illustrations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_gmv_weights(excess_returns):\n",
    "    cov = excess_returns.cov()\n",
    "    inv_cov = np.linalg.pinv(cov)\n",
    "    ones = np.ones(excess_returns.shape[1])\n",
    "    gmv_weights = inv_cov.dot(ones) / ones.dot(inv_cov).dot(ones)\n",
    "    return gmv_weights\n",
    "\n",
    "def calc_tang_weights(excess_returns):\n",
    "    cov = excess_returns.cov()\n",
    "    inv_cov = np.linalg.pinv(cov)\n",
    "    ones = np.ones(excess_returns.shape[1])\n",
    "    tangency_weights = inv_cov.dot(excess_returns.mean()) / ones.dot(inv_cov).dot(excess_returns.mean())\n",
    "    return tangency_weights\n",
    "\n",
    "def calc_variance(weights, excess_returns):\n",
    "    return weights.dot(excess_returns.cov()).dot(weights)\n",
    "\n",
    "def calc_expected_return(weights, excess_returns):\n",
    "    return weights.dot(excess_returns.mean())\n",
    "\n",
    "def calc_efficient_frontier(excess_returns, min_alpha=-1, max_alpha=1.5, num_points=50):\n",
    "    gmw_weights = calc_gmv_weights(excess_returns)\n",
    "    tang_weights = calc_tang_weights(excess_returns)\n",
    "    efficient_frontier = {}\n",
    "    for alpha in np.linspace(min_alpha, max_alpha, num_points):\n",
    "        weights = alpha * tang_weights + (1 - alpha) * gmw_weights\n",
    "        efficient_frontier[alpha] = [calc_variance(weights, excess_returns), calc_expected_return(weights, excess_returns)]\n",
    "    for imp in [0, 1]:\n",
    "        if imp not in efficient_frontier.keys():\n",
    "            weights = imp * tang_weights + (1 - imp) * gmw_weights\n",
    "            efficient_frontier[imp] = [calc_variance(weights, excess_returns), calc_expected_return(weights, excess_returns)]\n",
    "    return (\n",
    "        pd.DataFrame(efficient_frontier)\n",
    "        .transpose()\n",
    "        .set_axis(['variance', 'expected_return'], axis=1)\n",
    "        .assign(volatility=lambda df: df['variance'].apply(np.sqrt))\n",
    "        .loc[:, ['expected_return', 'volatility']]\n",
    "        .assign(volatility=lambda df: df['volatility'] * np.sqrt(252))\n",
    "        .sort_index()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Agric</th>\n",
       "      <th>Food</th>\n",
       "      <th>Soda</th>\n",
       "      <th>Beer</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>Toys</th>\n",
       "      <th>Fun</th>\n",
       "      <th>Books</th>\n",
       "      <th>Hshld</th>\n",
       "      <th>Clths</th>\n",
       "      <th>...</th>\n",
       "      <th>Boxes</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Whlsl</th>\n",
       "      <th>Rtail</th>\n",
       "      <th>Meals</th>\n",
       "      <th>Banks</th>\n",
       "      <th>Insur</th>\n",
       "      <th>RlEst</th>\n",
       "      <th>Fin</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.0341</td>\n",
       "      <td>-0.0237</td>\n",
       "      <td>-0.0052</td>\n",
       "      <td>-0.0116</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>-0.0057</td>\n",
       "      <td>-0.0168</td>\n",
       "      <td>-0.0255</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-0.0241</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0174</td>\n",
       "      <td>-0.0085</td>\n",
       "      <td>-0.0065</td>\n",
       "      <td>-0.0257</td>\n",
       "      <td>-0.0212</td>\n",
       "      <td>-0.0460</td>\n",
       "      <td>-0.0307</td>\n",
       "      <td>-0.0237</td>\n",
       "      <td>-0.0417</td>\n",
       "      <td>-0.0073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.0268</td>\n",
       "      <td>-0.0227</td>\n",
       "      <td>-0.0219</td>\n",
       "      <td>-0.0111</td>\n",
       "      <td>0.0064</td>\n",
       "      <td>-0.0303</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>-0.0275</td>\n",
       "      <td>-0.0210</td>\n",
       "      <td>-0.0361</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0184</td>\n",
       "      <td>-0.0257</td>\n",
       "      <td>-0.0317</td>\n",
       "      <td>-0.0346</td>\n",
       "      <td>-0.0182</td>\n",
       "      <td>-0.0385</td>\n",
       "      <td>-0.0361</td>\n",
       "      <td>-0.0131</td>\n",
       "      <td>-0.0460</td>\n",
       "      <td>-0.0213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.0300</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0133</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0217</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-0.0142</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0208</td>\n",
       "      <td>-0.0097</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>-0.0038</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>-0.0209</td>\n",
       "      <td>0.0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0369</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>-0.0128</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0181</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0269</td>\n",
       "      <td>-0.0045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0170</td>\n",
       "      <td>0.0306</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.0073</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>0.0200</td>\n",
       "      <td>-0.0021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0525</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>-0.0127</td>\n",
       "      <td>0.0540</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0060</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.0479</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>0.0426</td>\n",
       "      <td>0.0141</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-23</th>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>-0.0034</td>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>-0.0160</td>\n",
       "      <td>-0.0157</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0057</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0086</td>\n",
       "      <td>-0.0013</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>-0.0091</td>\n",
       "      <td>-0.0059</td>\n",
       "      <td>-0.0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>-0.0189</td>\n",
       "      <td>-0.0127</td>\n",
       "      <td>-0.0098</td>\n",
       "      <td>-0.0079</td>\n",
       "      <td>-0.0158</td>\n",
       "      <td>-0.0149</td>\n",
       "      <td>-0.0281</td>\n",
       "      <td>-0.0173</td>\n",
       "      <td>-0.0128</td>\n",
       "      <td>-0.0230</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0201</td>\n",
       "      <td>-0.0174</td>\n",
       "      <td>-0.0150</td>\n",
       "      <td>-0.0130</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0083</td>\n",
       "      <td>-0.0182</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>-0.0083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>-0.0014</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0018</td>\n",
       "      <td>0.0387</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0184</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0147</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0151</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>-0.0015</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>-0.0054</td>\n",
       "      <td>-0.0078</td>\n",
       "      <td>-0.0010</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>-0.0017</td>\n",
       "      <td>-0.0074</td>\n",
       "      <td>0.0011</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>-0.0081</td>\n",
       "      <td>-0.0056</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>-0.0031</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0045</td>\n",
       "      <td>-0.0016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5787 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Agric   Food    Soda    Beer    Smoke   Toys    Fun     Books  \\\n",
       "date                                                                         \n",
       "2000-01-03 -0.0341 -0.0237 -0.0052 -0.0116  0.0167 -0.0057 -0.0168 -0.0255   \n",
       "2000-01-04 -0.0268 -0.0227 -0.0219 -0.0111  0.0064 -0.0303  0.0027 -0.0275   \n",
       "2000-01-05  0.0300 -0.0003  0.0112  0.0133 -0.0041 -0.0217 -0.0021 -0.0220   \n",
       "2000-01-06  0.0024  0.0099  0.0369  0.0129 -0.0128  0.0003 -0.0181  0.0027   \n",
       "2000-01-07  0.0277  0.0118  0.0149  0.0525  0.0275  0.0071  0.0085 -0.0127   \n",
       "...            ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2022-12-23  0.0085  0.0053  0.0073  0.0057  0.0089 -0.0021 -0.0034  0.0187   \n",
       "2022-12-27  0.0042  0.0052  0.0055  0.0035  0.0081 -0.0160 -0.0157 -0.0043   \n",
       "2022-12-28 -0.0189 -0.0127 -0.0098 -0.0079 -0.0158 -0.0149 -0.0281 -0.0173   \n",
       "2022-12-29 -0.0014  0.0029  0.0063  0.0039  0.0018  0.0387  0.0363  0.0184   \n",
       "2022-12-30 -0.0015 -0.0032 -0.0054 -0.0078 -0.0010 -0.0016  0.0103 -0.0017   \n",
       "\n",
       "             Hshld   Clths  ...   Boxes   Trans   Whlsl   Rtail   Meals  \\\n",
       "date                        ...                                           \n",
       "2000-01-03 -0.0275 -0.0241  ... -0.0174 -0.0085 -0.0065 -0.0257 -0.0212   \n",
       "2000-01-04 -0.0210 -0.0361  ... -0.0184 -0.0257 -0.0317 -0.0346 -0.0182   \n",
       "2000-01-05 -0.0142  0.0196  ...  0.0155  0.0031  0.0208 -0.0097  0.0119   \n",
       "2000-01-06  0.0269 -0.0045  ...  0.0170  0.0306  0.0315 -0.0014 -0.0073   \n",
       "2000-01-07  0.0540  0.0108  ... -0.0060  0.0112  0.0282  0.0479  0.0150   \n",
       "...            ...     ...  ...     ...     ...     ...     ...     ...   \n",
       "2022-12-23  0.0029 -0.0012  ...  0.0126  0.0075  0.0057  0.0116  0.0053   \n",
       "2022-12-27  0.0089  0.0134  ...  0.0057 -0.0025 -0.0016 -0.0086 -0.0013   \n",
       "2022-12-28 -0.0128 -0.0230  ... -0.0201 -0.0174 -0.0150 -0.0130 -0.0100   \n",
       "2022-12-29  0.0090  0.0239  ...  0.0147  0.0158  0.0101  0.0167  0.0110   \n",
       "2022-12-30 -0.0074  0.0011  ... -0.0067 -0.0081 -0.0056 -0.0037 -0.0067   \n",
       "\n",
       "             Banks   Insur   RlEst   Fin     Other  \n",
       "date                                                \n",
       "2000-01-03 -0.0460 -0.0307 -0.0237 -0.0417 -0.0073  \n",
       "2000-01-04 -0.0385 -0.0361 -0.0131 -0.0460 -0.0213  \n",
       "2000-01-05 -0.0038  0.0032  0.0121 -0.0209  0.0007  \n",
       "2000-01-06  0.0400  0.0238 -0.0063  0.0200 -0.0021  \n",
       "2000-01-07  0.0117  0.0426  0.0141  0.0130  0.0045  \n",
       "...            ...     ...     ...     ...     ...  \n",
       "2022-12-23  0.0057  0.0065  0.0091  0.0046  0.0095  \n",
       "2022-12-27  0.0024  0.0014 -0.0091 -0.0059 -0.0003  \n",
       "2022-12-28 -0.0041 -0.0083 -0.0182 -0.0078 -0.0083  \n",
       "2022-12-29  0.0151  0.0071  0.0277  0.0164  0.0167  \n",
       "2022-12-30 -0.0004 -0.0031 -0.0019 -0.0045 -0.0016  \n",
       "\n",
       "[5787 rows x 49 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_raw = pd.read_csv(\"data/industry_portfolios_49_daily.csv\", parse_dates=True, index_col=0)\n",
    "data = data_raw.loc[\"2000-01-01\":\"2023-01-01\"]\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expected_return</th>\n",
       "      <th>volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.000000</th>\n",
       "      <td>-0.000870</td>\n",
       "      <td>0.270883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.948980</th>\n",
       "      <td>-0.000808</td>\n",
       "      <td>0.260241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.897959</th>\n",
       "      <td>-0.000745</td>\n",
       "      <td>0.249739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.846939</th>\n",
       "      <td>-0.000682</td>\n",
       "      <td>0.239393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.795918</th>\n",
       "      <td>-0.000620</td>\n",
       "      <td>0.229226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           expected_return  volatility\n",
       "-1.000000        -0.000870    0.270883\n",
       "-0.948980        -0.000808    0.260241\n",
       "-0.897959        -0.000745    0.249739\n",
       "-0.846939        -0.000682    0.239393\n",
       "-0.795918        -0.000620    0.229226"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "efficient_frontier = calc_efficient_frontier(data)\n",
    "efficient_frontier.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "plt.axhline(y=0, linestyle='--', color='black', alpha=0.5) \n",
    "plt.axvline(x=0, linestyle='--', color='black', alpha=0.5)\n",
    "plt.plot(\n",
    "    efficient_frontier[\"volatility\"].loc[lambda df: df.index >= 0], efficient_frontier['expected_return'].loc[lambda df: df.index >= 0],\n",
    "    linestyle='-', color='black', label=\"Efficient Frontier\"\n",
    ")\n",
    "plt.plot(\n",
    "    efficient_frontier[\"volatility\"].loc[lambda df: df.index <= 0], efficient_frontier['expected_return'].loc[lambda df: df.index <= 0],\n",
    "    linestyle='--', color='black', label='Alpha < 0'\n",
    ")\n",
    "plt.plot(\n",
    "    efficient_frontier[\"volatility\"].loc[lambda df: df.index == 0],\n",
    "    efficient_frontier['expected_return'].loc[lambda df: df.index == 0],\n",
    "    linestyle='', marker='x', color='black', label='GMV Portfolio', markersize=10\n",
    ")\n",
    "plt.plot(\n",
    "    efficient_frontier[\"volatility\"].loc[lambda df: df.index == 1],\n",
    "    efficient_frontier['expected_return'].loc[lambda df: df.index == 1],\n",
    "    linestyle='', marker='o', color='black', label='Tangency Portfolio', markersize=10\n",
    ")\n",
    "plt.title('Efficient Frontier')\n",
    "plt.ylabel('Expected Return')\n",
    "plt.gca().yaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1))\n",
    "plt.xlabel('Annualized Volatility')\n",
    "plt.gca().xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1))\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('reports/graphics/report_efficient_frontier.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_ASSETS = 10\n",
    "np.random.seed(42)\n",
    "std_norm = {}\n",
    "for var in np.linspace(0, .05, 10_000):\n",
    "    weights = np.random.normal(1 / N_ASSETS, var, N_ASSETS)\n",
    "    weights /= weights.sum()\n",
    "    std_norm[weights.std()] = np.linalg.norm(weights, ord=2)\n",
    "std_norm_plot = (\n",
    "    pd.DataFrame(std_norm, index=[\"norm\"])\n",
    "    .transpose()\n",
    "    .sort_index()\n",
    ")\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.plot(\n",
    "    std_norm_plot.iloc[0].index, std_norm_plot[\"norm\"].iloc[0],\n",
    "    linestyle='', color='black', markersize=15,\n",
    "    label='Norm for $w_i = \\\\frac{1}{n}, \\\\quad  i = 1, \\\\ldots, 10$',\n",
    "    marker=(8, 1, 0),\n",
    ")\n",
    "plt.plot(\n",
    "    std_norm_plot.index, std_norm_plot[\"norm\"],\n",
    "    linestyle='-', color='black'\n",
    ")\n",
    "plt.title('Euclidean Norm ($\\\\| w \\\\|_2$) given Standard Deviation of Weights (Example with $n=10$)')\n",
    "plt.ylabel('Euclidean Norm')\n",
    "plt.xlabel('Standard Deviation of Weights')\n",
    "plt.xticks(np.linspace(0, .11, 12))\n",
    "plt.gca().xaxis.set_major_formatter(plt.matplotlib.ticker.PercentFormatter(1))\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('reports/graphics/report_euclidean_norm.png')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "blank",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}