{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
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  "kernelspec": {
   "name": "python38564bitbookconda5e3307fcc1d14fa189ab964662d95352",
   "display_name": "Python 3.8.5 64-bit ('book': conda)"
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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Yield Curve"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import QuantLib as ql\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = [ql.Date(15,6,2020), ql.Date(15,6,2021), ql.Date(15,6,2022)]\n",
    "zeros = [0.01, 0.02, 0.03]\n",
    "curve = ql.ZeroCurve(dates, zeros, ql.ActualActual(), ql.TARGET())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "((Date(15,6,2020), 0.01), (Date(15,6,2021), 0.02), (Date(15,6,2022), 0.03))"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "curve.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f6933e5ddf0>]"
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.plot(*list(zip(*[(dt.to_date(), rate) for dt,rate in curve.nodes()])), marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}