{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "recreational-deployment",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Brazilian Securities\n",
    "\n",
    "---\n",
    "\n",
    "### Wilson Freitas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medieval-corporation",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Brazilian Securities\n",
    "\n",
    "---\n",
    "\n",
    "### Wilson Freitas\n",
    "\n",
    "# Lecture 4\n",
    "\n",
    "- DI x IPCA Spread Futures - DAP\n",
    "    - DI x IPCA Spread Curve\n",
    "- Ibovespa Futures - IND\n",
    "\t- DI x Ibovespa Spread Curve\n",
    "- Commodities Futures\n",
    "\t- Cash Settled Live Cattle Futures - BGI\n",
    "\t- Cash Settled Corn Futures - CCM\n",
    "- Debêntures\n",
    "\t- Fixed Rate\n",
    "\t- Floating Rate: CDI+spread, %CDI\n",
    "\t- Indexed: IPCA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "alpha-compilation",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "surface-dressing",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from bcb import currency, sgs\n",
    "import bizdays\n",
    "\n",
    "MARKET_CALENDAR = bizdays.Calendar.load('ANBIMA.cal')\n",
    "ACTUAL_CALENDAR = bizdays.Calendar(name='actual')\n",
    "\n",
    "import myfuncs as my"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "injured-waste",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_parquet('data/contracts_202109_202111.parquet').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unable-staff",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## DI x IPCA Spread Futures\n",
    "\n",
    "\n",
    "- This is an interest rate future on the spread between the **compounded DI rate** and **IPCA variation**.\n",
    "    - It is the **real interest rate**, as DI rates are nominal rates.\n",
    "    - IPCA is an inflation index published by IBGE.\n",
    "- The future is called **DAP** Future.\n",
    "\n",
    "\n",
    "### Real Interest Rates\n",
    "\n",
    "\n",
    "$$\n",
    "\\left( 1 + c(t,T) \\right)^{DU(t,T)/252} = \\frac{\\Pi_{i=t}^{T}(1 + CDI_i)^{1/252}}{\\frac{IPCA_{T}}{IPCA_{t}}}\n",
    "$$\n",
    "\n",
    "where\n",
    "\n",
    "- $c(t,T)$ is an interest rate - spread between DI1 rates and inflation.\n",
    "- $DU(t,T)$ is the total number of business days between $t$ e $T$.\n",
    "- $IPCA_{t}$ the IPCA at $t$\n",
    "- $IPCA_{T}$ the IPCA expectation at $T$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-calvin",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "- The DAP Futures are priced as bullet bonds.\n",
    "- $c(t,T)$ is the real interest rate\n",
    "\n",
    "$$\n",
    "PU_{DAP}(t,T) = \\frac{100000}{(1 + c(t,T))^{DU(t,T)/252}}\n",
    "$$\n",
    "\n",
    "where\n",
    "\n",
    "- $t$: current instant of time (reference date)\n",
    "- $T$: maturity date (date when the contract expires)\n",
    "- $DU(t,T)$: the amount of business days between maturity and reference dates, according to specific calendar delivered by ANBIMA\n",
    "- $c(t,T)$: spot interest rate between reference date and contract maturity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "capable-converter",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "dap_curve = my.build_curve('DAP', df, refdate=pd.to_datetime('2021-11-01'),\n",
    "                           cal=MARKET_CALENDAR, notional=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "thirty-administration",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "curve = dap_curve()\n",
    "curve.plot(x='Maturity', y='Rate', figsize=(20,6), style='-o',\n",
    "           ylabel='Rate', xlabel='Date', title='DI x IPCA Spread Rates - 2021-11-01');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "packed-number",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "- Let's compare with the real interest rates obtained from NTN-B contracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "smooth-supervision",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ANBIMA's parameters for real interest rates\n",
    "par = (0.0557, -0.0766, 0.1308, -0.0326, 2.0284, 1.0558)\n",
    "ipca_gov_curve = my.nss_curve(par)\n",
    "ipca_gov_rates = ipca_gov_curve(curve['DU'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "armed-prayer",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ppd+klMamlMb279+/GcqWJEmSJEnSljRHePQMMDIihkdEJ+D9wPh6bcYDFxTunwM8mFJKwL3AfhHRpRAqvQN4BUnSJsZNmsORVz/I8Mv+yZFXP8i4SfVzekmSJEnaIB+9NH17Y7Z72lpKqSYiPkM+CCoGbkopvRwRVwETU0rjgRuB30fEFGAJ+YCJlNLSiPgR+QAqAfeklP65vTVJUnszbtIcLr/zRSqq89cUmLOsgsvvfBGAM8fUX2ZOkiRJUkdXXl7O4sWLN1k0e93V1srLy5vcV2xt2tQajB07Nk2cODHrMiSpxRx59YPMWVaxyfbBvToz4bJ3ZlCRJEmS1LGNmzSHa+59nbnLKhjUqzOXnrxnq/ofu9XV1cyePbvBq6qVl5czZMgQSktLN9peWId6bP32WS+YLUnagnnLKxoMjiA/Aum8Xz/B4N6dGdwr/zOoV+f1j8tLi1u42hb0wh3wwFWwfDb0HALHXwH7n5t1VZIkSeoA2sLMgNLSUoYPH94sfRkeSVIrtHxNNfe8NI9xk+bw9IwljbbrXFpMLiWemraEecsryNUbTNq3aycG9+7MoJ4bAqVBvTozpHf+tneX0q2+TGer8MIdcPfnoLoQqi2flX8MBkiSJKlZtfbRJWpeuVyiqjbH2uoca2tqqSzcrq0p3FbnWFuT46p/vLw+OFqnorqWa+59vV2+PwyPJKmVqKyu5cHX3mbcpDk89PpCqmpz7NavK58/fg+6dCrmR/e9sdEfqM6lxXzv7P3W/3Gqqc0xf0Ulc5dVMmfZGuYuq2T20vyopSkLV/HwGws3+QPXubQ4Hy71WjdyqbwQMnVhUK9yBvQop6S4Oa6tsJVytbB2BVQuh8rC7doVG+7/9zsbgqN1qivgn1+CFXOhpBxKyhq5rXu/08aPiztBWw3THIUlSVKza2h0yWV3vkDKJc46aEjG1bW8lgrSampzhbCmXoBTndskxKmsrhPs1DQW+uRYW2i3oX29Pgvbq2pz21X73EZmDLR1rnkkSRmqzSWemLqYcZPncO9L81m5tob+3cs4/YBBnDl6MPsO7rF+ZND2/rFOKbF0TTVzl1Uwe2kFc5flg6U5SyuYuzx/u3h11UbHFAUM6FG+0aildWHTkMLjrmUl9U8EVas3DnvWB0HLNx8KrdtXtWq7X9tt1uTgqU7gtNljNnNsSVmdn3IoLoOirQzr6o/CAijtDKf9zABJkqStVFWTY/qi1by+YCVvzF/JDY9Oo7Km8TChuCgojqCoiMJt1NmWvy0u2nh/SVFQVNheXPf+un422Va3n7rHb9x20z7Z6Ph1tdU9/4a+abCmDdvgyWmLuXnCWxuFK52Kg/MOGcp+g3tuFMBsHMzUC33qBjj1Ap5122vrD6ffSqXFQVlJMWUlRZSVFFFeWkynkiLKSjdsKyspprw0f1tWumHbuvZlJUWF7XWOKS2mvHD7iVsnctjqB/hKyR0MikXMTf34Qc25PNvjxDa9JqlrHklSK5FS4sU5y7lr8lzufn4ub69cS7eyEk7ZdwBnjh7M4SP6Uly06eiXM4sncGbZVVA+G8qGQPEVQNPDgYigT9dO9OnaiX0H92ywTWV1LXMWL+ftt99m8eKFLFu6iJXL5rFmxVKqpi6lpmI5i1hDFatZHBVMYw19iivoXVxJz1hD17Sa8txqilJtg/2vV1QC5T2hrEf+trwH9B0B5b3y9+tuX99u3f2e8Jtj8qNs6us5BD79DNRUQs3aJt4W7teubdoxFUsb319btWlNW6vJYVTh9tXxDY/CuufSfBhX3KmBn9L88evub25/UUnbG43lSCypafysbOBrsbEO8HrU5hIzl6zh9fkreWPBhp9pC1dTUwguiouC2lzi9KLHNgkIxueO4nPHj6Q2l6M2B7mUqM3lf9bd37Ctzv6UyNVpV1P/mBxU1+bqHV+3T9Zv26i/9cev28b6bc3p9KLH+EqnjV+L3z9xVINtNwpc6ocwJcV07VpCeQPBzbrAZqNjShsIduodu659p5KiBv8t3dx+ud9U9n32BjpH/t9/Q2IR3y+9gZdGDQPabnjUGEceSVILeWvxasZNmstdz89h2sLVdCou4tg9+3PmmMG8c6+dNr+4dVNHl+Ry+cCg7uieTUb6bGHUT82mV2Oor6a0O1Ul3ago6spKurAs14XFNWUsqCpnSW05K1MXVtCFlakLlcVdKevWh649+tCjV1969+nPzn17Mbh3Fwb36syAnuV0Kmkno21yuXyAtDWhVWMhVFOPXTZzxz+v4k75UVF1g6aSTvXCpzr7S+oFUcX1gqot7q93ruJOm99fN+Bqre+NLHWAL4FN5muxgZ+VDTrya5FS/oc6ty/+Bf75xY1fj5LO8J5rYZ+z1x24cR91t9V/vNk2NKHN9p0rpRwLVqxl2sJVTF+0imkLVzN90SpmLFq9fgRNkBjUszO79evKbv27MKxfV3br14Uhvbvw61//nE/U/JHyqF5/ispUym9LPshnL/lsvefSUB07YPtW9pGrEz6llKM2QcpBba6W2pRIaUObfPiUy29LidpcjpSDHIl7/v4HPlnyj01ei1/XnMr73/8ROpUEnYqLKC0uorQ4CGLj+japsYHnkHm7rehr3CWwZtGm+3vuAl94qYHztQ2NjTwyPJKkHWjhyrX884W5jJs8l8mzlgFw6PA+nDlmMO/edyA9u5RuvoN1frxvflHo+oo7Qd+RG8KftStp8I9cXSWdmza6Z92+uu3KekBZdyhqOOhKKbGioiY/HW5ZBXOWrmHu8krmFNZemrOsgoUr1250TATs1L1so2lxQ+pNketRvunr9Mz4X7PLc9ewU1rE29GPWQdeysGnf7Jpr2d70th7o8dg+MRDhVFVVVBbXbit+1PdwP61G7etqWr+/c0uNgRJ1avz/yKur6gUdh5VCJqK87dFxfmfzT4ugSiq97h4Q9uN+lvXbnNtmnrOho5vYn9RvGH6Y1v5UpzqfyFq6pfOrTjupb/BPV+BmnpfiE/5How6o84xhW9V9b9Qb2lfo+0LxzTaF1t5nvp90cTz1Nv2z0uhYvGmv4vOveHEqzb0V7fPdf1stD1Xb3tqZHtD7dNm+sltXPsWz1v43G/LeedOanjkaFEp9N9rw/toc7/jLbahefpZ/5beUpvNnaPO50TSDhBw5bKsi9hmhkeS1EJWra3hPy/PZ9zkuUyYsojaXGLvgT04c/QgTjtgEIN6dW5aRxXLYNpDMPUBeO7WxtvtdeoWgqB1+wr3Szo1x9PcZpXVtcxfXlknYMrfrluDad6yyk0WKuxeXlJY0DsfKC1dXcW/X55Pde2Gv2FlJUV85rjdOXbPnVr6KWVqzqO38I7XvrV+yDRARerEhFFXMPydH91ojYS6azKsW9OgoXUVdqiUIFdTCJbqBU219YKmRvfXDb423p+e/BUNPYMExMiTIdXmz5+rzf+sf1yTHzmWq9lMm4Ye19A6v4hFPkzKVTe+v1O3wv26XzZpZFudfc0V5qiDiHwAG+tui+psW7c9Gtlev31soZ/C7Rb7qdfXtIcaL3/Pd+ePgw0jHNefo97t5vY12IZt7Keh4+of00jfTWnz3283/nqccOWmr8e6fjba1lxtaLBNRXUtC1ZWsWBFJQtWrGXBikrmr1jL6qr8tPlE0LVTMTv1KGdAz3J27tF5/W2XTsXr+2lSPX//RP1XYYP33rjptoaezw7ZviP6ppHt+fvp92c1/jf2I3c11EkD52tqm1bUrrE2t78fVr296S5HHrUehkeSWpuqmhyPvLGQcZPncP+rC6iszjG4V2fOGD2IM8cMZo+du2+5k1wtzJ2cD4um3A+zJ+a/pJb1LHyRbuDKDW38j1NDcrnEolVrmb0uUKoTLq1b6HtFZU3WZbYqG9ZiWMzc1Hf9WgzbamsW/ywpKqIoaGShziYs/llcp79tXPyzpM7+U+4/gUFsOoR8Lv149qxHN1n8s27/9YO2xtpuUlMkislRHDmKUy1F5G+LI0dsMXxqIIxKtRv2Nfq4af2lx37U+D/0D/sUjX5R22jbVnwJ3OHH1Xu8Nee77woadcrVrP/yHEV1+qn3xXp9AFF/X1G9bWxm35b6YivOU38fTT/PrWfAqvmbvhbdB8LH7qPx8CUa2d5A+wa/cLVCjY3gbId/Y5ukFb0ea6pqmPL2qvXrEr2+YBVvLljJvOUbpth37VTMHgO6s+fO3dlj5+7sOSB/269bp/UXHdkurej1yJyvxcbayujereSC2ZLUzHK5xLMzlzJu0hz++eI8lq2ppneXUs45aAhnjh7MQbv23vI/WlbOh6kP5sOiqf+FiiVAwKAxcPQXYfcTYPBYePnOhv84Hb+ZL0NtVFFRsFOPcnbqUc6BQ3s32Gb4Zf9sdOzCDR/Z5G9du/bxWycyPncU46s2DYt++v7R9RbapPGFOjdZdLNpi3/W5Db0t8minU1c/LO27uKe27n45+Sic7m69Aa61BmJtSZ14urqcxn/p0k75HewORFsGroVwrb6IVlJ8bpArJTi6FQI7Rq4Us5GIdemwV3dgO4L6VYGx6Zh2jz6MXnw5+haVkK3wk/XsuLCbQmlxVu5Dllb8PRvG//Sc9glLV9P1k76VsN/V068Cnrtkl1dWTj+ig7zN7ZJMng9qmpyTFu0qs7i1at4Y8FKZi5Zs37QYqeSIkbu1I3Dd+u7PiwauXM3Bvfq3DwhUWN8f2zga7GxdQFRB1lLz/BIkrbS6/NXMm7yHMZPnsucZRWUlxZx4qgBnDl6EEeP7L/5xZ9rqmDWk/mwaMqDsODF/PauO8EeJ+fDot2Og659Nz6ug/1x2pJBvTozZ9mmI7EG9+rMCaN2zqCi7AzezGtxxujBGVTUMuqGS3UDp1N+Ws5lK9lkJNaTXY/nvo8fWieQYuPj64ZZ9QK2+qFb3XBr3baaOm0bDMjWt234iju1dcOyegFd/aCtJpdjbU2iNrFJEFg/dKuq3kyY9ofnGn19y0qK1gdJG4VL5aV0Kyuma6f8vu7l+duuZSV0L1t3v7jOMSWUlRTt2C92TeWXno35d2UDX4uN7cDXozaXeGvx6vXh0OsLVvLG/JVMX7TxFc5269eVfQf15OwxQ9hzQDf22Lk7u/bt2iJX0NqE748NfC02tf+5Heb5O21Nkppg7rIKxj8/l3GT5vDa/JUUFwVH7d6PM8cM4qRRA+hatpksfsk0mPJA/mf6I/kFfYtKYehhsPvxMOJ42HnfDQvcaovGTZrD5Xe+SEV17fptnUuL+d7Z+3HmmPYbmDTE12Jjvh4bO/LqBzloxX2bhGlPdTueWy46hFWVNaxaW8PqtbWsXlvDyrU1rC78rFq7bt+GNnW3ramq3XIBQElRbHcItS6I6tKpeLuCKBfa39i4SXO45t7XmbusgkG9OnPpyXt2yM8J+Fo0t5QSc5dX8sb8DQHR6wtWMuXtVayt2bCu4dA+XQpTzbqtn3I2vF9Xyko2cwVaSTuU09YkaSstW1PFPS/OZ9zkOTw9fQkAY4b24pun78N79h9Iv25lDR+4dhXMeLQQGN0PS6fnt/ceBqM/kA+Lhh+dv2qZtsm6f9D7D31fi/p8PTZ26cl7cvmdVRtNa+xcWsz33rU3ew3osV191+YSq6s2hE0rKzcETPXDp42DqVpWVFQzd1lFnWCqhs3MRlwvArp1qhMu1QmhupWV0K28/mipkvz+shKefWsp1z09lLU1P13fX/kzRVw5YCanjx5EUQQR+bWt8j+0jhFTO0j9oHXOsgouvzM/GrajfV58LTbV1DAtpcSiVVX5UUTr1yVayZsLVrFq7Yb1CQf0KGePAd05YkRf9iisTTRy52506eTXUamtcOSRJNVRWV3LA6++zbjJc3jo9beprk3s1r8rZ44ezBmjB7Fr366bHpQSLHipMBXtAZj5ZP4KR6VdYPgx+bBo9+Oh74iWf0KSOry2MKIipURFde3Go6AqC2FTVZ37a2tYtbaWVWurGw2qVq2t2ehKjNtjQ5iUD5KK6oRLdfflH9fdX2hftPn26xZib/DYpp6raF37uvsLC7xvtH/j/u6YOIvVazcdPdatrIQLjtiVIN8uCi9EFF6PutujTsDW4L7C4/z+un0UHjex/6h//Gb73vh4Nnq8aR8EfPmO51m8esPUznX6devErz88luLCwvxNWkB/CxcKaAsaG8H5/07dm5E7d6+zLlF+baIldV673l1K1y9YvX7x6p2607NLaRZPRdI28GprktSI2lzi8amLGDdpLve+PJ9Va2vYqXsZpx8wiDNGD2bfwT02/b/Pa5YUFrp+IH91tFUL8tt33hdGvDO/dtHQw6CkkdFJkqQdZm1N7foQal2w9L7rn2i0/eXv2otcyq8xlVJafz+XKDxOdfbn15pqrH0ulzZ/bKq/v4FzFdbZ2tz++v2lujXUeVyba7iWlZu5amVxUZBSIsH6xYrVPOqGS3WvVtn4VSabfjXLkvr9bHLlyKZdzfJ3j8/Y7PsDWuAKZ5Iy47Q1SaojpcSLc5YzbtJc7n5hLgtXrqV7WQnv2ncAZ44ZzGG79d14UcbaGpgzcUNYNOc5IEHn3vkFrnc/IR8a9RiY2XOSJOWVlRRTVlJMn66d1m/b3OLyn3xHxxsZeuTVDzb6eky47J2bbE+FQCqtuw+Fx2l9wFT3cf12bGZfyu/cpM9EPozbYt8bba/Tron1feLWZ1m0au0mz7lv10788NwDNnvVyoYW0F+/mH2dxes3LKrfhAX01/dJA4vy17tSZS5RVZPbZAH9TWuDmlxu44X4G7o65hbCwt9deHDLXOFMUqtjeCSpQ5mxaPX6K6VNW7SaTsVFHLdXf84cPZjj9tqJ8tI6CzQun71h3aJpD8Pa5RBFMORgOPbyfGA0aDQUuaijJLV2+fWfNp2Kc+nJe2ZYVXa29vVYN82s8GjHF9iC/u89ezcyTWsUx+65U4aVtbyUEkd+/0HmLqvcZN/gXp05bq+O9XpI2sDwSFK7t3DlWv7xwlzGTZ7L87OWEQGHDu/DJ47ZjXftO3DDPPzqCpjy+IbRRQtfy2/vPghGnZ4Pi3Z7R360kSSpTXEx9Y35emzga7FBRPCVk/cyaJW0Cdc8ktQurVpbw70v5a+UNmHKInIJRg3swZljBnHaAYMY2LNzfvz6ojfzI4umPgAzHoOaSigug12PyC9yvfsJ0H8vcGi2JEnqINrCQvuSdgwXzJbU7lXV5HjkjYWMmzyH+19dQGV1jiG9O6+/UtrInbtD5fL8FLSpD+RHGC2flT+478h8ULT78bDrkdCpS7ZPRpIkSZJamAtmS2qXcrnExLeWMm7yHO55cR7L1lTTu0sp7ztoF84cM4gDd+lJzHseXr8e/vkAzHoaUi106p6fgnb0F2HE8dB716yfiiRJkiS1SoZHklqtzQ2Zfm3+ivyV0p6fy5xlFXQuLeakfXbmjNGDOHpgonTGQzDxJ/DnB2HN4nyHA0fDUZ/PjzAacjAUl2b0zCRJkiSp7TA8ktQqjZs0Z6PFGucsq+Crf3uB+19dwJS3V/Ha/JUUFwVHj+zHV04czknd36LzzLvgofth/ov5Trr2L0xFOwF2Ow669c/wGUmSJElS22R4JKlVuube1zmx9mG+0ukOBsUi5qZ+/KDmXMa/cBQHDu3FD0/oycnlL9Ft1u/h3kegahUUlcAuh8LxV+Snog3YH4qKsn4qkiRJktSmGR5JapXGrriP75XeQJeoAmBILOKa0t9wTu4RjqmpgMem5hv2Ggr7n5sPi4YfA+U9MqxakiRJktofwyNJrdLlnf5CF6o22lYWNRxV/BL0PQkO+UR+OlrfERCRUZWSJEmS1P4ZHklqlXZmUYPbg4Dz/9LC1UiSJElSx+ViIJJap55DGtwcjWyXJEmSJO0YhkeSWqWZQ88ipXobSzvnF8OWJEmSJLUYwyNJrU/VGspevZNl0Y1cj8FAQM9d4LSf5RfHliRJkiS1GNc8ktTqLP/H/zGgZjZ/3edXnPO+87MuR5IkSZI6NEceSWpdpj9Kzxdu5NbaU3jHKedkXY0kSZIkdXiGR5Jaj7UryY37FDPSQF7d94v0716WdUWSJEmS1OEZHklqPf7zf7B8Nl+s+iQfPnrvrKuRJEmSJGF4JKm1ePN+ePZm/lB8OmXDD2fUoB5ZVyRJkiRJwvBIUmtQsRTGf4YV3Xfn26vP5KKjhmddkSRJkiSpwPBIUvb+dRmsXsg3Sz7LgL69OH6vnbKuSJIkSZJUYHgkKVuv3g0v3M68/T/N3+b156NHDKOoKLKuSpIkSZJUYHgkKTurF8Hdn4cB+/O91afSvbyE943dJeuqJEmSJEl1GB5JykZK8I8vwNoVLDjhp/zz5UW8/+Bd6FpWknVlkiRJkqQ6DI8kZeOlv8Gr4+G4r3PTm51JKXHBEcOyrkqSJEmSVI/hkaSWt2Ie/PNLMOQQVh90CX96aian7DuAIb27ZF2ZJEmSJKkewyNJLSsluPtzULMWzryOOyfPY0VlDR87anjWlUmSJEmSGmB4JKllTboN3vwPnPhNcn1GcNOEGRwwpCcHDu2ddWWSJEmSpAYYHklqOctmwr8vh2FHw8H/w0NvvM30Rau56KjhRETW1UmSJEmSGtAs4VFEnBIRr0fElIi4rIH9ZRHx58L+pyJiWL39QyNiVUR8uTnqkdQK5XJw16eBBGf8EoqKuPGx6QzoUc679xuYdXWSJEmSpEZsd3gUEcXAL4F3AaOAD0TEqHrNPgYsTSntDvwY+H69/T8C/rW9tUhqxZ65AaY/Aid/F3rvymvzVzBhymI+csSulBY7CFKSJEmSWqvm+MZ2CDAlpTQtpVQF3A6cUa/NGcAthft/BY6PwhyViDgTmA683Ay1SGqNFk+F+78Bu58IB34EgJsem055aREfPGRoxsVJkiRJkjanOcKjwcCsOo9nF7Y12CalVAMsB/pGRDfgq8A3t3SSiPhEREyMiIkLFy5shrIltYhcLYy7BIpL4fSfQwSLVq1l3OS5vPfAIfTq0inrCiVJkiRJm5H1XJErgR+nlFZtqWFK6TcppbEppbH9+/ff8ZVJah5P/AJmPQXvvhZ65Nc2+sOTM6mqyfHRI4dnXJwkSZIkaUtKmqGPOcAudR4PKWxrqM3siCgBegKLgUOBcyLiB0AvIBcRlSmlXzRDXZKy9var8OC3Ye/TYL/3AbC2ppbfP/kWx+7Zn9136pZxgZIkSZKkLWmO8OgZYGREDCcfEr0f+GC9NuOBC4AngHOAB1NKCTh6XYOIuBJYZXAktRO11fD3i6GsB7znx5Bf5oy7n5/HolVruchRR5IkSZLUJmx3eJRSqomIzwD3AsXATSmllyPiKmBiSmk8cCPw+4iYAiwhHzBJas8e/RHMmwzn/h665aeappS46bHpjNypG0eP7JdtfZIkSZKkJmmOkUeklO4B7qm37Yo69yuB922hjyuboxZJrcDcyfDID2C/c2HU6es3PzltCa/MW8H3zt6PwgUXJUmSJEmtXNYLZktqb2rW5qerde0P7/7BRrtumjCd3l1KOWtM/QsySpIkSZJaK8MjSc3roe/Bwlfh9J9D597rN89YtJr7X13A+YfuSnlpcYYFSpIkSZK2huGRpOYz62mY8FM48CMw8sSNdt38+AxKioIPH75rRsVJkiRJkraF4ZGk5lG1Jj9drccQOOk7G+1aUVnNXybO4tT9B7Fzj/KMCpQkSZIkbYtmWTBbknjgm7BkKlxwN5T32GjXHc/MYnVVLRcdOTyj4iRJkiRJ28qRR5K23/RH4Knr4dCLYfgxG+2qqc3xuwkzOGRYH/Yb0jOjAiVJkiRJ28rwSNL2WbsSxn0a+oyA47+xye77XlnAnGUVXHSUo44kSZIkqS1y2pqk7XPv12HFbPjov6FTl0123/jYdHbp05kTR+2cQXGSJEmSpO3lyCNJ2+7N++C5W+CIz8HQQzfZ/fysZUx8aykXHjGc4qLIoEBJkiRJ0vYyPJK0bSqWwvjPQv+94bivNdjkpgnT6VZWwrljh7RwcZIkSZKk5mJ4JGnb/OursHohnHUdlJRtsnv+8kr++cI8zh27C93LSzMoUJIkSZLUHAyPJG29V8bDC3+GYy6FQWMabHLrEzOoTYkLjxjWsrVJkiRJkpqV4ZGkrbN6EfzjCzDwADj6Sw02qaiq5Y9Pz+SkUTsztO+mi2hLkiRJktoOr7YmqelSgn98HtaugLP+AcUNT0e7c9Jslq2p5qIjh7dsfZIkSZKkZufII0lN9+Jf4dW74bivw057N9gkl0vc9Nh09h3cg0OG92nhAiVJkiRJzc3wSFLTrJgH93wJhhwCR3y20WaPvLmQqQtXc9GRw4mIFixQkiRJkrQjGB5J2rKU4O7PQU0VnHU9FBU32vSmCTPo372MU/cf1IIFSpIkSZJ2FMMjSVs26ffw5n/gxG9C3xGNNntzwUoeeWMhHzlsVzqV+J8XSZIkSWoP/HYnafOWvgX/vhyGHQ0H/89mm940YQZlJUV88NChLVScJEmSJGlHMzyS1LhcDu76NBBwxi+hqPH/ZCxZXcWdz83mrDGD6dutrOVqlCRJkiTtUCVZFyCpFXvmBpjxKJz+c+i962ab/unpmaytyXHRUcNbqDhJkiRJUktw5JGkhi2eCvddASNPgjEf3mzTqpoctzw+g6NH9mOPnbu3UIGSJEmSpJZgeCRpU7la+PvFUFIGp/0MIjbb/J4X5/H2yrWOOpIkSZKkdshpa5I29fjPYfbTcPYN0GPgZpumlLjxsens1r8r7xjZv4UKlCRJkiS1FEceSdrY26/Cf78De58G+52zxeYT31rKi3OWc9GRwykq2vwIJUmSJElS22N4JGmD2mr4+yehrAe858dbnK4GcOOj0+nZuZSzDxzcAgVKkiRJklqa4ZGkDR79Icx7Hk79MXTb8hS0WUvW8J9X5vPBQ4fSpZOzYCVJkiSpPTI8kpQ3dzI8cg3sfx6MOr1Jh9z8+AyKIvjI4bvu2NokSZIkSZkxPJIENWvzV1fr2h/e9f0mHbKyspo/PzOLd+83kIE9O+/gAiVJkiRJWXGeiST473dh4atw/l+hc+8mHfKXibNZtbaGi44avoOLkyRJkiRlyZFHUkc38yl4/Gdw4AUw8sQmHVKbS9z8+AwO2rU3o3fptWPrkyRJkiRlyvBI6siq1sC4S6DnEDj5O00+7P5XFzBzyRouOtJRR5IkSZLU3jltTerIHvgmLJkKF9wNZd2bfNhNj01ncK/OnLzPzjuwOEmSJElSa+DII6mjmv4IPHU9HHoxDD+myYe9NGc5T01fwgVH7EpJsf8JkSRJkqT2zm9+UkdUuQLGfRr6jIDjv7FVh940YTpdOhVz3sFDd1BxkiRJkqTWxGlrUkf0n6/Ditlw0b3QqUuTD3t7RSV3Pz+XDx4ylJ6dS3dggZIkSZKk1sKRR1JH8+Z98NytcMTnYJdDturQ2558i5pc4kIXypYkSZKkDsPwSOpI1iyBuz4D/feG4762VYdWVtdy21MzOX6vnRjer+sOKlCSJEmS1No4bU3qSP71VVizCD74Zygp26pD75o8hyWrq7joKEcdSZIkSVJH4sgjqaN4ZTy8eAcccykMGr1Vh6aUuPGx6ew1oDuH79Z3x9QnSZIkSWqVDI+kjmDVQvjHF2DgAXD0l7b68AlTFvPGglV87KjhRMQOKFCSJEmS1Fo5bU1q71KCf34B1q6As/4BxVt/lbQbH5tGv26dOO2AQTugQEmSJElSa+bII6m9e/Ev8Ord8M7/g5323urDpy5cxX9fX8iHDtuV8tLiHVCgJEmSJKk1MzyS2rMVc+GeL8Muh8Lhn9mmLn43YTqdios4/9Bdm7k4SZIkSVJbYHgktVcpwfjPQU0VnHkdFG39qKFla6r427NzOGP0IPp337qrs0mSJEmS2odmCY8i4pSIeD0ipkTEZQ3sL4uIPxf2PxURwwrbT4yIZyPixcLtO5ujHknAc7fClPvgxKug74ht6uJPT8+iorqWjx45vJmLkyRJkiS1FdsdHkVEMfBL4F3AKOADETGqXrOPAUtTSrsDPwa+X9i+CDgtpbQfcAHw++2tRxKw9C2492sw/Bg4+OPb1EV1bY5bn5jBESP6MmpQj2YuUJIkSZLUVjTHyKNDgCkppWkppSrgduCMem3OAG4p3P8rcHxEREppUkppbmH7y0DniHBujLQ9cjm469NAwBm/hKJt+5j/66X5zFteyUWOOpIkSZKkDq05wqPBwKw6j2cXtjXYJqVUAywH+tZr817guZTS2oZOEhGfiIiJETFx4cKFzVC21E4981uY8Sic8l3oNXSbu7npsekM69uFd+61UzMWJ0mSJElqa1rFgtkRsQ/5qWyfbKxNSuk3KaWxKaWx/fv3b7nipLZk0RS47xsw8iQY8+Ft7ubZt5YyedYyPnrkcIqKohkLlCRJkiS1Nc0RHs0BdqnzeEhhW4NtIqIE6AksLjweAvwd+EhKaWoz1CN1TLlaGHcJlJTBaT+D2PbQ56YJ0+leXsI5Bw1pxgIlSZIkSW1Rc4RHzwAjI2J4RHQC3g+Mr9dmPPkFsQHOAR5MKaWI6AX8E7gspTShGWqROq7Hfw6zn4Z3Xws9Bm5zN3OWVfDvl+bzgUOG0rWspBkLlCRJkiS1RdsdHhXWMPoMcC/wKnBHSunliLgqIk4vNLsR6BsRU4AvApcVtn8G2B24IiImF35cYEXaWgtegf9+B/Y+HfY7Z7u6uvXxGQBccMSw7a9LkiRJktTmNcuwgpTSPcA99bZdUed+JfC+Bo77NvDt5qhB6rBqq+Hvn4SyHnDqj7drutrqtTX88emZnLLPAAb36tyMRUqSJEmS2irnpEht3SPXwvwX4LzboGu/7erqb8/NZmVlDRcdNbyZipMkSZIktXWt4mprkrbR3Enw6LWw/3mw92nb1VUul/jdhBkcsEsvDhzaq3nqkyRJkiS1eYZHUltVXQl/vwS69od3fX+7u/vv628zfdFqPnbUcGI7pr5JkiRJktoXp61JbdVD34WFr8L5f4POvbe7uxsfm87AnuW8a98BzVCcJEmSJKm9cOSR1BbNfAom/AwOvABGnrDd3b06bwWPT13MRw4fRmmx/1mQJEmSJG3gt0SpralaDeMuhl67wMnfaZYub3psOp1Li/nAIbs0S3+SJEmSpPbDaWtSW3P/N2HJNLjgH1DWfbu7W7RqLXdNnsu5Bw+hV5dOzVCgJEmSJKk9ceSR1JZMexie/jUcegkMP7pZurztybeoqs3x0SOHN0t/kiRJkqT2xfBIaisqV8Bdn4Y+I+D4K5qly7U1tdz25Fsct2d/RvTv1ix9SpIkSZLaF6etSW3Ff74OK+bARfdCpy7N0uX4yXNZtKqKi45y1JEkSZIkqWGOPJLagjf+A8/dCkf+L+xySLN0mVLipgkz2GPnbhy1e79m6VOSJEmS1P4YHkmt3ZolMP6zsNMoOPbyZuv2iWmLeXXeCi46cjgR0Wz9SpIkSZLaF6etSa3dv74CaxbB+XdASVmzdXvTYzPo07UTZ44Z3Gx9SpIkSZLaH0ceSa3ZK3fBi3+BY74CAw9otm5nLFrNA68t4PxDh1JeWtxs/UqSJEmS2h/DI6m1WrUQ/vEFGDgajv5is3Z98+MzKCkKPnzYrs3aryRJkiSp/XHamtTavHAHPHAVLJ+Vf3zEZ6G4tNm6X15RzR0TZ3Ha/oPYqUd5s/UrSZIkSWqfHHkktSYv3AF3f25DcATw8Pfz25vJHc/MYk1VLRcdNbzZ+pQkSZIktV+GR1Jrcv+VUF2x8bbqivxIpGZQU5vj5sdncMjwPuw7uGez9ClJkiRJat8Mj6TWYOUC+M//gxVzGt6/fHaznOY/ryxgzrIKPuaoI0mSJElSE7nmkZSlZbPg8Z/Bc7dCbRWUdoHqNZu26zmkWU5342PTGdqnCyfsvXOz9CdJkiRJav8Mj6QsLJ4Kj/0Inr8dCDjg/XDUF2DOs/k1j+pOXSvtDMdfsd2nnDxrGc++tZQrTh1FcVFsd3+SJEmSpI7B8EhqSQtegUd/CC/fCcWdYOxFcMTnoNcu+f19R+RvH7gqP1Wt55B8cLT/udt96psem063shLeN7Z5RjFJkiRJkjoGwyOpJcx5Lh8avfYP6NQNDv9M/qd7A9PH9j+3WcKiuuYtr+CeF+dxwRHD6F5e2qx9S5IkSZLaN8MjaUd663F45FqY+gCU94R3XAaHfhK69GnRMm594i1yKXHhEcNa9LySJEmSpLbP8EhqbinB1AfzodHMx6FrfzjhShj7MSjv0eLlVFTV8senZnLSqAHs0qdLi59fkiRJktS2GR5JzSWXg9fvgUevhbmToMdgOOX7cOBHoFN2oc3fnpvN8opqLjpqeGY1SJIkSZLaLsMjaXvlauHlv+fXNHr7Feg9DE77KRzwASgpy7a0XOJ3E6az3+CeHDysd6a1SJIkSZLaJsMjaVvVVMELf4bHfgRLpkH/veDs38I+Z0Nx6/hoPfzmQqYuXM2PzzuAiMi6HEmSJElSG9Q6vuFKbUl1BUy6DR77CayYDQMPgHN/D3udCkVFWVe3kZsem85O3ct4z36Dsi5FkiRJktRGGR5JTbV2JUy8CR7/Bax+G3Y5DE77Cex+ArTCUT1vLFjJo28u4ssn7UGnktYVakmSJEmS2g7DI2lLKpbCU7+BJ38Flctgt2PhmN/Brke2ytBond9NmE5ZSREfPHTXrEuRJEmSJLVhhkdSY1YthCd/CU/fAFUrYc93w9FfgiFjs65si5asruLO5+Zw9oGD6dO1U9blSJIkSZLaMMMjqb7lc+Dxn8OzN0NNJexzVj40GrBv1pU12R+feou1NTkuOnJ41qVIkiRJkto4wyNpnSXT8otgT/4jkGD/8+CoL0C/kVlXtlWqanLc+sRbHD2yHyN37p51OZIkSZKkNs7wSHr7NXjsR/DiX6CoFA78CBz5v9C7ba4V9M8X5/L2yrX84Jz9sy5FkiRJktQOGB6p45r3PDxyLbx6N5R2hsM+BUd8FroPyLqybZZS4sbHpjOif1eOGdk/63IkSZIkSe2A4ZE6nplPwSPXwJT7oKwnHPNlOPQS6No368q22zMzlvLSnBV856x9KSpqvVeCkyRJkiS1HYZH6hhSgmkPwaM/hBmPQpe+8M7/B4f8D5T3zLq6ZnPjY9Po1aWUs8cMyboUSZIkSVI7YXik9i0leOPf+elpcyZC94Fw8nfhoAuhU9esq2tWMxev4T+vLOCSd4ygc6firMuRJEmSJLUThkdqn3K18Mpd+ZFGC16CXkPh1B/D6POhpCzr6naImx+fQXEEHzl8WNalSJIkSZLaEcMjtS+11fmrpj36I1j8JvTbA868HvY7B4pLs65uh1lZWc0dE2fxnv0HMqBnedblSJIkSZLaEcMjtQ/VlTD5NpjwU1g2E3beD953M+x9OhS1/ylcd0yczaq1NVx05PCsS5EkSZIktTOGR2rbqlbDxN/B4z+HVfNhyMHw7mth5EkQHeNqY7W5xM2PT2fsrr05YJdeWZcjSZIkSWpnDI/UNlUsg2d+C0/8CiqWwLCj4ezfwPBjOkxotM59ryxg1pIKLn/X3lmXIkmSJElqhwyP1LasXgxP/gqe/g2sXQEjT4Zjvgy7HJJ1ZZm5acJ0BvfqzEmjds66FEmSJElSO1TUHJ1ExCkR8XpETImIyxrYXxYRfy7sfyoihtXZd3lh++sRcXJz1KN2aMU8+PfX4Cf75q+gNuI4+OQjcP4dHTo4emnOcp6evoQLjxhGSXGzfJwlSZIkSdrIdo88iohi4JfAicBs4JmIGJ9SeqVOs48BS1NKu0fE+4HvA+dFxCjg/cA+wCDg/ojYI6VUu711qZ1Y+hZM+AlMug1ytbDf++DoL0L/PbOurFW46bHpdO1UzHmH7JJ1KZIkSZKkdqo5pq0dAkxJKU0DiIjbgTOAuuHRGcCVhft/BX4REVHYfntKaS0wPSKmFPp7ohnqUlvxwh3wwFWwfDb0HALHXwGDxsCjP4IX/py/WtroD8KRn4c+Xk1snbdXVHL3C3M5/9Bd6VFemnU5kiRJkqR2qjnCo8HArDqPZwOHNtYmpVQTEcuBvoXtT9Y7dnBDJ4mITwCfABg6dGgzlK1W4YU74O7PQXVF/vHyWfD3iyHVQklnOPSTcMRnocegbOtshX7/5FvU5BIXHjEs61IkSZIkSe1Ym1kwO6X0G+A3AGPHjk0Zl6Pm8sBVG4KjdVItlHWHz06Cbv2zqauVq6yu5Q9PzeT4vXZmWL+uWZcjSZIkSWrHmmOF3TlA3QVXhhS2NdgmIkqAnsDiJh6r9mz57Ia3r11lcLQZ4ybNYcnqKj52lNP4JEmSJEk7VnOER88AIyNieER0Ir8A9vh6bcYDFxTunwM8mFJKhe3vL1yNbTgwEni6GWpSa7dyPvzjC0Ajg8h6DmnRctqSlBI3TZjO3gN7cNhufbIuR5IkSZLUzm33tLXCGkafAe4FioGbUkovR8RVwMSU0njgRuD3hQWxl5APmCi0u4P84to1wKe90lo7V7kCHv8ZPPFLqK2C4cfBrCegpnJDm9LO+UWz1aDHpizijQWruPZ9B5Bfd16SJEmSpB2nWdY8SindA9xTb9sVde5XAu9r5NjvAN9pjjrUitWshWduhEeugYolsO974bivQ98RDV9tbf9zs6641brxsen061bGaQcMzLoUSZIkSVIH0GYWzFYblcvBi3+BB78Ny2fCbsfCCVfCoDEb2ux/rmFRE015exUPvb6QL5ywB2UlxVmXI0mSJEnqAAyPtGOkBFPuh/u/CQtehAH7w+k/hRHvzLqyNu13E6bTqaSI8w8bmnUpkiRJkqQOwvBIzW/2s3D/N2DGo9B7GLz3RtjnbChqjvXZO65la6r423OzOXP0IPp1K8u6HEmSJElSB2F4pOazaAo8eBW8chd06QfvugYOuhBKOmVdWbvwx6dnUlmd46KjhmddiiRJkiSpAzE80vZbOR8euhqeuxVKyuHYy+HwT0NZ96wrazeqa3Pc+vhbHLl7X/Ya0CPrciRJkiRJHYjhkbZd5XKY8DN48ldQWwUHfwyOuRS67ZR1Ze3OPS/OY/6KSr5z1r5ZlyJJkiRJ6mAMj7T1atbCMzfAI9dCxRLY9xx459ehz25ZV9YupZS46bHpDO/XleP2NJiTJEmSJLUswyM1Xa4WXvwLPPgdWD4TdjsOTrgSBo3OurJ27bmZS3l+9nKuOmMfiooi63IkSZIkSR2M4ZG2LCWYcj/cfyUseAkGHgCn/xRGvDPryjqEmx6bQY/yEt574JCsS5EkSZIkdUCGR9q82RPhvm/AW49B7+Fwzk0w6iwoKsq6sg5h9tI1/OulefzP0bvRtcyPqyRJkiSp5fltVA1b9CY8cBW8Oh669od3XwsHXgAlnbKurEO59Ym3iAg+csSwrEuRJEmSJHVQhkfa2Mr58NDV8NytUNoZjr0cDv80lHXPurIOZ/XaGv709ExO2XcAg3t1zrocSZIkSVIHZXikvMrlMOGn8MSvIFcDB38cjrkUuvXPurIO66/PzmZlZQ0XHTk861IkSZIkSR2Y4VFHV10Jz9wAj14LFUthv/fBcV+HPgYWWRk3aQ4/uPc15i6rpLQ4mLVkDQft2jvrsiRJkiRJHZThUUeVq4UX7oD/fgeWz8pfOe2EK/NXUlNmxk2aw+V3vkhFdS0A1bWJy+98EYAzxwzOsjRJkiRJUgdleNTRpARv3gf3XwlvvwwDR8PpP4cRx2VdmYBr7n19fXC0TkV1Ldfc+7rhkSRJkiQpE4ZHHcmsZ+D+b8BbE6D3cDjndzDqTCgqyroyARVVtcxZVtHgvrmNbJckSZIkaUczPOoIFr0JD3wTXr0buvaHd18LB10IxaVZVyZg+Zpqbn1iBr97fEajbQZ5tTVJkiRJUkYMj9qzFfPg4avhud9DaWc49mtw+KehrFvWlQlYsKKSGx6dxh+fmsnqqlreuddO7Du4B799ZPpGU9c6lxZz6cl7ZlipJEmSJKkjMzxqjyqWwYSfwpPXQa4GDvkfOPrL0K1/1pUJmL5oNb9+eCp3PjeHmlyO0w4YxMXvGMHeA3sAsFu/blxz7+vMXVbBoF6dufTkPV3vSJIkSZKUGcOj9qS6Ep65AR69FiqWwn7nwnFfgz7Ds65MwEtzlnPdQ1O556V5lBYXce7BQ/jE0SMY2rfLRu3OHDPYsEiSJEmS1GoYHrUHuVp44c/w3+/C8lkw4ng44Rsw8ICsK+vwUko8MW0x1z00lUffXET3shIueccIPnrkcPp3L8u6PEmSJEmStsjwqC1LCd78D9x/Jbz9CgwaA2f8EnZ7R9aVdXi5XOK+Vxdw3UNTmTxrGf26lfHVU/bi/MOG0qPchcolSZIkSW2H4VFbNesZuP8b8NYE6LMbvO9mGHUmRGRdWYdWXZvjrslzuf7hqUx5exW79OnMt8/cl3MOGkJ5aXHW5UmSJEmStNUMj9qahW/AA9+E1/4BXXeC9/wQDrwAih3NkqU1VTX8+ZlZ/PaRacxdXsleA7rz0/eP5j37DaSkuCjr8iRJkiRJ2maGR23Firnw0NUw6fdQ2hWO+z847BIo65Z1ZR3asjVV3PrEW/xuwnSWrqnmkGF9+M5Z+3Hsnv0JR4FJkiRJktoBw6PWrmIZTPgJPHldfmHsQz4Jx3wZuvbLurIObf7ySm54dBp/fHoma6pqOX6vnbjk2BGMHdYn69IkSZIkSWpWhketVXUlPPNbeORaqFwO+70P3vl16D0s68o6tGkLV/Hrh6dx56TZ5BKctv9ALj52BHsN6JF1aZIkSZIk7RCGR61Nrhaevx3++11YMRt2PwGO/wYM3D/ryjq0F2cv57qHp/Cvl+bTqbiIDxwylP85ejd26dMl69IkSZIkSdqhDI9ai5TgjXvh/ith4aswaAyc+SvY7R1ZV9ZhpZR4Yupirnt4Ko++uYju5SV86tgRXHjEcPp3L8u6PEmSJEmSWoThURZeuAMeuAqWz4aeQ2D0+TD9EZj5OPQZAe+7GUadCS64nIlcLvGfVxZw3cNTeX7WMvp3L+Oyd+3F+YcOpXu5V7WTJEmSJHUshkct7YU74O7PQXVF/vHyWfDw1dCpO7znR3DgR6DYgCILVTU57po8h+sfnsrUhasZ2qcL3zlrX9574BDKS4uzLk+SJEmSpEwYHrW0B67aEBzVVd4DDv5Yy9cj1lTVcPvTs7jh0WnMXV7J3gN78PMPjOFd+w6gpLgo6/IkSZIkScqU4VFLWz674e0r5rZsHWLp6ipueWIGtzw+g6VrqjlkeB++e/Z+vGOP/oRTBiVJkiRJAgyPWl7PIfmpag1tV4uYt7yCGx6dzp+ensmaqlpO2HsnLjl2BAft2ifr0iRJkiRJanUMj1ra8VdsvOYRQGnn/HbtUFMXruLXD0/l75PmkEtwxgGD+OQ7RrDngO5ZlyZJkiRJUqtleNTS9j83f1v3amvHX7Fhu5rdC7OXcd1DU/n3y/PpVFzEBw8ZyseP3o1d+nTJujRJkiRJklo9w6Ms7H+uYdEOllLi8amLue6hqTw2ZRE9ykv49LG7c+GRw+jXrSzr8iRJkiRJajMMj9Su5HKJ/7wyn+semsrzs5ezU/cyvvbuvfjAIUPpXl6adXmSJEmSJLU5hkdqF6pqcoybPIfrH57KtIWrGda3C987ez/OGjOY8tLirMuTJEmSJKnNMjxSm7Z6bQ23PzOLGx6dxrzllewzqAe/+OAY3rXvQIqLIuvyJEmSJElq8wyP1CYtXV3FzY/P4JYnZrBsTTWH7daHq9+7P8eM7EeEoZEkSZIkSc3F8EhtyrzlFfz2ken86emZVFTXcuKonbnk2BEcOLR31qVJkiRJktQuGR6pTZjy9ip+/fBUxk2eQ0pw+uhBXPKOEYzcuXvWpUmSJEmS1K4ZHqlVe37WMq57aCr3vjKfspIizj90Vz5+9HCG9O6SdWmSJEmSJHUI2xUeRUQf4M/AMGAGcG5KaWkD7S4A/q/w8NsppVsiogvwF2AEUAvcnVK6bHvqUds0btIcrrn3deYuq2BQr858+aQ96N+9nOsensKEKYvpUV7CZ47bnQuPGEbfbmVZlytJkiRJUocSKaVtPzjiB8CSlNLVEXEZ0Dul9NV6bfoAE4GxQAKeBQ4C1gKHppT+GxGdgAeA76aU/rWl844dOzZNnDhxm+tW6zFu0hwuv/NFKqpr12+LgJRg5x5lfPyo3fjAoUPpVuYgOUmSJEmSdqSIeDalNLb+9u39Rn4GcGzh/i3AQ8BX67U5GbgvpbSkUMh9wCkppT8B/wVIKVVFxHPAkO2sR23MNfe+vlFwBPngqFfnUh75ynGUlRRnVJkkSZIkSQIo2s7jd04pzSvcnw/s3ECbwcCsOo9nF7atFxG9gNPIjz5qUER8IiImRsTEhQsXblfRaj3mLqtocPvyimqDI0mSJEmSWoEtjjyKiPuBAQ3s+nrdBymlFBFbPQcuIkqAPwE/SylNa6xdSuk3wG8gP21ta8+j1mlAz3LmLa/cZPugXp0zqEaSJEmSJNW3xfAopXRCY/siYkFEDEwpzYuIgcDbDTSbw4apbZCfmvZQnce/Ad5MKf2kKQWr/cjlEn26lm4SHnUuLebSk/fMqCpJkiRJklTX9k5bGw9cULh/AXBXA23uBU6KiN4R0Rs4qbCNiPg20BP4/HbWoTbouoen8vLclZw9ZhCDe3UmgMG9OvO9s/fjzDGDt3i8JEmSJEna8bZ3weyrgTsi4mPAW8C5ABExFrg4pfTxlNKSiPgW8EzhmKsK24aQn/r2GvBcRAD8IqV0w3bWpDbg0TcXcu1/Xuf0Awbxw3NHU/j9S5IkSZKkViZSanvLB40dOzZNnDgx6zK0jeYsq+DUnz3KTt3L+funj6BLp+3NMCVJkiRJ0vaKiGdTSmPrb9/eaWvSVllbU8unbnuWmtrEdR860OBIkiRJkqRWzm/ualHfvPsVnp+9nOs/dBC79e+WdTmSJEmSJGkLHHmkFvOXibP441MzufgdIzhl3wFZlyNJkiRJkprA8Egt4qU5y/m/cS9xxIi+fPmkPbIuR5IkSZIkNZHhkXa45WuqueQPz9K7Syd+9oExlBT7tpMkSZIkqa1wzSPtULlc4vN/nsT85ZX8+ZOH069bWdYlSZIkSZKkreAQEO1QP39wCv99fSFXnDqKA4f2zrocSZIkSZK0lQyPtMM89Prb/OSBNzh7zGA+dNiuWZcjSZIkSZK2geGRdohZS9bwv7dPZs+du/Ods/YjIrIuSZIkSZIkbQPDIzW7yupaPvWH58ilxPUfOojOnYqzLkmSJEmSJG0jF8xWs7ty/Mu8OGc5N3xkLMP6dc26HEmSJEmStB0ceaRm9ednZnL7M7P4zHG7c8KonbMuR5IkSZIkbSfDIzWbF2cv5//d9TJHj+zHF07cI+tyJEmSJElSMzA8UrNYurqKi297lv7dyvjp+8dQXOQC2ZIkSZIktQeueaTtVptL/O+fJ7Nw5Vr+cvHh9OnaKeuSJEmSJElSM3HkkbbbTx94k0feWMg3Th/FAbv0yrocSZIkSZLUjAyPtF0efG0BP3vgTc45aAgfPGRo1uVIkiRJkqRmZnikbTZz8Ro+f/tkRg3swbfP3JcI1zmSJEmSJKm9MTzSNqmsruXi254F4PoPHUR5aXHGFUmSJEmSpB3BBbO11VJK/N+4l3hl3gpuunAsQ/t2ybokSZIkSZK0gzjySFvtT0/P4q/PzuZzx4/knXvtnHU5kiRJkiRpBzI80lZ5ftYyrhz/Msfs0Z//PX5k1uVIkiRJkqQdzPBITbZkdRWX3PYs/buX8dPzRlNc5ALZkiRJkiS1d655pCapzSU+96dJLFpdxd8uPoLeXTtlXZIkSZIkSWoBjjxSk/z4vjd4bMoivnXGPuw3pGfW5UiSJEmSpBZieKQtuu+VBfziv1M4b+wunHfw0KzLkSRJkiRJLcjwSJs1Y9FqvnjHZPYb3JNvnrFP1uVIkiRJkqQWZnikRlVU1XLxbc9SXBT86vwDKS8tzrokSZIkSZLUwlwwWw1KKfG1v7/I6wtW8rsLD2aXPl2yLkmSJEmSJGXAkUdq0G1PvsXfJ83h88fvwbF77pR1OZIkSZIkKSOGR9rEczOXctU/XuG4Pfvz2XfunnU5kiRJkiQpQ4ZH2siiVWv51G3PMaBnOT85bwxFRZF1SZIkSZIkKUOueaT1ampzfPaPk1i6poq/XXIEPbuUZl2SJEmSJEnKmOGR1rv2P2/wxLTFXHPO/uw7uGfW5UiSJEmSpFbAaWsC4N8vzef6h6fywUOH8r6xu2RdjiRJkiRJaiUMj8S0hav48l+e54AhPfnGaaOyLkeSJEmSJLUihkcd3JqqGi6+7VlKi4NffeggykqKsy5JkiRJkiS1Iq551IGllLjsby/y5turuPWiQxjcq3PWJUmSJEmSpFbGkUcd2C2Pz2D883P58kl7cvTI/lmXI0mSJEmSWiHDow5q4owlfPufr3LC3jtxyTtGZF2OJEmSJElqpQyPOqC3V1by6T8+x+DenfnhuaMpKoqsS5IkSZIkSa2U4VEHU1Ob47N/nMTyimqu/9BB9OxcmnVJkiRJkiSpFXPB7A7mB/e+zlPTl/Dj8w5g74E9si5HkiRJkiS1co486kDueXEev3lkGh8+bFfOGjMk63IkSZIkSVIbYHjUQUx5exWX/uV5xgztxf87dVTW5UiSJEmSpDZiu8KjiOgTEfdFxJuF296NtLug0ObNiLiggf3jI+Kl7alFjVu9toaLb3uW8tJifnX+gXQqMTOUJEmSJElNs70pwmXAAymlkcADhccbiYg+wDeAQ4FDgG/UDZki4mxg1XbWoUaklPjK315g2sJV/PwDYxjYs3PWJUmSJEmSpDZke8OjM4BbCvdvAc5soM3JwH0ppSUppaXAfcApABHRDfgi8O3trEONuPGx6fzzhXlcevJeHLF7v6zLkSRJkiRJbcz2hkc7p5TmFe7PB3ZuoM1gYFadx7ML2wC+BfwQWLOlE0XEJyJiYkRMXLhw4XaU3HE8PX0J3/vXa5y8z85c/I7dsi5HkiRJkiS1QSVbahAR9wMDGtj19boPUkopIlJTTxwRo4ERKaUvRMSwLbVPKf0G+A3A2LFjm3yejurtFZV8+o/PMbRPF6553wFERNYlSZIkSZKkNmiL4VFK6YTG9kXEgogYmFKaFxEDgbcbaDYHOLbO4yHAQ8DhwNiImFGoY6eIeCildCzaLtW1OT79x+dYVVnDbR87lB7lpVmXJEmSJEmS2qjtnbY2Hlh39bQLgLsaaHMvcFJE9C4slH0ScG9K6bqU0qCU0jDgKOANg6Pm8b17XuOZGUu5+r37seeA7lmXI0mSJEmS2rDtDY+uBk6MiDeBEwqPiYixEXEDQEppCfm1jZ4p/FxV2KYd4O7n53LThOlceMQwzhg9eMsHSJIkSZIkbUak1PaWDxo7dmyaOHFi1mW0Om8uWMkZv5zA3gN78Kf/OYxOJdubDUqSJEmSpI4iIp5NKY2tv910oZ1YWVnNJ297li6dSvjV+QcaHEmSJEmSpGaxxQWz1fqllLj0Ly/w1uI1/OHjh7Jzj/KsS5IkSZIkSe2Ew1Pagd8+Oo1/vzyfy07Zi8N265t1OZIkSZIkqR0xPGrjnpi6mKv/9Rrv3m8AHz96eNblSJIkSZKkdsbwqA2bv7ySz/7pOYb368oPzjmAiMi6JEmSJEmS1M645lEbVVWT41N/eJY1VbXc/onD6Fbmr1KSJEmSJDU/E4c26rv3vMpzM5fxiw+OYfedumddjiRJkiRJaqecttYGjZs0h5sfn8HHjhrOqfsPyrocSZIkSZLUjhketTGvzV/B5Xe+yCHD+nDZu/bKuhxJkiRJktTOGR61ISsqq7n498/SrbyEX3xwDKXF/vokSZIkSdKOZfrQRuRyiS/d8Tyzl1bwq/MPZKce5VmXJEmSJEmSOgDDozbi+kemct8rC7j83Xtz8LA+WZcjSZIkSZI6CMOjNmDClEVce+/rnLr/QC46cljW5UiSJEmSpA7E8KiVm7usgs/+aRIj+nfj++/dn4jIuiRJkiRJktSBGB61YmtravnUH56jqibHdR86iK5lJVmXJEmSJEmSOhjTiFbsW/94hcmzlnHd+Qey+07dsi5HkiRJkiR1QI48aqX+9uxsbntyJp88Zjfetd/ArMuRJEmSJEkdlOFRK/TK3BV87e8vcthufbj05D2zLkeSJEmSJHVghketzPI11Vx827P06lLKzz9wICXF/ookSZIkSVJ2XPOoFcnlEl+8YzJzl1Xw508eRv/uZVmXJEmSJEmSOjiHtbQiv3poCg+89jb/79RRHLRrn6zLkSRJkiRJMjxqLR55YyE/vO8Nzhg9iI8cvmvW5UiSJEmSJAGGR63C7KVr+N/bJ7HHTt353tn7ERFZlyRJkiRJkgQYHmWusrqWT/3hOWpqE9d/+CC6dHIZKkmSJEmS1HqYVGRg3KQ5XHPv68xdVkHnTsWsqarlNx8+iOH9umZdmiRJkiRJ0kYMj1rYuElzuPzOF6morgVgTVUtJUXBmqrajCuTJEmSJEnalNPWWtg1976+PjhapyaXuObe1zOqSJIkSZIkqXGGRy1s7rKKrdouSZIkSZKUJcOjFjaoV+et2i5JkiRJkpQlw6MWdunJe9K5tHijbZ1Li7n05D0zqkiSJEmSJKlxLpjdws4cMxhg/dXWBvXqzKUn77l+uyRJkiRJUmtieJSBM8cMNiySJEmSJEltgtPWJEmSJEmS1CjDI0mSJEmSJDXK8EiSJEmSJEmNMjySJEmSJElSowyPJEmSJEmS1CjDI0mSJEmSJDXK8EiSJEmSJEmNMjySJEmSJElSowyPJEmSJEmS1CjDI0mSJEmSJDUqUkpZ17DVImIh8FbWdTSDfsCirItQq+X7Q43xvaHG+N7Q5vj+UGN8b6gxvjfUGN8b7deuKaX+9Te2yfCovYiIiSmlsVnXodbJ94ca43tDjfG9oc3x/aHG+N5QY3xvqDG+Nzoep61JkiRJkiSpUYZHkiRJkiRJapThUbZ+k3UBatV8f6gxvjfUGN8b2hzfH2qM7w01xveGGuN7o4NxzSNJkiRJkiQ1ypFHkiRJkiRJapThUUYi4pSIeD0ipkTEZVnXo9YhInaJiP9GxCsR8XJE/G/WNal1iYjiiJgUEf/Iuha1LhHRKyL+GhGvRcSrEXF41jWpdYiILxT+prwUEX+KiPKsa1J2IuKmiHg7Il6qs61PRNwXEW8WbntnWaOy0ch745rC35UXIuLvEdErwxKVkYbeG3X2fSkiUkT0y6I2tRzDowxERDHwS+BdwCjgAxExKtuq1ErUAF9KKY0CDgM+7XtD9fwv8GrWRahV+inw75TSXsAB+D4REBGDgc8BY1NK+wLFwPuzrUoZuxk4pd62y4AHUkojgQcKj9Xx3Mym7437gH1TSvsDbwCXt3RRahVuZtP3BhGxC3ASMLOlC1LLMzzKxiHAlJTStJRSFXA7cEbGNakVSCnNSyk9V7i/kvyXv8HZVqXWIiKGAO8Bbsi6FrUuEdETOAa4ESClVJVSWpZpUWpNSoDOEVECdAHmZlyPMpRSegRYUm/zGcAthfu3AGe2ZE1qHRp6b6SU/pNSqik8fBIY0uKFKXON/HcD4MfAVwAXUu4ADI+yMRiYVefxbAwIVE9EDAPGAE9lXIpaj5+Q/wOdy7gOtT7DgYXA7wrTGm+IiK5ZF6XspZTmANeS/7/C84DlKaX/ZFuVWqGdU0rzCvfnAztnWYxarYuAf2VdhFqHiDgDmJNSej7rWtQyDI+kVigiugF/Az6fUlqRdT3KXkScCrydUno261rUKpUABwLXpZTGAKtx2omAwto1Z5APGAcBXSPiQ9lWpdYs5S/F7CgCbSQivk5+eYU/ZF2LshcRXYCvAVdkXYtajuFRNuYAu9R5PKSwTSIiSskHR39IKd2ZdT1qNY4ETo+IGeSnur4zIm7LtiS1IrOB2SmldSMV/0o+TJJOAKanlBamlKqBO4EjMq5Jrc+CiBgIULh9O+N61IpExIXAqcD5hXBRGkH+f0o8X/i36RDguYgYkGlV2qEMj7LxDDAyIoZHRCfyC1eOz7gmtQIREeTXLHk1pfSjrOtR65FSujylNCSlNIz8fzMeTCk5ekAApJTmA7MiYs/CpuOBVzIsSa3HTOCwiOhS+BtzPC6mrk2NBy4o3L8AuCvDWtSKRMQp5KfMn55SWpN1PWodUkovppR2SikNK/zbdDZwYOHfI2qnDI8yUFh07jPAveT/AXdHSunlbKtSK3Ek8GHyo0omF37enXVRktqEzwJ/iIgXgNHAd7MtR61BYTTaX4HngBfJ/9vvN5kWpUxFxJ+AJ4A9I2J2RHwMuBo4MSLeJD9a7eosa1Q2Gnlv/ALoDtxX+Hfp9ZkWqUw08t5QBxOOPJQkSZIkSVJjHHkkSZIkSZKkRhkeSZIkSZIkqVGGR5IkSZIkSWqU4ZEkSZIkSZIaZXgkSZIkSZKkRhkeSZIkbaWIqC1ctvqliLg7Inptof3oiHh3C5UnSZLUrAyPJEmStl5FSml0SmlfYAnw6S20Hw0YHkmSpDbJ8EiSJGn7PAEMBoiIQyLiiYiYFBGPR8SeEdEJuAo4rzBa6byI6BoRN0XE04W2Z2T6DCRJkjYjUkpZ1yBJktSmRMSqlFK3iCgGbgduTCn9OyJ6AGtSSjURcQJwSUrpvRFxITA2pfSZwvHfBV5JKd1WmPL2NDAmpbQ6m2ckSZLUuJKsC5AkSWqDOkfEZPIjjl4F7its7wncEhEjgQSUNnL8ScDpEfHlwuNyYGihL0mSpFbFaWuSJElbryKlNBrYFQg2rHn0LeC/hbWQTiMfCjUkgPcW1k0anVIamlIyOJIkSa2S4ZEkSdI2SimtAT4HfCkiSsiPPJpT2H1hnaYrge51Ht8LfDYiAiAixuz4aiVJkraN4ZEkSdJ2SClNAl4APgD8APheRExi4+UB/guMWrdgNvkRSqXACxHxcuGxJElSq+SC2ZIkSZIkSWqUI48kSZIkSZLUKMMjSZIkSZIkNcrwSJIkSZIkSY0yPJIkSZIkSVKjDI8kSZIkSZLUKMMjSZIkSZIkNcrwSJIkSZIkSY0yPJIkSZIkSVKj/j/UoCrNaZTRxAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "ax.set_xlabel('Rate')\n",
    "ax.set_title('DI x IPCA Spread Rates - 2021-11-01 - Real Interest Rates')\n",
    "ax.legend(['DAP rates', 'IPCA gov rates'])\n",
    "ax.plot(curve['Rate'], '-o')\n",
    "ax.plot(ipca_gov_rates, '-o')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "educational-advertising",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ibovespa Futures - IND\n",
    "\n",
    "- This trades the future values of Ibovespa Index.\n",
    "\n",
    "\n",
    "### Pricing\n",
    "\n",
    "- From arbitrage theory the future price of Ibovespa can be written as\n",
    "\n",
    "$$\n",
    "PU_{IND}(T) = S_{t} \\frac{( 1 + r(t,T) )^{DU(t,T)/252}}{(1 + c(t,T)) ^ {DU(t,T)/252}}\n",
    "$$\n",
    "\n",
    "where\n",
    "\n",
    "- $t$: current instant of time (reference date)\n",
    "- $T$: maturity date (date when the contract expires)\n",
    "- $S_{t}$ the spot value at $t$\n",
    "- $PU_{IND}(T)$ the future value of Ibovespa at $T$\n",
    "- $DU(t,T)$: the total number of business days between $t$ e $T$\n",
    "- $c(t,T)$: spot interest rate between reference date and contract maturity\n",
    "- $r(t,T)$: risk free interest rate (DI1 Futures rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bacterial-conversation",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ibovespa Spread Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "affected-possibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "di1_curve = my.build_curve('DI1', df, refdate=pd.to_datetime('2021-11-01'),\n",
    "                           cal=MARKET_CALENDAR, notional=100000)\n",
    "ind_curve = my.build_curve('IND', df, refdate=pd.to_datetime('2021-11-01'),\n",
    "                           cal=MARKET_CALENDAR, spot=105822, rf_curve=di1_curve)\n",
    "\n",
    "ind_curve().plot(x='Maturity', y='Rate', figsize=(20,6), style='-o',\n",
    "               ylabel='Rate', xlabel='Date', title='DI x Ibovespa Spread Rates - 2021-11-01');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "industrial-antigua",
   "metadata": {},
   "source": [
    "- https://arquivos.b3.com.br/Web/Consolidated?lang=en\n",
    "- [Economic Indicators](https://arquivos.b3.com.br/tabelas/EconomicIndicatorPrice/2021-11-30?lang=en)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suspected-helmet",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Boi Gordo Commodity Futures - BGI (Live Cattle Futures)\n",
    "\n",
    "- This trades the future values of male steers (Boi Gordo).\n",
    "    - Male steers with at least 16 net arrobas carcass weight and a maximum age of 42 months.\n",
    "\n",
    "\n",
    "### Risk Factors\n",
    "\n",
    "- B3 CORE Risk Engine considers the future prices itself as risk factors for Boi Gordo Futures."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suffering-opposition",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "- Alternatively, from arbitrage theory the future price of Boi Gordo can be written as\n",
    "\n",
    "$$\n",
    "PU_{BGI}(T) = S_{t} \\frac{( 1 + r(t,T) )^{DU(t,T)/252}}{(1 + c(t,T)) ^ {DU(t,T)/252}}\n",
    "$$\n",
    "\n",
    "where\n",
    "\n",
    "- $t$: current instant of time (reference date)\n",
    "- $T$: maturity date (date when the contract expires)\n",
    "- $S_{t}$ the spot value at $t$\n",
    "- $PU_{BGI}(T)$ the future value of Boi Gordo at $T$\n",
    "- $DU(t,T)$: the total number of business days between $t$ e $T$\n",
    "- $c(t,T)$: spot interest rate between reference date and contract maturity\n",
    "- $r(t,T)$: risk free interest rate (DI1 Futures rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpreted-concern",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### BGI Futures Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "standard-jimmy",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgi_fut = my.build_bgi_futures(df, cal=MARKET_CALENDAR, refdate=pd.to_datetime('2021-11-01'),\n",
    "                               spot=257.77)\n",
    "\n",
    "bgi_fut.plot(x='Maturity', y='PU', figsize=(20,6), style='-o',\n",
    "             ylabel='Price', xlabel='Date', title='BGI Futures - 2021-11-01');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "urban-wyoming",
   "metadata": {},
   "source": [
    "- [Indicadores agropecuários\n",
    "](https://www.b3.com.br/pt_br/market-data-e-indices/servicos-de-dados/market-data/consultas/mercado-de-derivativos/indicadores/indicadores-agropecuarios/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rational-journal",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### BGI Futures Cost of Carry Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "unauthorized-bikini",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "bgi_curve = my.build_curve('BGI', df, refdate=pd.to_datetime('2021-11-01'),\n",
    "                           cal=MARKET_CALENDAR, spot=257.77, rf_curve=di1_curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "exciting-correction",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgi_curve().plot(x='Maturity', y='Rate', figsize=(20,6), style='-o',\n",
    "               ylabel='Rate', xlabel='Date', title='BGI Cost of Carry Rates - 2021-11-01');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "military-exclusion",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### BGI implied Cost of Carry\n",
    "\n",
    "- Commodities have additional costs, such as storage costs, for example.\n",
    "\n",
    "- These costs are taken into account when dealing with futures.\n",
    "\n",
    "- In a risk free world the future value of a commodity can be given by:\n",
    "\n",
    "$$\n",
    "S_t ( 1 + r(t,T) )^{DU(t,T)/252}\n",
    "$$\n",
    "\n",
    "- Considering the storage costs is known and has a present value $U$, the future value can be defined such that\n",
    "\n",
    "$$\n",
    "(S_t + U) ( 1 + r(t,T) )^{DU(t,T)/252}\n",
    "$$\n",
    "\n",
    "- $U$ can be estimated from the future prices, following the above equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "major-subdivision",
   "metadata": {},
   "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>DataRef</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>DU</th>\n",
       "      <th>DC</th>\n",
       "      <th>PU</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2021-11-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>257.77</td>\n",
       "      <td>-0.075392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>19</td>\n",
       "      <td>29</td>\n",
       "      <td>274.90</td>\n",
       "      <td>15.602552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>42</td>\n",
       "      <td>60</td>\n",
       "      <td>291.05</td>\n",
       "      <td>29.408951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-01-31</td>\n",
       "      <td>63</td>\n",
       "      <td>91</td>\n",
       "      <td>300.00</td>\n",
       "      <td>36.004676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-02-25</td>\n",
       "      <td>82</td>\n",
       "      <td>116</td>\n",
       "      <td>300.00</td>\n",
       "      <td>33.633674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-03-31</td>\n",
       "      <td>104</td>\n",
       "      <td>150</td>\n",
       "      <td>306.25</td>\n",
       "      <td>36.629114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-04-29</td>\n",
       "      <td>123</td>\n",
       "      <td>179</td>\n",
       "      <td>306.25</td>\n",
       "      <td>33.938555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-05-31</td>\n",
       "      <td>145</td>\n",
       "      <td>211</td>\n",
       "      <td>300.20</td>\n",
       "      <td>24.945678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-10-31</td>\n",
       "      <td>251</td>\n",
       "      <td>364</td>\n",
       "      <td>329.15</td>\n",
       "      <td>36.010623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>271</td>\n",
       "      <td>394</td>\n",
       "      <td>329.15</td>\n",
       "      <td>33.097832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DataRef   Maturity   DU   DC      PU          U\n",
       "0 2021-11-01 2021-11-03    1    2  257.77  -0.075392\n",
       "1 2021-11-01 2021-11-30   19   29  274.90  15.602552\n",
       "2 2021-11-01 2021-12-31   42   60  291.05  29.408951\n",
       "3 2021-11-01 2022-01-31   63   91  300.00  36.004676\n",
       "4 2021-11-01 2022-02-25   82  116  300.00  33.633674\n",
       "5 2021-11-01 2022-03-31  104  150  306.25  36.629114\n",
       "6 2021-11-01 2022-04-29  123  179  306.25  33.938555\n",
       "7 2021-11-01 2022-05-31  145  211  300.20  24.945678\n",
       "8 2021-11-01 2022-10-31  251  364  329.15  36.010623\n",
       "9 2021-11-01 2022-11-30  271  394  329.15  33.097832"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# U for BGI Futures\n",
    "bgi_fut['U'] = bgi_fut['PU'] / ( (1 + di1_curve(bgi_fut['DU'])['Rate']) ** (bgi_fut['DU']/252) ) - 257.77\n",
    "bgi_fut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frank-stamp",
   "metadata": {},
   "source": [
    "- $U$ is positive, indicating value that must be paid.\n",
    "    - Negative values of $U$, as happens in IND futures, represent received values for the asset owner.\n",
    "\n",
    "- $U$ can be writen in terms of $S_t$ and as a constant rate $c(t,T)$.\n",
    "\n",
    "$$\n",
    "S_{t} \\frac{( 1 + r(t,T) )^{DU(t,T)/252}}{(1 + c(t,T)) ^ {DU(t,T)/252}}\n",
    "$$\n",
    "\n",
    "- Positive values of $U$ yield negative values for $c(t,T)$ (BGI futures for example).\n",
    "- Negative values of $U$ yield positive values for $c(t,T)$ (IND futures for example)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "atlantic-student",
   "metadata": {},
   "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>DataRef</th>\n",
       "      <th>Maturity</th>\n",
       "      <th>DU</th>\n",
       "      <th>DC</th>\n",
       "      <th>PU</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2021-11-03</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>105822.0</td>\n",
       "      <td>-30.950523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>106383.0</td>\n",
       "      <td>-422.229627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-02-16</td>\n",
       "      <td>75</td>\n",
       "      <td>107</td>\n",
       "      <td>107953.0</td>\n",
       "      <td>-646.159276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-04-13</td>\n",
       "      <td>113</td>\n",
       "      <td>163</td>\n",
       "      <td>109688.0</td>\n",
       "      <td>-835.631960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-06-15</td>\n",
       "      <td>156</td>\n",
       "      <td>226</td>\n",
       "      <td>111874.0</td>\n",
       "      <td>-1048.608267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-08-17</td>\n",
       "      <td>200</td>\n",
       "      <td>289</td>\n",
       "      <td>114166.0</td>\n",
       "      <td>-1267.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-10-13</td>\n",
       "      <td>239</td>\n",
       "      <td>346</td>\n",
       "      <td>116233.0</td>\n",
       "      <td>-1459.424077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2022-12-14</td>\n",
       "      <td>281</td>\n",
       "      <td>408</td>\n",
       "      <td>118453.0</td>\n",
       "      <td>-1667.153391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2023-02-15</td>\n",
       "      <td>326</td>\n",
       "      <td>471</td>\n",
       "      <td>120819.0</td>\n",
       "      <td>-1888.515135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2023-04-12</td>\n",
       "      <td>363</td>\n",
       "      <td>527</td>\n",
       "      <td>122780.0</td>\n",
       "      <td>-2070.921647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2023-06-14</td>\n",
       "      <td>405</td>\n",
       "      <td>590</td>\n",
       "      <td>125072.0</td>\n",
       "      <td>-2276.952736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2023-08-16</td>\n",
       "      <td>450</td>\n",
       "      <td>653</td>\n",
       "      <td>127524.0</td>\n",
       "      <td>-2496.782597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2023-10-18</td>\n",
       "      <td>493</td>\n",
       "      <td>716</td>\n",
       "      <td>129858.0</td>\n",
       "      <td>-2707.318339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-11-01</td>\n",
       "      <td>2023-12-13</td>\n",
       "      <td>531</td>\n",
       "      <td>772</td>\n",
       "      <td>131873.0</td>\n",
       "      <td>-2892.075417</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DataRef   Maturity   DU   DC        PU            U\n",
       "0  2021-11-01 2021-11-03    1    2  105822.0   -30.950523\n",
       "1  2021-11-01 2021-12-15   30   44  106383.0  -422.229627\n",
       "2  2021-11-01 2022-02-16   75  107  107953.0  -646.159276\n",
       "3  2021-11-01 2022-04-13  113  163  109688.0  -835.631960\n",
       "4  2021-11-01 2022-06-15  156  226  111874.0 -1048.608267\n",
       "5  2021-11-01 2022-08-17  200  289  114166.0 -1267.421053\n",
       "6  2021-11-01 2022-10-13  239  346  116233.0 -1459.424077\n",
       "7  2021-11-01 2022-12-14  281  408  118453.0 -1667.153391\n",
       "8  2021-11-01 2023-02-15  326  471  120819.0 -1888.515135\n",
       "9  2021-11-01 2023-04-12  363  527  122780.0 -2070.921647\n",
       "10 2021-11-01 2023-06-14  405  590  125072.0 -2276.952736\n",
       "11 2021-11-01 2023-08-16  450  653  127524.0 -2496.782597\n",
       "12 2021-11-01 2023-10-18  493  716  129858.0 -2707.318339\n",
       "13 2021-11-01 2023-12-13  531  772  131873.0 -2892.075417"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# U for IND Futures\n",
    "ind_fut = my.build_ind_futures(df, refdate=pd.to_datetime('2021-11-01'),\n",
    "                           cal=MARKET_CALENDAR, spot=105822)\n",
    "ind_fut['U'] = ind_fut['PU'] / ( (1 + di1_curve(ind_fut['DU'])['Rate']) ** (ind_fut['DU']/252) ) - 105822\n",
    "ind_fut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minute-question",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Corn Futures - CCM\n",
    "\n",
    "- This trades the future values of yellow corn in bulk.\n",
    "    - Yellow corn in bulk, with regular odor and appearance, hard or semihard from the latest crop.\n",
    "\n",
    "### Risk Factors\n",
    "\n",
    "- B3 CORE Risk Engine considers the future prices itself as risk factors for Corn Futures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mobile-blame",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "- Alternatively, from arbitrage theory the future price of Corn Futures can be written as\n",
    "\n",
    "$$\n",
    "PU_{CCM}(T) = S_{t} \\frac{( 1 + r(t,T) )^{DU(t,T)/252}}{(1 + c(t,T)) ^ {DU(t,T)/252}}\n",
    "$$\n",
    "\n",
    "where\n",
    "\n",
    "- $t$: current instant of time (reference date)\n",
    "- $T$: maturity date (date when the contract expires)\n",
    "- $S_{t}$ the spot value at $t$\n",
    "- $PU_{CCM}(T)$ the future value of Corn at $T$\n",
    "- $DU(t,T)$: the total number of business days between $t$ e $T$\n",
    "- $c(t,T)$: spot interest rate between reference date and contract maturity\n",
    "- $r(t,T)$: risk free interest rate (DI1 Futures rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interested-guide",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### CCM Futures Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "piano-hierarchy",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccm_fut = my.build_ccm_futures(df, cal=MARKET_CALENDAR, refdate=pd.to_datetime('2021-11-01'),\n",
    "                               spot=86.9)\n",
    "\n",
    "ccm_fut.plot(x='Maturity', y='PU', figsize=(20,6), style='-o',\n",
    "             ylabel='Price', xlabel='Date', title='CCM Futures - 2021-11-01');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "complete-overhead",
   "metadata": {},
   "source": [
    "- [Indicadores agropecuários\n",
    "](https://www.b3.com.br/pt_br/market-data-e-indices/servicos-de-dados/market-data/consultas/mercado-de-derivativos/indicadores/indicadores-agropecuarios/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "focused-chancellor",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### CCM Futures Cost of Carry Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "incredible-tolerance",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ccm_curve = my.build_curve('CCM', df, refdate=pd.to_datetime('2021-11-01'), cal=MARKET_CALENDAR,\n",
    "                           spot=86.9, rf_curve=di1_curve)\n",
    "\n",
    "ccm_curve().plot(x='Maturity', y='Rate', figsize=(20,6), style='-o',\n",
    "               ylabel='Rate', xlabel='Date', title='CCM Cost of Carry Rates - 2021-11-01');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprising-abuse",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Debêntures\n",
    "\n",
    "* Debêntures are fixed income instruments issued by companies\n",
    "\n",
    "* Commonly indexed by\n",
    "  * Fixed rate\n",
    "  * Floating (DI rates) + *spread*\n",
    "  * Floating %CDI\n",
    "  * IPCA inflation index\n",
    "\n",
    "## Data\n",
    "\n",
    "* [CALC B3](https://calculadorarendafixa.com.br/#/navbar/calculadora)\n",
    "\n",
    "* [ANBIMA|Data](https://data.anbima.com.br/)\n",
    "    \n",
    "* [ANBIMA|Prices](https://www.anbima.com.br/pt_br/informar/taxas-de-debentures.htm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "combined-trance",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overview\n",
    "\n",
    "- Generic cashflow for any bond that pays coupons\n",
    "\n",
    "![](images/fluxo-de-caixa-debenture-simples1.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pleasant-plumbing",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Overview\n",
    "\n",
    "- Generic cashflow for any bond that pays coupons and has amortizations\n",
    "\n",
    "![](images/fluxo-de-caixa-debenture-com-amort.png)\n",
    "\n",
    "- Once you have these future payments the price is obtained by discounting the cashflows with a discount curve and a spread.\n",
    "- But first, how to calculate the cashflows?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attractive-admission",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### VNR - Remaining Notional Value\n",
    "\n",
    "- After an amortization is paid, the amount of notional value that remains unpaid is the $VNR$\n",
    "\n",
    "- At the begining $VNR$ is equal to the notional amount $N$\n",
    "\n",
    "$$\n",
    "VNR_0 = N\n",
    "$$\n",
    "\n",
    "- After the first amortization we have\n",
    "\n",
    "$$\n",
    "VNR_1 = N - A_1\n",
    "$$\n",
    "\n",
    "- After $n$ amortizations\n",
    "\n",
    "$$\n",
    "VNR_n = N - \\sum^n_{i=1} A_i\n",
    "$$\n",
    "\n",
    "- After amortizations the coupons are calculated on $VNR_i$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fancy-singer",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from abc import ABC, abstractmethod\n",
    "\n",
    "def vna_ipca(start_date, end_date):\n",
    "    freq = '1M'\n",
    "    b_dates = pd.date_range(start=start_date, end=end_date, freq=freq, closed=None).to_series()\n",
    "    refdates = b_dates - pd.DateOffset(months=1)\n",
    "    b_dates = b_dates.map(lambda dt: dt.replace(day=15))\n",
    "    refdates = refdates.map(lambda dt: dt.replace(day=1))\n",
    "\n",
    "    dates = pd.DataFrame({\n",
    "        'RefDate': refdates,\n",
    "        'BirthDate': b_dates,\n",
    "        'BirthDateBefore': b_dates.shift()\n",
    "    }).reset_index(drop=True)\n",
    "\n",
    "    ipca = sgs.get(('IPCA', 433))\n",
    "\n",
    "    VNA = pd.merge(dates, ipca, left_on='RefDate', right_on='date')\n",
    "    VNA['VNA'] = 1 + VNA['IPCA']/100\n",
    "    VNA.loc[0, 'VNA'] = 1000\n",
    "    VNA['VNA'] = np.cumprod(VNA['VNA'])\n",
    "\n",
    "    return VNA[['BirthDate', 'VNA']].rename(columns={'BirthDate': 'Date'})\n",
    "\n",
    "\n",
    "class Debenture(ABC):\n",
    "    def __init__(self, **bond_details):\n",
    "        self.issuer = bond_details['issuer']\n",
    "        self.symbol = bond_details['codbond']\n",
    "        self.issue_date = pd.to_datetime(bond_details['issuedate'])\n",
    "        self.start_date = pd.to_datetime(bond_details['startingdate'])\n",
    "        self.maturity_date = pd.to_datetime(bond_details['expiredate'])\n",
    "        self.type = bond_details['method']\n",
    "        self.coupon_rate = bond_details['yield']/100\n",
    "        self.calendar = bond_details['calendar']\n",
    "        self.vne = bond_details['vne']\n",
    "        self.anniversary = bond_details['anniversaryday']\n",
    "        self._cashflow = self._build_cashflow(bond_details['events'])\n",
    "        self.amortization_cashflow = self._build_amortization_cashflow(self._cashflow)\n",
    "        self.coupon_cashflow = self._build_coupon_cashflow(self._cashflow)\n",
    "        self.cashflow = self._build_bond_cashflow()\n",
    "\n",
    "    def _build_cashflow(self, events):\n",
    "        cashflow = pd.DataFrame(events)\n",
    "        cashflow = cashflow.rename(columns={'date': 'Dates'})\n",
    "        cashflow['Dates'] = pd.to_datetime(cashflow['Dates'])\n",
    "        return cashflow.sort_values('Dates').reset_index(drop=True)\n",
    "\n",
    "    def _build_amortization_cashflow(self, cashflow):\n",
    "        amortization_cashflow = cashflow\\\n",
    "            .query('eventType != \"J\"')\\\n",
    "            .rename(columns={'yield': 'Amortization_p'})\\\n",
    "            .assign(Amortization_p=lambda df: df['Amortization_p']/100)\\\n",
    "            .reset_index(drop=True)\n",
    "        \n",
    "        cmp = 1 - amortization_cashflow['Amortization_p'].sum()\n",
    "        sqs = [amortization_cashflow, pd.DataFrame({'Dates': self.maturity_date, 'Amortization_p': [cmp]})]\n",
    "        amortization_cashflow = pd.concat(sqs, axis=0).reset_index(drop=True)\n",
    "        amortization_cashflow['Dates'] = pd.to_datetime(amortization_cashflow['Dates'])\n",
    "        \n",
    "        return amortization_cashflow[['Dates', 'Amortization_p']]\n",
    "\n",
    "    def _build_coupon_cashflow(self, cashflow):\n",
    "        coupon_cashflow = cashflow\\\n",
    "            .query('eventType == \"J\"')\\\n",
    "            .assign(Coupon_p=0)\\\n",
    "            .reset_index(drop=True)\n",
    "        return coupon_cashflow[['Dates', 'Coupon_p']]\n",
    "\n",
    "    def _build_bond_cashflow(self):\n",
    "        bond_cashflow = pd.merge(self.amortization_cashflow, self.coupon_cashflow, on='Dates', how='outer')\n",
    "        bond_cashflow = bond_cashflow.sort_values('Dates').reset_index(drop=True)\n",
    "        \n",
    "        bond_cashflow.loc[np.isnan(bond_cashflow['Amortization_p']), 'Amortization_p'] = 0\n",
    "        \n",
    "        bond_cashflow['c_amt_l'] = np.cumsum(bond_cashflow['Amortization_p'])\n",
    "        bond_cashflow['VNR_p'] = 1 - bond_cashflow['c_amt_l']\n",
    "        bond_cashflow = bond_cashflow.drop('c_amt_l', 1)\n",
    "        bond_cashflow['Fixings'] = list(self.calendar.vec.adjust_next(bond_cashflow['Dates']))\n",
    "        \n",
    "        dates = pd.to_datetime(pd.concat([pd.Series([self.start_date]), bond_cashflow['Fixings']]))\n",
    "        bond_cashflow['DaysBetween'] = my.bizdiff(dates, MARKET_CALENDAR)\n",
    "        \n",
    "        return bond_cashflow\n",
    "    \n",
    "    def market_cashflow(self, refdate, **kwargs):\n",
    "        market_cashflow = self.cashflow[self.cashflow['Dates'] > refdate].reset_index(drop=True)\n",
    "        market_cashflow['BusinessDays'] = list(self.calendar.vec.bizdays(refdate, market_cashflow['Fixings']))\n",
    "        sqs = [market_cashflow['BusinessDays'].head(1), market_cashflow['DaysBetween'].tail(-1)]\n",
    "        market_cashflow['CouponDays'] = pd.concat(sqs)\n",
    "        \n",
    "        self.calculate_notional(refdate, **kwargs)\n",
    "        \n",
    "        market_cashflow['VNR'] = self.notional * market_cashflow['VNR_p']\n",
    "        market_cashflow['Amortizations'] = self.notional * market_cashflow['Amortization_p']\n",
    "\n",
    "        self.vnr = market_cashflow['VNR'].iloc[0]\n",
    "        dx = self.cashflow.loc[self.cashflow['Dates'] <= refdate, 'Dates'].iloc[-1]\n",
    "        self.last_coupon_date = pd.to_datetime(self.calendar.following(dx))\n",
    "        \n",
    "        market_cashflow['Coupon_p'] = self.coupon_rates(market_cashflow, **kwargs)\n",
    "        self.calculate_vna(refdate, **kwargs)\n",
    "        \n",
    "        _N = market_cashflow['VNR'].shift(fill_value=self.vna)\n",
    "        _M = market_cashflow['VNR'].shift(fill_value=self.vnr)\n",
    "        market_cashflow['Coupons'] = _N * (1 + market_cashflow['Coupon_p']) ** (market_cashflow['CouponDays']/252) - _M\n",
    "        market_cashflow['Payments'] = market_cashflow['Coupons'] + market_cashflow['Amortizations']\n",
    "\n",
    "        return market_cashflow\n",
    "    \n",
    "    @abstractmethod\n",
    "    def coupon_rates(self, market_cashflow, **kwargs):\n",
    "        raise NotImplemented()\n",
    "\n",
    "    @abstractmethod\n",
    "    def calculate_vna(self, refdate, **kwargs):\n",
    "        raise NotImplemented()\n",
    "\n",
    "    @abstractmethod\n",
    "    def calculate_notional(self, refdate, **kwargs):\n",
    "        raise NotImplemented()\n",
    "\n",
    "\n",
    "class FixedRateDebenture(Debenture):\n",
    "    def coupon_rates(self, market_cashflow, **kwargs):\n",
    "        return self.coupon_rate\n",
    "    \n",
    "    def calculate_vna(self, refdate, **kwargs):\n",
    "        du = self.calendar.bizdays(self.last_coupon_date, refdate)\n",
    "        self.vna = self.vnr * (1 + self.coupon_rate)**(du/252)\n",
    "\n",
    "    def calculate_notional(self, refdate, **kwargs):\n",
    "        self.notional = self.vne\n",
    "\n",
    "\n",
    "class FloatingRateDebenture(Debenture):\n",
    "    def coupon_rates(self, market_cashflow, **kwargs):\n",
    "        zero_curve = kwargs['zero_curve']\n",
    "        z_rate = zero_curve(market_cashflow['BusinessDays'])\n",
    "\n",
    "        comp1 = (1 + z_rate['Rate']) ** (market_cashflow['BusinessDays']/252)\n",
    "        comp2 = comp1.shift()\n",
    "        \n",
    "        du1 = market_cashflow['BusinessDays']\n",
    "        du2 = du1.shift()\n",
    "        \n",
    "        f_rate = (comp1/comp2) ** (252/(du1 - du2)) - 1\n",
    "        c_rate = pd.concat([z_rate['Rate'].head(1), f_rate.tail(-1)])\n",
    "\n",
    "        fc1 = (1 + c_rate)**(market_cashflow['CouponDays']/252)\n",
    "        fc2 = (1 + self.coupon_rate)**(market_cashflow['CouponDays']/252)\n",
    "        c_rate = (fc1 * fc2)**(252/market_cashflow['CouponDays']) - 1\n",
    "\n",
    "        return c_rate\n",
    "\n",
    "    def calculate_vna(self, refdate, **kwargs):\n",
    "        cdi = kwargs['historical_rates']\n",
    "        cdi_ = cdi[(cdi.index >= self.last_coupon_date) & (cdi.index < refdate)]\n",
    "\n",
    "        c_cdi = np.prod( (1 + cdi_['CDI'] / 100) ** (1/252) * (1 + self.coupon_rate) ** (1/252) )\n",
    "        self.vna = self.vnr * c_cdi\n",
    "\n",
    "    def calculate_notional(self, refdate, **kwargs):\n",
    "        self.notional = self.vne\n",
    "\n",
    "\n",
    "class FloatingPercentualDebenture(Debenture):\n",
    "    def coupon_rates(self, market_cashflow, **kwargs):\n",
    "        zero_curve = kwargs['zero_curve']\n",
    "        z_rate = zero_curve(market_cashflow['BusinessDays'])\n",
    "\n",
    "        comp1 = (1 + z_rate['Rate']) ** (market_cashflow['BusinessDays']/252)\n",
    "        comp2 = comp1.shift()\n",
    "        \n",
    "        du1 = market_cashflow['BusinessDays']\n",
    "        du2 = du1.shift()\n",
    "        \n",
    "        f_rate = (comp1/comp2) ** (252/(du1 - du2)) - 1\n",
    "        c_rate = pd.concat([z_rate['Rate'].head(1), f_rate.tail(-1)])\n",
    "\n",
    "        fc1 = (( (1 + c_rate)**(1/252) - 1 )*self.coupon_rate + 1)**market_cashflow['CouponDays']\n",
    "        c_rate = fc1**(252/market_cashflow['CouponDays']) - 1\n",
    "\n",
    "        return c_rate\n",
    "\n",
    "    def calculate_vna(self, refdate, **kwargs):\n",
    "        cdi = kwargs['historical_rates']\n",
    "        _date = pd.to_datetime(self.calendar.offset(refdate, -1))\n",
    "        cdi_ = cdi[(cdi.index >= self.last_coupon_date) & (cdi.index < _date)]\n",
    "\n",
    "        c_cdi = np.prod( ((1 + cdi_['CDI'] / 100) ** (1/252) - 1) * self.coupon_rate + 1 )\n",
    "        self.vna = self.vnr * c_cdi\n",
    "\n",
    "    def calculate_notional(self, refdate, **kwargs):\n",
    "        self.notional = self.vne\n",
    "\n",
    "\n",
    "class InflationIndexedDebenture(Debenture):\n",
    "    def coupon_rates(self, market_cashflow, **kwargs):\n",
    "        return self.coupon_rate\n",
    "    \n",
    "    def calculate_vna(self, refdate, **kwargs):\n",
    "        du = self.calendar.bizdays(self.last_coupon_date, refdate)\n",
    "        self.vna = self.vnr * (1 + self.coupon_rate)**(du/252)\n",
    "\n",
    "    def calculate_notional(self, refdate, **kwargs):\n",
    "        anniversary = datetime.datetime(self.start_date.year, self.start_date.month, self.anniversary)\n",
    "        anniversary = pd.Timestamp(anniversary)\n",
    "        zero_anniversary = anniversary - pd.DateOffset(months=1) if self.start_date < anniversary else anniversary\n",
    "        first_anniversary = anniversary if self.start_date < anniversary else anniversary + pd.DateOffset(months=1)\n",
    "\n",
    "        VNA_IPCA = kwargs['inflation_index']\n",
    "\n",
    "        N1 = VNA_IPCA.loc[VNA_IPCA['Date'] == zero_anniversary, 'VNA'].values\n",
    "        N2 = VNA_IPCA.loc[VNA_IPCA['Date'] == first_anniversary, 'VNA'].values\n",
    "        dt1 = MARKET_CALENDAR.bizdays(self.start_date, first_anniversary)\n",
    "        dn1 = MARKET_CALENDAR.bizdays(zero_anniversary, first_anniversary)\n",
    "\n",
    "        # last aniversary\n",
    "        anniversary = refdate.replace(day=self.anniversary)\n",
    "        last_anniversary = anniversary - pd.DateOffset(months=1) if refdate < anniversary else anniversary\n",
    "        next_anniversary = anniversary if refdate <= anniversary else anniversary + pd.DateOffset(months=1)\n",
    "        N3 = VNA_IPCA.loc[VNA_IPCA['Date'] == last_anniversary, 'VNA'].values\n",
    "\n",
    "        dt2 = MARKET_CALENDAR.bizdays(last_anniversary, refdate)\n",
    "        dn2 = MARKET_CALENDAR.bizdays(last_anniversary, next_anniversary)\n",
    "\n",
    "        proj = kwargs['projection']\n",
    "        self.notional = self.vne * (N2/N1) ** (dt1/dn1) * (N3/N2) * (1 + proj) ** (dt2/dn2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alpha-liverpool",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Prefixada - Fixed Rate\n",
    "\n",
    "- This is a Fixed Rate Bond\n",
    "- https://data.anbima.com.br/debentures/LMSP12/caracteristicas\n",
    "\n",
    "#### Accrued notional\n",
    "\n",
    "$$\n",
    "N (1 + c) ^ {DU(0,T)/252}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "brave-easter",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'anniversaryday': 1,\n",
       " 'codbond': 'LMSP12',\n",
       " 'events': [{'date': '2026-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2028-04-01', 'eventType': 'A', 'yield': 6.749998},\n",
       "  {'date': '2020-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2021-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2025-04-01', 'eventType': 'A', 'yield': 2.549972},\n",
       "  {'date': '2026-04-01', 'eventType': 'A', 'yield': 5.799981},\n",
       "  {'date': '2029-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2021-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2028-10-01', 'eventType': 'A', 'yield': 6.750023},\n",
       "  {'date': '2025-10-01', 'eventType': 'A', 'yield': 2.550007},\n",
       "  {'date': '2024-10-01', 'eventType': 'A', 'yield': 4.800007},\n",
       "  {'date': '2023-04-01', 'eventType': 'A', 'yield': 5.149973},\n",
       "  {'date': '2029-10-01', 'eventType': 'A', 'yield': 7.399999},\n",
       "  {'date': '2025-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2022-10-01', 'eventType': 'A', 'yield': 4.39999},\n",
       "  {'date': '2022-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2027-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2023-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2027-04-01', 'eventType': 'A', 'yield': 5.650013},\n",
       "  {'date': '2024-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2028-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2024-04-01', 'eventType': 'A', 'yield': 4.800041},\n",
       "  {'date': '2029-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2030-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2025-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2027-10-01', 'eventType': 'A', 'yield': 5.650009},\n",
       "  {'date': '2026-10-01', 'eventType': 'A', 'yield': 5.799972},\n",
       "  {'date': '2029-04-01', 'eventType': 'A', 'yield': 7.399997},\n",
       "  {'date': '2026-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2027-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2022-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2022-04-01', 'eventType': 'A', 'yield': 4.4},\n",
       "  {'date': '2023-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2024-10-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2028-04-01', 'eventType': 'J', 'yield': 9.76},\n",
       "  {'date': '2023-10-01', 'eventType': 'A', 'yield': 5.150009}],\n",
       " 'expiredate': '2030-04-01',\n",
       " 'firstDay': None,\n",
       " 'issuedate': '2020-04-01',\n",
       " 'issuer': 'CONCESSIONARIA DAS LINHAS 5 E 17 DO METRO DE SAO PAULO S.A.',\n",
       " 'method': 'PRE',\n",
       " 'note': None,\n",
       " 'startingdate': '2020-04-03',\n",
       " 'status': 'A',\n",
       " 'tipoIF': {'codigo': 'DEBENTURE', 'codigoAsString': 'DEB', 'id': 1},\n",
       " 'vne': 1000,\n",
       " 'yield': 9.76}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_url = 'https://calculadorarendafixa.com.br/bond-calculator-web/free/getbonddetails/LMSP12'\n",
    "res = requests.get(site_url)\n",
    "\n",
    "bond_details = res.json()\n",
    "bond_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "expired-peeing",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
       "      <th>VNR_p</th>\n",
       "      <th>Fixings</th>\n",
       "      <th>DaysBetween</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2021-10-01</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9560</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>2022-10-03</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8605</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8090</td>\n",
       "      <td>2023-10-02</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>0.0480</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7610</td>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024-10-01</td>\n",
       "      <td>0.0480</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7130</td>\n",
       "      <td>2024-10-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025-04-01</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>2025-04-01</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2025-10-01</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6620</td>\n",
       "      <td>2025-10-01</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5460</td>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2027-04-01</td>\n",
       "      <td>0.0565</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4895</td>\n",
       "      <td>2027-04-01</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2027-10-01</td>\n",
       "      <td>0.0565</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4330</td>\n",
       "      <td>2027-10-01</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2028-04-01</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3655</td>\n",
       "      <td>2028-04-03</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2028-10-01</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2980</td>\n",
       "      <td>2028-10-02</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2029-04-01</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2240</td>\n",
       "      <td>2029-04-02</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2029-10-01</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>2029-10-01</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2030-04-01</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2030-04-01</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Dates  Amortization_p  Coupon_p   VNR_p     Fixings  DaysBetween\n",
       "0  2020-10-01          0.0000         0  1.0000  2020-10-01          124\n",
       "1  2021-04-01          0.0000         0  1.0000  2021-04-01          124\n",
       "2  2021-10-01          0.0000         0  1.0000  2021-10-01          127\n",
       "3  2022-04-01          0.0440         0  0.9560  2022-04-01          125\n",
       "4  2022-10-01          0.0440         0  0.9120  2022-10-03          127\n",
       "5  2023-04-01          0.0515         0  0.8605  2023-04-03          125\n",
       "6  2023-10-01          0.0515         0  0.8090  2023-10-02          125\n",
       "7  2024-04-01          0.0480         0  0.7610  2024-04-01          122\n",
       "8  2024-10-01          0.0480         0  0.7130  2024-10-01          129\n",
       "9  2025-04-01          0.0255         0  0.6875  2025-04-01          125\n",
       "10 2025-10-01          0.0255         0  0.6620  2025-10-01          127\n",
       "11 2026-04-01          0.0580         0  0.6040  2026-04-01          126\n",
       "12 2026-10-01          0.0580         0  0.5460  2026-10-01          126\n",
       "13 2027-04-01          0.0565         0  0.4895  2027-04-01          123\n",
       "14 2027-10-01          0.0565         0  0.4330  2027-10-01          128\n",
       "15 2028-04-01          0.0675         0  0.3655  2028-04-03          126\n",
       "16 2028-10-01          0.0675         0  0.2980  2028-10-02          125\n",
       "17 2029-04-01          0.0740         0  0.2240  2029-04-02          122\n",
       "18 2029-10-01          0.0740         0  0.1500  2029-10-01          127\n",
       "19 2030-04-01          0.1500         0  0.0000  2030-04-01          123"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bond_details['calendar'] = MARKET_CALENDAR\n",
    "deb = FixedRateDebenture(**bond_details)\n",
    "deb.cashflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technological-gather",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Prefixada - Fixed Rate\n",
    "\n",
    "![](images/fluxo-de-caixa-debenture-prefixada.png)\n",
    "\n",
    "- $c$ is the coupon rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "turkish-warrant",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
       "      <th>VNR_p</th>\n",
       "      <th>Fixings</th>\n",
       "      <th>DaysBetween</th>\n",
       "      <th>BusinessDays</th>\n",
       "      <th>CouponDays</th>\n",
       "      <th>VNR</th>\n",
       "      <th>Amortizations</th>\n",
       "      <th>Coupons</th>\n",
       "      <th>Payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.9560</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>125</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>956.00000</td>\n",
       "      <td>44.00000</td>\n",
       "      <td>45.196791</td>\n",
       "      <td>89.196791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.9120</td>\n",
       "      <td>2022-10-03</td>\n",
       "      <td>127</td>\n",
       "      <td>232</td>\n",
       "      <td>127</td>\n",
       "      <td>912.00010</td>\n",
       "      <td>43.99990</td>\n",
       "      <td>45.937044</td>\n",
       "      <td>89.936944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.8605</td>\n",
       "      <td>2023-04-03</td>\n",
       "      <td>125</td>\n",
       "      <td>357</td>\n",
       "      <td>125</td>\n",
       "      <td>860.50037</td>\n",
       "      <td>51.49973</td>\n",
       "      <td>43.116609</td>\n",
       "      <td>94.616339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.8090</td>\n",
       "      <td>2023-10-02</td>\n",
       "      <td>125</td>\n",
       "      <td>482</td>\n",
       "      <td>125</td>\n",
       "      <td>809.00028</td>\n",
       "      <td>51.50009</td>\n",
       "      <td>40.681857</td>\n",
       "      <td>92.181947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>0.0480</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.7610</td>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>122</td>\n",
       "      <td>604</td>\n",
       "      <td>122</td>\n",
       "      <td>760.99987</td>\n",
       "      <td>48.00041</td>\n",
       "      <td>37.308315</td>\n",
       "      <td>85.308725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-10-01</td>\n",
       "      <td>0.0480</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.7130</td>\n",
       "      <td>2024-10-01</td>\n",
       "      <td>129</td>\n",
       "      <td>733</td>\n",
       "      <td>129</td>\n",
       "      <td>712.99980</td>\n",
       "      <td>48.00007</td>\n",
       "      <td>37.156730</td>\n",
       "      <td>85.156800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2025-04-01</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>2025-04-01</td>\n",
       "      <td>125</td>\n",
       "      <td>858</td>\n",
       "      <td>125</td>\n",
       "      <td>687.50008</td>\n",
       "      <td>25.49972</td>\n",
       "      <td>33.708476</td>\n",
       "      <td>59.208196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2025-10-01</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.6620</td>\n",
       "      <td>2025-10-01</td>\n",
       "      <td>127</td>\n",
       "      <td>985</td>\n",
       "      <td>127</td>\n",
       "      <td>662.00001</td>\n",
       "      <td>25.50007</td>\n",
       "      <td>33.035274</td>\n",
       "      <td>58.535344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>126</td>\n",
       "      <td>1111</td>\n",
       "      <td>126</td>\n",
       "      <td>604.00020</td>\n",
       "      <td>57.99981</td>\n",
       "      <td>31.553614</td>\n",
       "      <td>89.553424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>0.0580</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.5460</td>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>126</td>\n",
       "      <td>1237</td>\n",
       "      <td>126</td>\n",
       "      <td>546.00048</td>\n",
       "      <td>57.99972</td>\n",
       "      <td>28.789107</td>\n",
       "      <td>86.788827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2027-04-01</td>\n",
       "      <td>0.0565</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.4895</td>\n",
       "      <td>2027-04-01</td>\n",
       "      <td>123</td>\n",
       "      <td>1360</td>\n",
       "      <td>123</td>\n",
       "      <td>489.50035</td>\n",
       "      <td>56.50013</td>\n",
       "      <td>25.390784</td>\n",
       "      <td>81.890914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2027-10-01</td>\n",
       "      <td>0.0565</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.4330</td>\n",
       "      <td>2027-10-01</td>\n",
       "      <td>128</td>\n",
       "      <td>1488</td>\n",
       "      <td>128</td>\n",
       "      <td>433.00026</td>\n",
       "      <td>56.50009</td>\n",
       "      <td>23.710750</td>\n",
       "      <td>80.210840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2028-04-01</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.3655</td>\n",
       "      <td>2028-04-03</td>\n",
       "      <td>126</td>\n",
       "      <td>1614</td>\n",
       "      <td>126</td>\n",
       "      <td>365.50028</td>\n",
       "      <td>67.49998</td>\n",
       "      <td>20.638554</td>\n",
       "      <td>88.138534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2028-10-01</td>\n",
       "      <td>0.0675</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.2980</td>\n",
       "      <td>2028-10-02</td>\n",
       "      <td>125</td>\n",
       "      <td>1739</td>\n",
       "      <td>125</td>\n",
       "      <td>298.00005</td>\n",
       "      <td>67.50023</td>\n",
       "      <td>17.279749</td>\n",
       "      <td>84.779979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2029-04-01</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.2240</td>\n",
       "      <td>2029-04-02</td>\n",
       "      <td>122</td>\n",
       "      <td>1861</td>\n",
       "      <td>122</td>\n",
       "      <td>224.00008</td>\n",
       "      <td>73.99997</td>\n",
       "      <td>13.742739</td>\n",
       "      <td>87.742709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2029-10-01</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>2029-10-01</td>\n",
       "      <td>127</td>\n",
       "      <td>1988</td>\n",
       "      <td>127</td>\n",
       "      <td>150.00009</td>\n",
       "      <td>73.99999</td>\n",
       "      <td>10.763495</td>\n",
       "      <td>84.763485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2030-04-01</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>0.0976</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2030-04-01</td>\n",
       "      <td>123</td>\n",
       "      <td>2111</td>\n",
       "      <td>123</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>150.00009</td>\n",
       "      <td>6.975488</td>\n",
       "      <td>156.975578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Dates  Amortization_p  Coupon_p   VNR_p     Fixings  DaysBetween  \\\n",
       "0  2022-04-01          0.0440    0.0976  0.9560  2022-04-01          125   \n",
       "1  2022-10-01          0.0440    0.0976  0.9120  2022-10-03          127   \n",
       "2  2023-04-01          0.0515    0.0976  0.8605  2023-04-03          125   \n",
       "3  2023-10-01          0.0515    0.0976  0.8090  2023-10-02          125   \n",
       "4  2024-04-01          0.0480    0.0976  0.7610  2024-04-01          122   \n",
       "5  2024-10-01          0.0480    0.0976  0.7130  2024-10-01          129   \n",
       "6  2025-04-01          0.0255    0.0976  0.6875  2025-04-01          125   \n",
       "7  2025-10-01          0.0255    0.0976  0.6620  2025-10-01          127   \n",
       "8  2026-04-01          0.0580    0.0976  0.6040  2026-04-01          126   \n",
       "9  2026-10-01          0.0580    0.0976  0.5460  2026-10-01          126   \n",
       "10 2027-04-01          0.0565    0.0976  0.4895  2027-04-01          123   \n",
       "11 2027-10-01          0.0565    0.0976  0.4330  2027-10-01          128   \n",
       "12 2028-04-01          0.0675    0.0976  0.3655  2028-04-03          126   \n",
       "13 2028-10-01          0.0675    0.0976  0.2980  2028-10-02          125   \n",
       "14 2029-04-01          0.0740    0.0976  0.2240  2029-04-02          122   \n",
       "15 2029-10-01          0.0740    0.0976  0.1500  2029-10-01          127   \n",
       "16 2030-04-01          0.1500    0.0976  0.0000  2030-04-01          123   \n",
       "\n",
       "    BusinessDays  CouponDays        VNR  Amortizations    Coupons    Payments  \n",
       "0            105         105  956.00000       44.00000  45.196791   89.196791  \n",
       "1            232         127  912.00010       43.99990  45.937044   89.936944  \n",
       "2            357         125  860.50037       51.49973  43.116609   94.616339  \n",
       "3            482         125  809.00028       51.50009  40.681857   92.181947  \n",
       "4            604         122  760.99987       48.00041  37.308315   85.308725  \n",
       "5            733         129  712.99980       48.00007  37.156730   85.156800  \n",
       "6            858         125  687.50008       25.49972  33.708476   59.208196  \n",
       "7            985         127  662.00001       25.50007  33.035274   58.535344  \n",
       "8           1111         126  604.00020       57.99981  31.553614   89.553424  \n",
       "9           1237         126  546.00048       57.99972  28.789107   86.788827  \n",
       "10          1360         123  489.50035       56.50013  25.390784   81.890914  \n",
       "11          1488         128  433.00026       56.50009  23.710750   80.210840  \n",
       "12          1614         126  365.50028       67.49998  20.638554   88.138534  \n",
       "13          1739         125  298.00005       67.50023  17.279749   84.779979  \n",
       "14          1861         122  224.00008       73.99997  13.742739   87.742709  \n",
       "15          1988         127  150.00009       73.99999  10.763495   84.763485  \n",
       "16          2111         123    0.00000      150.00009   6.975488  156.975578  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refdate = pd.to_datetime('2021-11-01')\n",
    "deb.market_cashflow(refdate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "challenging-amplifier",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "$$\n",
    "P = \\sum_{i=1}^{M} \\frac{F_i}{(1 + c^\\prime)^{DU_i/252}}\n",
    "$$\n",
    "\n",
    "- $c^\\prime$ is the fixed rate used by market participants\n",
    "\n",
    "- $c^\\prime$ is the internal rate of return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "anonymous-annotation",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Pós-fixada - Floating Rate\n",
    "\n",
    "- This is a Floating Rate Bond with a spread over the floating interest rate.\n",
    "- The floating interest rate is the DI rate.\n",
    "- For future values the rates are obtained from the **DI1 curve**.\n",
    "- https://data.anbima.com.br/debentures/ALGT12/caracteristicas\n",
    "\n",
    "#### Accrued notional\n",
    "\n",
    "$$\n",
    "N \\prod^T_{t=1} \\left[ (1 + CDI_t) ^ {1/252} (1 + c) ^ {1/252} \\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "normal-secretariat",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
       "      <th>VNR_p</th>\n",
       "      <th>Fixings</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2019-10-18</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2020-12-11</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2021-10-18</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-04-18</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-04-18</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-10-18</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-04-18</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2023-04-18</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2023-10-18</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates  Amortization_p  Coupon_p  VNR_p     Fixings  DaysBetween\n",
       "0 2019-10-18            0.00         0   1.00  2019-10-18          128\n",
       "1 2020-04-18            0.00         0   1.00  2020-04-20          125\n",
       "2 2020-10-18            0.00         0   1.00  2020-10-19          125\n",
       "3 2020-12-11            0.25         0   0.75  2020-12-11           38\n",
       "4 2021-10-18            0.00         0   0.75  2021-10-18          212\n",
       "5 2022-04-18            0.25         0   0.50  2022-04-18          125\n",
       "6 2022-10-18            0.00         0   0.50  2022-10-18          127\n",
       "7 2023-04-18            0.25         0   0.25  2023-04-18          125\n",
       "8 2023-10-18            0.00         0   0.25  2023-10-18          126\n",
       "9 2024-04-18            0.25         0   0.00  2024-04-18          124"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_url = 'https://calculadorarendafixa.com.br/bond-calculator-web/free/getbonddetails/ALGT12'\n",
    "res = requests.get(site_url)\n",
    "\n",
    "bond_details = res.json()\n",
    "bond_details['calendar'] = MARKET_CALENDAR\n",
    "\n",
    "deb = FloatingRateDebenture(**bond_details)\n",
    "deb.cashflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operating-sense",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Pós-fixada - Floating Rate\n",
    "\n",
    "![](images/fluxo-de-caixa-debenture-posfixada.png)\n",
    "\n",
    "\n",
    "- $c$ is the spread\n",
    "\n",
    "- $f_i$ are forward rates between payment dates\n",
    "\n",
    "- These forward rates come from the DI1 curve\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "acknowledged-oklahoma",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
       "      <th>VNR_p</th>\n",
       "      <th>Fixings</th>\n",
       "      <th>DaysBetween</th>\n",
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       "      <th>CouponDays</th>\n",
       "      <th>VNR</th>\n",
       "      <th>Amortizations</th>\n",
       "      <th>Coupons</th>\n",
       "      <th>Payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-18</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.121848</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-04-18</td>\n",
       "      <td>125</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>500.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>28.597028</td>\n",
       "      <td>278.597028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.155713</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-10-18</td>\n",
       "      <td>127</td>\n",
       "      <td>242</td>\n",
       "      <td>127</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.829169</td>\n",
       "      <td>37.829169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-04-18</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.150397</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2023-04-18</td>\n",
       "      <td>125</td>\n",
       "      <td>367</td>\n",
       "      <td>125</td>\n",
       "      <td>250.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>35.984811</td>\n",
       "      <td>285.984811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10-18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.147236</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2023-10-18</td>\n",
       "      <td>126</td>\n",
       "      <td>493</td>\n",
       "      <td>126</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.772803</td>\n",
       "      <td>17.772803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.142707</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>124</td>\n",
       "      <td>617</td>\n",
       "      <td>124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>16.960869</td>\n",
       "      <td>266.960869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates  Amortization_p  Coupon_p  VNR_p     Fixings  DaysBetween  \\\n",
       "0 2022-04-18            0.25  0.121848   0.50  2022-04-18          125   \n",
       "1 2022-10-18            0.00  0.155713   0.50  2022-10-18          127   \n",
       "2 2023-04-18            0.25  0.150397   0.25  2023-04-18          125   \n",
       "3 2023-10-18            0.00  0.147236   0.25  2023-10-18          126   \n",
       "4 2024-04-18            0.25  0.142707   0.00  2024-04-18          124   \n",
       "\n",
       "   BusinessDays  CouponDays    VNR  Amortizations    Coupons    Payments  \n",
       "0           115         115  500.0          250.0  28.597028  278.597028  \n",
       "1           242         127  500.0            0.0  37.829169   37.829169  \n",
       "2           367         125  250.0          250.0  35.984811  285.984811  \n",
       "3           493         126  250.0            0.0  17.772803   17.772803  \n",
       "4           617         124    0.0          250.0  16.960869  266.960869  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refdate = pd.to_datetime('2021-11-01')\n",
    "di1_curve = my.build_curve('DI1', df, notional=100000, cal=MARKET_CALENDAR, refdate=refdate)\n",
    "cdi = sgs.get(('CDI', 4389))\n",
    "deb.market_cashflow(refdate, historical_rates=cdi, zero_curve=di1_curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-polymer",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "$$\n",
    "P = \\sum_{i=1}^{M} \\frac{F_i}{(1 + r(DU_i))^{DU_i/252}(1 + c^\\prime)^{DU_i/252}}\n",
    "$$\n",
    "\n",
    "- $c^\\prime$ is the fixed rate used by market participants\n",
    "- $r(DU_i)$ represents DI rates obtained from the DI1 curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "christian-university",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Pós-fixada % CDI - % Floating Rate\n",
    "\n",
    "- This is a Floating Rate Bond with the floating interest rate multiplied by a factor (percentual).\n",
    "- The floating interest rate is the DI rate.\n",
    "- For future values the rates are obtained from the **DI1 curve**.\n",
    "- https://data.anbima.com.br/debentures/CEPE18/caracteristicas\n",
    "\n",
    "#### Accrued notional\n",
    "\n",
    "$$\n",
    "N \\prod^T_{t=1} \\left[1 + c \\cdot \\left( (1 + CDI_t) ^ {1/252} - 1 \\right) \\right]\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "welsh-mediterranean",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2018-08-08</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2019-08-08</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>2020-08-10</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-02-08</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>2021-02-08</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-08-08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2021-08-09</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-02-08</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2022-02-08</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022-08-08</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2022-08-08</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-02-08</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2023-02-08</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates  Amortization_p  Coupon_p  VNR_p     Fixings  DaysBetween\n",
       "0 2018-08-08            0.00         0   1.00  2018-08-08          120\n",
       "1 2019-02-08            0.00         0   1.00  2019-02-08          126\n",
       "2 2019-08-08            0.00         0   1.00  2019-08-08          124\n",
       "3 2020-02-08            0.00         0   1.00  2020-02-10          129\n",
       "4 2020-08-08            0.05         0   0.95  2020-08-10          124\n",
       "5 2021-02-08            0.05         0   0.90  2021-02-08          125\n",
       "6 2021-08-08            0.15         0   0.75  2021-08-09          125\n",
       "7 2022-02-08            0.25         0   0.50  2022-02-08          127\n",
       "8 2022-08-08            0.25         0   0.25  2022-08-08          124\n",
       "9 2023-02-08            0.25         0   0.00  2023-02-08          128"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_url = 'https://calculadorarendafixa.com.br/bond-calculator-web/free/getbonddetails/CEPE18'\n",
    "res = requests.get(site_url)\n",
    "\n",
    "bond_details = res.json()\n",
    "bond_details['calendar'] = MARKET_CALENDAR\n",
    "\n",
    "deb = FloatingPercentualDebenture(**bond_details)\n",
    "deb.cashflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "refined-rough",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Pós-fixada % CDI - % Floating Rate\n",
    "\n",
    "![](images/fluxo-de-caixa-debenture-posfixada2.png)\n",
    "\n",
    "- $c$ is the percetual applied to DI rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "progressive-commander",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <td>2022-02-08</td>\n",
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       "      <td>5000.0</td>\n",
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       "      <td>0.25</td>\n",
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       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>370.525642</td>\n",
       "      <td>2870.525642</td>\n",
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       "      <td>321</td>\n",
       "      <td>128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>193.964542</td>\n",
       "      <td>2693.964542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates  Amortization_p  Coupon_p  VNR_p     Fixings  DaysBetween  \\\n",
       "0 2022-02-08            0.25  0.105826   0.50  2022-02-08          127   \n",
       "1 2022-08-08            0.25  0.156365   0.25  2022-08-08          124   \n",
       "2 2023-02-08            0.25  0.158483   0.00  2023-02-08          128   \n",
       "\n",
       "   BusinessDays  CouponDays     VNR  Amortizations     Coupons     Payments  \n",
       "0            69          69  5000.0         2500.0  214.647845  2714.647845  \n",
       "1           193         124  2500.0         2500.0  370.525642  2870.525642  \n",
       "2           321         128     0.0         2500.0  193.964542  2693.964542  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refdate = pd.to_datetime('2021-11-01')\n",
    "di1_curve = my.build_curve('DI1', df, notional=100000, cal=MARKET_CALENDAR, refdate=refdate)\n",
    "cdi = sgs.get(('CDI', 4389))\n",
    "deb.market_cashflow(refdate, historical_rates=cdi, zero_curve=di1_curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rough-importance",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "$$\n",
    "P = \\sum_{i=1}^{M} \\frac{F_i}{(((1 + r(DU_i))^{1/252} - 1)\\cdot c^\\prime + 1)^{DU_i}}\n",
    "$$\n",
    "\n",
    "- $c^\\prime$ is the percentual used by the market participants\n",
    "- $r(DU_i)$ represents DI rates obtained from the DI1 curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "presidential-impact",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Indexada - Inflation Indexed\n",
    "\n",
    "- This is a Inflation Indexed Bond with the notional amount indexed to the IPCA Index and that pays a spread over the variation of the inflation.\n",
    "- https://data.anbima.com.br/debentures/AESL17/caracteristicas\n",
    "\n",
    "#### Accrued notional\n",
    "\n",
    "$$\n",
    "N \\frac{I_T}{I_0} (1+c)^{DU(0,T)/252}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "russian-possible",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
       "      <th>VNR_p</th>\n",
       "      <th>Fixings</th>\n",
       "      <th>DaysBetween</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-08-15</td>\n",
       "      <td>124</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-08-16</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-15</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-08-15</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-02-15</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024-08-15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2024-08-15</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2025-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2025-02-17</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2025-08-15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2025-08-15</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Dates  Amortization_p  Coupon_p  VNR_p     Fixings  DaysBetween\n",
       "0  2019-02-15             0.0         0    1.0  2019-02-15          110\n",
       "1  2019-08-15             0.0         0    1.0  2019-08-15          124\n",
       "2  2020-02-15             0.0         0    1.0  2020-02-17          129\n",
       "3  2020-08-15             0.0         0    1.0  2020-08-17          124\n",
       "4  2021-02-15             0.0         0    1.0  2021-02-17          125\n",
       "5  2021-08-15             0.0         0    1.0  2021-08-16          125\n",
       "6  2022-02-15             0.0         0    1.0  2022-02-15          127\n",
       "7  2022-08-15             0.0         0    1.0  2022-08-15          124\n",
       "8  2023-02-15             0.0         0    1.0  2023-02-15          128\n",
       "9  2023-08-15             0.0         0    1.0  2023-08-15          123\n",
       "10 2024-02-15             0.0         0    1.0  2024-02-15          124\n",
       "11 2024-08-15             0.5         0    0.5  2024-08-15          127\n",
       "12 2025-02-15             0.0         0    0.5  2025-02-17          129\n",
       "13 2025-08-15             0.5         0    0.0  2025-08-15          123"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "site_url = 'https://calculadorarendafixa.com.br/bond-calculator-web/free/getbonddetails/AESL17'\n",
    "res = requests.get(site_url)\n",
    "\n",
    "bond_details = res.json()\n",
    "bond_details['calendar'] = MARKET_CALENDAR\n",
    "\n",
    "deb = InflationIndexedDebenture(**bond_details)\n",
    "deb.cashflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affecting-swing",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Debênture Indexada - Inflation Indexed\n",
    "\n",
    "![](images/fluxo-de-caixa-debenture-prefixada.png)\n",
    "\n",
    "- $VNA_t = N \\cdot \\frac{I_t}{I_0}$ is indexed to the inflation\n",
    "\n",
    "- $VNR_t = VNA_t \\cdot VNR\\%_t$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "gorgeous-criticism",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>Amortization_p</th>\n",
       "      <th>Coupon_p</th>\n",
       "      <th>VNR_p</th>\n",
       "      <th>Fixings</th>\n",
       "      <th>DaysBetween</th>\n",
       "      <th>BusinessDays</th>\n",
       "      <th>CouponDays</th>\n",
       "      <th>VNR</th>\n",
       "      <th>Amortizations</th>\n",
       "      <th>Coupons</th>\n",
       "      <th>Payments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-02-15</td>\n",
       "      <td>127</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>1182.285199</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>34.075143</td>\n",
       "      <td>34.075143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2022-08-15</td>\n",
       "      <td>124</td>\n",
       "      <td>198</td>\n",
       "      <td>124</td>\n",
       "      <td>1182.285199</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>33.259002</td>\n",
       "      <td>33.259002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-02-15</td>\n",
       "      <td>128</td>\n",
       "      <td>326</td>\n",
       "      <td>128</td>\n",
       "      <td>1182.285199</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>34.347311</td>\n",
       "      <td>34.347311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-08-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2023-08-15</td>\n",
       "      <td>123</td>\n",
       "      <td>449</td>\n",
       "      <td>123</td>\n",
       "      <td>1182.285199</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>32.987077</td>\n",
       "      <td>32.987077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2024-02-15</td>\n",
       "      <td>124</td>\n",
       "      <td>573</td>\n",
       "      <td>124</td>\n",
       "      <td>1182.285199</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>33.259002</td>\n",
       "      <td>33.259002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024-08-15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2024-08-15</td>\n",
       "      <td>127</td>\n",
       "      <td>700</td>\n",
       "      <td>127</td>\n",
       "      <td>591.142600</td>\n",
       "      <td>591.1426</td>\n",
       "      <td>34.075143</td>\n",
       "      <td>625.217742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2025-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2025-02-17</td>\n",
       "      <td>129</td>\n",
       "      <td>829</td>\n",
       "      <td>129</td>\n",
       "      <td>591.142600</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>17.309770</td>\n",
       "      <td>17.309770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2025-08-15</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2025-08-15</td>\n",
       "      <td>123</td>\n",
       "      <td>952</td>\n",
       "      <td>123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>591.1426</td>\n",
       "      <td>16.493539</td>\n",
       "      <td>607.636138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates  Amortization_p  Coupon_p  VNR_p     Fixings  DaysBetween  \\\n",
       "0 2022-02-15             0.0     0.058    1.0  2022-02-15          127   \n",
       "1 2022-08-15             0.0     0.058    1.0  2022-08-15          124   \n",
       "2 2023-02-15             0.0     0.058    1.0  2023-02-15          128   \n",
       "3 2023-08-15             0.0     0.058    1.0  2023-08-15          123   \n",
       "4 2024-02-15             0.0     0.058    1.0  2024-02-15          124   \n",
       "5 2024-08-15             0.5     0.058    0.5  2024-08-15          127   \n",
       "6 2025-02-15             0.0     0.058    0.5  2025-02-17          129   \n",
       "7 2025-08-15             0.5     0.058    0.0  2025-08-15          123   \n",
       "\n",
       "   BusinessDays  CouponDays          VNR  Amortizations    Coupons    Payments  \n",
       "0            74          74  1182.285199         0.0000  34.075143   34.075143  \n",
       "1           198         124  1182.285199         0.0000  33.259002   33.259002  \n",
       "2           326         128  1182.285199         0.0000  34.347311   34.347311  \n",
       "3           449         123  1182.285199         0.0000  32.987077   32.987077  \n",
       "4           573         124  1182.285199         0.0000  33.259002   33.259002  \n",
       "5           700         127   591.142600       591.1426  34.075143  625.217742  \n",
       "6           829         129   591.142600         0.0000  17.309770   17.309770  \n",
       "7           952         123     0.000000       591.1426  16.493539  607.636138  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "refdate = pd.to_datetime('2021-11-01')\n",
    "ipca_index = vna_ipca(pd.to_datetime('2000-01-15'), datetime.datetime.today())\n",
    "deb.market_cashflow(refdate, projection=1.03/100, inflation_index=ipca_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "announced-memorial",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Pricing\n",
    "\n",
    "$$\n",
    "P = \\sum_{i=1}{M} \\frac{F_i}{(1 + c^\\prime)^{DU_i/252}}\n",
    "$$\n",
    "\n",
    "- $c^\\prime$ is the fixed rate used by market participants"
   ]
  }
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