├── 1 Introduction.ipynb
├── 10 Stocks.ipynb
├── 11 Dividend Discount Model.ipynb
├── 12 EAA.ipynb
├── 13 IRR.ipynb
├── 14 Risk.ipynb
├── 15 Beta.ipynb
├── 16 CAPM.ipynb
├── 17 WACC.ipynb
├── 2 Taxes.ipynb
├── 3 Compound Interest.ipynb
├── 4 Plotting Compound Interest.ipynb
├── 5 Present Value.ipynb
├── 6 Present Value Multiple Cash Flows.ipynb
├── 7 Perpetetuities.ipynb
├── 8 Annuities.ipynb
├── 9 Bonds.ipynb
└── README.md


/1 Introduction.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "X = 5"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "metadata": {},
 18 |    "outputs": [
 19 |     {
 20 |      "name": "stdout",
 21 |      "output_type": "stream",
 22 |      "text": [
 23 |       "5\n"
 24 |      ]
 25 |     }
 26 |    ],
 27 |    "source": [
 28 |     "print(X)"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 3,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "name": "stdout",
 38 |      "output_type": "stream",
 39 |      "text": [
 40 |       "8\n"
 41 |      ]
 42 |     }
 43 |    ],
 44 |    "source": [
 45 |     "Y = 3\n",
 46 |     "Z = X+Y\n",
 47 |     "print(Z)"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 4,
 53 |    "metadata": {},
 54 |    "outputs": [
 55 |     {
 56 |      "name": "stdout",
 57 |      "output_type": "stream",
 58 |      "text": [
 59 |       "30\n"
 60 |      ]
 61 |     }
 62 |    ],
 63 |    "source": [
 64 |     "X = 5\n",
 65 |     "Y=10\n",
 66 |     "Z=15\n",
 67 |     "total=X+Y+Z\n",
 68 |     "print(total)"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": 5,
 74 |    "metadata": {},
 75 |    "outputs": [
 76 |     {
 77 |      "name": "stdout",
 78 |      "output_type": "stream",
 79 |      "text": [
 80 |       "30\n"
 81 |      ]
 82 |     }
 83 |    ],
 84 |    "source": [
 85 |     "print(5+10+15)"
 86 |    ]
 87 |   }
 88 |  ],
 89 |  "metadata": {
 90 |   "anaconda-cloud": {},
 91 |   "kernelspec": {
 92 |    "display_name": "Python 3",
 93 |    "language": "python",
 94 |    "name": "python3"
 95 |   },
 96 |   "language_info": {
 97 |    "codemirror_mode": {
 98 |     "name": "ipython",
 99 |     "version": 3
100 |    },
101 |    "file_extension": ".py",
102 |    "mimetype": "text/x-python",
103 |    "name": "python",
104 |    "nbconvert_exporter": "python",
105 |    "pygments_lexer": "ipython3",
106 |    "version": "3.6.1"
107 |   }
108 |  },
109 |  "nbformat": 4,
110 |  "nbformat_minor": 1
111 | }
112 | 


--------------------------------------------------------------------------------
/10 Stocks.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "class Stock:\n",
 12 |     "    def __init__(self,price,dYield,r):\n",
 13 |     "        self.price = price\n",
 14 |     "        self.dYield = dYield\n",
 15 |     "        self.r = r"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 2,
 21 |    "metadata": {
 22 |     "collapsed": true
 23 |    },
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "AAPL = Stock(100,.02,.05)"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 3,
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "name": "stdout",
 36 |      "output_type": "stream",
 37 |      "text": [
 38 |       "100\n",
 39 |       "0.02\n",
 40 |       "0.05\n"
 41 |      ]
 42 |     }
 43 |    ],
 44 |    "source": [
 45 |     "print(AAPL.price)\n",
 46 |     "print(AAPL.dYield)\n",
 47 |     "print(AAPL.r)"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 5,
 53 |    "metadata": {
 54 |     "collapsed": true
 55 |    },
 56 |    "outputs": [],
 57 |    "source": [
 58 |     "class Stock:\n",
 59 |     "    def __init__(self,price,dYield,r):\n",
 60 |     "        self.price = price\n",
 61 |     "        self.dYield = dYield\n",
 62 |     "        self.r = r\n",
 63 |     "    def findDividend(self):\n",
 64 |     "        return self.dYield*self.price"
 65 |    ]
 66 |   },
 67 |   {
 68 |    "cell_type": "code",
 69 |    "execution_count": 6,
 70 |    "metadata": {},
 71 |    "outputs": [
 72 |     {
 73 |      "data": {
 74 |       "text/plain": [
 75 |        "2.0"
 76 |       ]
 77 |      },
 78 |      "execution_count": 6,
 79 |      "metadata": {},
 80 |      "output_type": "execute_result"
 81 |     }
 82 |    ],
 83 |    "source": [
 84 |     "AAPL = Stock(100,.02,.05)\n",
 85 |     "AAPL.findDividend()"
 86 |    ]
 87 |   },
 88 |   {
 89 |    "cell_type": "code",
 90 |    "execution_count": 7,
 91 |    "metadata": {
 92 |     "collapsed": true
 93 |    },
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "class Stock:\n",
 97 |     "    def __init__(self,price,dYield,r):\n",
 98 |     "        self.price = price\n",
 99 |     "        self.dYield = dYield\n",
100 |     "        self.r = r\n",
101 |     "    def findDividend(self):\n",
102 |     "        return self.dYield*self.price\n",
103 |     "    def findPE(self,EPS):\n",
104 |     "        self.PE = self.price/EPS\n",
105 |     "    def findExpectedPrice(self):\n",
106 |     "        expectedPriceWithout = self.price*(1+self.r)\n",
107 |     "        self.expectedPrice = expectedPriceWithout-expectedPriceWithout*(self.dYield)"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 8,
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "name": "stdout",
117 |      "output_type": "stream",
118 |      "text": [
119 |       "5.0\n",
120 |       "102.9\n"
121 |      ]
122 |     }
123 |    ],
124 |    "source": [
125 |     "AAPL = Stock(100,.02,.05)\n",
126 |     "\n",
127 |     "AAPL.findPE(20)\n",
128 |     "AAPL.findExpectedPrice()\n",
129 |     "\n",
130 |     "print(AAPL.PE)\n",
131 |     "print(AAPL.expectedPrice)"
132 |    ]
133 |   }
134 |  ],
135 |  "metadata": {
136 |   "anaconda-cloud": {},
137 |   "kernelspec": {
138 |    "display_name": "Python 3",
139 |    "language": "python",
140 |    "name": "python3"
141 |   },
142 |   "language_info": {
143 |    "codemirror_mode": {
144 |     "name": "ipython",
145 |     "version": 3
146 |    },
147 |    "file_extension": ".py",
148 |    "mimetype": "text/x-python",
149 |    "name": "python",
150 |    "nbconvert_exporter": "python",
151 |    "pygments_lexer": "ipython3",
152 |    "version": "3.6.1"
153 |   }
154 |  },
155 |  "nbformat": 4,
156 |  "nbformat_minor": 1
157 | }
158 | 


--------------------------------------------------------------------------------
/11 Dividend Discount Model.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "data": {
 10 |       "image/png": 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uDbocERFAQT/qbr20gYlFufzLY29qXr2IjAsK+lFWlBfjs1dO58WNe3hmg+bVi0jwFPRp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\nsxxbqPkPAAAAAElFTkSuQmCC\n",
 11 |       "text/plain": [
 12 |        "<matplotlib.figure.Figure at 0x110dc1940>"
 13 |       ]
 14 |      },
 15 |      "metadata": {},
 16 |      "output_type": "display_data"
 17 |     }
 18 |    ],
 19 |    "source": [
 20 |     "dividend = 100\n",
 21 |     "r = .1\n",
 22 |     "year=0\n",
 23 |     "years=[]\n",
 24 |     "values=[]\n",
 25 |     "for _ in range(101):\n",
 26 |     "    years+=[year]\n",
 27 |     "    values+=[dividend]\n",
 28 |     "    year+=1\n",
 29 |     "    dividend=dividend/(1+r)\n",
 30 |     "\n",
 31 |     "import matplotlib.pyplot as plt\n",
 32 |     "plt.plot(years, values)\n",
 33 |     "plt.show()"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 2,
 39 |    "metadata": {},
 40 |    "outputs": [
 41 |     {
 42 |      "data": {
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tWkR+fj5KKXJycrC1tWXmzJlMmjQJW1vb6g4ZMC39WSql1FwR2Y6pLLcAQ5RS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\noVAo9osSCIVCoVDsFyUQCoVCodgv/x9m8i+XlbGvHAAAAABJRU5ErkJggg==\n",
 44 |       "text/plain": [
 45 |        "<matplotlib.figure.Figure at 0x11714cc50>"
 46 |       ]
 47 |      },
 48 |      "metadata": {},
 49 |      "output_type": "display_data"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "import matplotlib.pyplot as plt\n",
 54 |     "growth=0\n",
 55 |     "for _ in range(6):\n",
 56 |     "    dividend = 100\n",
 57 |     "    r = .1\n",
 58 |     "    year=0\n",
 59 |     "    years=[]\n",
 60 |     "    values=[]\n",
 61 |     "    for _ in range(101):\n",
 62 |     "        years+=[year]\n",
 63 |     "        values+=[dividend]\n",
 64 |     "        year+=1\n",
 65 |     "        dividend=dividend*(1+growth)/(1+r)\n",
 66 |     "    growth+=.02\n",
 67 |     "    plt.plot(years, values)\n",
 68 |     "plt.ylim((0,110))\n",
 69 |     "plt.xlabel(\"Year\")\n",
 70 |     "plt.ylabel(\"Dividend Value\")\n",
 71 |     "plt.legend([\"0%\",\"2%\",\"4%\",\"6%\",\"8%\",\"10%\"],title=\"Growth Rate\")\n",
 72 |     "\n",
 73 |     "plt.title(\"Dividend Value at r=10%\")\n",
 74 |     "plt.show()"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "code",
 79 |    "execution_count": 3,
 80 |    "metadata": {
 81 |     "collapsed": true
 82 |    },
 83 |    "outputs": [],
 84 |    "source": [
 85 |     "def DD(div,g,r):\n",
 86 |     "    return div/(r-g)"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 4,
 92 |    "metadata": {},
 93 |    "outputs": [
 94 |     {
 95 |      "name": "stdout",
 96 |      "output_type": "stream",
 97 |      "text": [
 98 |       "1000.0\n",
 99 |       "1250.0\n",
100 |       "1666.6666666666665\n",
101 |       "2499.9999999999995\n",
102 |       "4999.999999999999\n"
103 |      ]
104 |     }
105 |    ],
106 |    "source": [
107 |     "print(DD(100,0,.1))\n",
108 |     "print(DD(100,.02,.1))\n",
109 |     "print(DD(100,.04,.1))\n",
110 |     "print(DD(100,.06,.1))\n",
111 |     "print(DD(100,.08,.1))"
112 |    ]
113 |   }
114 |  ],
115 |  "metadata": {
116 |   "anaconda-cloud": {},
117 |   "kernelspec": {
118 |    "display_name": "Python 3",
119 |    "language": "python",
120 |    "name": "python3"
121 |   },
122 |   "language_info": {
123 |    "codemirror_mode": {
124 |     "name": "ipython",
125 |     "version": 3
126 |    },
127 |    "file_extension": ".py",
128 |    "mimetype": "text/x-python",
129 |    "name": "python",
130 |    "nbconvert_exporter": "python",
131 |    "pygments_lexer": "ipython3",
132 |    "version": "3.6.1"
133 |   }
134 |  },
135 |  "nbformat": 4,
136 |  "nbformat_minor": 1
137 | }
138 | 


--------------------------------------------------------------------------------
/12 EAA.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def annuityValue(year,payment,r):\n",
 12 |     "    annuity_value = payment*(1-(1+r)**(-year))/r\n",
 13 |     "    return annuity_value"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "name": "stdout",
 23 |      "output_type": "stream",
 24 |      "text": [
 25 |       "16.473833531541146\n",
 26 |       "11.162401468523967\n"
 27 |      ]
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "print(annuityValue(5,50,.05)-200)\n",
 32 |     "print(annuityValue(3,50,.05)-125)"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 3,
 38 |    "metadata": {
 39 |     "collapsed": true
 40 |    },
 41 |    "outputs": [],
 42 |    "source": [
 43 |     "A= annuityValue(5,50,.05)-200\n",
 44 |     "B= annuityValue(3,50,.05)-125"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 4,
 50 |    "metadata": {
 51 |     "collapsed": true
 52 |    },
 53 |    "outputs": [],
 54 |    "source": [
 55 |     "def aFactor(r,t):\n",
 56 |     "    return (1-(1+r)**(-t))/r"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 5,
 62 |    "metadata": {},
 63 |    "outputs": [
 64 |     {
 65 |      "name": "stdout",
 66 |      "output_type": "stream",
 67 |      "text": [
 68 |       "3.805040374346408\n",
 69 |       "4.098929421094386\n"
 70 |      ]
 71 |     }
 72 |    ],
 73 |    "source": [
 74 |     "print(A/aFactor(.05,5))\n",
 75 |     "print(B/aFactor(.05,3))"
 76 |    ]
 77 |   }
 78 |  ],
 79 |  "metadata": {
 80 |   "anaconda-cloud": {},
 81 |   "kernelspec": {
 82 |    "display_name": "Python 3",
 83 |    "language": "python",
 84 |    "name": "python3"
 85 |   },
 86 |   "language_info": {
 87 |    "codemirror_mode": {
 88 |     "name": "ipython",
 89 |     "version": 3
 90 |    },
 91 |    "file_extension": ".py",
 92 |    "mimetype": "text/x-python",
 93 |    "name": "python",
 94 |    "nbconvert_exporter": "python",
 95 |    "pygments_lexer": "ipython3",
 96 |    "version": "3.6.1"
 97 |   }
 98 |  },
 99 |  "nbformat": 4,
100 |  "nbformat_minor": 1
101 | }
102 | 


--------------------------------------------------------------------------------
/13 IRR.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def TVM(p,r,t,type):\n",
 12 |     "    discount = (1+r)**t\n",
 13 |     "    if type==\"FV\":\n",
 14 |     "        A = p*discount\n",
 15 |     "        return A\n",
 16 |     "    if type==\"PV\":\n",
 17 |     "        A = p/discount\n",
 18 |     "        return A\n",
 19 |     "\n",
 20 |     "def totalTVM(rate,payments,years):\n",
 21 |     "    total = 0\n",
 22 |     "    for index in range(0,len(payments)):\n",
 23 |     "        total+= TVM(payments[index],years[index],rate,\"PV\")\n",
 24 |     "    return total"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 2,
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "image/png": 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NmweU87/vDIxxzp1yzm0CNgANvcgoIlmvfPE4RtyTwMj7Ejh95hx3vDefRz9b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\n",
 35 |       "text/plain": [
 36 |        "<matplotlib.figure.Figure at 0x1066d49e8>"
 37 |       ]
 38 |      },
 39 |      "metadata": {},
 40 |      "output_type": "display_data"
 41 |     }
 42 |    ],
 43 |    "source": [
 44 |     "import matplotlib.pyplot as plt\n",
 45 |     "r=0\n",
 46 |     "payments=[-300,100,150,50,50]\n",
 47 |     "years=[0,2,4,6,8]\n",
 48 |     "rates = []\n",
 49 |     "values= []\n",
 50 |     "for _ in range(20):\n",
 51 |     "    \n",
 52 |     "    rates+=[r]\n",
 53 |     "    v=totalTVM(r,payments,years)\n",
 54 |     "    values +=[v]\n",
 55 |     "    r+=.01\n",
 56 |     "plt.plot(rates,values)\n",
 57 |     "plt.xlabel(\"IRR\")\n",
 58 |     "plt.ylabel(\"Present Value\")\n",
 59 |     "plt.title(\"Finding IRR\")\n",
 60 |     "plt.show()"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": 3,
 66 |    "metadata": {},
 67 |    "outputs": [
 68 |     {
 69 |      "data": {
 70 |       "text/plain": [
 71 |        "0.097026857685357931"
 72 |       ]
 73 |      },
 74 |      "execution_count": 3,
 75 |      "metadata": {},
 76 |      "output_type": "execute_result"
 77 |     }
 78 |    ],
 79 |    "source": [
 80 |     "from scipy.optimize import fsolve\n",
 81 |     "def findIRR(payments,years):\n",
 82 |     "    return fsolve(totalTVM, 0,(payments,years))[0]\n",
 83 |     "findIRR(payments,years)"
 84 |    ]
 85 |   }
 86 |  ],
 87 |  "metadata": {
 88 |   "anaconda-cloud": {},
 89 |   "kernelspec": {
 90 |    "display_name": "Python 3",
 91 |    "language": "python",
 92 |    "name": "python3"
 93 |   },
 94 |   "language_info": {
 95 |    "codemirror_mode": {
 96 |     "name": "ipython",
 97 |     "version": 3
 98 |    },
 99 |    "file_extension": ".py",
100 |    "mimetype": "text/x-python",
101 |    "name": "python",
102 |    "nbconvert_exporter": "python",
103 |    "pygments_lexer": "ipython3",
104 |    "version": "3.6.1"
105 |   }
106 |  },
107 |  "nbformat": 4,
108 |  "nbformat_minor": 1
109 | }
110 | 


--------------------------------------------------------------------------------
/14 Risk.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [],
 8 |    "source": [
 9 |     "import numpy as np\n",
10 |     "ret = [.25,.1,-.1,-.25]"
11 |    ]
12 |   },
13 |   {
14 |    "cell_type": "code",
15 |    "execution_count": 2,
16 |    "metadata": {},
17 |    "outputs": [
18 |     {
19 |      "name": "stdout",
20 |      "output_type": "stream",
21 |      "text": [
22 |       "0.0483333333333\n",
23 |       "0.219848432638\n"
24 |      ]
25 |     }
26 |    ],
27 |    "source": [
28 |     "print(np.var(ret, ddof=1))\n",
29 |     "print(np.std(ret, ddof=1))"
30 |    ]
31 |   },
32 |   {
33 |    "cell_type": "code",
34 |    "execution_count": 3,
35 |    "metadata": {},
36 |    "outputs": [
37 |     {
38 |      "name": "stdout",
39 |      "output_type": "stream",
40 |      "text": [
41 |       "0.0104166666667\n",
42 |       "0.102062072616\n"
43 |      ]
44 |     }
45 |    ],
46 |    "source": [
47 |     "ret = [.1,.2,.25,.4,.2,.3] \n",
48 |     "\n",
49 |     "print( np.var(ret, ddof=1) ) \n",
50 |     "print( np.std(ret, ddof=1) )"
51 |    ]
52 |   }
53 |  ],
54 |  "metadata": {
55 |   "anaconda-cloud": {},
56 |   "kernelspec": {
57 |    "display_name": "Python 3",
58 |    "language": "python",
59 |    "name": "python3"
60 |   },
61 |   "language_info": {
62 |    "codemirror_mode": {
63 |     "name": "ipython",
64 |     "version": 3
65 |    },
66 |    "file_extension": ".py",
67 |    "mimetype": "text/x-python",
68 |    "name": "python",
69 |    "nbconvert_exporter": "python",
70 |    "pygments_lexer": "ipython3",
71 |    "version": "3.6.1"
72 |   }
73 |  },
74 |  "nbformat": 4,
75 |  "nbformat_minor": 1
76 | }
77 | 


--------------------------------------------------------------------------------
/15 Beta.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "[[ 0.00375  0.0075 ]\n",
13 |       " [ 0.0075   0.015  ]]\n"
14 |      ]
15 |     }
16 |    ],
17 |    "source": [
18 |     "import numpy as np\n",
19 |     "index = [.1,.05,-.05,.05,.1]\n",
20 |     "A = [.2,.1,-.1,.1,.2]\n",
21 |     "B = [.15,0,.05,0,.2]\n",
22 |     "C = [.1,.15,.1,.15,.1]\n",
23 |     "print(np.cov(index,A))"
24 |    ]
25 |   },
26 |   {
27 |    "cell_type": "code",
28 |    "execution_count": 2,
29 |    "metadata": {},
30 |    "outputs": [
31 |     {
32 |      "name": "stdout",
33 |      "output_type": "stream",
34 |      "text": [
35 |       "0.0075\n"
36 |      ]
37 |     }
38 |    ],
39 |    "source": [
40 |     "print(np.cov(index,A)[0][1])"
41 |    ]
42 |   },
43 |   {
44 |    "cell_type": "code",
45 |    "execution_count": 3,
46 |    "metadata": {
47 |     "collapsed": true
48 |    },
49 |    "outputs": [],
50 |    "source": [
51 |     "def beta(indx,stock):\n",
52 |     "    return np.cov(indx,stock)[0][1]/np.var(indx, ddof=1)"
53 |    ]
54 |   },
55 |   {
56 |    "cell_type": "code",
57 |    "execution_count": 4,
58 |    "metadata": {},
59 |    "outputs": [
60 |     {
61 |      "name": "stdout",
62 |      "output_type": "stream",
63 |      "text": [
64 |       "2.0\n",
65 |       "0.833333333333\n",
66 |       "2.08166817117e-18\n"
67 |      ]
68 |     }
69 |    ],
70 |    "source": [
71 |     "print(beta(index,A))\n",
72 |     "print(beta(index,B))\n",
73 |     "print(beta(index,C))"
74 |    ]
75 |   }
76 |  ],
77 |  "metadata": {
78 |   "kernelspec": {
79 |    "display_name": "Python 3",
80 |    "language": "python",
81 |    "name": "python3"
82 |   },
83 |   "language_info": {
84 |    "codemirror_mode": {
85 |     "name": "ipython",
86 |     "version": 3
87 |    },
88 |    "file_extension": ".py",
89 |    "mimetype": "text/x-python",
90 |    "name": "python",
91 |    "nbconvert_exporter": "python",
92 |    "pygments_lexer": "ipython3",
93 |    "version": "3.6.1"
94 |   }
95 |  },
96 |  "nbformat": 4,
97 |  "nbformat_minor": 1
98 | }
99 | 


--------------------------------------------------------------------------------
/16 CAPM.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "def CAPM(rf,market,beta):\n",
12 |     "    return rf+(beta*(market-rf))"
13 |    ]
14 |   },
15 |   {
16 |    "cell_type": "code",
17 |    "execution_count": 2,
18 |    "metadata": {},
19 |    "outputs": [
20 |     {
21 |      "data": {
22 |       "image/png": 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HynMVLkWCX6QJrTUzZ87E39+fS5cu0atXL3r16kUml3hsNPVs22a6cqZMMbMO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\nI8EvhBAeRoJfCCE8zP8BqyDGomOWG6EAAAAASUVORK5CYII=\n",
23 |       "text/plain": [
24 |        "<matplotlib.figure.Figure at 0x105a1ba58>"
25 |       ]
26 |      },
27 |      "metadata": {},
28 |      "output_type": "display_data"
29 |     }
30 |    ],
31 |    "source": [
32 |     "import matplotlib.pyplot as plt\n",
33 |     "\n",
34 |     "betas = []\n",
35 |     "returns = []\n",
36 |     "beta = 0\n",
37 |     "rf = .02\n",
38 |     "market = .08\n",
39 |     "for x in range(31):\n",
40 |     "    betas+=[beta]\n",
41 |     "    r = CAPM(rf,market,beta)\n",
42 |     "    returns +=[r]\n",
43 |     "    beta+=.1\n",
44 |     "plt.plot(betas,returns,\"blue\")\n",
45 |     "\n",
46 |     "betas = []\n",
47 |     "returns = []\n",
48 |     "beta = 0\n",
49 |     "rf = .04\n",
50 |     "market = .08\n",
51 |     "for x in range(31):\n",
52 |     "    betas+=[beta]\n",
53 |     "    r = CAPM(rf,market,beta)\n",
54 |     "    returns +=[r]\n",
55 |     "    beta+=.1\n",
56 |     "plt.plot(betas,returns,\"red\")\n",
57 |     "\n",
58 |     "\n",
59 |     "betas = []\n",
60 |     "returns = []\n",
61 |     "beta = 0\n",
62 |     "rf = .02\n",
63 |     "market = .12\n",
64 |     "for x in range(31):\n",
65 |     "    betas+=[beta]\n",
66 |     "    r = CAPM(rf,market,beta)\n",
67 |     "    returns +=[r]\n",
68 |     "    beta+=.1\n",
69 |     "plt.plot(betas,returns,\"black\")\n",
70 |     "\n",
71 |     "plt.show()"
72 |    ]
73 |   }
74 |  ],
75 |  "metadata": {
76 |   "anaconda-cloud": {},
77 |   "kernelspec": {
78 |    "display_name": "Python 3",
79 |    "language": "python",
80 |    "name": "python3"
81 |   },
82 |   "language_info": {
83 |    "codemirror_mode": {
84 |     "name": "ipython",
85 |     "version": 3
86 |    },
87 |    "file_extension": ".py",
88 |    "mimetype": "text/x-python",
89 |    "name": "python",
90 |    "nbconvert_exporter": "python",
91 |    "pygments_lexer": "ipython3",
92 |    "version": "3.6.1"
93 |   }
94 |  },
95 |  "nbformat": 4,
96 |  "nbformat_minor": 1
97 | }
98 | 


--------------------------------------------------------------------------------
/17 WACC.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "0.07833333333333334\n"
13 |      ]
14 |     }
15 |    ],
16 |    "source": [
17 |     "def WACC(price,outstanding,debtValue,debtYield,stockReturn,taxRate):\n",
18 |     "    equity = price*outstanding\n",
19 |     "    total = equity+debtValue\n",
20 |     "    return (equity/total)*stockReturn+(debtValue/total)*debtYield*(1-taxRate)\n",
21 |     "print(WACC(20.0,100,1000.0,.05,.1,.3))"
22 |    ]
23 |   }
24 |  ],
25 |  "metadata": {
26 |   "anaconda-cloud": {},
27 |   "kernelspec": {
28 |    "display_name": "Python 3",
29 |    "language": "python",
30 |    "name": "python3"
31 |   },
32 |   "language_info": {
33 |    "codemirror_mode": {
34 |     "name": "ipython",
35 |     "version": 3
36 |    },
37 |    "file_extension": ".py",
38 |    "mimetype": "text/x-python",
39 |    "name": "python",
40 |    "nbconvert_exporter": "python",
41 |    "pygments_lexer": "ipython3",
42 |    "version": "3.6.1"
43 |   }
44 |  },
45 |  "nbformat": 4,
46 |  "nbformat_minor": 1
47 | }
48 | 


--------------------------------------------------------------------------------
/2 Taxes.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "Meal = 100"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 2,
 17 |    "metadata": {
 18 |     "collapsed": true
 19 |    },
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "TaxRate = .15"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 3,
 28 |    "metadata": {},
 29 |    "outputs": [
 30 |     {
 31 |      "name": "stdout",
 32 |      "output_type": "stream",
 33 |      "text": [
 34 |       "15.0\n"
 35 |      ]
 36 |     }
 37 |    ],
 38 |    "source": [
 39 |     "Tax = Meal*TaxRate\n",
 40 |     "print(Tax)"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 4,
 46 |    "metadata": {},
 47 |    "outputs": [
 48 |     {
 49 |      "name": "stdout",
 50 |      "output_type": "stream",
 51 |      "text": [
 52 |       "115.0\n"
 53 |      ]
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "Total = Meal+Tax\n",
 58 |     "print(Total)"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "code",
 63 |    "execution_count": 5,
 64 |    "metadata": {
 65 |     "collapsed": true
 66 |    },
 67 |    "outputs": [],
 68 |    "source": [
 69 |     "def Calculator(meal, taxRate):\n",
 70 |     "    tax = meal*taxRate\n",
 71 |     "    print(tax)"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "code",
 76 |    "execution_count": 6,
 77 |    "metadata": {},
 78 |    "outputs": [
 79 |     {
 80 |      "name": "stdout",
 81 |      "output_type": "stream",
 82 |      "text": [
 83 |       "15.0\n"
 84 |      ]
 85 |     }
 86 |    ],
 87 |    "source": [
 88 |     "Calculator(100,.15)"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": 7,
 94 |    "metadata": {},
 95 |    "outputs": [
 96 |     {
 97 |      "name": "stdout",
 98 |      "output_type": "stream",
 99 |      "text": [
100 |       "115.0\n"
101 |      ]
102 |     }
103 |    ],
104 |    "source": [
105 |     "def Calculator(meal, taxRate):\n",
106 |     "    tax = meal*taxRate\n",
107 |     "    print(tax+meal)\n",
108 |     "Calculator(100,.15)"
109 |    ]
110 |   }
111 |  ],
112 |  "metadata": {
113 |   "anaconda-cloud": {},
114 |   "kernelspec": {
115 |    "display_name": "Python 3",
116 |    "language": "python",
117 |    "name": "python3"
118 |   },
119 |   "language_info": {
120 |    "codemirror_mode": {
121 |     "name": "ipython",
122 |     "version": 3
123 |    },
124 |    "file_extension": ".py",
125 |    "mimetype": "text/x-python",
126 |    "name": "python",
127 |    "nbconvert_exporter": "python",
128 |    "pygments_lexer": "ipython3",
129 |    "version": "3.6.1"
130 |   }
131 |  },
132 |  "nbformat": 4,
133 |  "nbformat_minor": 1
134 | }
135 | 


--------------------------------------------------------------------------------
/3 Compound Interest.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "def compoundInterest(p,r,t):\n",
12 |     "     A = p*(1+r)**t\n",
13 |     "     return A"
14 |    ]
15 |   },
16 |   {
17 |    "cell_type": "code",
18 |    "execution_count": 2,
19 |    "metadata": {},
20 |    "outputs": [
21 |     {
22 |      "name": "stdout",
23 |      "output_type": "stream",
24 |      "text": [
25 |       "105.0\n"
26 |      ]
27 |     }
28 |    ],
29 |    "source": [
30 |     "print( compoundInterest(100,.05,1) )"
31 |    ]
32 |   }
33 |  ],
34 |  "metadata": {
35 |   "kernelspec": {
36 |    "display_name": "Python 3",
37 |    "language": "python",
38 |    "name": "python3"
39 |   },
40 |   "language_info": {
41 |    "codemirror_mode": {
42 |     "name": "ipython",
43 |     "version": 3
44 |    },
45 |    "file_extension": ".py",
46 |    "mimetype": "text/x-python",
47 |    "name": "python",
48 |    "nbconvert_exporter": "python",
49 |    "pygments_lexer": "ipython3",
50 |    "version": "3.6.1"
51 |   }
52 |  },
53 |  "nbformat": 4,
54 |  "nbformat_minor": 2
55 | }
56 | 


--------------------------------------------------------------------------------
/4 Plotting Compound Interest.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def compoundInterest(p,r,t):\n",
 12 |     "    A = p*(1+r)**t\n",
 13 |     "    return A"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "name": "stdout",
 23 |      "output_type": "stream",
 24 |      "text": [
 25 |       "1\n",
 26 |       "2\n",
 27 |       "3\n",
 28 |       "4\n",
 29 |       "5\n",
 30 |       "6\n",
 31 |       "7\n",
 32 |       "8\n",
 33 |       "9\n",
 34 |       "10\n",
 35 |       "11\n",
 36 |       "12\n",
 37 |       "13\n",
 38 |       "14\n",
 39 |       "15\n",
 40 |       "16\n",
 41 |       "17\n",
 42 |       "18\n",
 43 |       "19\n",
 44 |       "20\n",
 45 |       "21\n",
 46 |       "22\n",
 47 |       "23\n",
 48 |       "24\n",
 49 |       "25\n"
 50 |      ]
 51 |     }
 52 |    ],
 53 |    "source": [
 54 |     "for time in range(1,26):\n",
 55 |     "     print(time)"
 56 |    ]
 57 |   },
 58 |   {
 59 |    "cell_type": "code",
 60 |    "execution_count": 3,
 61 |    "metadata": {},
 62 |    "outputs": [
 63 |     {
 64 |      "name": "stdout",
 65 |      "output_type": "stream",
 66 |      "text": [
 67 |       "105.0\n",
 68 |       "110.25\n",
 69 |       "115.76250000000002\n",
 70 |       "121.55062500000003\n",
 71 |       "127.62815625000003\n",
 72 |       "134.00956406250003\n",
 73 |       "140.71004226562505\n",
 74 |       "147.7455443789063\n",
 75 |       "155.13282159785163\n",
 76 |       "162.8894626777442\n",
 77 |       "171.0339358116314\n",
 78 |       "179.585632602213\n",
 79 |       "188.56491423232367\n",
 80 |       "197.99315994393987\n",
 81 |       "207.89281794113688\n",
 82 |       "218.2874588381937\n",
 83 |       "229.20183178010342\n",
 84 |       "240.66192336910856\n",
 85 |       "252.69501953756404\n",
 86 |       "265.3297705144422\n",
 87 |       "278.59625904016434\n",
 88 |       "292.5260719921726\n",
 89 |       "307.1523755917812\n",
 90 |       "322.5099943713703\n",
 91 |       "338.63549408993885\n"
 92 |      ]
 93 |     }
 94 |    ],
 95 |    "source": [
 96 |     "for time in range(1,26):\n",
 97 |     "     print( compoundInterest(100,.05, time) )"
 98 |    ]
 99 |   },
100 |   {
101 |    "cell_type": "code",
102 |    "execution_count": 4,
103 |    "metadata": {},
104 |    "outputs": [
105 |     {
106 |      "name": "stdout",
107 |      "output_type": "stream",
108 |      "text": [
109 |       "[105.0, 110.25, 115.76250000000002, 121.55062500000003, 127.62815625000003, 134.00956406250003, 140.71004226562505, 147.7455443789063, 155.13282159785163, 162.8894626777442, 171.0339358116314, 179.585632602213, 188.56491423232367, 197.99315994393987, 207.89281794113688, 218.2874588381937, 229.20183178010342, 240.66192336910856, 252.69501953756404, 265.3297705144422, 278.59625904016434, 292.5260719921726, 307.1523755917812, 322.5099943713703, 338.63549408993885]\n",
110 |       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]\n"
111 |      ]
112 |     }
113 |    ],
114 |    "source": [
115 |     "values = [] \n",
116 |     "years = [] \n",
117 |     "for time in range(1,26): \n",
118 |     "    values.append(compoundInterest(100,.05, time)) \n",
119 |     "    years.append(time) \n",
120 |     "print(values) \n",
121 |     "print(years)"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "code",
126 |    "execution_count": 5,
127 |    "metadata": {},
128 |    "outputs": [
129 |     {
130 |      "name": "stdout",
131 |      "output_type": "stream",
132 |      "text": [
133 |       "[100, 105.0, 110.25, 115.76250000000002, 121.55062500000003, 127.62815625000003, 134.00956406250003, 140.71004226562505, 147.7455443789063, 155.13282159785163, 162.8894626777442, 171.0339358116314, 179.585632602213, 188.56491423232367, 197.99315994393987, 207.89281794113688, 218.2874588381937, 229.20183178010342, 240.66192336910856, 252.69501953756404, 265.3297705144422, 278.59625904016434, 292.5260719921726, 307.1523755917812, 322.5099943713703, 338.63549408993885]\n",
134 |       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]\n"
135 |      ]
136 |     }
137 |    ],
138 |    "source": [
139 |     "values = [100] \n",
140 |     "years = [0] \n",
141 |     "for time in range(1,26): \n",
142 |     "    values.append(compoundInterest(100,.05, time)) \n",
143 |     "    years.append(time) \n",
144 |     "print(values) \n",
145 |     "print(years)"
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "code",
150 |    "execution_count": 6,
151 |    "metadata": {},
152 |    "outputs": [
153 |     {
154 |      "name": "stdout",
155 |      "output_type": "stream",
156 |      "text": [
157 |       "100\n",
158 |       "105.0\n"
159 |      ]
160 |     }
161 |    ],
162 |    "source": [
163 |     "print(values[0])\n",
164 |     "print(values[1])"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 7,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "data": {
174 |       "image/png": 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C5k0acu2gKG4a1p2EmPY00Lh3EQkiCvoq+n7XjHMwond77rs8lmsGdqFFhH6U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\n6EVEwpyCXkQkzCnoRUTC3P8B4oK8VWMxQ74AAAAASUVORK5CYII=\n",
175 |       "text/plain": [
176 |        "<matplotlib.figure.Figure at 0x1091ae898>"
177 |       ]
178 |      },
179 |      "metadata": {},
180 |      "output_type": "display_data"
181 |     }
182 |    ],
183 |    "source": [
184 |     "import matplotlib.pyplot as plt\n",
185 |     "plt.plot(years, values)\n",
186 |     "plt.show()"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "code",
191 |    "execution_count": 8,
192 |    "metadata": {
193 |     "collapsed": true
194 |    },
195 |    "outputs": [],
196 |    "source": [
197 |     "def createArray(p,r,t):\n",
198 |     "    values = [p]\n",
199 |     "    years =[0]\n",
200 |     "    for time in range(1,t+1):\n",
201 |     "        values.append(compoundInterest(p,r, time))\n",
202 |     "        years.append(time)\n",
203 |     "    return values,years"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 9,
209 |    "metadata": {},
210 |    "outputs": [
211 |     {
212 |      "data": {
213 |       "image/png": 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u+7rGSn595f7zhidTRRrYcyBDs4dydP7RTBs3jWG9hjGs1zCG9hrKkOwh6l4p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\n",
214 |       "text/plain": [
215 |        "<matplotlib.figure.Figure at 0x11297fa90>"
216 |       ]
217 |      },
218 |      "metadata": {},
219 |      "output_type": "display_data"
220 |     }
221 |    ],
222 |    "source": [
223 |     "import matplotlib.pyplot as plt\n",
224 |     "values,years = createArray(100,.02,25)\n",
225 |     "plt.plot(years, values, \"r\")\n",
226 |     "values,years = createArray(100,.07,25)\n",
227 |     "plt.plot(years, values, \"g\")\n",
228 |     "values,years = createArray(100,.14,25)\n",
229 |     "plt.plot(years, values, \"b\")\n",
230 |     "plt.show()"
231 |    ]
232 |   }
233 |  ],
234 |  "metadata": {
235 |   "kernelspec": {
236 |    "display_name": "Python 3",
237 |    "language": "python",
238 |    "name": "python3"
239 |   },
240 |   "language_info": {
241 |    "codemirror_mode": {
242 |     "name": "ipython",
243 |     "version": 3
244 |    },
245 |    "file_extension": ".py",
246 |    "mimetype": "text/x-python",
247 |    "name": "python",
248 |    "nbconvert_exporter": "python",
249 |    "pygments_lexer": "ipython3",
250 |    "version": "3.6.1"
251 |   }
252 |  },
253 |  "nbformat": 4,
254 |  "nbformat_minor": 2
255 | }
256 | 


--------------------------------------------------------------------------------
/5 Present Value.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def compoundInterest(p,r,t):\n",
 12 |     "    A = p*(1+r)**t\n",
 13 |     "    return A"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "metadata": {
 20 |     "collapsed": true
 21 |    },
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "def presentValue(p,r,t):\n",
 25 |     "    A = p/(1+r)**t\n",
 26 |     "    return A"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 3,
 32 |    "metadata": {
 33 |     "collapsed": true
 34 |    },
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "principal = 100\n",
 38 |     "rate = .08\n",
 39 |     "time = 5"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 4,
 45 |    "metadata": {},
 46 |    "outputs": [
 47 |     {
 48 |      "name": "stdout",
 49 |      "output_type": "stream",
 50 |      "text": [
 51 |       "1.4693280768000005\n"
 52 |      ]
 53 |     }
 54 |    ],
 55 |    "source": [
 56 |     "discount = (1+rate)**time\n",
 57 |     "print(discount)"
 58 |    ]
 59 |   },
 60 |   {
 61 |    "cell_type": "code",
 62 |    "execution_count": 3,
 63 |    "metadata": {},
 64 |    "outputs": [
 65 |     {
 66 |      "name": "stdout",
 67 |      "output_type": "stream",
 68 |      "text": [
 69 |       "146.93280768\n"
 70 |      ]
 71 |     }
 72 |    ],
 73 |    "source": [
 74 |     "future = principal * discount\n",
 75 |     "print future"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 4,
 81 |    "metadata": {},
 82 |    "outputs": [
 83 |     {
 84 |      "name": "stdout",
 85 |      "output_type": "stream",
 86 |      "text": [
 87 |       "68.0583197034\n"
 88 |      ]
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "present = principal / discount\n",
 93 |     "print present"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 5,
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "name": "stdout",
103 |      "output_type": "stream",
104 |      "text": [
105 |       "True\n",
106 |       "False\n"
107 |      ]
108 |     }
109 |    ],
110 |    "source": [
111 |     "print 5==5\n",
112 |     "print 6==5"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": 6,
118 |    "metadata": {
119 |     "collapsed": true
120 |    },
121 |    "outputs": [],
122 |    "source": [
123 |     "def TVM(p,r,t,type):\n",
124 |     "    discount = (1+r)**t\n",
125 |     "    if type==\"FV\":\n",
126 |     "        A = p*discount\n",
127 |     "        return A\n",
128 |     "    if type==\"PV\":\n",
129 |     "        A = p/discount\n",
130 |     "        return A"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "code",
135 |    "execution_count": 7,
136 |    "metadata": {},
137 |    "outputs": [
138 |     {
139 |      "name": "stdout",
140 |      "output_type": "stream",
141 |      "text": [
142 |       "68.0583197034\n",
143 |       "146.93280768\n"
144 |      ]
145 |     }
146 |    ],
147 |    "source": [
148 |     "print TVM(100,.08,5,\"PV\")\n",
149 |     "print TVM(100,.08,5,\"FV\")"
150 |    ]
151 |   }
152 |  ],
153 |  "metadata": {
154 |   "anaconda-cloud": {},
155 |   "kernelspec": {
156 |    "display_name": "Python 3",
157 |    "language": "python",
158 |    "name": "python3"
159 |   },
160 |   "language_info": {
161 |    "codemirror_mode": {
162 |     "name": "ipython",
163 |     "version": 3
164 |    },
165 |    "file_extension": ".py",
166 |    "mimetype": "text/x-python",
167 |    "name": "python",
168 |    "nbconvert_exporter": "python",
169 |    "pygments_lexer": "ipython3",
170 |    "version": "3.6.1"
171 |   }
172 |  },
173 |  "nbformat": 4,
174 |  "nbformat_minor": 1
175 | }
176 | 


--------------------------------------------------------------------------------
/6 Present Value Multiple Cash Flows.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "payments = [100,200,300,400]\n",
 12 |     "years = [5,10,15,20]\n",
 13 |     "rates = [.01,.02,.03,.04]"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 2,
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "name": "stdout",
 23 |      "output_type": "stream",
 24 |      "text": [
 25 |       "100\n",
 26 |       "5\n",
 27 |       "0.01\n",
 28 |       "\n",
 29 |       "200\n",
 30 |       "10\n",
 31 |       "0.02\n",
 32 |       "\n",
 33 |       "300\n",
 34 |       "15\n",
 35 |       "0.03\n",
 36 |       "\n",
 37 |       "400\n",
 38 |       "20\n",
 39 |       "0.04\n",
 40 |       "\n"
 41 |      ]
 42 |     }
 43 |    ],
 44 |    "source": [
 45 |     "for index in range(0,len(payments)):\n",
 46 |     "    print(payments[index])\n",
 47 |     "    print(years[index])\n",
 48 |     "    print(rates[index])\n",
 49 |     "    print(\"\")"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 3,
 55 |    "metadata": {
 56 |     "collapsed": true
 57 |    },
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "def TVM(p,r,t,type):\n",
 61 |     "    discount = (1+r)**t\n",
 62 |     "    if type==\"FV\":\n",
 63 |     "        A = p*discount\n",
 64 |     "        return A\n",
 65 |     "    if type==\"PV\":\n",
 66 |     "        A = p/discount\n",
 67 |     "        return A"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "code",
 72 |    "execution_count": 4,
 73 |    "metadata": {},
 74 |    "outputs": [
 75 |     {
 76 |      "name": "stdout",
 77 |      "output_type": "stream",
 78 |      "text": [
 79 |       "98.22419709752423\n",
 80 |       "190.63478193082776\n",
 81 |       "276.0562951874625\n",
 82 |       "354.1369311851404\n"
 83 |      ]
 84 |     }
 85 |    ],
 86 |    "source": [
 87 |     "for index in range(0,len(payments)):\n",
 88 |     "    print(TVM(payments[index],years[index],rates[index],\"PV\"))"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": 5,
 94 |    "metadata": {},
 95 |    "outputs": [
 96 |     {
 97 |      "name": "stdout",
 98 |      "output_type": "stream",
 99 |      "text": [
100 |       "919.0522054009549\n"
101 |      ]
102 |     }
103 |    ],
104 |    "source": [
105 |     "total = 0\n",
106 |     "for index in range(0,len(payments)):\n",
107 |     "    total+= TVM(payments[index],years[index],rates[index],\"PV\")\n",
108 |     "print(total)"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 6,
114 |    "metadata": {
115 |     "collapsed": true
116 |    },
117 |    "outputs": [],
118 |    "source": [
119 |     "def totalTVM(payments,years,rates):\n",
120 |     "    total = 0\n",
121 |     "    for index in range(0,len(payments)):\n",
122 |     "        total+= TVM(payments[index],years[index],rates[index],\"PV\")\n",
123 |     "    return total"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "code",
128 |    "execution_count": 7,
129 |    "metadata": {},
130 |    "outputs": [
131 |     {
132 |      "data": {
133 |       "text/plain": [
134 |        "919.0522054009549"
135 |       ]
136 |      },
137 |      "execution_count": 7,
138 |      "metadata": {},
139 |      "output_type": "execute_result"
140 |     }
141 |    ],
142 |    "source": [
143 |     "totalTVM([100,200,300,400],[5,10,15,20],[.01,.02,.03,.04])"
144 |    ]
145 |   },
146 |   {
147 |    "cell_type": "code",
148 |    "execution_count": 8,
149 |    "metadata": {},
150 |    "outputs": [
151 |     {
152 |      "data": {
153 |       "text/plain": [
154 |        "919.0522054009549"
155 |       ]
156 |      },
157 |      "execution_count": 8,
158 |      "metadata": {},
159 |      "output_type": "execute_result"
160 |     }
161 |    ],
162 |    "source": [
163 |     "payments = [100,200,300,400]\n",
164 |     "years = [5,10,15,20]\n",
165 |     "rates = [.01,.02,.03,.04]\n",
166 |     "totalTVM(payments,years,rates)"
167 |    ]
168 |   },
169 |   {
170 |    "cell_type": "code",
171 |    "execution_count": 9,
172 |    "metadata": {},
173 |    "outputs": [
174 |     {
175 |      "data": {
176 |       "text/plain": [
177 |        "19.052205400954904"
178 |       ]
179 |      },
180 |      "execution_count": 9,
181 |      "metadata": {},
182 |      "output_type": "execute_result"
183 |     }
184 |    ],
185 |    "source": [
186 |     "payments = [-900,100,200,300,400]\n",
187 |     "years = [0,5,10,15,20]\n",
188 |     "rates = [.01,.01,.02,.03,.04]\n",
189 |     "totalTVM(payments,years,rates)"
190 |    ]
191 |   }
192 |  ],
193 |  "metadata": {
194 |   "anaconda-cloud": {},
195 |   "kernelspec": {
196 |    "display_name": "Python 3",
197 |    "language": "python",
198 |    "name": "python3"
199 |   },
200 |   "language_info": {
201 |    "codemirror_mode": {
202 |     "name": "ipython",
203 |     "version": 3
204 |    },
205 |    "file_extension": ".py",
206 |    "mimetype": "text/x-python",
207 |    "name": "python",
208 |    "nbconvert_exporter": "python",
209 |    "pygments_lexer": "ipython3",
210 |    "version": "3.6.1"
211 |   }
212 |  },
213 |  "nbformat": 4,
214 |  "nbformat_minor": 1
215 | }
216 | 


--------------------------------------------------------------------------------
/7 Perpetetuities.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "def TVM(p,r,t,type):\n",
12 |     "    discount = (1+r)**t\n",
13 |     "    if type==\"FV\":\n",
14 |     "        A = p*discount\n",
15 |     "        return A\n",
16 |     "    if type==\"PV\":\n",
17 |     "        A = p/discount\n",
18 |     "        return A"
19 |    ]
20 |   },
21 |   {
22 |    "cell_type": "code",
23 |    "execution_count": 2,
24 |    "metadata": {},
25 |    "outputs": [
26 |     {
27 |      "name": "stdout",
28 |      "output_type": "stream",
29 |      "text": [
30 |       "82.03482998751551\n",
31 |       "13.803296719774542\n",
32 |       "2.5108931858165077e-07\n",
33 |       "9.960528462617324e-85\n"
34 |      ]
35 |     }
36 |    ],
37 |    "source": [
38 |     "print(TVM(100,.02,10,\"PV\"))\n",
39 |     "print(TVM(100,.02,100,\"PV\"))\n",
40 |     "print(TVM(100,.02,1000,\"PV\"))\n",
41 |     "print(TVM(100,.02,10000,\"PV\"))"
42 |    ]
43 |   },
44 |   {
45 |    "cell_type": "code",
46 |    "execution_count": 3,
47 |    "metadata": {},
48 |    "outputs": [
49 |     {
50 |      "name": "stdout",
51 |      "output_type": "stream",
52 |      "text": [
53 |       "4999.999987445526\n"
54 |      ]
55 |     }
56 |    ],
57 |    "source": [
58 |     "total=0\n",
59 |     "for year in range(1,1001):\n",
60 |     "    total+= TVM(100,.02,year,\"PV\")\n",
61 |     "print(total)"
62 |    ]
63 |   }
64 |  ],
65 |  "metadata": {
66 |   "kernelspec": {
67 |    "display_name": "Python 3",
68 |    "language": "python",
69 |    "name": "python3"
70 |   },
71 |   "language_info": {
72 |    "codemirror_mode": {
73 |     "name": "ipython",
74 |     "version": 3
75 |    },
76 |    "file_extension": ".py",
77 |    "mimetype": "text/x-python",
78 |    "name": "python",
79 |    "nbconvert_exporter": "python",
80 |    "pygments_lexer": "ipython3",
81 |    "version": "3.6.1"
82 |   }
83 |  },
84 |  "nbformat": 4,
85 |  "nbformat_minor": 1
86 | }
87 | 


--------------------------------------------------------------------------------
/8 Annuities.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def TVM(p,r,t,type):\n",
 12 |     "    discount = (1+r)**t\n",
 13 |     "    if type==\"FV\":\n",
 14 |     "        A = p*discount\n",
 15 |     "        return A\n",
 16 |     "    if type==\"PV\":\n",
 17 |     "        A = p/discount\n",
 18 |     "        return A"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 2,
 24 |    "metadata": {},
 25 |    "outputs": [
 26 |     {
 27 |      "name": "stdout",
 28 |      "output_type": "stream",
 29 |      "text": [
 30 |       "772.1734929184811\n"
 31 |      ]
 32 |     }
 33 |    ],
 34 |    "source": [
 35 |     "payment=100\n",
 36 |     "rate=.05\n",
 37 |     "years=10\n",
 38 |     "\n",
 39 |     "total=0\n",
 40 |     "for year in range(1,years+1):\n",
 41 |     "    total+=TVM(payment,rate,year,\"PV\")\n",
 42 |     "print(total)"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 3,
 48 |    "metadata": {},
 49 |    "outputs": [
 50 |     {
 51 |      "name": "stdout",
 52 |      "output_type": "stream",
 53 |      "text": [
 54 |       "810.7821675644052\n"
 55 |      ]
 56 |     }
 57 |    ],
 58 |    "source": [
 59 |     "payment=100\n",
 60 |     "rate=.05\n",
 61 |     "years=10\n",
 62 |     "\n",
 63 |     "total=0\n",
 64 |     "for year in range(1,years+1):\n",
 65 |     "    total+=TVM(payment,rate,year,\"PV\")\n",
 66 |     "print(total*(1+rate))"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 4,
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "name": "stdout",
 76 |      "output_type": "stream",
 77 |      "text": [
 78 |       "810.7821675644052\n"
 79 |      ]
 80 |     }
 81 |    ],
 82 |    "source": [
 83 |     "payment=100\n",
 84 |     "rate=.05\n",
 85 |     "years=10\n",
 86 |     "\n",
 87 |     "total=0\n",
 88 |     "for year in range(0,years):\n",
 89 |     "    total+=TVM(payment,rate,year,\"PV\")\n",
 90 |     "print(total)"
 91 |    ]
 92 |   }
 93 |  ],
 94 |  "metadata": {
 95 |   "kernelspec": {
 96 |    "display_name": "Python 3",
 97 |    "language": "python",
 98 |    "name": "python3"
 99 |   },
100 |   "language_info": {
101 |    "codemirror_mode": {
102 |     "name": "ipython",
103 |     "version": 3
104 |    },
105 |    "file_extension": ".py",
106 |    "mimetype": "text/x-python",
107 |    "name": "python",
108 |    "nbconvert_exporter": "python",
109 |    "pygments_lexer": "ipython3",
110 |    "version": "3.6.1"
111 |   }
112 |  },
113 |  "nbformat": 4,
114 |  "nbformat_minor": 1
115 | }
116 | 


--------------------------------------------------------------------------------
/9 Bonds.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def bondValue(fv,year,cr,r):\n",
 12 |     "    final_pay = fv/(1+r)**year\n",
 13 |     "    coupon_payment = (fv*cr)\n",
 14 |     "    annuity_value = coupon_payment*(1-(1+r)**(-year))/r\n",
 15 |     "    return annuity_value+final_pay"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 2,
 21 |    "metadata": {},
 22 |    "outputs": [
 23 |     {
 24 |      "name": "stdout",
 25 |      "output_type": "stream",
 26 |      "text": [
 27 |       "1141.4037852551264\n"
 28 |      ]
 29 |     }
 30 |    ],
 31 |    "source": [
 32 |     "print(bondValue(1000,5,.05,.02))"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 3,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "data": {
 42 |       "text/plain": [
 43 |        "0.05000000000103398"
 44 |       ]
 45 |      },
 46 |      "execution_count": 3,
 47 |      "metadata": {},
 48 |      "output_type": "execute_result"
 49 |     }
 50 |    ],
 51 |    "source": [
 52 |     "def findCR(pv,fv,year,r): \n",
 53 |     "    final_pay = fv/(1+r)**year\n",
 54 |     "    annuity_factor = (1-(1+r)**(-year))/r\n",
 55 |     "    annuity_value = pv-final_pay\n",
 56 |     "    coupon_payment = annuity_value/annuity_factor\n",
 57 |     "    cr = coupon_payment/fv\n",
 58 |     "    return cr\n",
 59 |     "\n",
 60 |     "findCR(1141.40378526,1000,5,.02)"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": 4,
 66 |    "metadata": {},
 67 |    "outputs": [],
 68 |    "source": [
 69 |     "def function(r,price,fv,year,cr):\n",
 70 |     "    final_pay = fv/(1+r)**year\n",
 71 |     "    coupon_payment = (fv*cr)\n",
 72 |     "    annuity_value = coupon_payment*(1-(1+r)**(-year))/r\n",
 73 |     "    return annuity_value+final_pay-price"
 74 |    ]
 75 |   },
 76 |   {
 77 |    "cell_type": "code",
 78 |    "execution_count": 5,
 79 |    "metadata": {},
 80 |    "outputs": [
 81 |     {
 82 |      "data": {
 83 |       "text/plain": [
 84 |        "-4.873527359450236e-09"
 85 |       ]
 86 |      },
 87 |      "execution_count": 5,
 88 |      "metadata": {},
 89 |      "output_type": "execute_result"
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "function(.02,1141.40378526,1000,5,.05)"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 6,
 99 |    "metadata": {},
100 |    "outputs": [],
101 |    "source": [
102 |     "from scipy.optimize import fsolve\n",
103 |     "def findYield(price,fv,year,cr):\n",
104 |     "    return fsolve(function, cr,(price,fv,year,cr))[0]"
105 |    ]
106 |   },
107 |   {
108 |    "cell_type": "code",
109 |    "execution_count": 7,
110 |    "metadata": {},
111 |    "outputs": [
112 |     {
113 |      "data": {
114 |       "text/plain": [
115 |        "0.019999999999048713"
116 |       ]
117 |      },
118 |      "execution_count": 7,
119 |      "metadata": {},
120 |      "output_type": "execute_result"
121 |     }
122 |    ],
123 |    "source": [
124 |     "findYield(1141.40378526,1000,5,.05)"
125 |    ]
126 |   }
127 |  ],
128 |  "metadata": {
129 |   "kernelspec": {
130 |    "display_name": "Python 3",
131 |    "language": "python",
132 |    "name": "python3"
133 |   },
134 |   "language_info": {
135 |    "codemirror_mode": {
136 |     "name": "ipython",
137 |     "version": 3
138 |    },
139 |    "file_extension": ".py",
140 |    "mimetype": "text/x-python",
141 |    "name": "python",
142 |    "nbconvert_exporter": "python",
143 |    "pygments_lexer": "ipython3",
144 |    "version": "3.6.1"
145 |   }
146 |  },
147 |  "nbformat": 4,
148 |  "nbformat_minor": 1
149 | }
150 | 


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/README.md:
--------------------------------------------------------------------------------
1 | # FinanceAndPython.com-BasicFinance
2 | The files in this repository are the source code files for the basic finance course on <a href="http://www.FinanceAndPython.com">FinanceAndPython.com.</a>
3 | 


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