3_inverted_rate_curve.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import requests\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "import QuantLib as ql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_date():\n",
    "    headers = {'User-Agent': 'Mozilla/5.0'}\n",
    "    req = requests.get('https://www.wsj.com/market-data/bonds', headers=headers)\n",
    "    html = req.text\n",
    "    soup = BeautifulSoup(html, 'html.parser')\n",
    "    data = soup.find(\"span\", class_ = \"WSJBase--card__timestamp--3F2HxyAE\")\n",
    "    \n",
    "    # JS path\n",
    "    # document.querySelector(\"#root > div > div > div > div:nth-child(2) > div.style--grid--SxS2So51 > div > div:nth-child(2) > h3 > span.WSJBase--card__timestamp--3F2HxyAE > span\")\n",
    "\n",
    "    date = data.text\n",
    "    date = datetime.datetime.strptime(date, \"%m/%d/%y\").date()\n",
    "    return date\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_quote(reference_date):\n",
    "    tenors = ['01M', '03M', '06M', '01Y', '02Y', '03Y','05Y','07Y','10Y','30Y']\n",
    "\n",
    "    # create empty lists\n",
    "    maturities = []\n",
    "    days = []\n",
    "    prices = []\n",
    "    coupons = []\n",
    "    headers = {'User-Agent': 'Mozilla/5.0'}\n",
    "\n",
    "    # get market informations\n",
    "    for i, tenor in enumerate(tenors):\n",
    "        url = \"https://www.wsj.com/market-data/quotes/bond/BX/TMUBMUSD\"+tenor+\"?mod=md_bond_overview_quote\"\n",
    "        req = requests.get(url, headers=headers)\n",
    "        html = req.text\n",
    "        soup = BeautifulSoup(html, 'html.parser')\n",
    "\n",
    "        # Price \n",
    "        data_src = soup.find(\"span\", id=\"quote_val\") \n",
    "        price = data_src.text\n",
    "        price = float(price[:-1])\n",
    "    \n",
    "        data_src2 = soup.find_all(\"span\", class_=\"data_data\")\n",
    "\n",
    "        # Coupon\n",
    "        coupon = data_src2[2].text\n",
    "        if coupon != '':\n",
    "            coupon = float(coupon[:-1])\n",
    "        else:\n",
    "            coupon = 0.0\n",
    "        \n",
    "        # Maturity Date\n",
    "        maturity = data_src2[3].text\n",
    "        maturity = datetime.datetime.strptime(maturity, '%m/%d/%y').date()\n",
    "\n",
    "        # Send to lists\n",
    "        days.append((maturity - reference_date).days)\n",
    "        prices.append(price)\n",
    "        coupons.append(coupon)\n",
    "        maturities.append(maturity)\n",
    "    \n",
    "    # create dataframe\n",
    "    df = pd.DataFrame([maturities, days, prices, coupons]).transpose()\n",
    "    headers = ['maturity', 'days', 'price', 'coupon']\n",
    "    df.columns = headers\n",
    "    df.set_index('maturity', inplace=True)\n",
    "\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def treasury_curve(date, quote):\n",
    "    \n",
    "    # Divide Quotes\n",
    "    tbill = quote[0:4]\n",
    "    tbond = quote[4:]\n",
    "    \n",
    "    # Set Evaluation Date\n",
    "    eval_date = ql.Date(date.day, date.month, date.year)\n",
    "    ql.Settings.instance().evaluationDate = eval_date\n",
    "    \n",
    "    # Set Market Conventions\n",
    "    calendar = ql.UnitedStates()\n",
    "    convention = ql.ModifiedFollowing\n",
    "    day_counter = ql.ActualActual()\n",
    "    end_of_month = False\n",
    "    fixing_days = 1\n",
    "    face_amount = 100\n",
    "    coupon_frequency = ql.Period(ql.Semiannual)\n",
    "    \n",
    "    # Construct Treasury Bill Helpers\n",
    "    bill_helpers = [ql.DepositRateHelper(ql.QuoteHandle(ql.SimpleQuote(r/100.0)),\n",
    "                                         ql.Period(m, ql.Days),\n",
    "                                         fixing_days,\n",
    "                                         calendar,\n",
    "                                         convention,\n",
    "                                         end_of_month,\n",
    "                                         day_counter)\n",
    "                    for r, m in zip(tbill['price'], tbill['days'])]\n",
    "    \n",
    "    # Construct Treasury Bond Helpers\n",
    "    bond_helpers = []\n",
    "    for p, c, m in zip(tbond['price'], tbond['coupon'], tbond['days']):\n",
    "        termination_date = eval_date + ql.Period(m, ql.Days)\n",
    "        schedule = ql.Schedule(eval_date,\n",
    "                               termination_date,\n",
    "                               coupon_frequency,\n",
    "                               calendar,\n",
    "                               convention,\n",
    "                               convention,\n",
    "                               ql.DateGeneration.Backward,\n",
    "                               end_of_month)\n",
    "        bond_helper = ql.FixedRateBondHelper(ql.QuoteHandle(ql.SimpleQuote(100)),\n",
    "                                             fixing_days,\n",
    "                                             face_amount,\n",
    "                                             schedule,\n",
    "                                             [c/100.0],\n",
    "                                             day_counter,\n",
    "                                             convention)\n",
    "        bond_helpers.append(bond_helper)\n",
    "    \n",
    "    # Bind Helpers\n",
    "    rate_helper = bill_helpers + bond_helpers\n",
    "    \n",
    "    # Build Curve\n",
    "    yc_linearzero = ql.PiecewiseLinearZero(eval_date, rate_helper, day_counter)\n",
    "    \n",
    "    return yc_linearzero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discount_factor(date, curve):\n",
    "    # returns discount factors of each day\n",
    "    # use quantlib date type\n",
    "    print(date)\n",
    "    date = ql.Date(date.day, date.month, date.year)\n",
    "    return curve.discount(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_rate(date, curve):\n",
    "    date = ql.Date(date.day, date.month, date.year)\n",
    "    day_counter = ql.ActualActual()\n",
    "    compounding = ql.Compounded\n",
    "    freq = ql.Continuous\n",
    "    zero_rate = curve.zeroRate(date, day_counter, compounding, freq).rate()\n",
    "    return zero_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             days  price coupon\n",
      "maturity                       \n",
      "2022-10-25     27   2.53    0.0\n",
      "2022-12-29     92  3.301    0.0\n",
      "2023-03-30    183  3.914    0.0\n",
      "2023-09-07    344  3.803    0.0\n",
      "2024-09-30    733  4.204   4.25\n",
      "2025-09-15   1083  4.216    3.5\n",
      "2027-09-30   1828  4.013  4.125\n",
      "2029-09-30   2559  3.911  3.875\n",
      "2032-08-15   3609  3.773   2.75\n",
      "2052-08-15  10914  3.707    3.0\n"
     ]
    }
   ],
   "source": [
    "ref_date = get_date()\n",
    "quote = get_quote(ref_date)\n",
    "print(quote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "curve = treasury_curve(ref_date, quote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-09-07\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9647560437201531"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discount_factor(quote.index[3], curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-10-25\n",
      "2022-12-29\n",
      "2023-03-30\n",
      "2023-09-07\n",
      "2024-09-30\n",
      "2025-09-15\n",
      "2027-09-30\n",
      "2029-09-30\n",
      "2032-08-15\n",
      "2052-08-15\n",
      "            discount factor  zero rate\n",
      "maturity                              \n",
      "2022-10-25         0.998133   0.025425\n",
      "2022-12-29         0.992677   0.029375\n",
      "2023-03-30         0.982763   0.034982\n",
      "2023-09-07         0.964756   0.038435\n",
      "2024-09-30         0.918859   0.042629\n",
      "2025-09-15         0.902767   0.034806\n",
      "2027-09-30         0.814004   0.041538\n",
      "2029-09-30         0.764772   0.038650\n",
      "2032-08-15         0.772329   0.026319\n",
      "2052-08-15         0.407590   0.030263\n"
     ]
    }
   ],
   "source": [
    "quote['discount factor'] = np.nan\n",
    "quote['zero rate'] = np.nan\n",
    "\n",
    "for date in quote.index:\n",
    "    quote.loc[date, 'discount factor'] = discount_factor(date, curve)\n",
    "    quote.loc[date, 'zero rate'] = zero_rate(date, curve)\n",
    "\n",
    "print(quote[['discount factor', 'zero rate']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'discount factor')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(quote['zero rate'], 'b-')\n",
    "plt.title('zero curve', loc='center')\n",
    "plt.xlabel('maturity')\n",
    "plt.ylabel('zero rate')\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(quote['discount factor'], 'r-')\n",
    "plt.title('discount curve', loc='center')\n",
    "plt.xlabel('maturity')\n",
    "plt.ylabel('discount factor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}