├── BETA.ipynb
├── Beta_for_PG.ipynb
├── Div_non-div_risk.ipynb
├── Markowitz_algorithm.ipynb
├── Porfolio_returns.ipynb
├── README.md
├── Regression.ipynb
├── Simple_n_log_returns.ipynb
├── Standard_Deviation.ipynb
└── var_cov_correlation.ipynb


/BETA.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "from pandas_datareader import data as wb\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "import fix_yahoo_finance as yf"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": 29,
 19 |    "metadata": {},
 20 |    "outputs": [
 21 |     {
 22 |      "name": "stdout",
 23 |      "output_type": "stream",
 24 |      "text": [
 25 |       "[*********************100%***********************]  1 of 1 downloaded\n",
 26 |       "[*********************100%***********************]  1 of 1 downloaded\n"
 27 |      ]
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "#for beta of microsoft\n",
 32 |     "tickers = ['MSFT','^GSPC']\n",
 33 |     "\n",
 34 |     "data = pd.DataFrame()\n",
 35 |     "\n",
 36 |     "for t in tickers :\n",
 37 |     "    data[t] = yf.download(t, start = '2012-01-01', end='2017-01-01')['Adj Close']"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 30,
 43 |    "metadata": {},
 44 |    "outputs": [],
 45 |    "source": [
 46 |     "sec_returns = np.log(data/data.shift(1))"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 32,
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "data": {
 56 |       "text/html": [
 57 |        "<div>\n",
 58 |        "<style scoped>\n",
 59 |        "    .dataframe tbody tr th:only-of-type {\n",
 60 |        "        vertical-align: middle;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe tbody tr th {\n",
 64 |        "        vertical-align: top;\n",
 65 |        "    }\n",
 66 |        "\n",
 67 |        "    .dataframe thead th {\n",
 68 |        "        text-align: right;\n",
 69 |        "    }\n",
 70 |        "</style>\n",
 71 |        "<table border=\"1\" class=\"dataframe\">\n",
 72 |        "  <thead>\n",
 73 |        "    <tr style=\"text-align: right;\">\n",
 74 |        "      <th></th>\n",
 75 |        "      <th>MSFT</th>\n",
 76 |        "      <th>^GSPC</th>\n",
 77 |        "    </tr>\n",
 78 |        "  </thead>\n",
 79 |        "  <tbody>\n",
 80 |        "    <tr>\n",
 81 |        "      <th>MSFT</th>\n",
 82 |        "      <td>0.053781</td>\n",
 83 |        "      <td>0.018213</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>^GSPC</th>\n",
 87 |        "      <td>0.018213</td>\n",
 88 |        "      <td>0.016362</td>\n",
 89 |        "    </tr>\n",
 90 |        "  </tbody>\n",
 91 |        "</table>\n",
 92 |        "</div>"
 93 |       ],
 94 |       "text/plain": [
 95 |        "           MSFT     ^GSPC\n",
 96 |        "MSFT   0.053781  0.018213\n",
 97 |        "^GSPC  0.018213  0.016362"
 98 |       ]
 99 |      },
100 |      "execution_count": 32,
101 |      "metadata": {},
102 |      "output_type": "execute_result"
103 |     }
104 |    ],
105 |    "source": [
106 |     "cov = sec_returns.cov() * 250\n",
107 |     "cov"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 33,
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "data": {
117 |       "text/plain": [
118 |        "0.018212758362452473"
119 |       ]
120 |      },
121 |      "execution_count": 33,
122 |      "metadata": {},
123 |      "output_type": "execute_result"
124 |     }
125 |    ],
126 |    "source": [
127 |     "cov_with_market = cov.iloc[0,1]\n",
128 |     "cov_with_market"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 34,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "data": {
138 |       "text/plain": [
139 |        "0.016361631022038074"
140 |       ]
141 |      },
142 |      "execution_count": 34,
143 |      "metadata": {},
144 |      "output_type": "execute_result"
145 |     }
146 |    ],
147 |    "source": [
148 |     "market_var = sec_returns['^GSPC'].var()*250\n",
149 |     "market_var"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": 35,
155 |    "metadata": {},
156 |    "outputs": [
157 |     {
158 |      "data": {
159 |       "text/plain": [
160 |        "1.1131383135288315"
161 |       ]
162 |      },
163 |      "execution_count": 35,
164 |      "metadata": {},
165 |      "output_type": "execute_result"
166 |     }
167 |    ],
168 |    "source": [
169 |     "msft_beta = cov_with_market / market_var\n",
170 |     "msft_beta"
171 |    ]
172 |   }
173 |  ],
174 |  "metadata": {
175 |   "kernelspec": {
176 |    "display_name": "Python 3",
177 |    "language": "python",
178 |    "name": "python3"
179 |   },
180 |   "language_info": {
181 |    "codemirror_mode": {
182 |     "name": "ipython",
183 |     "version": 3
184 |    },
185 |    "file_extension": ".py",
186 |    "mimetype": "text/x-python",
187 |    "name": "python",
188 |    "nbconvert_exporter": "python",
189 |    "pygments_lexer": "ipython3",
190 |    "version": "3.6.4"
191 |   }
192 |  },
193 |  "nbformat": 4,
194 |  "nbformat_minor": 2
195 | }
196 | 


--------------------------------------------------------------------------------
/Beta_for_PG.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "from pandas_datareader import data as wb\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "import fix_yahoo_finance as yf"
 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%***********************]  1 of 1 downloaded\n",
 26 |       "[*********************100%***********************]  1 of 1 downloaded\n"
 27 |      ]
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "#for beta of P&G\n",
 32 |     "tickers = ['PG','^GSPC']\n",
 33 |     "\n",
 34 |     "data = pd.DataFrame()\n",
 35 |     "\n",
 36 |     "for t in tickers :\n",
 37 |     "    data[t] = yf.download(t, start = '2012-01-01', end='2017-01-01')['Adj Close']"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 3,
 43 |    "metadata": {},
 44 |    "outputs": [],
 45 |    "source": [
 46 |     "sec_returns = np.log(data/data.shift(1))"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 4,
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "data": {
 56 |       "text/html": [
 57 |        "<div>\n",
 58 |        "<style scoped>\n",
 59 |        "    .dataframe tbody tr th:only-of-type {\n",
 60 |        "        vertical-align: middle;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe tbody tr th {\n",
 64 |        "        vertical-align: top;\n",
 65 |        "    }\n",
 66 |        "\n",
 67 |        "    .dataframe thead th {\n",
 68 |        "        text-align: right;\n",
 69 |        "    }\n",
 70 |        "</style>\n",
 71 |        "<table border=\"1\" class=\"dataframe\">\n",
 72 |        "  <thead>\n",
 73 |        "    <tr style=\"text-align: right;\">\n",
 74 |        "      <th></th>\n",
 75 |        "      <th>PG</th>\n",
 76 |        "      <th>^GSPC</th>\n",
 77 |        "    </tr>\n",
 78 |        "  </thead>\n",
 79 |        "  <tbody>\n",
 80 |        "    <tr>\n",
 81 |        "      <th>PG</th>\n",
 82 |        "      <td>0.020409</td>\n",
 83 |        "      <td>0.010078</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>^GSPC</th>\n",
 87 |        "      <td>0.010078</td>\n",
 88 |        "      <td>0.016362</td>\n",
 89 |        "    </tr>\n",
 90 |        "  </tbody>\n",
 91 |        "</table>\n",
 92 |        "</div>"
 93 |       ],
 94 |       "text/plain": [
 95 |        "             PG     ^GSPC\n",
 96 |        "PG     0.020409  0.010078\n",
 97 |        "^GSPC  0.010078  0.016362"
 98 |       ]
 99 |      },
100 |      "execution_count": 4,
101 |      "metadata": {},
102 |      "output_type": "execute_result"
103 |     }
104 |    ],
105 |    "source": [
106 |     "cov = sec_returns.cov() * 250\n",
107 |     "cov"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 5,
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "data": {
117 |       "text/plain": [
118 |        "0.010078082426284392"
119 |       ]
120 |      },
121 |      "execution_count": 5,
122 |      "metadata": {},
123 |      "output_type": "execute_result"
124 |     }
125 |    ],
126 |    "source": [
127 |     "cov_with_market = cov.iloc[0,1]\n",
128 |     "cov_with_market"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 6,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "data": {
138 |       "text/plain": [
139 |        "0.016361631022038074"
140 |       ]
141 |      },
142 |      "execution_count": 6,
143 |      "metadata": {},
144 |      "output_type": "execute_result"
145 |     }
146 |    ],
147 |    "source": [
148 |     "market_var = sec_returns['^GSPC'].var()*250\n",
149 |     "market_var"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": 7,
155 |    "metadata": {},
156 |    "outputs": [
157 |     {
158 |      "data": {
159 |       "text/plain": [
160 |        "0.6159582997996873"
161 |       ]
162 |      },
163 |      "execution_count": 7,
164 |      "metadata": {},
165 |      "output_type": "execute_result"
166 |     }
167 |    ],
168 |    "source": [
169 |     "msft_beta = cov_with_market / market_var\n",
170 |     "msft_beta"
171 |    ]
172 |   }
173 |  ],
174 |  "metadata": {
175 |   "kernelspec": {
176 |    "display_name": "Python 3",
177 |    "language": "python",
178 |    "name": "python3"
179 |   },
180 |   "language_info": {
181 |    "codemirror_mode": {
182 |     "name": "ipython",
183 |     "version": 3
184 |    },
185 |    "file_extension": ".py",
186 |    "mimetype": "text/x-python",
187 |    "name": "python",
188 |    "nbconvert_exporter": "python",
189 |    "pygments_lexer": "ipython3",
190 |    "version": "3.6.4"
191 |   }
192 |  },
193 |  "nbformat": 4,
194 |  "nbformat_minor": 2
195 | }
196 | 


--------------------------------------------------------------------------------
/Div_non-div_risk.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "from pandas_datareader import data as wb\n",
 12 |     "import matplotlib.pyplot as plt"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "tickers = ['PG','MSFT']\n",
 22 |     "\n",
 23 |     "sec_data = pd.DataFrame()\n",
 24 |     "\n",
 25 |     "for t in tickers :\n",
 26 |     "    sec_data[t] = wb.DataReader(t,'quandl','2007-1-1','2018-3-1')['AdjClose']"
 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 |       "<class 'pandas.core.frame.DataFrame'>\n",
 39 |       "DatetimeIndex: 2809 entries, 2018-03-01 to 2007-01-03\n",
 40 |       "Data columns (total 2 columns):\n",
 41 |       "PG      2809 non-null float64\n",
 42 |       "MSFT    2809 non-null float64\n",
 43 |       "dtypes: float64(2)\n",
 44 |       "memory usage: 65.8 KB\n"
 45 |      ]
 46 |     }
 47 |    ],
 48 |    "source": [
 49 |     "sec_data.info()"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 4,
 55 |    "metadata": {},
 56 |    "outputs": [
 57 |     {
 58 |      "data": {
 59 |       "text/html": [
 60 |        "<div>\n",
 61 |        "<style scoped>\n",
 62 |        "    .dataframe tbody tr th:only-of-type {\n",
 63 |        "        vertical-align: middle;\n",
 64 |        "    }\n",
 65 |        "\n",
 66 |        "    .dataframe tbody tr th {\n",
 67 |        "        vertical-align: top;\n",
 68 |        "    }\n",
 69 |        "\n",
 70 |        "    .dataframe thead th {\n",
 71 |        "        text-align: right;\n",
 72 |        "    }\n",
 73 |        "</style>\n",
 74 |        "<table border=\"1\" class=\"dataframe\">\n",
 75 |        "  <thead>\n",
 76 |        "    <tr style=\"text-align: right;\">\n",
 77 |        "      <th></th>\n",
 78 |        "      <th>PG</th>\n",
 79 |        "      <th>MSFT</th>\n",
 80 |        "    </tr>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>Date</th>\n",
 83 |        "      <th></th>\n",
 84 |        "      <th></th>\n",
 85 |        "    </tr>\n",
 86 |        "  </thead>\n",
 87 |        "  <tbody>\n",
 88 |        "    <tr>\n",
 89 |        "      <th>2007-01-03</th>\n",
 90 |        "      <td>NaN</td>\n",
 91 |        "      <td>NaN</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>2007-01-04</th>\n",
 95 |        "      <td>-0.007621</td>\n",
 96 |        "      <td>-0.001676</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <th>2007-01-05</th>\n",
100 |        "      <td>-0.008624</td>\n",
101 |        "      <td>-0.005719</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>2007-01-08</th>\n",
105 |        "      <td>0.002202</td>\n",
106 |        "      <td>0.009737</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>2007-01-09</th>\n",
110 |        "      <td>-0.002517</td>\n",
111 |        "      <td>0.001002</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>2007-01-10</th>\n",
115 |        "      <td>0.011901</td>\n",
116 |        "      <td>-0.010064</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>2007-01-11</th>\n",
120 |        "      <td>0.006207</td>\n",
121 |        "      <td>0.034463</td>\n",
122 |        "    </tr>\n",
123 |        "    <tr>\n",
124 |        "      <th>2007-01-12</th>\n",
125 |        "      <td>0.005554</td>\n",
126 |        "      <td>0.016476</td>\n",
127 |        "    </tr>\n",
128 |        "    <tr>\n",
129 |        "      <th>2007-01-16</th>\n",
130 |        "      <td>0.000000</td>\n",
131 |        "      <td>-0.001603</td>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <th>2007-01-17</th>\n",
135 |        "      <td>0.010255</td>\n",
136 |        "      <td>-0.001927</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <th>2007-01-18</th>\n",
140 |        "      <td>0.002750</td>\n",
141 |        "      <td>-0.003221</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>2007-01-19</th>\n",
145 |        "      <td>0.008357</td>\n",
146 |        "      <td>0.003542</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>2007-01-22</th>\n",
150 |        "      <td>-0.005614</td>\n",
151 |        "      <td>-0.012615</td>\n",
152 |        "    </tr>\n",
153 |        "    <tr>\n",
154 |        "      <th>2007-01-23</th>\n",
155 |        "      <td>-0.001218</td>\n",
156 |        "      <td>0.000651</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>2007-01-24</th>\n",
160 |        "      <td>-0.001982</td>\n",
161 |        "      <td>0.011321</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>2007-01-25</th>\n",
165 |        "      <td>-0.009047</td>\n",
166 |        "      <td>-0.020800</td>\n",
167 |        "    </tr>\n",
168 |        "    <tr>\n",
169 |        "      <th>2007-01-26</th>\n",
170 |        "      <td>-0.000925</td>\n",
171 |        "      <td>0.004914</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>2007-01-29</th>\n",
175 |        "      <td>0.000308</td>\n",
176 |        "      <td>-0.002290</td>\n",
177 |        "    </tr>\n",
178 |        "    <tr>\n",
179 |        "      <th>2007-01-30</th>\n",
180 |        "      <td>-0.004480</td>\n",
181 |        "      <td>-0.001639</td>\n",
182 |        "    </tr>\n",
183 |        "    <tr>\n",
184 |        "      <th>2007-01-31</th>\n",
185 |        "      <td>0.004326</td>\n",
186 |        "      <td>0.012390</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>2007-02-01</th>\n",
190 |        "      <td>0.007219</td>\n",
191 |        "      <td>-0.009769</td>\n",
192 |        "    </tr>\n",
193 |        "    <tr>\n",
194 |        "      <th>2007-02-02</th>\n",
195 |        "      <td>0.000000</td>\n",
196 |        "      <td>-0.012181</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>2007-02-05</th>\n",
200 |        "      <td>-0.002912</td>\n",
201 |        "      <td>-0.019399</td>\n",
202 |        "    </tr>\n",
203 |        "    <tr>\n",
204 |        "      <th>2007-02-06</th>\n",
205 |        "      <td>-0.008633</td>\n",
206 |        "      <td>-0.003383</td>\n",
207 |        "    </tr>\n",
208 |        "    <tr>\n",
209 |        "      <th>2007-02-07</th>\n",
210 |        "      <td>-0.001705</td>\n",
211 |        "      <td>-0.004755</td>\n",
212 |        "    </tr>\n",
213 |        "    <tr>\n",
214 |        "      <th>2007-02-08</th>\n",
215 |        "      <td>0.001705</td>\n",
216 |        "      <td>-0.003752</td>\n",
217 |        "    </tr>\n",
218 |        "    <tr>\n",
219 |        "      <th>2007-02-09</th>\n",
220 |        "      <td>-0.002480</td>\n",
221 |        "      <td>-0.009615</td>\n",
222 |        "    </tr>\n",
223 |        "    <tr>\n",
224 |        "      <th>2007-02-12</th>\n",
225 |        "      <td>-0.004199</td>\n",
226 |        "      <td>-0.001381</td>\n",
227 |        "    </tr>\n",
228 |        "    <tr>\n",
229 |        "      <th>2007-02-13</th>\n",
230 |        "      <td>0.008536</td>\n",
231 |        "      <td>0.005857</td>\n",
232 |        "    </tr>\n",
233 |        "    <tr>\n",
234 |        "      <th>2007-02-14</th>\n",
235 |        "      <td>0.002624</td>\n",
236 |        "      <td>0.013354</td>\n",
237 |        "    </tr>\n",
238 |        "    <tr>\n",
239 |        "      <th>...</th>\n",
240 |        "      <td>...</td>\n",
241 |        "      <td>...</td>\n",
242 |        "    </tr>\n",
243 |        "    <tr>\n",
244 |        "      <th>2018-01-18</th>\n",
245 |        "      <td>-0.011247</td>\n",
246 |        "      <td>-0.000444</td>\n",
247 |        "    </tr>\n",
248 |        "    <tr>\n",
249 |        "      <th>2018-01-19</th>\n",
250 |        "      <td>0.009821</td>\n",
251 |        "      <td>-0.001110</td>\n",
252 |        "    </tr>\n",
253 |        "    <tr>\n",
254 |        "      <th>2018-01-22</th>\n",
255 |        "      <td>0.008964</td>\n",
256 |        "      <td>0.017731</td>\n",
257 |        "    </tr>\n",
258 |        "    <tr>\n",
259 |        "      <th>2018-01-23</th>\n",
260 |        "      <td>-0.031394</td>\n",
261 |        "      <td>0.003161</td>\n",
262 |        "    </tr>\n",
263 |        "    <tr>\n",
264 |        "      <th>2018-01-24</th>\n",
265 |        "      <td>-0.008345</td>\n",
266 |        "      <td>-0.000871</td>\n",
267 |        "    </tr>\n",
268 |        "    <tr>\n",
269 |        "      <th>2018-01-25</th>\n",
270 |        "      <td>0.000340</td>\n",
271 |        "      <td>0.005539</td>\n",
272 |        "    </tr>\n",
273 |        "    <tr>\n",
274 |        "      <th>2018-01-26</th>\n",
275 |        "      <td>-0.006929</td>\n",
276 |        "      <td>0.018564</td>\n",
277 |        "    </tr>\n",
278 |        "    <tr>\n",
279 |        "      <th>2018-01-29</th>\n",
280 |        "      <td>-0.009966</td>\n",
281 |        "      <td>-0.001490</td>\n",
282 |        "    </tr>\n",
283 |        "    <tr>\n",
284 |        "      <th>2018-01-30</th>\n",
285 |        "      <td>0.001036</td>\n",
286 |        "      <td>-0.012643</td>\n",
287 |        "    </tr>\n",
288 |        "    <tr>\n",
289 |        "      <th>2018-01-31</th>\n",
290 |        "      <td>-0.007040</td>\n",
291 |        "      <td>0.024182</td>\n",
292 |        "    </tr>\n",
293 |        "    <tr>\n",
294 |        "      <th>2018-02-01</th>\n",
295 |        "      <td>-0.005691</td>\n",
296 |        "      <td>-0.007925</td>\n",
297 |        "    </tr>\n",
298 |        "    <tr>\n",
299 |        "      <th>2018-02-02</th>\n",
300 |        "      <td>-0.018813</td>\n",
301 |        "      <td>-0.026663</td>\n",
302 |        "    </tr>\n",
303 |        "    <tr>\n",
304 |        "      <th>2018-02-05</th>\n",
305 |        "      <td>-0.038599</td>\n",
306 |        "      <td>-0.030196</td>\n",
307 |        "    </tr>\n",
308 |        "    <tr>\n",
309 |        "      <th>2018-02-06</th>\n",
310 |        "      <td>0.016153</td>\n",
311 |        "      <td>0.025281</td>\n",
312 |        "    </tr>\n",
313 |        "    <tr>\n",
314 |        "      <th>2018-02-07</th>\n",
315 |        "      <td>-0.006577</td>\n",
316 |        "      <td>-0.019012</td>\n",
317 |        "    </tr>\n",
318 |        "    <tr>\n",
319 |        "      <th>2018-02-08</th>\n",
320 |        "      <td>-0.019993</td>\n",
321 |        "      <td>-0.054111</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>2018-02-09</th>\n",
325 |        "      <td>-0.003747</td>\n",
326 |        "      <td>0.037116</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>2018-02-12</th>\n",
330 |        "      <td>0.017489</td>\n",
331 |        "      <td>0.011623</td>\n",
332 |        "    </tr>\n",
333 |        "    <tr>\n",
334 |        "      <th>2018-02-13</th>\n",
335 |        "      <td>0.002088</td>\n",
336 |        "      <td>0.007823</td>\n",
337 |        "    </tr>\n",
338 |        "    <tr>\n",
339 |        "      <th>2018-02-14</th>\n",
340 |        "      <td>-0.010112</td>\n",
341 |        "      <td>0.010850</td>\n",
342 |        "    </tr>\n",
343 |        "    <tr>\n",
344 |        "      <th>2018-02-15</th>\n",
345 |        "      <td>0.021216</td>\n",
346 |        "      <td>0.020167</td>\n",
347 |        "    </tr>\n",
348 |        "    <tr>\n",
349 |        "      <th>2018-02-16</th>\n",
350 |        "      <td>0.002303</td>\n",
351 |        "      <td>-0.007148</td>\n",
352 |        "    </tr>\n",
353 |        "    <tr>\n",
354 |        "      <th>2018-02-20</th>\n",
355 |        "      <td>-0.015372</td>\n",
356 |        "      <td>0.007796</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>2018-02-21</th>\n",
360 |        "      <td>-0.013616</td>\n",
361 |        "      <td>-0.013355</td>\n",
362 |        "    </tr>\n",
363 |        "    <tr>\n",
364 |        "      <th>2018-02-22</th>\n",
365 |        "      <td>0.007450</td>\n",
366 |        "      <td>0.002729</td>\n",
367 |        "    </tr>\n",
368 |        "    <tr>\n",
369 |        "      <th>2018-02-23</th>\n",
370 |        "      <td>0.002594</td>\n",
371 |        "      <td>0.024974</td>\n",
372 |        "    </tr>\n",
373 |        "    <tr>\n",
374 |        "      <th>2018-02-26</th>\n",
375 |        "      <td>0.007131</td>\n",
376 |        "      <td>0.014355</td>\n",
377 |        "    </tr>\n",
378 |        "    <tr>\n",
379 |        "      <th>2018-02-27</th>\n",
380 |        "      <td>-0.013443</td>\n",
381 |        "      <td>-0.012868</td>\n",
382 |        "    </tr>\n",
383 |        "    <tr>\n",
384 |        "      <th>2018-02-28</th>\n",
385 |        "      <td>-0.025401</td>\n",
386 |        "      <td>-0.004575</td>\n",
387 |        "    </tr>\n",
388 |        "    <tr>\n",
389 |        "      <th>2018-03-01</th>\n",
390 |        "      <td>0.002290</td>\n",
391 |        "      <td>-0.009860</td>\n",
392 |        "    </tr>\n",
393 |        "  </tbody>\n",
394 |        "</table>\n",
395 |        "<p>2809 rows × 2 columns</p>\n",
396 |        "</div>"
397 |       ],
398 |       "text/plain": [
399 |        "                  PG      MSFT\n",
400 |        "Date                          \n",
401 |        "2007-01-03       NaN       NaN\n",
402 |        "2007-01-04 -0.007621 -0.001676\n",
403 |        "2007-01-05 -0.008624 -0.005719\n",
404 |        "2007-01-08  0.002202  0.009737\n",
405 |        "2007-01-09 -0.002517  0.001002\n",
406 |        "2007-01-10  0.011901 -0.010064\n",
407 |        "2007-01-11  0.006207  0.034463\n",
408 |        "2007-01-12  0.005554  0.016476\n",
409 |        "2007-01-16  0.000000 -0.001603\n",
410 |        "2007-01-17  0.010255 -0.001927\n",
411 |        "2007-01-18  0.002750 -0.003221\n",
412 |        "2007-01-19  0.008357  0.003542\n",
413 |        "2007-01-22 -0.005614 -0.012615\n",
414 |        "2007-01-23 -0.001218  0.000651\n",
415 |        "2007-01-24 -0.001982  0.011321\n",
416 |        "2007-01-25 -0.009047 -0.020800\n",
417 |        "2007-01-26 -0.000925  0.004914\n",
418 |        "2007-01-29  0.000308 -0.002290\n",
419 |        "2007-01-30 -0.004480 -0.001639\n",
420 |        "2007-01-31  0.004326  0.012390\n",
421 |        "2007-02-01  0.007219 -0.009769\n",
422 |        "2007-02-02  0.000000 -0.012181\n",
423 |        "2007-02-05 -0.002912 -0.019399\n",
424 |        "2007-02-06 -0.008633 -0.003383\n",
425 |        "2007-02-07 -0.001705 -0.004755\n",
426 |        "2007-02-08  0.001705 -0.003752\n",
427 |        "2007-02-09 -0.002480 -0.009615\n",
428 |        "2007-02-12 -0.004199 -0.001381\n",
429 |        "2007-02-13  0.008536  0.005857\n",
430 |        "2007-02-14  0.002624  0.013354\n",
431 |        "...              ...       ...\n",
432 |        "2018-01-18 -0.011247 -0.000444\n",
433 |        "2018-01-19  0.009821 -0.001110\n",
434 |        "2018-01-22  0.008964  0.017731\n",
435 |        "2018-01-23 -0.031394  0.003161\n",
436 |        "2018-01-24 -0.008345 -0.000871\n",
437 |        "2018-01-25  0.000340  0.005539\n",
438 |        "2018-01-26 -0.006929  0.018564\n",
439 |        "2018-01-29 -0.009966 -0.001490\n",
440 |        "2018-01-30  0.001036 -0.012643\n",
441 |        "2018-01-31 -0.007040  0.024182\n",
442 |        "2018-02-01 -0.005691 -0.007925\n",
443 |        "2018-02-02 -0.018813 -0.026663\n",
444 |        "2018-02-05 -0.038599 -0.030196\n",
445 |        "2018-02-06  0.016153  0.025281\n",
446 |        "2018-02-07 -0.006577 -0.019012\n",
447 |        "2018-02-08 -0.019993 -0.054111\n",
448 |        "2018-02-09 -0.003747  0.037116\n",
449 |        "2018-02-12  0.017489  0.011623\n",
450 |        "2018-02-13  0.002088  0.007823\n",
451 |        "2018-02-14 -0.010112  0.010850\n",
452 |        "2018-02-15  0.021216  0.020167\n",
453 |        "2018-02-16  0.002303 -0.007148\n",
454 |        "2018-02-20 -0.015372  0.007796\n",
455 |        "2018-02-21 -0.013616 -0.013355\n",
456 |        "2018-02-22  0.007450  0.002729\n",
457 |        "2018-02-23  0.002594  0.024974\n",
458 |        "2018-02-26  0.007131  0.014355\n",
459 |        "2018-02-27 -0.013443 -0.012868\n",
460 |        "2018-02-28 -0.025401 -0.004575\n",
461 |        "2018-03-01  0.002290 -0.009860\n",
462 |        "\n",
463 |        "[2809 rows x 2 columns]"
464 |       ]
465 |      },
466 |      "execution_count": 4,
467 |      "metadata": {},
468 |      "output_type": "execute_result"
469 |     }
470 |    ],
471 |    "source": [
472 |     "sec_data = sec_data.iloc[::-1]\n",
473 |     "sec_returns = np.log(sec_data / sec_data.shift(1))\n",
474 |     "sec_returns"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "code",
479 |    "execution_count": 5,
480 |    "metadata": {},
481 |    "outputs": [],
482 |    "source": [
483 |     "weights = np.array([0.5,0.5])"
484 |    ]
485 |   },
486 |   {
487 |    "cell_type": "code",
488 |    "execution_count": 12,
489 |    "metadata": {},
490 |    "outputs": [],
491 |    "source": [
492 |     "PG_var_a = sec_returns['PG'].var() * 250\n",
493 |     "MSFT_var_a = sec_returns['MSFT'].var() * 250"
494 |    ]
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": 13,
499 |    "metadata": {},
500 |    "outputs": [
501 |     {
502 |      "name": "stdout",
503 |      "output_type": "stream",
504 |      "text": [
505 |       "0.03676485457868782\n",
506 |       "3.676 %\n"
507 |      ]
508 |     }
509 |    ],
510 |    "source": [
511 |     "pfolio_var = np.dot(weights.T,np.dot(sec_returns.cov() * 250,weights))\n",
512 |     "print(pfolio_var)\n",
513 |     "print(str(round(pfolio_var * 100,3)) + ' %')"
514 |    ]
515 |   },
516 |   {
517 |    "cell_type": "code",
518 |    "execution_count": 9,
519 |    "metadata": {},
520 |    "outputs": [
521 |     {
522 |      "name": "stdout",
523 |      "output_type": "stream",
524 |      "text": [
525 |       "1.082 %\n"
526 |      ]
527 |     }
528 |    ],
529 |    "source": [
530 |     "#diversifiable risk\n",
531 |     "dr = pfolio_var - (weights[0] ** 2 * PG_var_a) - (weights[1] ** 2 * MSFT_var_a)\n",
532 |     "print(str(round(dr*100,3)) + ' %')"
533 |    ]
534 |   },
535 |   {
536 |    "cell_type": "code",
537 |    "execution_count": 10,
538 |    "metadata": {},
539 |    "outputs": [
540 |     {
541 |      "name": "stdout",
542 |      "output_type": "stream",
543 |      "text": [
544 |       "2.595 %\n"
545 |      ]
546 |     }
547 |    ],
548 |    "source": [
549 |     "#non-diversifiable risk\n",
550 |     "n_dr_1 = pfolio_var - dr\n",
551 |     "print(str(round(n_dr_1*100,3)) + ' %')"
552 |    ]
553 |   },
554 |   {
555 |    "cell_type": "code",
556 |    "execution_count": 14,
557 |    "metadata": {},
558 |    "outputs": [
559 |     {
560 |      "name": "stdout",
561 |      "output_type": "stream",
562 |      "text": [
563 |       "2.595 %\n"
564 |      ]
565 |     }
566 |    ],
567 |    "source": [
568 |     "n_dr_2 = (weights[0] ** 2 * PG_var_a) + (weights[1] ** 2 * MSFT_var_a)\n",
569 |     "print(str(round(n_dr_2*100,3)) + ' %')"
570 |    ]
571 |   }
572 |  ],
573 |  "metadata": {
574 |   "kernelspec": {
575 |    "display_name": "Python 3",
576 |    "language": "python",
577 |    "name": "python3"
578 |   },
579 |   "language_info": {
580 |    "codemirror_mode": {
581 |     "name": "ipython",
582 |     "version": 3
583 |    },
584 |    "file_extension": ".py",
585 |    "mimetype": "text/x-python",
586 |    "name": "python",
587 |    "nbconvert_exporter": "python",
588 |    "pygments_lexer": "ipython3",
589 |    "version": "3.6.4"
590 |   }
591 |  },
592 |  "nbformat": 4,
593 |  "nbformat_minor": 2
594 | }
595 | 


--------------------------------------------------------------------------------
/Markowitz_algorithm.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 1,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "import pandas as pd\n",
  10 |     "import numpy as np\n",
  11 |     "from pandas_datareader import data as wb\n",
  12 |     "import matplotlib.pyplot as plt\n",
  13 |     "%matplotlib inline"
  14 |    ]
  15 |   },
  16 |   {
  17 |    "cell_type": "code",
  18 |    "execution_count": 41,
  19 |    "metadata": {},
  20 |    "outputs": [],
  21 |    "source": [
  22 |     "assets = ['MSFT','GOOGL']\n",
  23 |     "pf_data = pd.DataFrame()"
  24 |    ]
  25 |   },
  26 |   {
  27 |    "cell_type": "code",
  28 |    "execution_count": 42,
  29 |    "metadata": {},
  30 |    "outputs": [],
  31 |    "source": [
  32 |     "for a in assets :\n",
  33 |     "    pf_data[a] = wb.DataReader(a,'quandl', '2010-1-1', '2018-1-1')['AdjClose']"
  34 |    ]
  35 |   },
  36 |   {
  37 |    "cell_type": "code",
  38 |    "execution_count": 54,
  39 |    "metadata": {},
  40 |    "outputs": [
  41 |     {
  42 |      "data": {
  43 |       "text/plain": [
  44 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1e3bbff9630>"
  45 |       ]
  46 |      },
  47 |      "execution_count": 54,
  48 |      "metadata": {},
  49 |      "output_type": "execute_result"
  50 |     },
  51 |     {
  52 |      "data": {
  53 |       "image/png": 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\n",
  54 |       "text/plain": [
  55 |        "<matplotlib.figure.Figure at 0x1e3bc0040b8>"
  56 |       ]
  57 |      },
  58 |      "metadata": {},
  59 |      "output_type": "display_data"
  60 |     }
  61 |    ],
  62 |    "source": [
  63 |     "#pf_data = pf_data.iloc[::-1]\n",
  64 |     "(pf_data / pf_data.iloc[0] * 100).plot(figsize=(10,5))"
  65 |    ]
  66 |   },
  67 |   {
  68 |    "cell_type": "code",
  69 |    "execution_count": 56,
  70 |    "metadata": {},
  71 |    "outputs": [
  72 |     {
  73 |      "data": {
  74 |       "text/html": [
  75 |        "<div>\n",
  76 |        "<style scoped>\n",
  77 |        "    .dataframe tbody tr th:only-of-type {\n",
  78 |        "        vertical-align: middle;\n",
  79 |        "    }\n",
  80 |        "\n",
  81 |        "    .dataframe tbody tr th {\n",
  82 |        "        vertical-align: top;\n",
  83 |        "    }\n",
  84 |        "\n",
  85 |        "    .dataframe thead th {\n",
  86 |        "        text-align: right;\n",
  87 |        "    }\n",
  88 |        "</style>\n",
  89 |        "<table border=\"1\" class=\"dataframe\">\n",
  90 |        "  <thead>\n",
  91 |        "    <tr style=\"text-align: right;\">\n",
  92 |        "      <th></th>\n",
  93 |        "      <th>MSFT</th>\n",
  94 |        "      <th>GOOGL</th>\n",
  95 |        "    </tr>\n",
  96 |        "    <tr>\n",
  97 |        "      <th>Date</th>\n",
  98 |        "      <th></th>\n",
  99 |        "      <th></th>\n",
 100 |        "    </tr>\n",
 101 |        "  </thead>\n",
 102 |        "  <tbody>\n",
 103 |        "    <tr>\n",
 104 |        "      <th>2010-01-04</th>\n",
 105 |        "      <td>NaN</td>\n",
 106 |        "      <td>NaN</td>\n",
 107 |        "    </tr>\n",
 108 |        "    <tr>\n",
 109 |        "      <th>2010-01-05</th>\n",
 110 |        "      <td>0.000323</td>\n",
 111 |        "      <td>-0.004413</td>\n",
 112 |        "    </tr>\n",
 113 |        "    <tr>\n",
 114 |        "      <th>2010-01-06</th>\n",
 115 |        "      <td>-0.006156</td>\n",
 116 |        "      <td>-0.025532</td>\n",
 117 |        "    </tr>\n",
 118 |        "    <tr>\n",
 119 |        "      <th>2010-01-07</th>\n",
 120 |        "      <td>-0.010389</td>\n",
 121 |        "      <td>-0.023555</td>\n",
 122 |        "    </tr>\n",
 123 |        "    <tr>\n",
 124 |        "      <th>2010-01-08</th>\n",
 125 |        "      <td>0.006807</td>\n",
 126 |        "      <td>0.013243</td>\n",
 127 |        "    </tr>\n",
 128 |        "    <tr>\n",
 129 |        "      <th>2010-01-11</th>\n",
 130 |        "      <td>-0.012802</td>\n",
 131 |        "      <td>-0.001513</td>\n",
 132 |        "    </tr>\n",
 133 |        "    <tr>\n",
 134 |        "      <th>2010-01-12</th>\n",
 135 |        "      <td>-0.006629</td>\n",
 136 |        "      <td>-0.017842</td>\n",
 137 |        "    </tr>\n",
 138 |        "    <tr>\n",
 139 |        "      <th>2010-01-13</th>\n",
 140 |        "      <td>0.009269</td>\n",
 141 |        "      <td>-0.005758</td>\n",
 142 |        "    </tr>\n",
 143 |        "    <tr>\n",
 144 |        "      <th>2010-01-14</th>\n",
 145 |        "      <td>0.019900</td>\n",
 146 |        "      <td>0.004690</td>\n",
 147 |        "    </tr>\n",
 148 |        "    <tr>\n",
 149 |        "      <th>2010-01-15</th>\n",
 150 |        "      <td>-0.003235</td>\n",
 151 |        "      <td>-0.016840</td>\n",
 152 |        "    </tr>\n",
 153 |        "    <tr>\n",
 154 |        "      <th>2010-01-19</th>\n",
 155 |        "      <td>0.007747</td>\n",
 156 |        "      <td>0.013052</td>\n",
 157 |        "    </tr>\n",
 158 |        "    <tr>\n",
 159 |        "      <th>2010-01-20</th>\n",
 160 |        "      <td>-0.016698</td>\n",
 161 |        "      <td>-0.012346</td>\n",
 162 |        "    </tr>\n",
 163 |        "    <tr>\n",
 164 |        "      <th>2010-01-21</th>\n",
 165 |        "      <td>-0.018979</td>\n",
 166 |        "      <td>0.004418</td>\n",
 167 |        "    </tr>\n",
 168 |        "    <tr>\n",
 169 |        "      <th>2010-01-22</th>\n",
 170 |        "      <td>-0.035615</td>\n",
 171 |        "      <td>-0.058216</td>\n",
 172 |        "    </tr>\n",
 173 |        "    <tr>\n",
 174 |        "      <th>2010-01-25</th>\n",
 175 |        "      <td>0.012354</td>\n",
 176 |        "      <td>-0.018367</td>\n",
 177 |        "    </tr>\n",
 178 |        "    <tr>\n",
 179 |        "      <th>2010-01-26</th>\n",
 180 |        "      <td>0.006120</td>\n",
 181 |        "      <td>0.004471</td>\n",
 182 |        "    </tr>\n",
 183 |        "    <tr>\n",
 184 |        "      <th>2010-01-27</th>\n",
 185 |        "      <td>0.005746</td>\n",
 186 |        "      <td>-0.000590</td>\n",
 187 |        "    </tr>\n",
 188 |        "    <tr>\n",
 189 |        "      <th>2010-01-28</th>\n",
 190 |        "      <td>-0.017339</td>\n",
 191 |        "      <td>-0.014512</td>\n",
 192 |        "    </tr>\n",
 193 |        "    <tr>\n",
 194 |        "      <th>2010-01-29</th>\n",
 195 |        "      <td>-0.034185</td>\n",
 196 |        "      <td>-0.008167</td>\n",
 197 |        "    </tr>\n",
 198 |        "    <tr>\n",
 199 |        "      <th>2010-02-01</th>\n",
 200 |        "      <td>0.008129</td>\n",
 201 |        "      <td>0.005788</td>\n",
 202 |        "    </tr>\n",
 203 |        "    <tr>\n",
 204 |        "      <th>2010-02-02</th>\n",
 205 |        "      <td>0.001758</td>\n",
 206 |        "      <td>-0.003571</td>\n",
 207 |        "    </tr>\n",
 208 |        "    <tr>\n",
 209 |        "      <th>2010-02-03</th>\n",
 210 |        "      <td>0.005956</td>\n",
 211 |        "      <td>0.018099</td>\n",
 212 |        "    </tr>\n",
 213 |        "    <tr>\n",
 214 |        "      <th>2010-02-04</th>\n",
 215 |        "      <td>-0.027981</td>\n",
 216 |        "      <td>-0.026307</td>\n",
 217 |        "    </tr>\n",
 218 |        "    <tr>\n",
 219 |        "      <th>2010-02-05</th>\n",
 220 |        "      <td>0.006445</td>\n",
 221 |        "      <td>0.008529</td>\n",
 222 |        "    </tr>\n",
 223 |        "    <tr>\n",
 224 |        "      <th>2010-02-08</th>\n",
 225 |        "      <td>-0.010764</td>\n",
 226 |        "      <td>0.004095</td>\n",
 227 |        "    </tr>\n",
 228 |        "    <tr>\n",
 229 |        "      <th>2010-02-09</th>\n",
 230 |        "      <td>0.010407</td>\n",
 231 |        "      <td>0.005552</td>\n",
 232 |        "    </tr>\n",
 233 |        "    <tr>\n",
 234 |        "      <th>2010-02-10</th>\n",
 235 |        "      <td>-0.000714</td>\n",
 236 |        "      <td>-0.003726</td>\n",
 237 |        "    </tr>\n",
 238 |        "    <tr>\n",
 239 |        "      <th>2010-02-11</th>\n",
 240 |        "      <td>0.004634</td>\n",
 241 |        "      <td>0.003651</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>2010-02-12</th>\n",
 245 |        "      <td>-0.006780</td>\n",
 246 |        "      <td>-0.006134</td>\n",
 247 |        "    </tr>\n",
 248 |        "    <tr>\n",
 249 |        "      <th>2010-02-16</th>\n",
 250 |        "      <td>0.019501</td>\n",
 251 |        "      <td>0.015227</td>\n",
 252 |        "    </tr>\n",
 253 |        "    <tr>\n",
 254 |        "      <th>...</th>\n",
 255 |        "      <td>...</td>\n",
 256 |        "      <td>...</td>\n",
 257 |        "    </tr>\n",
 258 |        "    <tr>\n",
 259 |        "      <th>2017-11-16</th>\n",
 260 |        "      <td>0.002648</td>\n",
 261 |        "      <td>0.011569</td>\n",
 262 |        "    </tr>\n",
 263 |        "    <tr>\n",
 264 |        "      <th>2017-11-17</th>\n",
 265 |        "      <td>-0.009662</td>\n",
 266 |        "      <td>-0.012071</td>\n",
 267 |        "    </tr>\n",
 268 |        "    <tr>\n",
 269 |        "      <th>2017-11-20</th>\n",
 270 |        "      <td>0.001576</td>\n",
 271 |        "      <td>-0.001188</td>\n",
 272 |        "    </tr>\n",
 273 |        "    <tr>\n",
 274 |        "      <th>2017-11-21</th>\n",
 275 |        "      <td>0.014316</td>\n",
 276 |        "      <td>0.015003</td>\n",
 277 |        "    </tr>\n",
 278 |        "    <tr>\n",
 279 |        "      <th>2017-11-22</th>\n",
 280 |        "      <td>-0.007313</td>\n",
 281 |        "      <td>0.001541</td>\n",
 282 |        "    </tr>\n",
 283 |        "    <tr>\n",
 284 |        "      <th>2017-11-24</th>\n",
 285 |        "      <td>0.001803</td>\n",
 286 |        "      <td>0.004363</td>\n",
 287 |        "    </tr>\n",
 288 |        "    <tr>\n",
 289 |        "      <th>2017-11-27</th>\n",
 290 |        "      <td>0.007300</td>\n",
 291 |        "      <td>0.014555</td>\n",
 292 |        "    </tr>\n",
 293 |        "    <tr>\n",
 294 |        "      <th>2017-11-28</th>\n",
 295 |        "      <td>0.011971</td>\n",
 296 |        "      <td>-0.008168</td>\n",
 297 |        "    </tr>\n",
 298 |        "    <tr>\n",
 299 |        "      <th>2017-11-29</th>\n",
 300 |        "      <td>-0.018310</td>\n",
 301 |        "      <td>-0.024670</td>\n",
 302 |        "    </tr>\n",
 303 |        "    <tr>\n",
 304 |        "      <th>2017-11-30</th>\n",
 305 |        "      <td>0.009910</td>\n",
 306 |        "      <td>-0.001167</td>\n",
 307 |        "    </tr>\n",
 308 |        "    <tr>\n",
 309 |        "      <th>2017-12-01</th>\n",
 310 |        "      <td>0.001069</td>\n",
 311 |        "      <td>-0.010770</td>\n",
 312 |        "    </tr>\n",
 313 |        "    <tr>\n",
 314 |        "      <th>2017-12-04</th>\n",
 315 |        "      <td>-0.038471</td>\n",
 316 |        "      <td>-0.012961</td>\n",
 317 |        "    </tr>\n",
 318 |        "    <tr>\n",
 319 |        "      <th>2017-12-05</th>\n",
 320 |        "      <td>0.006270</td>\n",
 321 |        "      <td>0.007610</td>\n",
 322 |        "    </tr>\n",
 323 |        "    <tr>\n",
 324 |        "      <th>2017-12-06</th>\n",
 325 |        "      <td>0.014480</td>\n",
 326 |        "      <td>0.012786</td>\n",
 327 |        "    </tr>\n",
 328 |        "    <tr>\n",
 329 |        "      <th>2017-12-07</th>\n",
 330 |        "      <td>-0.003509</td>\n",
 331 |        "      <td>0.011409</td>\n",
 332 |        "    </tr>\n",
 333 |        "    <tr>\n",
 334 |        "      <th>2017-12-08</th>\n",
 335 |        "      <td>0.020043</td>\n",
 336 |        "      <td>0.004594</td>\n",
 337 |        "    </tr>\n",
 338 |        "    <tr>\n",
 339 |        "      <th>2017-12-11</th>\n",
 340 |        "      <td>0.012634</td>\n",
 341 |        "      <td>0.002465</td>\n",
 342 |        "    </tr>\n",
 343 |        "    <tr>\n",
 344 |        "      <th>2017-12-12</th>\n",
 345 |        "      <td>0.004098</td>\n",
 346 |        "      <td>-0.003047</td>\n",
 347 |        "    </tr>\n",
 348 |        "    <tr>\n",
 349 |        "      <th>2017-12-13</th>\n",
 350 |        "      <td>-0.002691</td>\n",
 351 |        "      <td>0.002495</td>\n",
 352 |        "    </tr>\n",
 353 |        "    <tr>\n",
 354 |        "      <th>2017-12-14</th>\n",
 355 |        "      <td>-0.007763</td>\n",
 356 |        "      <td>0.005766</td>\n",
 357 |        "    </tr>\n",
 358 |        "    <tr>\n",
 359 |        "      <th>2017-12-15</th>\n",
 360 |        "      <td>0.025185</td>\n",
 361 |        "      <td>0.013647</td>\n",
 362 |        "    </tr>\n",
 363 |        "    <tr>\n",
 364 |        "      <th>2017-12-18</th>\n",
 365 |        "      <td>-0.005426</td>\n",
 366 |        "      <td>0.012137</td>\n",
 367 |        "    </tr>\n",
 368 |        "    <tr>\n",
 369 |        "      <th>2017-12-19</th>\n",
 370 |        "      <td>-0.006388</td>\n",
 371 |        "      <td>-0.004906</td>\n",
 372 |        "    </tr>\n",
 373 |        "    <tr>\n",
 374 |        "      <th>2017-12-20</th>\n",
 375 |        "      <td>-0.003618</td>\n",
 376 |        "      <td>-0.005777</td>\n",
 377 |        "    </tr>\n",
 378 |        "    <tr>\n",
 379 |        "      <th>2017-12-21</th>\n",
 380 |        "      <td>-0.000234</td>\n",
 381 |        "      <td>-0.002528</td>\n",
 382 |        "    </tr>\n",
 383 |        "    <tr>\n",
 384 |        "      <th>2017-12-22</th>\n",
 385 |        "      <td>0.000117</td>\n",
 386 |        "      <td>-0.001860</td>\n",
 387 |        "    </tr>\n",
 388 |        "    <tr>\n",
 389 |        "      <th>2017-12-26</th>\n",
 390 |        "      <td>-0.001287</td>\n",
 391 |        "      <td>-0.002820</td>\n",
 392 |        "    </tr>\n",
 393 |        "    <tr>\n",
 394 |        "      <th>2017-12-27</th>\n",
 395 |        "      <td>0.003623</td>\n",
 396 |        "      <td>-0.005315</td>\n",
 397 |        "    </tr>\n",
 398 |        "    <tr>\n",
 399 |        "      <th>2017-12-28</th>\n",
 400 |        "      <td>0.000117</td>\n",
 401 |        "      <td>-0.004017</td>\n",
 402 |        "    </tr>\n",
 403 |        "    <tr>\n",
 404 |        "      <th>2017-12-29</th>\n",
 405 |        "      <td>-0.002102</td>\n",
 406 |        "      <td>-0.002418</td>\n",
 407 |        "    </tr>\n",
 408 |        "  </tbody>\n",
 409 |        "</table>\n",
 410 |        "<p>2012 rows × 2 columns</p>\n",
 411 |        "</div>"
 412 |       ],
 413 |       "text/plain": [
 414 |        "                MSFT     GOOGL\n",
 415 |        "Date                          \n",
 416 |        "2010-01-04       NaN       NaN\n",
 417 |        "2010-01-05  0.000323 -0.004413\n",
 418 |        "2010-01-06 -0.006156 -0.025532\n",
 419 |        "2010-01-07 -0.010389 -0.023555\n",
 420 |        "2010-01-08  0.006807  0.013243\n",
 421 |        "2010-01-11 -0.012802 -0.001513\n",
 422 |        "2010-01-12 -0.006629 -0.017842\n",
 423 |        "2010-01-13  0.009269 -0.005758\n",
 424 |        "2010-01-14  0.019900  0.004690\n",
 425 |        "2010-01-15 -0.003235 -0.016840\n",
 426 |        "2010-01-19  0.007747  0.013052\n",
 427 |        "2010-01-20 -0.016698 -0.012346\n",
 428 |        "2010-01-21 -0.018979  0.004418\n",
 429 |        "2010-01-22 -0.035615 -0.058216\n",
 430 |        "2010-01-25  0.012354 -0.018367\n",
 431 |        "2010-01-26  0.006120  0.004471\n",
 432 |        "2010-01-27  0.005746 -0.000590\n",
 433 |        "2010-01-28 -0.017339 -0.014512\n",
 434 |        "2010-01-29 -0.034185 -0.008167\n",
 435 |        "2010-02-01  0.008129  0.005788\n",
 436 |        "2010-02-02  0.001758 -0.003571\n",
 437 |        "2010-02-03  0.005956  0.018099\n",
 438 |        "2010-02-04 -0.027981 -0.026307\n",
 439 |        "2010-02-05  0.006445  0.008529\n",
 440 |        "2010-02-08 -0.010764  0.004095\n",
 441 |        "2010-02-09  0.010407  0.005552\n",
 442 |        "2010-02-10 -0.000714 -0.003726\n",
 443 |        "2010-02-11  0.004634  0.003651\n",
 444 |        "2010-02-12 -0.006780 -0.006134\n",
 445 |        "2010-02-16  0.019501  0.015227\n",
 446 |        "...              ...       ...\n",
 447 |        "2017-11-16  0.002648  0.011569\n",
 448 |        "2017-11-17 -0.009662 -0.012071\n",
 449 |        "2017-11-20  0.001576 -0.001188\n",
 450 |        "2017-11-21  0.014316  0.015003\n",
 451 |        "2017-11-22 -0.007313  0.001541\n",
 452 |        "2017-11-24  0.001803  0.004363\n",
 453 |        "2017-11-27  0.007300  0.014555\n",
 454 |        "2017-11-28  0.011971 -0.008168\n",
 455 |        "2017-11-29 -0.018310 -0.024670\n",
 456 |        "2017-11-30  0.009910 -0.001167\n",
 457 |        "2017-12-01  0.001069 -0.010770\n",
 458 |        "2017-12-04 -0.038471 -0.012961\n",
 459 |        "2017-12-05  0.006270  0.007610\n",
 460 |        "2017-12-06  0.014480  0.012786\n",
 461 |        "2017-12-07 -0.003509  0.011409\n",
 462 |        "2017-12-08  0.020043  0.004594\n",
 463 |        "2017-12-11  0.012634  0.002465\n",
 464 |        "2017-12-12  0.004098 -0.003047\n",
 465 |        "2017-12-13 -0.002691  0.002495\n",
 466 |        "2017-12-14 -0.007763  0.005766\n",
 467 |        "2017-12-15  0.025185  0.013647\n",
 468 |        "2017-12-18 -0.005426  0.012137\n",
 469 |        "2017-12-19 -0.006388 -0.004906\n",
 470 |        "2017-12-20 -0.003618 -0.005777\n",
 471 |        "2017-12-21 -0.000234 -0.002528\n",
 472 |        "2017-12-22  0.000117 -0.001860\n",
 473 |        "2017-12-26 -0.001287 -0.002820\n",
 474 |        "2017-12-27  0.003623 -0.005315\n",
 475 |        "2017-12-28  0.000117 -0.004017\n",
 476 |        "2017-12-29 -0.002102 -0.002418\n",
 477 |        "\n",
 478 |        "[2012 rows x 2 columns]"
 479 |       ]
 480 |      },
 481 |      "execution_count": 56,
 482 |      "metadata": {},
 483 |      "output_type": "execute_result"
 484 |     }
 485 |    ],
 486 |    "source": [
 487 |     "pf_data = pf_data.iloc[::-1]\n",
 488 |     "log_returns = np.log(pf_data/pf_data.shift(1))\n",
 489 |     "log_returns"
 490 |    ]
 491 |   },
 492 |   {
 493 |    "cell_type": "code",
 494 |    "execution_count": 59,
 495 |    "metadata": {},
 496 |    "outputs": [
 497 |     {
 498 |      "name": "stdout",
 499 |      "output_type": "stream",
 500 |      "text": [
 501 |       "MSFT     16.429452\n",
 502 |       "GOOGL    16.216976\n",
 503 |       "dtype: float64 %\n"
 504 |      ]
 505 |     }
 506 |    ],
 507 |    "source": [
 508 |     "print(str((((log_returns.mean() + 1)**250) - 1)*100) + ' %')"
 509 |    ]
 510 |   },
 511 |   {
 512 |    "cell_type": "code",
 513 |    "execution_count": 60,
 514 |    "metadata": {},
 515 |    "outputs": [
 516 |     {
 517 |      "data": {
 518 |       "text/html": [
 519 |        "<div>\n",
 520 |        "<style scoped>\n",
 521 |        "    .dataframe tbody tr th:only-of-type {\n",
 522 |        "        vertical-align: middle;\n",
 523 |        "    }\n",
 524 |        "\n",
 525 |        "    .dataframe tbody tr th {\n",
 526 |        "        vertical-align: top;\n",
 527 |        "    }\n",
 528 |        "\n",
 529 |        "    .dataframe thead th {\n",
 530 |        "        text-align: right;\n",
 531 |        "    }\n",
 532 |        "</style>\n",
 533 |        "<table border=\"1\" class=\"dataframe\">\n",
 534 |        "  <thead>\n",
 535 |        "    <tr style=\"text-align: right;\">\n",
 536 |        "      <th></th>\n",
 537 |        "      <th>MSFT</th>\n",
 538 |        "      <th>GOOGL</th>\n",
 539 |        "    </tr>\n",
 540 |        "  </thead>\n",
 541 |        "  <tbody>\n",
 542 |        "    <tr>\n",
 543 |        "      <th>MSFT</th>\n",
 544 |        "      <td>0.049235</td>\n",
 545 |        "      <td>0.025028</td>\n",
 546 |        "    </tr>\n",
 547 |        "    <tr>\n",
 548 |        "      <th>GOOGL</th>\n",
 549 |        "      <td>0.025028</td>\n",
 550 |        "      <td>0.055883</td>\n",
 551 |        "    </tr>\n",
 552 |        "  </tbody>\n",
 553 |        "</table>\n",
 554 |        "</div>"
 555 |       ],
 556 |       "text/plain": [
 557 |        "           MSFT     GOOGL\n",
 558 |        "MSFT   0.049235  0.025028\n",
 559 |        "GOOGL  0.025028  0.055883"
 560 |       ]
 561 |      },
 562 |      "execution_count": 60,
 563 |      "metadata": {},
 564 |      "output_type": "execute_result"
 565 |     }
 566 |    ],
 567 |    "source": [
 568 |     "log_returns.cov() *250"
 569 |    ]
 570 |   },
 571 |   {
 572 |    "cell_type": "code",
 573 |    "execution_count": 61,
 574 |    "metadata": {},
 575 |    "outputs": [
 576 |     {
 577 |      "data": {
 578 |       "text/html": [
 579 |        "<div>\n",
 580 |        "<style scoped>\n",
 581 |        "    .dataframe tbody tr th:only-of-type {\n",
 582 |        "        vertical-align: middle;\n",
 583 |        "    }\n",
 584 |        "\n",
 585 |        "    .dataframe tbody tr th {\n",
 586 |        "        vertical-align: top;\n",
 587 |        "    }\n",
 588 |        "\n",
 589 |        "    .dataframe thead th {\n",
 590 |        "        text-align: right;\n",
 591 |        "    }\n",
 592 |        "</style>\n",
 593 |        "<table border=\"1\" class=\"dataframe\">\n",
 594 |        "  <thead>\n",
 595 |        "    <tr style=\"text-align: right;\">\n",
 596 |        "      <th></th>\n",
 597 |        "      <th>MSFT</th>\n",
 598 |        "      <th>GOOGL</th>\n",
 599 |        "    </tr>\n",
 600 |        "  </thead>\n",
 601 |        "  <tbody>\n",
 602 |        "    <tr>\n",
 603 |        "      <th>MSFT</th>\n",
 604 |        "      <td>1.00000</td>\n",
 605 |        "      <td>0.47714</td>\n",
 606 |        "    </tr>\n",
 607 |        "    <tr>\n",
 608 |        "      <th>GOOGL</th>\n",
 609 |        "      <td>0.47714</td>\n",
 610 |        "      <td>1.00000</td>\n",
 611 |        "    </tr>\n",
 612 |        "  </tbody>\n",
 613 |        "</table>\n",
 614 |        "</div>"
 615 |       ],
 616 |       "text/plain": [
 617 |        "          MSFT    GOOGL\n",
 618 |        "MSFT   1.00000  0.47714\n",
 619 |        "GOOGL  0.47714  1.00000"
 620 |       ]
 621 |      },
 622 |      "execution_count": 61,
 623 |      "metadata": {},
 624 |      "output_type": "execute_result"
 625 |     }
 626 |    ],
 627 |    "source": [
 628 |     "log_returns.corr()"
 629 |    ]
 630 |   },
 631 |   {
 632 |    "cell_type": "code",
 633 |    "execution_count": 63,
 634 |    "metadata": {},
 635 |    "outputs": [],
 636 |    "source": [
 637 |     "num_assets = len(assets)\n",
 638 |     "weights = np.random.random(num_assets)"
 639 |    ]
 640 |   },
 641 |   {
 642 |    "cell_type": "code",
 643 |    "execution_count": 65,
 644 |    "metadata": {},
 645 |    "outputs": [
 646 |     {
 647 |      "data": {
 648 |       "text/plain": [
 649 |        "array([0.51613153, 0.48386847])"
 650 |       ]
 651 |      },
 652 |      "execution_count": 65,
 653 |      "metadata": {},
 654 |      "output_type": "execute_result"
 655 |     }
 656 |    ],
 657 |    "source": [
 658 |     "weights = np.random.random(num_assets)\n",
 659 |     "weights/=np.sum(weights)\n",
 660 |     "weights"
 661 |    ]
 662 |   },
 663 |   {
 664 |    "cell_type": "code",
 665 |    "execution_count": 66,
 666 |    "metadata": {},
 667 |    "outputs": [],
 668 |    "source": [
 669 |     "log_annual = ((log_returns.mean() + 1)**250) - 1"
 670 |    ]
 671 |   },
 672 |   {
 673 |    "cell_type": "code",
 674 |    "execution_count": 69,
 675 |    "metadata": {},
 676 |    "outputs": [
 677 |     {
 678 |      "data": {
 679 |       "text/plain": [
 680 |        "0.16326641729894442"
 681 |       ]
 682 |      },
 683 |      "execution_count": 69,
 684 |      "metadata": {},
 685 |      "output_type": "execute_result"
 686 |     }
 687 |    ],
 688 |    "source": [
 689 |     "#Expected portfolio returns\n",
 690 |     "np.sum(weights * log_annual)"
 691 |    ]
 692 |   },
 693 |   {
 694 |    "cell_type": "code",
 695 |    "execution_count": 71,
 696 |    "metadata": {},
 697 |    "outputs": [
 698 |     {
 699 |      "data": {
 700 |       "text/plain": [
 701 |        "0.03870036449082226"
 702 |       ]
 703 |      },
 704 |      "execution_count": 71,
 705 |      "metadata": {},
 706 |      "output_type": "execute_result"
 707 |     }
 708 |    ],
 709 |    "source": [
 710 |     " #Expected Portfolio Variance\n",
 711 |     "np.dot(weights.T, np.dot(log_returns.cov() * 250,weights))"
 712 |    ]
 713 |   },
 714 |   {
 715 |    "cell_type": "code",
 716 |    "execution_count": 72,
 717 |    "metadata": {},
 718 |    "outputs": [
 719 |     {
 720 |      "data": {
 721 |       "text/plain": [
 722 |        "0.19672408213236695"
 723 |       ]
 724 |      },
 725 |      "execution_count": 72,
 726 |      "metadata": {},
 727 |      "output_type": "execute_result"
 728 |     }
 729 |    ],
 730 |    "source": [
 731 |     "#Expected Portfolio Volatility\n",
 732 |     "np.sqrt(np.dot(weights.T,np.dot(log_returns.cov() * 250,weights)))"
 733 |    ]
 734 |   },
 735 |   {
 736 |    "cell_type": "code",
 737 |    "execution_count": 108,
 738 |    "metadata": {},
 739 |    "outputs": [
 740 |     {
 741 |      "name": "stdout",
 742 |      "output_type": "stream",
 743 |      "text": [
 744 |       "Microsoft : 0.5929498477894943 Google : 0.40705015221050567\n",
 745 |       "Microsoft : 0.5792341086932384 Google : 0.4207658913067615\n",
 746 |       "Microsoft : 0.5917284264458144 Google : 0.4082715735541857\n",
 747 |       "Microsoft : 0.5802529990002616 Google : 0.41974700099973844\n",
 748 |       "Microsoft : 0.5859028374660776 Google : 0.41409716253392226\n",
 749 |       "Microsoft : 0.587074495600213 Google : 0.412925504399787\n",
 750 |       "Microsoft : 0.5876933817896055 Google : 0.4123066182103944\n",
 751 |       "Microsoft : 0.5846175309161378 Google : 0.4153824690838621\n",
 752 |       "Microsoft : 0.587420322732071 Google : 0.41257967726792893\n",
 753 |       "Microsoft : 0.5910965722098238 Google : 0.40890342779017624\n",
 754 |       "Microsoft : 0.5903560955924934 Google : 0.40964390440750664\n",
 755 |       "Microsoft : 0.5899639866283974 Google : 0.4100360133716026\n",
 756 |       "Microsoft : 0.590812623493713 Google : 0.40918737650628706\n",
 757 |       "Microsoft : 0.5835238426319063 Google : 0.4164761573680937\n",
 758 |       "Microsoft : 0.5848939674770928 Google : 0.4151060325229074\n",
 759 |       "Microsoft : 0.5907174870343446 Google : 0.4092825129656555\n",
 760 |       "Microsoft : 0.5914147244204458 Google : 0.40858527557955404\n",
 761 |       "Microsoft : 0.5877156189308469 Google : 0.41228438106915305\n",
 762 |       "Microsoft : 0.5811522262205125 Google : 0.41884777377948745\n",
 763 |       "Microsoft : 0.5805746009939525 Google : 0.4194253990060474\n"
 764 |      ]
 765 |     }
 766 |    ],
 767 |    "source": [
 768 |     "pfolio_returns = []\n",
 769 |     "pfolio_volatilities = []\n",
 770 |     "for x in range(1000):\n",
 771 |     "    weights = np.random.random(num_assets)\n",
 772 |     "    weights /= np.sum(weights)\n",
 773 |     "    pfolio_returns.append(np.sum(weights * log_annual))\n",
 774 |     "    pfolio_volatilities.append(np.sqrt(np.dot(weights.T,np.dot(log_returns.cov() * 250,weights))))\n",
 775 |     "    if pfolio_returns[x]>0.1634 and pfolio_volatilities[x]<0.1966 :\n",
 776 |     "        print('Microsoft : ' + str(weights[0]) + ' Google : ' + str(weights[1]))\n",
 777 |     "    \n",
 778 |     "pfolio_returns = np.array(pfolio_returns)\n",
 779 |     "pfolio_volatilities = np.array(pfolio_volatilities)\n",
 780 |     "#pfolio_returns,pfolio_volatilities"
 781 |    ]
 782 |   },
 783 |   {
 784 |    "cell_type": "code",
 785 |    "execution_count": 78,
 786 |    "metadata": {},
 787 |    "outputs": [],
 788 |    "source": [
 789 |     "portfolio = pd.DataFrame({'Return': pfolio_returns, 'Volatility': pfolio_volatilities})"
 790 |    ]
 791 |   },
 792 |   {
 793 |    "cell_type": "code",
 794 |    "execution_count": 79,
 795 |    "metadata": {},
 796 |    "outputs": [
 797 |     {
 798 |      "data": {
 799 |       "text/html": [
 800 |        "<div>\n",
 801 |        "<style scoped>\n",
 802 |        "    .dataframe tbody tr th:only-of-type {\n",
 803 |        "        vertical-align: middle;\n",
 804 |        "    }\n",
 805 |        "\n",
 806 |        "    .dataframe tbody tr th {\n",
 807 |        "        vertical-align: top;\n",
 808 |        "    }\n",
 809 |        "\n",
 810 |        "    .dataframe thead th {\n",
 811 |        "        text-align: right;\n",
 812 |        "    }\n",
 813 |        "</style>\n",
 814 |        "<table border=\"1\" class=\"dataframe\">\n",
 815 |        "  <thead>\n",
 816 |        "    <tr style=\"text-align: right;\">\n",
 817 |        "      <th></th>\n",
 818 |        "      <th>Return</th>\n",
 819 |        "      <th>Volatility</th>\n",
 820 |        "    </tr>\n",
 821 |        "  </thead>\n",
 822 |        "  <tbody>\n",
 823 |        "    <tr>\n",
 824 |        "      <th>0</th>\n",
 825 |        "      <td>0.163909</td>\n",
 826 |        "      <td>0.205590</td>\n",
 827 |        "    </tr>\n",
 828 |        "    <tr>\n",
 829 |        "      <th>1</th>\n",
 830 |        "      <td>0.163127</td>\n",
 831 |        "      <td>0.198139</td>\n",
 832 |        "    </tr>\n",
 833 |        "    <tr>\n",
 834 |        "      <th>2</th>\n",
 835 |        "      <td>0.162905</td>\n",
 836 |        "      <td>0.202798</td>\n",
 837 |        "    </tr>\n",
 838 |        "    <tr>\n",
 839 |        "      <th>3</th>\n",
 840 |        "      <td>0.163991</td>\n",
 841 |        "      <td>0.208443</td>\n",
 842 |        "    </tr>\n",
 843 |        "    <tr>\n",
 844 |        "      <th>4</th>\n",
 845 |        "      <td>0.164170</td>\n",
 846 |        "      <td>0.215816</td>\n",
 847 |        "    </tr>\n",
 848 |        "  </tbody>\n",
 849 |        "</table>\n",
 850 |        "</div>"
 851 |       ],
 852 |       "text/plain": [
 853 |        "     Return  Volatility\n",
 854 |        "0  0.163909    0.205590\n",
 855 |        "1  0.163127    0.198139\n",
 856 |        "2  0.162905    0.202798\n",
 857 |        "3  0.163991    0.208443\n",
 858 |        "4  0.164170    0.215816"
 859 |       ]
 860 |      },
 861 |      "execution_count": 79,
 862 |      "metadata": {},
 863 |      "output_type": "execute_result"
 864 |     }
 865 |    ],
 866 |    "source": [
 867 |     "portfolio.head()"
 868 |    ]
 869 |   },
 870 |   {
 871 |    "cell_type": "code",
 872 |    "execution_count": 80,
 873 |    "metadata": {},
 874 |    "outputs": [
 875 |     {
 876 |      "data": {
 877 |       "text/html": [
 878 |        "<div>\n",
 879 |        "<style scoped>\n",
 880 |        "    .dataframe tbody tr th:only-of-type {\n",
 881 |        "        vertical-align: middle;\n",
 882 |        "    }\n",
 883 |        "\n",
 884 |        "    .dataframe tbody tr th {\n",
 885 |        "        vertical-align: top;\n",
 886 |        "    }\n",
 887 |        "\n",
 888 |        "    .dataframe thead th {\n",
 889 |        "        text-align: right;\n",
 890 |        "    }\n",
 891 |        "</style>\n",
 892 |        "<table border=\"1\" class=\"dataframe\">\n",
 893 |        "  <thead>\n",
 894 |        "    <tr style=\"text-align: right;\">\n",
 895 |        "      <th></th>\n",
 896 |        "      <th>Return</th>\n",
 897 |        "      <th>Volatility</th>\n",
 898 |        "    </tr>\n",
 899 |        "  </thead>\n",
 900 |        "  <tbody>\n",
 901 |        "    <tr>\n",
 902 |        "      <th>995</th>\n",
 903 |        "      <td>0.162644</td>\n",
 904 |        "      <td>0.211787</td>\n",
 905 |        "    </tr>\n",
 906 |        "    <tr>\n",
 907 |        "      <th>996</th>\n",
 908 |        "      <td>0.163464</td>\n",
 909 |        "      <td>0.196781</td>\n",
 910 |        "    </tr>\n",
 911 |        "    <tr>\n",
 912 |        "      <th>997</th>\n",
 913 |        "      <td>0.163122</td>\n",
 914 |        "      <td>0.198213</td>\n",
 915 |        "    </tr>\n",
 916 |        "    <tr>\n",
 917 |        "      <th>998</th>\n",
 918 |        "      <td>0.163386</td>\n",
 919 |        "      <td>0.196470</td>\n",
 920 |        "    </tr>\n",
 921 |        "    <tr>\n",
 922 |        "      <th>999</th>\n",
 923 |        "      <td>0.163227</td>\n",
 924 |        "      <td>0.197005</td>\n",
 925 |        "    </tr>\n",
 926 |        "  </tbody>\n",
 927 |        "</table>\n",
 928 |        "</div>"
 929 |       ],
 930 |       "text/plain": [
 931 |        "       Return  Volatility\n",
 932 |        "995  0.162644    0.211787\n",
 933 |        "996  0.163464    0.196781\n",
 934 |        "997  0.163122    0.198213\n",
 935 |        "998  0.163386    0.196470\n",
 936 |        "999  0.163227    0.197005"
 937 |       ]
 938 |      },
 939 |      "execution_count": 80,
 940 |      "metadata": {},
 941 |      "output_type": "execute_result"
 942 |     }
 943 |    ],
 944 |    "source": [
 945 |     "portfolio.tail()"
 946 |    ]
 947 |   },
 948 |   {
 949 |    "cell_type": "code",
 950 |    "execution_count": 92,
 951 |    "metadata": {},
 952 |    "outputs": [
 953 |     {
 954 |      "data": {
 955 |       "text/plain": [
 956 |        "Text(0,0.5,'Expected Return')"
 957 |       ]
 958 |      },
 959 |      "execution_count": 92,
 960 |      "metadata": {},
 961 |      "output_type": "execute_result"
 962 |     },
 963 |     {
 964 |      "data": {
 965 |       "image/png": 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\n",
 966 |       "text/plain": [
 967 |        "<matplotlib.figure.Figure at 0x1e3bc671400>"
 968 |       ]
 969 |      },
 970 |      "metadata": {},
 971 |      "output_type": "display_data"
 972 |     }
 973 |    ],
 974 |    "source": [
 975 |     "portfolios.plot(x='Volatility',y='Return',kind='scatter',figsize=(15,8))\n",
 976 |     "plt.xlabel('Expected Volatality')\n",
 977 |     "plt.ylabel('Expected Return')"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": 94,
 983 |    "metadata": {},
 984 |    "outputs": [
 985 |     {
 986 |      "data": {
 987 |       "text/plain": [
 988 |        "0.19644997010794701"
 989 |       ]
 990 |      },
 991 |      "execution_count": 94,
 992 |      "metadata": {},
 993 |      "output_type": "execute_result"
 994 |     }
 995 |    ],
 996 |    "source": [
 997 |     "min(pfolio_volatilities)"
 998 |    ]
 999 |   }
1000 |  ],
1001 |  "metadata": {
1002 |   "kernelspec": {
1003 |    "display_name": "Python 3",
1004 |    "language": "python",
1005 |    "name": "python3"
1006 |   },
1007 |   "language_info": {
1008 |    "codemirror_mode": {
1009 |     "name": "ipython",
1010 |     "version": 3
1011 |    },
1012 |    "file_extension": ".py",
1013 |    "mimetype": "text/x-python",
1014 |    "name": "python",
1015 |    "nbconvert_exporter": "python",
1016 |    "pygments_lexer": "ipython3",
1017 |    "version": "3.6.4"
1018 |   }
1019 |  },
1020 |  "nbformat": 4,
1021 |  "nbformat_minor": 2
1022 | }
1023 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Stock-Market-Analysis-With-Python
 2 | Performing the Financial Analysis on Historic Stock Market Data such as calculating various risks, returns,etc.
 3 | 
 4 | 1) Simple_n_Log_returns : It predicts the daily and annual returns(simple and logarithmic) based on historic data.
 5 | 
 6 | Note : Simple returns are taken into consideration when we are considering multiple stocks while Logarithmmic return for individual stock consideration.
 7 | 
 8 | 2) Portfolio_returns : Predicting the combined returns of stocks in a portfolio.
 9 | 
10 | 3) Var_cov_correlation : The variance refers to the spread of the data set—how far apart the numbers are in relation to the mean, for instance. Variance is particularly useful when calculating the probability of future events or performance.
11 | A covariance refers to the measure of how two random variables will change together and is used to calculate the correlation between variables.
12 | Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together.
13 | 
14 | 4) Standard Deviation : Standard deviation is a number used to tell how measurements for a group are spread out from the average (mean), or expected value i.e, what is the percentage that inverstors will be surprised by the result.
15 | 
16 | 5) Div_non_Div_risk  : It calculates diversifiable risk and non-diversifiable risk i.e, risk that are beyond our control and risk that are organizational and can be controled.
17 | 
18 | 6) Regression : The relation between house prices and house area is taken and checked how close is the relation with lots of characteristics to see from.
19 | 
20 | 7) Markowitz_Algorithm : The algorithm that determines the best combination of weights in a pportfolio to get the minimum risk and maximum returns.
21 | 
22 | 8) BETA and Beta_for_PG : Beta is a variable that tells about how much a stock moves with respect to the market.
23 | If BETA < 1 then stock is defensive , if BETA > 1 then stock is aggressive, if BETA = 0 then there is no relationship.
24 | 


--------------------------------------------------------------------------------
/Regression.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stderr",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "C:\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
 13 |       "  from pandas.core import datetools\n"
 14 |      ]
 15 |     }
 16 |    ],
 17 |    "source": [
 18 |     "import pandas as pd\n",
 19 |     "import numpy as np\n",
 20 |     "from pandas_datareader import data as wb\n",
 21 |     "import matplotlib.pyplot as plt\n",
 22 |     "from scipy import stats\n",
 23 |     "import statsmodels.api as sm"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 5,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "data = pd.read_excel(\"F:\\Housing.xlsx\")"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 6,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "data": {
 42 |       "text/html": [
 43 |        "<div>\n",
 44 |        "<style scoped>\n",
 45 |        "    .dataframe tbody tr th:only-of-type {\n",
 46 |        "        vertical-align: middle;\n",
 47 |        "    }\n",
 48 |        "\n",
 49 |        "    .dataframe tbody tr th {\n",
 50 |        "        vertical-align: top;\n",
 51 |        "    }\n",
 52 |        "\n",
 53 |        "    .dataframe thead th {\n",
 54 |        "        text-align: right;\n",
 55 |        "    }\n",
 56 |        "</style>\n",
 57 |        "<table border=\"1\" class=\"dataframe\">\n",
 58 |        "  <thead>\n",
 59 |        "    <tr style=\"text-align: right;\">\n",
 60 |        "      <th></th>\n",
 61 |        "      <th>House Price</th>\n",
 62 |        "      <th>House Size (sq.ft.)</th>\n",
 63 |        "      <th>State</th>\n",
 64 |        "      <th>Number of Rooms</th>\n",
 65 |        "      <th>Year of Construction</th>\n",
 66 |        "    </tr>\n",
 67 |        "  </thead>\n",
 68 |        "  <tbody>\n",
 69 |        "    <tr>\n",
 70 |        "      <th>0</th>\n",
 71 |        "      <td>1116000</td>\n",
 72 |        "      <td>1940</td>\n",
 73 |        "      <td>IN</td>\n",
 74 |        "      <td>8</td>\n",
 75 |        "      <td>2002</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>1</th>\n",
 79 |        "      <td>860000</td>\n",
 80 |        "      <td>1300</td>\n",
 81 |        "      <td>IN</td>\n",
 82 |        "      <td>5</td>\n",
 83 |        "      <td>1992</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2</th>\n",
 87 |        "      <td>818400</td>\n",
 88 |        "      <td>1420</td>\n",
 89 |        "      <td>IN</td>\n",
 90 |        "      <td>6</td>\n",
 91 |        "      <td>1987</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>3</th>\n",
 95 |        "      <td>1000000</td>\n",
 96 |        "      <td>1680</td>\n",
 97 |        "      <td>IN</td>\n",
 98 |        "      <td>7</td>\n",
 99 |        "      <td>2000</td>\n",
100 |        "    </tr>\n",
101 |        "    <tr>\n",
102 |        "      <th>4</th>\n",
103 |        "      <td>640000</td>\n",
104 |        "      <td>1270</td>\n",
105 |        "      <td>IN</td>\n",
106 |        "      <td>5</td>\n",
107 |        "      <td>1995</td>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <th>5</th>\n",
111 |        "      <td>1010000</td>\n",
112 |        "      <td>1850</td>\n",
113 |        "      <td>IN</td>\n",
114 |        "      <td>7</td>\n",
115 |        "      <td>1998</td>\n",
116 |        "    </tr>\n",
117 |        "    <tr>\n",
118 |        "      <th>6</th>\n",
119 |        "      <td>600000</td>\n",
120 |        "      <td>1000</td>\n",
121 |        "      <td>IN</td>\n",
122 |        "      <td>4</td>\n",
123 |        "      <td>2015</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>7</th>\n",
127 |        "      <td>700000</td>\n",
128 |        "      <td>1100</td>\n",
129 |        "      <td>LA</td>\n",
130 |        "      <td>4</td>\n",
131 |        "      <td>2014</td>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <th>8</th>\n",
135 |        "      <td>1100000</td>\n",
136 |        "      <td>1600</td>\n",
137 |        "      <td>LA</td>\n",
138 |        "      <td>7</td>\n",
139 |        "      <td>2017</td>\n",
140 |        "    </tr>\n",
141 |        "    <tr>\n",
142 |        "      <th>9</th>\n",
143 |        "      <td>570000</td>\n",
144 |        "      <td>1000</td>\n",
145 |        "      <td>NY</td>\n",
146 |        "      <td>5</td>\n",
147 |        "      <td>1997</td>\n",
148 |        "    </tr>\n",
149 |        "    <tr>\n",
150 |        "      <th>10</th>\n",
151 |        "      <td>860000</td>\n",
152 |        "      <td>2150</td>\n",
153 |        "      <td>NY</td>\n",
154 |        "      <td>9</td>\n",
155 |        "      <td>1997</td>\n",
156 |        "    </tr>\n",
157 |        "    <tr>\n",
158 |        "      <th>11</th>\n",
159 |        "      <td>1085000</td>\n",
160 |        "      <td>1900</td>\n",
161 |        "      <td>NY</td>\n",
162 |        "      <td>9</td>\n",
163 |        "      <td>2000</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>12</th>\n",
167 |        "      <td>1250000</td>\n",
168 |        "      <td>2200</td>\n",
169 |        "      <td>NY</td>\n",
170 |        "      <td>9</td>\n",
171 |        "      <td>2014</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>13</th>\n",
175 |        "      <td>850000</td>\n",
176 |        "      <td>1100</td>\n",
177 |        "      <td>TX</td>\n",
178 |        "      <td>4</td>\n",
179 |        "      <td>2017</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <th>14</th>\n",
183 |        "      <td>640000</td>\n",
184 |        "      <td>860</td>\n",
185 |        "      <td>TX</td>\n",
186 |        "      <td>4</td>\n",
187 |        "      <td>1997</td>\n",
188 |        "    </tr>\n",
189 |        "    <tr>\n",
190 |        "      <th>15</th>\n",
191 |        "      <td>900000</td>\n",
192 |        "      <td>1325</td>\n",
193 |        "      <td>TX</td>\n",
194 |        "      <td>6</td>\n",
195 |        "      <td>1997</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>16</th>\n",
199 |        "      <td>730000</td>\n",
200 |        "      <td>1350</td>\n",
201 |        "      <td>TX</td>\n",
202 |        "      <td>6</td>\n",
203 |        "      <td>2000</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>17</th>\n",
207 |        "      <td>750000</td>\n",
208 |        "      <td>1600</td>\n",
209 |        "      <td>TX</td>\n",
210 |        "      <td>6</td>\n",
211 |        "      <td>1992</td>\n",
212 |        "    </tr>\n",
213 |        "    <tr>\n",
214 |        "      <th>18</th>\n",
215 |        "      <td>650000</td>\n",
216 |        "      <td>950</td>\n",
217 |        "      <td>TX</td>\n",
218 |        "      <td>2</td>\n",
219 |        "      <td>1987</td>\n",
220 |        "    </tr>\n",
221 |        "    <tr>\n",
222 |        "      <th>19</th>\n",
223 |        "      <td>680000</td>\n",
224 |        "      <td>1250</td>\n",
225 |        "      <td>TX</td>\n",
226 |        "      <td>4</td>\n",
227 |        "      <td>2000</td>\n",
228 |        "    </tr>\n",
229 |        "  </tbody>\n",
230 |        "</table>\n",
231 |        "</div>"
232 |       ],
233 |       "text/plain": [
234 |        "    House Price  House Size (sq.ft.) State  Number of Rooms  \\\n",
235 |        "0       1116000                 1940    IN                8   \n",
236 |        "1        860000                 1300    IN                5   \n",
237 |        "2        818400                 1420    IN                6   \n",
238 |        "3       1000000                 1680    IN                7   \n",
239 |        "4        640000                 1270    IN                5   \n",
240 |        "5       1010000                 1850    IN                7   \n",
241 |        "6        600000                 1000    IN                4   \n",
242 |        "7        700000                 1100   LA                 4   \n",
243 |        "8       1100000                 1600   LA                 7   \n",
244 |        "9        570000                 1000    NY                5   \n",
245 |        "10       860000                 2150    NY                9   \n",
246 |        "11      1085000                 1900    NY                9   \n",
247 |        "12      1250000                 2200    NY                9   \n",
248 |        "13       850000                 1100    TX                4   \n",
249 |        "14       640000                  860    TX                4   \n",
250 |        "15       900000                 1325    TX                6   \n",
251 |        "16       730000                 1350    TX                6   \n",
252 |        "17       750000                 1600    TX                6   \n",
253 |        "18       650000                  950    TX                2   \n",
254 |        "19       680000                 1250    TX                4   \n",
255 |        "\n",
256 |        "    Year of Construction  \n",
257 |        "0                   2002  \n",
258 |        "1                   1992  \n",
259 |        "2                   1987  \n",
260 |        "3                   2000  \n",
261 |        "4                   1995  \n",
262 |        "5                   1998  \n",
263 |        "6                   2015  \n",
264 |        "7                   2014  \n",
265 |        "8                   2017  \n",
266 |        "9                   1997  \n",
267 |        "10                  1997  \n",
268 |        "11                  2000  \n",
269 |        "12                  2014  \n",
270 |        "13                  2017  \n",
271 |        "14                  1997  \n",
272 |        "15                  1997  \n",
273 |        "16                  2000  \n",
274 |        "17                  1992  \n",
275 |        "18                  1987  \n",
276 |        "19                  2000  "
277 |       ]
278 |      },
279 |      "execution_count": 6,
280 |      "metadata": {},
281 |      "output_type": "execute_result"
282 |     }
283 |    ],
284 |    "source": [
285 |     "data"
286 |    ]
287 |   },
288 |   {
289 |    "cell_type": "code",
290 |    "execution_count": 7,
291 |    "metadata": {},
292 |    "outputs": [
293 |     {
294 |      "data": {
295 |       "text/html": [
296 |        "<div>\n",
297 |        "<style scoped>\n",
298 |        "    .dataframe tbody tr th:only-of-type {\n",
299 |        "        vertical-align: middle;\n",
300 |        "    }\n",
301 |        "\n",
302 |        "    .dataframe tbody tr th {\n",
303 |        "        vertical-align: top;\n",
304 |        "    }\n",
305 |        "\n",
306 |        "    .dataframe thead th {\n",
307 |        "        text-align: right;\n",
308 |        "    }\n",
309 |        "</style>\n",
310 |        "<table border=\"1\" class=\"dataframe\">\n",
311 |        "  <thead>\n",
312 |        "    <tr style=\"text-align: right;\">\n",
313 |        "      <th></th>\n",
314 |        "      <th>House Price</th>\n",
315 |        "      <th>House Size (sq.ft.)</th>\n",
316 |        "    </tr>\n",
317 |        "  </thead>\n",
318 |        "  <tbody>\n",
319 |        "    <tr>\n",
320 |        "      <th>0</th>\n",
321 |        "      <td>1116000</td>\n",
322 |        "      <td>1940</td>\n",
323 |        "    </tr>\n",
324 |        "    <tr>\n",
325 |        "      <th>1</th>\n",
326 |        "      <td>860000</td>\n",
327 |        "      <td>1300</td>\n",
328 |        "    </tr>\n",
329 |        "    <tr>\n",
330 |        "      <th>2</th>\n",
331 |        "      <td>818400</td>\n",
332 |        "      <td>1420</td>\n",
333 |        "    </tr>\n",
334 |        "    <tr>\n",
335 |        "      <th>3</th>\n",
336 |        "      <td>1000000</td>\n",
337 |        "      <td>1680</td>\n",
338 |        "    </tr>\n",
339 |        "    <tr>\n",
340 |        "      <th>4</th>\n",
341 |        "      <td>640000</td>\n",
342 |        "      <td>1270</td>\n",
343 |        "    </tr>\n",
344 |        "    <tr>\n",
345 |        "      <th>5</th>\n",
346 |        "      <td>1010000</td>\n",
347 |        "      <td>1850</td>\n",
348 |        "    </tr>\n",
349 |        "    <tr>\n",
350 |        "      <th>6</th>\n",
351 |        "      <td>600000</td>\n",
352 |        "      <td>1000</td>\n",
353 |        "    </tr>\n",
354 |        "    <tr>\n",
355 |        "      <th>7</th>\n",
356 |        "      <td>700000</td>\n",
357 |        "      <td>1100</td>\n",
358 |        "    </tr>\n",
359 |        "    <tr>\n",
360 |        "      <th>8</th>\n",
361 |        "      <td>1100000</td>\n",
362 |        "      <td>1600</td>\n",
363 |        "    </tr>\n",
364 |        "    <tr>\n",
365 |        "      <th>9</th>\n",
366 |        "      <td>570000</td>\n",
367 |        "      <td>1000</td>\n",
368 |        "    </tr>\n",
369 |        "    <tr>\n",
370 |        "      <th>10</th>\n",
371 |        "      <td>860000</td>\n",
372 |        "      <td>2150</td>\n",
373 |        "    </tr>\n",
374 |        "    <tr>\n",
375 |        "      <th>11</th>\n",
376 |        "      <td>1085000</td>\n",
377 |        "      <td>1900</td>\n",
378 |        "    </tr>\n",
379 |        "    <tr>\n",
380 |        "      <th>12</th>\n",
381 |        "      <td>1250000</td>\n",
382 |        "      <td>2200</td>\n",
383 |        "    </tr>\n",
384 |        "    <tr>\n",
385 |        "      <th>13</th>\n",
386 |        "      <td>850000</td>\n",
387 |        "      <td>1100</td>\n",
388 |        "    </tr>\n",
389 |        "    <tr>\n",
390 |        "      <th>14</th>\n",
391 |        "      <td>640000</td>\n",
392 |        "      <td>860</td>\n",
393 |        "    </tr>\n",
394 |        "    <tr>\n",
395 |        "      <th>15</th>\n",
396 |        "      <td>900000</td>\n",
397 |        "      <td>1325</td>\n",
398 |        "    </tr>\n",
399 |        "    <tr>\n",
400 |        "      <th>16</th>\n",
401 |        "      <td>730000</td>\n",
402 |        "      <td>1350</td>\n",
403 |        "    </tr>\n",
404 |        "    <tr>\n",
405 |        "      <th>17</th>\n",
406 |        "      <td>750000</td>\n",
407 |        "      <td>1600</td>\n",
408 |        "    </tr>\n",
409 |        "    <tr>\n",
410 |        "      <th>18</th>\n",
411 |        "      <td>650000</td>\n",
412 |        "      <td>950</td>\n",
413 |        "    </tr>\n",
414 |        "    <tr>\n",
415 |        "      <th>19</th>\n",
416 |        "      <td>680000</td>\n",
417 |        "      <td>1250</td>\n",
418 |        "    </tr>\n",
419 |        "  </tbody>\n",
420 |        "</table>\n",
421 |        "</div>"
422 |       ],
423 |       "text/plain": [
424 |        "    House Price  House Size (sq.ft.)\n",
425 |        "0       1116000                 1940\n",
426 |        "1        860000                 1300\n",
427 |        "2        818400                 1420\n",
428 |        "3       1000000                 1680\n",
429 |        "4        640000                 1270\n",
430 |        "5       1010000                 1850\n",
431 |        "6        600000                 1000\n",
432 |        "7        700000                 1100\n",
433 |        "8       1100000                 1600\n",
434 |        "9        570000                 1000\n",
435 |        "10       860000                 2150\n",
436 |        "11      1085000                 1900\n",
437 |        "12      1250000                 2200\n",
438 |        "13       850000                 1100\n",
439 |        "14       640000                  860\n",
440 |        "15       900000                 1325\n",
441 |        "16       730000                 1350\n",
442 |        "17       750000                 1600\n",
443 |        "18       650000                  950\n",
444 |        "19       680000                 1250"
445 |       ]
446 |      },
447 |      "execution_count": 7,
448 |      "metadata": {},
449 |      "output_type": "execute_result"
450 |     }
451 |    ],
452 |    "source": [
453 |     "data[['House Price', 'House Size (sq.ft.)']]"
454 |    ]
455 |   },
456 |   {
457 |    "cell_type": "code",
458 |    "execution_count": 9,
459 |    "metadata": {},
460 |    "outputs": [],
461 |    "source": [
462 |     "x = data['House Size (sq.ft.)']\n",
463 |     "y = data['House Price']"
464 |    ]
465 |   },
466 |   {
467 |    "cell_type": "code",
468 |    "execution_count": 10,
469 |    "metadata": {},
470 |    "outputs": [
471 |     {
472 |      "data": {
473 |       "text/plain": [
474 |        "0     1940\n",
475 |        "1     1300\n",
476 |        "2     1420\n",
477 |        "3     1680\n",
478 |        "4     1270\n",
479 |        "5     1850\n",
480 |        "6     1000\n",
481 |        "7     1100\n",
482 |        "8     1600\n",
483 |        "9     1000\n",
484 |        "10    2150\n",
485 |        "11    1900\n",
486 |        "12    2200\n",
487 |        "13    1100\n",
488 |        "14     860\n",
489 |        "15    1325\n",
490 |        "16    1350\n",
491 |        "17    1600\n",
492 |        "18     950\n",
493 |        "19    1250\n",
494 |        "Name: House Size (sq.ft.), dtype: int64"
495 |       ]
496 |      },
497 |      "execution_count": 10,
498 |      "metadata": {},
499 |      "output_type": "execute_result"
500 |     }
501 |    ],
502 |    "source": [
503 |     "x"
504 |    ]
505 |   },
506 |   {
507 |    "cell_type": "code",
508 |    "execution_count": 11,
509 |    "metadata": {},
510 |    "outputs": [
511 |     {
512 |      "data": {
513 |       "text/plain": [
514 |        "0     1116000\n",
515 |        "1      860000\n",
516 |        "2      818400\n",
517 |        "3     1000000\n",
518 |        "4      640000\n",
519 |        "5     1010000\n",
520 |        "6      600000\n",
521 |        "7      700000\n",
522 |        "8     1100000\n",
523 |        "9      570000\n",
524 |        "10     860000\n",
525 |        "11    1085000\n",
526 |        "12    1250000\n",
527 |        "13     850000\n",
528 |        "14     640000\n",
529 |        "15     900000\n",
530 |        "16     730000\n",
531 |        "17     750000\n",
532 |        "18     650000\n",
533 |        "19     680000\n",
534 |        "Name: House Price, dtype: int64"
535 |       ]
536 |      },
537 |      "execution_count": 11,
538 |      "metadata": {},
539 |      "output_type": "execute_result"
540 |     }
541 |    ],
542 |    "source": [
543 |     "y"
544 |    ]
545 |   },
546 |   {
547 |    "cell_type": "code",
548 |    "execution_count": 15,
549 |    "metadata": {},
550 |    "outputs": [
551 |     {
552 |      "data": {
553 |       "image/png": 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\n",
554 |       "text/plain": [
555 |        "<matplotlib.figure.Figure at 0x2bfdfb38940>"
556 |       ]
557 |      },
558 |      "metadata": {},
559 |      "output_type": "display_data"
560 |     }
561 |    ],
562 |    "source": [
563 |     "plt.scatter(x,y)\n",
564 |     "plt.axis([0, 2500, 0, 1500000])\n",
565 |     "plt.ylabel('House Price')\n",
566 |     "plt.xlabel('House Size(in sq.ft)')\n",
567 |     "plt.show()"
568 |    ]
569 |   },
570 |   {
571 |    "cell_type": "code",
572 |    "execution_count": 16,
573 |    "metadata": {},
574 |    "outputs": [],
575 |    "source": [
576 |     "x1 = sm.add_constant(x)\n",
577 |     "reg = sm.OLS(y,x1).fit()"
578 |    ]
579 |   },
580 |   {
581 |    "cell_type": "code",
582 |    "execution_count": 17,
583 |    "metadata": {},
584 |    "outputs": [
585 |     {
586 |      "data": {
587 |       "text/html": [
588 |        "<table class=\"simpletable\">\n",
589 |        "<caption>OLS Regression Results</caption>\n",
590 |        "<tr>\n",
591 |        "  <th>Dep. Variable:</th>       <td>House Price</td>   <th>  R-squared:         </th> <td>   0.678</td>\n",
592 |        "</tr>\n",
593 |        "<tr>\n",
594 |        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.660</td>\n",
595 |        "</tr>\n",
596 |        "<tr>\n",
597 |        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   37.95</td>\n",
598 |        "</tr>\n",
599 |        "<tr>\n",
600 |        "  <th>Date:</th>             <td>Tue, 03 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>8.13e-06</td>\n",
601 |        "</tr>\n",
602 |        "<tr>\n",
603 |        "  <th>Time:</th>                 <td>23:27:41</td>     <th>  Log-Likelihood:    </th> <td> -260.43</td>\n",
604 |        "</tr>\n",
605 |        "<tr>\n",
606 |        "  <th>No. Observations:</th>      <td>    20</td>      <th>  AIC:               </th> <td>   524.9</td>\n",
607 |        "</tr>\n",
608 |        "<tr>\n",
609 |        "  <th>Df Residuals:</th>          <td>    18</td>      <th>  BIC:               </th> <td>   526.8</td>\n",
610 |        "</tr>\n",
611 |        "<tr>\n",
612 |        "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
613 |        "</tr>\n",
614 |        "<tr>\n",
615 |        "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
616 |        "</tr>\n",
617 |        "</table>\n",
618 |        "<table class=\"simpletable\">\n",
619 |        "<tr>\n",
620 |        "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
621 |        "</tr>\n",
622 |        "<tr>\n",
623 |        "  <th>const</th>               <td> 2.608e+05</td> <td> 9.76e+04</td> <td>    2.673</td> <td> 0.016</td> <td> 5.58e+04</td> <td> 4.66e+05</td>\n",
624 |        "</tr>\n",
625 |        "<tr>\n",
626 |        "  <th>House Size (sq.ft.)</th> <td>  401.9163</td> <td>   65.243</td> <td>    6.160</td> <td> 0.000</td> <td>  264.846</td> <td>  538.987</td>\n",
627 |        "</tr>\n",
628 |        "</table>\n",
629 |        "<table class=\"simpletable\">\n",
630 |        "<tr>\n",
631 |        "  <th>Omnibus:</th>       <td> 1.238</td> <th>  Durbin-Watson:     </th> <td>   1.810</td>\n",
632 |        "</tr>\n",
633 |        "<tr>\n",
634 |        "  <th>Prob(Omnibus):</th> <td> 0.538</td> <th>  Jarque-Bera (JB):  </th> <td>   0.715</td>\n",
635 |        "</tr>\n",
636 |        "<tr>\n",
637 |        "  <th>Skew:</th>          <td>-0.459</td> <th>  Prob(JB):          </th> <td>   0.699</td>\n",
638 |        "</tr>\n",
639 |        "<tr>\n",
640 |        "  <th>Kurtosis:</th>      <td> 2.884</td> <th>  Cond. No.          </th> <td>5.66e+03</td>\n",
641 |        "</tr>\n",
642 |        "</table>"
643 |       ],
644 |       "text/plain": [
645 |        "<class 'statsmodels.iolib.summary.Summary'>\n",
646 |        "\"\"\"\n",
647 |        "                            OLS Regression Results                            \n",
648 |        "==============================================================================\n",
649 |        "Dep. Variable:            House Price   R-squared:                       0.678\n",
650 |        "Model:                            OLS   Adj. R-squared:                  0.660\n",
651 |        "Method:                 Least Squares   F-statistic:                     37.95\n",
652 |        "Date:                Tue, 03 Apr 2018   Prob (F-statistic):           8.13e-06\n",
653 |        "Time:                        23:27:41   Log-Likelihood:                -260.43\n",
654 |        "No. Observations:                  20   AIC:                             524.9\n",
655 |        "Df Residuals:                      18   BIC:                             526.8\n",
656 |        "Df Model:                           1                                         \n",
657 |        "Covariance Type:            nonrobust                                         \n",
658 |        "=======================================================================================\n",
659 |        "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
660 |        "---------------------------------------------------------------------------------------\n",
661 |        "const                2.608e+05   9.76e+04      2.673      0.016    5.58e+04    4.66e+05\n",
662 |        "House Size (sq.ft.)   401.9163     65.243      6.160      0.000     264.846     538.987\n",
663 |        "==============================================================================\n",
664 |        "Omnibus:                        1.238   Durbin-Watson:                   1.810\n",
665 |        "Prob(Omnibus):                  0.538   Jarque-Bera (JB):                0.715\n",
666 |        "Skew:                          -0.459   Prob(JB):                        0.699\n",
667 |        "Kurtosis:                       2.884   Cond. No.                     5.66e+03\n",
668 |        "==============================================================================\n",
669 |        "\n",
670 |        "Warnings:\n",
671 |        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
672 |        "[2] The condition number is large, 5.66e+03. This might indicate that there are\n",
673 |        "strong multicollinearity or other numerical problems.\n",
674 |        "\"\"\""
675 |       ]
676 |      },
677 |      "execution_count": 17,
678 |      "metadata": {},
679 |      "output_type": "execute_result"
680 |     }
681 |    ],
682 |    "source": [
683 |     "reg.summary()"
684 |    ]
685 |   },
686 |   {
687 |    "cell_type": "code",
688 |    "execution_count": 18,
689 |    "metadata": {},
690 |    "outputs": [
691 |     {
692 |      "data": {
693 |       "text/plain": [
694 |        "662800"
695 |       ]
696 |      },
697 |      "execution_count": 18,
698 |      "metadata": {},
699 |      "output_type": "execute_result"
700 |     }
701 |    ],
702 |    "source": [
703 |     "260800 + 402 * 1000"
704 |    ]
705 |   },
706 |   {
707 |    "cell_type": "code",
708 |    "execution_count": 19,
709 |    "metadata": {},
710 |    "outputs": [],
711 |    "source": [
712 |     "slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)"
713 |    ]
714 |   },
715 |   {
716 |    "cell_type": "code",
717 |    "execution_count": 20,
718 |    "metadata": {},
719 |    "outputs": [
720 |     {
721 |      "data": {
722 |       "text/plain": [
723 |        "0.678279986579124"
724 |       ]
725 |      },
726 |      "execution_count": 20,
727 |      "metadata": {},
728 |      "output_type": "execute_result"
729 |     }
730 |    ],
731 |    "source": [
732 |     "r_value**2"
733 |    ]
734 |   },
735 |   {
736 |    "cell_type": "code",
737 |    "execution_count": 21,
738 |    "metadata": {},
739 |    "outputs": [
740 |     {
741 |      "data": {
742 |       "text/plain": [
743 |        "401.91628631922595"
744 |       ]
745 |      },
746 |      "execution_count": 21,
747 |      "metadata": {},
748 |      "output_type": "execute_result"
749 |     }
750 |    ],
751 |    "source": [
752 |     "slope"
753 |    ]
754 |   },
755 |   {
756 |    "cell_type": "code",
757 |    "execution_count": 22,
758 |    "metadata": {},
759 |    "outputs": [
760 |     {
761 |      "data": {
762 |       "text/plain": [
763 |        "260806.2360560964"
764 |       ]
765 |      },
766 |      "execution_count": 22,
767 |      "metadata": {},
768 |      "output_type": "execute_result"
769 |     }
770 |    ],
771 |    "source": [
772 |     "intercept"
773 |    ]
774 |   },
775 |   {
776 |    "cell_type": "code",
777 |    "execution_count": 23,
778 |    "metadata": {},
779 |    "outputs": [
780 |     {
781 |      "data": {
782 |       "text/plain": [
783 |        "65.24299510636492"
784 |       ]
785 |      },
786 |      "execution_count": 23,
787 |      "metadata": {},
788 |      "output_type": "execute_result"
789 |     }
790 |    ],
791 |    "source": [
792 |     "std_err"
793 |    ]
794 |   },
795 |   {
796 |    "cell_type": "code",
797 |    "execution_count": 24,
798 |    "metadata": {},
799 |    "outputs": [
800 |     {
801 |      "data": {
802 |       "text/plain": [
803 |        "8.129642377231308e-06"
804 |       ]
805 |      },
806 |      "execution_count": 24,
807 |      "metadata": {},
808 |      "output_type": "execute_result"
809 |     }
810 |    ],
811 |    "source": [
812 |     "p_value"
813 |    ]
814 |   }
815 |  ],
816 |  "metadata": {
817 |   "kernelspec": {
818 |    "display_name": "Python 3",
819 |    "language": "python",
820 |    "name": "python3"
821 |   },
822 |   "language_info": {
823 |    "codemirror_mode": {
824 |     "name": "ipython",
825 |     "version": 3
826 |    },
827 |    "file_extension": ".py",
828 |    "mimetype": "text/x-python",
829 |    "name": "python",
830 |    "nbconvert_exporter": "python",
831 |    "pygments_lexer": "ipython3",
832 |    "version": "3.6.4"
833 |   }
834 |  },
835 |  "nbformat": 4,
836 |  "nbformat_minor": 2
837 | }
838 | 


--------------------------------------------------------------------------------
/Standard_Deviation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "from pandas_datareader import data as wb\n",
 12 |     "import matplotlib.pyplot as plt"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 3,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "tickers = ['PG','AAPL']\n",
 22 |     "\n",
 23 |     "sec_data = pd.DataFrame()\n",
 24 |     "\n",
 25 |     "for t in tickers :\n",
 26 |     "    sec_data[t] = wb.DataReader(t,'quandl','2007-1-1','2018-3-1')['AdjClose']"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 4,
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "data": {
 36 |       "text/html": [
 37 |        "<div>\n",
 38 |        "<style scoped>\n",
 39 |        "    .dataframe tbody tr th:only-of-type {\n",
 40 |        "        vertical-align: middle;\n",
 41 |        "    }\n",
 42 |        "\n",
 43 |        "    .dataframe tbody tr th {\n",
 44 |        "        vertical-align: top;\n",
 45 |        "    }\n",
 46 |        "\n",
 47 |        "    .dataframe thead th {\n",
 48 |        "        text-align: right;\n",
 49 |        "    }\n",
 50 |        "</style>\n",
 51 |        "<table border=\"1\" class=\"dataframe\">\n",
 52 |        "  <thead>\n",
 53 |        "    <tr style=\"text-align: right;\">\n",
 54 |        "      <th></th>\n",
 55 |        "      <th>PG</th>\n",
 56 |        "      <th>AAPL</th>\n",
 57 |        "    </tr>\n",
 58 |        "    <tr>\n",
 59 |        "      <th>Date</th>\n",
 60 |        "      <th></th>\n",
 61 |        "      <th></th>\n",
 62 |        "    </tr>\n",
 63 |        "  </thead>\n",
 64 |        "  <tbody>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>2007-01-03</th>\n",
 67 |        "      <td>NaN</td>\n",
 68 |        "      <td>NaN</td>\n",
 69 |        "    </tr>\n",
 70 |        "    <tr>\n",
 71 |        "      <th>2007-01-04</th>\n",
 72 |        "      <td>-0.007621</td>\n",
 73 |        "      <td>0.021953</td>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>2007-01-05</th>\n",
 77 |        "      <td>-0.008624</td>\n",
 78 |        "      <td>-0.007147</td>\n",
 79 |        "    </tr>\n",
 80 |        "    <tr>\n",
 81 |        "      <th>2007-01-08</th>\n",
 82 |        "      <td>0.002202</td>\n",
 83 |        "      <td>0.004926</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2007-01-09</th>\n",
 87 |        "      <td>-0.002517</td>\n",
 88 |        "      <td>0.079800</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>2007-01-10</th>\n",
 92 |        "      <td>0.011901</td>\n",
 93 |        "      <td>0.046746</td>\n",
 94 |        "    </tr>\n",
 95 |        "    <tr>\n",
 96 |        "      <th>2007-01-11</th>\n",
 97 |        "      <td>0.006207</td>\n",
 98 |        "      <td>-0.012448</td>\n",
 99 |        "    </tr>\n",
100 |        "    <tr>\n",
101 |        "      <th>2007-01-12</th>\n",
102 |        "      <td>0.005554</td>\n",
103 |        "      <td>-0.012394</td>\n",
104 |        "    </tr>\n",
105 |        "    <tr>\n",
106 |        "      <th>2007-01-16</th>\n",
107 |        "      <td>0.000000</td>\n",
108 |        "      <td>0.025873</td>\n",
109 |        "    </tr>\n",
110 |        "    <tr>\n",
111 |        "      <th>2007-01-17</th>\n",
112 |        "      <td>0.010255</td>\n",
113 |        "      <td>-0.022391</td>\n",
114 |        "    </tr>\n",
115 |        "    <tr>\n",
116 |        "      <th>2007-01-18</th>\n",
117 |        "      <td>0.002750</td>\n",
118 |        "      <td>-0.063928</td>\n",
119 |        "    </tr>\n",
120 |        "    <tr>\n",
121 |        "      <th>2007-01-19</th>\n",
122 |        "      <td>0.008357</td>\n",
123 |        "      <td>-0.006420</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>2007-01-22</th>\n",
127 |        "      <td>-0.005614</td>\n",
128 |        "      <td>-0.019511</td>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>2007-01-23</th>\n",
132 |        "      <td>-0.001218</td>\n",
133 |        "      <td>-0.012639</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>2007-01-24</th>\n",
137 |        "      <td>-0.001982</td>\n",
138 |        "      <td>0.011601</td>\n",
139 |        "    </tr>\n",
140 |        "    <tr>\n",
141 |        "      <th>2007-01-25</th>\n",
142 |        "      <td>-0.009047</td>\n",
143 |        "      <td>-0.005204</td>\n",
144 |        "    </tr>\n",
145 |        "    <tr>\n",
146 |        "      <th>2007-01-26</th>\n",
147 |        "      <td>-0.000925</td>\n",
148 |        "      <td>-0.010138</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>2007-01-29</th>\n",
152 |        "      <td>0.000308</td>\n",
153 |        "      <td>0.006537</td>\n",
154 |        "    </tr>\n",
155 |        "    <tr>\n",
156 |        "      <th>2007-01-30</th>\n",
157 |        "      <td>-0.004480</td>\n",
158 |        "      <td>-0.004548</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>2007-01-31</th>\n",
162 |        "      <td>0.004326</td>\n",
163 |        "      <td>0.002102</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>2007-02-01</th>\n",
167 |        "      <td>0.007219</td>\n",
168 |        "      <td>-0.011615</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>2007-02-02</th>\n",
172 |        "      <td>0.000000</td>\n",
173 |        "      <td>0.000118</td>\n",
174 |        "    </tr>\n",
175 |        "    <tr>\n",
176 |        "      <th>2007-02-05</th>\n",
177 |        "      <td>-0.002912</td>\n",
178 |        "      <td>-0.009603</td>\n",
179 |        "    </tr>\n",
180 |        "    <tr>\n",
181 |        "      <th>2007-02-06</th>\n",
182 |        "      <td>-0.008633</td>\n",
183 |        "      <td>0.002499</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>2007-02-07</th>\n",
187 |        "      <td>-0.001705</td>\n",
188 |        "      <td>0.023489</td>\n",
189 |        "    </tr>\n",
190 |        "    <tr>\n",
191 |        "      <th>2007-02-08</th>\n",
192 |        "      <td>0.001705</td>\n",
193 |        "      <td>0.000348</td>\n",
194 |        "    </tr>\n",
195 |        "    <tr>\n",
196 |        "      <th>2007-02-09</th>\n",
197 |        "      <td>-0.002480</td>\n",
198 |        "      <td>-0.034350</td>\n",
199 |        "    </tr>\n",
200 |        "    <tr>\n",
201 |        "      <th>2007-02-12</th>\n",
202 |        "      <td>-0.004199</td>\n",
203 |        "      <td>0.019150</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>2007-02-13</th>\n",
207 |        "      <td>0.008536</td>\n",
208 |        "      <td>-0.002123</td>\n",
209 |        "    </tr>\n",
210 |        "    <tr>\n",
211 |        "      <th>2007-02-14</th>\n",
212 |        "      <td>0.002624</td>\n",
213 |        "      <td>0.007059</td>\n",
214 |        "    </tr>\n",
215 |        "    <tr>\n",
216 |        "      <th>...</th>\n",
217 |        "      <td>...</td>\n",
218 |        "      <td>...</td>\n",
219 |        "    </tr>\n",
220 |        "    <tr>\n",
221 |        "      <th>2018-01-18</th>\n",
222 |        "      <td>-0.011247</td>\n",
223 |        "      <td>0.000893</td>\n",
224 |        "    </tr>\n",
225 |        "    <tr>\n",
226 |        "      <th>2018-01-19</th>\n",
227 |        "      <td>0.009821</td>\n",
228 |        "      <td>-0.004473</td>\n",
229 |        "    </tr>\n",
230 |        "    <tr>\n",
231 |        "      <th>2018-01-22</th>\n",
232 |        "      <td>0.008964</td>\n",
233 |        "      <td>-0.008215</td>\n",
234 |        "    </tr>\n",
235 |        "    <tr>\n",
236 |        "      <th>2018-01-23</th>\n",
237 |        "      <td>-0.031394</td>\n",
238 |        "      <td>0.000226</td>\n",
239 |        "    </tr>\n",
240 |        "    <tr>\n",
241 |        "      <th>2018-01-24</th>\n",
242 |        "      <td>-0.008345</td>\n",
243 |        "      <td>-0.016057</td>\n",
244 |        "    </tr>\n",
245 |        "    <tr>\n",
246 |        "      <th>2018-01-25</th>\n",
247 |        "      <td>0.000340</td>\n",
248 |        "      <td>-0.018012</td>\n",
249 |        "    </tr>\n",
250 |        "    <tr>\n",
251 |        "      <th>2018-01-26</th>\n",
252 |        "      <td>-0.006929</td>\n",
253 |        "      <td>0.002335</td>\n",
254 |        "    </tr>\n",
255 |        "    <tr>\n",
256 |        "      <th>2018-01-29</th>\n",
257 |        "      <td>-0.009966</td>\n",
258 |        "      <td>-0.020916</td>\n",
259 |        "    </tr>\n",
260 |        "    <tr>\n",
261 |        "      <th>2018-01-30</th>\n",
262 |        "      <td>0.001036</td>\n",
263 |        "      <td>-0.005912</td>\n",
264 |        "    </tr>\n",
265 |        "    <tr>\n",
266 |        "      <th>2018-01-31</th>\n",
267 |        "      <td>-0.007040</td>\n",
268 |        "      <td>0.002751</td>\n",
269 |        "    </tr>\n",
270 |        "    <tr>\n",
271 |        "      <th>2018-02-01</th>\n",
272 |        "      <td>-0.005691</td>\n",
273 |        "      <td>0.002088</td>\n",
274 |        "    </tr>\n",
275 |        "    <tr>\n",
276 |        "      <th>2018-02-02</th>\n",
277 |        "      <td>-0.018813</td>\n",
278 |        "      <td>-0.045170</td>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <th>2018-02-05</th>\n",
282 |        "      <td>-0.038599</td>\n",
283 |        "      <td>-0.018122</td>\n",
284 |        "    </tr>\n",
285 |        "    <tr>\n",
286 |        "      <th>2018-02-06</th>\n",
287 |        "      <td>0.016153</td>\n",
288 |        "      <td>0.034572</td>\n",
289 |        "    </tr>\n",
290 |        "    <tr>\n",
291 |        "      <th>2018-02-07</th>\n",
292 |        "      <td>-0.006577</td>\n",
293 |        "      <td>-0.021640</td>\n",
294 |        "    </tr>\n",
295 |        "    <tr>\n",
296 |        "      <th>2018-02-08</th>\n",
297 |        "      <td>-0.019993</td>\n",
298 |        "      <td>-0.026807</td>\n",
299 |        "    </tr>\n",
300 |        "    <tr>\n",
301 |        "      <th>2018-02-09</th>\n",
302 |        "      <td>-0.003747</td>\n",
303 |        "      <td>0.004176</td>\n",
304 |        "    </tr>\n",
305 |        "    <tr>\n",
306 |        "      <th>2018-02-12</th>\n",
307 |        "      <td>0.017489</td>\n",
308 |        "      <td>0.042306</td>\n",
309 |        "    </tr>\n",
310 |        "    <tr>\n",
311 |        "      <th>2018-02-13</th>\n",
312 |        "      <td>0.002088</td>\n",
313 |        "      <td>0.009968</td>\n",
314 |        "    </tr>\n",
315 |        "    <tr>\n",
316 |        "      <th>2018-02-14</th>\n",
317 |        "      <td>-0.010112</td>\n",
318 |        "      <td>0.018269</td>\n",
319 |        "    </tr>\n",
320 |        "    <tr>\n",
321 |        "      <th>2018-02-15</th>\n",
322 |        "      <td>0.021216</td>\n",
323 |        "      <td>0.033027</td>\n",
324 |        "    </tr>\n",
325 |        "    <tr>\n",
326 |        "      <th>2018-02-16</th>\n",
327 |        "      <td>0.002303</td>\n",
328 |        "      <td>-0.003242</td>\n",
329 |        "    </tr>\n",
330 |        "    <tr>\n",
331 |        "      <th>2018-02-20</th>\n",
332 |        "      <td>-0.015372</td>\n",
333 |        "      <td>-0.003369</td>\n",
334 |        "    </tr>\n",
335 |        "    <tr>\n",
336 |        "      <th>2018-02-21</th>\n",
337 |        "      <td>-0.013616</td>\n",
338 |        "      <td>-0.004549</td>\n",
339 |        "    </tr>\n",
340 |        "    <tr>\n",
341 |        "      <th>2018-02-22</th>\n",
342 |        "      <td>0.007450</td>\n",
343 |        "      <td>0.008904</td>\n",
344 |        "    </tr>\n",
345 |        "    <tr>\n",
346 |        "      <th>2018-02-23</th>\n",
347 |        "      <td>0.002594</td>\n",
348 |        "      <td>0.016976</td>\n",
349 |        "    </tr>\n",
350 |        "    <tr>\n",
351 |        "      <th>2018-02-26</th>\n",
352 |        "      <td>0.007131</td>\n",
353 |        "      <td>0.019266</td>\n",
354 |        "    </tr>\n",
355 |        "    <tr>\n",
356 |        "      <th>2018-02-27</th>\n",
357 |        "      <td>-0.013443</td>\n",
358 |        "      <td>-0.003246</td>\n",
359 |        "    </tr>\n",
360 |        "    <tr>\n",
361 |        "      <th>2018-02-28</th>\n",
362 |        "      <td>-0.025401</td>\n",
363 |        "      <td>-0.001515</td>\n",
364 |        "    </tr>\n",
365 |        "    <tr>\n",
366 |        "      <th>2018-03-01</th>\n",
367 |        "      <td>0.002290</td>\n",
368 |        "      <td>-0.017672</td>\n",
369 |        "    </tr>\n",
370 |        "  </tbody>\n",
371 |        "</table>\n",
372 |        "<p>2809 rows × 2 columns</p>\n",
373 |        "</div>"
374 |       ],
375 |       "text/plain": [
376 |        "                  PG      AAPL\n",
377 |        "Date                          \n",
378 |        "2007-01-03       NaN       NaN\n",
379 |        "2007-01-04 -0.007621  0.021953\n",
380 |        "2007-01-05 -0.008624 -0.007147\n",
381 |        "2007-01-08  0.002202  0.004926\n",
382 |        "2007-01-09 -0.002517  0.079800\n",
383 |        "2007-01-10  0.011901  0.046746\n",
384 |        "2007-01-11  0.006207 -0.012448\n",
385 |        "2007-01-12  0.005554 -0.012394\n",
386 |        "2007-01-16  0.000000  0.025873\n",
387 |        "2007-01-17  0.010255 -0.022391\n",
388 |        "2007-01-18  0.002750 -0.063928\n",
389 |        "2007-01-19  0.008357 -0.006420\n",
390 |        "2007-01-22 -0.005614 -0.019511\n",
391 |        "2007-01-23 -0.001218 -0.012639\n",
392 |        "2007-01-24 -0.001982  0.011601\n",
393 |        "2007-01-25 -0.009047 -0.005204\n",
394 |        "2007-01-26 -0.000925 -0.010138\n",
395 |        "2007-01-29  0.000308  0.006537\n",
396 |        "2007-01-30 -0.004480 -0.004548\n",
397 |        "2007-01-31  0.004326  0.002102\n",
398 |        "2007-02-01  0.007219 -0.011615\n",
399 |        "2007-02-02  0.000000  0.000118\n",
400 |        "2007-02-05 -0.002912 -0.009603\n",
401 |        "2007-02-06 -0.008633  0.002499\n",
402 |        "2007-02-07 -0.001705  0.023489\n",
403 |        "2007-02-08  0.001705  0.000348\n",
404 |        "2007-02-09 -0.002480 -0.034350\n",
405 |        "2007-02-12 -0.004199  0.019150\n",
406 |        "2007-02-13  0.008536 -0.002123\n",
407 |        "2007-02-14  0.002624  0.007059\n",
408 |        "...              ...       ...\n",
409 |        "2018-01-18 -0.011247  0.000893\n",
410 |        "2018-01-19  0.009821 -0.004473\n",
411 |        "2018-01-22  0.008964 -0.008215\n",
412 |        "2018-01-23 -0.031394  0.000226\n",
413 |        "2018-01-24 -0.008345 -0.016057\n",
414 |        "2018-01-25  0.000340 -0.018012\n",
415 |        "2018-01-26 -0.006929  0.002335\n",
416 |        "2018-01-29 -0.009966 -0.020916\n",
417 |        "2018-01-30  0.001036 -0.005912\n",
418 |        "2018-01-31 -0.007040  0.002751\n",
419 |        "2018-02-01 -0.005691  0.002088\n",
420 |        "2018-02-02 -0.018813 -0.045170\n",
421 |        "2018-02-05 -0.038599 -0.018122\n",
422 |        "2018-02-06  0.016153  0.034572\n",
423 |        "2018-02-07 -0.006577 -0.021640\n",
424 |        "2018-02-08 -0.019993 -0.026807\n",
425 |        "2018-02-09 -0.003747  0.004176\n",
426 |        "2018-02-12  0.017489  0.042306\n",
427 |        "2018-02-13  0.002088  0.009968\n",
428 |        "2018-02-14 -0.010112  0.018269\n",
429 |        "2018-02-15  0.021216  0.033027\n",
430 |        "2018-02-16  0.002303 -0.003242\n",
431 |        "2018-02-20 -0.015372 -0.003369\n",
432 |        "2018-02-21 -0.013616 -0.004549\n",
433 |        "2018-02-22  0.007450  0.008904\n",
434 |        "2018-02-23  0.002594  0.016976\n",
435 |        "2018-02-26  0.007131  0.019266\n",
436 |        "2018-02-27 -0.013443 -0.003246\n",
437 |        "2018-02-28 -0.025401 -0.001515\n",
438 |        "2018-03-01  0.002290 -0.017672\n",
439 |        "\n",
440 |        "[2809 rows x 2 columns]"
441 |       ]
442 |      },
443 |      "execution_count": 4,
444 |      "metadata": {},
445 |      "output_type": "execute_result"
446 |     }
447 |    ],
448 |    "source": [
449 |     "sec_data = sec_data.iloc[::-1]\n",
450 |     "sec_returns = np.log(sec_data / sec_data.shift(1))\n",
451 |     "sec_returns"
452 |    ]
453 |   },
454 |   {
455 |    "cell_type": "code",
456 |    "execution_count": 27,
457 |    "metadata": {},
458 |    "outputs": [
459 |     {
460 |      "data": {
461 |       "text/plain": [
462 |        "0.00018767102795901115"
463 |       ]
464 |      },
465 |      "execution_count": 27,
466 |      "metadata": {},
467 |      "output_type": "execute_result"
468 |     }
469 |    ],
470 |    "source": [
471 |     "sec_returns['PG'].mean()"
472 |    ]
473 |   },
474 |   {
475 |    "cell_type": "code",
476 |    "execution_count": 7,
477 |    "metadata": {},
478 |    "outputs": [
479 |     {
480 |      "data": {
481 |       "text/plain": [
482 |        "PG      0.000188\n",
483 |        "AAPL    0.000985\n",
484 |        "dtype: float64"
485 |       ]
486 |      },
487 |      "execution_count": 7,
488 |      "metadata": {},
489 |      "output_type": "execute_result"
490 |     }
491 |    ],
492 |    "source": [
493 |     "daily = sec_returns[['PG','AAPL']].mean()\n",
494 |     "daily"
495 |    ]
496 |   },
497 |   {
498 |    "cell_type": "code",
499 |    "execution_count": 34,
500 |    "metadata": {},
501 |    "outputs": [
502 |     {
503 |      "name": "stdout",
504 |      "output_type": "stream",
505 |      "text": [
506 |       "PG       4.803120\n",
507 |       "AAPL    27.915995\n",
508 |       "dtype: float64%\n"
509 |      ]
510 |     }
511 |    ],
512 |    "source": [
513 |     "annual = (((daily+1)**250)-1)*100\n",
514 |     "print(str(annual) + '%')"
515 |    ]
516 |   },
517 |   {
518 |    "cell_type": "code",
519 |    "execution_count": 35,
520 |    "metadata": {},
521 |    "outputs": [
522 |     {
523 |      "data": {
524 |       "text/plain": [
525 |        "PG      0.010931\n",
526 |        "AAPL    0.020057\n",
527 |        "dtype: float64"
528 |       ]
529 |      },
530 |      "execution_count": 35,
531 |      "metadata": {},
532 |      "output_type": "execute_result"
533 |     }
534 |    ],
535 |    "source": [
536 |     "daily_std = sec_returns[['PG','AAPL']].std()\n",
537 |     "daily_std"
538 |    ]
539 |   },
540 |   {
541 |    "cell_type": "code",
542 |    "execution_count": 38,
543 |    "metadata": {},
544 |    "outputs": [
545 |     {
546 |      "data": {
547 |       "text/plain": [
548 |        "PG      0.172832\n",
549 |        "AAPL    0.317125\n",
550 |        "dtype: float64"
551 |       ]
552 |      },
553 |      "execution_count": 38,
554 |      "metadata": {},
555 |      "output_type": "execute_result"
556 |     }
557 |    ],
558 |    "source": [
559 |     "annual_std = sec_returns[['PG','AAPL']].std()*250**0.5\n",
560 |     "annual_std"
561 |    ]
562 |   }
563 |  ],
564 |  "metadata": {
565 |   "kernelspec": {
566 |    "display_name": "Python 3",
567 |    "language": "python",
568 |    "name": "python3"
569 |   },
570 |   "language_info": {
571 |    "codemirror_mode": {
572 |     "name": "ipython",
573 |     "version": 3
574 |    },
575 |    "file_extension": ".py",
576 |    "mimetype": "text/x-python",
577 |    "name": "python",
578 |    "nbconvert_exporter": "python",
579 |    "pygments_lexer": "ipython3",
580 |    "version": "3.6.4"
581 |   }
582 |  },
583 |  "nbformat": 4,
584 |  "nbformat_minor": 2
585 | }
586 | 


--------------------------------------------------------------------------------
/var_cov_correlation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import pandas as pd\n",
 10 |     "import numpy as np\n",
 11 |     "from pandas_datareader import data as wb\n",
 12 |     "import matplotlib.pyplot as plt"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 15,
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "tickers = ['PG','MSFT']\n",
 22 |     "\n",
 23 |     "sec_data = pd.DataFrame()\n",
 24 |     "\n",
 25 |     "for t in tickers :\n",
 26 |     "    sec_data[t] = wb.DataReader(t,'quandl','2007-1-1','2018-3-1')['AdjClose']"
 27 |    ]
 28 |   },
 29 |   {
 30 |    "cell_type": "code",
 31 |    "execution_count": 16,
 32 |    "metadata": {},
 33 |    "outputs": [
 34 |     {
 35 |      "name": "stdout",
 36 |      "output_type": "stream",
 37 |      "text": [
 38 |       "<class 'pandas.core.frame.DataFrame'>\n",
 39 |       "DatetimeIndex: 2809 entries, 2018-03-01 to 2007-01-03\n",
 40 |       "Data columns (total 2 columns):\n",
 41 |       "PG      2809 non-null float64\n",
 42 |       "MSFT    2809 non-null float64\n",
 43 |       "dtypes: float64(2)\n",
 44 |       "memory usage: 65.8 KB\n"
 45 |      ]
 46 |     }
 47 |    ],
 48 |    "source": [
 49 |     "sec_data.info()"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 17,
 55 |    "metadata": {},
 56 |    "outputs": [
 57 |     {
 58 |      "data": {
 59 |       "text/html": [
 60 |        "<div>\n",
 61 |        "<style scoped>\n",
 62 |        "    .dataframe tbody tr th:only-of-type {\n",
 63 |        "        vertical-align: middle;\n",
 64 |        "    }\n",
 65 |        "\n",
 66 |        "    .dataframe tbody tr th {\n",
 67 |        "        vertical-align: top;\n",
 68 |        "    }\n",
 69 |        "\n",
 70 |        "    .dataframe thead th {\n",
 71 |        "        text-align: right;\n",
 72 |        "    }\n",
 73 |        "</style>\n",
 74 |        "<table border=\"1\" class=\"dataframe\">\n",
 75 |        "  <thead>\n",
 76 |        "    <tr style=\"text-align: right;\">\n",
 77 |        "      <th></th>\n",
 78 |        "      <th>PG</th>\n",
 79 |        "      <th>MSFT</th>\n",
 80 |        "    </tr>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>Date</th>\n",
 83 |        "      <th></th>\n",
 84 |        "      <th></th>\n",
 85 |        "    </tr>\n",
 86 |        "  </thead>\n",
 87 |        "  <tbody>\n",
 88 |        "    <tr>\n",
 89 |        "      <th>2007-01-03</th>\n",
 90 |        "      <td>NaN</td>\n",
 91 |        "      <td>NaN</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>2007-01-04</th>\n",
 95 |        "      <td>-0.007621</td>\n",
 96 |        "      <td>-0.001676</td>\n",
 97 |        "    </tr>\n",
 98 |        "    <tr>\n",
 99 |        "      <th>2007-01-05</th>\n",
100 |        "      <td>-0.008624</td>\n",
101 |        "      <td>-0.005719</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>2007-01-08</th>\n",
105 |        "      <td>0.002202</td>\n",
106 |        "      <td>0.009737</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>2007-01-09</th>\n",
110 |        "      <td>-0.002517</td>\n",
111 |        "      <td>0.001002</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>2007-01-10</th>\n",
115 |        "      <td>0.011901</td>\n",
116 |        "      <td>-0.010064</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>2007-01-11</th>\n",
120 |        "      <td>0.006207</td>\n",
121 |        "      <td>0.034463</td>\n",
122 |        "    </tr>\n",
123 |        "    <tr>\n",
124 |        "      <th>2007-01-12</th>\n",
125 |        "      <td>0.005554</td>\n",
126 |        "      <td>0.016476</td>\n",
127 |        "    </tr>\n",
128 |        "    <tr>\n",
129 |        "      <th>2007-01-16</th>\n",
130 |        "      <td>0.000000</td>\n",
131 |        "      <td>-0.001603</td>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <th>2007-01-17</th>\n",
135 |        "      <td>0.010255</td>\n",
136 |        "      <td>-0.001927</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <th>2007-01-18</th>\n",
140 |        "      <td>0.002750</td>\n",
141 |        "      <td>-0.003221</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>2007-01-19</th>\n",
145 |        "      <td>0.008357</td>\n",
146 |        "      <td>0.003542</td>\n",
147 |        "    </tr>\n",
148 |        "    <tr>\n",
149 |        "      <th>2007-01-22</th>\n",
150 |        "      <td>-0.005614</td>\n",
151 |        "      <td>-0.012615</td>\n",
152 |        "    </tr>\n",
153 |        "    <tr>\n",
154 |        "      <th>2007-01-23</th>\n",
155 |        "      <td>-0.001218</td>\n",
156 |        "      <td>0.000651</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>2007-01-24</th>\n",
160 |        "      <td>-0.001982</td>\n",
161 |        "      <td>0.011321</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>2007-01-25</th>\n",
165 |        "      <td>-0.009047</td>\n",
166 |        "      <td>-0.020800</td>\n",
167 |        "    </tr>\n",
168 |        "    <tr>\n",
169 |        "      <th>2007-01-26</th>\n",
170 |        "      <td>-0.000925</td>\n",
171 |        "      <td>0.004914</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>2007-01-29</th>\n",
175 |        "      <td>0.000308</td>\n",
176 |        "      <td>-0.002290</td>\n",
177 |        "    </tr>\n",
178 |        "    <tr>\n",
179 |        "      <th>2007-01-30</th>\n",
180 |        "      <td>-0.004480</td>\n",
181 |        "      <td>-0.001639</td>\n",
182 |        "    </tr>\n",
183 |        "    <tr>\n",
184 |        "      <th>2007-01-31</th>\n",
185 |        "      <td>0.004326</td>\n",
186 |        "      <td>0.012390</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>2007-02-01</th>\n",
190 |        "      <td>0.007219</td>\n",
191 |        "      <td>-0.009769</td>\n",
192 |        "    </tr>\n",
193 |        "    <tr>\n",
194 |        "      <th>2007-02-02</th>\n",
195 |        "      <td>0.000000</td>\n",
196 |        "      <td>-0.012181</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>2007-02-05</th>\n",
200 |        "      <td>-0.002912</td>\n",
201 |        "      <td>-0.019399</td>\n",
202 |        "    </tr>\n",
203 |        "    <tr>\n",
204 |        "      <th>2007-02-06</th>\n",
205 |        "      <td>-0.008633</td>\n",
206 |        "      <td>-0.003383</td>\n",
207 |        "    </tr>\n",
208 |        "    <tr>\n",
209 |        "      <th>2007-02-07</th>\n",
210 |        "      <td>-0.001705</td>\n",
211 |        "      <td>-0.004755</td>\n",
212 |        "    </tr>\n",
213 |        "    <tr>\n",
214 |        "      <th>2007-02-08</th>\n",
215 |        "      <td>0.001705</td>\n",
216 |        "      <td>-0.003752</td>\n",
217 |        "    </tr>\n",
218 |        "    <tr>\n",
219 |        "      <th>2007-02-09</th>\n",
220 |        "      <td>-0.002480</td>\n",
221 |        "      <td>-0.009615</td>\n",
222 |        "    </tr>\n",
223 |        "    <tr>\n",
224 |        "      <th>2007-02-12</th>\n",
225 |        "      <td>-0.004199</td>\n",
226 |        "      <td>-0.001381</td>\n",
227 |        "    </tr>\n",
228 |        "    <tr>\n",
229 |        "      <th>2007-02-13</th>\n",
230 |        "      <td>0.008536</td>\n",
231 |        "      <td>0.005857</td>\n",
232 |        "    </tr>\n",
233 |        "    <tr>\n",
234 |        "      <th>2007-02-14</th>\n",
235 |        "      <td>0.002624</td>\n",
236 |        "      <td>0.013354</td>\n",
237 |        "    </tr>\n",
238 |        "    <tr>\n",
239 |        "      <th>...</th>\n",
240 |        "      <td>...</td>\n",
241 |        "      <td>...</td>\n",
242 |        "    </tr>\n",
243 |        "    <tr>\n",
244 |        "      <th>2018-01-18</th>\n",
245 |        "      <td>-0.011247</td>\n",
246 |        "      <td>-0.000444</td>\n",
247 |        "    </tr>\n",
248 |        "    <tr>\n",
249 |        "      <th>2018-01-19</th>\n",
250 |        "      <td>0.009821</td>\n",
251 |        "      <td>-0.001110</td>\n",
252 |        "    </tr>\n",
253 |        "    <tr>\n",
254 |        "      <th>2018-01-22</th>\n",
255 |        "      <td>0.008964</td>\n",
256 |        "      <td>0.017731</td>\n",
257 |        "    </tr>\n",
258 |        "    <tr>\n",
259 |        "      <th>2018-01-23</th>\n",
260 |        "      <td>-0.031394</td>\n",
261 |        "      <td>0.003161</td>\n",
262 |        "    </tr>\n",
263 |        "    <tr>\n",
264 |        "      <th>2018-01-24</th>\n",
265 |        "      <td>-0.008345</td>\n",
266 |        "      <td>-0.000871</td>\n",
267 |        "    </tr>\n",
268 |        "    <tr>\n",
269 |        "      <th>2018-01-25</th>\n",
270 |        "      <td>0.000340</td>\n",
271 |        "      <td>0.005539</td>\n",
272 |        "    </tr>\n",
273 |        "    <tr>\n",
274 |        "      <th>2018-01-26</th>\n",
275 |        "      <td>-0.006929</td>\n",
276 |        "      <td>0.018564</td>\n",
277 |        "    </tr>\n",
278 |        "    <tr>\n",
279 |        "      <th>2018-01-29</th>\n",
280 |        "      <td>-0.009966</td>\n",
281 |        "      <td>-0.001490</td>\n",
282 |        "    </tr>\n",
283 |        "    <tr>\n",
284 |        "      <th>2018-01-30</th>\n",
285 |        "      <td>0.001036</td>\n",
286 |        "      <td>-0.012643</td>\n",
287 |        "    </tr>\n",
288 |        "    <tr>\n",
289 |        "      <th>2018-01-31</th>\n",
290 |        "      <td>-0.007040</td>\n",
291 |        "      <td>0.024182</td>\n",
292 |        "    </tr>\n",
293 |        "    <tr>\n",
294 |        "      <th>2018-02-01</th>\n",
295 |        "      <td>-0.005691</td>\n",
296 |        "      <td>-0.007925</td>\n",
297 |        "    </tr>\n",
298 |        "    <tr>\n",
299 |        "      <th>2018-02-02</th>\n",
300 |        "      <td>-0.018813</td>\n",
301 |        "      <td>-0.026663</td>\n",
302 |        "    </tr>\n",
303 |        "    <tr>\n",
304 |        "      <th>2018-02-05</th>\n",
305 |        "      <td>-0.038599</td>\n",
306 |        "      <td>-0.030196</td>\n",
307 |        "    </tr>\n",
308 |        "    <tr>\n",
309 |        "      <th>2018-02-06</th>\n",
310 |        "      <td>0.016153</td>\n",
311 |        "      <td>0.025281</td>\n",
312 |        "    </tr>\n",
313 |        "    <tr>\n",
314 |        "      <th>2018-02-07</th>\n",
315 |        "      <td>-0.006577</td>\n",
316 |        "      <td>-0.019012</td>\n",
317 |        "    </tr>\n",
318 |        "    <tr>\n",
319 |        "      <th>2018-02-08</th>\n",
320 |        "      <td>-0.019993</td>\n",
321 |        "      <td>-0.054111</td>\n",
322 |        "    </tr>\n",
323 |        "    <tr>\n",
324 |        "      <th>2018-02-09</th>\n",
325 |        "      <td>-0.003747</td>\n",
326 |        "      <td>0.037116</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>2018-02-12</th>\n",
330 |        "      <td>0.017489</td>\n",
331 |        "      <td>0.011623</td>\n",
332 |        "    </tr>\n",
333 |        "    <tr>\n",
334 |        "      <th>2018-02-13</th>\n",
335 |        "      <td>0.002088</td>\n",
336 |        "      <td>0.007823</td>\n",
337 |        "    </tr>\n",
338 |        "    <tr>\n",
339 |        "      <th>2018-02-14</th>\n",
340 |        "      <td>-0.010112</td>\n",
341 |        "      <td>0.010850</td>\n",
342 |        "    </tr>\n",
343 |        "    <tr>\n",
344 |        "      <th>2018-02-15</th>\n",
345 |        "      <td>0.021216</td>\n",
346 |        "      <td>0.020167</td>\n",
347 |        "    </tr>\n",
348 |        "    <tr>\n",
349 |        "      <th>2018-02-16</th>\n",
350 |        "      <td>0.002303</td>\n",
351 |        "      <td>-0.007148</td>\n",
352 |        "    </tr>\n",
353 |        "    <tr>\n",
354 |        "      <th>2018-02-20</th>\n",
355 |        "      <td>-0.015372</td>\n",
356 |        "      <td>0.007796</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>2018-02-21</th>\n",
360 |        "      <td>-0.013616</td>\n",
361 |        "      <td>-0.013355</td>\n",
362 |        "    </tr>\n",
363 |        "    <tr>\n",
364 |        "      <th>2018-02-22</th>\n",
365 |        "      <td>0.007450</td>\n",
366 |        "      <td>0.002729</td>\n",
367 |        "    </tr>\n",
368 |        "    <tr>\n",
369 |        "      <th>2018-02-23</th>\n",
370 |        "      <td>0.002594</td>\n",
371 |        "      <td>0.024974</td>\n",
372 |        "    </tr>\n",
373 |        "    <tr>\n",
374 |        "      <th>2018-02-26</th>\n",
375 |        "      <td>0.007131</td>\n",
376 |        "      <td>0.014355</td>\n",
377 |        "    </tr>\n",
378 |        "    <tr>\n",
379 |        "      <th>2018-02-27</th>\n",
380 |        "      <td>-0.013443</td>\n",
381 |        "      <td>-0.012868</td>\n",
382 |        "    </tr>\n",
383 |        "    <tr>\n",
384 |        "      <th>2018-02-28</th>\n",
385 |        "      <td>-0.025401</td>\n",
386 |        "      <td>-0.004575</td>\n",
387 |        "    </tr>\n",
388 |        "    <tr>\n",
389 |        "      <th>2018-03-01</th>\n",
390 |        "      <td>0.002290</td>\n",
391 |        "      <td>-0.009860</td>\n",
392 |        "    </tr>\n",
393 |        "  </tbody>\n",
394 |        "</table>\n",
395 |        "<p>2809 rows × 2 columns</p>\n",
396 |        "</div>"
397 |       ],
398 |       "text/plain": [
399 |        "                  PG      MSFT\n",
400 |        "Date                          \n",
401 |        "2007-01-03       NaN       NaN\n",
402 |        "2007-01-04 -0.007621 -0.001676\n",
403 |        "2007-01-05 -0.008624 -0.005719\n",
404 |        "2007-01-08  0.002202  0.009737\n",
405 |        "2007-01-09 -0.002517  0.001002\n",
406 |        "2007-01-10  0.011901 -0.010064\n",
407 |        "2007-01-11  0.006207  0.034463\n",
408 |        "2007-01-12  0.005554  0.016476\n",
409 |        "2007-01-16  0.000000 -0.001603\n",
410 |        "2007-01-17  0.010255 -0.001927\n",
411 |        "2007-01-18  0.002750 -0.003221\n",
412 |        "2007-01-19  0.008357  0.003542\n",
413 |        "2007-01-22 -0.005614 -0.012615\n",
414 |        "2007-01-23 -0.001218  0.000651\n",
415 |        "2007-01-24 -0.001982  0.011321\n",
416 |        "2007-01-25 -0.009047 -0.020800\n",
417 |        "2007-01-26 -0.000925  0.004914\n",
418 |        "2007-01-29  0.000308 -0.002290\n",
419 |        "2007-01-30 -0.004480 -0.001639\n",
420 |        "2007-01-31  0.004326  0.012390\n",
421 |        "2007-02-01  0.007219 -0.009769\n",
422 |        "2007-02-02  0.000000 -0.012181\n",
423 |        "2007-02-05 -0.002912 -0.019399\n",
424 |        "2007-02-06 -0.008633 -0.003383\n",
425 |        "2007-02-07 -0.001705 -0.004755\n",
426 |        "2007-02-08  0.001705 -0.003752\n",
427 |        "2007-02-09 -0.002480 -0.009615\n",
428 |        "2007-02-12 -0.004199 -0.001381\n",
429 |        "2007-02-13  0.008536  0.005857\n",
430 |        "2007-02-14  0.002624  0.013354\n",
431 |        "...              ...       ...\n",
432 |        "2018-01-18 -0.011247 -0.000444\n",
433 |        "2018-01-19  0.009821 -0.001110\n",
434 |        "2018-01-22  0.008964  0.017731\n",
435 |        "2018-01-23 -0.031394  0.003161\n",
436 |        "2018-01-24 -0.008345 -0.000871\n",
437 |        "2018-01-25  0.000340  0.005539\n",
438 |        "2018-01-26 -0.006929  0.018564\n",
439 |        "2018-01-29 -0.009966 -0.001490\n",
440 |        "2018-01-30  0.001036 -0.012643\n",
441 |        "2018-01-31 -0.007040  0.024182\n",
442 |        "2018-02-01 -0.005691 -0.007925\n",
443 |        "2018-02-02 -0.018813 -0.026663\n",
444 |        "2018-02-05 -0.038599 -0.030196\n",
445 |        "2018-02-06  0.016153  0.025281\n",
446 |        "2018-02-07 -0.006577 -0.019012\n",
447 |        "2018-02-08 -0.019993 -0.054111\n",
448 |        "2018-02-09 -0.003747  0.037116\n",
449 |        "2018-02-12  0.017489  0.011623\n",
450 |        "2018-02-13  0.002088  0.007823\n",
451 |        "2018-02-14 -0.010112  0.010850\n",
452 |        "2018-02-15  0.021216  0.020167\n",
453 |        "2018-02-16  0.002303 -0.007148\n",
454 |        "2018-02-20 -0.015372  0.007796\n",
455 |        "2018-02-21 -0.013616 -0.013355\n",
456 |        "2018-02-22  0.007450  0.002729\n",
457 |        "2018-02-23  0.002594  0.024974\n",
458 |        "2018-02-26  0.007131  0.014355\n",
459 |        "2018-02-27 -0.013443 -0.012868\n",
460 |        "2018-02-28 -0.025401 -0.004575\n",
461 |        "2018-03-01  0.002290 -0.009860\n",
462 |        "\n",
463 |        "[2809 rows x 2 columns]"
464 |       ]
465 |      },
466 |      "execution_count": 17,
467 |      "metadata": {},
468 |      "output_type": "execute_result"
469 |     }
470 |    ],
471 |    "source": [
472 |     "sec_data = sec_data.iloc[::-1]\n",
473 |     "sec_returns = np.log(sec_data / sec_data.shift(1))\n",
474 |     "sec_returns"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "code",
479 |    "execution_count": 21,
480 |    "metadata": {
481 |     "scrolled": true
482 |    },
483 |    "outputs": [
484 |     {
485 |      "name": "stdout",
486 |      "output_type": "stream",
487 |      "text": [
488 |       "P&G : 0.029870754208558788\n",
489 |       "Microsoft : 0.07392432143310333\n"
490 |      ]
491 |     }
492 |    ],
493 |    "source": [
494 |     "PG_var = sec_returns['PG'].var() * 250\n",
495 |     "MSFT_var = sec_returns['MSFT'].var() * 250\n",
496 |     "print('P&G : ' + str(PG_var) + '\\nMicrosoft : '+ str(MSFT_var))"
497 |    ]
498 |   },
499 |   {
500 |    "cell_type": "code",
501 |    "execution_count": 23,
502 |    "metadata": {},
503 |    "outputs": [
504 |     {
505 |      "data": {
506 |       "text/html": [
507 |        "<div>\n",
508 |        "<style scoped>\n",
509 |        "    .dataframe tbody tr th:only-of-type {\n",
510 |        "        vertical-align: middle;\n",
511 |        "    }\n",
512 |        "\n",
513 |        "    .dataframe tbody tr th {\n",
514 |        "        vertical-align: top;\n",
515 |        "    }\n",
516 |        "\n",
517 |        "    .dataframe thead th {\n",
518 |        "        text-align: right;\n",
519 |        "    }\n",
520 |        "</style>\n",
521 |        "<table border=\"1\" class=\"dataframe\">\n",
522 |        "  <thead>\n",
523 |        "    <tr style=\"text-align: right;\">\n",
524 |        "      <th></th>\n",
525 |        "      <th>PG</th>\n",
526 |        "      <th>MSFT</th>\n",
527 |        "    </tr>\n",
528 |        "  </thead>\n",
529 |        "  <tbody>\n",
530 |        "    <tr>\n",
531 |        "      <th>PG</th>\n",
532 |        "      <td>0.029871</td>\n",
533 |        "      <td>0.021632</td>\n",
534 |        "    </tr>\n",
535 |        "    <tr>\n",
536 |        "      <th>MSFT</th>\n",
537 |        "      <td>0.021632</td>\n",
538 |        "      <td>0.073924</td>\n",
539 |        "    </tr>\n",
540 |        "  </tbody>\n",
541 |        "</table>\n",
542 |        "</div>"
543 |       ],
544 |       "text/plain": [
545 |        "            PG      MSFT\n",
546 |        "PG    0.029871  0.021632\n",
547 |        "MSFT  0.021632  0.073924"
548 |       ]
549 |      },
550 |      "execution_count": 23,
551 |      "metadata": {},
552 |      "output_type": "execute_result"
553 |     }
554 |    ],
555 |    "source": [
556 |     "cov_matrix = sec_returns.cov() *250\n",
557 |     "cov_matrix"
558 |    ]
559 |   },
560 |   {
561 |    "cell_type": "code",
562 |    "execution_count": 24,
563 |    "metadata": {},
564 |    "outputs": [
565 |     {
566 |      "data": {
567 |       "text/html": [
568 |        "<div>\n",
569 |        "<style scoped>\n",
570 |        "    .dataframe tbody tr th:only-of-type {\n",
571 |        "        vertical-align: middle;\n",
572 |        "    }\n",
573 |        "\n",
574 |        "    .dataframe tbody tr th {\n",
575 |        "        vertical-align: top;\n",
576 |        "    }\n",
577 |        "\n",
578 |        "    .dataframe thead th {\n",
579 |        "        text-align: right;\n",
580 |        "    }\n",
581 |        "</style>\n",
582 |        "<table border=\"1\" class=\"dataframe\">\n",
583 |        "  <thead>\n",
584 |        "    <tr style=\"text-align: right;\">\n",
585 |        "      <th></th>\n",
586 |        "      <th>PG</th>\n",
587 |        "      <th>MSFT</th>\n",
588 |        "    </tr>\n",
589 |        "  </thead>\n",
590 |        "  <tbody>\n",
591 |        "    <tr>\n",
592 |        "      <th>PG</th>\n",
593 |        "      <td>1.000000</td>\n",
594 |        "      <td>0.460345</td>\n",
595 |        "    </tr>\n",
596 |        "    <tr>\n",
597 |        "      <th>MSFT</th>\n",
598 |        "      <td>0.460345</td>\n",
599 |        "      <td>1.000000</td>\n",
600 |        "    </tr>\n",
601 |        "  </tbody>\n",
602 |        "</table>\n",
603 |        "</div>"
604 |       ],
605 |       "text/plain": [
606 |        "            PG      MSFT\n",
607 |        "PG    1.000000  0.460345\n",
608 |        "MSFT  0.460345  1.000000"
609 |       ]
610 |      },
611 |      "execution_count": 24,
612 |      "metadata": {},
613 |      "output_type": "execute_result"
614 |     }
615 |    ],
616 |    "source": [
617 |     "corr_matrix = sec_returns.corr()\n",
618 |     "corr_matrix"
619 |    ]
620 |   }
621 |  ],
622 |  "metadata": {
623 |   "kernelspec": {
624 |    "display_name": "Python 3",
625 |    "language": "python",
626 |    "name": "python3"
627 |   },
628 |   "language_info": {
629 |    "codemirror_mode": {
630 |     "name": "ipython",
631 |     "version": 3
632 |    },
633 |    "file_extension": ".py",
634 |    "mimetype": "text/x-python",
635 |    "name": "python",
636 |    "nbconvert_exporter": "python",
637 |    "pygments_lexer": "ipython3",
638 |    "version": "3.6.4"
639 |   }
640 |  },
641 |  "nbformat": 4,
642 |  "nbformat_minor": 2
643 | }
644 | 


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