolkit





class Jubi_web():

    def __init__(self, send=None):

        cfg = Toolkit.getUserData('data.cfg')

        self.public_key = cfg['public_key']

        self.private_key = cfg['private_key']

        self.send=send

        from_mail = cfg['from_mail']

        password = cfg['password']

        to_mail = cfg['to_mail']

        smtp_server = 'smtp.qq.com'



        self.server = smtp_server

        self.username = from_mail.split("@")[0]

        self.from_mail = from_mail

        self.password = password

        self.to_mail = to_mail

        self.coin_list=['IFC','DOGE','EAC','DNC','MET','ZET','SKT','YTC','PLC','LKC',

                        'JBC','MRYC','GOOC','QEC','PEB','XRP','NXT','WDC','MAX','ZCC',

                        'HLB','RSS','PGC','RIO','XAS','TFC','BLK','FZ','ANS','XPM','VTC',

                        'KTC','VRC','XSGS','LSK','PPC','ETC','GAME','LTC','ETH','BTC']

        # 初始化邮箱设置读取需要股票信息

        # 这样子只登陆一次

        if self.send == 'msn':



            try:

                self.smtp = smtplib.SMTP_SSL(port=465)

                self.smtp.connect(self.server)

                self.smtp.login(self.username, self.password)

            except smtplib.SMTPException, e:

                print e

                return 0



        if send=='wechat':

            self.w_name=u'xxxxx'

            

            itchat.auto_login(hotReload=True)

            account=itchat.get_friends(self.w_name)





    def send_wechat(self,name,content):

        w_content=name+' '+content

        itchat.send(w_content,toUserName=self.toName)

        time.sleep(1)

        itchat.send(w_content,toUserName='filehelper')





    def send_text(self, name, content):



        subject = '%s' % name

        self.msg = MIMEText(content, 'plain', 'utf-8')

        self.msg['to'] = self.to_mail

        self.msg['from'] = self.from_mail

        self.msg['Subject'] = subject

        self.msg['Date'] = Utils.formatdate(localtime=1)

        try:

            self.smtp.sendmail(self.msg['from'], self.msg['to'], self.msg.as_string())

            self.smtp.quit()

            print "sent"

        except smtplib.SMTPException, e:

            print e

            return 0



    def warming(self, coin, up_price, down_price):

        url = 'https://www.jubi.com/api/v1/ticker/'

        while 1:

            time.sleep(5)

            try:

                data = requests.post(url, data={'coin': coin}).json()

            except Exception,e:

                print e

                print "time out. Retry"

                time.sleep(15)

                continue



            current = float(data['last'])

            if current >= up_price:

                print "Up to ", up_price

                print "current price ",current



                if self.send=='msn':

                    self.send_text(coin,str(current))

                if self.send=='wechat':

                    self.send_wechat(coin,str(current))



                time.sleep(1200)

            if current <= down_price:

                print "Down to ", down_price

                print "current price ",current

                if self.send=='msn':

                    self.send_text(coin,str(current))

                if self.send=='wechat':

                    self.send_wechat(coin,str(current))

                time.sleep(1200)

    #上面的内容尽量不用修改。





    def getContent(self):

        url = 'https://www.jubi.com/api/v1/trade_list'

        params_data = {'key': 'x', 'signature': 'x'}

        s = requests.get(url=url, params=params_data)



    def getHash(self, s):

        m = hashlib.md5()

        m.update(s)

        return m.hexdigest()



    def sha_convert(self, s):

        return hashlib.sha256(self.getHash(s)).hexdigest()



    def get_nonce(self):

        lens = 12

        return ''.join([str(random.randint(0, 9)) for i in range(lens)])



    def get_signiture(self):

        url = 'xxxxxxxxx'

        coin = 'zet'

        nonce = self.get_nonce()



        # sha=self.sha_convert(private_key)

        md5 = self.getHash(self.private_key)

        message = 'nonce=' + nonce + '&' + 'key=' + self.public_key

        # print message

        signature = hmac.new(md5, message, digestmod=hashlib.sha256).digest()

        # print signature



        # req=requests.post(url,data={'signature':signature,'key':public_key,'nonce':nonce,'coin':'zet'})

        req = requests.post(url, data={'coin': coin})

        print req.status_code

        print req.text



    def real_time_ticker(self, coin):

        url = 'xxxxxxxx'

        try:

            data = requests.post(url, data={'coin': coin}).json()

            #print data

        except Exception ,e:

            print e

        return data





    def real_time_depth(self, coin):

        url = 'xxxxxxxxx'

        data = requests.post(url, data={'coin': coin}).json()

        print data

        data_bids = data['bids']

        data_asks = data['asks']

        print "bids"

        for i in data_bids:

            print i[0],

            print ' ',

            print i[1]

        print "asks"

        for j in data_asks:

            print j[0],

            print ' ',

            print j[1]



    def list_all_price(self):

        for i in self.coin_list:

            print i,

            print " price: ",

            p=self.real_time_ticker(i.lower())

            if p is not None:

                print p[u'last']



    def getOrder(self,coin):

        url='https://www.jubi.com/api/v1/orders/'

        try:

            req=requests.get(url,params={'coin':coin})

        except Exception,e:

            print e



        data=req.json()

        return data

    # recent 100 trade turn over

    def turnover(self,coin):

        i=coin.lower()

        coins=Toolkit.getUserData('coins.csv')

        total=long(coins[i])

[i]        [/i]p=self.getOrder(i)

        print p

        amount=0.00

        for j in p:

            t= j[u'amount']

            amount=float(t)+amount

        #current=float(self.real_time_ticker(i)[u'last'])

        turn_over=amount*1.00/total*100

        print turn_over



    def multi_thread(self,coin_list,price_list):

        thread_num=len(coin_list)

        thread_list=

        for i in range(thread_num):

            t=threading.Thread(target=self.warming, args=(coin_list,price_list[0],price_list[1]),)

            thread_list.append(t)

        for j in thread_list:

            j.start()

        for k in thread_list:

            k.join()



if __name__ == '__main__':



    obj = Jubi_web(send='wechat')

    coin_list=['zet','doge']

    price_list=[[0.2,0.13],[0.03,0.024]]

    obj.multi_thread(coin_list,price_list)



[/i]

    #通过tushare获取股票信息

    df=ts.get_k_data('300580',start='2017-01-12',end='2017-05-26')

    #提取收盘价

    closed=df['close'].values

    

    upper,middle,lower=talib.BBANDS(closed,matype=talib.MA_Type.T3)

    

    print upper,middle,lower

    plt.plot(upper)

    plt.plot(middle)

    plt.plot(lower)

    plt.grid()

    plt.show()

    diff1=upper-middle

    diff2=middle-lower

    print diff1

    print diff2

    #通过tushare获取股票信息

    df=ts.get_k_data('300580',start='2017-01-12',end='2017-05-26')

    #提取收盘价

    closed=df['close'].values

    #获取均线的数据，通过timeperiod参数来分别获取 5,10,20 日均线的数据。

    ma5=talib.SMA(closed,timeperiod=5)

    ma10=talib.SMA(closed,timeperiod=10)

    ma20=talib.SMA(closed,timeperiod=20)



    #打印出来每一个数据

    print closed

    print ma5

    print ma10

    print ma20



    #通过plog函数可以很方便的绘制出每一条均线

    plt.plot(closed)

    plt.plot(ma5)

    plt.plot(ma10)

    plt.plot(ma20)

    #添加网格，可有可无，只是让图像好看点

    plt.grid()

    #记得加这一句，不然不会显示图像

    plt.show()

#-*-coding=utf-8-*-

import Tkinter as tk

from Tkinter import *

import ttk

import matplotlib.pyplot as plt



import numpy as np

import talib as ta



series = np.random.choice([1, -1], size=200)

close = np.cumsum(series).astype(float)



# 重叠指标

def overlap_process(event):

    print(event.widget.get())

    overlap = event.widget.get()



    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)

    fig, axes = plt.subplots(2, 1, sharex=True)

    ax1, ax2 = axes[0], axes[1]

    axes[0].plot(close, '', markersize=3)

    axes[0].plot(upperband, '')

    axes[0].plot(middleband, '')

    axes[0].plot(lowerband, '')

    axes[0].set_title(overlap, fontproperties="SimHei")



    if overlap == '布林线':

        pass

    elif overlap == '双指数移动平均线':

        real = ta.DEMA(close, timeperiod=30)

        axes[1].plot(real, '')

    elif overlap == '指数移动平均线 ':

        real = ta.EMA(close, timeperiod=30)

        axes[1].plot(real, '')

    elif overlap == '希尔伯特变换——瞬时趋势线':

        real = ta.HT_TRENDLINE(close)

        axes[1].plot(real, '')

    elif overlap == '考夫曼自适应移动平均线':

        real = ta.KAMA(close, timeperiod=30)

        axes[1].plot(real, '')

    elif overlap == '移动平均线':

        real = ta.MA(close, timeperiod=30, matype=0)

        axes[1].plot(real, '')

    elif overlap == 'MESA自适应移动平均':

        mama, fama = ta.MAMA(close, fastlimit=0, slowlimit=0)

        axes[1].plot(mama, '')

        axes[1].plot(fama, '')

    elif overlap == '变周期移动平均线':

        real = ta.MAVP(close, periods, minperiod=2, maxperiod=30, matype=0)

        axes[1].plot(real, '')

    elif overlap == '简单移动平均线':

        real = ta.SMA(close, timeperiod=30)

        axes[1].plot(real, '')

    elif overlap == '三指数移动平均线(T3)':

        real = ta.T3(close, timeperiod=5, vfactor=0)

        axes[1].plot(real, '')

    elif overlap == '三指数移动平均线':

        real = ta.TEMA(close, timeperiod=30)

        axes[1].plot(real, '')

    elif overlap == '三角形加权法 ':

        real = ta.TRIMA(close, timeperiod=30)

        axes[1].plot(real, '')

    elif overlap == '加权移动平均数':

        real = ta.WMA(close, timeperiod=30)

        axes[1].plot(real, '')

    plt.show()



# 动量指标

def momentum_process(event):

    print(event.widget.get())

    momentum = event.widget.get()



    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)

    fig, axes = plt.subplots(2, 1, sharex=True)

    ax1, ax2 = axes[0], axes[1]

    axes[0].plot(close, '', markersize=3)

    axes[0].plot(upperband, '')

    axes[0].plot(middleband, '')

    axes[0].plot(lowerband, '')

    axes[0].set_title(momentum, fontproperties="SimHei")



    if momentum == '绝对价格振荡器':

        real = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)

        axes[1].plot(real, '')

    elif momentum == '钱德动量摆动指标':

        real = ta.CMO(close, timeperiod=14)

        axes[1].plot(real, '')

    elif momentum == '移动平均收敛/散度':

        macd, macdsignal, macdhist = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)

        axes[1].plot(macd, '')

        axes[1].plot(macdsignal, '')

        axes[1].plot(macdhist, '')

    elif momentum == '带可控MA类型的MACD':

        macd, macdsignal, macdhist = ta.MACDEXT(close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)

        axes[1].plot(macd, '')

        axes[1].plot(macdsignal, '')

        axes[1].plot(macdhist, '')

    elif momentum == '移动平均收敛/散度 固定 12/26':

        macd, macdsignal, macdhist = ta.MACDFIX(close, signalperiod=9)

        axes[1].plot(macd, '')

        axes[1].plot(macdsignal, '')

        axes[1].plot(macdhist, '')

    elif momentum == '动量':

        real = ta.MOM(close, timeperiod=10)

        axes[1].plot(real, '')

    elif momentum == '比例价格振荡器':

        real = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)

        axes[1].plot(real, '')

    elif momentum == '变化率':

        real = ta.ROC(close, timeperiod=10)

        axes[1].plot(real, '')

    elif momentum == '变化率百分比':

        real = ta.ROCP(close, timeperiod=10)

        axes[1].plot(real, '')

    elif momentum == '变化率的比率':

        real = ta.ROCR(close, timeperiod=10)

        axes[1].plot(real, '')

    elif momentum == '变化率的比率100倍':

        real = ta.ROCR100(close, timeperiod=10)

        axes[1].plot(real, '')

    elif momentum == '相对强弱指数':

        real = ta.RSI(close, timeperiod=14)

        axes[1].plot(real, '')

    elif momentum == '随机相对强弱指标':

        fastk, fastd = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)

        axes[1].plot(fastk, '')

        axes[1].plot(fastd, '')

    elif momentum == '三重光滑EMA的日变化率':

        real = ta.TRIX(close, timeperiod=30)

        axes[1].plot(real, '')



    plt.show()



# 周期指标

def cycle_process(event):

    print(event.widget.get())

    cycle = event.widget.get()



    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)

    fig, axes = plt.subplots(2, 1, sharex=True)

    ax1, ax2 = axes[0], axes[1]

    axes[0].plot(close, '', markersize=3)

    axes[0].plot(upperband, '')

    axes[0].plot(middleband, '')

    axes[0].plot(lowerband, '')

    axes[0].set_title(cycle, fontproperties="SimHei")



    if cycle == '希尔伯特变换——主要的循环周期':

        real = ta.HT_DCPERIOD(close)

        axes[1].plot(real, '')

    elif cycle == '希尔伯特变换,占主导地位的周期阶段':

        real = ta.HT_DCPHASE(close)

        axes[1].plot(real, '')

    elif cycle == '希尔伯特变换——相量组件':

        inphase, quadrature = ta.HT_PHASOR(close)

        axes[1].plot(inphase, '')

        axes[1].plot(quadrature, '')

    elif cycle == '希尔伯特变换——正弦曲线':

        sine, leadsine = ta.HT_SINE(close)

        axes[1].plot(sine, '')

        axes[1].plot(leadsine, '')

    elif cycle == '希尔伯特变换——趋势和周期模式':

        integer = ta.HT_TRENDMODE(close)

        axes[1].plot(integer, '')



    plt.show()





# 统计功能

def statistic_process(event):

    print(event.widget.get())

    statistic = event.widget.get()



    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)

    fig, axes = plt.subplots(2, 1, sharex=True)

    ax1, ax2 = axes[0], axes[1]

    axes[0].plot(close, '', markersize=3)

    axes[0].plot(upperband, '')

    axes[0].plot(middleband, '')

    axes[0].plot(lowerband, '')

    axes[0].set_title(statistic, fontproperties="SimHei")



    if statistic == '线性回归':

        real = ta.LINEARREG(close, timeperiod=14)

        axes[1].plot(real, '')

    elif statistic == '线性回归角度':

        real = ta.LINEARREG_ANGLE(close, timeperiod=14)

        axes[1].plot(real, '')

    elif statistic == '线性回归截距':

        real = ta.LINEARREG_INTERCEPT(close, timeperiod=14)

        axes[1].plot(real, '')

    elif statistic == '线性回归斜率':

        real = ta.LINEARREG_SLOPE(close, timeperiod=14)

        axes[1].plot(real, '')

    elif statistic == '标准差':

        real = ta.STDDEV(close, timeperiod=5, nbdev=1)

        axes[1].plot(real, '')

    elif statistic == '时间序列预测':

        real = ta.TSF(close, timeperiod=14)

        axes[1].plot(real, '')

    elif statistic == '方差':

        real = ta.VAR(close, timeperiod=5, nbdev=1)

        axes[1].plot(real, '')



    plt.show()





# 数学变换

def math_transform_process(event):

    print(event.widget.get())

    math_transform = event.widget.get()



    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)

    fig, axes = plt.subplots(2, 1, sharex=True)

    ax1, ax2 = axes[0], axes[1]

    axes[0].plot(close, '', markersize=3)

    axes[0].plot(upperband, '')

    axes[0].plot(middleband, '')

    axes[0].plot(lowerband, '')

    axes[0].set_title(math_transform, fontproperties="SimHei")





    if math_transform == '反余弦':

        real = ta.ACOS(close)

        axes[1].plot(real, '')

    elif math_transform == '反正弦':

        real = ta.ASIN(close)

        axes[1].plot(real, '')

    elif math_transform == '反正切':

        real = ta.ATAN(close)

        axes[1].plot(real, '')

    elif math_transform == '向上取整':

        real = ta.CEIL(close)

        axes[1].plot(real, '')

    elif math_transform == '余弦':

        real = ta.COS(close)

        axes[1].plot(real, '')

    elif math_transform == '双曲余弦':

        real = ta.COSH(close)

        axes[1].plot(real, '')

    elif math_transform == '指数':

        real = ta.EXP(close)

        axes[1].plot(real, '')

    elif math_transform == '向下取整':

        real = ta.FLOOR(close)

        axes[1].plot(real, '')

    elif math_transform == '自然对数':

        real = ta.LN(close)

        axes[1].plot(real, '')

    elif math_transform == '常用对数':

        real = ta.LOG10(close)

        axes[1].plot(real, '')

    elif math_transform == '正弦':

        real = ta.SIN(close)

        axes[1].plot(real, '')

    elif math_transform == '双曲正弦':

        real = ta.SINH(close)

        axes[1].plot(real, '')

    elif math_transform == '平方根':

        real = ta.SQRT(close)

        axes[1].plot(real, '')

    elif math_transform == '正切':

        real = ta.TAN(close)

        axes[1].plot(real, '')

    elif math_transform == '双曲正切':

        real = ta.TANH(close)

        axes[1].plot(real, '')



    plt.show()





# 数学操作

def math_operator_process(event):

    print(event.widget.get())

    math_operator = event.widget.get()



    upperband, middleband, lowerband = ta.BBANDS(close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)

    fig, axes = plt.subplots(2, 1, sharex=True)

    ax1, ax2 = axes[0], axes[1]

    axes[0].plot(close, '', markersize=3)

    axes[0].plot(upperband, '')

    axes[0].plot(middleband, '')

    axes[0].plot(lowerband, '')

    axes[0].set_title(math_operator, fontproperties="SimHei")





    if math_operator == '指定的期间的最大值':

        real = ta.MAX(close, timeperiod=30)

        axes[1].plot(real, '')

    elif math_operator == '指定的期间的最大值的索引':

        integer = ta.MAXINDEX(close, timeperiod=30)

        axes[1].plot(integer, '')

    elif math_operator == '指定的期间的最小值':

        real = ta.MIN(close, timeperiod=30)

        axes[1].plot(real, '')

    elif math_operator == '指定的期间的最小值的索引':

        integer = ta.MININDEX(close, timeperiod=30)

        axes[1].plot(integer, '')

    elif math_operator == '指定的期间的最小和最大值':

        min, max = ta.MINMAX(close, timeperiod=30)

        axes[1].plot(min, '')

        axes[1].plot(max, '')

    elif math_operator == '指定的期间的最小和最大值的索引':

        minidx, maxidx = ta.MINMAXINDEX(close, timeperiod=30)

        axes[1].plot(minidx, '')

        axes[1].plot(maxidx, '')

    elif math_operator == '合计':

        real = ta.SUM(close, timeperiod=30)

        axes[1].plot(real, '')



    plt.show()





root = tk.Tk()



# 第一行：重叠指标

rowframe1 = tk.Frame(root)

rowframe1.pack(side=tk.TOP, ipadx=3, ipady=3)

tk.Label(rowframe1, text="重叠指标").pack(side=tk.LEFT)



overlap_indicator = tk.StringVar() # 重叠指标

combobox1 = ttk.Combobox(rowframe1, textvariable=overlap_indicator)

combobox1['values'] = ['布林线','双指数移动平均线','指数移动平均线 ','希尔伯特变换——瞬时趋势线',

                       '考夫曼自适应移动平均线','移动平均线','MESA自适应移动平均','变周期移动平均线',

                       '简单移动平均线','三指数移动平均线(T3)','三指数移动平均线','三角形加权法 ','加权移动平均数']

combobox1.current(0)

combobox1.pack(side=tk.LEFT)



combobox1.bind('<<ComboboxSelected>>', overlap_process)





# 第二行：动量指标

rowframe2 = tk.Frame(root)

rowframe2.pack(side=tk.TOP, ipadx=3, ipady=3)

tk.Label(rowframe2, text="动量指标").pack(side=tk.LEFT)



momentum_indicator = tk.StringVar() # 动量指标

combobox2 = ttk.Combobox(rowframe2, textvariable=momentum_indicator)

combobox2['values'] = ['绝对价格振荡器','钱德动量摆动指标','移动平均收敛/散度','带可控MA类型的MACD',

                       '移动平均收敛/散度 固定 12/26','动量','比例价格振荡器','变化率','变化率百分比',

                       '变化率的比率','变化率的比率100倍','相对强弱指数','随机相对强弱指标','三重光滑EMA的日变化率']



combobox2.current(0)

combobox2.pack(side=tk.LEFT)



combobox2.bind('<<ComboboxSelected>>', momentum_process)







# 第三行：周期指标

rowframe3 = tk.Frame(root)

rowframe3.pack(side=tk.TOP, ipadx=3, ipady=3)

tk.Label(rowframe3, text="周期指标").pack(side=tk.LEFT)



cycle_indicator = tk.StringVar() # 周期指标

combobox3 = ttk.Combobox(rowframe3, textvariable=cycle_indicator)

combobox3['values'] = ['希尔伯特变换——主要的循环周期','希尔伯特变换——主要的周期阶段','希尔伯特变换——相量组件',

                       '希尔伯特变换——正弦曲线','希尔伯特变换——趋势和周期模式']



combobox3.current(0)

combobox3.pack(side=tk.LEFT)



combobox3.bind('<<ComboboxSelected>>', cycle_process)





# 第四行：统计功能

rowframe4 = tk.Frame(root)

rowframe4.pack(side=tk.TOP, ipadx=3, ipady=3)

tk.Label(rowframe4, text="统计功能").pack(side=tk.LEFT)



statistic_indicator = tk.StringVar() # 统计功能

combobox4 = ttk.Combobox(rowframe4, textvariable=statistic_indicator)

combobox4['values'] = ['贝塔系数；投资风险与股市风险系数','皮尔逊相关系数','线性回归','线性回归角度',

                       '线性回归截距','线性回归斜率','标准差','时间序列预测','方差']



combobox4.current(0)

combobox4.pack(side=tk.LEFT)



combobox4.bind('<<ComboboxSelected>>', statistic_process)





# 第五行：数学变换

rowframe5 = tk.Frame(root)

rowframe5.pack(side=tk.TOP, ipadx=3, ipady=3)

tk.Label(rowframe5, text="数学变换").pack(side=tk.LEFT)



math_transform = tk.StringVar() # 数学变换

combobox5 = ttk.Combobox(rowframe5, textvariable=math_transform_process)

combobox5['values'] = ['反余弦','反正弦','反正切','向上取整','余弦','双曲余弦','指数','向下取整',

                       '自然对数','常用对数','正弦','双曲正弦','平方根','正切','双曲正切']



combobox5.current(0)

combobox5.pack(side=tk.LEFT)



combobox5.bind('<<ComboboxSelected>>', math_transform_process)





# 第六行：数学操作

rowframe6 = tk.Frame(root)

rowframe6.pack(side=tk.TOP, ipadx=3, ipady=3)

tk.Label(rowframe6, text="数学操作").pack(side=tk.LEFT)



math_operator = tk.StringVar() # 数学操作

combobox6 = ttk.Combobox(rowframe6, textvariable=math_operator_process)

combobox6['values'] = ['指定期间的最大值','指定期间的最大值的索引','指定期间的最小值','指定期间的最小值的索引',

                       '指定期间的最小和最大值','指定期间的最小和最大值的索引','合计']



combobox6.current(0)

combobox6.pack(side=tk.LEFT)



combobox6.bind('<<ComboboxSelected>>', math_operator_process)









root.mainloop()

# -*-coding=utf-8-*-

__author__ = 'Rocky'

'''

http://30daydo.com

Contact: weigesysu@qq.com

'''

import smtplib, time, os, datetime

from email.mime.text import MIMEText

from email.header import Header

from toolkit import Toolkit

from email.mime.multipart import MIMEMultipart

from email import Encoders, Utils

from toolkit import Toolkit

import tushare as ts

from pandas import Series

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import threading



#监测涨停板开板监测

class break_monitor():

    def __init__(self,send=True):

        self.send=send

        if self.send==True:

            cfg = Toolkit.getUserData('data.cfg')

            from_mail = cfg['from_mail']

            password = cfg['password']

            to_mail = cfg['to_mail']

            smtp_server = 'smtp.qq.com'



            self.server = smtp_server

            self.username = from_mail.split("@")[0]

            self.from_mail = from_mail

            self.password = password

            self.to_mail = to_mail

            # 初始化邮箱设置读取需要股票信息

            # 这样子只登陆一次

            try:

                self.smtp = smtplib.SMTP_SSL(port=465)

                self.smtp.connect(self.server)

                self.smtp.login(self.username, self.password)

            except smtplib.SMTPException, e:

                print e

                return 0

        self.bases = pd.read_csv('bases.csv', dtype={'code': np.str})

        self.stocklist = Toolkit.read_stock('monitor_list.log')





    

    def send_txt(self, name, content):



        subject = '%s' % name

        self.msg = MIMEText(content, 'plain', 'utf-8')

        self.msg['to'] = self.to_mail

        self.msg['from'] = self.from_mail

        self.msg['Subject'] = subject

        self.msg['Date'] = Utils.formatdate(localtime=1)

        try:

            self.smtp.sendmail(self.msg['from'], self.msg['to'], self.msg.as_string())

            self.smtp.quit()

            print "sent"

        except smtplib.SMTPException, e:

            print e

            return 0



    #开板提示

    def break_ceil(self, code):

        print threading.current_thread().name

        while 1:

            #print code

            time.sleep(2)

            try:

                df = ts.get_realtime_quotes(code)

            except:

                time.sleep(5)

                continue

            v = long(df['b1_v'].values[0])



            if v <= 1000:

                print datetime.datetime.now().strftime("%H:%M:%S")

                print u"小于万手，小心！跑"

                if self.send==True:

                    self.push_msg('break', 10, 10, 'down')

                #这里可以优化，不必每次都登陆。





    def monitor_break(self,send=True):

        thread_num = len(self.stocklist)

        thread_list = []

        join_list = []

        for i in range(thread_num):

            t = threading.Thread(target=self.break_ceil, args=(self.stocklist[i],))

            thread_list.append(t)



        for j in thread_list:

            j.start()



        for k in thread_list:

            k.join()





if __name__ == '__main__':

    path = os.path.join(os.getcwd(), 'data')

    if os.path.exists(path) == False:

        os.mkdir(path)

    os.chdir(path)

    obj = break_monitor(send=False)

    obj.monitor_break()

# -*-coding=utf-8-*-

__author__ = 'Rocky'

'''

http://30daydo.com

Contact: weigesysu@qq.com

'''

#分析新股的开板时机

import tushare as ts

import os

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

class New_Stock_Break():

    def __init__(self):

        #为了文件整齐，新建一个文件夹data用来专门存放数据

        current = os.getcwd()

        folder = os.path.join(current, 'data')

        if os.path.exists(folder) == False:

            os.mkdir(folder)

        os.chdir(folder)

        #调用tushare接口，获取A股信息

        df0=ts.get_stock_basics()

        #df0=pd.read_csv('bases.csv',dtype={'code':np.str})

        self.bases=df0.sort_values('timeToMarket',ascending=False)



        #获取样本， 获取最近一个年的新股情况



        self.cxg=self.bases[(self.bases['timeToMarket']>20160101) & (self.bases['timeToMarket']<20170101)]

        self.codes= self.cxg['code'].values



    def calc_open_by_percent(self,code):

        cont=100000000

        #total_vol=self.bases[self.bases['code']==code]['totals'].values[0]

        acutal_vol=self.bases[self.bases['code']==code]['outstanding'].values[0]

        all_vol= acutal_vol*cont

        df1=ts.get_k_data(code)

        i=1

        while 1:

            s=df1.ix[1]

            if s['high']!=s['low']:

                #date=s['date']

                break

            i=i+1



        j=i-1

        date_end=df1.ix[j]['date']

        date_start=df1.ix[0]['date']

        df3=df1[(df1['date']>=date_start) & (df1['date']<=date_end)]

        v_total_break=df3['volume'].sum()

        l=len(df3)

        print l

        print v_total_break

        rate=v_total_break*100*100.00/all_vol #手和股 注意

        print round(rate,6)

        return rate,l





    def getData(self):

        result=

        max_line=

        k=

        for i in self.codes:

            print i

            name=self.bases[self.bases['code']==i]['name'].values[0]

            rate,l=self.calc_open_by_percent(i)

            if rate is not None:

                result.append(rate)

                max_line.append([name,l,rate])

                k.append(l)

        #写入文件

        f=open('2016-2017-cixin.csv','w')

        for x in max_line:

            #f.write(';'.join(x))

            f.write(x[0])

            f.write('-')

            f.write(str(x[1]))

            f.write('-')

            f.write(str(x[2]))

            f.write('\n')



        f.close()



def main():

    obj=New_Stock_Break()

    obj.testcase2()



main()

 
运行后得到的数据如下：

 

    def Trade_list(self, coin):

        '''

        Trade_list（挂单查询）

        您指定时间后的挂单，可以根据类型查询，比如查看正在挂单和全部挂单

        Path：/api/v1/trade_list/

        Request类型：POST

        参数

        key - API key

        signature - signature

        nonce - nonce

        since - unix timestamp(utc timezone) default == 0, i.e. 返回所有

        coin - 币种简称,例如btc、ltc、xas

        type - 挂单类型[open:正在挂单, all:所有挂单]



        返回JSON dictionary

        id - 挂单ID

        datetime - date and time

        type - "buy" or "sell"

        price - price

        amount_original - 下单时数量

        amount_outstanding - 当前剩余数量

        '''

        url = self.host + '/api/v1/trade_list/'

        time.sleep(random.random())

        nonce = self.get_nonce_time()

        types = 'all'

        since = 0

        parameters = {'key': self.public_key, 'nonce': str(nonce), 'type': types, 'coin': coin, 'signature': ''}

        # print parameters

        post_data = ''

        for k, v in parameters.items():

            if not isinstance(v, str):

                #if type(v) is not types.StringType:

                v = str(v)

            post_data = post_data + k

            post_data = post_data + '=' + v + '&'



        #print 'post-data:\n',post_data

        post_data = post_data[:-1]

        post_data = post_data.replace('&signature=', '')

        #print post_data



        signature = hmac.new(self.md5, post_data, digestmod=hashlib.sha256).digest()

        sig = self.toHex(signature)

        parameters['signature'] = sig

        #print parameters

        r = requests.post(url=url, data=parameters)

        s = r.json()

        #print s

        return s

一、API使用说明

1、请求过程说明

1.1 构造请求数据，用户数据按照Jubi提供的接口规则，通过程序生成签名和要传输给Jubi的数据集合；

1.2 发送请求数据，把构造完成的数据集合通过POST/GET提交的方式传递给Jubi；

1.3 Jubi对请求数据进行处理，服务器在接收到请求后，会首先进行安全校验，验证通过后便会处理该次发送过来的请求；

1.4 返回响应结果数据，Jubi把响应结果以JSON的格式反馈给用户，具体的响应格式，错误代码参见接口部分；

1.5 对获取的返回结果数据进行处理；

2、安全认证

所有的private API都需要经过认证

Api的申请可以到财务中心 -> API，申请得到私钥和公钥，私钥Jubi将不做储存，一旦丢失将无法找回

注意:请勿向任何人泄露这两个参数，这像您的密码一样重要

2.签名机制

每次请求private api 都需要验证签名，发送的参数示例：

$param = array(

amount => 1,

price => 10000,

type => 'buy',

nonce => 141377098123

key => 5zi7w-4mnes-swmc4-egg9b-f2iqw-396z4-g541b

signature => 459c69d25c496765191582d9611028b9974830e9dfafd762854669809290ed82

);

nonce 可以理解为一个递增的整数：http://zh.wikipedia.org/wiki/Nonce

key 是申请到的公钥

signature是签名，是将amount price type nonce key等参数通过'&'字符连接起来通过md5(私钥)为key进行sha256算法加密得到的值.

 

    def real_time_ticker(coin):

        url = 'https://www.jubi.com/api/v1/ticker/'

        try:

            data = requests.post(url, data={'coin': coin}).json()

            

        except Exception ,e:

            print e

        return data

{u'sell': u'0.179000', u'volume': 21828245.102822, u'buy': u'0.175010', u'last': u'0.179000', u'vol': 108290769.9171, u'high': u'0.289000', u'low': u'0.119141'}

每次请求private api 都需要验证签名，发送的参数示例：

$param = array(

amount => 1,

price => 10000,

type => 'buy',

nonce => 141377098123

key => 5zi7w-4mnes-swmc4-egg9b-f2iqw-396z4-g541b

signature => 459c69d25c496765191582d9611028b9974830e9dfafd762854669809290ed82

);

nonce 可以理解为一个递增的整数：http://zh.wikipedia.org/wiki/Nonce

key 是申请到的公钥

signature是签名，是将amount price type nonce key等参数通过'&'字符连接起来通过md5(私钥)为key进行sha256算法加密得到的值.

Trade_add（下单）
Path：/api/v1/trade_add/
Request类型：POST
 
参数
key - API key
signature - signature
nonce - nonce
amount - 购买数量
price - 购买价格
type - 买单或者卖单
coin - 币种简称,例如btc、ltc、xas
id - 挂单ID
result - true(成功), false(失败)
{"result":true, "id":"11"}
 
返回JSON dictionary
id - 挂单ID
result - true(成功), false(失败)
 
返回结果示例：
{"result":true, "id":"11"}
 

    def get_nonce(self):

        lens=12

        return ''.join([str(random.randint(0, 9)) for i in range(lens)])

    def get_nonce_time(self):

        lens = 12

        curr_stamp = time.time()*100

        nonece=int(curr_stamp)

        return nonece

    def getHash(self,s):

        m=hashlib.md5()

        m.update(s)

        return m.hexdigest()

nonce=self.get_nonce_time

type='buy'

amount='10000'

key='xxxxxxxxxxx‘  #这个是聚币网给你的公钥，同样在设置里头可以找到

price='10' #你要设置的价格为10

coin='zet'

message = "amount=“+amount+”&nonce="+str(nonce)+"&type="+type+"&key="+key+'&price="+price+"&coin"+coin

 

signature = hmac.new(sha_256key, message, digestmod=hashlib.sha256).digest()



这样获得signature之后，就可以通过签名来进行post操作。

 

data_wrap={'nonce':nonce,'key':key_value,'signature':signature}

 

js=requests.post(url,data=data_wrap).json()

    def toHex(self,str):

        lst = 

        for ch in str:

            hv = hex(ord(ch)).replace('0x', '')

            if len(hv) == 1:

                hv = '0' + hv

            lst.append(hv)

        return reduce(lambda x, y: x + y, lst)

    def getAccount(self):

        url='https://www.jubi.com/api/v1/balance/'



        nonce_value=self.get_nonce_time()

        print nonce_value

        key_value=self.public_key

        private_key=self.private_key



        s='nonce='+str(nonce_value)+'&'+'key='+key_value



        print s



        #signature是签名，是将amount price type nonce key等参数通过'&'字符连接起来通过md5(私钥)为key进行sha256算法加密得到的值.

        md5=self.getHash(private_key)

        print md5

        print type(md5)



        msg=bytes(s).encode('utf-8')

        key=bytes(md5).encode('utf-8')

        signature =hmac.new(key,msg,digestmod=hashlib.sha256).digest()

        print signature

        print type(signature)

        sig=self.toHex(signature)



        print sig

        data_wrap={'nonce':nonce_value,'key':key_value,'signature':sig}



        print data_wrap



        data_en=urllib.urlencode(data_wrap)

        req=urllib2.Request(url,data=data_en)

        resp=urllib2.urlopen(req).read()

        print resp





    def toHex(self,str):

        lst = 

        for ch in str:

            hv = hex(ord(ch)).replace('0x', '')

            if len(hv) == 1:

                hv = '0' + hv

            lst.append(hv)

        return reduce(lambda x, y: x + y, lst)

{"uid":123456,"nameauth":1,"moflag":1,"asset":,"btc_balance":0,"btc_lock":0,"drk_balance":0,"drk_lock":0,"blk_balance":0,"blk_lock":0,"vrc_balance":0,"vrc_lock":0,"tfc_balance":0,"tfc_lock":0,"jbc_balance":0,"jbc_lock":0,"ltc_balance":0,"ltc_lock":0,"doge_balance":0,"doge_lock":0,"xpm_balance":0,"xpm_lock":0,"ppc_balance":0,"ppc_lock":0,"wdc_balance":0,"wdc_lock":0,"vtc_balance":0,"vtc_lock":0,"max_balance":0,"max_lock":0,"ifc_balance":0,"ifc_lock":0,"zcc_balance":0,"zcc_lock":0,"zet_balance":0,"zet_lock":0,"eac_balance":0,"eac_lock":0,"fz_balance":0,"fz_lock":0,"skt_balance":0,"skt_lock":0,"plc_balance":0,"plc_lock":0,"mtc_balance":0,"mtc_lock":0,"qec_balance":0,"qec_lock":0,"lkc_balance":10,"lkc_lock":0,"met_balance":0,"met_lock":0,"ytc_balance":0,"ytc_lock":0,"hlb_balance":0,"hlb_lock":0,"game_balance":0,"game_lock":0,"rss_balance":0,"rss_lock":0,"rio_balance":0,"rio_lock":0,"ktc_balance":0,"ktc_lock":0,"pgc_balance":0,"pgc_lock":0,"mryc_balance":0,"mryc_lock":0,"eth_balance":0,"eth_lock":0,"etc_balance":0,"etc_lock":0,"dnc_balance":0,"dnc_lock":0,"gooc_balance":0,"gooc_lock":0,"xrp_balance":0,"xrp_lock":0,"nxt_balance":0,"nxt_lock":0,"lsk_balance":0,"lsk_lock":0,"xas_balance":0,"xas_lock":0,"peb_balance":0,"peb_lock":0,"nhgh_balance":0,"nhgh_lock":0,"xsgs_balance":0,"xsgs_lock":0,"ans_balance":0,"ans_lock":0,"bts_balance":0,"bts_lock":0,"cny_balance":0,"cny_lock":0}

# -*-coding=utf-8-*-

__author__ = 'Rocky'

#计算某个股票的某个时间段的成交量

import  tushare as ts

import pandas as pd

import datetime

pd.set_option('display.max_rows',None)

class amount_calculation():

    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 calc(self,start,end):

        s0=datetime.datetime.strptime(start,'%H:%M:%S')

        e0=datetime.datetime.strptime(end,'%H:%M:%S')

        new_df=self.df[(self.df['time']>=s0) & (self.df['time']<e0) ]

        part=new_df['volume'].sum()

        print part

        rate=round(part*1.00/self.total*100,2)

        print rate

        return rate









def main():

    obj=amount_calculation('600908')

    #s1=obj.calc('09:24:00','10:30:00')

    #s2=obj.calc('10:30:00','11:30:00')

    #s3=obj.calc('13:00:00','14:00:00')

    s4=obj.calc('14:35:00','15:05:00')

    #print s1+s2+s3+s4



main()

    base=ts.get_stock_basics()

    base.to_csv('2.xls')

    base.to_excel('base.xls',encoding='GBK')

    base.to_excel('111.xls',encoding='utf8')

    base.to_excel('111.xls‘)

import tushare as ts



ts.get_stock_basics()

    self.base=ts.get_stock_basics()

  File "C:\Python27\lib\site-packages\tushare\stock\fundamental.py", line 44, in get_stock_basics

    text = urlopen(request, timeout=10).read()

  File "C:\Python27\lib\socket.py", line 351, in read

    data = self._sock.recv(rbufsize)

  File "C:\Python27\lib\httplib.py", line 567, in read

    s = self.fp.read(amt)

  File "C:\Python27\lib\socket.py", line 380, in read

    data = self._sock.recv(left)

socket.timeout: timed out

pip install tushare --upgrade

    def count_area():

        base=ts.get_stock_basics()

        count=base['area'].value_counts()

        ＃这一句的意思，就是获取所有股票的上市地区的数据，然后计算出现的次数

        print count

        print type(count)

Password:

The directory '/Users/rocky/Library/Caches/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.

The directory '/Users/rocky/Library/Caches/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.

Requirement already satisfied: matplotlib in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python

Requirement already satisfied: numpy>=1.5 in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from matplotlib)

Requirement already satisfied: python-dateutil in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from matplotlib)

Collecting tornado (from matplotlib)

  Downloading tornado-4.4.3.tar.gz (463kB)

    100% |████████████████████████████████| 471kB 84kB/s 

Requirement already satisfied: pyparsing>=1.5.6 in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from matplotlib)

Collecting nose (from matplotlib)

  Downloading nose-1.3.7-py2-none-any.whl (154kB)

    100% |████████████████████████████████| 163kB 75kB/s 

Collecting singledispatch (from tornado->matplotlib)

  Downloading singledispatch-3.4.0.3-py2.py3-none-any.whl

Collecting certifi (from tornado->matplotlib)

  Downloading certifi-2017.1.23-py2.py3-none-any.whl (382kB)

    100% |████████████████████████████████| 389kB 105kB/s 

Collecting backports_abc>=0.4 (from tornado->matplotlib)

  Downloading backports_abc-0.5-py2.py3-none-any.whl

Requirement already satisfied: six in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from singledispatch->tornado->matplotlib)

Installing collected packages: singledispatch, certifi, backports-abc, tornado, nose

  Running setup.py install for tornado ... done

Exception:

Traceback (most recent call last):

  File "/Library/Python/2.7/site-packages/pip/basecommand.py", line 215, in main

    status = self.run(options, args)

  File "/Library/Python/2.7/site-packages/pip/commands/install.py", line 342, in run

    prefix=options.prefix_path,

  File "/Library/Python/2.7/site-packages/pip/req/req_set.py", line 784, in install

    **kwargs

  File "/Library/Python/2.7/site-packages/pip/req/req_install.py", line 851, in install

    self.move_wheel_files(self.source_dir, root=root, prefix=prefix)

  File "/Library/Python/2.7/site-packages/pip/req/req_install.py", line 1064, in move_wheel_files

    isolated=self.isolated,

  File "/Library/Python/2.7/site-packages/pip/wheel.py", line 377, in move_wheel_files

    clobber(source, dest, False, fixer=fixer, filter=filter)

  File "/Library/Python/2.7/site-packages/pip/wheel.py", line 316, in clobber

    ensure_dir(destdir)

  File "/Library/Python/2.7/site-packages/pip/utils/__init__.py", line 83, in ensure_dir

    os.makedirs(path)

  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/os.py", line 150, in makedirs

    makedirs(head, mode)

  File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/os.py", line 157, in makedirs

    mkdir(name, mode)

OSError: [Errno 1] Operation not permitted: '/System/Library/Frameworks/Python.framework/Versions/2.7/man'