ind49_weighting_strategies.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "369a0553",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from Index_weighting_functions import FinancialMetrics, PortfolioOptimization, PortfolioWeighting, PortfolioAnalysis, PortfolioStrategy, KenFrenchIndustryPortfolios\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "154354f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_portfolios = KenFrenchIndustryPortfolios(n_inds=49)\n",
    "ind49_rets = kf_portfolios.get_ind_returns(weighting=\"vw\")[\"1974\":]\n",
    "ind49_mcap = kf_portfolios.get_ind_market_caps(weights=True)[\"1974\":]\n",
    "cov = ind49_rets.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6a7df19",
   "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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-01</th>\n",
       "      <td>0.0229</td>\n",
       "      <td>0.0615</td>\n",
       "      <td>-0.0540</td>\n",
       "      <td>-0.0083</td>\n",
       "      <td>0.0301</td>\n",
       "      <td>0.0899</td>\n",
       "      <td>0.0858</td>\n",
       "      <td>-0.0101</td>\n",
       "      <td>-0.0260</td>\n",
       "      <td>0.0965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0552</td>\n",
       "      <td>0.0776</td>\n",
       "      <td>0.0144</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>-0.0162</td>\n",
       "      <td>0.1311</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-02</th>\n",
       "      <td>0.1382</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>-0.0236</td>\n",
       "      <td>-0.0174</td>\n",
       "      <td>0.0185</td>\n",
       "      <td>-0.0257</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>-0.0528</td>\n",
       "      <td>-0.0342</td>\n",
       "      <td>0.0230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0787</td>\n",
       "      <td>0.0239</td>\n",
       "      <td>0.0137</td>\n",
       "      <td>-0.0477</td>\n",
       "      <td>0.0165</td>\n",
       "      <td>-0.0219</td>\n",
       "      <td>-0.0249</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>-0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-03</th>\n",
       "      <td>-0.0157</td>\n",
       "      <td>-0.0156</td>\n",
       "      <td>-0.0406</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>-0.0600</td>\n",
       "      <td>0.0517</td>\n",
       "      <td>0.0361</td>\n",
       "      <td>0.0579</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>-0.0158</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0151</td>\n",
       "      <td>-0.0485</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>-0.0452</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>-0.0438</td>\n",
       "      <td>-0.0453</td>\n",
       "      <td>-0.0216</td>\n",
       "      <td>-0.0399</td>\n",
       "      <td>-0.0306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-04</th>\n",
       "      <td>-0.0926</td>\n",
       "      <td>-0.0265</td>\n",
       "      <td>-0.0415</td>\n",
       "      <td>-0.0366</td>\n",
       "      <td>-0.0277</td>\n",
       "      <td>-0.0749</td>\n",
       "      <td>-0.0796</td>\n",
       "      <td>-0.0646</td>\n",
       "      <td>-0.0419</td>\n",
       "      <td>-0.0477</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0269</td>\n",
       "      <td>-0.0393</td>\n",
       "      <td>-0.0194</td>\n",
       "      <td>-0.0206</td>\n",
       "      <td>-0.0296</td>\n",
       "      <td>-0.0510</td>\n",
       "      <td>-0.0919</td>\n",
       "      <td>-0.1227</td>\n",
       "      <td>-0.0684</td>\n",
       "      <td>-0.0535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-05</th>\n",
       "      <td>-0.0703</td>\n",
       "      <td>-0.0488</td>\n",
       "      <td>-0.0115</td>\n",
       "      <td>-0.0448</td>\n",
       "      <td>0.0559</td>\n",
       "      <td>0.0238</td>\n",
       "      <td>-0.0089</td>\n",
       "      <td>-0.0133</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>-0.0691</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0130</td>\n",
       "      <td>-0.0469</td>\n",
       "      <td>-0.0390</td>\n",
       "      <td>0.0220</td>\n",
       "      <td>-0.0305</td>\n",
       "      <td>-0.1089</td>\n",
       "      <td>-0.0882</td>\n",
       "      <td>-0.1146</td>\n",
       "      <td>-0.0998</td>\n",
       "      <td>-0.0677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Agric    Food    Soda    Beer   Smoke    Toys     Fun   Books  \\\n",
       "1974-01  0.0229  0.0615 -0.0540 -0.0083  0.0301  0.0899  0.0858 -0.0101   \n",
       "1974-02  0.1382  0.0140 -0.0236 -0.0174  0.0185 -0.0257 -0.0032 -0.0528   \n",
       "1974-03 -0.0157 -0.0156 -0.0406 -0.0220 -0.0600  0.0517  0.0361  0.0579   \n",
       "1974-04 -0.0926 -0.0265 -0.0415 -0.0366 -0.0277 -0.0749 -0.0796 -0.0646   \n",
       "1974-05 -0.0703 -0.0488 -0.0115 -0.0448  0.0559  0.0238 -0.0089 -0.0133   \n",
       "\n",
       "          Hshld   Clths  ...   Boxes   Trans   Whlsl   Rtail   Meals   Banks  \\\n",
       "1974-01 -0.0260  0.0965  ...  0.0050  0.0048  0.0552  0.0776  0.0144  0.0014   \n",
       "1974-02 -0.0342  0.0230  ...  0.0156  0.0787  0.0239  0.0137 -0.0477  0.0165   \n",
       "1974-03 -0.0023 -0.0158  ... -0.0151 -0.0485  0.0159 -0.0452  0.0373 -0.0438   \n",
       "1974-04 -0.0419 -0.0477  ... -0.0269 -0.0393 -0.0194 -0.0206 -0.0296 -0.0510   \n",
       "1974-05  0.0102 -0.0691  ... -0.0130 -0.0469 -0.0390  0.0220 -0.0305 -0.1089   \n",
       "\n",
       "          Insur   RlEst     Fin   Other  \n",
       "1974-01 -0.0162  0.1311  0.0083  0.0102  \n",
       "1974-02 -0.0219 -0.0249  0.0129 -0.0100  \n",
       "1974-03 -0.0453 -0.0216 -0.0399 -0.0306  \n",
       "1974-04 -0.0919 -0.1227 -0.0684 -0.0535  \n",
       "1974-05 -0.0882 -0.1146 -0.0998 -0.0677  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind49_rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4897f92f",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-01</th>\n",
       "      <td>0.000757</td>\n",
       "      <td>0.023874</td>\n",
       "      <td>0.014288</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.008435</td>\n",
       "      <td>0.004557</td>\n",
       "      <td>0.002918</td>\n",
       "      <td>0.006314</td>\n",
       "      <td>0.087971</td>\n",
       "      <td>0.003453</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016800</td>\n",
       "      <td>0.014330</td>\n",
       "      <td>0.005279</td>\n",
       "      <td>0.046936</td>\n",
       "      <td>0.006213</td>\n",
       "      <td>0.031837</td>\n",
       "      <td>0.020093</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.040329</td>\n",
       "      <td>0.044887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-02</th>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.025248</td>\n",
       "      <td>0.013489</td>\n",
       "      <td>0.006172</td>\n",
       "      <td>0.008666</td>\n",
       "      <td>0.004958</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>0.006053</td>\n",
       "      <td>0.085401</td>\n",
       "      <td>0.003770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016839</td>\n",
       "      <td>0.014344</td>\n",
       "      <td>0.005555</td>\n",
       "      <td>0.050377</td>\n",
       "      <td>0.006280</td>\n",
       "      <td>0.031768</td>\n",
       "      <td>0.019675</td>\n",
       "      <td>0.002004</td>\n",
       "      <td>0.040422</td>\n",
       "      <td>0.045171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-03</th>\n",
       "      <td>0.000883</td>\n",
       "      <td>0.025604</td>\n",
       "      <td>0.013220</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>0.008796</td>\n",
       "      <td>0.004838</td>\n",
       "      <td>0.003161</td>\n",
       "      <td>0.005738</td>\n",
       "      <td>0.082550</td>\n",
       "      <td>0.003859</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017098</td>\n",
       "      <td>0.015476</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>0.051104</td>\n",
       "      <td>0.006005</td>\n",
       "      <td>0.032384</td>\n",
       "      <td>0.019254</td>\n",
       "      <td>0.001954</td>\n",
       "      <td>0.040982</td>\n",
       "      <td>0.044799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-04</th>\n",
       "      <td>0.000891</td>\n",
       "      <td>0.025714</td>\n",
       "      <td>0.012954</td>\n",
       "      <td>0.006074</td>\n",
       "      <td>0.008467</td>\n",
       "      <td>0.005081</td>\n",
       "      <td>0.003358</td>\n",
       "      <td>0.006207</td>\n",
       "      <td>0.084434</td>\n",
       "      <td>0.003871</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017267</td>\n",
       "      <td>0.015057</td>\n",
       "      <td>0.005921</td>\n",
       "      <td>0.049980</td>\n",
       "      <td>0.006376</td>\n",
       "      <td>0.031559</td>\n",
       "      <td>0.018815</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.040202</td>\n",
       "      <td>0.044434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-05</th>\n",
       "      <td>0.000846</td>\n",
       "      <td>0.026251</td>\n",
       "      <td>0.013041</td>\n",
       "      <td>0.006144</td>\n",
       "      <td>0.008643</td>\n",
       "      <td>0.004822</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.006097</td>\n",
       "      <td>0.084895</td>\n",
       "      <td>0.003852</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017598</td>\n",
       "      <td>0.015178</td>\n",
       "      <td>0.006092</td>\n",
       "      <td>0.051341</td>\n",
       "      <td>0.006481</td>\n",
       "      <td>0.031371</td>\n",
       "      <td>0.017868</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.039215</td>\n",
       "      <td>0.044104</td>\n",
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       "<p>5 rows × 49 columns</p>\n",
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      "text/plain": [
       "            Agric      Food      Soda      Beer     Smoke      Toys       Fun  \\\n",
       "1974-01  0.000757  0.023874  0.014288  0.006235  0.008435  0.004557  0.002918   \n",
       "1974-02  0.000773  0.025248  0.013489  0.006172  0.008666  0.004958  0.003160   \n",
       "1974-03  0.000883  0.025604  0.013220  0.006077  0.008796  0.004838  0.003161   \n",
       "1974-04  0.000891  0.025714  0.012954  0.006074  0.008467  0.005081  0.003358   \n",
       "1974-05  0.000846  0.026251  0.013041  0.006144  0.008643  0.004822  0.003241   \n",
       "\n",
       "            Books     Hshld     Clths  ...     Boxes     Trans     Whlsl  \\\n",
       "1974-01  0.006314  0.087971  0.003453  ...  0.016800  0.014330  0.005279   \n",
       "1974-02  0.006053  0.085401  0.003770  ...  0.016839  0.014344  0.005555   \n",
       "1974-03  0.005738  0.082550  0.003859  ...  0.017098  0.015476  0.005700   \n",
       "1974-04  0.006207  0.084434  0.003871  ...  0.017267  0.015057  0.005921   \n",
       "1974-05  0.006097  0.084895  0.003852  ...  0.017598  0.015178  0.006092   \n",
       "\n",
       "            Rtail     Meals     Banks     Insur     RlEst       Fin     Other  \n",
       "1974-01  0.046936  0.006213  0.031837  0.020093  0.001807  0.040329  0.044887  \n",
       "1974-02  0.050377  0.006280  0.031768  0.019675  0.002004  0.040422  0.045171  \n",
       "1974-03  0.051104  0.006005  0.032384  0.019254  0.001954  0.040982  0.044799  \n",
       "1974-04  0.049980  0.006376  0.031559  0.018815  0.001956  0.040202  0.044434  \n",
       "1974-05  0.051341  0.006481  0.031371  0.017868  0.001795  0.039215  0.044104  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind49_mcap.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec631cd",
   "metadata": {},
   "source": [
    "## Risk Parity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea3b2b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "PortfolioOptimization.risk_contribution(PortfolioWeighting.weight_ew(ind49_rets), cov).plot.bar(title=\"Risk Contributions of an EW portfolio\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d44f669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "PortfolioOptimization.risk_contribution(PortfolioOptimization.equal_risk_contributions(cov), cov).plot.bar(title=\"Risk Contributions of an ERC portfolio\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58989a2a",
   "metadata": {},
   "source": [
    "## K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9494d6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_transposed = ind49_rets.transpose().dropna()\n",
    "scaler = StandardScaler()\n",
    "r_scaled = scaler.fit_transform(r_transposed)\n",
    "sse = {}\n",
    "silhouette_coefficients = []\n",
    "K_range = range(2, 11)\n",
    "\n",
    "for k in K_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(r_scaled)\n",
    "    sse[k] = kmeans.inertia_\n",
    "    score = silhouette_score(r_scaled, kmeans.labels_)\n",
    "    silhouette_coefficients.append(score)\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(list(sse.keys()), list(sse.values()), 'bo-')\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"SSE (Inertia)\")\n",
    "plt.title(\"Elbow Method for Optimal Number of Clusters\")\n",
    "plt.xticks(K_range)\n",
    "plt.grid(True)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(K_range, silhouette_coefficients, 'bo-')\n",
    "plt.xticks(K_range)\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"Silhouette Coefficient\")\n",
    "plt.title(\"Silhouette Score for Optimal Number of Clusters\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca2da6d8",
   "metadata": {},
   "source": [
    "## Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c9fd0e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(r_scaled, method='ward')\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "dendrogram(Z, labels=r_transposed.index, leaf_rotation=90, leaf_font_size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0a32fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "max_d = 7\n",
    "clusters = fcluster(Z, max_d, criterion='distance')\n",
    "asset_cluster = pd.DataFrame({'asset': r_transposed.index, 'cluster': clusters})\n",
    "\n",
    "#print(asset_cluster.sort_values('cluster'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4b3a849",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 6\n",
    "clusters = fcluster(Z, k, criterion='maxclust')\n",
    "asset_cluster_k = pd.DataFrame({'asset': r_transposed.index, 'cluster': clusters})\n",
    "\n",
    "#print(asset_cluster_k.sort_values('cluster'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8544df65",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2de56aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA().fit(r_scaled)\n",
    "cumulative_explained_variance = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(cumulative_explained_variance)\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Cumulative Explained Variance as a Function of the Number of Components')\n",
    "plt.axhline(y=0.9, color='r', linestyle='--')\n",
    "plt.axhline(y=0.95, color='g', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8286392e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of components for 90% explained variance: 31\n"
     ]
    }
   ],
   "source": [
    "for i, cum_var in enumerate(cumulative_explained_variance):\n",
    "    if cum_var >= 0.9:\n",
    "        print(f\"Number of components for 90% explained variance: {i+1}\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482ce73f",
   "metadata": {},
   "source": [
    "## Strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1dce6ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "strategies = {\n",
    "    \"CW\": lambda r: PortfolioWeighting.weight_cw(r, cap_weights=ind49_mcap),\n",
    "    \"EW\": lambda r: PortfolioWeighting.weight_ew(r),\n",
    "    \"EW-Tethered\": lambda r: PortfolioWeighting.weight_ew(r, cap_weights=ind49_mcap, max_cw_mult=5, microcap_threshold=.019),\n",
    "    \"GMV\": lambda r: PortfolioWeighting.weight_gmv(r, cov_estimator=PortfolioOptimization.sample_cov),\n",
    "    \"GMV-CC\": lambda r: PortfolioWeighting.weight_gmv(r, cov_estimator=PortfolioOptimization.cc_cov),\n",
    "    \"GMV-SC\": lambda r: PortfolioWeighting.weight_gmv(r, cov_estimator=PortfolioOptimization.shrinkage_cov, delta=0.8),\n",
    "    \"ERC\": lambda r: PortfolioWeighting.weight_erc(r, cov_estimator=PortfolioOptimization.sample_cov),\n",
    "    \"ERC-A\": lambda r: PortfolioWeighting.weight_erc(r, cov_estimator=PortfolioOptimization.cc_cov),\n",
    "    \"ERC-B\": lambda r: PortfolioWeighting.weight_erc(r, cov_estimator=PortfolioOptimization.shrinkage_cov, delta=0.4),\n",
    "    \"MCA\": lambda r: PortfolioWeighting.weight_min_corr(r),\n",
    "    \"MaxDiv\": lambda r: PortfolioWeighting.weight_max_div(r),\n",
    "    \"MOM\": lambda r: PortfolioWeighting.weight_mom(r, lookback_period=12),\n",
    "    \"K-Means\": lambda r: PortfolioWeighting.weight_kmeans(r, n_clusters=6),\n",
    "    \"Hier Clust\": lambda r: PortfolioWeighting.weight_hier(r, n_clusters=27),\n",
    "    \"HRP\": lambda r: PortfolioWeighting.weight_hrp(r),\n",
    "    \"PCA\": lambda r: PortfolioWeighting.weight_pca(r, n_components=31),\n",
    "    \"Tail Risk\": lambda r: PortfolioWeighting.weight_trp(r, alpha=0.05),\n",
    "    \"Wavelet\": lambda r: PortfolioWeighting.weight_wavelet(r, wavelet_name='bior3.5', max_level=4),\n",
    "    \"Algo\": lambda r: PortfolioWeighting.weight_algo(r),\n",
    "    \"Random Forest\": lambda r: PortfolioWeighting.weight_rf(r, lookback_periods=24),\n",
    "    \"MSR\": lambda r: PortfolioWeighting.weight_msr(r, risk_free_rate=0.03, periods_per_year=12)\n",
    "}\n",
    "\n",
    "portfolio_strategy = PortfolioStrategy(ind49_rets)\n",
    "for name, strategy in strategies.items():\n",
    "    wts, btr = portfolio_strategy.add_strategy(name, strategy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e603cba2",
   "metadata": {},
   "source": [
    "## Summary Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f481b7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annualized Return</th>\n",
       "      <th>Annualized Volatility</th>\n",
       "      <th>Semi-Deviation</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Kurtosis</th>\n",
       "      <th>Historic VaR (5%)</th>\n",
       "      <th>Cornish-Fisher VaR (5%)</th>\n",
       "      <th>Historic CVaR (5%)</th>\n",
       "      <th>Return on VaR</th>\n",
       "      <th>Sharpe Ratio</th>\n",
       "      <th>Sortino Ratio</th>\n",
       "      <th>Calmar Ratio</th>\n",
       "      <th>Modified Burke Ratio</th>\n",
       "      <th>Max Drawdown</th>\n",
       "      <th>Net Profit to Worst Drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CW</th>\n",
       "      <td>0.1173</td>\n",
       "      <td>0.1512</td>\n",
       "      <td>0.1143</td>\n",
       "      <td>-0.7435</td>\n",
       "      <td>5.3467</td>\n",
       "      <td>0.0706</td>\n",
       "      <td>0.0682</td>\n",
       "      <td>0.0978</td>\n",
       "      <td>1.6625</td>\n",
       "      <td>0.5619</td>\n",
       "      <td>0.7400</td>\n",
       "      <td>0.1695</td>\n",
       "      <td>0.6734</td>\n",
       "      <td>-0.5012</td>\n",
       "      <td>166.5793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EW</th>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.1608</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>-0.7571</td>\n",
       "      <td>6.6450</td>\n",
       "      <td>0.0635</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>1.9483</td>\n",
       "      <td>0.5665</td>\n",
       "      <td>0.7383</td>\n",
       "      <td>0.1724</td>\n",
       "      <td>0.9339</td>\n",
       "      <td>-0.5283</td>\n",
       "      <td>198.5728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EW-Tethered</th>\n",
       "      <td>0.1256</td>\n",
       "      <td>0.1499</td>\n",
       "      <td>0.1113</td>\n",
       "      <td>-0.6703</td>\n",
       "      <td>5.1883</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.0662</td>\n",
       "      <td>0.0952</td>\n",
       "      <td>1.8826</td>\n",
       "      <td>0.6206</td>\n",
       "      <td>0.8304</td>\n",
       "      <td>0.1871</td>\n",
       "      <td>0.8953</td>\n",
       "      <td>-0.4973</td>\n",
       "      <td>226.5880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMV</th>\n",
       "      <td>0.1141</td>\n",
       "      <td>0.1172</td>\n",
       "      <td>0.0878</td>\n",
       "      <td>-0.6230</td>\n",
       "      <td>5.6475</td>\n",
       "      <td>0.0425</td>\n",
       "      <td>0.0499</td>\n",
       "      <td>0.0731</td>\n",
       "      <td>2.6854</td>\n",
       "      <td>0.6982</td>\n",
       "      <td>0.9336</td>\n",
       "      <td>0.2142</td>\n",
       "      <td>1.0812</td>\n",
       "      <td>-0.3820</td>\n",
       "      <td>194.4157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMV-CC</th>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.1181</td>\n",
       "      <td>0.0854</td>\n",
       "      <td>-0.4760</td>\n",
       "      <td>4.5199</td>\n",
       "      <td>0.0466</td>\n",
       "      <td>0.0492</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>2.6373</td>\n",
       "      <td>0.7658</td>\n",
       "      <td>1.0595</td>\n",
       "      <td>0.2573</td>\n",
       "      <td>1.4057</td>\n",
       "      <td>-0.3516</td>\n",
       "      <td>291.3344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMV-SC</th>\n",
       "      <td>0.1223</td>\n",
       "      <td>0.1173</td>\n",
       "      <td>0.0846</td>\n",
       "      <td>-0.4609</td>\n",
       "      <td>4.5612</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>0.0486</td>\n",
       "      <td>0.0705</td>\n",
       "      <td>2.6299</td>\n",
       "      <td>0.7662</td>\n",
       "      <td>1.0614</td>\n",
       "      <td>0.2515</td>\n",
       "      <td>1.3885</td>\n",
       "      <td>-0.3572</td>\n",
       "      <td>280.1532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERC</th>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>0.1177</td>\n",
       "      <td>-0.8425</td>\n",
       "      <td>6.8759</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.0667</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>2.1236</td>\n",
       "      <td>0.6080</td>\n",
       "      <td>0.7753</td>\n",
       "      <td>0.1814</td>\n",
       "      <td>0.9886</td>\n",
       "      <td>-0.5020</td>\n",
       "      <td>208.8619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERC-A</th>\n",
       "      <td>0.1248</td>\n",
       "      <td>0.1540</td>\n",
       "      <td>0.1204</td>\n",
       "      <td>-0.8245</td>\n",
       "      <td>6.7422</td>\n",
       "      <td>0.0609</td>\n",
       "      <td>0.0687</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>2.0509</td>\n",
       "      <td>0.5989</td>\n",
       "      <td>0.7673</td>\n",
       "      <td>0.1790</td>\n",
       "      <td>0.9740</td>\n",
       "      <td>-0.5155</td>\n",
       "      <td>212.3436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERC-B</th>\n",
       "      <td>0.1245</td>\n",
       "      <td>0.1517</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>-0.8360</td>\n",
       "      <td>6.8117</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>0.0987</td>\n",
       "      <td>2.0877</td>\n",
       "      <td>0.6064</td>\n",
       "      <td>0.7737</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.9879</td>\n",
       "      <td>-0.5076</td>\n",
       "      <td>213.3236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCA</th>\n",
       "      <td>0.1191</td>\n",
       "      <td>0.2683</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.7743</td>\n",
       "      <td>6.5803</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>0.0915</td>\n",
       "      <td>0.1434</td>\n",
       "      <td>1.1308</td>\n",
       "      <td>0.3230</td>\n",
       "      <td>0.5531</td>\n",
       "      <td>0.1405</td>\n",
       "      <td>0.3589</td>\n",
       "      <td>-0.6168</td>\n",
       "      <td>144.6428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxDiv</th>\n",
       "      <td>0.1134</td>\n",
       "      <td>0.1403</td>\n",
       "      <td>0.1038</td>\n",
       "      <td>-0.5563</td>\n",
       "      <td>6.5065</td>\n",
       "      <td>0.0529</td>\n",
       "      <td>0.0600</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>2.1431</td>\n",
       "      <td>0.5781</td>\n",
       "      <td>0.7841</td>\n",
       "      <td>0.1832</td>\n",
       "      <td>0.8941</td>\n",
       "      <td>-0.4427</td>\n",
       "      <td>163.4440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOM</th>\n",
       "      <td>0.1462</td>\n",
       "      <td>0.1742</td>\n",
       "      <td>0.1305</td>\n",
       "      <td>-0.7620</td>\n",
       "      <td>5.6839</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.1106</td>\n",
       "      <td>2.2948</td>\n",
       "      <td>0.6491</td>\n",
       "      <td>0.8684</td>\n",
       "      <td>0.2157</td>\n",
       "      <td>0.9208</td>\n",
       "      <td>-0.5243</td>\n",
       "      <td>446.0811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K-Means</th>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.1799</td>\n",
       "      <td>0.1253</td>\n",
       "      <td>-0.4443</td>\n",
       "      <td>5.8850</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.1063</td>\n",
       "      <td>1.8789</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.7380</td>\n",
       "      <td>0.1859</td>\n",
       "      <td>0.8829</td>\n",
       "      <td>-0.4974</td>\n",
       "      <td>221.9027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hier Clust</th>\n",
       "      <td>0.1204</td>\n",
       "      <td>0.1683</td>\n",
       "      <td>0.1283</td>\n",
       "      <td>-0.6892</td>\n",
       "      <td>6.6308</td>\n",
       "      <td>0.0657</td>\n",
       "      <td>0.0746</td>\n",
       "      <td>0.1080</td>\n",
       "      <td>1.8309</td>\n",
       "      <td>0.5224</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.1609</td>\n",
       "      <td>0.8547</td>\n",
       "      <td>-0.5462</td>\n",
       "      <td>170.7314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HRP</th>\n",
       "      <td>0.1223</td>\n",
       "      <td>0.1705</td>\n",
       "      <td>0.1266</td>\n",
       "      <td>-0.6346</td>\n",
       "      <td>6.1708</td>\n",
       "      <td>0.0616</td>\n",
       "      <td>0.0754</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>1.9858</td>\n",
       "      <td>0.5268</td>\n",
       "      <td>0.7102</td>\n",
       "      <td>0.1705</td>\n",
       "      <td>0.8868</td>\n",
       "      <td>-0.5269</td>\n",
       "      <td>190.1140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCA</th>\n",
       "      <td>0.1241</td>\n",
       "      <td>0.1647</td>\n",
       "      <td>0.1260</td>\n",
       "      <td>-0.7324</td>\n",
       "      <td>6.5116</td>\n",
       "      <td>0.0656</td>\n",
       "      <td>0.0732</td>\n",
       "      <td>0.1067</td>\n",
       "      <td>1.8912</td>\n",
       "      <td>0.5558</td>\n",
       "      <td>0.7280</td>\n",
       "      <td>0.1693</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>-0.5409</td>\n",
       "      <td>197.2866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tail Risk</th>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.1648</td>\n",
       "      <td>0.1210</td>\n",
       "      <td>-0.5521</td>\n",
       "      <td>5.5773</td>\n",
       "      <td>0.0719</td>\n",
       "      <td>0.0710</td>\n",
       "      <td>0.1035</td>\n",
       "      <td>1.9105</td>\n",
       "      <td>0.6338</td>\n",
       "      <td>0.8650</td>\n",
       "      <td>0.2092</td>\n",
       "      <td>1.1954</td>\n",
       "      <td>-0.4993</td>\n",
       "      <td>342.7802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wavelet</th>\n",
       "      <td>0.1292</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>-0.8527</td>\n",
       "      <td>6.6481</td>\n",
       "      <td>0.0568</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.0951</td>\n",
       "      <td>2.2738</td>\n",
       "      <td>0.6593</td>\n",
       "      <td>0.8418</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>1.0998</td>\n",
       "      <td>-0.4852</td>\n",
       "      <td>264.3902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algo</th>\n",
       "      <td>0.1242</td>\n",
       "      <td>0.1610</td>\n",
       "      <td>0.1233</td>\n",
       "      <td>-0.7456</td>\n",
       "      <td>6.6072</td>\n",
       "      <td>0.0634</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.1039</td>\n",
       "      <td>1.9605</td>\n",
       "      <td>0.5694</td>\n",
       "      <td>0.7441</td>\n",
       "      <td>0.1742</td>\n",
       "      <td>0.9445</td>\n",
       "      <td>-0.5264</td>\n",
       "      <td>203.5660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.1236</td>\n",
       "      <td>0.1588</td>\n",
       "      <td>0.1212</td>\n",
       "      <td>-0.6927</td>\n",
       "      <td>6.5378</td>\n",
       "      <td>0.0639</td>\n",
       "      <td>0.0698</td>\n",
       "      <td>0.1021</td>\n",
       "      <td>1.9334</td>\n",
       "      <td>0.5732</td>\n",
       "      <td>0.7522</td>\n",
       "      <td>0.1743</td>\n",
       "      <td>0.9710</td>\n",
       "      <td>-0.5223</td>\n",
       "      <td>200.3870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSR</th>\n",
       "      <td>0.1320</td>\n",
       "      <td>0.1673</td>\n",
       "      <td>0.1141</td>\n",
       "      <td>-0.3600</td>\n",
       "      <td>4.5483</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.0711</td>\n",
       "      <td>0.1012</td>\n",
       "      <td>2.0730</td>\n",
       "      <td>0.5931</td>\n",
       "      <td>0.8690</td>\n",
       "      <td>0.2091</td>\n",
       "      <td>0.8187</td>\n",
       "      <td>-0.4747</td>\n",
       "      <td>298.1032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Annualized Return  Annualized Volatility  Semi-Deviation  \\\n",
       "CW                        0.1173                 0.1512          0.1143   \n",
       "EW                        0.1236                 0.1608          0.1235   \n",
       "EW-Tethered               0.1256                 0.1499          0.1113   \n",
       "GMV                       0.1141                 0.1172          0.0878   \n",
       "GMV-CC                    0.1230                 0.1181          0.0854   \n",
       "GMV-SC                    0.1223                 0.1173          0.0846   \n",
       "ERC                       0.1236                 0.1498          0.1177   \n",
       "ERC-A                     0.1248                 0.1540          0.1204   \n",
       "ERC-B                     0.1245                 0.1517          0.1190   \n",
       "MCA                       0.1191                 0.2683          0.1566   \n",
       "MaxDiv                    0.1134                 0.1403          0.1038   \n",
       "MOM                       0.1462                 0.1742          0.1305   \n",
       "K-Means                   0.1250                 0.1799          0.1253   \n",
       "Hier Clust                0.1204                 0.1683          0.1283   \n",
       "HRP                       0.1223                 0.1705          0.1266   \n",
       "PCA                       0.1241                 0.1647          0.1260   \n",
       "Tail Risk                 0.1374                 0.1648          0.1210   \n",
       "Wavelet                   0.1292                 0.1465          0.1149   \n",
       "Algo                      0.1242                 0.1610          0.1233   \n",
       "Random Forest             0.1236                 0.1588          0.1212   \n",
       "MSR                       0.1320                 0.1673          0.1141   \n",
       "\n",
       "               Skewness  Kurtosis  Historic VaR (5%)  Cornish-Fisher VaR (5%)  \\\n",
       "CW              -0.7435    5.3467             0.0706                   0.0682   \n",
       "EW              -0.7571    6.6450             0.0635                   0.0715   \n",
       "EW-Tethered     -0.6703    5.1883             0.0667                   0.0662   \n",
       "GMV             -0.6230    5.6475             0.0425                   0.0499   \n",
       "GMV-CC          -0.4760    4.5199             0.0466                   0.0492   \n",
       "GMV-SC          -0.4609    4.5612             0.0465                   0.0486   \n",
       "ERC             -0.8425    6.8759             0.0582                   0.0667   \n",
       "ERC-A           -0.8245    6.7422             0.0609                   0.0687   \n",
       "ERC-B           -0.8360    6.8117             0.0596                   0.0676   \n",
       "MCA              0.7743    6.5803             0.1054                   0.0915   \n",
       "MaxDiv          -0.5563    6.5065             0.0529                   0.0600   \n",
       "MOM             -0.7620    5.6839             0.0637                   0.0775   \n",
       "K-Means         -0.4443    5.8850             0.0666                   0.0775   \n",
       "Hier Clust      -0.6892    6.6308             0.0657                   0.0746   \n",
       "HRP             -0.6346    6.1708             0.0616                   0.0754   \n",
       "PCA             -0.7324    6.5116             0.0656                   0.0732   \n",
       "Tail Risk       -0.5521    5.5773             0.0719                   0.0710   \n",
       "Wavelet         -0.8527    6.6481             0.0568                   0.0649   \n",
       "Algo            -0.7456    6.6072             0.0634                   0.0714   \n",
       "Random Forest   -0.6927    6.5378             0.0639                   0.0698   \n",
       "MSR             -0.3600    4.5483             0.0637                   0.0711   \n",
       "\n",
       "               Historic CVaR (5%)  Return on VaR  Sharpe Ratio  Sortino Ratio  \\\n",
       "CW                         0.0978         1.6625        0.5619         0.7400   \n",
       "EW                         0.1040         1.9483        0.5665         0.7383   \n",
       "EW-Tethered                0.0952         1.8826        0.6206         0.8304   \n",
       "GMV                        0.0731         2.6854        0.6982         0.9336   \n",
       "GMV-CC                     0.0714         2.6373        0.7658         1.0595   \n",
       "GMV-SC                     0.0705         2.6299        0.7662         1.0614   \n",
       "ERC                        0.0973         2.1236        0.6080         0.7753   \n",
       "ERC-A                      0.1003         2.0509        0.5989         0.7673   \n",
       "ERC-B                      0.0987         2.0877        0.6064         0.7737   \n",
       "MCA                        0.1434         1.1308        0.3230         0.5531   \n",
       "MaxDiv                     0.0869         2.1431        0.5781         0.7841   \n",
       "MOM                        0.1106         2.2948        0.6491         0.8684   \n",
       "K-Means                    0.1063         1.8789        0.5140         0.7380   \n",
       "Hier Clust                 0.1080         1.8309        0.5224         0.6875   \n",
       "HRP                        0.1061         1.9858        0.5268         0.7102   \n",
       "PCA                        0.1067         1.8912        0.5558         0.7280   \n",
       "Tail Risk                  0.1035         1.9105        0.6338         0.8650   \n",
       "Wavelet                    0.0951         2.2738        0.6593         0.8418   \n",
       "Algo                       0.1039         1.9605        0.5694         0.7441   \n",
       "Random Forest              0.1021         1.9334        0.5732         0.7522   \n",
       "MSR                        0.1012         2.0730        0.5931         0.8690   \n",
       "\n",
       "               Calmar Ratio  Modified Burke Ratio  Max Drawdown  \\\n",
       "CW                   0.1695                0.6734       -0.5012   \n",
       "EW                   0.1724                0.9339       -0.5283   \n",
       "EW-Tethered          0.1871                0.8953       -0.4973   \n",
       "GMV                  0.2142                1.0812       -0.3820   \n",
       "GMV-CC               0.2573                1.4057       -0.3516   \n",
       "GMV-SC               0.2515                1.3885       -0.3572   \n",
       "ERC                  0.1814                0.9886       -0.5020   \n",
       "ERC-A                0.1790                0.9740       -0.5155   \n",
       "ERC-B                0.1812                0.9879       -0.5076   \n",
       "MCA                  0.1405                0.3589       -0.6168   \n",
       "MaxDiv               0.1832                0.8941       -0.4427   \n",
       "MOM                  0.2157                0.9208       -0.5243   \n",
       "K-Means              0.1859                0.8829       -0.4974   \n",
       "Hier Clust           0.1609                0.8547       -0.5462   \n",
       "HRP                  0.1705                0.8868       -0.5269   \n",
       "PCA                  0.1693                0.9125       -0.5409   \n",
       "Tail Risk            0.2092                1.1954       -0.4993   \n",
       "Wavelet              0.1990                1.0998       -0.4852   \n",
       "Algo                 0.1742                0.9445       -0.5264   \n",
       "Random Forest        0.1743                0.9710       -0.5223   \n",
       "MSR                  0.2091                0.8187       -0.4747   \n",
       "\n",
       "               Net Profit to Worst Drawdown  \n",
       "CW                                 166.5793  \n",
       "EW                                 198.5728  \n",
       "EW-Tethered                        226.5880  \n",
       "GMV                                194.4157  \n",
       "GMV-CC                             291.3344  \n",
       "GMV-SC                             280.1532  \n",
       "ERC                                208.8619  \n",
       "ERC-A                              212.3436  \n",
       "ERC-B                              213.3236  \n",
       "MCA                                144.6428  \n",
       "MaxDiv                             163.4440  \n",
       "MOM                                446.0811  \n",
       "K-Means                            221.9027  \n",
       "Hier Clust                         170.7314  \n",
       "HRP                                190.1140  \n",
       "PCA                                197.2866  \n",
       "Tail Risk                          342.7802  \n",
       "Wavelet                            264.3902  \n",
       "Algo                               203.5660  \n",
       "Random Forest                      200.3870  \n",
       "MSR                                298.1032  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinancialMetrics.summary_stats(btr.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d50cedc",
   "metadata": {},
   "outputs": [
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           "style": "light"
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           "bgcolor": "#E5ECF6",
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          },
          "scene": {
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           "zaxis": {
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            "gridwidth": 2,
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            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
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           "line": {
            "color": "#2a3f5f"
           }
          },
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           "aaxis": {
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            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Portfolio Evolution"
        },
        "xaxis": {
         "title": {
          "text": "Date"
         }
        },
        "yaxis": {
         "title": {
          "text": "Cumulative Returns"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PortfolioAnalysis.plot_cumulative_returns(btr, title=\"Portfolio Evolution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b1cb044d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor date in wts.index:\\n    current_data = wts.loc[date]\\n    weights_df = pd.DataFrame()\\n    for strategy, values in current_data.items():\\n        for asset, weight in values.items():\\n            if strategy not in weights_df.columns:\\n                weights_df[strategy] = pd.Series(dtype=float)\\n            if asset not in weights_df.index:\\n                weights_df.loc[asset] = pd.Series(dtype=float)\\n            weights_df.at[asset, strategy] = weights_df.at[asset, strategy] + weight if pd.notna(weights_df.at[asset, strategy]) else weight\\n    PortfolioAnalysis.plot_weights(weights_df, title=\"Portfolio Weights - {}\".format(date))\\n'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "for date in wts.index:\n",
    "    current_data = wts.loc[date]\n",
    "    weights_df = pd.DataFrame()\n",
    "    for strategy, values in current_data.items():\n",
    "        for asset, weight in values.items():\n",
    "            if strategy not in weights_df.columns:\n",
    "                weights_df[strategy] = pd.Series(dtype=float)\n",
    "            if asset not in weights_df.index:\n",
    "                weights_df.loc[asset] = pd.Series(dtype=float)\n",
    "            weights_df.at[asset, strategy] = weights_df.at[asset, strategy] + weight if pd.notna(weights_df.at[asset, strategy]) else weight\n",
    "    PortfolioAnalysis.plot_weights(weights_df, title=\"Portfolio Weights - {}\".format(date))\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a98c0f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_weights = pd.DataFrame()\n",
    "for column in wts.columns:\n",
    "    mean_weights[column] = pd.DataFrame(list(wts[column])).mean()\n",
    "\n",
    "PortfolioAnalysis.plot_weights(mean_weights, title=\"Mean Weights Across All Dates for Each Strategy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ffb2b273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracking error for EW is: 0.2856\n",
      "Tracking error for EW_Tethered is: 0.1553\n",
      "Tracking error for GMV is: 0.5977\n",
      "Tracking error for GMV_CC is: 0.6206\n",
      "Tracking error for GMV_SC is: 0.6218\n",
      "Tracking error for ERC is: 0.2744\n",
      "Tracking error for ERC_A is: 0.2635\n",
      "Tracking error for ERC_B is: 0.2655\n",
      "Tracking error for MCA is: 1.5898\n",
      "Tracking error for MaxDiv is: 0.5885\n",
      "Tracking error for MOM is: 0.5576\n",
      "Tracking error for K_Means is: 0.6291\n",
      "Tracking error for Hier_Clust is: 0.3631\n",
      "Tracking error for HRP is: 0.4945\n",
      "Tracking error for PCA is: 0.2908\n",
      "Tracking error for Tail_Risk is: 0.3670\n",
      "Tracking error for Wavelet is: 0.2596\n",
      "Tracking error for Algo is: 0.2866\n",
      "Tracking error for Random_Forest is: 0.2979\n",
      "Tracking error for MSR is: 0.6228\n"
     ]
    }
   ],
   "source": [
    "PortfolioAnalysis.print_tracking_errors(btr['CW'], EW=btr['EW'], EW_Tethered=btr['EW-Tethered'], GMV=btr['GMV'], GMV_CC=btr['GMV-CC'], GMV_SC=btr['GMV-SC'], ERC=btr['ERC'], ERC_A=btr['ERC-A'], ERC_B=btr['ERC-B'], MCA=btr['MCA'], MaxDiv=btr['MaxDiv'], MOM=btr['MOM'], K_Means=btr['K-Means'], Hier_Clust=btr['Hier Clust'], HRP=btr['HRP'], PCA=btr['PCA'], Tail_Risk=btr['Tail Risk'], Wavelet=btr['Wavelet'], Algo=btr['Algo'], Random_Forest=btr['Random Forest'], MSR=btr['MSR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5a0ff90b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information Ratio for EW is: 0.0465\n",
      "Information Ratio for EW_Tethered is: 0.0856\n",
      "Information Ratio for GMV is: -0.0232\n",
      "Information Ratio for GMV_CC is: 0.0015\n",
      "Information Ratio for GMV_SC is: -0.0006\n",
      "Information Ratio for ERC is: 0.0368\n",
      "Information Ratio for ERC_A is: 0.0503\n",
      "Information Ratio for ERC_B is: 0.0455\n",
      "Information Ratio for MCA is: 0.0284\n",
      "Information Ratio for MaxDiv is: -0.0162\n",
      "Information Ratio for MOM is: 0.0977\n",
      "Information Ratio for K_Means is: 0.0341\n",
      "Information Ratio for Hier_Clust is: 0.0280\n",
      "Information Ratio for HRP is: 0.0285\n",
      "Information Ratio for PCA is: 0.0525\n",
      "Information Ratio for Tail_Risk is: 0.1005\n",
      "Information Ratio for Wavelet is: 0.0709\n",
      "Information Ratio for Algo is: 0.0499\n",
      "Information Ratio for Random_Forest is: 0.0421\n",
      "Information Ratio for MSR is: 0.0459\n"
     ]
    }
   ],
   "source": [
    "PortfolioAnalysis.print_information_ratios(btr['CW'], EW=btr['EW'], EW_Tethered=btr['EW-Tethered'], GMV=btr['GMV'], GMV_CC=btr['GMV-CC'], GMV_SC=btr['GMV-SC'], ERC=btr['ERC'], ERC_A=btr['ERC-A'], ERC_B=btr['ERC-B'], MCA=btr['MCA'], MaxDiv=btr['MaxDiv'], MOM=btr['MOM'], K_Means=btr['K-Means'], Hier_Clust=btr['Hier Clust'], HRP=btr['HRP'], PCA=btr['PCA'], Tail_Risk=btr['Tail Risk'], Wavelet=btr['Wavelet'], Algo=btr['Algo'], Random_Forest=btr['Random Forest'], MSR=btr['MSR'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}