可转债跟踪---溢价跟踪（简版）¶
2018年末，可转债成交量出现异动，对于日内操作，无异于是机会。
可转债套利，该套利依旧有风险，因为转债与正股有联动，出现溢价买入转债后转正股，卖出操作需隔日；融券操作，了解了券商市场，能融到指定可转债正股的难度非常大；尾盘操作，依旧需要承担风险。笔者的金融开始是以日内回转为开始，所以自身对于可转债的套利思路，单边操作可转债为最佳。2019年不少交易员按照此手法进行套利交易，至于怎么操作，笔者不多说，让各位思考。至于溢价跟踪模型，能带来什么效益，可以做到机械化跟踪和预警，但是部分重要的日内操作因素没加入跟踪模型。
友情提醒：溢价率直接获取方式可直接爬取集思路数据，该篇文章为自己爬取数据及自己计算溢价率。
# 导入需求库
from jqdata import *
from jqlib.technical_analysis import *
import numpy as np
import pandas as pd
import time
import requests
import json
import re
code_depot = 0
一、转债代码及正股信息爬取¶
目前转债基本信息获取以集思路为来源获取！
# 可转债爬虫
def code_spider():
base_url = 'https://www.jisilu.cn/data/cbnew/cb_list/?___jsl=LST___t=1551579067513'
code_ini_data = get_code_dict(base_url)
return code_ini_data
# 可转债信息爬虫内部
def get_code_dict(url):
headers = {
'Referer': 'https://www.jisilu.cn/data/cbnew/',
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36 SE 2.X MetaSr 1.0'
}
response = requests.get(url, headers= headers)
text = response.text
# 爬取转债代码
bond_list = re.findall('pre_bond_id\"\:\"s[h|z]\d{6}', text)
bond_list = re.findall('s[h|z]\d{6}', str(bond_list))
# 爬取转债名称
bond_name_list = re.findall(u'bond_nm\"\:\"(.+?)\"', text)
# 爬取正股代码
stock_list = re.findall('stock_cd\"\:\"\d{6}', text)
stock_list = re.findall('\d{6}', str(stock_list))
# 爬取正股名称
stock_name_list = re.findall(u'stock_nm\"\:\"(.+?)\"', text)
# 爬取转股日期
convert_list = re.findall('convert_price\"\:\"(.+?)\"', text)
convert_list = re.findall('\d{1,2}.\d{3}', str(convert_list))
convert_list = map(float, convert_list)
convert_dt_list = re.findall('convert_dt\"\:\"(.+?)\"', text)
# 爬取最后日期
maturity_dt_list = re.findall('\"maturity_dt\"\:\"(.+?)\"', text)
data = {"bond_code": bond_list,"bond_name": bond_name_list ,"stock_code": stock_list, "stock_name": stock_name_list, "convert_price": convert_list, "convert_dt": convert_dt_list, "maturity_dt": maturity_dt_list}
code_ini_data = pd.DataFrame(data)
# 修改为sina交易代码
for i in range(len(code_ini_data.index)):
if code_ini_data['bond_code'][i][0:2] == 'sz':
code_ini_data.loc[[i], ['stock_code']] = 'sz' + code_ini_data['stock_code'][i]
elif code_ini_data['bond_code'][i][0:2] == 'sh':
code_ini_data.loc[[i], ['stock_code']] = 'sh' + code_ini_data['stock_code'][i]
return code_ini_data
code_depot = code_spider()
code_depot.head(5)
二、可转债行情获取（SINA）¶
# 可转债盘口数据
def get_bond_data(url):
real_price = 0
a1_v = 0
a1_p = 0
a2_v = 0
a2_p = 0
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36 SE 2.X MetaSr 1.0'
}
response = requests.get(url, headers= headers)
text = response.text
if len(text) >= 100:
ret = re.split(',',text)
real_price = float(ret[3])
a1_v = int(ret[20]) / 10
a1_p = float(ret[21])
a2_v = int(ret[22]) / 10
a2_p = float(ret[23])
return real_price, a1_v, a1_p, a2_v, a2_p
# 可转债盘口数据打印
real_price, a1_v, a1_p, a2_v, a2_p = get_bond_data('http://hq.sinajs.cn/list=sh113539')
print('可转债：{}，最新价格：{}，卖一量：{}，卖一价：{}，卖二量：{}，卖二价：{}'.format('113539',real_price, a1_v, a1_p, a2_v, a2_p))
三、正股行情获取（SINA）¶
# 正股盘口数据
def get_stock_data(url):
real_price = 0
b1_v = 0
b1_p = 0
b2_v = 0
b2_p = 0
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36 SE 2.X MetaSr 1.0'
}
response = requests.get(url, headers= headers)
text = response.text
if len(text) >= 100:
ret = re.split(',',text)
real_price = float(ret[3])
b1_v = int(ret[10]) / 100
b1_p = float(ret[11])
b2_v = int(ret[12]) / 100
b2_p = float(ret[13])
return real_price, b1_v, b1_p, b2_v, b2_p
# 正股盘口数据打印
real_price, b1_v, b1_p, b2_v, b2_p = get_stock_data('http://hq.sinajs.cn/list=sh603079')
print('正股：{}，最新价格：{}，买一量：{}，买一价：{}，买二量：{}，买二价：{}'.format('603079',real_price, b1_v, b1_p, b2_v, b2_p))
四、可转债/正股数据整合¶
# 整合数据集
def bond_spider():
code_data = []
bond_list = []
stock_list = []
bond_price_list = []
bond_a1_v_list = []
bond_a1_p_list = []
bond_a2_v_list = []
bond_a2_p_list = []
stock_price_list = []
stock_b1_v_list = []
stock_b1_p_list = []
stock_b2_v_list = []
stock_b2_p_list = []
base_url = 'http://hq.sinajs.cn/list={}'
code_dict = code_depot
bond_list = code_dict['bond_code']
stock_list = code_dict['stock_code']
# 遍历可转债及正股行情数据
for i in range(len(code_dict.index)):
get_bond = base_url.format(code_dict['bond_code'][i])
bond_price, bond_a1_v, bond_a1_p, bond_a2_v, bond_a2_p = get_bond_data(get_bond)
bond_price_list.append(bond_price)
bond_a1_v_list.append(bond_a1_v)
bond_a1_p_list.append(bond_a1_p)
bond_a2_v_list.append(bond_a2_v)
bond_a2_p_list.append(bond_a2_p)
get_stock = base_url.format(code_dict['stock_code'][i])
stock_price, stock_b1_v, stock_b1_p, stock_b2_v, stock_b2_p = get_stock_data(get_stock)
stock_price_list.append(stock_price)
stock_b1_v_list.append(stock_b1_v)
stock_b1_p_list.append(stock_b1_p)
stock_b2_v_list.append(stock_b2_v)
stock_b2_p_list.append(stock_b2_p)
# 整合数据
data = {"bond_code": bond_list, "bond_price": bond_price_list, "bond_a1_p": bond_a1_p_list, "bond_a1_v": bond_a1_v_list, \
"bond_a2_p": bond_a2_p_list, "bond_a2_v": bond_a2_v_list, "stock_code": stock_list,"stock_price": stock_price_list, \
"stock_b1_p": stock_b1_p_list, "stock_b1_v": stock_b1_v_list, "stock_b2_p": stock_b2_p_list, "stock_b2_v": stock_b2_v_list}
code_data_init = pd.DataFrame(data)
code_data = pd.merge(code_data_init,code_dict,on=None,how='outer',sort=False)
code_data = code_data[code_data['bond_price'] != 0]
return code_data
# 获取整合数据并打印
code_data = bond_spider()
code_data.head(5)
五、筛选溢价数据¶
# 溢价筛选
def get_bond_premium():
code_data = bond_spider()
# 转股价值计算
code_data['convertible_value'] = code_data['stock_price'] * 100 / code_data['convert_price']
# 溢价率计算
code_data['premium_rate'] = code_data['bond_price'] / code_data['convertible_value'] - 1
code_data = code_data.sort_index(axis = 0,ascending = True,by = 'premium_rate').reset_index(drop=True)
code_data = code_data.head(1)
code_data['convertible_value'] = code_data['convertible_value'].apply(lambda x: format(x, '.2f'))
code_data['premium_rate'] = code_data['premium_rate'].apply(lambda x: format(x, '.2%'))
if len(code_data.index) > 0:
print('------------{}可转债溢价报价------------'.format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())))
for i in range(0,len(code_data.index)):
print('\n套利时间:{0},\n套利溢价:{1},\n转债代码:{2},\n转债名称：{3},\n债券卖一:{4},\n卖一数量:{5},\n转债卖二:{6},\n卖二数量:{7},\n转股价值:{8},\n正股代码:{9},\n正股名称:{10}\n正股买一:{11},\n买一数量:{12},\n正股买二:{13},\n买二数量:{14},\n转股日期:{15},\n到期时间：{16}'.format( \
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()), code_data['premium_rate'][i], code_data['bond_code'][i], code_data['bond_name'][i], \
code_data['bond_a1_p'][i], code_data['bond_a1_v'][i], code_data['bond_a2_p'][i], code_data['bond_a2_v'][i], code_data['convertible_value'][i], \
code_data['stock_code'][i], code_data['stock_name'][i], code_data['stock_b1_p'][i], code_data['stock_b1_v'][i], code_data['stock_b2_p'][i], \
code_data['stock_b2_v'][i], code_data['maturity_dt'][i], code_data['convert_dt'][i]))
# 打印数据
get_bond_premium()
六、总结¶
上文内容，无剔除尚不可转股的可转债，并且并无剔除低量可转债。
可转债套利模式，多样，更多数据整合，还需要再深入分析，但是万变不离量价两者关系，特别是市场强势时候最为明显。
可共同交流可转债内容，如有时间，将继续更新可转债其他内容。
