from  setting import get_mysql_conn,get_engine

import pandas as pd

import pymongo

from pyecharts import Geo,Style,Map

engine = get_engine('db_stock',local='local')

# 堆叠图

from pyecharts import Bar

df = pd.read_sql('tb_bond_jisilu',con=engine)



result ={}

for name,grades in df.groupby('评级'):

#     print(name,grades[['可转债名称','可转债价格']])

    for each in grades['可转债价格']:

        result.setdefault(name,)

        result[name].append(each)

        

        

# 确定价格的范围



value = [str(i) for i in range(85,140)]

ret = [0]*len(value)

ret1 = dict(zip(value,ret))



ret_A_add = ret1.copy()

for item in result['A+']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            ret_A_add[k]+=1

            

retAA_ = ret1.copy()

for item in result['']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAA_[k]+=1



retAA = ret1.copy()

for item in result['AA']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAA[k]+=1

            

retAA_add = ret1.copy()

for item in result['AA+']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAA_add[k]+=1

            

retAAA = ret1.copy()

for item in result['AAA']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAAA[k]+=1



bar = Bar('可转债价格分布')

bar.add('A+',value,list(ret_A_add.values()),is_stack=True,yaxis_max=11)

bar.add('',value,list(retAA_.values()),is_stack=True,yaxis_max=11)

bar.add('AA',value,list(retAA.values()),is_stack=True,yaxis_max=11)

bar.add('AA+',value,list(retAA_add.values()),is_stack=True,yaxis_max=11)

bar.add('AAA',value,list(retAAA.values()),is_stack=True,yaxis_max=11)

#-*-coding=utf-8

'''

可转债监控

'''

import tushare as ts

from setting import get_engine

engine = get_engine('db_bond')

import pandas as pd

import datetime

class ConvertBond():



    def __init__(self):

        self.conn=ts.get_apis()

        self.allBonds=ts.new_cbonds(pause=2)

        self.onSellBond=self.allBonds.dropna(subset=['marketprice'])

        self.today=datetime.datetime.now().strftime('%Y-%m-%d %H:%M')



    def stockPrice(self,code):

        stock_df = ts.get_realtime_quotes(code)

        price = float(stock_df['price'].values[0])

        return price



    def dataframe(self):

        price_list=

        for code in self.onSellBond['scode']:

            price_list.append(self.stockPrice(code))

        self.onSellBond['stock_price']=price_list

        self.onSellBond['ratio'] = (

self.onSellBond['marketprice']

/(self.onSellBond['stock_price'] / self.onSellBond['convprice'])-1)*100

        self.onSellBond['Updated']=self.today

        self.onSellBond.to_sql('tb_bond',engine,if_exists='replace')



    def closed(self):

        ts.close_apis(self.conn)



def main():

    bond=ConvertBond()

    bond.dataframe()

    bond.closed()

if __name__=='__main__':

    main()

import os

import MySQLdb

MYSQL_USER = *********

MYSQL_PASSWORD = ********

MYSQL_HOST = *********

MYSQL_PORT = *****



def get_engine(db):

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

    return engine

    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'])

from pytdx.hq import TdxHq_API

api = TdxHq_API()

with api.connect('119.147.212.81', 7709):

    data = api.get_security_bars(7, 0, '123045', 0, 240) # 123045 为转债代码 ，240 为获取 240个转债数据

    df = api.to_df(data)

    df=df.sort_values('datetime')

order-按数量买卖

order(security, amount, limit_price=None)

买卖标的。



注意：



由于下述原因，回测中实际买入或者卖出的股票数量有时候可能与委托设置的不一样，针对上述内容调整，系统会在日志中增加警告信息：



根据委托买入数量与价格经计算后的资金数量，大于当前可用资金；

委托卖出数量大于当前可用持仓数量；

每次交易数量只能是100的整数倍，但是卖出所有股票时不受此限制；

股票停牌、股票未上市或者退市、股票不存在；

回测中每天结束时会取消所有未完成交易；

order_target(security, amount, limit_price=None)

买卖股票，直到股票最终数量达到指定的amount。



注意：该函数在委托股票时取整100股，委托可转债时取整100张。



参数

security: 股票代码(str)；



amount: 期望的最终数量(int)；



limit_price：买卖限价(float)；



返回

Order对象中的id或者None。如果创建订单成功，则返回Order对象的id，失败则返回None(str)。

order_value - 指定目标价值买卖

order_value(security, value, limit_price=None)

买卖指定价值为value的股票。



注意：该函数在委托股票时取整100股，委托可转债时取整100张。

order_target_value - 指定持仓市值买卖

order_target_value(security, value, limit_price=None)

调整股票仓位到value价值



注意：该函数在委托股票时取整100股，委托可转债时取整100张。

order_market - 按市价进行委托

order_market(security, amount, market_type=None, limit_price=None)

可以使用多种市价类型进行委托。



注意：该函数仅在股票交易模块可用。

tick_data（可选）

tick_data(context, data)

该函数会每隔3秒执行一次。



注意 ：



该函数仅在交易模块可用。



该函数在9:30之后才能执行。



该函数中只能使用order_tick进行对应的下单操作。

order_tick(sid, amount, priceGear='1', limit_price=None)

买卖股票下单，可设定价格档位进行委托



注意：该函数仅在交易模块可用。



参数

sid：股票代码(str)；



amount：交易数量，正数表示买入，负数表示卖出(int)



priceGear：盘口档位，level1:1~5买档/-1~-5卖档，level2:1~10买档/-1~-10卖档(str)



limit_price：买卖限价，当输入参数中也包含priceGear时，下单价格以limit_price为主(float)；

def initialize(context):

    g.security = "600570.SS"

    set_universe(g.security)



def tick_data(context,data):

    security = g.security

    current_price = eval(data[security]['tick']['bid_grp'][0])[1][0]

    if current_price > 56 and current_price < 57:

        # 以买一档下单

        order_tick(g.security, -100, "1")

        # 以卖二档下单

        order_tick(g.security, 100, "-2")

        # 以指定价格下单

        order_tick(g.security, 100, limit_price=56.5)



def handle_data(context, data):

    pass

xtdata是xtquant库中提供行情相关数据的模块，本模块旨在提供精简直接的数据满足量化交易者的数

据需求，作为python库的形式可以被灵活添加到各种策略脚本中。

主要提供行情数据（历史和实时的K线和分笔）、财务数据、合约基础信息、板块和行业分类信息等通

用的行情数据

# 创建策略

#coding=utf-8

from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback

from xtquant.xtquant import StockAccount

from xtquant import xtconstant



class MyXtQuantTraderCallback(XtQuantTraderCallback):

    def on_disconnected(self):

        """

        连接断开

        :return:

        """

        print("connection lost")

    def on_stock_order(self, order):

        """

        委托回报推送

        :param order: XtOrder对象

        :return:

        """

        print("on order callback:")

        print(order.stock_code, order.order_status, order.order_sysid)

    def on_stock_asset(self, asset):

        """

        资金变动推送

        :param asset: XtAsset对象

        :return:

        """

        print("on asset callback")

        print(asset.account_id, asset.cash, asset.total_asset)

    def on_stock_trade(self, trade):

        """

        成交变动推送

        :param trade: XtTrade对象

        :return:

        """

        print("on trade callback")

        print(trade.account_id, trade.stock_code, trade.order_id)

    def on_stock_position(self, position):

        """

        持仓变动推送

        :param position: XtPosition对象

        :return:

        """

        print("on position callback")

        print(position.stock_code, position.volume)

    def on_order_error(self, order_error):

        """

        委托失败推送

        :param order_error:XtOrderError 对象

        :return:

        """

        print("on order_error callback")



        print(order_error.order_id, order_error.error_id, order_error.error_msg)

    

    def on_cancel_error(self, cancel_error):

        """

        撤单失败推送

        :param cancel_error: XtCancelError 对象

        :return:

        """

        print("on cancel_error callback")

        print(cancel_error.order_id, cancel_error.error_id,

        cancel_error.error_msg)

    def on_order_stock_async_response(self, response):

        """

        异步下单回报推送

        :param response: XtOrderResponse 对象

        :return:

        """

        print("on_order_stock_async_response")

        print(response.account_id, response.order_id, response.seq)

    

if __name__ == "__main__":

    print("demo test")

    # path为mini qmt客户端安装目录下userdata_mini路径

    path = 'D:\\迅投极速交易终端 睿智融科版\\userdata_mini'

    # session_id为会话编号，策略使用方对于不同的Python策略需要使用不同的会话编号

    session_id = 123456

    xt_trader = XtQuantTrader(path, session_id)

    

    # 创建资金账号为1000000365的证券账号对象

    acc = StockAccount('1000000365')

    

    # 创建交易回调类对象，并声明接收回调

    callback = MyXtQuantTraderCallback()

    xt_trader.register_callback(callback)

    

    # 启动交易线程

    xt_trader.start()

    

    # 建立交易连接，返回0表示连接成功

    connect_result = xt_trader.connect()

    print(connect_result)

    

    # 对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功

    subscribe_result = xt_trader.subscribe(acc)

    print(subscribe_result)

    stock_code = '600000.SH'

    

    # 使用指定价下单，接口返回订单编号，后续可以用于撤单操作以及查询委托状态

    print("order using the fix price:")

    fix_result_order_id = xt_trader.order_stock(acc, stock_code,

    xtconstant.STOCK_BUY, 200, xtconstant.FIX_PRICE, 10.5, 'strategy_name',

    'remark')

    print(fix_result_order_id)

    

    # 使用订单编号撤单

    print("cancel order:")

    cancel_order_result = xt_trader.cancel_order_stock(acc, fix_result_order_id)

    print(cancel_order_result)

    

    # 使用异步下单接口，接口返回下单请求序号seq，seq可以和on_order_stock_async_response

    的委托反馈response对应起来

    print("order using async api:")

    async_seq = xt_trader.order_stock(acc, stock_code, xtconstant.STOCK_BUY,

    200, xtconstant.FIX_PRICE, 10.5, 'strategy_name', 'remark')

    print(async_seq)

    

    # 查询证券资产

    

    print("query asset:")

    asset = xt_trader.query_stock_asset(acc)

    if asset:

        print("asset:")

        print("cash {0}".format(asset.cash))

        # 根据订单编号查询委托

        print("query order:")

    order = xt_trader.query_stock_order(acc, fix_result_order_id)

    if order:

        print("order:")

        print("order {0}".format(order.order_id))

        # 查询当日所有的委托

        print("query orders:")

        orders = xt_trader.query_stock_orders(acc)

        print("orders:", len(orders))

    

    if len(orders) != 0:

        print("last order:")

        print("{0} {1} {2}".format(orders[-1].stock_code,

        orders[-1].order_volume, orders[-1].price))

        # 查询当日所有的成交

        print("query trade:")

    trades = xt_trader.query_stock_trades(acc)

    print("trades:", len(trades))

    

    if len(trades) != 0:

        print("last trade:")

        print("{0} {1} {2}".format(trades[-1].stock_code,

        trades[-1].traded_volume, trades[-1].traded_price))

    # 查询当日所有的持仓

    print("query positions:")

    positions = xt_trader.query_stock_positions(acc)

    print("positions:", len(positions))

    

    if len(positions) != 0:

        print("last position:")

        print("{0} {1} {2}".format(positions[-1].account_id,

        positions[-1].stock_code, positions[-1].volume))

    

    # 根据股票代码查询对应持仓

    print("query position:")

    position = xt_trader.query_stock_position(acc, stock_code)

    if position:

        print("position:")

        print("{0} {1} {2}".format(position.account_id, position.stock_code,

        position.volume))

        # 阻塞线程，接收交易推送

    xt_trader.run_forever()

import time



def initialize(context):

	g.flag = False

	log.info("initialize g.flag=" + str(g.flag) )



def before_trading_start(context, data):

	g.flag = False

	log.info("before_trading_start g.flag=" + str(g.flag) )

    

def handle_data(context, data):

	if not g.flag and time.strftime("%H:%M:%S", time.localtime()) > '09:35:00':

		# 自动打新

		log.info("自动打新")

		ipo_stocks_order()

		g.flag = True



def on_order_response(context, order_list):

	# 该函数会在委托回报返回时响应

	log.info(order_list)

	ticker	secShortName	secFullName	y_chgPct

31	601288	农业银行	中国农业银行股份有限公司	-0.341178

11	600015	华夏银行	华夏银行股份有限公司	1.856174

45	601988	中国银行	中国银行股份有限公司	2.248533

32	601328	交通银行	交通银行股份有限公司	3.532657

30	601229	上海银行	上海银行股份有限公司	3.725781

35	601398	工商银行	中国工商银行股份有限公司	4.771403

40	601818	光大银行	中国光大银行股份有限公司	5.643119

27	601169	北京银行	北京银行股份有限公司	6.205580

14	600016	民生银行	中国民生银行股份有限公司	7.092815

5	002936	郑州银行	郑州银行股份有限公司	7.551112

49	601998	中信银行	中信银行股份有限公司	8.181956

43	601939	建设银行	中国建设银行股份有限公司	9.402651

41	601838	成都银行	成都银行股份有限公司	9.424554

52	603323	苏农银行	江苏苏州农村商业银行股份有限公司	12.375732

20	600926	杭州银行	杭州银行股份有限公司	12.933645

8	600000	浦发银行	上海浦东发展银行股份有限公司	14.752244

39	601577	长沙银行	长沙银行股份有限公司	14.792683

18	600908	无锡银行	无锡农村商业银行股份有限公司	16.181704

3	002807	江阴银行	江苏江阴农村商业银行股份有限公司	19.274586

48	601997	贵阳银行	贵阳银行股份有限公司	20.489563

4	002839	张家港行	江苏张家港农村商业银行股份有限公司	20.599511

25	601166	兴业银行	兴业银行股份有限公司	21.206503

24	601128	常熟银行	江苏常熟农村商业银行股份有限公司	21.571187

19	600919	江苏银行	江苏银行股份有限公司	23.218299

22	601009	南京银行	南京银行股份有限公司	26.297500

16	600036	招商银行	招商银行股份有限公司	27.518708

0	000001	平安银行	平安银行股份有限公司	31.624747

2	002142	宁波银行	宁波银行股份有限公司	31.729062

6	002948	青岛银行	青岛银行股份有限公司	48.602573

7	002958	青农商行	青岛农村商业银行股份有限公司	108.983776

42	601860	紫金银行	江苏紫金农村商业银行股份有限公司	115.147347

21	600928	西安银行	西安银行股份有限公司	128.496683

from  setting import get_mysql_conn,get_engine

import pandas as pd

import pymongo

from pyecharts import Geo,Style,Map

engine = get_engine('db_stock',local='local')

# 堆叠图

from pyecharts import Bar

df = pd.read_sql('tb_bond_jisilu',con=engine)



result ={}

for name,grades in df.groupby('评级'):

#     print(name,grades[['可转债名称','可转债价格']])

    for each in grades['可转债价格']:

        result.setdefault(name,)

        result[name].append(each)

        

        

# 确定价格的范围



value = [str(i) for i in range(85,140)]

ret = [0]*len(value)

ret1 = dict(zip(value,ret))



ret_A_add = ret1.copy()

for item in result['A+']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            ret_A_add[k]+=1

            

retAA_ = ret1.copy()

for item in result['']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAA_[k]+=1



retAA = ret1.copy()

for item in result['AA']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAA[k]+=1

            

retAA_add = ret1.copy()

for item in result['AA+']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAA_add[k]+=1

            

retAAA = ret1.copy()

for item in result['AAA']:

    for k in ret1:

        if float(k)+0.5>item and float(k)-0.5<=item:

            retAAA[k]+=1



bar = Bar('可转债价格分布')

bar.add('A+',value,list(ret_A_add.values()),is_stack=True,yaxis_max=11)

bar.add('',value,list(retAA_.values()),is_stack=True,yaxis_max=11)

bar.add('AA',value,list(retAA.values()),is_stack=True,yaxis_max=11)

bar.add('AA+',value,list(retAA_add.values()),is_stack=True,yaxis_max=11)

bar.add('AAA',value,list(retAAA.values()),is_stack=True,yaxis_max=11)

numpy.logspace

numpy.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)[source]

Return numbers spaced evenly on a log scale.

In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

Parameters:

start : float

base ** start is the starting value of the sequence.

stop : float

base ** stop is the final value of the sequence, unless endpoint is False. In that case, num + 1 values are spaced over the interval in log-space, of which all but the last (a sequence of length num) are returned.

num : integer, optional

Number of samples to generate. Default is 50.

endpoint : boolean, optional

If true, stop is the last sample. Otherwise, it is not included. Default is True.

base : float, optional

The base of the log space. The step size between the elements in ln(samples) / ln(base) (or log_base(samples)) is uniform. Default is 10.0.

dtype : dtype

The type of the output array. If dtype is not given, infer the data type from the other input arguments.

Returns:

samples : ndarray

num samples, equally spaced on a log scale

array([1.00000000e+00, 1.77827941e+01, 3.16227766e+02, 5.62341325e+03,

       1.00000000e+05, 1.77827941e+06, 3.16227766e+07, 5.62341325e+08,

       1.00000000e+10])

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



# @Time : 2018/10/23 9:26

# @File : money_reward.py

import requests

from collections import OrderedDict

import time

import datetime

import pymongo

import config



session = requests.Session()

def get_proxy(retry=10):

    proxyurl = 'http://{}:8081/dynamicIp/common/getDynamicIp.do'.format(config.PROXY)

    count = 0

    for i in range(retry):

        try:

            r = requests.get(proxyurl, timeout=10)

        except Exception as e:

            print(e)

            count += 1

            print('代理获取失败,重试' + str(count))

            time.sleep(1)



        else:

            js = r.json()

            proxyServer = 'http://{0}:{1}'.format(js.get('ip'), js.get('port'))

            proxies_random = {

                'http': proxyServer

            }

            return proxies_random





def get_content(url):

    headers = {

        # 此处添加个人的header信息

    }

    try:

        proxy = get_proxy()

    except Exception as e:

        print(e)

        proxy = get_proxy()



    try:

        r = session.get(url=url, headers=headers,proxies=proxy,timeout=10)

    except Exception as e:

        print(e)

        proxy = get_proxy()

        r = session.get(url=url, headers=headers,proxies=proxy,timeout=10)



    return r





def parse_content(post_id):

    url = 'https://xueqiu.com/statuses/reward/list_by_user.json?status_id={}&page=1&size=99999999'.format(post_id)

    r = get_content(url)

    print(r.text)

    if r.status_code != 200:

        print('status code != 200')

        failed_doc.insert({'post_id':post_id,'status':0})

        return None



    try:



        js_data = r.json()

    except Exception as e:

        print(e)

        print('can not parse to json')

        print(post_id)

        failed_doc.insert({'post_id': post_id, 'status': 0})

        return



    ret = 

    been_reward_user = '元卫南'

    for item in js_data.get('items'):

        name = item.get('name')

        amount = item.get('amount')

        description = item.get('description')

        user_id = item.get('user_id')

        created_at = item.get('created_at')

        if created_at:

            created_at = datetime.datetime.fromtimestamp(int(created_at) / 1000).strftime('%Y-%m-%d %H:%M:%S')



        d = OrderedDict()

        d['name'] = name

        d['user_id'] = user_id

        d['amount'] = amount / 100

        d['description'] = description

        d['created_at'] = created_at

        d['been_reward'] = been_reward_user

        ret.append(d)



    print(ret)

    if ret:

        doc.insert_many(ret)

        failed_doc.insert({'post_id':post_id,'status':1})







def get_all_page_id(user_id):

    doc = db['db_parker']['xueqiu_zhuanglan']



    get_page_url = 'https://xueqiu.com/statuses/original/timeline.json?user_id={}&page=1'.format(user_id)

    r = get_content(get_page_url)

    max_page = int(r.json().get('maxPage'))



    for i in range(1, max_page + 1):

        url = 'https://xueqiu.com/statuses/original/timeline.json?user_id=2227798650&page={}'.format(i)

        r = get_content(url)

        js_data = r.json()

        ret = 



        for item in js_data.get('list'):

            d = OrderedDict()



            d['article_id'] = item.get('id')

            d['title'] = item.get('title')

            d['description'] = item.get('description')

            d['view_count'] = item.get('view_count')

            d['target'] = 'https://xueqiu.com/' + item.get('target')

            d['user_id']= item.get('user_id')

            d['created_at'] = datetime.datetime.fromtimestamp(int(item.get('created_at')) / 1000).strftime(

                '%Y-%m-%d %H:%M:%S')



            ret.append(d)

        print(d)

        doc.insert_many(ret)



def loop_page_id():

    doc = db['db_parker']['xueqiu_zhuanglan']

    ret = doc.find({},{'article_id':1})

    failed_doc = db['db_parker']['xueqiu_reward_status']

    failed_ret = failed_doc.find({'status':1})

    article_id_list =

    for i in failed_ret:

        article_id_list.append(i.get('article_id'))



    for item in ret:

        article_id = item.get('article_id')

        print(article_id)

        if article_id in article_id_list:

            continue

        else:

            parse_content(article_id)



loop_page_id()

all_stocks = DataAPI.SecTypeRegionRelGet(secID=u"",ticker=u"",typeID=u"",field=u"",pandas="1")

fbl = 



for code in all_stocks['secID']:

    df = DataAPI.MktEqudGet(secID=code,ticker=u"",tradeDate=u"",beginDate=u"20120101",endDate=u"",isOpen="",field=u"",pandas="1")

    df['ztj']=map(lambda x:round(x,2),df['preClosePrice']*1.1)

    df['chgPct']=df['chgPct']*100

    

    # 非一字板

    zt = df[(df['ztj']==df['highestPrice']) & (df['openPrice']!=df['highestPrice'])]

    fz= df[(df['ztj']==df['highestPrice']) & (df['openPrice']!=df['highestPrice'])&(df['closePrice']==df['highestPrice'])]

    try:

        f = len(fz)*1.00/len(zt)*100

        fbl.append((code,f))

    except Exception,e:

        print e

        print code

dx= dict(fbl)

x = np.array(dx.values())

print x.mean()

#-*-coding=utf-8

'''

可转债监控

'''

import tushare as ts

from setting import get_engine

engine = get_engine('db_bond')

import pandas as pd

import datetime

class ConvertBond():



    def __init__(self):

        self.conn=ts.get_apis()

        self.allBonds=ts.new_cbonds(pause=2)

        self.onSellBond=self.allBonds.dropna(subset=['marketprice'])

        self.today=datetime.datetime.now().strftime('%Y-%m-%d %H:%M')



    def stockPrice(self,code):

        stock_df = ts.get_realtime_quotes(code)

        price = float(stock_df['price'].values[0])

        return price



    def dataframe(self):

        price_list=

        for code in self.onSellBond['scode']:

            price_list.append(self.stockPrice(code))

        self.onSellBond['stock_price']=price_list

        self.onSellBond['ratio'] = (

self.onSellBond['marketprice']

/(self.onSellBond['stock_price'] / self.onSellBond['convprice'])-1)*100

        self.onSellBond['Updated']=self.today

        self.onSellBond.to_sql('tb_bond',engine,if_exists='replace')



    def closed(self):

        ts.close_apis(self.conn)



def main():

    bond=ConvertBond()

    bond.dataframe()

    bond.closed()

if __name__=='__main__':

    main()

import os

import MySQLdb

MYSQL_USER = *********

MYSQL_PASSWORD = ********

MYSQL_HOST = *********

MYSQL_PORT = *****



def get_engine(db):

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

    return engine

code='300527'

df = ts.get_today_ticks(code)

df.sort_values(by='volume',ascending=False).head(100)

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