convertible_pricing-CRR_Approach.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the relationship between binomial-tree price and the market price of convertible bonds listed in China\n",
    "\n",
    "Author：Zheng Liu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Exploring-the-relationship-between-binomial-tree-price-and-the-market-price-of-convertible-bonds-listed-in-China\" data-toc-modified-id=\"Exploring-the-relationship-between-binomial-tree-price-and-the-market-price-of-convertible-bonds-listed-in-China-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Exploring the relationship between binomial-tree price and the market price of convertible bonds listed in China</a></span></li><li><span><a href=\"#Abstract\" data-toc-modified-id=\"Abstract-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Abstract</a></span></li><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Method-Illustration\" data-toc-modified-id=\"Method-Illustration-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Method Illustration</a></span></li><li><span><a href=\"#The-relationship-between-binomial-tree-price-and-market-price-at-the-first-day-of-listing\" data-toc-modified-id=\"The-relationship-between-binomial-tree-price-and-market-price-at-the-first-day-of-listing-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>The relationship between binomial-tree price and market price at the first day of listing</a></span></li><li><span><a href=\"#The-changes-of-average-cal_to_real-during-the-duration-of-convertible-bonds\" data-toc-modified-id=\"The-changes-of-average-cal_to_real-during-the-duration-of-convertible-bonds-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>The changes of average cal_to_real during the duration of convertible bonds</a></span><ul class=\"toc-item\"><li><span><a href=\"#The-overall-trend-of-cal_to_real-ratio\" data-toc-modified-id=\"The-overall-trend-of-cal_to_real-ratio-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>The overall trend of cal_to_real ratio</a></span></li><li><span><a href=\"#Exploring-the-overall-changes-by-eliminating-the-linear-trend\" data-toc-modified-id=\"Exploring-the-overall-changes-by-eliminating-the-linear-trend-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>Exploring the overall changes by eliminating the linear trend</a></span></li><li><span><a href=\"#Build-trading-strategy-based-on-cal_to_real-ratio\" data-toc-modified-id=\"Build-trading-strategy-based-on-cal_to_real-ratio-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>Build trading strategy based on cal_to_real ratio</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abstract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This article tries to explore the relationship between the theoretical binomial-tree price and the real market price of all convertible bonds issued in China. The goal is to evaluate whether the binomial-tree price can accurately measure the market price.\n",
    "\n",
    "The main conclusions are as followed:\n",
    "\n",
    "(1)\tOn the first day of listing, the binomial-tree price and the real market price maintain the stationary relationship. But binomial-tree price systematically undervalued the real price for about 10%.\n",
    "\n",
    "(2)\tIn the duration of a single convertible bond, on average, the binomial-tree price and the market price are non-stationary: in the early stage of listing, binomial-tree price is below the market price; with time going on, binomial-tree price gradually becomes closer to the market price.\n",
    "\n",
    "(3)\tBy adopting a strategy that takes advantage of the maximum deviation between the binomial-tree price and the market price (if we assume them to become equal eventually), we could achieve 9% annualized return historically. But according to the results of back-testing, the historical returns are highly volatile, indicating that the strategy is not ideal.\n",
    "\n",
    "(4)\tSince the facts that the number of listed convertible bonds in China is limited (around 300 bonds in total) and that they massively went into open market during 2017-2019 and were strongly influenced by the bull market during this period, the relationship between the binomial-tree price and the market price is unstable as we expected. Although in general it is hard to measure the market price with binomial-tree price, this model still has some predicting power for some individual bond.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convertible bonds are hybrid securities issued by a firm where the holder has the right to convert the bond into the common stocks of the firm according to pre-specified conditions.\n",
    "\n",
    "The market of convertible bonds is booming in China. For the first half of 2019, there are 62 convertible bonds, worth of 20 billion USD, that went into public. The number of bonds increased by 210% and the amount of money increased by 570% over the same period last year. The latter even exceeded last year's total amount. Accordingly, there are tons of capital pouring into this market. Some have claimed that the new era of the market of convertible bonds has come in China.\n",
    "\n",
    "There are many reasons behind this boom. Firstly, since Chinese government put more restrictions on financing methods during 2018, listed company have to resort to other ways, such as convertible bonds, to fund themselves. In addition, the embedded option to convert bonds to stocks makes convertible bonds more stock-like and easier to speculate. Compared to those of the Chinese A-share stock market, the characters of the convertible bonds market, such as T+0 trading system and no limit on the rise of fall in a single trading day, are more favorable for many institutional investors. \n",
    "\n",
    "Thanks to the pioneer works in financial engineering, such as Black-Scholes-Merton Option Pricing Model, it is no longer a difficult task to price the intrinsic value of simplified convertible bonds. Even with various provisions embedded into the convertible bonds, their values should be easier to accurately modeled than stocks. Nevertheless, market of convertible bonds in China seems to be pretty confused recently: dramatic change in prices and overreaction happen all the time. Most of the large financial investors trade convertible bonds based on fundamental analysis, rather than more specific pricing tools. \n",
    "\n",
    "Can we accurately price the convertible bonds listed in China? That’s the main question that the author want to address in the series of articles.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Method Illustration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In theory, the value of typical listed convertible bonds in China = \n",
    "+ the value of bond \n",
    "+ +the value of conversion provisions (call option on the equity) \n",
    "- -the value of redemption provisions (call option on the equity) \n",
    "+ +the value of reset provisions (the conversion price is adjusted downwards if the underlying stock price does not exceed pre-specified prices) \n",
    "- -put provisions of the bond.\n",
    "\n",
    "All the embedded options above are American options.\n",
    "\n",
    "Given that the boundary of reset provisions and put provisions of the bond are hard to judge with binomial model and that they are not the main factors, this article only considers the first three parts. Further with Monte Carlo simulation, we could address the values of reset clause.\n",
    "\n",
    "This article describes the relationship with the ratio (cal_to_real), which is the calculated price divided by the market price. If cal_to_real is above 1, the market price is undervalued; if cal_to_real is below 1, the market price is overvalued. \n",
    "\n",
    "Also, in the first sixth month after listing, the right to convert bonds to stocks could not be exercised. Hence when calculating bonds within this period, we must offset the value of conversion right at this time. \n",
    "For example, for the convertible bonds with 6-year duration, its value the first day of listing = \n",
    "+ bond value \n",
    "+ +the value of conversion right from present to the sixth year in the future  \n",
    "- -the value of conversion right from present to the sixth month in the future \n",
    "- -the value of redemption right from present to the sixth year in the future \n",
    "+ +the value of redemption right from present to the sixth month in the future\n",
    "\n",
    "The bond value is calculated via discounted cash flow method. The embedded American options are calculated via Cox-Ross-Rubinstein model(hereinafter referred to as CRR model).\n",
    "\n",
    "We take the treasury yield at different time point as the discount rate. To mitigate the market influence on the volatility of certain point, the volatility of stock price, which is needed in CRR model, are assumed as annualized 65-day exponential average of the daily volatility. \n",
    "\n",
    "All the date comes from RiceQuant, an online quantitative trading platform(https://www.ricequant.com/welcome/#research). \n",
    "\n",
    "The code is shown below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/rqdatac/client.py:166: UserWarning: rqdatac is already inited. Settings will be changed.\n",
      "  warnings.warn(\"rqdatac is already inited. Settings will be changed.\", stacklevel=0)\n"
     ]
    }
   ],
   "source": [
    "import rqdatac as rq\n",
    "rq.init()\n",
    "from rqdatac import *\n",
    "import pandas as pd\n",
    "import sys\n",
    "import numpy as np\n",
    "from utility import zwdata as zw\n",
    "import QuantLib as ql \n",
    "import talib as ta\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "code_folding": [
     0,
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   "source": [
    "def get_convertible_call_price(bond_name,date):\n",
    "    '''\n",
    "    Calculate the values of embedded options\n",
    "    Assuming that the dividend yield equals to 0\n",
    "    Assuming volatility as 65-day EMA of daily volatility\n",
    "    The outputs are option values per ￥100 convertible bonds\n",
    "    '''\n",
    "    \n",
    "    # Assuming that the dividend yield equals to 0\n",
    "    dividend_rate = 0\n",
    "    \n",
    "    # Acquire each bond information\n",
    "    all_info = rq.convertible.all_instruments()\n",
    "    info = all_info[all_info['symbol'] == bond_name]\n",
    "    order_book_id = info['order_book_id'].iloc[0]\n",
    "    stock_code = info['stock_code'].iloc[0]\n",
    "    maturity_date = info['maturity_date'].iloc[0]\n",
    "    conversion_start_date = info['conversion_start_date'].iloc[0]\n",
    "    value_date = info['value_date'].iloc[0]\n",
    "    de_listed_date = info['de_listed_date'].iloc[0]\n",
    "    \n",
    "    # Acquire redemption multiple\n",
    "    redumption = pd.read_excel('赎回条款.xlsx')\n",
    "    redumption = redumption.dropna(subset = ['证券代码'])\n",
    "    try:\n",
    "        multiple = redumption[redumption['证券代码'].str.contains(order_book_id[:-5])].iloc[0,-2]/100\n",
    "    except:\n",
    "        multiple = 1.3\n",
    "    \n",
    "    # Judge whether the time period could be accepted \n",
    "    date_pd = pd.Timestamp(date)\n",
    "    if date_pd > maturity_date:\n",
    "        #print(bond_name+':time of pricing is after maturity')\n",
    "        sys.exit(0)\n",
    "    elif date_pd > de_listed_date:\n",
    "        #print(bond_name+':time of pricing is after the de-listed date')\n",
    "        sys.exit(0)\n",
    "    \n",
    "    # Get the yeild curve of treasury\n",
    "    # Get yeild rate of the pricing date (from present to the maturity date)\n",
    "    yc = rq.get_yield_curve(start_date=date, end_date=date)\n",
    "\n",
    "    date_pd = pd.Timestamp(date)\n",
    "    time_remain_month = ((maturity_date - date_pd).days)/30\n",
    "    time_remain_year = ((maturity_date - date_pd).days)/365\n",
    "\n",
    "    if time_remain_year <= 1:\n",
    "        if time_remain_month <= 1:\n",
    "            risk_free = yc['1M'].iloc[0]\n",
    "        elif time_remain_month <= 2:\n",
    "             risk_free = yc['2M'].iloc[0]\n",
    "        elif time_remain_month <= 3:\n",
    "            risk_free = yc['3M'].iloc[0]\n",
    "        elif time_remain_month <= 6:\n",
    "             risk_free = yc['6M'].iloc[0]\n",
    "        elif time_remain_month <= 9:\n",
    "             risk_free = yc['9M'].iloc[0]\n",
    "        else: \n",
    "            risk_free = yc['1Y'].iloc[0]\n",
    "    else:\n",
    "        year = math.ceil(time_remain_year)\n",
    "        risk_free = yc[str(year)+'Y'].iloc[0]\n",
    "        if str(risk_free) == 'nan':\n",
    "            risk_free = (yc[str(year-1)+'Y'].iloc[0] + yc[str(year+1)+'Y'].iloc[0] )/2\n",
    "        \n",
    "    # Get the stock price\n",
    "    start = date[:2] + str(int(date[2:4]) - 3) + date[4:]\n",
    "    stock_price = zw.get_stocks_price([stock_code],start,date).dropna(subset = ['close'])\n",
    "    spot_price = stock_price['close'].iloc[-1]\n",
    "\n",
    "    # Calculate volatility(EMA65)\n",
    "    stock_price['ret'] = stock_price['close']/stock_price['close'].shift(1) - 1\n",
    "    stock_price['vol'] = pd.Series()\n",
    "    for i in range(120,len(stock_price)):\n",
    "        stock_price['vol'].iloc[i] = np.std(stock_price['ret'].iloc[i-120:i])    \n",
    "\n",
    "    stock_price['vol_ema'] = ta.EMA(stock_price['vol'],65)\n",
    "    spot_vol = stock_price['vol_ema'][-1]*np.sqrt(252)\n",
    "    \n",
    "    # Get the conversion price table\n",
    "    conversion_price = rq.convertible.get_conversion_price(order_book_id)\n",
    "    \n",
    "    # Get the conversion price of the pricing date\n",
    "    date_pd = pd.Timestamp(date)\n",
    "    strike_price = -1\n",
    "\n",
    "    if date_pd >= conversion_price.index[-1][1]:\n",
    "        strike_price = conversion_price['conversion_price'].iloc[-1]\n",
    "    else:\n",
    "        for i in range(len(conversion_price) - 1):\n",
    "            if date_pd >= conversion_price.index[i][1] and date_pd < conversion_price.index[i+1][1]:\n",
    "                strike_price = conversion_price['conversion_price'].iloc[i]\n",
    "                break\n",
    "\n",
    "    if strike_price == -1:\n",
    "        #print(bond_name + ':计算时间在初次转股价格发布日之前')\n",
    "        sys.exit(0) \n",
    "\n",
    "    \n",
    "    ### Calculate the value of long option on the stock\n",
    "    # Modify the format of time according to quantlib\n",
    "    maturity_date_ql = ql.Date(maturity_date.day, maturity_date.month, maturity_date.year)\n",
    "    day_count = ql.Actual365Fixed()\n",
    "    calendar = ql.China()\n",
    "    calculation_date = ql.Date(date_pd.day, date_pd.month, date_pd.year)\n",
    "    ql.Settings.instance().evaluationDate = calculation_date\n",
    "    \n",
    "    # Build American option\n",
    "    option_type = ql.Option.Call\n",
    "    payoff = ql.PlainVanillaPayoff(option_type, strike_price)\n",
    "    settlement = calculation_date\n",
    "\n",
    "    am_exercise = ql.AmericanExercise(settlement, maturity_date_ql)\n",
    "    american_option = ql.VanillaOption(payoff, am_exercise)\n",
    "    \n",
    "    # Black-Scholes-Merton process\n",
    "    spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))\n",
    "    flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, risk_free, day_count))\n",
    "    dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                               dividend_yield, \n",
    "                                               flat_ts, \n",
    "                                               flat_vol_ts)    \n",
    "    \n",
    "    # Calculate option price using CRR approach\n",
    "    steps = 400\n",
    "    binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "    american_option.setPricingEngine(binomial_engine)\n",
    "    \n",
    "    # Convert the option price to per 100 convertible bond\n",
    "    option_position1 = round((100/spot_price) * american_option.NPV(),2)\n",
    "    delta_position1 =  round((100/spot_price) * american_option.delta(),2)\n",
    "    gamma_position1 = round((100/spot_price) * american_option.gamma(),2)\n",
    "    \n",
    "            \n",
    "    # calculate vega\n",
    "    # calculate p_u\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol+ 0.0001, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                               dividend_yield, \n",
    "                                               flat_ts, \n",
    "                                               flat_vol_ts)\n",
    "    steps = 400\n",
    "    binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "    american_option.setPricingEngine(binomial_engine)\n",
    "    p_u = american_option.NPV()\n",
    "\n",
    "    # calculate p_d\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol- 0.0001, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                               dividend_yield, \n",
    "                                               flat_ts, \n",
    "                                               flat_vol_ts)\n",
    "    steps = 400\n",
    "    binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "    american_option.setPricingEngine(binomial_engine)\n",
    "    p_d = american_option.NPV()\n",
    "    vega = (p_u - p_d)/(2*0.0001)\n",
    "    \n",
    "    vega_position1 = round((100/spot_price) * vega,2)\n",
    "    \n",
    "    \n",
    "    \n",
    "    # calculate the short value of long option when strike cap is 130%\n",
    "    option_type = ql.Option.Call\n",
    "    payoff = ql.PlainVanillaPayoff(option_type, strike_price*multiple)\n",
    "    settlement = calculation_date\n",
    "\n",
    "    am_exercise = ql.AmericanExercise(settlement, maturity_date_ql)\n",
    "    american_option = ql.VanillaOption(payoff, am_exercise)\n",
    "    \n",
    "    spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))\n",
    "    flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, risk_free, day_count))\n",
    "    dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                               dividend_yield, \n",
    "                                               flat_ts, \n",
    "                                               flat_vol_ts)    \n",
    "    \n",
    "    # Calculate price using CRR approach.\n",
    "    steps = 400\n",
    "    binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "    american_option.setPricingEngine(binomial_engine)\n",
    "    option_position2 = round((100/spot_price) * american_option.NPV(),2)\n",
    "    delta_position2 =  round((100/spot_price) * american_option.delta(),2)\n",
    "    gamma_position2 = round((100/spot_price) * american_option.gamma(),2)\n",
    "    \n",
    "    # calculate vega\n",
    "    # Calculate p_u\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol+ 0.0001, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                               dividend_yield, \n",
    "                                               flat_ts, \n",
    "                                               flat_vol_ts)\n",
    "    steps = 400\n",
    "    binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "    american_option.setPricingEngine(binomial_engine)\n",
    "    p_u = american_option.NPV()\n",
    "\n",
    "    # Calculate p_d\n",
    "    flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol- 0.0001, day_count))\n",
    "    bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                               dividend_yield, \n",
    "                                               flat_ts, \n",
    "                                               flat_vol_ts)\n",
    "    steps = 400\n",
    "    binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "    american_option.setPricingEngine(binomial_engine)\n",
    "    p_d = american_option.NPV()\n",
    "    vega = (p_u - p_d)/(2*0.0001)\n",
    "    \n",
    "    vega_position2 = round((100/spot_price) * vega,2)    \n",
    "\n",
    "    \n",
    "    # If the pricing date is pre exercise date, we should offset option value at this period\n",
    "    if date_pd < conversion_start_date:\n",
    "        # get the risk free rate\n",
    "        date_pd = pd.Timestamp(date)\n",
    "        time_remain_month = ((conversion_start_date - date_pd).days)/30\n",
    "        time_remain_year = ((conversion_start_date - date_pd).days)/365\n",
    "\n",
    "        if time_remain_year <= 1:\n",
    "            if time_remain_month <= 1:\n",
    "                risk_free = yc['1M'].iloc[0]\n",
    "            elif time_remain_month <= 2:\n",
    "                 risk_free = yc['2M'].iloc[0]\n",
    "            elif time_remain_month <= 3:\n",
    "                risk_free = yc['3M'].iloc[0]\n",
    "            elif time_remain_month <= 6:\n",
    "                 risk_free = yc['6M'].iloc[0]\n",
    "            elif time_remain_month <= 9:\n",
    "                 risk_free = yc['9M'].iloc[0]\n",
    "            else: \n",
    "                risk_free = yc['1Y'].iloc[0]\n",
    "        else:\n",
    "            year = math.ceil(time_remain_year)\n",
    "            risk_free = yc[str(year)+'Y'].iloc[0]\n",
    "            if str(risk_free) == 'nan':\n",
    "                risk_free = (yc[str(year-1)+'Y'].iloc[0] + yc[str(year+1)+'Y'].iloc[0] )/2\n",
    "    \n",
    "        # Modify the format of time according to quantlib\n",
    "        maturity_date_ql = ql.Date(conversion_start_date.day, conversion_start_date.month, conversion_start_date.year)\n",
    "        day_count = ql.Actual365Fixed()\n",
    "        calendar = ql.China()\n",
    "        calculation_date = ql.Date(date_pd.day, date_pd.month, date_pd.year)\n",
    "        ql.Settings.instance().evaluationDate = calculation_date\n",
    "\n",
    "        # Build American option\n",
    "        option_type = ql.Option.Call\n",
    "        payoff = ql.PlainVanillaPayoff(option_type, strike_price)\n",
    "        settlement = calculation_date\n",
    "\n",
    "        am_exercise = ql.AmericanExercise(settlement, maturity_date_ql)\n",
    "        american_option = ql.VanillaOption(payoff, am_exercise)\n",
    "\n",
    "        #  Black-Scholes-Merton process\n",
    "        spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))\n",
    "        flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, risk_free, day_count))\n",
    "        dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))\n",
    "        flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol, day_count))\n",
    "        bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                                   dividend_yield, \n",
    "                                                   flat_ts, \n",
    "                                                   flat_vol_ts)    \n",
    "\n",
    "        # Calculate price using CRR approach.\n",
    "        steps = 400\n",
    "        binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "        american_option.setPricingEngine(binomial_engine)\n",
    "\n",
    "        # Convert the option price to per 100 convertible bond\n",
    "        option_position3 = round((100/spot_price) * american_option.NPV(),2)\n",
    "        delta_position3 =  round((100/spot_price) * american_option.delta(),2)\n",
    "        gamma_position3 = round((100/spot_price) * american_option.gamma(),2)\n",
    "\n",
    "\n",
    "        # calculate vega\n",
    "        # calculate p_u\n",
    "        flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol+ 0.0001, day_count))\n",
    "        bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                                   dividend_yield, \n",
    "                                                   flat_ts, \n",
    "                                                   flat_vol_ts)\n",
    "        steps = 400\n",
    "        binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "        american_option.setPricingEngine(binomial_engine)\n",
    "        p_u = american_option.NPV()\n",
    "\n",
    "        # calculate p_d\n",
    "        flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol- 0.0001, day_count))\n",
    "        bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                                   dividend_yield, \n",
    "                                                   flat_ts, \n",
    "                                                   flat_vol_ts)\n",
    "        steps = 400\n",
    "        binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "        american_option.setPricingEngine(binomial_engine)\n",
    "        p_d = american_option.NPV()\n",
    "        vega = (p_u - p_d)/(2*0.0001)\n",
    "\n",
    "        vega_position3 = round((100/spot_price) * vega,2)\n",
    "        \n",
    "        is_pre_exercise ='yes'\n",
    "        \n",
    "        \n",
    "        # Build American option\n",
    "        option_type = ql.Option.Call\n",
    "        payoff = ql.PlainVanillaPayoff(option_type, strike_price*multiple)\n",
    "        settlement = calculation_date\n",
    "\n",
    "        am_exercise = ql.AmericanExercise(settlement, maturity_date_ql)\n",
    "        american_option = ql.VanillaOption(payoff, am_exercise)\n",
    "\n",
    "        #  Black-Scholes-Merton process\n",
    "        spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))\n",
    "        flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, risk_free, day_count))\n",
    "        dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date, dividend_rate, day_count))\n",
    "        flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol, day_count))\n",
    "        bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                                   dividend_yield, \n",
    "                                                   flat_ts, \n",
    "                                                   flat_vol_ts)    \n",
    "\n",
    "        # Calculate price using CRR approach.\n",
    "        steps = 400\n",
    "        binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "        american_option.setPricingEngine(binomial_engine)\n",
    "\n",
    "        # Convert the option price to per 100 convertible bond\n",
    "        option_position4 = round((100/spot_price) * american_option.NPV(),2)\n",
    "        delta_position4 =  round((100/spot_price) * american_option.delta(),2)\n",
    "        gamma_position4 = round((100/spot_price) * american_option.gamma(),2)\n",
    "\n",
    "\n",
    "        # calculate vega\n",
    "        # calculate p_u\n",
    "        flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol+ 0.0001, day_count))\n",
    "        bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                                   dividend_yield, \n",
    "                                                   flat_ts, \n",
    "                                                   flat_vol_ts)\n",
    "        steps = 400\n",
    "        binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "        american_option.setPricingEngine(binomial_engine)\n",
    "        p_u = american_option.NPV()\n",
    "\n",
    "        # calculate p_d\n",
    "        flat_vol_ts = ql.BlackVolTermStructureHandle(ql.BlackConstantVol(calculation_date, calendar, spot_vol- 0.0001, day_count))\n",
    "        bsm_process = ql.BlackScholesMertonProcess(spot_handle, \n",
    "                                                   dividend_yield, \n",
    "                                                   flat_ts, \n",
    "                                                   flat_vol_ts)\n",
    "        steps = 400\n",
    "        binomial_engine = ql.BinomialVanillaEngine(bsm_process, \"crr\", steps)\n",
    "        american_option.setPricingEngine(binomial_engine)\n",
    "        p_d = american_option.NPV()\n",
    "        vega = (p_u - p_d)/(2*0.0001)\n",
    "\n",
    "        vega_position4 = round((100/spot_price) * vega,2)                \n",
    "        \n",
    "    else:\n",
    "        option_position3 = 0\n",
    "        delta_position3 =  0\n",
    "        gamma_position3 = 0\n",
    "        vega_position3 = 0\n",
    "        option_position4 = 0\n",
    "        delta_position4 =  0\n",
    "        gamma_position4 = 0\n",
    "        vega_position4 = 0\n",
    "        is_pre_exercise ='no'\n",
    "    \n",
    "    option_position = option_position1 - option_position2 - option_position3 + option_position4\n",
    "    delta_position = delta_position1 - delta_position2 - delta_position3 + delta_position4\n",
    "    gamma_position = gamma_position1 - gamma_position2 - gamma_position3 + gamma_position4\n",
    "    vega_position = vega_position1 - vega_position2 - vega_position3 + vega_position4\n",
    "    \n",
    "    return option_position1,option_position2, option_position3,option_position4,delta_position, gamma_position, vega_position,is_pre_exercise\n",
    "\n",
    "def get_bond_price(bond_name, date):\n",
    "\n",
    "    \n",
    "    # get the id of convertible bonds\n",
    "    all_info = rq.convertible.all_instruments()\n",
    "    order_book_id = all_info[all_info['symbol'] == bond_name]['order_book_id'].iloc[0]\n",
    "    maturity_date =  all_info[all_info['symbol'] == bond_name]['maturity_date'].iloc[0]\n",
    "    \n",
    "    # gte the yeild curve of pricing date\n",
    "    yc = rq.get_yield_curve(start_date=date, end_date=date)\n",
    "    \n",
    "    # get the risk-free rate of pricing date\n",
    "    date_pd = pd.Timestamp(date)\n",
    "    time_remain_month = ((maturity_date - date_pd).days)/30\n",
    "    time_remain_year = ((maturity_date - date_pd).days)/365\n",
    "\n",
    "    if time_remain_year <= 1:\n",
    "        if time_remain_month <= 1:\n",
    "            risk_free = yc['1M'].iloc[0]\n",
    "        elif time_remain_month <= 2:\n",
    "             risk_free = yc['2M'].iloc[0]\n",
    "        elif time_remain_month <= 3:\n",
    "            risk_free = yc['3M'].iloc[0]\n",
    "        elif time_remain_month <= 6:\n",
    "             risk_free = yc['6M'].iloc[0]\n",
    "        elif time_remain_month <= 9:\n",
    "             risk_free = yc['9M'].iloc[0]\n",
    "        else: \n",
    "            risk_free = yc['1Y'].iloc[0]\n",
    "    else:\n",
    "        year = math.ceil(time_remain_year)\n",
    "        risk_free = yc[str(year)+'Y'].iloc[0]\n",
    "        if str(risk_free) == 'nan':\n",
    "            risk_free = (yc[str(year-1)+'Y'].iloc[0] + yc[str(year+1)+'Y'].iloc[0] )/2    \n",
    "    \n",
    "    # get the cash flow table of convertible bonds\n",
    "    cf = convertible.get_cash_flow(order_book_id)\n",
    "    \n",
    "    # find the nearest cash flow\n",
    "    first_cf = -1\n",
    "\n",
    "    if date_pd <= cf.index[0][1]:\n",
    "        first_cf = 0\n",
    "    else:\n",
    "        for i in range(len(cf.index) - 1):\n",
    "            if date_pd > cf.index[i][1] and date_pd <= cf.index[i+1][1]:\n",
    "                first_cf = i + 1\n",
    "                break\n",
    "\n",
    "    if first_cf == -1:\n",
    "        #print(bond_name + ':输入日期超出债券退市日期')\n",
    "        sys.exit(0)\n",
    "    \n",
    "    # calculate bond value\n",
    "    bond_value = 0\n",
    "    for i in range(first_cf, len(cf.index)):\n",
    "        time_diff = (cf.index[i][1] - date_pd).days\n",
    "        discount = cf['cash_flow_pretax'].iloc[i]/np.power(1+risk_free, time_diff/365)\n",
    "        bond_value = bond_value + discount\n",
    "    \n",
    "    return bond_value\n",
    "\n",
    "def get_convertible_bond_price(bond_name, date):\n",
    "    # get the id of convertible bonds\n",
    "    all_info = rq.convertible.all_instruments()\n",
    "    order_book_id = all_info[all_info['symbol'] == bond_name]['order_book_id'].iloc[0]    \n",
    "    bond_price = rq.get_price(order_book_id, start_date=date, end_date=date)['close'][0]\n",
    "    \n",
    "    return bond_price\n",
    "\n",
    "def chunks(List, n):\n",
    "    return [List[i:i + n] for i in range(0, len(List), n)]\n",
    "\n",
    "def get_table(trading_date,all_info):\n",
    "    for date in trading_date:\n",
    "        select = all_info[all_info['maturity_date'] > pd.Timestamp(date)]\n",
    "        bond_list = select['symbol']\n",
    "\n",
    "        stats = []\n",
    "        for bond in bond_list:\n",
    "            try:\n",
    "                maturity_date = str(all_info[all_info['symbol'] == bond]['maturity_date'].iloc[0])[:10]\n",
    "                bond_price = get_bond_price(bond, date)\n",
    "                option_position1,option_position2, option_position3, option_position4, delta_position, gamma_position, vega_position,is_pre_exercise = get_convertible_call_price(bond, date)\n",
    "                convertible_price = bond_price + option_position1 - option_position2 - option_position3 +  option_position4\n",
    "                real_price = get_convertible_bond_price(bond,  date)\n",
    "                stats.append([bond,date,maturity_date,is_pre_exercise,bond_price, \n",
    "                              option_position1,option_position2,option_position3,option_position4, \n",
    "                              convertible_price, real_price, delta_position, gamma_position, vega_position])\n",
    "            except:\n",
    "                continue\n",
    "        #df_stats = pd.DataFrame(stats, columns = ['bond_name','calculation_date','maturity_date','is_pre_exercise','bond_price','option1','option2','option3','option4',\n",
    "        #                                      'calculation_price','real_price'])\n",
    "        print(date + ' finished')\n",
    "        return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     8,
     13
    ]
   },
   "outputs": [],
   "source": [
    "trading_date = zw.get_tradingDates( '2017-01-01',  '2019-04-01')\n",
    "trading_list = [str(x)[:10] for x in trading_date]\n",
    "chunk = chunks(trading_list, 1)\n",
    "\n",
    "all_info = rq.convertible.all_instruments()\n",
    "\n",
    "pool = Pool(processes= 8)\n",
    "result = []\n",
    "for ll in chunk:\n",
    "    result.append(pool.apply_async(get_table,(ll,all_info)))\n",
    "pool.close()\n",
    "pool.join()\n",
    "df = pd.DataFrame()\n",
    "for res in result:\n",
    "    b = pd.DataFrame(res.get(),columns = ['bond_name','calculation_date','maturity_date','is_pre_exercise','bond_price','option1','option2','option3','option4',\n",
    "                                          'calculation_price','real_price','delta_position', 'gamma_position', 'vega_position'])\n",
    "    df = df.append(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calculation results are as followed："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bond_name</th>\n",
       "      <th>calculation_date</th>\n",
       "      <th>maturity_date</th>\n",
       "      <th>is_pre_exercise</th>\n",
       "      <th>bond_price</th>\n",
       "      <th>option1</th>\n",
       "      <th>option2</th>\n",
       "      <th>option3</th>\n",
       "      <th>option4</th>\n",
       "      <th>calculation_price</th>\n",
       "      <th>real_price</th>\n",
       "      <th>delta_position</th>\n",
       "      <th>gamma_position</th>\n",
       "      <th>vega_position</th>\n",
       "      <th>stock_include</th>\n",
       "      <th>spot_vol</th>\n",
       "      <th>cal_to_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>格力转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2019-12-25</td>\n",
       "      <td>no</td>\n",
       "      <td>99.956733</td>\n",
       "      <td>12.88</td>\n",
       "      <td>6.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.616733</td>\n",
       "      <td>114.040</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.49</td>\n",
       "      <td>11.57</td>\n",
       "      <td>74.999293</td>\n",
       "      <td>0.296356</td>\n",
       "      <td>0.934906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>航信转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>no</td>\n",
       "      <td>98.505468</td>\n",
       "      <td>2.42</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.215468</td>\n",
       "      <td>108.500</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.06</td>\n",
       "      <td>25.85</td>\n",
       "      <td>46.037475</td>\n",
       "      <td>0.222267</td>\n",
       "      <td>0.923645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>三一转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>no</td>\n",
       "      <td>95.678911</td>\n",
       "      <td>18.72</td>\n",
       "      <td>10.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.138911</td>\n",
       "      <td>111.290</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.09</td>\n",
       "      <td>2.66</td>\n",
       "      <td>81.534553</td>\n",
       "      <td>0.236706</td>\n",
       "      <td>0.935744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>国贸转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>no</td>\n",
       "      <td>97.740194</td>\n",
       "      <td>30.48</td>\n",
       "      <td>21.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.690194</td>\n",
       "      <td>115.320</td>\n",
       "      <td>1.69</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>90.055963</td>\n",
       "      <td>0.331152</td>\n",
       "      <td>0.925166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>九州转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>no</td>\n",
       "      <td>96.544357</td>\n",
       "      <td>39.05</td>\n",
       "      <td>29.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.234357</td>\n",
       "      <td>123.530</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-15.28</td>\n",
       "      <td>112.578732</td>\n",
       "      <td>0.333674</td>\n",
       "      <td>0.859988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>白云转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>no</td>\n",
       "      <td>99.106204</td>\n",
       "      <td>9.56</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>105.266204</td>\n",
       "      <td>125.210</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.35</td>\n",
       "      <td>19.66</td>\n",
       "      <td>75.714232</td>\n",
       "      <td>0.191534</td>\n",
       "      <td>0.840717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>广汽转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>no</td>\n",
       "      <td>95.789904</td>\n",
       "      <td>20.63</td>\n",
       "      <td>12.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.479904</td>\n",
       "      <td>116.680</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3.78</td>\n",
       "      <td>72.894405</td>\n",
       "      <td>0.300505</td>\n",
       "      <td>0.886869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>江南转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-03-18</td>\n",
       "      <td>no</td>\n",
       "      <td>95.651439</td>\n",
       "      <td>30.78</td>\n",
       "      <td>22.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.791439</td>\n",
       "      <td>114.800</td>\n",
       "      <td>1.59</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-5.95</td>\n",
       "      <td>80.124534</td>\n",
       "      <td>0.373626</td>\n",
       "      <td>0.904107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16以岭EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-04-18</td>\n",
       "      <td>yes</td>\n",
       "      <td>99.386114</td>\n",
       "      <td>25.28</td>\n",
       "      <td>15.39</td>\n",
       "      <td>4.59</td>\n",
       "      <td>0.14</td>\n",
       "      <td>104.826114</td>\n",
       "      <td>114.830</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-25.02</td>\n",
       "      <td>96.703543</td>\n",
       "      <td>0.264857</td>\n",
       "      <td>0.912881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>蓝标转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-12-18</td>\n",
       "      <td>no</td>\n",
       "      <td>98.395053</td>\n",
       "      <td>20.41</td>\n",
       "      <td>13.39</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>105.415053</td>\n",
       "      <td>108.502</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.03</td>\n",
       "      <td>8.96</td>\n",
       "      <td>64.701983</td>\n",
       "      <td>0.345247</td>\n",
       "      <td>0.971549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>海印转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-06-08</td>\n",
       "      <td>no</td>\n",
       "      <td>98.911410</td>\n",
       "      <td>31.40</td>\n",
       "      <td>22.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>108.041410</td>\n",
       "      <td>113.800</td>\n",
       "      <td>2.82</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-10.59</td>\n",
       "      <td>92.339094</td>\n",
       "      <td>0.312896</td>\n",
       "      <td>0.949397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>歌尔转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2020-12-12</td>\n",
       "      <td>no</td>\n",
       "      <td>99.178097</td>\n",
       "      <td>9.19</td>\n",
       "      <td>4.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.588097</td>\n",
       "      <td>121.548</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.07</td>\n",
       "      <td>17.42</td>\n",
       "      <td>51.253428</td>\n",
       "      <td>0.329258</td>\n",
       "      <td>0.852240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>顺昌转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>no</td>\n",
       "      <td>98.189351</td>\n",
       "      <td>44.00</td>\n",
       "      <td>36.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.019351</td>\n",
       "      <td>119.705</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-10.63</td>\n",
       "      <td>98.036069</td>\n",
       "      <td>0.467736</td>\n",
       "      <td>0.885672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>汽模转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-03-02</td>\n",
       "      <td>no</td>\n",
       "      <td>99.074018</td>\n",
       "      <td>41.03</td>\n",
       "      <td>31.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>108.834018</td>\n",
       "      <td>127.654</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-16.30</td>\n",
       "      <td>118.127297</td>\n",
       "      <td>0.330001</td>\n",
       "      <td>0.852570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>辉丰转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-04-21</td>\n",
       "      <td>no</td>\n",
       "      <td>91.472883</td>\n",
       "      <td>19.14</td>\n",
       "      <td>12.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>98.302883</td>\n",
       "      <td>108.001</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.16</td>\n",
       "      <td>3.19</td>\n",
       "      <td>62.627395</td>\n",
       "      <td>0.326284</td>\n",
       "      <td>0.910203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>洪涛转债</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>yes</td>\n",
       "      <td>96.615034</td>\n",
       "      <td>31.92</td>\n",
       "      <td>23.95</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.505034</td>\n",
       "      <td>113.200</td>\n",
       "      <td>1.18</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>77.918567</td>\n",
       "      <td>0.380423</td>\n",
       "      <td>0.923189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14宝钢EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2017-12-10</td>\n",
       "      <td>no</td>\n",
       "      <td>100.423744</td>\n",
       "      <td>12.72</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>109.913744</td>\n",
       "      <td>114.070</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.48</td>\n",
       "      <td>103.154958</td>\n",
       "      <td>0.260270</td>\n",
       "      <td>0.963564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>15天集EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>no</td>\n",
       "      <td>97.349029</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.389029</td>\n",
       "      <td>106.180</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.03</td>\n",
       "      <td>24.89</td>\n",
       "      <td>50.437984</td>\n",
       "      <td>0.249117</td>\n",
       "      <td>0.936043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15清控EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2018-10-26</td>\n",
       "      <td>no</td>\n",
       "      <td>97.072413</td>\n",
       "      <td>7.25</td>\n",
       "      <td>2.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>102.192413</td>\n",
       "      <td>109.620</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.25</td>\n",
       "      <td>23.36</td>\n",
       "      <td>78.372953</td>\n",
       "      <td>0.265674</td>\n",
       "      <td>0.932242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15国盛EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-11-05</td>\n",
       "      <td>no</td>\n",
       "      <td>94.355165</td>\n",
       "      <td>5.01</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>97.225165</td>\n",
       "      <td>99.260</td>\n",
       "      <td>2.67</td>\n",
       "      <td>1.28</td>\n",
       "      <td>27.48</td>\n",
       "      <td>44.615817</td>\n",
       "      <td>0.265700</td>\n",
       "      <td>0.979500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15国资EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>no</td>\n",
       "      <td>102.583083</td>\n",
       "      <td>5.27</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.553083</td>\n",
       "      <td>111.010</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.07</td>\n",
       "      <td>36.75</td>\n",
       "      <td>69.936139</td>\n",
       "      <td>0.171734</td>\n",
       "      <td>0.959851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16皖新EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>yes</td>\n",
       "      <td>102.397751</td>\n",
       "      <td>39.10</td>\n",
       "      <td>30.38</td>\n",
       "      <td>13.18</td>\n",
       "      <td>3.90</td>\n",
       "      <td>101.837751</td>\n",
       "      <td>113.240</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-14.41</td>\n",
       "      <td>102.993925</td>\n",
       "      <td>0.412853</td>\n",
       "      <td>0.899309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>16凤凰EB</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>yes</td>\n",
       "      <td>96.135166</td>\n",
       "      <td>7.78</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.775166</td>\n",
       "      <td>104.010</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.08</td>\n",
       "      <td>26.40</td>\n",
       "      <td>63.046169</td>\n",
       "      <td>0.211700</td>\n",
       "      <td>0.968899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>格力转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2019-12-25</td>\n",
       "      <td>no</td>\n",
       "      <td>99.868999</td>\n",
       "      <td>13.11</td>\n",
       "      <td>6.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.638999</td>\n",
       "      <td>114.230</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.47</td>\n",
       "      <td>11.37</td>\n",
       "      <td>75.768517</td>\n",
       "      <td>0.294630</td>\n",
       "      <td>0.933546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>航信转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>no</td>\n",
       "      <td>98.321101</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.981101</td>\n",
       "      <td>108.620</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.06</td>\n",
       "      <td>26.13</td>\n",
       "      <td>45.969905</td>\n",
       "      <td>0.220437</td>\n",
       "      <td>0.920467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>三一转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>no</td>\n",
       "      <td>95.604491</td>\n",
       "      <td>19.18</td>\n",
       "      <td>10.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.194491</td>\n",
       "      <td>111.510</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.06</td>\n",
       "      <td>2.49</td>\n",
       "      <td>82.318539</td>\n",
       "      <td>0.236918</td>\n",
       "      <td>0.934396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>国贸转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>no</td>\n",
       "      <td>97.560435</td>\n",
       "      <td>30.78</td>\n",
       "      <td>21.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.560435</td>\n",
       "      <td>116.170</td>\n",
       "      <td>1.68</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-9.72</td>\n",
       "      <td>91.021999</td>\n",
       "      <td>0.329436</td>\n",
       "      <td>0.917280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>九州转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>no</td>\n",
       "      <td>96.364663</td>\n",
       "      <td>38.86</td>\n",
       "      <td>29.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.064663</td>\n",
       "      <td>125.030</td>\n",
       "      <td>0.57</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-13.75</td>\n",
       "      <td>111.885776</td>\n",
       "      <td>0.333268</td>\n",
       "      <td>0.848314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>白云转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>no</td>\n",
       "      <td>99.095187</td>\n",
       "      <td>9.82</td>\n",
       "      <td>3.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>105.385187</td>\n",
       "      <td>126.500</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.33</td>\n",
       "      <td>24.87</td>\n",
       "      <td>76.244073</td>\n",
       "      <td>0.191398</td>\n",
       "      <td>0.833084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>广汽转债</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>2022-01-22</td>\n",
       "      <td>no</td>\n",
       "      <td>95.611977</td>\n",
       "      <td>20.60</td>\n",
       "      <td>12.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.311977</td>\n",
       "      <td>117.550</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.64</td>\n",
       "      <td>72.862985</td>\n",
       "      <td>0.299713</td>\n",
       "      <td>0.878877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35271</th>\n",
       "      <td>利欧转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-03-22</td>\n",
       "      <td>no</td>\n",
       "      <td>98.429684</td>\n",
       "      <td>52.12</td>\n",
       "      <td>43.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.679684</td>\n",
       "      <td>145.500</td>\n",
       "      <td>3.22</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-14.51</td>\n",
       "      <td>136.627907</td>\n",
       "      <td>0.438776</td>\n",
       "      <td>0.733194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35272</th>\n",
       "      <td>三力转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-06-08</td>\n",
       "      <td>no</td>\n",
       "      <td>94.698929</td>\n",
       "      <td>45.15</td>\n",
       "      <td>36.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>103.718929</td>\n",
       "      <td>120.635</td>\n",
       "      <td>1.37</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-15.55</td>\n",
       "      <td>120.034247</td>\n",
       "      <td>0.379122</td>\n",
       "      <td>0.859775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35273</th>\n",
       "      <td>华通转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-06-14</td>\n",
       "      <td>no</td>\n",
       "      <td>96.998918</td>\n",
       "      <td>40.74</td>\n",
       "      <td>32.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>105.448918</td>\n",
       "      <td>109.020</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-12.10</td>\n",
       "      <td>99.301310</td>\n",
       "      <td>0.406672</td>\n",
       "      <td>0.967244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35274</th>\n",
       "      <td>盛路转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-07-17</td>\n",
       "      <td>no</td>\n",
       "      <td>98.628850</td>\n",
       "      <td>64.66</td>\n",
       "      <td>58.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.718850</td>\n",
       "      <td>156.486</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-10.98</td>\n",
       "      <td>157.267442</td>\n",
       "      <td>0.583967</td>\n",
       "      <td>0.669190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35275</th>\n",
       "      <td>凯中转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-07-30</td>\n",
       "      <td>no</td>\n",
       "      <td>100.026626</td>\n",
       "      <td>35.74</td>\n",
       "      <td>26.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>109.116626</td>\n",
       "      <td>110.230</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-11.80</td>\n",
       "      <td>97.735849</td>\n",
       "      <td>0.341865</td>\n",
       "      <td>0.989900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35276</th>\n",
       "      <td>东音转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-08-02</td>\n",
       "      <td>no</td>\n",
       "      <td>98.477375</td>\n",
       "      <td>47.03</td>\n",
       "      <td>38.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.017375</td>\n",
       "      <td>117.697</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-13.03</td>\n",
       "      <td>118.110236</td>\n",
       "      <td>0.408168</td>\n",
       "      <td>0.909262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35277</th>\n",
       "      <td>岭南转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>no</td>\n",
       "      <td>95.462347</td>\n",
       "      <td>42.89</td>\n",
       "      <td>34.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.122347</td>\n",
       "      <td>114.910</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-11.95</td>\n",
       "      <td>107.142857</td>\n",
       "      <td>0.392776</td>\n",
       "      <td>0.906121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35278</th>\n",
       "      <td>机电转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-08-27</td>\n",
       "      <td>no</td>\n",
       "      <td>93.564118</td>\n",
       "      <td>44.25</td>\n",
       "      <td>35.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>102.124118</td>\n",
       "      <td>120.050</td>\n",
       "      <td>1.12</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-13.97</td>\n",
       "      <td>109.921671</td>\n",
       "      <td>0.398502</td>\n",
       "      <td>0.850680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35279</th>\n",
       "      <td>利尔转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-10-17</td>\n",
       "      <td>yes</td>\n",
       "      <td>95.991492</td>\n",
       "      <td>41.57</td>\n",
       "      <td>33.67</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>102.601492</td>\n",
       "      <td>114.000</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-16.71</td>\n",
       "      <td>91.514501</td>\n",
       "      <td>0.433467</td>\n",
       "      <td>0.900013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35280</th>\n",
       "      <td>光电转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.489723</td>\n",
       "      <td>39.41</td>\n",
       "      <td>30.38</td>\n",
       "      <td>6.29</td>\n",
       "      <td>0.10</td>\n",
       "      <td>93.329723</td>\n",
       "      <td>118.721</td>\n",
       "      <td>-1.20</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-23.77</td>\n",
       "      <td>103.179334</td>\n",
       "      <td>0.352060</td>\n",
       "      <td>0.786126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35281</th>\n",
       "      <td>张行转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-11-12</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.728294</td>\n",
       "      <td>51.16</td>\n",
       "      <td>43.42</td>\n",
       "      <td>16.11</td>\n",
       "      <td>2.73</td>\n",
       "      <td>85.088294</td>\n",
       "      <td>121.100</td>\n",
       "      <td>-6.96</td>\n",
       "      <td>1.39</td>\n",
       "      <td>-9.06</td>\n",
       "      <td>117.161716</td>\n",
       "      <td>0.459307</td>\n",
       "      <td>0.702628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35282</th>\n",
       "      <td>华源转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-11-27</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.822730</td>\n",
       "      <td>53.49</td>\n",
       "      <td>45.85</td>\n",
       "      <td>22.18</td>\n",
       "      <td>6.60</td>\n",
       "      <td>83.882730</td>\n",
       "      <td>119.800</td>\n",
       "      <td>-3.67</td>\n",
       "      <td>1.32</td>\n",
       "      <td>-3.62</td>\n",
       "      <td>126.684280</td>\n",
       "      <td>0.458521</td>\n",
       "      <td>0.700190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35283</th>\n",
       "      <td>钧达转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-12-10</td>\n",
       "      <td>yes</td>\n",
       "      <td>92.100175</td>\n",
       "      <td>62.03</td>\n",
       "      <td>56.50</td>\n",
       "      <td>14.79</td>\n",
       "      <td>4.93</td>\n",
       "      <td>87.770175</td>\n",
       "      <td>110.200</td>\n",
       "      <td>-1.27</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-10.13</td>\n",
       "      <td>106.025759</td>\n",
       "      <td>0.662850</td>\n",
       "      <td>0.796463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35284</th>\n",
       "      <td>光华转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-12-14</td>\n",
       "      <td>yes</td>\n",
       "      <td>91.960390</td>\n",
       "      <td>42.20</td>\n",
       "      <td>34.41</td>\n",
       "      <td>4.57</td>\n",
       "      <td>0.42</td>\n",
       "      <td>95.600390</td>\n",
       "      <td>107.589</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-22.51</td>\n",
       "      <td>90.663535</td>\n",
       "      <td>0.437099</td>\n",
       "      <td>0.888570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35285</th>\n",
       "      <td>凯龙转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2024-12-21</td>\n",
       "      <td>yes</td>\n",
       "      <td>90.625933</td>\n",
       "      <td>57.86</td>\n",
       "      <td>49.86</td>\n",
       "      <td>37.46</td>\n",
       "      <td>20.09</td>\n",
       "      <td>81.255933</td>\n",
       "      <td>134.013</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.72</td>\n",
       "      <td>-4.44</td>\n",
       "      <td>158.967001</td>\n",
       "      <td>0.414737</td>\n",
       "      <td>0.606329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35286</th>\n",
       "      <td>尚荣转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2025-02-14</td>\n",
       "      <td>yes</td>\n",
       "      <td>96.706021</td>\n",
       "      <td>49.40</td>\n",
       "      <td>40.59</td>\n",
       "      <td>24.68</td>\n",
       "      <td>9.40</td>\n",
       "      <td>90.236021</td>\n",
       "      <td>120.000</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>2.14</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>129.554656</td>\n",
       "      <td>0.370618</td>\n",
       "      <td>0.751967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35287</th>\n",
       "      <td>中宠转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2025-02-15</td>\n",
       "      <td>yes</td>\n",
       "      <td>96.959147</td>\n",
       "      <td>43.71</td>\n",
       "      <td>35.62</td>\n",
       "      <td>10.24</td>\n",
       "      <td>2.36</td>\n",
       "      <td>97.169147</td>\n",
       "      <td>114.320</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-20.29</td>\n",
       "      <td>99.025546</td>\n",
       "      <td>0.413183</td>\n",
       "      <td>0.849975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35288</th>\n",
       "      <td>今飞转债</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2025-02-28</td>\n",
       "      <td>yes</td>\n",
       "      <td>101.944409</td>\n",
       "      <td>51.08</td>\n",
       "      <td>43.77</td>\n",
       "      <td>16.06</td>\n",
       "      <td>5.90</td>\n",
       "      <td>99.094409</td>\n",
       "      <td>109.528</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-12.54</td>\n",
       "      <td>106.764706</td>\n",
       "      <td>0.480942</td>\n",
       "      <td>0.904740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35289</th>\n",
       "      <td>15国盛EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2021-11-05</td>\n",
       "      <td>no</td>\n",
       "      <td>98.852159</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.812159</td>\n",
       "      <td>98.170</td>\n",
       "      <td>2.89</td>\n",
       "      <td>2.69</td>\n",
       "      <td>22.39</td>\n",
       "      <td>59.722222</td>\n",
       "      <td>0.228174</td>\n",
       "      <td>1.026914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35290</th>\n",
       "      <td>15国资EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>no</td>\n",
       "      <td>106.279806</td>\n",
       "      <td>18.68</td>\n",
       "      <td>9.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>115.209806</td>\n",
       "      <td>115.470</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.98</td>\n",
       "      <td>94.924966</td>\n",
       "      <td>0.370562</td>\n",
       "      <td>0.997747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35291</th>\n",
       "      <td>16皖新EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>no</td>\n",
       "      <td>107.379878</td>\n",
       "      <td>4.69</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.259878</td>\n",
       "      <td>104.450</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.32</td>\n",
       "      <td>17.38</td>\n",
       "      <td>52.398990</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>1.055624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35292</th>\n",
       "      <td>16凤凰EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2021-10-31</td>\n",
       "      <td>no</td>\n",
       "      <td>100.753298</td>\n",
       "      <td>8.84</td>\n",
       "      <td>4.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>105.253298</td>\n",
       "      <td>98.440</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.20</td>\n",
       "      <td>13.41</td>\n",
       "      <td>57.161458</td>\n",
       "      <td>0.374585</td>\n",
       "      <td>1.069213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35293</th>\n",
       "      <td>17山高EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2022-04-24</td>\n",
       "      <td>no</td>\n",
       "      <td>103.663234</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>105.953234</td>\n",
       "      <td>100.100</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.09</td>\n",
       "      <td>24.25</td>\n",
       "      <td>53.638254</td>\n",
       "      <td>0.256242</td>\n",
       "      <td>1.058474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35294</th>\n",
       "      <td>17中油EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2022-07-13</td>\n",
       "      <td>no</td>\n",
       "      <td>99.445773</td>\n",
       "      <td>15.97</td>\n",
       "      <td>7.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.935773</td>\n",
       "      <td>101.640</td>\n",
       "      <td>3.01</td>\n",
       "      <td>0.14</td>\n",
       "      <td>4.95</td>\n",
       "      <td>87.785388</td>\n",
       "      <td>0.242830</td>\n",
       "      <td>1.061942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35295</th>\n",
       "      <td>17桐昆EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2020-08-03</td>\n",
       "      <td>no</td>\n",
       "      <td>100.537452</td>\n",
       "      <td>34.72</td>\n",
       "      <td>23.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>111.787452</td>\n",
       "      <td>129.600</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-9.97</td>\n",
       "      <td>130.509868</td>\n",
       "      <td>0.481180</td>\n",
       "      <td>0.862558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35296</th>\n",
       "      <td>17浙报EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2022-08-17</td>\n",
       "      <td>no</td>\n",
       "      <td>97.358360</td>\n",
       "      <td>6.42</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.618360</td>\n",
       "      <td>93.900</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.12</td>\n",
       "      <td>17.63</td>\n",
       "      <td>43.624436</td>\n",
       "      <td>0.363176</td>\n",
       "      <td>1.071548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35297</th>\n",
       "      <td>17巨化EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2020-09-04</td>\n",
       "      <td>no</td>\n",
       "      <td>100.313754</td>\n",
       "      <td>12.10</td>\n",
       "      <td>4.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.643754</td>\n",
       "      <td>103.460</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.26</td>\n",
       "      <td>8.51</td>\n",
       "      <td>87.743191</td>\n",
       "      <td>0.333141</td>\n",
       "      <td>1.040438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35298</th>\n",
       "      <td>17宝武EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2020-11-24</td>\n",
       "      <td>no</td>\n",
       "      <td>100.708633</td>\n",
       "      <td>8.73</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.368633</td>\n",
       "      <td>102.990</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.58</td>\n",
       "      <td>17.60</td>\n",
       "      <td>78.952880</td>\n",
       "      <td>0.309114</td>\n",
       "      <td>1.032805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35299</th>\n",
       "      <td>18中化EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2023-04-24</td>\n",
       "      <td>yes</td>\n",
       "      <td>98.186200</td>\n",
       "      <td>20.12</td>\n",
       "      <td>11.32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>106.946200</td>\n",
       "      <td>105.800</td>\n",
       "      <td>2.66</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>86.292428</td>\n",
       "      <td>0.265372</td>\n",
       "      <td>1.010834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35300</th>\n",
       "      <td>18中油EB</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>no</td>\n",
       "      <td>99.384163</td>\n",
       "      <td>16.09</td>\n",
       "      <td>7.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>107.544163</td>\n",
       "      <td>100.460</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.13</td>\n",
       "      <td>9.65</td>\n",
       "      <td>83.496200</td>\n",
       "      <td>0.242830</td>\n",
       "      <td>1.070517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35301 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bond_name calculation_date maturity_date is_pre_exercise  bond_price  \\\n",
       "0          格力转债       2017-01-03    2019-12-25              no   99.956733   \n",
       "1          航信转债       2017-01-03    2021-06-12              no   98.505468   \n",
       "2          三一转债       2017-01-03    2022-01-04              no   95.678911   \n",
       "3          国贸转债       2017-01-03    2022-01-05              no   97.740194   \n",
       "4          九州转债       2017-01-03    2022-01-15              no   96.544357   \n",
       "5          白云转债       2017-01-03    2021-02-26              no   99.106204   \n",
       "6          广汽转债       2017-01-03    2022-01-22              no   95.789904   \n",
       "7          江南转债       2017-01-03    2022-03-18              no   95.651439   \n",
       "8        16以岭EB       2017-01-03    2021-04-18             yes   99.386114   \n",
       "9          蓝标转债       2017-01-03    2021-12-18              no   98.395053   \n",
       "10         海印转债       2017-01-03    2022-06-08              no   98.911410   \n",
       "11         歌尔转债       2017-01-03    2020-12-12              no   99.178097   \n",
       "12         顺昌转债       2017-01-03    2022-01-22              no   98.189351   \n",
       "13         汽模转债       2017-01-03    2022-03-02              no   99.074018   \n",
       "14         辉丰转债       2017-01-03    2022-04-21              no   91.472883   \n",
       "15         洪涛转债       2017-01-03    2022-07-29             yes   96.615034   \n",
       "16       14宝钢EB       2017-01-03    2017-12-10              no  100.423744   \n",
       "17       15天集EB       2017-01-03    2020-06-08              no   97.349029   \n",
       "18       15清控EB       2017-01-03    2018-10-26              no   97.072413   \n",
       "19       15国盛EB       2017-01-03    2021-11-05              no   94.355165   \n",
       "20       15国资EB       2017-01-03    2020-12-08              no  102.583083   \n",
       "21       16皖新EB       2017-01-03    2021-06-23             yes  102.397751   \n",
       "22       16凤凰EB       2017-01-03    2021-10-31             yes   96.135166   \n",
       "23         格力转债       2017-01-04    2019-12-25              no   99.868999   \n",
       "24         航信转债       2017-01-04    2021-06-12              no   98.321101   \n",
       "25         三一转债       2017-01-04    2022-01-04              no   95.604491   \n",
       "26         国贸转债       2017-01-04    2022-01-05              no   97.560435   \n",
       "27         九州转债       2017-01-04    2022-01-15              no   96.364663   \n",
       "28         白云转债       2017-01-04    2021-02-26              no   99.095187   \n",
       "29         广汽转债       2017-01-04    2022-01-22              no   95.611977   \n",
       "...         ...              ...           ...             ...         ...   \n",
       "35271      利欧转债       2019-04-01    2024-03-22              no   98.429684   \n",
       "35272      三力转债       2019-04-01    2024-06-08              no   94.698929   \n",
       "35273      华通转债       2019-04-01    2024-06-14              no   96.998918   \n",
       "35274      盛路转债       2019-04-01    2024-07-17              no   98.628850   \n",
       "35275      凯中转债       2019-04-01    2024-07-30              no  100.026626   \n",
       "35276      东音转债       2019-04-01    2024-08-02              no   98.477375   \n",
       "35277      岭南转债       2019-04-01    2024-08-14              no   95.462347   \n",
       "35278      机电转债       2019-04-01    2024-08-27              no   93.564118   \n",
       "35279      利尔转债       2019-04-01    2024-10-17             yes   95.991492   \n",
       "35280      光电转债       2019-04-01    2024-11-05             yes   90.489723   \n",
       "35281      张行转债       2019-04-01    2024-11-12             yes   90.728294   \n",
       "35282      华源转债       2019-04-01    2024-11-27             yes   91.822730   \n",
       "35283      钧达转债       2019-04-01    2024-12-10             yes   92.100175   \n",
       "35284      光华转债       2019-04-01    2024-12-14             yes   91.960390   \n",
       "35285      凯龙转债       2019-04-01    2024-12-21             yes   90.625933   \n",
       "35286      尚荣转债       2019-04-01    2025-02-14             yes   96.706021   \n",
       "35287      中宠转债       2019-04-01    2025-02-15             yes   96.959147   \n",
       "35288      今飞转债       2019-04-01    2025-02-28             yes  101.944409   \n",
       "35289    15国盛EB       2019-04-01    2021-11-05              no   98.852159   \n",
       "35290    15国资EB       2019-04-01    2020-12-08              no  106.279806   \n",
       "35291    16皖新EB       2019-04-01    2021-06-23              no  107.379878   \n",
       "35292    16凤凰EB       2019-04-01    2021-10-31              no  100.753298   \n",
       "35293    17山高EB       2019-04-01    2022-04-24              no  103.663234   \n",
       "35294    17中油EB       2019-04-01    2022-07-13              no   99.445773   \n",
       "35295    17桐昆EB       2019-04-01    2020-08-03              no  100.537452   \n",
       "35296    17浙报EB       2019-04-01    2022-08-17              no   97.358360   \n",
       "35297    17巨化EB       2019-04-01    2020-09-04              no  100.313754   \n",
       "35298    17宝武EB       2019-04-01    2020-11-24              no  100.708633   \n",
       "35299    18中化EB       2019-04-01    2023-04-24             yes   98.186200   \n",
       "35300    18中油EB       2019-04-01    2023-02-01              no   99.384163   \n",
       "\n",
       "       option1  option2  option3  option4  calculation_price  real_price  \\\n",
       "0        12.88     6.22     0.00     0.00         106.616733     114.040   \n",
       "1         2.42     0.71     0.00     0.00         100.215468     108.500   \n",
       "2        18.72    10.26     0.00     0.00         104.138911     111.290   \n",
       "3        30.48    21.53     0.00     0.00         106.690194     115.320   \n",
       "4        39.05    29.36     0.00     0.00         106.234357     123.530   \n",
       "5         9.56     3.40     0.00     0.00         105.266204     125.210   \n",
       "6        20.63    12.94     0.00     0.00         103.479904     116.680   \n",
       "7        30.78    22.64     0.00     0.00         103.791439     114.800   \n",
       "8        25.28    15.39     4.59     0.14         104.826114     114.830   \n",
       "9        20.41    13.39     0.00     0.00         105.415053     108.502   \n",
       "10       31.40    22.27     0.00     0.00         108.041410     113.800   \n",
       "11        9.19     4.78     0.00     0.00         103.588097     121.548   \n",
       "12       44.00    36.17     0.00     0.00         106.019351     119.705   \n",
       "13       41.03    31.27     0.00     0.00         108.834018     127.654   \n",
       "14       19.14    12.31     0.00     0.00          98.302883     108.001   \n",
       "15       31.92    23.95     0.08     0.00         104.505034     113.200   \n",
       "16       12.72     3.23     0.00     0.00         109.913744     114.070   \n",
       "17        2.93     0.89     0.00     0.00          99.389029     106.180   \n",
       "18        7.25     2.13     0.00     0.00         102.192413     109.620   \n",
       "19        5.01     2.14     0.00     0.00          97.225165      99.260   \n",
       "20        5.27     1.30     0.00     0.00         106.553083     111.010   \n",
       "21       39.10    30.38    13.18     3.90         101.837751     113.240   \n",
       "22        7.78     3.05     0.09     0.00         100.775166     104.010   \n",
       "23       13.11     6.34     0.00     0.00         106.638999     114.230   \n",
       "24        2.34     0.68     0.00     0.00          99.981101     108.620   \n",
       "25       19.18    10.59     0.00     0.00         104.194491     111.510   \n",
       "26       30.78    21.78     0.00     0.00         106.560435     116.170   \n",
       "27       38.86    29.16     0.00     0.00         106.064663     125.030   \n",
       "28        9.82     3.53     0.00     0.00         105.385187     126.500   \n",
       "29       20.60    12.90     0.00     0.00         103.311977     117.550   \n",
       "...        ...      ...      ...      ...                ...         ...   \n",
       "35271    52.12    43.87     0.00     0.00         106.679684     145.500   \n",
       "35272    45.15    36.13     0.00     0.00         103.718929     120.635   \n",
       "35273    40.74    32.29     0.00     0.00         105.448918     109.020   \n",
       "35274    64.66    58.57     0.00     0.00         104.718850     156.486   \n",
       "35275    35.74    26.65     0.00     0.00         109.116626     110.230   \n",
       "35276    47.03    38.49     0.00     0.00         107.017375     117.697   \n",
       "35277    42.89    34.23     0.00     0.00         104.122347     114.910   \n",
       "35278    44.25    35.69     0.00     0.00         102.124118     120.050   \n",
       "35279    41.57    33.67     1.29     0.00         102.601492     114.000   \n",
       "35280    39.41    30.38     6.29     0.10          93.329723     118.721   \n",
       "35281    51.16    43.42    16.11     2.73          85.088294     121.100   \n",
       "35282    53.49    45.85    22.18     6.60          83.882730     119.800   \n",
       "35283    62.03    56.50    14.79     4.93          87.770175     110.200   \n",
       "35284    42.20    34.41     4.57     0.42          95.600390     107.589   \n",
       "35285    57.86    49.86    37.46    20.09          81.255933     134.013   \n",
       "35286    49.40    40.59    24.68     9.40          90.236021     120.000   \n",
       "35287    43.71    35.62    10.24     2.36          97.169147     114.320   \n",
       "35288    51.08    43.77    16.06     5.90          99.094409     109.528   \n",
       "35289     2.50     0.54     0.00     0.00         100.812159      98.170   \n",
       "35290    18.68     9.75     0.00     0.00         115.209806     115.470   \n",
       "35291     4.69     1.81     0.00     0.00         110.259878     104.450   \n",
       "35292     8.84     4.34     0.00     0.00         105.253298      98.440   \n",
       "35293     3.28     0.99     0.00     0.00         105.953234     100.100   \n",
       "35294    15.97     7.48     0.00     0.00         107.935773     101.640   \n",
       "35295    34.72    23.47     0.00     0.00         111.787452     129.600   \n",
       "35296     6.42     3.16     0.00     0.00         100.618360      93.900   \n",
       "35297    12.10     4.77     0.00     0.00         107.643754     103.460   \n",
       "35298     8.73     3.07     0.00     0.00         106.368633     102.990   \n",
       "35299    20.12    11.32     0.04     0.00         106.946200     105.800   \n",
       "35300    16.09     7.93     0.00     0.00         107.544163     100.460   \n",
       "\n",
       "       delta_position  gamma_position  vega_position  stock_include  spot_vol  \\\n",
       "0                3.44            0.49          11.57      74.999293  0.296356   \n",
       "1                0.41            0.06          25.85      46.037475  0.222267   \n",
       "2                3.19            0.09           2.66      81.534553  0.236706   \n",
       "3                1.69           -0.07          -9.66      90.055963  0.331152   \n",
       "4                0.57           -0.02         -15.28     112.578732  0.333674   \n",
       "5                2.40            0.35          19.66      75.714232  0.191534   \n",
       "6                0.96            0.01           3.78      72.894405  0.300505   \n",
       "7                1.59           -0.04          -5.95      80.124534  0.373626   \n",
       "8               -1.42           -0.80         -25.02      96.703543  0.264857   \n",
       "9                1.35            0.03           8.96      64.701983  0.345247   \n",
       "10               2.82           -0.23         -10.59      92.339094  0.312896   \n",
       "11               0.91            0.07          17.42      51.253428  0.329258   \n",
       "12               0.94           -0.05         -10.63      98.036069  0.467736   \n",
       "13               1.68           -0.21         -16.30     118.127297  0.330001   \n",
       "14               2.78            0.16           3.19      62.627395  0.326284   \n",
       "15               1.18           -0.67          -7.39      77.918567  0.380423   \n",
       "16               0.89            0.02           5.48     103.154958  0.260270   \n",
       "17               0.33            0.03          24.89      50.437984  0.249117   \n",
       "18               1.68            0.25          23.36      78.372953  0.265674   \n",
       "19               2.67            1.28          27.48      44.615817  0.265700   \n",
       "20               0.73            0.07          36.75      69.936139  0.171734   \n",
       "21              -1.46           -0.10         -14.41     102.993925  0.412853   \n",
       "22               1.55            0.08          26.40      63.046169  0.211700   \n",
       "23               3.44            0.47          11.37      75.768517  0.294630   \n",
       "24               0.41            0.06          26.13      45.969905  0.220437   \n",
       "25               3.16            0.06           2.49      82.318539  0.236918   \n",
       "26               1.68           -0.08          -9.72      91.021999  0.329436   \n",
       "27               0.57           -0.02         -13.75     111.885776  0.333268   \n",
       "28               2.41            0.33          24.87      76.244073  0.191398   \n",
       "29               0.97            0.00           3.64      72.862985  0.299713   \n",
       "...               ...             ...            ...            ...       ...   \n",
       "35271            3.22           -1.15         -14.51     136.627907  0.438776   \n",
       "35272            1.37           -0.15         -15.55     120.034247  0.379122   \n",
       "35273            0.88           -0.04         -12.10      99.301310  0.406672   \n",
       "35274            0.42           -0.03         -10.98     157.267442  0.583967   \n",
       "35275            0.94           -0.03         -11.80      97.735849  0.341865   \n",
       "35276            0.65           -0.03         -13.03     118.110236  0.408168   \n",
       "35277            1.02           -0.06         -11.95     107.142857  0.392776   \n",
       "35278            1.12           -0.09         -13.97     109.921671  0.398502   \n",
       "35279           -0.76           -0.97         -16.71      91.514501  0.433467   \n",
       "35280           -1.20           -0.17         -23.77     103.179334  0.352060   \n",
       "35281           -6.96            1.39          -9.06     117.161716  0.459307   \n",
       "35282           -3.67            1.32          -3.62     126.684280  0.458521   \n",
       "35283           -1.27           -0.03         -10.13     106.025759  0.662850   \n",
       "35284           -1.44           -0.56         -22.51      90.663535  0.437099   \n",
       "35285           -0.54            0.72          -4.44     158.967001  0.414737   \n",
       "35286           -4.06            2.14          -1.62     129.554656  0.370618   \n",
       "35287           -0.72           -0.05         -20.29      99.025546  0.413183   \n",
       "35288           -3.46           -0.12         -12.54     106.764706  0.480942   \n",
       "35289            2.89            2.69          22.39      59.722222  0.228174   \n",
       "35290            0.62            0.01           0.98      94.924966  0.370562   \n",
       "35291            1.32            0.32          17.38      52.398990  0.353976   \n",
       "35292            1.52            0.20          13.41      57.161458  0.374585   \n",
       "35293            2.06            1.09          24.25      53.638254  0.256242   \n",
       "35294            3.01            0.14           4.95      87.785388  0.242830   \n",
       "35295            0.99           -0.07          -9.97     130.509868  0.481180   \n",
       "35296            0.93            0.12          17.63      43.624436  0.363176   \n",
       "35297            2.68            0.26           8.51      87.743191  0.333141   \n",
       "35298            2.85            0.58          17.60      78.952880  0.309114   \n",
       "35299            2.66           -1.42          -0.60      86.292428  0.265372   \n",
       "35300            2.77            0.13           9.65      83.496200  0.242830   \n",
       "\n",
       "       cal_to_real  \n",
       "0         0.934906  \n",
       "1         0.923645  \n",
       "2         0.935744  \n",
       "3         0.925166  \n",
       "4         0.859988  \n",
       "5         0.840717  \n",
       "6         0.886869  \n",
       "7         0.904107  \n",
       "8         0.912881  \n",
       "9         0.971549  \n",
       "10        0.949397  \n",
       "11        0.852240  \n",
       "12        0.885672  \n",
       "13        0.852570  \n",
       "14        0.910203  \n",
       "15        0.923189  \n",
       "16        0.963564  \n",
       "17        0.936043  \n",
       "18        0.932242  \n",
       "19        0.979500  \n",
       "20        0.959851  \n",
       "21        0.899309  \n",
       "22        0.968899  \n",
       "23        0.933546  \n",
       "24        0.920467  \n",
       "25        0.934396  \n",
       "26        0.917280  \n",
       "27        0.848314  \n",
       "28        0.833084  \n",
       "29        0.878877  \n",
       "...            ...  \n",
       "35271     0.733194  \n",
       "35272     0.859775  \n",
       "35273     0.967244  \n",
       "35274     0.669190  \n",
       "35275     0.989900  \n",
       "35276     0.909262  \n",
       "35277     0.906121  \n",
       "35278     0.850680  \n",
       "35279     0.900013  \n",
       "35280     0.786126  \n",
       "35281     0.702628  \n",
       "35282     0.700190  \n",
       "35283     0.796463  \n",
       "35284     0.888570  \n",
       "35285     0.606329  \n",
       "35286     0.751967  \n",
       "35287     0.849975  \n",
       "35288     0.904740  \n",
       "35289     1.026914  \n",
       "35290     0.997747  \n",
       "35291     1.055624  \n",
       "35292     1.069213  \n",
       "35293     1.058474  \n",
       "35294     1.061942  \n",
       "35295     0.862558  \n",
       "35296     1.071548  \n",
       "35297     1.040438  \n",
       "35298     1.032805  \n",
       "35299     1.010834  \n",
       "35300     1.070517  \n",
       "\n",
       "[35301 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h5 = pd.HDFStore('convertible_pricing3.h5')\n",
    "all_df = h5['convertible_pricing']\n",
    "h5.close()\n",
    "all_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The relationship between binomial-tree price and market price at the first day of listing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate whether the model price can accurately reflect the real price, we first examine their relationship on the first day of listing.\n",
    "\n",
    "We use cal_to_real (model price to real price) to show this relationship.\n",
    "\n",
    "From the results, the average cal_to_real ratio on the first day of listing is 0.891088. The time theory of the ratio is stationary (verified by ADF test).\n",
    "\n",
    "This result indicates that on the first day of listing, our binomial-tree model systematically undervalues the price of convertible bonds.\n",
    "\n",
    "The potential cause could be:\n",
    "+ (1)\tWe ignore the reset clause and put clause of convertible bonds, leading to some imperfection of our model\n",
    "+ (2)\tThe assumption regards risk-free rate and volatility may slightly deviate from those of the real market.\n",
    "+ (3)\tInvestors are over-confident on the value of convertible bonds in general.\n",
    "\n",
    "The calculation process is shown below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Testing the relathionship on the first day of listing\n",
    "all_info = rq.convertible.all_instruments()\n",
    "bond_list = all_df['bond_name'].drop_duplicates()\n",
    "first_open_list =[]\n",
    "for bond in bond_list:\n",
    "    bond_info = all_info[all_info['symbol'] == bond]\n",
    "    value_date = str(bond_info['value_date'].iloc[0])[:10]\n",
    "    order_book_id = bond_info['order_book_id'].iloc[0]\n",
    "    # get the information on the first day of listing\n",
    "    first_day = rq.get_price(order_book_id, start_date=value_date, end_date='2019-04-01', frequency='1d', fields=None, \n",
    "                             adjust_type='none', skip_suspended =False, market='cn')\n",
    "    first_date = str(first_day.index[0])[:10]\n",
    "    first_open = first_day['open'].iloc[0]\n",
    "    # get the calculation price on the first day of listing\n",
    "    try:\n",
    "        first_pricing = all_df[(all_df['bond_name'] == bond) & (all_df['calculation_date'] == first_date)]['calculation_price'].iloc[0]\n",
    "        first_open_list.append([bond,first_date,first_open,first_pricing])\n",
    "    except:\n",
    "        maturity_date = str(all_info[all_info['symbol'] == bond]['maturity_date'].iloc[0])[:10]\n",
    "        bond_price = get_bond_price(bond, first_date)\n",
    "        option_position1,option_position2, option_position3, option_position4, delta_position, gamma_position, vega_position,is_pre_exercise = get_convertible_call_price(bond, first_date)\n",
    "        convertible_price = bond_price + option_position1 - option_position2 - option_position3 +  option_position4\n",
    "        first_open_list.append([bond,first_date,first_open,convertible_price])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bond_name</th>\n",
       "      <th>listed_date</th>\n",
       "      <th>listed_open</th>\n",
       "      <th>calculation_price</th>\n",
       "      <th>cal_to_real</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14宝钢EB</td>\n",
       "      <td>2014-12-24</td>\n",
       "      <td>117.000</td>\n",
       "      <td>97.975069</td>\n",
       "      <td>0.837394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>歌尔转债</td>\n",
       "      <td>2014-12-26</td>\n",
       "      <td>119.900</td>\n",
       "      <td>98.066259</td>\n",
       "      <td>0.817900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>格力转债</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>138.000</td>\n",
       "      <td>97.878908</td>\n",
       "      <td>0.709267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>电气转债</td>\n",
       "      <td>2015-02-16</td>\n",
       "      <td>131.010</td>\n",
       "      <td>93.035971</td>\n",
       "      <td>0.710144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>航信转债</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>120.000</td>\n",
       "      <td>97.483910</td>\n",
       "      <td>0.812366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15天集EB</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>133.000</td>\n",
       "      <td>98.201299</td>\n",
       "      <td>0.738356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15清控EB</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>150.000</td>\n",
       "      <td>92.827621</td>\n",
       "      <td>0.618851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15国盛EB</td>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>117.000</td>\n",
       "      <td>92.137502</td>\n",
       "      <td>0.787500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15国资EB</td>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>112.500</td>\n",
       "      <td>104.201086</td>\n",
       "      <td>0.926232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>蓝标转债</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>115.000</td>\n",
       "      <td>100.120767</td>\n",
       "      <td>0.870615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>三一转债</td>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>107.110</td>\n",
       "      <td>93.963832</td>\n",
       "      <td>0.877265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>国贸转债</td>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>110.300</td>\n",
       "      <td>95.248476</td>\n",
       "      <td>0.863540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>九州转债</td>\n",
       "      <td>2016-01-29</td>\n",
       "      <td>118.000</td>\n",
       "      <td>92.374960</td>\n",
       "      <td>0.782839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>广汽转债</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>118.000</td>\n",
       "      <td>96.895987</td>\n",
       "      <td>0.821152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>顺昌转债</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>130.000</td>\n",
       "      <td>94.832591</td>\n",
       "      <td>0.729481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>白云转债</td>\n",
       "      <td>2016-03-15</td>\n",
       "      <td>120.250</td>\n",
       "      <td>102.602346</td>\n",
       "      <td>0.853242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>汽模转债</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>130.000</td>\n",
       "      <td>100.505199</td>\n",
       "      <td>0.773117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>江南转债</td>\n",
       "      <td>2016-04-05</td>\n",
       "      <td>125.090</td>\n",
       "      <td>99.632836</td>\n",
       "      <td>0.796489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>辉丰转债</td>\n",
       "      <td>2016-05-17</td>\n",
       "      <td>118.390</td>\n",
       "      <td>95.060143</td>\n",
       "      <td>0.802941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16以岭EB</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>109.000</td>\n",
       "      <td>99.692917</td>\n",
       "      <td>0.914614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>海印转债</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>117.000</td>\n",
       "      <td>96.332073</td>\n",
       "      <td>0.823351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16皖新EB</td>\n",
       "      <td>2016-07-29</td>\n",
       "      <td>104.890</td>\n",
       "      <td>104.498548</td>\n",
       "      <td>0.996268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>洪涛转债</td>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>120.880</td>\n",
       "      <td>94.821537</td>\n",
       "      <td>0.784427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16凤凰EB</td>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>110.000</td>\n",
       "      <td>102.240532</td>\n",
       "      <td>0.929459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>光大转债</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>102.000</td>\n",
       "      <td>100.812405</td>\n",
       "      <td>0.988357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>骆驼转债</td>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>111.500</td>\n",
       "      <td>91.493128</td>\n",
       "      <td>0.820566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>17山高EB</td>\n",
       "      <td>2017-05-11</td>\n",
       "      <td>95.000</td>\n",
       "      <td>101.275168</td>\n",
       "      <td>1.066054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>永东转债</td>\n",
       "      <td>2017-05-12</td>\n",
       "      <td>100.513</td>\n",
       "      <td>97.361441</td>\n",
       "      <td>0.968645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>模塑转债</td>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>110.220</td>\n",
       "      <td>101.200639</td>\n",
       "      <td>0.918169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>久其转债</td>\n",
       "      <td>2017-06-27</td>\n",
       "      <td>117.200</td>\n",
       "      <td>93.476071</td>\n",
       "      <td>0.797577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>圆通转债</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>101.010</td>\n",
       "      <td>91.046998</td>\n",
       "      <td>0.901366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>华源转债</td>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>96.440</td>\n",
       "      <td>90.692085</td>\n",
       "      <td>0.940399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>寒锐转债</td>\n",
       "      <td>2018-12-24</td>\n",
       "      <td>97.050</td>\n",
       "      <td>86.670137</td>\n",
       "      <td>0.893046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>特发转债</td>\n",
       "      <td>2018-12-25</td>\n",
       "      <td>102.800</td>\n",
       "      <td>88.714521</td>\n",
       "      <td>0.862982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>伟明转债</td>\n",
       "      <td>2018-12-26</td>\n",
       "      <td>100.000</td>\n",
       "      <td>89.015513</td>\n",
       "      <td>0.890155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>钧达转债</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>97.230</td>\n",
       "      <td>89.597630</td>\n",
       "      <td>0.921502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>福能转债</td>\n",
       "      <td>2018-12-28</td>\n",
       "      <td>104.000</td>\n",
       "      <td>90.305417</td>\n",
       "      <td>0.868321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>奇精转债</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>96.190</td>\n",
       "      <td>89.152584</td>\n",
       "      <td>0.926838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>光华转债</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>96.501</td>\n",
       "      <td>92.947853</td>\n",
       "      <td>0.963180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>18中原EB</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>105.200</td>\n",
       "      <td>99.720449</td>\n",
       "      <td>0.947913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>台华转债</td>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>99.900</td>\n",
       "      <td>93.745418</td>\n",
       "      <td>0.938393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>海尔转债</td>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>110.700</td>\n",
       "      <td>88.670096</td>\n",
       "      <td>0.800995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>佳都转债</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>103.500</td>\n",
       "      <td>89.141900</td>\n",
       "      <td>0.861274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>凯龙转债</td>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>106.000</td>\n",
       "      <td>87.188971</td>\n",
       "      <td>0.822537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>溢利转债</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>100.000</td>\n",
       "      <td>92.573703</td>\n",
       "      <td>0.925737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>联泰转债</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>103.700</td>\n",
       "      <td>92.150475</td>\n",
       "      <td>0.888626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>平银转债</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>114.000</td>\n",
       "      <td>95.363888</td>\n",
       "      <td>0.836525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>维格转债</td>\n",
       "      <td>2019-02-19</td>\n",
       "      <td>106.300</td>\n",
       "      <td>91.738707</td>\n",
       "      <td>0.863017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>冰轮转债</td>\n",
       "      <td>2019-02-21</td>\n",
       "      <td>112.100</td>\n",
       "      <td>89.851478</td>\n",
       "      <td>0.801530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>尚荣转债</td>\n",
       "      <td>2019-03-07</td>\n",
       "      <td>122.220</td>\n",
       "      <td>91.108546</td>\n",
       "      <td>0.745447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>中宠转债</td>\n",
       "      <td>2019-03-14</td>\n",
       "      <td>109.750</td>\n",
       "      <td>96.140664</td>\n",
       "      <td>0.875997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>中信转债</td>\n",
       "      <td>2019-03-19</td>\n",
       "      <td>107.650</td>\n",
       "      <td>107.256134</td>\n",
       "      <td>0.996341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>长城转债</td>\n",
       "      <td>2019-03-20</td>\n",
       "      <td>113.500</td>\n",
       "      <td>96.060042</td>\n",
       "      <td>0.846344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>中天转债</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>111.810</td>\n",
       "      <td>96.782542</td>\n",
       "      <td>0.865598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>中来转债</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>114.750</td>\n",
       "      <td>100.023184</td>\n",
       "      <td>0.871662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>今飞转债</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>109.000</td>\n",
       "      <td>99.128191</td>\n",
       "      <td>0.909433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>万信转2</td>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>115.000</td>\n",
       "      <td>94.223298</td>\n",
       "      <td>0.819333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>浙商转债</td>\n",
       "      <td>2019-03-28</td>\n",
       "      <td>106.000</td>\n",
       "      <td>93.410711</td>\n",
       "      <td>0.881233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>贵广转债</td>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>119.990</td>\n",
       "      <td>94.187510</td>\n",
       "      <td>0.784961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>富祥转债</td>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>120.000</td>\n",
       "      <td>96.808232</td>\n",
       "      <td>0.806735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bond_name listed_date  listed_open  calculation_price  cal_to_real\n",
       "0      14宝钢EB  2014-12-24      117.000          97.975069     0.837394\n",
       "1        歌尔转债  2014-12-26      119.900          98.066259     0.817900\n",
       "2        格力转债  2015-01-13      138.000          97.878908     0.709267\n",
       "3        电气转债  2015-02-16      131.010          93.035971     0.710144\n",
       "4        航信转债  2015-06-30      120.000          97.483910     0.812366\n",
       "5      15天集EB  2015-07-02      133.000          98.201299     0.738356\n",
       "6      15清控EB  2015-11-20      150.000          92.827621     0.618851\n",
       "7      15国盛EB  2015-11-25      117.000          92.137502     0.787500\n",
       "8      15国资EB  2015-12-23      112.500         104.201086     0.926232\n",
       "9        蓝标转债  2016-01-18      115.000         100.120767     0.870615\n",
       "10       三一转债  2016-01-18      107.110          93.963832     0.877265\n",
       "11       国贸转债  2016-01-19      110.300          95.248476     0.863540\n",
       "12       九州转债  2016-01-29      118.000          92.374960     0.782839\n",
       "13       广汽转债  2016-02-04      118.000          96.895987     0.821152\n",
       "14       顺昌转债  2016-02-23      130.000          94.832591     0.729481\n",
       "15       白云转债  2016-03-15      120.250         102.602346     0.853242\n",
       "16       汽模转债  2016-03-24      130.000         100.505199     0.773117\n",
       "17       江南转债  2016-04-05      125.090          99.632836     0.796489\n",
       "18       辉丰转债  2016-05-17      118.390          95.060143     0.802941\n",
       "19     16以岭EB  2016-06-16      109.000          99.692917     0.914614\n",
       "20       海印转债  2016-07-01      117.000          96.332073     0.823351\n",
       "21     16皖新EB  2016-07-29      104.890         104.498548     0.996268\n",
       "22       洪涛转债  2016-08-23      120.880          94.821537     0.784427\n",
       "23     16凤凰EB  2016-11-30      110.000         102.240532     0.929459\n",
       "24       光大转债  2017-04-05      102.000         100.812405     0.988357\n",
       "25       骆驼转债  2017-04-13      111.500          91.493128     0.820566\n",
       "26     17山高EB  2017-05-11       95.000         101.275168     1.066054\n",
       "27       永东转债  2017-05-12      100.513          97.361441     0.968645\n",
       "28       模塑转债  2017-06-26      110.220         101.200639     0.918169\n",
       "29       久其转债  2017-06-27      117.200          93.476071     0.797577\n",
       "..        ...         ...          ...                ...          ...\n",
       "123      圆通转债  2018-12-18      101.010          91.046998     0.901366\n",
       "124      华源转债  2018-12-20       96.440          90.692085     0.940399\n",
       "125      寒锐转债  2018-12-24       97.050          86.670137     0.893046\n",
       "126      特发转债  2018-12-25      102.800          88.714521     0.862982\n",
       "127      伟明转债  2018-12-26      100.000          89.015513     0.890155\n",
       "128      钧达转债  2018-12-27       97.230          89.597630     0.921502\n",
       "129      福能转债  2018-12-28      104.000          90.305417     0.868321\n",
       "130      奇精转债  2019-01-07       96.190          89.152584     0.926838\n",
       "131      光华转债  2019-01-09       96.501          92.947853     0.963180\n",
       "132    18中原EB  2019-01-11      105.200          99.720449     0.947913\n",
       "133      台华转债  2019-01-11       99.900          93.745418     0.938393\n",
       "134      海尔转债  2019-01-18      110.700          88.670096     0.800995\n",
       "135      佳都转债  2019-01-21      103.500          89.141900     0.861274\n",
       "136      凯龙转债  2019-01-21      106.000          87.188971     0.822537\n",
       "137      溢利转债  2019-01-23      100.000          92.573703     0.925737\n",
       "138      联泰转债  2019-02-18      103.700          92.150475     0.888626\n",
       "139      平银转债  2019-02-18      114.000          95.363888     0.836525\n",
       "140      维格转债  2019-02-19      106.300          91.738707     0.863017\n",
       "141      冰轮转债  2019-02-21      112.100          89.851478     0.801530\n",
       "142      尚荣转债  2019-03-07      122.220          91.108546     0.745447\n",
       "143      中宠转债  2019-03-14      109.750          96.140664     0.875997\n",
       "144      中信转债  2019-03-19      107.650         107.256134     0.996341\n",
       "145      长城转债  2019-03-20      113.500          96.060042     0.846344\n",
       "146      中天转债  2019-03-22      111.810          96.782542     0.865598\n",
       "147      中来转债  2019-03-22      114.750         100.023184     0.871662\n",
       "148      今飞转债  2019-03-22      109.000          99.128191     0.909433\n",
       "149      万信转2  2019-03-25      115.000          94.223298     0.819333\n",
       "150      浙商转债  2019-03-28      106.000          93.410711     0.881233\n",
       "151      贵广转债  2019-03-29      119.990          94.187510     0.784961\n",
       "152      富祥转债  2019-03-29      120.000          96.808232     0.806735\n",
       "\n",
       "[153 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first_open = pd.DataFrame(first_open_list,columns = ['bond_name','listed_date','listed_open','calculation_price'])\n",
    "df_first_open = df_first_open.sort_values(by= ['listed_date'])\n",
    "df_first_open['cal_to_real'] = df_first_open['calculation_price']/df_first_open['listed_open']\n",
    "df_first_open = df_first_open.reset_index(drop = True)\n",
    "df_first_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '上市首日计算价格/真实开盘价')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([pd.Timestamp(x) for x in df_first_open['listed_date']],df_first_open['cal_to_real'])\n",
    "plt.plot([pd.Timestamp(x) for x in df_first_open['listed_date']],df_first_open['cal_to_real'])\n",
    "plt.title('上市首日计算价格/真实开盘价')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-01T15:04:05.819960Z",
     "start_time": "2019-09-01T15:04:05.816180Z"
    },
    "code_folding": []
   },
   "source": [
    "The picture above shows the time series of cal_to_real. For each point, the x-coordinate represents its first listing date, while the y-coordinate is its cal_to_real on that date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_first_open['cal_to_real'], bins=40, density=0, facecolor=\"blue\", edgecolor=\"black\", alpha=0.7)\n",
    "# show the label of horizontal axis\n",
    "plt.xlabel(\"区间\")\n",
    "# show the label of longitudinal axis\n",
    "plt.ylabel(\"频数/频率\")\n",
    "# show the title\n",
    "plt.title(\"上市首日计算价格/真实开盘价分布直方图\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart above is the frequency histogram of the cal_to_real on the first day of listing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    153.000000\n",
       "mean       0.891088\n",
       "std        0.091278\n",
       "min        0.618851\n",
       "25%        0.822537\n",
       "50%        0.900403\n",
       "75%        0.952971\n",
       "max        1.076130\n",
       "Name: cal_to_real, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical description\n",
    "df_first_open['cal_to_real'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# stationary test using ADF test under 95% confidence level\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "def check_for_stationarity(X, cutoff=0.05):\n",
    "    # We must observe significant p-value to convince ourselves that the series is stationary\n",
    "    pvalue = adfuller(X)[1]\n",
    "    if pvalue < cutoff:\n",
    "        #print('p-value = ' + str(pvalue) + '. This time_throry'  +' is likely stationary.')\n",
    "        return True\n",
    "    else:\n",
    "        #print('p-value = ' + str(pvalue) + '. This time_throry' + ' is likely non-stationary.')\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_for_stationarity(df_first_open['cal_to_real'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The changes of average cal_to_real during the duration of convertible bonds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The overall trend of cal_to_real ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we select all convertible bonds that went into public between 2017 and April 2019. Then we calculate the average cal_to_real ratio based on the number of trading days after the first listed date, thus inducing the overall trend of all cal_to_real ratio.\n",
    "\n",
    "In the chart below, [day,number,mean,std,score] separately refer to the number of trading days after listed, the number of convertible bonds included, the mean of all cal_to_real, the stand deviation of cal_to_real and score = mean + 1.645std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "code_folding": [],
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>number</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>129</td>\n",
       "      <td>0.905191</td>\n",
       "      <td>0.087977</td>\n",
       "      <td>1.049914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>0.909063</td>\n",
       "      <td>0.088356</td>\n",
       "      <td>1.054408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>127</td>\n",
       "      <td>0.911574</td>\n",
       "      <td>0.088504</td>\n",
       "      <td>1.057163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>0.913938</td>\n",
       "      <td>0.087456</td>\n",
       "      <td>1.057803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>126</td>\n",
       "      <td>0.913109</td>\n",
       "      <td>0.088250</td>\n",
       "      <td>1.058279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>126</td>\n",
       "      <td>0.911398</td>\n",
       "      <td>0.090849</td>\n",
       "      <td>1.060845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>125</td>\n",
       "      <td>0.912967</td>\n",
       "      <td>0.090546</td>\n",
       "      <td>1.061916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>122</td>\n",
       "      <td>0.911624</td>\n",
       "      <td>0.094431</td>\n",
       "      <td>1.066962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>122</td>\n",
       "      <td>0.914103</td>\n",
       "      <td>0.094141</td>\n",
       "      <td>1.068964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>121</td>\n",
       "      <td>0.913569</td>\n",
       "      <td>0.094427</td>\n",
       "      <td>1.068900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>120</td>\n",
       "      <td>0.913247</td>\n",
       "      <td>0.096537</td>\n",
       "      <td>1.072050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>120</td>\n",
       "      <td>0.915004</td>\n",
       "      <td>0.096221</td>\n",
       "      <td>1.073287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "      <td>0.916380</td>\n",
       "      <td>0.097047</td>\n",
       "      <td>1.076023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>119</td>\n",
       "      <td>0.919026</td>\n",
       "      <td>0.096746</td>\n",
       "      <td>1.078174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>119</td>\n",
       "      <td>0.919665</td>\n",
       "      <td>0.098949</td>\n",
       "      <td>1.082436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>119</td>\n",
       "      <td>0.917990</td>\n",
       "      <td>0.100394</td>\n",
       "      <td>1.083138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>119</td>\n",
       "      <td>0.916387</td>\n",
       "      <td>0.104130</td>\n",
       "      <td>1.087681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>119</td>\n",
       "      <td>0.916406</td>\n",
       "      <td>0.105024</td>\n",
       "      <td>1.089169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>118</td>\n",
       "      <td>0.916813</td>\n",
       "      <td>0.103979</td>\n",
       "      <td>1.087858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>118</td>\n",
       "      <td>0.913489</td>\n",
       "      <td>0.107435</td>\n",
       "      <td>1.090220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>118</td>\n",
       "      <td>0.913198</td>\n",
       "      <td>0.111252</td>\n",
       "      <td>1.096207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>118</td>\n",
       "      <td>0.912334</td>\n",
       "      <td>0.113117</td>\n",
       "      <td>1.098412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>118</td>\n",
       "      <td>0.914646</td>\n",
       "      <td>0.113200</td>\n",
       "      <td>1.100860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>118</td>\n",
       "      <td>0.914501</td>\n",
       "      <td>0.114657</td>\n",
       "      <td>1.103111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>118</td>\n",
       "      <td>0.914140</td>\n",
       "      <td>0.118939</td>\n",
       "      <td>1.109794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>118</td>\n",
       "      <td>0.912932</td>\n",
       "      <td>0.123194</td>\n",
       "      <td>1.115587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>118</td>\n",
       "      <td>0.912511</td>\n",
       "      <td>0.125741</td>\n",
       "      <td>1.119355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>118</td>\n",
       "      <td>0.914345</td>\n",
       "      <td>0.128035</td>\n",
       "      <td>1.124962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>117</td>\n",
       "      <td>0.915094</td>\n",
       "      <td>0.125504</td>\n",
       "      <td>1.121547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>117</td>\n",
       "      <td>0.916197</td>\n",
       "      <td>0.124856</td>\n",
       "      <td>1.121586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>516</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>517</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>518</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>519</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>520</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>521</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>522</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>523</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>524</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>526</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>527</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>528</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>529</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>530</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>531</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>532</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>533</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>534</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>535</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>536</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>537</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>539</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>540</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>541</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>542</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>543</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>544</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>546 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     day  number      mean       std     score\n",
       "0      0     129  0.905191  0.087977  1.049914\n",
       "1      1     129  0.909063  0.088356  1.054408\n",
       "2      2     127  0.911574  0.088504  1.057163\n",
       "3      3     126  0.913938  0.087456  1.057803\n",
       "4      4     126  0.913109  0.088250  1.058279\n",
       "5      5     126  0.911398  0.090849  1.060845\n",
       "6      6     125  0.912967  0.090546  1.061916\n",
       "7      7     122  0.911624  0.094431  1.066962\n",
       "8      8     122  0.914103  0.094141  1.068964\n",
       "9      9     121  0.913569  0.094427  1.068900\n",
       "10    10     120  0.913247  0.096537  1.072050\n",
       "11    11     120  0.915004  0.096221  1.073287\n",
       "12    12     120  0.916380  0.097047  1.076023\n",
       "13    13     119  0.919026  0.096746  1.078174\n",
       "14    14     119  0.919665  0.098949  1.082436\n",
       "15    15     119  0.917990  0.100394  1.083138\n",
       "16    16     119  0.916387  0.104130  1.087681\n",
       "17    17     119  0.916406  0.105024  1.089169\n",
       "18    18     118  0.916813  0.103979  1.087858\n",
       "19    19     118  0.913489  0.107435  1.090220\n",
       "20    20     118  0.913198  0.111252  1.096207\n",
       "21    21     118  0.912334  0.113117  1.098412\n",
       "22    22     118  0.914646  0.113200  1.100860\n",
       "23    23     118  0.914501  0.114657  1.103111\n",
       "24    24     118  0.914140  0.118939  1.109794\n",
       "25    25     118  0.912932  0.123194  1.115587\n",
       "26    26     118  0.912511  0.125741  1.119355\n",
       "27    27     118  0.914345  0.128035  1.124962\n",
       "28    28     117  0.915094  0.125504  1.121547\n",
       "29    29     117  0.916197  0.124856  1.121586\n",
       "..   ...     ...       ...       ...       ...\n",
       "516  516       0       NaN       NaN       NaN\n",
       "517  517       0       NaN       NaN       NaN\n",
       "518  518       0       NaN       NaN       NaN\n",
       "519  519       0       NaN       NaN       NaN\n",
       "520  520       0       NaN       NaN       NaN\n",
       "521  521       0       NaN       NaN       NaN\n",
       "522  522       0       NaN       NaN       NaN\n",
       "523  523       0       NaN       NaN       NaN\n",
       "524  524       0       NaN       NaN       NaN\n",
       "525  525       0       NaN       NaN       NaN\n",
       "526  526       0       NaN       NaN       NaN\n",
       "527  527       0       NaN       NaN       NaN\n",
       "528  528       0       NaN       NaN       NaN\n",
       "529  529       0       NaN       NaN       NaN\n",
       "530  530       0       NaN       NaN       NaN\n",
       "531  531       0       NaN       NaN       NaN\n",
       "532  532       0       NaN       NaN       NaN\n",
       "533  533       0       NaN       NaN       NaN\n",
       "534  534       0       NaN       NaN       NaN\n",
       "535  535       0       NaN       NaN       NaN\n",
       "536  536       0       NaN       NaN       NaN\n",
       "537  537       0       NaN       NaN       NaN\n",
       "538  538       0       NaN       NaN       NaN\n",
       "539  539       0       NaN       NaN       NaN\n",
       "540  540       0       NaN       NaN       NaN\n",
       "541  541       0       NaN       NaN       NaN\n",
       "542  542       0       NaN       NaN       NaN\n",
       "543  543       0       NaN       NaN       NaN\n",
       "544  544       0       NaN       NaN       NaN\n",
       "545  545       0       NaN       NaN       NaN\n",
       "\n",
       "[546 rows x 5 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the average trend of all cal_to_real ratio during the duration of bonds\n",
    "bond_list_2 = df_first_open['bond_name'][24:]\n",
    "#ratio_sum = pd.Series( [0 for x in range(len(zw.get_tradingDates('2017-01-01','2019-04-01')))])\n",
    "ratio_sum = pd.DataFrame(index =  range(len(zw.get_tradingDates('2017-01-01','2019-04-01'))))\n",
    "for bond in bond_list_2:\n",
    "    ratio_ts = all_df[all_df['bond_name'] == bond].reset_index(drop = True)\n",
    "    ratio_ts = ratio_ts['cal_to_real']\n",
    "    ratio_sum[bond] = pd.Series(ratio_ts, index = ratio_sum.index)\n",
    "res = []\n",
    "for i in range(len(ratio_sum)):\n",
    "    select = ratio_sum.iloc[i].dropna()\n",
    "    number = len(select)\n",
    "    mean = select.mean()\n",
    "    std = select.std()\n",
    "    res.append([i,number,mean,std])\n",
    "df_res = pd.DataFrame(res, columns = ['day','number','mean','std'])\n",
    "df_res['score'] = df_res['mean'] + 1.645*df_res['std']\n",
    "df_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_res['number'])\n",
    "plt.title('每个交易存续期的转债数量')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the chart above:\n",
    "\n",
    "horizontal axis -- number of days after first listed date\n",
    "\n",
    "longitudinal axis -- number of convertible bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_res['mean'])\n",
    "plt.title('每个交易cal_to_real的均值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the chart above:\n",
    "    \n",
    "horizontal axis -- number of days after first listed date\n",
    "\n",
    "longitudinal axis -- the mean of cal_to_real\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart above, we could find that the mean of cal_to_real increased steadily in the first 300 days and came back to near 1, indicating that the market price reverts back to the modeled price. The deviation between the binomial-tree price and the market price restored in some degree.\n",
    "\n",
    "But after 300 days, this trend may seem to revert once again. This is because after 292 days, the number of convertible bonds included becomes lower than 30, introducing bias both from the individual bonds and the market influence.\n",
    "\n",
    "For such a result, we further focus on the trend of cal_to_real between day 0 to day 292.\n",
    "\n",
    "We run regression on the time series of the remaining cal_to_real ratio to calculate its trend line. The result is: Y = 0.00040023*days + 0.9218. Both the regression coefficient and intercept passed the significance test of 1% level and the r-squre = 0.76. From this analysis, we could tell that the cal_to_real ratio has a very strong linear trend between day 0 to day 292, suggesting that the market price generally revert back to the modeled price.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run regression to create the trend line\n",
    "from sklearn.linear_model import LinearRegression\n",
    "Y = df_res[df_res['number'] > 30]['mean']\n",
    "X = Y.index.values.reshape(-1, 1)\n",
    "reg = LinearRegression().fit(X,Y)\n",
    "\n",
    "# get r-squre of the regression\n",
    "reg.score(X, Y)\n",
    "\n",
    "reg.coef_\n",
    "\n",
    "reg.intercept_ \n",
    "\n",
    "trend = reg.predict(X)\n",
    "\n",
    "plt.figure(figsize=[12,6])\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, reg.predict(X), color='red', linewidth=1)\n",
    "plt.title('每个交易cal_to_real的均值及其趋势线（number > 30）')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the chart above:\n",
    "\n",
    "horizontal axis – number of days after first listed date\n",
    "\n",
    "longitudinal axis -- mean of cal_to_real ratio\n",
    "\n",
    "red line -- trend line\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the overall changes by eliminating the linear trend "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the ideal situation, by eliminating the linear trend, the remaining part of the cal_to_real ratio would be stationary. With such condition, we could tell that the cal_to_real ratio is mean-reverting around the trend line, meaning that the modeled price could accurately measure the market price in a way.\n",
    "\n",
    "However, the remaining part fails to pass the ADF test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Eliminating the trend line and test for stationary\n",
    "de_trend = Y - trend\n",
    "plt.figure(figsize=[12,6])\n",
    "plt.plot(de_trend)\n",
    "plt.title('每个交易cal_to_real的均值减去趋势线（number > 30）')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_for_stationarity(de_trend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, if we subtract the linear trend from cal_to_real ratio of individual bonds, only 5 of them passed the stationary test, suggesting that our model may not well reflect the market price. \n",
    "\n",
    "The followings are the name of bonds that passed the stationary test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "code_folding": [
     2
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "小康转债\n",
      "济川转债\n",
      "17宝武EB\n",
      "安井转债\n",
      "中信转债\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/statsmodels/regression/linear_model.py:1510: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  return np.dot(wresid, wresid) / self.df_resid\n",
      "/opt/anaconda3/lib/python3.7/site-packages/statsmodels/regression/linear_model.py:846: RuntimeWarning: divide by zero encountered in log\n",
      "  llf = -nobs2*np.log(2*np.pi) - nobs2*np.log(ssr / nobs) - nobs2\n"
     ]
    }
   ],
   "source": [
    "ratio_sum_30 = ratio_sum.iloc[:len(trend)]\n",
    "count = 0\n",
    "for bond in ratio_sum_30.columns:\n",
    "    ts_minues = (ratio_sum_30[bond] -trend ).dropna()\n",
    "    try:\n",
    "        if check_for_stationarity(ts_minues)  == True:\n",
    "            count = count +1\n",
    "            print(bond)\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build trading strategy based on cal_to_real ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to test whether the modeled price could accurately reflect market price is to build a trading strategy based on it. If the binomial-tree price is a good indicator of market price, when the deviation between the two is high, i.e., the cal_to_real ratio is high, we would expect the market price will repair itself to the modeled price. Thus, when cal_to_real ratio is very high, the market price is undervalued and we could long the convertible bonds to profit. \n",
    "\n",
    "Here we use score to evaluate the degree of deviation between the two prices. \n",
    "\n",
    "score = mean of all bonds + 1.645std of all bonds(mean + 1.645std is the upper boundary of 90% confidence interval under normal distribution).\n",
    "\n",
    "For each date, if the cal_to_real ratio of bond A is higher than the score of that day, we would long the bonds A. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[12.,6])\n",
    "for bond in ratio_sum_30.columns:\n",
    "    plt.plot(ratio_sum_30.index,ratio_sum_30[bond],linewidth=1)\n",
    "plt.title('所有转债存续期内cal_to_real变动情况')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the chart above:\n",
    "    \n",
    "horizontal axis -- number of days after first listed date\n",
    "\n",
    "longitudinal axis -- cal_to_real ratio\n",
    "\n",
    "Each line represents a series of cal_to_real ratio for an individual bonds\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below selects the bonds that has cal_to_real ratio higher than score in some time.\n",
    "\n",
    "Only 20 of 150 bonds satisfy our criterion. The occurrence of these peak cal_to_real ratio is continuous for days, suggesting that when the extreme happens, the market price doesn’t revert immediately.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "code_folding": [],
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days</th>\n",
       "      <th>bond</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>宝信转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>湖广转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>横河转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>凯中转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>宝信转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>湖广转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>湖广转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>湖广转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>万顺转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9</td>\n",
       "      <td>17山高EB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>三力转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9</td>\n",
       "      <td>万顺转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>266</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>272</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>273</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>274</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>275</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>276</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>277</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>278</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>279</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>280</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>281</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>282</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>283</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>283</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>284</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>284</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>285</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>285</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>286</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>286</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>287</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>287</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>288</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>288</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>289</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>289</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>290</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>290</td>\n",
       "      <td>时达转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>291</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>292</td>\n",
       "      <td>模塑转债</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>340 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     days    bond\n",
       "0       0  17山高EB\n",
       "1       0    宝信转债\n",
       "2       0    三力转债\n",
       "3       0    湖广转债\n",
       "4       0    横河转债\n",
       "5       0    凯中转债\n",
       "6       1  17山高EB\n",
       "7       1    宝信转债\n",
       "8       1    三力转债\n",
       "9       1    湖广转债\n",
       "10      2  17山高EB\n",
       "11      2    三力转债\n",
       "12      2    湖广转债\n",
       "13      3  17山高EB\n",
       "14      3    三力转债\n",
       "15      3    湖广转债\n",
       "16      4  17山高EB\n",
       "17      4    三力转债\n",
       "18      5  17山高EB\n",
       "19      5    三力转债\n",
       "20      6  17山高EB\n",
       "21      6    三力转债\n",
       "22      7  17山高EB\n",
       "23      7    三力转债\n",
       "24      8  17山高EB\n",
       "25      8    三力转债\n",
       "26      8    万顺转债\n",
       "27      9  17山高EB\n",
       "28      9    三力转债\n",
       "29      9    万顺转债\n",
       "..    ...     ...\n",
       "310   266    模塑转债\n",
       "311   272    模塑转债\n",
       "312   273    模塑转债\n",
       "313   274    模塑转债\n",
       "314   275    模塑转债\n",
       "315   276    模塑转债\n",
       "316   277    模塑转债\n",
       "317   278    模塑转债\n",
       "318   279    模塑转债\n",
       "319   280    模塑转债\n",
       "320   281    模塑转债\n",
       "321   282    模塑转债\n",
       "322   283    模塑转债\n",
       "323   283    时达转债\n",
       "324   284    模塑转债\n",
       "325   284    时达转债\n",
       "326   285    模塑转债\n",
       "327   285    时达转债\n",
       "328   286    模塑转债\n",
       "329   286    时达转债\n",
       "330   287    模塑转债\n",
       "331   287    时达转债\n",
       "332   288    模塑转债\n",
       "333   288    时达转债\n",
       "334   289    模塑转债\n",
       "335   289    时达转债\n",
       "336   290    模塑转债\n",
       "337   290    时达转债\n",
       "338   291    模塑转债\n",
       "339   292    模塑转债\n",
       "\n",
       "[340 rows x 2 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_upper = []\n",
    "for i in range(len(ratio_sum_30)):\n",
    "    for bond in ratio_sum_30.columns:\n",
    "        if ratio_sum_30[bond].iloc[i] > df_res['score'].iloc[i]:\n",
    "               std_upper.append([i,bond])\n",
    "df_std = pd.DataFrame(std_upper,columns=['days', 'bond'])\n",
    "df_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of days for the 20 bonds that has the extreme cal_to_real ratio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17山高EB 16\n",
      "宝信转债 2\n",
      "三力转债 12\n",
      "湖广转债 4\n",
      "横河转债 5\n",
      "凯中转债 7\n",
      "万顺转债 2\n",
      "蓝盾转债 25\n",
      "东音转债 7\n",
      "凯发转债 3\n",
      "岩土转债 40\n",
      "利欧转债 52\n",
      "盛路转债 3\n",
      "威帝转债 3\n",
      "嘉澳转债 16\n",
      "众兴转债 23\n",
      "亚太转债 4\n",
      "金农转债 16\n",
      "杭电转债 16\n",
      "时达转债 18\n",
      "模塑转债 66\n"
     ]
    }
   ],
   "source": [
    "for bond in df_std['bond'].drop_duplicates():\n",
    "    a = df_std[df_std['bond'] == bond]\n",
    "    print(bond,len(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating the annualized return of the 20 bonds by holding long position when the extreme cal_to_real ratio emerges and closing the position when the extreme ratio disappears.\n",
    "\n",
    "The results are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "code_folding": [
     3,
     6
    ],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bond</th>\n",
       "      <th>max_days</th>\n",
       "      <th>annual_ret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>盛路转债</td>\n",
       "      <td>3</td>\n",
       "      <td>2.139616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>威帝转债</td>\n",
       "      <td>5</td>\n",
       "      <td>1.152994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>金农转债</td>\n",
       "      <td>18</td>\n",
       "      <td>0.528213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17山高EB</td>\n",
       "      <td>15</td>\n",
       "      <td>0.202912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>亚太转债</td>\n",
       "      <td>41</td>\n",
       "      <td>0.107187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>时达转债</td>\n",
       "      <td>148</td>\n",
       "      <td>0.098487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>众兴转债</td>\n",
       "      <td>133</td>\n",
       "      <td>0.091885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>利欧转债</td>\n",
       "      <td>156</td>\n",
       "      <td>0.071602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>嘉澳转债</td>\n",
       "      <td>89</td>\n",
       "      <td>0.070502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>三力转债</td>\n",
       "      <td>11</td>\n",
       "      <td>0.060560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>模塑转债</td>\n",
       "      <td>90</td>\n",
       "      <td>0.042587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>岩土转债</td>\n",
       "      <td>133</td>\n",
       "      <td>0.015225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>杭电转债</td>\n",
       "      <td>15</td>\n",
       "      <td>-0.022916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宝信转债</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.098816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>湖广转债</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.134452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>凯中转债</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.225312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>蓝盾转债</td>\n",
       "      <td>24</td>\n",
       "      <td>-0.244371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>横河转债</td>\n",
       "      <td>36</td>\n",
       "      <td>-0.285223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>东音转债</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.410540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>万顺转债</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.468764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>凯发转债</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.798214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bond  max_days  annual_ret\n",
       "12    盛路转债         3    2.139616\n",
       "13    威帝转债         5    1.152994\n",
       "17    金农转债        18    0.528213\n",
       "0   17山高EB        15    0.202912\n",
       "16    亚太转债        41    0.107187\n",
       "19    时达转债       148    0.098487\n",
       "15    众兴转债       133    0.091885\n",
       "11    利欧转债       156    0.071602\n",
       "14    嘉澳转债        89    0.070502\n",
       "2     三力转债        11    0.060560\n",
       "20    模塑转债        90    0.042587\n",
       "10    岩土转债       133    0.015225\n",
       "18    杭电转债        15   -0.022916\n",
       "1     宝信转债         1   -0.098816\n",
       "3     湖广转债         3   -0.134452\n",
       "5     凯中转债        28   -0.225312\n",
       "7     蓝盾转债        24   -0.244371\n",
       "4     横河转债        36   -0.285223\n",
       "8     东音转债         6   -0.410540\n",
       "6     万顺转债         1   -0.468764\n",
       "9     凯发转债         2   -0.798214"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep_first = df_std.drop_duplicates(subset = 'bond',keep = 'first')\n",
    "keep_last = df_std.drop_duplicates(subset = 'bond',keep = 'last')\n",
    "max_ret = []\n",
    "for bond in df_std['bond'].drop_duplicates():\n",
    "    first_max = keep_first[keep_first['bond'] == bond]['days'].iloc[0]\n",
    "    last_max = keep_last[keep_last['bond'] == bond]['days'].iloc[0]\n",
    "    ret_max = (all_df[all_df['bond_name'] == bond]['real_price'].iloc[last_max] / \n",
    "               all_df[all_df['bond_name'] == bond]['real_price'].iloc[first_max]) - 1\n",
    "    ret_max_annual = np.power(1+ret_max,252/(last_max - first_max)) - 1\n",
    "    max_ret.append([bond, last_max - first_max,  ret_max_annual])\n",
    "df_max_ret = pd.DataFrame(max_ret, columns= ['bond','max_days','annual_ret'])\n",
    "df_max_ret.sort_values(by = ['annual_ret'], ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "从出现极值到极值消失，所有转债的平均年化收益率为0.09015064335073644, 胜率为0.5714285714285714\n"
     ]
    }
   ],
   "source": [
    "max_mean = df_max_ret['annual_ret'].mean()\n",
    "win_percent = len(df_max_ret[df_max_ret['annual_ret']>0]) / len(df_max_ret)\n",
    "print('从出现极值到极值消失，所有转债的平均年化收益率为'+str(max_mean)+', 胜率为'+str(win_percent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this strategy, the annualized return of all bonds is 9% and the chance of profiting is 57%. As shown above, we could profit based on the extreme number of cal_to_real ratio, but profitability is not stable for different bonds and time, suggesting that our model to measure the market price is not highly accurate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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