final_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "companies_selected = pd.read_excel(\"Companies.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79, 5)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies_selected.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Beta Value</th>\n",
       "      <th>Grade</th>\n",
       "      <th>TSR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.49</td>\n",
       "      <td>B</td>\n",
       "      <td>53.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.38</td>\n",
       "      <td>C</td>\n",
       "      <td>57.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.50</td>\n",
       "      <td>B</td>\n",
       "      <td>54.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.34</td>\n",
       "      <td>A</td>\n",
       "      <td>-8.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.69</td>\n",
       "      <td>A</td>\n",
       "      <td>87.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Company Name   Industry  Beta Value Grade    TSR\n",
       "0  NextEra Energy (NEE)  Utilities        0.49     B  53.47\n",
       "1     Duke Energy (DUK)  Utilities        0.38     C  57.51\n",
       "2    Atmos Energy (ATO)  Utilities        0.50     B  54.13\n",
       "3        Avangrid (AGR)  Utilities        0.34     A  -8.57\n",
       "4     Otter Tail (OTTR)  Utilities        0.69     A  87.72"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies_selected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# since TSR is in % we will grade wight in % as well\n",
    "grade_weight = {\"A\": 90, \"B\": 80, \"C\": 70, \"D\": 60, \"E\": 50, \"F\": 40}\n",
    "companies_selected[\"GradeWeight\"] = companies_selected[\"Grade\"].map(grade_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_grade_score(data, x):\n",
    "    return (1 - x) * data[\"TSR\"] + x * data[\"GradeWeight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def monte_carlo_simulation(\n",
    "    data, max_industry_allocation=0.16, max_stock_allocation=0.045, target_beta=1.0\n",
    "):\n",
    "    best_portfolio = None\n",
    "    best_portfolio_beta = float(\"inf\")\n",
    "    best_run = None\n",
    "\n",
    "    for iteration in range(\n",
    "        65000, 80000\n",
    "    ):  # We want to keep the grade wight at a minumum of 0.65 of the total caluclation\n",
    "        weight_factor = iteration / 100000  # Fraction for Grade Score calculation\n",
    "\n",
    "        # Normalize TSR:\n",
    "        data[\"Industry TSR total\"] = data.groupby(\"Industry\")[\"TSR\"].transform(\"sum\")\n",
    "        data[\"Weighted TSR\"] = data[\"TSR\"] / data[\"Industry TSR total\"]\n",
    "\n",
    "        # Calculate Grade Score:\n",
    "        data[\"Grade Score\"] = data.apply(\n",
    "            calculate_grade_score, axis=1, args=(weight_factor,)\n",
    "        )\n",
    "\n",
    "        # Calculate Weight:\n",
    "        data[\"Weight\"] = data[\"Grade Score\"] / data[\"Grade Score\"].sum()\n",
    "\n",
    "        # Apply industry-level restrictions:\n",
    "        data[\"Industry Allocation\"] = data.groupby(\"Industry\")[\"Weight\"].transform(\n",
    "            \"sum\"\n",
    "        )\n",
    "        data[\"Allocation_by_restriction\"] = np.where(\n",
    "            data[\"Industry Allocation\"] > max_industry_allocation,\n",
    "            max_industry_allocation,\n",
    "            data[\"Industry Allocation\"],\n",
    "        )\n",
    "\n",
    "        # Normalize Weights based on restrictions:\n",
    "        data[\"Normalised Weight\"] = np.where(\n",
    "            data[\"Allocation_by_restriction\"] == max_industry_allocation,\n",
    "            (data[\"Weight\"] / data[\"Industry Allocation\"]) * max_industry_allocation,\n",
    "            data[\"Weight\"],\n",
    "        )\n",
    "\n",
    "        # Calculate Portfolio Beta:\n",
    "        portfolio_beta = (data[\"Beta Value\"] * data[\"Normalised Weight\"]).sum()\n",
    "\n",
    "        # Check if this portfolio is better:\n",
    "        if portfolio_beta < best_portfolio_beta:\n",
    "            test = weight_factor\n",
    "            best_portfolio_beta = portfolio_beta\n",
    "            best_run = iteration\n",
    "            best_portfolio = data.copy()\n",
    "            print(best_run, best_portfolio_beta)\n",
    "\n",
    "    return test, best_run, best_portfolio_beta, best_portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65000 0.8960902104464805\n"
     ]
    }
   ],
   "source": [
    "test, run_no, final_beta, ideal_portfolio = monte_carlo_simulation(companies_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8960902104464805"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Beta Value</th>\n",
       "      <th>Grade</th>\n",
       "      <th>TSR</th>\n",
       "      <th>GradeWeight</th>\n",
       "      <th>Industry TSR total</th>\n",
       "      <th>Weighted TSR</th>\n",
       "      <th>Grade Score</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Industry Allocation</th>\n",
       "      <th>Allocation_by_restriction</th>\n",
       "      <th>Normalised Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.49</td>\n",
       "      <td>B</td>\n",
       "      <td>53.47</td>\n",
       "      <td>80</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.149091</td>\n",
       "      <td>70.7145</td>\n",
       "      <td>0.008818</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.008818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.38</td>\n",
       "      <td>C</td>\n",
       "      <td>57.51</td>\n",
       "      <td>70</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.160356</td>\n",
       "      <td>65.6285</td>\n",
       "      <td>0.008184</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.008184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.50</td>\n",
       "      <td>B</td>\n",
       "      <td>54.13</td>\n",
       "      <td>80</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.150931</td>\n",
       "      <td>70.9455</td>\n",
       "      <td>0.008847</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.008847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.34</td>\n",
       "      <td>A</td>\n",
       "      <td>-8.57</td>\n",
       "      <td>90</td>\n",
       "      <td>358.64</td>\n",
       "      <td>-0.023896</td>\n",
       "      <td>55.5005</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.006921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.69</td>\n",
       "      <td>A</td>\n",
       "      <td>87.72</td>\n",
       "      <td>90</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.244591</td>\n",
       "      <td>89.2020</td>\n",
       "      <td>0.011123</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.011123</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.79</td>\n",
       "      <td>D</td>\n",
       "      <td>222.00</td>\n",
       "      <td>60</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.319839</td>\n",
       "      <td>116.7000</td>\n",
       "      <td>0.014552</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.014552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.45</td>\n",
       "      <td>B</td>\n",
       "      <td>-51.22</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>-0.073793</td>\n",
       "      <td>34.0730</td>\n",
       "      <td>0.004249</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.004249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.41</td>\n",
       "      <td>A</td>\n",
       "      <td>59.00</td>\n",
       "      <td>90</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.085002</td>\n",
       "      <td>79.1500</td>\n",
       "      <td>0.009870</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.009870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.37</td>\n",
       "      <td>B</td>\n",
       "      <td>38.00</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.054747</td>\n",
       "      <td>65.3000</td>\n",
       "      <td>0.008143</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.008143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.23</td>\n",
       "      <td>B</td>\n",
       "      <td>40.00</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.057629</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.008230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Company Name          Industry  Beta Value  \\\n",
       "0                       NextEra Energy (NEE)         Utilities        0.49   \n",
       "1                          Duke Energy (DUK)         Utilities        0.38   \n",
       "2                         Atmos Energy (ATO)         Utilities        0.50   \n",
       "3                             Avangrid (AGR)         Utilities        0.34   \n",
       "4                          Otter Tail (OTTR)         Utilities        0.69   \n",
       "..                                       ...               ...         ...   \n",
       "74  COSTCO WHOLESALE CORPORATION (XNAS:COST)  Consumer Staples        0.79   \n",
       "75      DOLLAR GENERAL CORPORATION (XNYS:DG)  Consumer Staples        0.45   \n",
       "76       COLGATE-PALMOLIVE COMPANY (XNYS:CL)  Consumer Staples        0.41   \n",
       "77            THE HERSHEY COMPANY (XNYS:HSY)  Consumer Staples        0.37   \n",
       "78                    UNILEVER PLC (XBUE:UL)  Consumer Staples        0.23   \n",
       "\n",
       "   Grade     TSR  GradeWeight  Industry TSR total  Weighted TSR  Grade Score  \\\n",
       "0      B   53.47           80              358.64      0.149091      70.7145   \n",
       "1      C   57.51           70              358.64      0.160356      65.6285   \n",
       "2      B   54.13           80              358.64      0.150931      70.9455   \n",
       "3      A   -8.57           90              358.64     -0.023896      55.5005   \n",
       "4      A   87.72           90              358.64      0.244591      89.2020   \n",
       "..   ...     ...          ...                 ...           ...          ...   \n",
       "74     D  222.00           60              694.10      0.319839     116.7000   \n",
       "75     B  -51.22           80              694.10     -0.073793      34.0730   \n",
       "76     A   59.00           90              694.10      0.085002      79.1500   \n",
       "77     B   38.00           80              694.10      0.054747      65.3000   \n",
       "78     B   40.00           80              694.10      0.057629      66.0000   \n",
       "\n",
       "      Weight  Industry Allocation  Allocation_by_restriction  \\\n",
       "0   0.008818             0.081304                   0.081304   \n",
       "1   0.008184             0.081304                   0.081304   \n",
       "2   0.008847             0.081304                   0.081304   \n",
       "3   0.006921             0.081304                   0.081304   \n",
       "4   0.011123             0.081304                   0.081304   \n",
       "..       ...                  ...                        ...   \n",
       "74  0.014552             0.095134                   0.095134   \n",
       "75  0.004249             0.095134                   0.095134   \n",
       "76  0.009870             0.095134                   0.095134   \n",
       "77  0.008143             0.095134                   0.095134   \n",
       "78  0.008230             0.095134                   0.095134   \n",
       "\n",
       "    Normalised Weight  \n",
       "0            0.008818  \n",
       "1            0.008184  \n",
       "2            0.008847  \n",
       "3            0.006921  \n",
       "4            0.011123  \n",
       "..                ...  \n",
       "74           0.014552  \n",
       "75           0.004249  \n",
       "76           0.009870  \n",
       "77           0.008143  \n",
       "78           0.008230  \n",
       "\n",
       "[79 rows x 13 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Industry\n",
       "Consumer Staples    0.095134\n",
       "Energy              0.109765\n",
       "Finance             0.129784\n",
       "Healthcare          0.127471\n",
       "Industruals         0.109167\n",
       "Real Estate         0.078241\n",
       "Technology          0.160000\n",
       "Utilities           0.081304\n",
       "Name: Allocation_by_restriction, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio.groupby(\"Industry\")[\"Allocation_by_restriction\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8715170000000001"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.077816 + 0.117657 + 0.139116 + 0.136636 + 0.069275 + 0.083867 + 0.160000 + 0.087150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10913266324038762"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_over = 1 - ideal_portfolio[\"Normalised Weight\"].sum()\n",
    "left_over"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_change = len(ideal_portfolio[ideal_portfolio[\"Allocation_by_restriction\"] >= 0.16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "change = len(ideal_portfolio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "addition = left_over / (change - no_change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0015590380462912517"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ideal_portfolio[\"Allocation_by_restriction\"] > 0.1599).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "ideal_portfolio[\"final_weight\"] = np.where(\n",
    "    ideal_portfolio[\"Allocation_by_restriction\"] >= 0.1600,\n",
    "    ideal_portfolio[\"Normalised Weight\"],\n",
    "    ideal_portfolio[\"Normalised Weight\"] + addition,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio[\"final_weight\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Industry\n",
       "Consumer Staples    0.110724\n",
       "Energy              0.125356\n",
       "Finance             0.145375\n",
       "Healthcare          0.143061\n",
       "Industruals         0.124758\n",
       "Real Estate         0.093832\n",
       "Technology          0.160000\n",
       "Utilities           0.096894\n",
       "Name: final_weight, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio.groupby(\"Industry\")[\"final_weight\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ideal_portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------------------------------------------------------- for capping stocks at 4.5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "ideal_portfolio[\"Capped_weight\"] = np.minimum(ideal_portfolio[\"final_weight\"], 4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Company Name</th>\n",
       "      <th>final_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NVDA</td>\n",
       "      <td>0.073294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Eli Lilly and Company (LLY)</td>\n",
       "      <td>0.036699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>0.036409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Interactive Brokers (IBKR)</td>\n",
       "      <td>0.019668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LPL Financial (LPLA)</td>\n",
       "      <td>0.019320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>EQR</td>\n",
       "      <td>0.007350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>PEAK</td>\n",
       "      <td>0.007050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.005808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>COIN</td>\n",
       "      <td>0.003581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SNOW</td>\n",
       "      <td>0.003104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Company Name  final_weight\n",
       "44                                  NVDA      0.073294\n",
       "25           Eli Lilly and Company (LLY)      0.036699\n",
       "40                                  TSLA      0.036409\n",
       "10            Interactive Brokers (IBKR)      0.019668\n",
       "11                  LPL Financial (LPLA)      0.019320\n",
       "..                                   ...           ...\n",
       "51                                   EQR      0.007350\n",
       "57                                  PEAK      0.007050\n",
       "75  DOLLAR GENERAL CORPORATION (XNYS:DG)      0.005808\n",
       "48                                  COIN      0.003581\n",
       "47                                  SNOW      0.003104\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio.sort_values(\"final_weight\", ascending=False)[\n",
    "    [\"Company Name\", \"final_weight\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "ideal_portfolio[\"Capped_weight\"] = np.minimum(ideal_portfolio[\"final_weight\"], 0.045)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NVDA</td>\n",
       "      <td>0.045000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Eli Lilly and Company (LLY)</td>\n",
       "      <td>0.036699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>0.036409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Interactive Brokers (IBKR)</td>\n",
       "      <td>0.019668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LPL Financial (LPLA)</td>\n",
       "      <td>0.019320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>EQR</td>\n",
       "      <td>0.007350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>PEAK</td>\n",
       "      <td>0.007050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.005808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>COIN</td>\n",
       "      <td>0.003581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SNOW</td>\n",
       "      <td>0.003104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Company Name  Capped_weight\n",
       "44                                  NVDA       0.045000\n",
       "25           Eli Lilly and Company (LLY)       0.036699\n",
       "40                                  TSLA       0.036409\n",
       "10            Interactive Brokers (IBKR)       0.019668\n",
       "11                  LPL Financial (LPLA)       0.019320\n",
       "..                                   ...            ...\n",
       "51                                   EQR       0.007350\n",
       "57                                  PEAK       0.007050\n",
       "75  DOLLAR GENERAL CORPORATION (XNYS:DG)       0.005808\n",
       "48                                  COIN       0.003581\n",
       "47                                  SNOW       0.003104\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio.sort_values(\"Capped_weight\", ascending=False)[\n",
    "    [\"Company Name\", \"Capped_weight\"]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "while True:\n",
    "    industry_totals = ideal_portfolio.groupby(\"Industry\")[\"final_weight\"].transform(\n",
    "        \"sum\"\n",
    "    )\n",
    "    capped_totals = ideal_portfolio.groupby(\"Industry\")[\"Capped_weight\"].transform(\n",
    "        \"sum\"\n",
    "    )\n",
    "    excess_weight = industry_totals - capped_totals\n",
    "\n",
    "    if (excess_weight <= 0).all():\n",
    "        break\n",
    "\n",
    "    # Identify eligible stocks (below cap) for redistribution\n",
    "    eligible = ideal_portfolio[\"Capped_weight\"] < 0.045\n",
    "    eligible_totals = (\n",
    "        ideal_portfolio[eligible].groupby(\"Industry\")[\"Capped_weight\"].transform(\"sum\")\n",
    "    )\n",
    "\n",
    "    # Redistribute proportionally among eligible stocks\n",
    "    redistribution = (\n",
    "        ideal_portfolio[\"Capped_weight\"] / eligible_totals\n",
    "    ) * excess_weight\n",
    "    redistribution = redistribution.fillna(\n",
    "        0\n",
    "    )  # Avoid NaN for industries with no eligible stocks\n",
    "    ideal_portfolio.loc[eligible, \"Capped_weight\"] += redistribution[eligible]\n",
    "\n",
    "    # Reapply the cap\n",
    "    ideal_portfolio[\"Capped_weight\"] = np.minimum(\n",
    "        ideal_portfolio[\"Capped_weight\"], 0.045\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "ideal_portfolio[\"Capped_weight\"] *= 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VERIFY PORTFOLIO CONSTRATINTS -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(ideal_portfolio[\"Capped_weight\"].sum(), 2) == 100.00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Beta Value</th>\n",
       "      <th>Grade</th>\n",
       "      <th>TSR</th>\n",
       "      <th>GradeWeight</th>\n",
       "      <th>Industry TSR total</th>\n",
       "      <th>Weighted TSR</th>\n",
       "      <th>Grade Score</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Industry Allocation</th>\n",
       "      <th>Allocation_by_restriction</th>\n",
       "      <th>Normalised Weight</th>\n",
       "      <th>final_weight</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>NVDA</td>\n",
       "      <td>Technology</td>\n",
       "      <td>1.76</td>\n",
       "      <td>C</td>\n",
       "      <td>2694.86</td>\n",
       "      <td>70</td>\n",
       "      <td>4829.52</td>\n",
       "      <td>0.557997</td>\n",
       "      <td>988.7010</td>\n",
       "      <td>0.123286</td>\n",
       "      <td>0.269133</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.073294</td>\n",
       "      <td>0.073294</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>TSLA</td>\n",
       "      <td>Technology</td>\n",
       "      <td>2.12</td>\n",
       "      <td>A</td>\n",
       "      <td>1236.11</td>\n",
       "      <td>90</td>\n",
       "      <td>4829.52</td>\n",
       "      <td>0.255949</td>\n",
       "      <td>491.1385</td>\n",
       "      <td>0.061242</td>\n",
       "      <td>0.269133</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.036409</td>\n",
       "      <td>0.036409</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Eli Lilly and Company (LLY)</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>0.42</td>\n",
       "      <td>C</td>\n",
       "      <td>675.16</td>\n",
       "      <td>70</td>\n",
       "      <td>1435.04</td>\n",
       "      <td>0.470482</td>\n",
       "      <td>281.8060</td>\n",
       "      <td>0.035140</td>\n",
       "      <td>0.127471</td>\n",
       "      <td>0.127471</td>\n",
       "      <td>0.035140</td>\n",
       "      <td>0.036699</td>\n",
       "      <td>3.669877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Interactive Brokers (IBKR)</td>\n",
       "      <td>Finance</td>\n",
       "      <td>0.79</td>\n",
       "      <td>D</td>\n",
       "      <td>303.51</td>\n",
       "      <td>60</td>\n",
       "      <td>1525.19</td>\n",
       "      <td>0.198998</td>\n",
       "      <td>145.2285</td>\n",
       "      <td>0.018109</td>\n",
       "      <td>0.129784</td>\n",
       "      <td>0.129784</td>\n",
       "      <td>0.018109</td>\n",
       "      <td>0.019668</td>\n",
       "      <td>1.966827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LPL Financial (LPLA)</td>\n",
       "      <td>Finance</td>\n",
       "      <td>0.85</td>\n",
       "      <td>C</td>\n",
       "      <td>276.95</td>\n",
       "      <td>70</td>\n",
       "      <td>1525.19</td>\n",
       "      <td>0.181584</td>\n",
       "      <td>142.4325</td>\n",
       "      <td>0.017761</td>\n",
       "      <td>0.129784</td>\n",
       "      <td>0.129784</td>\n",
       "      <td>0.017761</td>\n",
       "      <td>0.019320</td>\n",
       "      <td>1.931962</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>EQR</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>0.85</td>\n",
       "      <td>C</td>\n",
       "      <td>2.70</td>\n",
       "      <td>70</td>\n",
       "      <td>307.04</td>\n",
       "      <td>0.008794</td>\n",
       "      <td>46.4450</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.078241</td>\n",
       "      <td>0.078241</td>\n",
       "      <td>0.005791</td>\n",
       "      <td>0.007350</td>\n",
       "      <td>0.735049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>PEAK</td>\n",
       "      <td>Real Estate</td>\n",
       "      <td>0.75</td>\n",
       "      <td>B</td>\n",
       "      <td>-22.76</td>\n",
       "      <td>80</td>\n",
       "      <td>307.04</td>\n",
       "      <td>-0.074127</td>\n",
       "      <td>44.0340</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.078241</td>\n",
       "      <td>0.078241</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>0.007050</td>\n",
       "      <td>0.704985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.45</td>\n",
       "      <td>B</td>\n",
       "      <td>-51.22</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>-0.073793</td>\n",
       "      <td>34.0730</td>\n",
       "      <td>0.004249</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.004249</td>\n",
       "      <td>0.005808</td>\n",
       "      <td>0.580776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>COIN</td>\n",
       "      <td>Technology</td>\n",
       "      <td>3.34</td>\n",
       "      <td>B</td>\n",
       "      <td>-10.57</td>\n",
       "      <td>80</td>\n",
       "      <td>4829.52</td>\n",
       "      <td>-0.002189</td>\n",
       "      <td>48.3005</td>\n",
       "      <td>0.006023</td>\n",
       "      <td>0.269133</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.003581</td>\n",
       "      <td>0.498316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SNOW</td>\n",
       "      <td>Technology</td>\n",
       "      <td>1.37</td>\n",
       "      <td>A</td>\n",
       "      <td>-47.52</td>\n",
       "      <td>90</td>\n",
       "      <td>4829.52</td>\n",
       "      <td>-0.009839</td>\n",
       "      <td>41.8680</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>0.269133</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.003104</td>\n",
       "      <td>0.003104</td>\n",
       "      <td>0.431952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Company Name          Industry  Beta Value Grade  \\\n",
       "44                                  NVDA        Technology        1.76     C   \n",
       "40                                  TSLA        Technology        2.12     A   \n",
       "25           Eli Lilly and Company (LLY)        Healthcare        0.42     C   \n",
       "10            Interactive Brokers (IBKR)           Finance        0.79     D   \n",
       "11                  LPL Financial (LPLA)           Finance        0.85     C   \n",
       "..                                   ...               ...         ...   ...   \n",
       "51                                   EQR       Real Estate        0.85     C   \n",
       "57                                  PEAK       Real Estate        0.75     B   \n",
       "75  DOLLAR GENERAL CORPORATION (XNYS:DG)  Consumer Staples        0.45     B   \n",
       "48                                  COIN        Technology        3.34     B   \n",
       "47                                  SNOW        Technology        1.37     A   \n",
       "\n",
       "        TSR  GradeWeight  Industry TSR total  Weighted TSR  Grade Score  \\\n",
       "44  2694.86           70             4829.52      0.557997     988.7010   \n",
       "40  1236.11           90             4829.52      0.255949     491.1385   \n",
       "25   675.16           70             1435.04      0.470482     281.8060   \n",
       "10   303.51           60             1525.19      0.198998     145.2285   \n",
       "11   276.95           70             1525.19      0.181584     142.4325   \n",
       "..      ...          ...                 ...           ...          ...   \n",
       "51     2.70           70              307.04      0.008794      46.4450   \n",
       "57   -22.76           80              307.04     -0.074127      44.0340   \n",
       "75   -51.22           80              694.10     -0.073793      34.0730   \n",
       "48   -10.57           80             4829.52     -0.002189      48.3005   \n",
       "47   -47.52           90             4829.52     -0.009839      41.8680   \n",
       "\n",
       "      Weight  Industry Allocation  Allocation_by_restriction  \\\n",
       "44  0.123286             0.269133                   0.160000   \n",
       "40  0.061242             0.269133                   0.160000   \n",
       "25  0.035140             0.127471                   0.127471   \n",
       "10  0.018109             0.129784                   0.129784   \n",
       "11  0.017761             0.129784                   0.129784   \n",
       "..       ...                  ...                        ...   \n",
       "51  0.005791             0.078241                   0.078241   \n",
       "57  0.005491             0.078241                   0.078241   \n",
       "75  0.004249             0.095134                   0.095134   \n",
       "48  0.006023             0.269133                   0.160000   \n",
       "47  0.005221             0.269133                   0.160000   \n",
       "\n",
       "    Normalised Weight  final_weight  Capped_weight  \n",
       "44           0.073294      0.073294       4.500000  \n",
       "40           0.036409      0.036409       4.500000  \n",
       "25           0.035140      0.036699       3.669877  \n",
       "10           0.018109      0.019668       1.966827  \n",
       "11           0.017761      0.019320       1.931962  \n",
       "..                ...           ...            ...  \n",
       "51           0.005791      0.007350       0.735049  \n",
       "57           0.005491      0.007050       0.704985  \n",
       "75           0.004249      0.005808       0.580776  \n",
       "48           0.003581      0.003581       0.498316  \n",
       "47           0.003104      0.003104       0.431952  \n",
       "\n",
       "[79 rows x 15 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio.sort_values(by=\"Capped_weight\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Company Name</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Beta Value</th>\n",
       "      <th>Grade</th>\n",
       "      <th>TSR</th>\n",
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       "      <th>Allocation_by_restriction</th>\n",
       "      <th>Normalised Weight</th>\n",
       "      <th>final_weight</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.49</td>\n",
       "      <td>B</td>\n",
       "      <td>53.47</td>\n",
       "      <td>80</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.149091</td>\n",
       "      <td>70.7145</td>\n",
       "      <td>0.008818</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.008818</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>1.037676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.38</td>\n",
       "      <td>C</td>\n",
       "      <td>57.51</td>\n",
       "      <td>70</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.160356</td>\n",
       "      <td>65.6285</td>\n",
       "      <td>0.008184</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.008184</td>\n",
       "      <td>0.009743</td>\n",
       "      <td>0.974257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.50</td>\n",
       "      <td>B</td>\n",
       "      <td>54.13</td>\n",
       "      <td>80</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.150931</td>\n",
       "      <td>70.9455</td>\n",
       "      <td>0.008847</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.008847</td>\n",
       "      <td>0.010406</td>\n",
       "      <td>1.040557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.34</td>\n",
       "      <td>A</td>\n",
       "      <td>-8.57</td>\n",
       "      <td>90</td>\n",
       "      <td>358.64</td>\n",
       "      <td>-0.023896</td>\n",
       "      <td>55.5005</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.006921</td>\n",
       "      <td>0.008480</td>\n",
       "      <td>0.847966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>Utilities</td>\n",
       "      <td>0.69</td>\n",
       "      <td>A</td>\n",
       "      <td>87.72</td>\n",
       "      <td>90</td>\n",
       "      <td>358.64</td>\n",
       "      <td>0.244591</td>\n",
       "      <td>89.2020</td>\n",
       "      <td>0.011123</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.081304</td>\n",
       "      <td>0.011123</td>\n",
       "      <td>0.012682</td>\n",
       "      <td>1.268206</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.79</td>\n",
       "      <td>D</td>\n",
       "      <td>222.00</td>\n",
       "      <td>60</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.319839</td>\n",
       "      <td>116.7000</td>\n",
       "      <td>0.014552</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.014552</td>\n",
       "      <td>0.016111</td>\n",
       "      <td>1.611092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.45</td>\n",
       "      <td>B</td>\n",
       "      <td>-51.22</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>-0.073793</td>\n",
       "      <td>34.0730</td>\n",
       "      <td>0.004249</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.004249</td>\n",
       "      <td>0.005808</td>\n",
       "      <td>0.580776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.41</td>\n",
       "      <td>A</td>\n",
       "      <td>59.00</td>\n",
       "      <td>90</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.085002</td>\n",
       "      <td>79.1500</td>\n",
       "      <td>0.009870</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.009870</td>\n",
       "      <td>0.011429</td>\n",
       "      <td>1.142863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.37</td>\n",
       "      <td>B</td>\n",
       "      <td>38.00</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.054747</td>\n",
       "      <td>65.3000</td>\n",
       "      <td>0.008143</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.008143</td>\n",
       "      <td>0.009702</td>\n",
       "      <td>0.970161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>Consumer Staples</td>\n",
       "      <td>0.23</td>\n",
       "      <td>B</td>\n",
       "      <td>40.00</td>\n",
       "      <td>80</td>\n",
       "      <td>694.10</td>\n",
       "      <td>0.057629</td>\n",
       "      <td>66.0000</td>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.095134</td>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.009789</td>\n",
       "      <td>0.978889</td>\n",
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       "<p>79 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "                                Company Name          Industry  Beta Value  \\\n",
       "0                       NextEra Energy (NEE)         Utilities        0.49   \n",
       "1                          Duke Energy (DUK)         Utilities        0.38   \n",
       "2                         Atmos Energy (ATO)         Utilities        0.50   \n",
       "3                             Avangrid (AGR)         Utilities        0.34   \n",
       "4                          Otter Tail (OTTR)         Utilities        0.69   \n",
       "..                                       ...               ...         ...   \n",
       "74  COSTCO WHOLESALE CORPORATION (XNAS:COST)  Consumer Staples        0.79   \n",
       "75      DOLLAR GENERAL CORPORATION (XNYS:DG)  Consumer Staples        0.45   \n",
       "76       COLGATE-PALMOLIVE COMPANY (XNYS:CL)  Consumer Staples        0.41   \n",
       "77            THE HERSHEY COMPANY (XNYS:HSY)  Consumer Staples        0.37   \n",
       "78                    UNILEVER PLC (XBUE:UL)  Consumer Staples        0.23   \n",
       "\n",
       "   Grade     TSR  GradeWeight  Industry TSR total  Weighted TSR  Grade Score  \\\n",
       "0      B   53.47           80              358.64      0.149091      70.7145   \n",
       "1      C   57.51           70              358.64      0.160356      65.6285   \n",
       "2      B   54.13           80              358.64      0.150931      70.9455   \n",
       "3      A   -8.57           90              358.64     -0.023896      55.5005   \n",
       "4      A   87.72           90              358.64      0.244591      89.2020   \n",
       "..   ...     ...          ...                 ...           ...          ...   \n",
       "74     D  222.00           60              694.10      0.319839     116.7000   \n",
       "75     B  -51.22           80              694.10     -0.073793      34.0730   \n",
       "76     A   59.00           90              694.10      0.085002      79.1500   \n",
       "77     B   38.00           80              694.10      0.054747      65.3000   \n",
       "78     B   40.00           80              694.10      0.057629      66.0000   \n",
       "\n",
       "      Weight  Industry Allocation  Allocation_by_restriction  \\\n",
       "0   0.008818             0.081304                   0.081304   \n",
       "1   0.008184             0.081304                   0.081304   \n",
       "2   0.008847             0.081304                   0.081304   \n",
       "3   0.006921             0.081304                   0.081304   \n",
       "4   0.011123             0.081304                   0.081304   \n",
       "..       ...                  ...                        ...   \n",
       "74  0.014552             0.095134                   0.095134   \n",
       "75  0.004249             0.095134                   0.095134   \n",
       "76  0.009870             0.095134                   0.095134   \n",
       "77  0.008143             0.095134                   0.095134   \n",
       "78  0.008230             0.095134                   0.095134   \n",
       "\n",
       "    Normalised Weight  final_weight  Capped_weight  \n",
       "0            0.008818      0.010377       1.037676  \n",
       "1            0.008184      0.009743       0.974257  \n",
       "2            0.008847      0.010406       1.040557  \n",
       "3            0.006921      0.008480       0.847966  \n",
       "4            0.011123      0.012682       1.268206  \n",
       "..                ...           ...            ...  \n",
       "74           0.014552      0.016111       1.611092  \n",
       "75           0.004249      0.005808       0.580776  \n",
       "76           0.009870      0.011429       1.142863  \n",
       "77           0.008143      0.009702       0.970161  \n",
       "78           0.008230      0.009789       0.978889  \n",
       "\n",
       "[79 rows x 15 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Industry\n",
       "Consumer Staples    0.110724\n",
       "Energy              0.125356\n",
       "Finance             0.145375\n",
       "Healthcare          0.143061\n",
       "Industruals         0.124758\n",
       "Real Estate         0.093832\n",
       "Technology          0.160000\n",
       "Utilities           0.096894\n",
       "Name: final_weight, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = ideal_portfolio.groupby(\"Industry\")[\"final_weight\"].sum()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------------------------------------------------------------------- EVERYTHING BELOW IS FOR TESTING OUT PORTFOLIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\asus\\AppData\\Local\\Temp\\ipykernel_26580\\3355036518.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  final_port[\"Ticker\"] = final_port[\"Company Name\"].map(mapping_dict)\n"
     ]
    },
    {
     "data": {
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       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight\n",
       "Ticker                                                         \n",
       "NEE                         NextEra Energy (NEE)       1.037676\n",
       "DUK                            Duke Energy (DUK)       0.974257\n",
       "ATO                           Atmos Energy (ATO)       1.040557\n",
       "AGR                               Avangrid (AGR)       0.847966\n",
       "OTTR                           Otter Tail (OTTR)       1.268206\n",
       "...                                          ...            ...\n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092\n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776\n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863\n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161\n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_port = ideal_portfolio[[\"Company Name\", \"Capped_weight\"]]\n",
    "ticker_map = pd.read_excel(\"ticker_map.xlsx\")\n",
    "mapping_dict = dict(zip(ticker_map[\"Company Name\"], ticker_map[\"Ticker\"]))\n",
    "final_port[\"Ticker\"] = final_port[\"Company Name\"].map(mapping_dict)\n",
    "\n",
    "final_port = final_port.set_index(\"Ticker\")\n",
    "final_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "      <th>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-07</th>\n",
       "      <th>2024-11-08</th>\n",
       "      <th>2024-11-09</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-11</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-11-25</th>\n",
       "      <th>2024-11-26</th>\n",
       "      <th>2024-11-27</th>\n",
       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>2024-12-02</th>\n",
       "      <th>2024-12-03</th>\n",
       "      <th>2024-12-04</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <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>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight  2024-11-04  \\\n",
       "Ticker                                                                        \n",
       "NEE                         NextEra Energy (NEE)       1.037676         NaN   \n",
       "DUK                            Duke Energy (DUK)       0.974257         NaN   \n",
       "ATO                           Atmos Energy (ATO)       1.040557         NaN   \n",
       "AGR                               Avangrid (AGR)       0.847966         NaN   \n",
       "OTTR                           Otter Tail (OTTR)       1.268206         NaN   \n",
       "...                                          ...            ...         ...   \n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092         NaN   \n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776         NaN   \n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863         NaN   \n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161         NaN   \n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889         NaN   \n",
       "\n",
       "        2024-11-05  2024-11-06  2024-11-07  2024-11-08  2024-11-09  \\\n",
       "Ticker                                                               \n",
       "NEE            NaN         NaN         NaN         NaN         NaN   \n",
       "DUK            NaN         NaN         NaN         NaN         NaN   \n",
       "ATO            NaN         NaN         NaN         NaN         NaN   \n",
       "AGR            NaN         NaN         NaN         NaN         NaN   \n",
       "OTTR           NaN         NaN         NaN         NaN         NaN   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST           NaN         NaN         NaN         NaN         NaN   \n",
       "DG             NaN         NaN         NaN         NaN         NaN   \n",
       "CLX            NaN         NaN         NaN         NaN         NaN   \n",
       "HSY            NaN         NaN         NaN         NaN         NaN   \n",
       "UL             NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        2024-11-10  2024-11-11  ...  2024-11-25  2024-11-26  2024-11-27  \\\n",
       "Ticker                          ...                                       \n",
       "NEE            NaN         NaN  ...         NaN         NaN         NaN   \n",
       "DUK            NaN         NaN  ...         NaN         NaN         NaN   \n",
       "ATO            NaN         NaN  ...         NaN         NaN         NaN   \n",
       "AGR            NaN         NaN  ...         NaN         NaN         NaN   \n",
       "OTTR           NaN         NaN  ...         NaN         NaN         NaN   \n",
       "...            ...         ...  ...         ...         ...         ...   \n",
       "COST           NaN         NaN  ...         NaN         NaN         NaN   \n",
       "DG             NaN         NaN  ...         NaN         NaN         NaN   \n",
       "CLX            NaN         NaN  ...         NaN         NaN         NaN   \n",
       "HSY            NaN         NaN  ...         NaN         NaN         NaN   \n",
       "UL             NaN         NaN  ...         NaN         NaN         NaN   \n",
       "\n",
       "        2024-11-28  2024-11-29  2024-11-30  2024-12-01  2024-12-02  \\\n",
       "Ticker                                                               \n",
       "NEE            NaN         NaN         NaN         NaN         NaN   \n",
       "DUK            NaN         NaN         NaN         NaN         NaN   \n",
       "ATO            NaN         NaN         NaN         NaN         NaN   \n",
       "AGR            NaN         NaN         NaN         NaN         NaN   \n",
       "OTTR           NaN         NaN         NaN         NaN         NaN   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST           NaN         NaN         NaN         NaN         NaN   \n",
       "DG             NaN         NaN         NaN         NaN         NaN   \n",
       "CLX            NaN         NaN         NaN         NaN         NaN   \n",
       "HSY            NaN         NaN         NaN         NaN         NaN   \n",
       "UL             NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        2024-12-03  2024-12-04  \n",
       "Ticker                          \n",
       "NEE            NaN         NaN  \n",
       "DUK            NaN         NaN  \n",
       "ATO            NaN         NaN  \n",
       "AGR            NaN         NaN  \n",
       "OTTR           NaN         NaN  \n",
       "...            ...         ...  \n",
       "COST           NaN         NaN  \n",
       "DG             NaN         NaN  \n",
       "CLX            NaN         NaN  \n",
       "HSY            NaN         NaN  \n",
       "UL             NaN         NaN  \n",
       "\n",
       "[79 rows x 33 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = pd.to_datetime(\"12-04-2024\")\n",
    "\n",
    "# Generate the previous 30 days, starting from today\n",
    "dates = pd.date_range(\n",
    "    start=start_date - pd.Timedelta(days=30), end=start_date, freq=\"D\"\n",
    ")\n",
    "\n",
    "# Create a DataFrame with 30 empty columns (NaN values) for each of the previous 30 days\n",
    "new_columns = pd.DataFrame(\n",
    "    np.nan, index=final_port.index, columns=dates.strftime(\"%Y-%m-%d\")\n",
    ")\n",
    "\n",
    "# Concatenate the existing DataFrame with the new columns\n",
    "df_updated = pd.concat([final_port, new_columns], axis=1)\n",
    "df_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
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       "      <th>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
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       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>2024-12-02</th>\n",
       "      <th>2024-12-03</th>\n",
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       "      <th>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.251116</td>\n",
       "      <td>-5.123960</td>\n",
       "      <td>-2.136045</td>\n",
       "      <td>-2.136045</td>\n",
       "      <td>-2.136045</td>\n",
       "      <td>-3.483785</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.902573</td>\n",
       "      <td>-0.814595</td>\n",
       "      <td>0.503780</td>\n",
       "      <td>0.503780</td>\n",
       "      <td>0.695778</td>\n",
       "      <td>0.695778</td>\n",
       "      <td>0.695778</td>\n",
       "      <td>-1.352183</td>\n",
       "      <td>-2.350563</td>\n",
       "      <td>-3.054554</td>\n",
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       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.394463</td>\n",
       "      <td>-2.629731</td>\n",
       "      <td>-0.745089</td>\n",
       "      <td>-0.745089</td>\n",
       "      <td>-0.745089</td>\n",
       "      <td>-1.420057</td>\n",
       "      <td>...</td>\n",
       "      <td>1.767301</td>\n",
       "      <td>3.572565</td>\n",
       "      <td>3.908837</td>\n",
       "      <td>3.908837</td>\n",
       "      <td>3.581416</td>\n",
       "      <td>3.581416</td>\n",
       "      <td>3.581416</td>\n",
       "      <td>1.926589</td>\n",
       "      <td>0.563791</td>\n",
       "      <td>0.245215</td>\n",
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       "    <tr>\n",
       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.652275</td>\n",
       "      <td>2.507623</td>\n",
       "      <td>4.500665</td>\n",
       "      <td>4.500665</td>\n",
       "      <td>4.500665</td>\n",
       "      <td>5.892165</td>\n",
       "      <td>...</td>\n",
       "      <td>9.476613</td>\n",
       "      <td>9.585965</td>\n",
       "      <td>10.387824</td>\n",
       "      <td>10.387824</td>\n",
       "      <td>10.307649</td>\n",
       "      <td>10.307649</td>\n",
       "      <td>10.307649</td>\n",
       "      <td>8.295686</td>\n",
       "      <td>6.451394</td>\n",
       "      <td>4.971587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.308205</td>\n",
       "      <td>-0.028025</td>\n",
       "      <td>0.588408</td>\n",
       "      <td>0.588408</td>\n",
       "      <td>0.588408</td>\n",
       "      <td>0.420282</td>\n",
       "      <td>...</td>\n",
       "      <td>1.288882</td>\n",
       "      <td>1.344909</td>\n",
       "      <td>1.288882</td>\n",
       "      <td>1.288882</td>\n",
       "      <td>1.148781</td>\n",
       "      <td>1.148781</td>\n",
       "      <td>1.148781</td>\n",
       "      <td>1.290602</td>\n",
       "      <td>1.177141</td>\n",
       "      <td>0.666585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.868179</td>\n",
       "      <td>1.152661</td>\n",
       "      <td>-3.053347</td>\n",
       "      <td>-3.053347</td>\n",
       "      <td>-3.053347</td>\n",
       "      <td>-0.147147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.072180</td>\n",
       "      <td>-1.037087</td>\n",
       "      <td>-0.235326</td>\n",
       "      <td>-0.235326</td>\n",
       "      <td>-0.531361</td>\n",
       "      <td>-0.531361</td>\n",
       "      <td>-0.531361</td>\n",
       "      <td>-0.222988</td>\n",
       "      <td>-0.408014</td>\n",
       "      <td>-1.345460</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>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.020032</td>\n",
       "      <td>2.669154</td>\n",
       "      <td>6.024693</td>\n",
       "      <td>6.024693</td>\n",
       "      <td>6.024693</td>\n",
       "      <td>4.797963</td>\n",
       "      <td>...</td>\n",
       "      <td>7.944554</td>\n",
       "      <td>9.136459</td>\n",
       "      <td>8.018694</td>\n",
       "      <td>8.018694</td>\n",
       "      <td>9.179148</td>\n",
       "      <td>9.179148</td>\n",
       "      <td>9.179148</td>\n",
       "      <td>9.529643</td>\n",
       "      <td>10.332863</td>\n",
       "      <td>11.318063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.099909</td>\n",
       "      <td>-4.290793</td>\n",
       "      <td>-5.663850</td>\n",
       "      <td>-5.663850</td>\n",
       "      <td>-5.663850</td>\n",
       "      <td>-6.472967</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.105190</td>\n",
       "      <td>-8.495771</td>\n",
       "      <td>-6.509743</td>\n",
       "      <td>-6.509743</td>\n",
       "      <td>-5.271550</td>\n",
       "      <td>-5.271550</td>\n",
       "      <td>-5.271550</td>\n",
       "      <td>-4.634055</td>\n",
       "      <td>-3.126153</td>\n",
       "      <td>-2.537697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172392</td>\n",
       "      <td>0.215494</td>\n",
       "      <td>1.686988</td>\n",
       "      <td>1.686988</td>\n",
       "      <td>1.686988</td>\n",
       "      <td>1.619262</td>\n",
       "      <td>...</td>\n",
       "      <td>2.401188</td>\n",
       "      <td>3.035345</td>\n",
       "      <td>2.912200</td>\n",
       "      <td>2.912200</td>\n",
       "      <td>2.924517</td>\n",
       "      <td>2.924517</td>\n",
       "      <td>2.924517</td>\n",
       "      <td>2.536630</td>\n",
       "      <td>2.899893</td>\n",
       "      <td>3.146165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.826660</td>\n",
       "      <td>-4.030864</td>\n",
       "      <td>-2.465164</td>\n",
       "      <td>-2.465164</td>\n",
       "      <td>-2.465164</td>\n",
       "      <td>-0.888345</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.749822</td>\n",
       "      <td>-3.009153</td>\n",
       "      <td>-2.561389</td>\n",
       "      <td>-2.561389</td>\n",
       "      <td>-1.419591</td>\n",
       "      <td>-1.419591</td>\n",
       "      <td>-1.419591</td>\n",
       "      <td>-0.473692</td>\n",
       "      <td>0.455407</td>\n",
       "      <td>-0.445714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.876093</td>\n",
       "      <td>-3.324678</td>\n",
       "      <td>-3.543752</td>\n",
       "      <td>-3.543752</td>\n",
       "      <td>-3.543752</td>\n",
       "      <td>-4.802589</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.903422</td>\n",
       "      <td>-3.380270</td>\n",
       "      <td>-2.333959</td>\n",
       "      <td>-2.333959</td>\n",
       "      <td>-2.170477</td>\n",
       "      <td>-2.170477</td>\n",
       "      <td>-2.170477</td>\n",
       "      <td>-2.056037</td>\n",
       "      <td>-2.922507</td>\n",
       "      <td>-2.938859</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight  2024-11-04  \\\n",
       "Ticker                                                                        \n",
       "NEE                         NextEra Energy (NEE)       1.037676         NaN   \n",
       "DUK                            Duke Energy (DUK)       0.974257         NaN   \n",
       "ATO                           Atmos Energy (ATO)       1.040557         NaN   \n",
       "AGR                               Avangrid (AGR)       0.847966         NaN   \n",
       "OTTR                           Otter Tail (OTTR)       1.268206         NaN   \n",
       "...                                          ...            ...         ...   \n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092         NaN   \n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776         NaN   \n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863         NaN   \n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161         NaN   \n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889         NaN   \n",
       "\n",
       "        2024-11-05  2024-11-06  2024-11-07  2024-11-08  2024-11-09  \\\n",
       "Ticker                                                               \n",
       "NEE            0.0   -5.251116   -5.123960   -2.136045   -2.136045   \n",
       "DUK            0.0   -0.394463   -2.629731   -0.745089   -0.745089   \n",
       "ATO            0.0    0.652275    2.507623    4.500665    4.500665   \n",
       "AGR            0.0   -0.308205   -0.028025    0.588408    0.588408   \n",
       "OTTR           0.0    4.868179    1.152661   -3.053347   -3.053347   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST           0.0    1.020032    2.669154    6.024693    6.024693   \n",
       "DG             0.0   -5.099909   -4.290793   -5.663850   -5.663850   \n",
       "CLX            0.0    0.172392    0.215494    1.686988    1.686988   \n",
       "HSY            0.0   -1.826660   -4.030864   -2.465164   -2.465164   \n",
       "UL             0.0   -3.876093   -3.324678   -3.543752   -3.543752   \n",
       "\n",
       "        2024-11-10  2024-11-11  ...  2024-11-25  2024-11-26  2024-11-27  \\\n",
       "Ticker                          ...                                       \n",
       "NEE      -2.136045   -3.483785  ...   -1.902573   -0.814595    0.503780   \n",
       "DUK      -0.745089   -1.420057  ...    1.767301    3.572565    3.908837   \n",
       "ATO       4.500665    5.892165  ...    9.476613    9.585965   10.387824   \n",
       "AGR       0.588408    0.420282  ...    1.288882    1.344909    1.288882   \n",
       "OTTR     -3.053347   -0.147147  ...    1.072180   -1.037087   -0.235326   \n",
       "...            ...         ...  ...         ...         ...         ...   \n",
       "COST      6.024693    4.797963  ...    7.944554    9.136459    8.018694   \n",
       "DG       -5.663850   -6.472967  ...   -6.105190   -8.495771   -6.509743   \n",
       "CLX       1.686988    1.619262  ...    2.401188    3.035345    2.912200   \n",
       "HSY      -2.465164   -0.888345  ...   -1.749822   -3.009153   -2.561389   \n",
       "UL       -3.543752   -4.802589  ...   -3.903422   -3.380270   -2.333959   \n",
       "\n",
       "        2024-11-28  2024-11-29  2024-11-30  2024-12-01  2024-12-02  \\\n",
       "Ticker                                                               \n",
       "NEE       0.503780    0.695778    0.695778    0.695778   -1.352183   \n",
       "DUK       3.908837    3.581416    3.581416    3.581416    1.926589   \n",
       "ATO      10.387824   10.307649   10.307649   10.307649    8.295686   \n",
       "AGR       1.288882    1.148781    1.148781    1.148781    1.290602   \n",
       "OTTR     -0.235326   -0.531361   -0.531361   -0.531361   -0.222988   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST      8.018694    9.179148    9.179148    9.179148    9.529643   \n",
       "DG       -6.509743   -5.271550   -5.271550   -5.271550   -4.634055   \n",
       "CLX       2.912200    2.924517    2.924517    2.924517    2.536630   \n",
       "HSY      -2.561389   -1.419591   -1.419591   -1.419591   -0.473692   \n",
       "UL       -2.333959   -2.170477   -2.170477   -2.170477   -2.056037   \n",
       "\n",
       "        2024-12-03  2024-12-04  \n",
       "Ticker                          \n",
       "NEE      -2.350563   -3.054554  \n",
       "DUK       0.563791    0.245215  \n",
       "ATO       6.451394    4.971587  \n",
       "AGR       1.177141    0.666585  \n",
       "OTTR     -0.408014   -1.345460  \n",
       "...            ...         ...  \n",
       "COST     10.332863   11.318063  \n",
       "DG       -3.126153   -2.537697  \n",
       "CLX       2.899893    3.146165  \n",
       "HSY       0.455407   -0.445714  \n",
       "UL       -2.922507   -2.938859  \n",
       "\n",
       "[79 rows x 33 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### USE THISS\n",
    "\n",
    "import yfinance as yf\n",
    "from datetime import datetime, timedelta\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "\n",
    "def get_returns(ticker, checkdate, intvl, start_of_year, mth, dys):\n",
    "    # Calculate the start of the year for the given date\n",
    "    try:\n",
    "        # start_of_year = \"2024-01-01\"\n",
    "\n",
    "        checkdate = checkdate + relativedelta(months=mth, days=dys)\n",
    "\n",
    "        # Fetch data using yfinance\n",
    "        data = yf.download(ticker, start=start_of_year, end=checkdate, interval=intvl)\n",
    "\n",
    "        # Get the adjusted close price on the first day of the year\n",
    "        start_price = data[\"Adj Close\"].iloc[0]  # Price on the start of the year\n",
    "\n",
    "        # Calculate the return as the percentage change from the start of the year\n",
    "        current_price = data[\"Adj Close\"].iloc[-1]  # Price on the current date\n",
    "\n",
    "        pct_change = ((current_price / start_price) - 1) * 100  # Percentage change\n",
    "\n",
    "        return pct_change\n",
    "\n",
    "    except Exception as e:\n",
    "        # Catch the specific error for failed downloads\n",
    "        print(f\"Failed to download data for {ticker}: {e}\")\n",
    "        return None\n",
    "\n",
    "\n",
    "for indx in df_updated.index:\n",
    "    for months in df_updated.columns[3:]:  # Skip the first column (e.g., Jan)\n",
    "        # Ensure `months` is handled as a datetime object\n",
    "        if isinstance(months, str):\n",
    "            month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "        else:\n",
    "            month = months  # Already a datetime object\n",
    "\n",
    "        # Update your DataFrame with the calculated return\n",
    "        if indx == \"PKST\":\n",
    "            break\n",
    "        else:\n",
    "            df_updated.at[indx, months] = get_returns(\n",
    "                indx,\n",
    "                month,\n",
    "                intvl=\"1d\",\n",
    "                start_of_year=pd.to_datetime(\"12-05-2024\") - timedelta(days=30),\n",
    "                mth=0,\n",
    "                dys=1,\n",
    "            ).values[0]\n",
    "\n",
    "a = df_updated\n",
    "df_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "      <th>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-07</th>\n",
       "      <th>2024-11-08</th>\n",
       "      <th>2024-11-09</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-11</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-11-25</th>\n",
       "      <th>2024-11-26</th>\n",
       "      <th>2024-11-27</th>\n",
       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>2024-12-02</th>\n",
       "      <th>2024-12-03</th>\n",
       "      <th>2024-12-04</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</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>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.448960</td>\n",
       "      <td>-5.317013</td>\n",
       "      <td>-2.216524</td>\n",
       "      <td>-2.216524</td>\n",
       "      <td>-2.216524</td>\n",
       "      <td>-3.615041</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.974255</td>\n",
       "      <td>-0.845286</td>\n",
       "      <td>0.522761</td>\n",
       "      <td>0.522761</td>\n",
       "      <td>0.721993</td>\n",
       "      <td>0.721993</td>\n",
       "      <td>0.721993</td>\n",
       "      <td>-1.403128</td>\n",
       "      <td>-2.439124</td>\n",
       "      <td>-3.169639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.384309</td>\n",
       "      <td>-2.562033</td>\n",
       "      <td>-0.725908</td>\n",
       "      <td>-0.725908</td>\n",
       "      <td>-0.725908</td>\n",
       "      <td>-1.383500</td>\n",
       "      <td>...</td>\n",
       "      <td>1.721804</td>\n",
       "      <td>3.480595</td>\n",
       "      <td>3.808211</td>\n",
       "      <td>3.808211</td>\n",
       "      <td>3.489219</td>\n",
       "      <td>3.489219</td>\n",
       "      <td>3.489219</td>\n",
       "      <td>1.876992</td>\n",
       "      <td>0.549277</td>\n",
       "      <td>0.238902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.678729</td>\n",
       "      <td>2.609324</td>\n",
       "      <td>4.683198</td>\n",
       "      <td>4.683198</td>\n",
       "      <td>4.683198</td>\n",
       "      <td>6.131133</td>\n",
       "      <td>...</td>\n",
       "      <td>9.860955</td>\n",
       "      <td>9.974742</td>\n",
       "      <td>10.809123</td>\n",
       "      <td>10.809123</td>\n",
       "      <td>10.725695</td>\n",
       "      <td>10.725695</td>\n",
       "      <td>10.725695</td>\n",
       "      <td>8.632133</td>\n",
       "      <td>6.713043</td>\n",
       "      <td>5.173220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.261348</td>\n",
       "      <td>-0.023764</td>\n",
       "      <td>0.498950</td>\n",
       "      <td>0.498950</td>\n",
       "      <td>0.498950</td>\n",
       "      <td>0.356385</td>\n",
       "      <td>...</td>\n",
       "      <td>1.092928</td>\n",
       "      <td>1.140437</td>\n",
       "      <td>1.092928</td>\n",
       "      <td>1.092928</td>\n",
       "      <td>0.974127</td>\n",
       "      <td>0.974127</td>\n",
       "      <td>0.974127</td>\n",
       "      <td>1.094387</td>\n",
       "      <td>0.998175</td>\n",
       "      <td>0.565241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.173853</td>\n",
       "      <td>1.461811</td>\n",
       "      <td>-3.872273</td>\n",
       "      <td>-3.872273</td>\n",
       "      <td>-3.872273</td>\n",
       "      <td>-0.186613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.359745</td>\n",
       "      <td>-1.315240</td>\n",
       "      <td>-0.298442</td>\n",
       "      <td>-0.298442</td>\n",
       "      <td>-0.673875</td>\n",
       "      <td>-0.673875</td>\n",
       "      <td>-0.673875</td>\n",
       "      <td>-0.282795</td>\n",
       "      <td>-0.517446</td>\n",
       "      <td>-1.706320</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>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.643365</td>\n",
       "      <td>4.300252</td>\n",
       "      <td>9.706331</td>\n",
       "      <td>9.706331</td>\n",
       "      <td>9.706331</td>\n",
       "      <td>7.729957</td>\n",
       "      <td>...</td>\n",
       "      <td>12.799403</td>\n",
       "      <td>14.719672</td>\n",
       "      <td>12.918849</td>\n",
       "      <td>12.918849</td>\n",
       "      <td>14.788448</td>\n",
       "      <td>14.788448</td>\n",
       "      <td>14.788448</td>\n",
       "      <td>15.353126</td>\n",
       "      <td>16.647187</td>\n",
       "      <td>18.234435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.961906</td>\n",
       "      <td>-2.491991</td>\n",
       "      <td>-3.289430</td>\n",
       "      <td>-3.289430</td>\n",
       "      <td>-3.289430</td>\n",
       "      <td>-3.759345</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.545749</td>\n",
       "      <td>-4.934142</td>\n",
       "      <td>-3.780704</td>\n",
       "      <td>-3.780704</td>\n",
       "      <td>-3.061591</td>\n",
       "      <td>-3.061591</td>\n",
       "      <td>-3.061591</td>\n",
       "      <td>-2.691349</td>\n",
       "      <td>-1.815595</td>\n",
       "      <td>-1.473834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.197020</td>\n",
       "      <td>0.246281</td>\n",
       "      <td>1.927995</td>\n",
       "      <td>1.927995</td>\n",
       "      <td>1.927995</td>\n",
       "      <td>1.850594</td>\n",
       "      <td>...</td>\n",
       "      <td>2.744228</td>\n",
       "      <td>3.468983</td>\n",
       "      <td>3.328245</td>\n",
       "      <td>3.328245</td>\n",
       "      <td>3.342321</td>\n",
       "      <td>3.342321</td>\n",
       "      <td>3.342321</td>\n",
       "      <td>2.899020</td>\n",
       "      <td>3.314180</td>\n",
       "      <td>3.595634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.772153</td>\n",
       "      <td>-3.910585</td>\n",
       "      <td>-2.391605</td>\n",
       "      <td>-2.391605</td>\n",
       "      <td>-2.391605</td>\n",
       "      <td>-0.861837</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.697608</td>\n",
       "      <td>-2.919361</td>\n",
       "      <td>-2.484958</td>\n",
       "      <td>-2.484958</td>\n",
       "      <td>-1.377231</td>\n",
       "      <td>-1.377231</td>\n",
       "      <td>-1.377231</td>\n",
       "      <td>-0.459558</td>\n",
       "      <td>0.441818</td>\n",
       "      <td>-0.432414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.794266</td>\n",
       "      <td>-3.254491</td>\n",
       "      <td>-3.468941</td>\n",
       "      <td>-3.468941</td>\n",
       "      <td>-3.468941</td>\n",
       "      <td>-4.701202</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.821017</td>\n",
       "      <td>-3.308909</td>\n",
       "      <td>-2.284687</td>\n",
       "      <td>-2.284687</td>\n",
       "      <td>-2.124656</td>\n",
       "      <td>-2.124656</td>\n",
       "      <td>-2.124656</td>\n",
       "      <td>-2.012633</td>\n",
       "      <td>-2.860810</td>\n",
       "      <td>-2.876817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight  2024-11-04  \\\n",
       "Ticker                                                                        \n",
       "NEE                         NextEra Energy (NEE)       1.037676         NaN   \n",
       "DUK                            Duke Energy (DUK)       0.974257         NaN   \n",
       "ATO                           Atmos Energy (ATO)       1.040557         NaN   \n",
       "AGR                               Avangrid (AGR)       0.847966         NaN   \n",
       "OTTR                           Otter Tail (OTTR)       1.268206         NaN   \n",
       "...                                          ...            ...         ...   \n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092         NaN   \n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776         NaN   \n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863         NaN   \n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161         NaN   \n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889         NaN   \n",
       "\n",
       "        2024-11-05  2024-11-06  2024-11-07  2024-11-08  2024-11-09  \\\n",
       "Ticker                                                               \n",
       "NEE            0.0   -5.448960   -5.317013   -2.216524   -2.216524   \n",
       "DUK            0.0   -0.384309   -2.562033   -0.725908   -0.725908   \n",
       "ATO            0.0    0.678729    2.609324    4.683198    4.683198   \n",
       "AGR            0.0   -0.261348   -0.023764    0.498950    0.498950   \n",
       "OTTR           0.0    6.173853    1.461811   -3.872273   -3.872273   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST           0.0    1.643365    4.300252    9.706331    9.706331   \n",
       "DG             0.0   -2.961906   -2.491991   -3.289430   -3.289430   \n",
       "CLX            0.0    0.197020    0.246281    1.927995    1.927995   \n",
       "HSY            0.0   -1.772153   -3.910585   -2.391605   -2.391605   \n",
       "UL             0.0   -3.794266   -3.254491   -3.468941   -3.468941   \n",
       "\n",
       "        2024-11-10  2024-11-11  ...  2024-11-25  2024-11-26  2024-11-27  \\\n",
       "Ticker                          ...                                       \n",
       "NEE      -2.216524   -3.615041  ...   -1.974255   -0.845286    0.522761   \n",
       "DUK      -0.725908   -1.383500  ...    1.721804    3.480595    3.808211   \n",
       "ATO       4.683198    6.131133  ...    9.860955    9.974742   10.809123   \n",
       "AGR       0.498950    0.356385  ...    1.092928    1.140437    1.092928   \n",
       "OTTR     -3.872273   -0.186613  ...    1.359745   -1.315240   -0.298442   \n",
       "...            ...         ...  ...         ...         ...         ...   \n",
       "COST      9.706331    7.729957  ...   12.799403   14.719672   12.918849   \n",
       "DG       -3.289430   -3.759345  ...   -3.545749   -4.934142   -3.780704   \n",
       "CLX       1.927995    1.850594  ...    2.744228    3.468983    3.328245   \n",
       "HSY      -2.391605   -0.861837  ...   -1.697608   -2.919361   -2.484958   \n",
       "UL       -3.468941   -4.701202  ...   -3.821017   -3.308909   -2.284687   \n",
       "\n",
       "        2024-11-28  2024-11-29  2024-11-30  2024-12-01  2024-12-02  \\\n",
       "Ticker                                                               \n",
       "NEE       0.522761    0.721993    0.721993    0.721993   -1.403128   \n",
       "DUK       3.808211    3.489219    3.489219    3.489219    1.876992   \n",
       "ATO      10.809123   10.725695   10.725695   10.725695    8.632133   \n",
       "AGR       1.092928    0.974127    0.974127    0.974127    1.094387   \n",
       "OTTR     -0.298442   -0.673875   -0.673875   -0.673875   -0.282795   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST     12.918849   14.788448   14.788448   14.788448   15.353126   \n",
       "DG       -3.780704   -3.061591   -3.061591   -3.061591   -2.691349   \n",
       "CLX       3.328245    3.342321    3.342321    3.342321    2.899020   \n",
       "HSY      -2.484958   -1.377231   -1.377231   -1.377231   -0.459558   \n",
       "UL       -2.284687   -2.124656   -2.124656   -2.124656   -2.012633   \n",
       "\n",
       "        2024-12-03  2024-12-04  \n",
       "Ticker                          \n",
       "NEE      -2.439124   -3.169639  \n",
       "DUK       0.549277    0.238902  \n",
       "ATO       6.713043    5.173220  \n",
       "AGR       0.998175    0.565241  \n",
       "OTTR     -0.517446   -1.706320  \n",
       "...            ...         ...  \n",
       "COST     16.647187   18.234435  \n",
       "DG       -1.815595   -1.473834  \n",
       "CLX       3.314180    3.595634  \n",
       "HSY       0.441818   -0.432414  \n",
       "UL       -2.860810   -2.876817  \n",
       "\n",
       "[79 rows x 33 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in df_updated.columns[2:]:\n",
    "    df_updated[col] = df_updated[col] * (df_updated[\"Capped_weight\"])\n",
    "df_updated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SECTOR PERFORMANCE -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-07</th>\n",
       "      <th>2024-11-08</th>\n",
       "      <th>2024-11-09</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-11</th>\n",
       "      <th>2024-11-12</th>\n",
       "      <th>2024-11-13</th>\n",
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       "      <th>2024-11-25</th>\n",
       "      <th>2024-11-26</th>\n",
       "      <th>2024-11-27</th>\n",
       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
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       "      <th>2024-12-03</th>\n",
       "      <th>2024-12-04</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.527823</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>5.080643</td>\n",
       "      <td>4.754221</td>\n",
       "      <td>4.805111</td>\n",
       "      <td>...</td>\n",
       "      <td>4.864785</td>\n",
       "      <td>5.412335</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.935313</td>\n",
       "      <td>5.984447</td>\n",
       "      <td>6.642561</td>\n",
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       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.020171</td>\n",
       "      <td>6.148211</td>\n",
       "      <td>6.148211</td>\n",
       "      <td>6.148211</td>\n",
       "      <td>6.969970</td>\n",
       "      <td>6.051103</td>\n",
       "      <td>6.275208</td>\n",
       "      <td>...</td>\n",
       "      <td>7.231430</td>\n",
       "      <td>7.358439</td>\n",
       "      <td>6.962492</td>\n",
       "      <td>6.962492</td>\n",
       "      <td>7.567612</td>\n",
       "      <td>7.567612</td>\n",
       "      <td>7.567612</td>\n",
       "      <td>6.775730</td>\n",
       "      <td>6.103390</td>\n",
       "      <td>6.566557</td>\n",
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       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.647013</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>0.211519</td>\n",
       "      <td>0.323497</td>\n",
       "      <td>0.622123</td>\n",
       "      <td>...</td>\n",
       "      <td>2.388949</td>\n",
       "      <td>2.811988</td>\n",
       "      <td>3.023517</td>\n",
       "      <td>3.023517</td>\n",
       "      <td>3.546097</td>\n",
       "      <td>3.546097</td>\n",
       "      <td>3.546097</td>\n",
       "      <td>3.421675</td>\n",
       "      <td>2.886649</td>\n",
       "      <td>2.600468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.974542</td>\n",
       "      <td>4.666741</td>\n",
       "      <td>4.666741</td>\n",
       "      <td>4.666741</td>\n",
       "      <td>5.258456</td>\n",
       "      <td>4.700233</td>\n",
       "      <td>5.515242</td>\n",
       "      <td>...</td>\n",
       "      <td>6.453052</td>\n",
       "      <td>6.307917</td>\n",
       "      <td>6.196275</td>\n",
       "      <td>6.196275</td>\n",
       "      <td>6.654013</td>\n",
       "      <td>6.654013</td>\n",
       "      <td>6.654013</td>\n",
       "      <td>5.526401</td>\n",
       "      <td>5.515242</td>\n",
       "      <td>2.969748</td>\n",
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       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.337081</td>\n",
       "      <td>6.287813</td>\n",
       "      <td>6.287813</td>\n",
       "      <td>6.287813</td>\n",
       "      <td>7.800347</td>\n",
       "      <td>7.476238</td>\n",
       "      <td>7.541058</td>\n",
       "      <td>...</td>\n",
       "      <td>10.328441</td>\n",
       "      <td>10.544514</td>\n",
       "      <td>10.760587</td>\n",
       "      <td>10.760587</td>\n",
       "      <td>10.933452</td>\n",
       "      <td>10.933452</td>\n",
       "      <td>10.933452</td>\n",
       "      <td>9.831468</td>\n",
       "      <td>9.010376</td>\n",
       "      <td>8.599826</td>\n",
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       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.594329</td>\n",
       "      <td>2.323355</td>\n",
       "      <td>2.323355</td>\n",
       "      <td>2.323355</td>\n",
       "      <td>1.723785</td>\n",
       "      <td>0.306600</td>\n",
       "      <td>0.040879</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.940250</td>\n",
       "      <td>-0.408807</td>\n",
       "      <td>0.122636</td>\n",
       "      <td>0.122636</td>\n",
       "      <td>0.436056</td>\n",
       "      <td>0.436056</td>\n",
       "      <td>0.436056</td>\n",
       "      <td>0.258901</td>\n",
       "      <td>0.034069</td>\n",
       "      <td>-0.020439</td>\n",
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       "      <th>XLRE</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.115694</td>\n",
       "      <td>1.596480</td>\n",
       "      <td>1.596480</td>\n",
       "      <td>1.596480</td>\n",
       "      <td>0.717255</td>\n",
       "      <td>-0.555302</td>\n",
       "      <td>0.231371</td>\n",
       "      <td>...</td>\n",
       "      <td>3.378063</td>\n",
       "      <td>3.887090</td>\n",
       "      <td>4.604345</td>\n",
       "      <td>4.604345</td>\n",
       "      <td>4.118461</td>\n",
       "      <td>4.118461</td>\n",
       "      <td>4.118461</td>\n",
       "      <td>2.545114</td>\n",
       "      <td>1.943545</td>\n",
       "      <td>1.665889</td>\n",
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       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.114236</td>\n",
       "      <td>5.998032</td>\n",
       "      <td>5.998032</td>\n",
       "      <td>5.998032</td>\n",
       "      <td>5.412533</td>\n",
       "      <td>5.546611</td>\n",
       "      <td>5.180118</td>\n",
       "      <td>...</td>\n",
       "      <td>4.402427</td>\n",
       "      <td>4.925355</td>\n",
       "      <td>3.504065</td>\n",
       "      <td>3.504065</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>5.457223</td>\n",
       "      <td>5.837131</td>\n",
       "      <td>7.772414</td>\n",
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       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.634722</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>2.849747</td>\n",
       "      <td>1.670986</td>\n",
       "      <td>1.489639</td>\n",
       "      <td>...</td>\n",
       "      <td>5.699484</td>\n",
       "      <td>7.383426</td>\n",
       "      <td>7.383426</td>\n",
       "      <td>7.383426</td>\n",
       "      <td>7.422284</td>\n",
       "      <td>7.422284</td>\n",
       "      <td>7.422284</td>\n",
       "      <td>5.116586</td>\n",
       "      <td>4.209845</td>\n",
       "      <td>4.196898</td>\n",
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      "text/plain": [
       "      2024-11-04  2024-11-05  2024-11-06  2024-11-07  2024-11-08  2024-11-09  \\\n",
       "SPY          NaN         NaN         NaN    4.527823    4.980608    4.980608   \n",
       "XLI          NaN         NaN         NaN    5.020171    6.148211    6.148211   \n",
       "XLP          NaN         NaN         NaN   -0.647013    0.559907    0.559907   \n",
       "XLE          NaN         NaN         NaN    3.974542    4.666741    4.666741   \n",
       "XLF          NaN         NaN         NaN    5.337081    6.287813    6.287813   \n",
       "XLV          NaN         NaN         NaN    1.594329    2.323355    2.323355   \n",
       "XLRE         NaN         NaN         NaN   -0.115694    1.596480    1.596480   \n",
       "XLK          NaN         NaN         NaN    6.114236    5.998032    5.998032   \n",
       "XLU          NaN         NaN         NaN    0.634722    2.564771    2.564771   \n",
       "\n",
       "      2024-11-10  2024-11-11  2024-11-12  2024-11-13  ...  2024-11-25  \\\n",
       "SPY     4.980608    5.080643    4.754221    4.805111  ...    4.864785   \n",
       "XLI     6.148211    6.969970    6.051103    6.275208  ...    7.231430   \n",
       "XLP     0.559907    0.211519    0.323497    0.622123  ...    2.388949   \n",
       "XLE     4.666741    5.258456    4.700233    5.515242  ...    6.453052   \n",
       "XLF     6.287813    7.800347    7.476238    7.541058  ...   10.328441   \n",
       "XLV     2.323355    1.723785    0.306600    0.040879  ...   -0.940250   \n",
       "XLRE    1.596480    0.717255   -0.555302    0.231371  ...    3.378063   \n",
       "XLK     5.998032    5.412533    5.546611    5.180118  ...    4.402427   \n",
       "XLU     2.564771    2.849747    1.670986    1.489639  ...    5.699484   \n",
       "\n",
       "      2024-11-26  2024-11-27  2024-11-28  2024-11-29  2024-11-30  2024-12-01  \\\n",
       "SPY     5.412335    5.092929    5.092929    5.745773    5.745773    5.745773   \n",
       "XLI     7.358439    6.962492    6.962492    7.567612    7.567612    7.567612   \n",
       "XLP     2.811988    3.023517    3.023517    3.546097    3.546097    3.546097   \n",
       "XLE     6.307917    6.196275    6.196275    6.654013    6.654013    6.654013   \n",
       "XLF    10.544514   10.760587   10.760587   10.933452   10.933452   10.933452   \n",
       "XLV    -0.408807    0.122636    0.122636    0.436056    0.436056    0.436056   \n",
       "XLRE    3.887090    4.604345    4.604345    4.118461    4.118461    4.118461   \n",
       "XLK     4.925355    3.504065    3.504065    4.465000    4.465000    4.465000   \n",
       "XLU     7.383426    7.383426    7.383426    7.422284    7.422284    7.422284   \n",
       "\n",
       "      2024-12-02  2024-12-03  2024-12-04  \n",
       "SPY     5.935313    5.984447    6.642561  \n",
       "XLI     6.775730    6.103390    6.566557  \n",
       "XLP     3.421675    2.886649    2.600468  \n",
       "XLE     5.526401    5.515242    2.969748  \n",
       "XLF     9.831468    9.010376    8.599826  \n",
       "XLV     0.258901    0.034069   -0.020439  \n",
       "XLRE    2.545114    1.943545    1.665889  \n",
       "XLK     5.457223    5.837131    7.772414  \n",
       "XLU     5.116586    4.209845    4.196898  \n",
       "\n",
       "[9 rows x 31 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sectors = [\"SPY\", \"XLI\", \"XLP\", \"XLE\", \"XLF\", \"XLV\", \"XLRE\", \"XLK\", \"XLU\"]\n",
    "\n",
    "df_sectors = pd.DataFrame(df_sectors, index=df_sectors)\n",
    "\n",
    "start_date = pd.to_datetime(\"12-04-2024\")\n",
    "\n",
    "\n",
    "# Generate the previous 30 days, starting from today\n",
    "dates = pd.date_range(\n",
    "    start=start_date - pd.Timedelta(days=30), end=start_date, freq=\"D\"\n",
    ")\n",
    "\n",
    "# Create a DataFrame with 30 empty columns (NaN values) for each of the previous 30 days\n",
    "new_columns = pd.DataFrame(\n",
    "    np.nan, index=df_sectors.index, columns=dates.strftime(\"%Y-%m-%d\")\n",
    ")\n",
    "\n",
    "# Concatenate the existing DataFrame with the new columns\n",
    "df_sectors = pd.concat([df_sectors, new_columns], axis=1)\n",
    "df_sectors = df_sectors.drop(df_sectors.columns[0], axis=1)\n",
    "df_sectors\n",
    "\n",
    "\n",
    "for indx in df_sectors.index:\n",
    "    for months in df_sectors.columns[3:]:  # Skip the first column (e.g., Jan)\n",
    "        # Ensure `months` is handled as a datetime object\n",
    "        if isinstance(months, str):\n",
    "            month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "        else:\n",
    "            month = months  # Already a datetime object\n",
    "        df_sectors.at[indx, months] = get_returns(\n",
    "            indx,\n",
    "            month,\n",
    "            intvl=\"1d\",\n",
    "            start_of_year=pd.to_datetime(\"12-04-2024\") - timedelta(days=30),\n",
    "            mth=0,\n",
    "            dys=1,\n",
    "        ).values[0]\n",
    "\n",
    "df_sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-07</th>\n",
       "      <th>2024-11-08</th>\n",
       "      <th>2024-11-09</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-11</th>\n",
       "      <th>2024-11-12</th>\n",
       "      <th>2024-11-13</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-11-25</th>\n",
       "      <th>2024-11-26</th>\n",
       "      <th>2024-11-27</th>\n",
       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>2024-12-02</th>\n",
       "      <th>2024-12-03</th>\n",
       "      <th>2024-12-04</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.527823</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>5.080643</td>\n",
       "      <td>4.754221</td>\n",
       "      <td>4.805111</td>\n",
       "      <td>...</td>\n",
       "      <td>4.864785</td>\n",
       "      <td>5.412335</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.935313</td>\n",
       "      <td>5.984447</td>\n",
       "      <td>6.642561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.020171</td>\n",
       "      <td>6.148211</td>\n",
       "      <td>6.148211</td>\n",
       "      <td>6.148211</td>\n",
       "      <td>6.969970</td>\n",
       "      <td>6.051103</td>\n",
       "      <td>6.275208</td>\n",
       "      <td>...</td>\n",
       "      <td>7.231430</td>\n",
       "      <td>7.358439</td>\n",
       "      <td>6.962492</td>\n",
       "      <td>6.962492</td>\n",
       "      <td>7.567612</td>\n",
       "      <td>7.567612</td>\n",
       "      <td>7.567612</td>\n",
       "      <td>6.775730</td>\n",
       "      <td>6.103390</td>\n",
       "      <td>6.566557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLP</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.647013</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>0.559907</td>\n",
       "      <td>0.211519</td>\n",
       "      <td>0.323497</td>\n",
       "      <td>0.622123</td>\n",
       "      <td>...</td>\n",
       "      <td>2.388949</td>\n",
       "      <td>2.811988</td>\n",
       "      <td>3.023517</td>\n",
       "      <td>3.023517</td>\n",
       "      <td>3.546097</td>\n",
       "      <td>3.546097</td>\n",
       "      <td>3.546097</td>\n",
       "      <td>3.421675</td>\n",
       "      <td>2.886649</td>\n",
       "      <td>2.600468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.974542</td>\n",
       "      <td>4.666741</td>\n",
       "      <td>4.666741</td>\n",
       "      <td>4.666741</td>\n",
       "      <td>5.258456</td>\n",
       "      <td>4.700233</td>\n",
       "      <td>5.515242</td>\n",
       "      <td>...</td>\n",
       "      <td>6.453052</td>\n",
       "      <td>6.307917</td>\n",
       "      <td>6.196275</td>\n",
       "      <td>6.196275</td>\n",
       "      <td>6.654013</td>\n",
       "      <td>6.654013</td>\n",
       "      <td>6.654013</td>\n",
       "      <td>5.526401</td>\n",
       "      <td>5.515242</td>\n",
       "      <td>2.969748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLF</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.337081</td>\n",
       "      <td>6.287813</td>\n",
       "      <td>6.287813</td>\n",
       "      <td>6.287813</td>\n",
       "      <td>7.800347</td>\n",
       "      <td>7.476238</td>\n",
       "      <td>7.541058</td>\n",
       "      <td>...</td>\n",
       "      <td>10.328441</td>\n",
       "      <td>10.544514</td>\n",
       "      <td>10.760587</td>\n",
       "      <td>10.760587</td>\n",
       "      <td>10.933452</td>\n",
       "      <td>10.933452</td>\n",
       "      <td>10.933452</td>\n",
       "      <td>9.831468</td>\n",
       "      <td>9.010376</td>\n",
       "      <td>8.599826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.594329</td>\n",
       "      <td>2.323355</td>\n",
       "      <td>2.323355</td>\n",
       "      <td>2.323355</td>\n",
       "      <td>1.723785</td>\n",
       "      <td>0.306600</td>\n",
       "      <td>0.040879</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.940250</td>\n",
       "      <td>-0.408807</td>\n",
       "      <td>0.122636</td>\n",
       "      <td>0.122636</td>\n",
       "      <td>0.436056</td>\n",
       "      <td>0.436056</td>\n",
       "      <td>0.436056</td>\n",
       "      <td>0.258901</td>\n",
       "      <td>0.034069</td>\n",
       "      <td>-0.020439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLRE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.115694</td>\n",
       "      <td>1.596480</td>\n",
       "      <td>1.596480</td>\n",
       "      <td>1.596480</td>\n",
       "      <td>0.717255</td>\n",
       "      <td>-0.555302</td>\n",
       "      <td>0.231371</td>\n",
       "      <td>...</td>\n",
       "      <td>3.378063</td>\n",
       "      <td>3.887090</td>\n",
       "      <td>4.604345</td>\n",
       "      <td>4.604345</td>\n",
       "      <td>4.118461</td>\n",
       "      <td>4.118461</td>\n",
       "      <td>4.118461</td>\n",
       "      <td>2.545114</td>\n",
       "      <td>1.943545</td>\n",
       "      <td>1.665889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLK</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.114236</td>\n",
       "      <td>5.998032</td>\n",
       "      <td>5.998032</td>\n",
       "      <td>5.998032</td>\n",
       "      <td>5.412533</td>\n",
       "      <td>5.546611</td>\n",
       "      <td>5.180118</td>\n",
       "      <td>...</td>\n",
       "      <td>4.402427</td>\n",
       "      <td>4.925355</td>\n",
       "      <td>3.504065</td>\n",
       "      <td>3.504065</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>5.457223</td>\n",
       "      <td>5.837131</td>\n",
       "      <td>7.772414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLU</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.634722</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>2.564771</td>\n",
       "      <td>2.849747</td>\n",
       "      <td>1.670986</td>\n",
       "      <td>1.489639</td>\n",
       "      <td>...</td>\n",
       "      <td>5.699484</td>\n",
       "      <td>7.383426</td>\n",
       "      <td>7.383426</td>\n",
       "      <td>7.383426</td>\n",
       "      <td>7.422284</td>\n",
       "      <td>7.422284</td>\n",
       "      <td>7.422284</td>\n",
       "      <td>5.116586</td>\n",
       "      <td>4.209845</td>\n",
       "      <td>4.196898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      2024-11-04  2024-11-05  2024-11-06  2024-11-07  2024-11-08  2024-11-09  \\\n",
       "SPY          0.0         0.0         0.0    4.527823    4.980608    4.980608   \n",
       "XLI          0.0         0.0         0.0    5.020171    6.148211    6.148211   \n",
       "XLP          0.0         0.0         0.0   -0.647013    0.559907    0.559907   \n",
       "XLE          0.0         0.0         0.0    3.974542    4.666741    4.666741   \n",
       "XLF          0.0         0.0         0.0    5.337081    6.287813    6.287813   \n",
       "XLV          0.0         0.0         0.0    1.594329    2.323355    2.323355   \n",
       "XLRE         0.0         0.0         0.0   -0.115694    1.596480    1.596480   \n",
       "XLK          0.0         0.0         0.0    6.114236    5.998032    5.998032   \n",
       "XLU          0.0         0.0         0.0    0.634722    2.564771    2.564771   \n",
       "\n",
       "      2024-11-10  2024-11-11  2024-11-12  2024-11-13  ...  2024-11-25  \\\n",
       "SPY     4.980608    5.080643    4.754221    4.805111  ...    4.864785   \n",
       "XLI     6.148211    6.969970    6.051103    6.275208  ...    7.231430   \n",
       "XLP     0.559907    0.211519    0.323497    0.622123  ...    2.388949   \n",
       "XLE     4.666741    5.258456    4.700233    5.515242  ...    6.453052   \n",
       "XLF     6.287813    7.800347    7.476238    7.541058  ...   10.328441   \n",
       "XLV     2.323355    1.723785    0.306600    0.040879  ...   -0.940250   \n",
       "XLRE    1.596480    0.717255   -0.555302    0.231371  ...    3.378063   \n",
       "XLK     5.998032    5.412533    5.546611    5.180118  ...    4.402427   \n",
       "XLU     2.564771    2.849747    1.670986    1.489639  ...    5.699484   \n",
       "\n",
       "      2024-11-26  2024-11-27  2024-11-28  2024-11-29  2024-11-30  2024-12-01  \\\n",
       "SPY     5.412335    5.092929    5.092929    5.745773    5.745773    5.745773   \n",
       "XLI     7.358439    6.962492    6.962492    7.567612    7.567612    7.567612   \n",
       "XLP     2.811988    3.023517    3.023517    3.546097    3.546097    3.546097   \n",
       "XLE     6.307917    6.196275    6.196275    6.654013    6.654013    6.654013   \n",
       "XLF    10.544514   10.760587   10.760587   10.933452   10.933452   10.933452   \n",
       "XLV    -0.408807    0.122636    0.122636    0.436056    0.436056    0.436056   \n",
       "XLRE    3.887090    4.604345    4.604345    4.118461    4.118461    4.118461   \n",
       "XLK     4.925355    3.504065    3.504065    4.465000    4.465000    4.465000   \n",
       "XLU     7.383426    7.383426    7.383426    7.422284    7.422284    7.422284   \n",
       "\n",
       "      2024-12-02  2024-12-03  2024-12-04  \n",
       "SPY     5.935313    5.984447    6.642561  \n",
       "XLI     6.775730    6.103390    6.566557  \n",
       "XLP     3.421675    2.886649    2.600468  \n",
       "XLE     5.526401    5.515242    2.969748  \n",
       "XLF     9.831468    9.010376    8.599826  \n",
       "XLV     0.258901    0.034069   -0.020439  \n",
       "XLRE    2.545114    1.943545    1.665889  \n",
       "XLK     5.457223    5.837131    7.772414  \n",
       "XLU     5.116586    4.209845    4.196898  \n",
       "\n",
       "[9 rows x 31 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sectors = df_sectors.fillna(0)\n",
    "df_sectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "for ticker in df_sectors.index:\n",
    "    # Select the data from the 3rd column onward\n",
    "    plt.plot(\n",
    "        df_sectors.columns[2:],\n",
    "        df_sectors.loc[ticker].values[2:],\n",
    "        label=ticker,\n",
    "        marker=\"o\",\n",
    "        markersize=3,\n",
    "    )\n",
    "\n",
    "plt.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=0.8)\n",
    "plt.legend(title=\"Ticker\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"% Change (30 days)\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"% Change per Industry\")\n",
    "plt.show()\n",
    "plt.savefig(\"sector_returns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XLI     6.566557\n",
       "XLP     2.600468\n",
       "XLE     2.969748\n",
       "XLF     8.599826\n",
       "XLV    -0.020439\n",
       "XLRE    1.665889\n",
       "XLK     7.772414\n",
       "XLU     4.196898\n",
       "Name: 2024-12-04, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sect_returns = df_sectors[1:][df_sectors.columns[-1]]\n",
    "sect_returns.mean()\n",
    "sect_returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PORTFOLIO VS SPY -"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = df_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
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      "[*********************100%***********************]  1 of 1 completed\n",
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      "[*********************100%***********************]  1 of 1 completed\n",
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      "[*********************100%***********************]  1 of 1 completed\n",
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      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "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",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-07</th>\n",
       "      <th>2024-11-08</th>\n",
       "      <th>2024-11-09</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-11</th>\n",
       "      <th>2024-11-12</th>\n",
       "      <th>2024-11-13</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-11-27</th>\n",
       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>2024-12-02</th>\n",
       "      <th>2024-12-03</th>\n",
       "      <th>2024-12-04</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>portfolio</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.998725</td>\n",
       "      <td>4.234054</td>\n",
       "      <td>5.919256</td>\n",
       "      <td>5.919256</td>\n",
       "      <td>5.919256</td>\n",
       "      <td>7.210174</td>\n",
       "      <td>5.950944</td>\n",
       "      <td>5.849599</td>\n",
       "      <td>...</td>\n",
       "      <td>8.028076</td>\n",
       "      <td>8.028076</td>\n",
       "      <td>8.724001</td>\n",
       "      <td>8.724001</td>\n",
       "      <td>8.724001</td>\n",
       "      <td>8.459807</td>\n",
       "      <td>8.198309</td>\n",
       "      <td>8.329815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.209178</td>\n",
       "      <td>3.725800</td>\n",
       "      <td>4.527823</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>5.080643</td>\n",
       "      <td>4.754221</td>\n",
       "      <td>4.805111</td>\n",
       "      <td>...</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.935313</td>\n",
       "      <td>5.984447</td>\n",
       "      <td>6.642561</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           2024-11-04  2024-11-05  2024-11-06  2024-11-07  2024-11-08  \\\n",
       "portfolio         NaN    0.000000    3.998725    4.234054    5.919256   \n",
       "SPY               NaN    1.209178    3.725800    4.527823    4.980608   \n",
       "\n",
       "           2024-11-09  2024-11-10  2024-11-11  2024-11-12  2024-11-13  ...  \\\n",
       "portfolio    5.919256    5.919256    7.210174    5.950944    5.849599  ...   \n",
       "SPY          4.980608    4.980608    5.080643    4.754221    4.805111  ...   \n",
       "\n",
       "           2024-11-27  2024-11-28  2024-11-29  2024-11-30  2024-12-01  \\\n",
       "portfolio    8.028076    8.028076    8.724001    8.724001    8.724001   \n",
       "SPY          5.092929    5.092929    5.745773    5.745773    5.745773   \n",
       "\n",
       "           2024-12-02  2024-12-03  2024-12-04  Company Name  Capped_weight  \n",
       "portfolio    8.459807    8.198309    8.329815           NaN            NaN  \n",
       "SPY          5.935313    5.984447    6.642561           NaN            NaN  \n",
       "\n",
       "[2 rows x 33 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the values of each column (from the 3rd column onward)\n",
    "column_wavg = df_updated.iloc[:, 2:].mean(axis=0)\n",
    "\n",
    "# Create a new DataFrame with the same columns\n",
    "df_avgs = pd.DataFrame(\n",
    "    column_wavg\n",
    ").T  # Transpose to match the structure of the original DataFrame\n",
    "df_avgs.columns = df_updated.columns[\n",
    "    2:\n",
    "]  # Assign the column names from the 3rd column onward\n",
    "\n",
    "# Add the two new index values: 'portfolio name' and 'SPY'\n",
    "df_avgs.index = [\"portfolio\"]\n",
    "spy_values = [np.NAN, np.NAN, np.NAN]\n",
    "\n",
    "# Iterate over the columns from the 3rd column onward\n",
    "for months in df_updated.columns[3:]:\n",
    "    if isinstance(months, str):\n",
    "        month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "    else:\n",
    "        month = months  # Already a datetime object\n",
    "    month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "    spy_value = get_returns(\n",
    "        \"SPY\",\n",
    "        month,\n",
    "        intvl=\"1d\",\n",
    "        start_of_year=pd.to_datetime(\"12-04-2024\") - timedelta(days=30),\n",
    "        mth=0,\n",
    "        dys=1,\n",
    "    ).values[0]\n",
    "    spy_values.append(spy_value)\n",
    "\n",
    "spy_values = pd.DataFrame([spy_values])\n",
    "spy_values.columns = df_updated.columns\n",
    "spy_values.index = [\"SPY\"]\n",
    "df_avgs = pd.concat([df_avgs, spy_values])\n",
    "df_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>2024-11-04</th>\n",
       "      <th>2024-11-05</th>\n",
       "      <th>2024-11-06</th>\n",
       "      <th>2024-11-07</th>\n",
       "      <th>2024-11-08</th>\n",
       "      <th>2024-11-09</th>\n",
       "      <th>2024-11-10</th>\n",
       "      <th>2024-11-11</th>\n",
       "      <th>2024-11-12</th>\n",
       "      <th>2024-11-13</th>\n",
       "      <th>...</th>\n",
       "      <th>2024-11-27</th>\n",
       "      <th>2024-11-28</th>\n",
       "      <th>2024-11-29</th>\n",
       "      <th>2024-11-30</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>2024-12-02</th>\n",
       "      <th>2024-12-03</th>\n",
       "      <th>2024-12-04</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>portfolio</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.998725</td>\n",
       "      <td>4.234054</td>\n",
       "      <td>5.919256</td>\n",
       "      <td>5.919256</td>\n",
       "      <td>5.919256</td>\n",
       "      <td>7.210174</td>\n",
       "      <td>5.950944</td>\n",
       "      <td>5.849599</td>\n",
       "      <td>...</td>\n",
       "      <td>8.028076</td>\n",
       "      <td>8.028076</td>\n",
       "      <td>8.724001</td>\n",
       "      <td>8.724001</td>\n",
       "      <td>8.724001</td>\n",
       "      <td>8.459807</td>\n",
       "      <td>8.198309</td>\n",
       "      <td>8.329815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.209178</td>\n",
       "      <td>3.725800</td>\n",
       "      <td>4.527823</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>4.980608</td>\n",
       "      <td>5.080643</td>\n",
       "      <td>4.754221</td>\n",
       "      <td>4.805111</td>\n",
       "      <td>...</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.092929</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.745773</td>\n",
       "      <td>5.935313</td>\n",
       "      <td>5.984447</td>\n",
       "      <td>6.642561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           2024-11-04  2024-11-05  2024-11-06  2024-11-07  2024-11-08  \\\n",
       "portfolio         0.0    0.000000    3.998725    4.234054    5.919256   \n",
       "SPY               0.0    1.209178    3.725800    4.527823    4.980608   \n",
       "\n",
       "           2024-11-09  2024-11-10  2024-11-11  2024-11-12  2024-11-13  ...  \\\n",
       "portfolio    5.919256    5.919256    7.210174    5.950944    5.849599  ...   \n",
       "SPY          4.980608    4.980608    5.080643    4.754221    4.805111  ...   \n",
       "\n",
       "           2024-11-27  2024-11-28  2024-11-29  2024-11-30  2024-12-01  \\\n",
       "portfolio    8.028076    8.028076    8.724001    8.724001    8.724001   \n",
       "SPY          5.092929    5.092929    5.745773    5.745773    5.745773   \n",
       "\n",
       "           2024-12-02  2024-12-03  2024-12-04  Company Name  Capped_weight  \n",
       "portfolio    8.459807    8.198309    8.329815           0.0            0.0  \n",
       "SPY          5.935313    5.984447    6.642561           0.0            0.0  \n",
       "\n",
       "[2 rows x 33 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avgs = df_avgs.fillna(0)\n",
    "df_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ltYiIiEg58+STT9K2bdtcM5579erFU089Rfv27fHy8uLDDz/koYceuux12rdvz5QpU3jkkUdo2bIlDz/8MA0bNiQzM5NNmzbx2Wef0ahRI2655ZZC5evTpw//93//xyuvvELXrl3ZvXs3r732GjVq1CArK6vQr9fBwYFXX32VESNGcOedd/Lggw8SHx/Pq6++SqVKlXBwOL+OY+DAgXz77bfcdNNNjB49mjZt2uDs7MzRo0f5+++/ue222+jbt+9ln8/FxYX33nuPM2fO0Lp1a1atWsWECRO48cYb6dSpEwA9e/akV69ePPvssyQmJtKxY0e2bt3KK6+8QvPmzRk0aFCBXlvjxo1ZtmwZP//8M5UqVcLb2/uyxfqbb75Jz549ue666xg7diwuLi58+umnbN++nVmzZhVqRXfdunUZPnw4H330EQ4ODtx4440cPHiQl156idDQUJ544okrXsPPz4++ffsyY8YM4uPjGTt2bK5fj61btzJq1KicTwG4uLjw119/sXXrVsaNG3fZazds2JDu3btz4403UqtWLdLS0vjvv/947733CA4OZujQoQV+rSIiIiJSslRai4iIiJQjS5cuZf78+ezYsSPX8aFDh7J//37GjRtHRkYGd9xxBy+88MIVrzds2DDatGnDpEmTePvtt4mOjsbZ2Znw8HDuueceRo0aVeiML7zwAikpKXz55ZdMnDiRBg0aMHXqVH788ccCb4R4seHDh2OxWJg4cSJ9+/YlLCyMcePG8dNPP3H48OGc8xwdHVm4cCEffPAB33zzDW+++SZOTk5UrVqVrl270rhx4ys+l7OzM7/88guPP/44EyZMwN3dnWHDhvHOO+/knGOxWFiwYAHjx49n+vTpvP766wQEBDBo0CDeeOONPCvNL+WDDz7g0UcfZeDAgaSkpNC1a9fL/jvq2rUrf/31F6+88gqDBw/GarXStGlTFi5cSJ8+fQr0nBeaMmUKtWrV4ssvv+STTz7B19eX3r178+abb+Y7vzw/Q4YMYdasWUDeTR1DQkKoVasWn376KUeOHMFisVCzZk3ee+89Hnvsscte96233mLx4sW8/vrrREdHk5WVRWhoKPfccw8vvPBCkcwJFxEREZHiYTGu9LlPEREREZEyKD4+nvDwcG6//XY+++yzIrnm4MGDmTt3LmfOnCmS64mIiIiIlEdaaS0iIiIiZV50dDSvv/461113Hf7+/hw6dIhJkyaRlJTE6NGjzY4nIiIiIiIXUGktIiIiImWeq6srBw8e5JFHHuHUqVN4eHjQrl07pk6dSsOGDc2OJyIiIiIiF9B4EBERERERERERERGxGw5XPkVEREREREREREREpGSotBYRERERERERERERu6HSWkRERERERERERETsRqneiNFqtXL8+HG8vb2xWCxmxxERERERERERERGRfBiGQVJSEpUrV8bB4fJrqUt1aX38+HFCQ0PNjiEiIiIiIiIiIiIiBXDkyBGqVq162XNKdWnt7e0N2F6oj4+PyWlEREREREREREREJD+JiYmEhobmdLqXU6pL63MjQXx8fFRai4iIiIiIiIiIiNi5gox51kaMIiIiIiIiIiIiImI3VFqLiIiIiIiIiIiIiN1QaS0iIiIiIiIiIiIidqNUz7QuCMMwyMrKIjs72+wochnOzs44OjqaHUNERERERERERERMVqZL64yMDKKiokhJSTE7ilyBxWKhatWqeHl5mR1FRERERERERERETFRmS2ur1cqBAwdwdHSkcuXKuLi4FGhnSil5hmEQGxvL0aNHqVOnjlZci4iIiIiIiIiIlGNltrTOyMjAarUSGhqKh4eH2XHkCgIDAzl48CCZmZkqrUVERERERERERMqxMr8Ro4NDmX+JZYJWwYuIiIiIiIiIiAiUg9JaREREREREREREREoPldYiIiIiIiIiIiIiYjdUWtuB8ePH06xZswKfb7FYWLBgQbHlERERERERERERETGLSutiZrFYLvs1ePBgxo4dy9KlS82OKiIiIiIiIiIiImI6J7MDlHVRUVE5/zx79mxefvlldu/enXPM3d0dLy8vvLy8zIgHQEZGBi4uLqY9v4iIiIiIiIiIiMg5WmldzEJCQnK+fH19sVgseY7lNx7kq6++omHDhri6ulKpUiVGjRp1yed47bXXCA4OZvPmzQCsWrWKLl264O7uTmhoKI8//jjJyck554eFhTFhwgQGDx6Mr68vw4YNK46XLiIiIiIiIiIiIlJoKq3t0JQpU3j00UcZPnw427ZtY+HChdSuXTvPeYZhMHr0aL788kv+/fdfmjVrxrZt2+jVqxf9+vVj69atzJ49m3///TdP6f3OO+/QqFEjNmzYwEsvvVRSL01ERERERERERETksjQexA5NmDCBp556itGjR+cca926da5zsrKyuP/++1m/fj0rV66katWqgK2MvueeexgzZgwAderU4cMPP6Rr165MmTIFNzc3AK6//nrGjh1bMi9IREREREREREqlRdujmLwkkgNxydQI8GRMjzr0blTJ7Fj5Ki1ZS0tOETOptLYzMTExHD9+nO7du1/2vCeeeAJXV1fWrFlDQEBAzvENGzawd+9evv3225xjhmFgtVo5cOAA9evXB6BVq1bF8wJEREREREREpExYtD2KkTM3YgEMYHd0EiNnbmREl5o0r1bB7Hi5bDp8mmn/7Lf7rJfKOfW+FiquRS6g0trOuLu7F+i8nj17MmvWLBYvXsy9996bc9xqtTJixAgef/zxPI+pVq1azj97enpee1gRERERERERKbMmL4nMKVe54HbaP/tNSnRlpSXrxTlf/3Un3eoG4ebsaFYkEbui0trOeHt7ExYWxtKlS7nuuusued6tt97KLbfcwj333IOjoyMDBw4EoEWLFuzYsSPfGdgiIiIiIiIiIgW1N+ZMTql6IQvQsrr9rF4G2HDodKnIeqmcR06n0vy1P+lYO4Ae9YO4vn4QQd5uJZ5PxF6otLZD48ePZ+TIkQQFBXHjjTeSlJTEypUreeyxx3Kd17dvX7755hsGDRqEk5MTd955J88++yzt2rXj0UcfZdiwYXh6erJz507+/PNPPvroI5NekYiIiIiIiIiUFlnZVt75YzdZ1rz1qsUC9UK8mftwBxOSXVrvyf+wOzopVyFsj1nzywng5GAhNTObJTtPsGTnCQCaVvWle/1gutcPokElHywWS8kHFjGJSms79MADD5CWlsakSZMYO3YsAQEB3Hnnnfmee+edd2K1Whk0aBAODg7069eP5cuX88ILL9C5c2cMw6BWrVoMGDCghF+FiIiIiIiIiJQ2p5IzeHzWJv7dG5dzzGIBwzh/O7p7uIkJ8zemRx3b/G07z3qpnB/f05zQih4s3RnD0p0n2HI0Iefr/T/3UNnXLafAblfTX2NEpMyzGIaR36cSSoXExER8fX1JSEjAx8cn131paWkcOHCAGjVq4Oamj1PYO/16iYiIiIiIiJhr+7EERnyzgWPxqbg7OzLxziY4O1r4YGkk+2OTqRnoyeju4fRuFGJ21Hwt2h5VKrIWJGdMYhp/7Yphyc4Y/t0bS1qmNec+DxdHOtcJoHv9YK6vF0SAl2tJvwSRq3K5LvdiKq3FLujXS0RERERERMQ88zYc5fkft5GeZaW6vwefDWpF3RBvs2MJkJaZzcq9cSzZGcNfu05wIjE95z6LBZqF+tHj7CrsusHeGiMidqswpbXGg4iIiIiIiIiIlFOZ2VYm/BLB16sPAXBd3UAmD2yOr7uzycnkHDdnx7OjQYIxjEZsP5bIkp0nWLrrBNuPJbLpcDybDsfzzuLdVPFzp0f9ILrXD6ZtzYq4OmmMiJROKq1FRERERERERMqhmKQ0Hv12I+sOngbg8e51GNO9Dg4OWqlrrywWC42r+tK4qi9P9AwnOiGNpbtOsHRnDCv3xnEsPpWvVx/i69WH8HRxpEt4IN3rB3Nd3UD8NUZEShGV1iIiIiIiIiIi5cyGQ6d55NsNnEhMx9vVifcHNKNng2CzY0khhfi6cW/b6tzbtjqpGdn8uzeOpTtPsHRXDLFJ6fy+PZrft0djsUCLahXoXj+IHvWDqRPkpTEiYtdUWouIiIiIiIiIlBOGYfDd2sOMX7iDzGyD2kFeTBvUklqBXmZHk2vk7uJIzwbB9GwQjNVqsO1YAkt3nmDJzhgiohLZcOg0Gw6dZuKi3YRWdKd7vWB61A+mTY2KuDg5mB1fJBeV1iIiIiIiIiIi5UBaZjav/LSD2euPAHBjoxDeuaspXq6qh8oaBwcLTUP9aBrqx5M31OV4fCpLd8WwdOcJVu07yZFTqcxYdZAZqw7i7ep0doxIENfVDaKCpwsAi7ZHMXlJJAfikqkR4MmYHnXo3aiSya9MyguLYRiG2SGu1uV2nExLS+PAgQPUqFEDNzc3kxJKQenXS0RERERERKT4HI9P5eFvN7LlSDwOFhjbqy4Pd62lERHlUHJ6Vs4Ykb92xRB3JiPnPgcLtKxegaoVPPhx0zEsgAE5t1Pva6HiWq7a5brci+mtNBERERERERGRMmzN/pM8+u1GTiZn4OfhzIcDm9MlPNDsWGIST1cnejUMoVfDEKxWgy1H41m6M4YlO0+wKzqJdQdP52zOeW6lqwFYLPDB0kiV1lIiVFqLiIiIiIiIiJRBhmHw1cqDvPHbTrKtBg0q+TBtUEtCK3qYHU3shIODhebVKtC8WgXG9qrL0dMp/LUrhlcW7uDi2QyGAbujk5i/8Sjd6gZR8ewYEZHioCnrwq5du2jXrh1ubm40a9asQI8ZPHgwt99+e8733bp1Y8yYMcWST0REREREREQKJzUjmzGzN/N/v0SQbTXo27wK8x7uoMJaLqtqBQ/ubx9G3WBv8hscYzXgyTlbaDnhT+6YsopPl+1ld3QSpXj6sNgprbQuRwYPHkx8fDwLFizIdfyVV17B09OT3bt34+V1dbsFz58/H2dn5yJIKSIiIiIiIiLX4vDJFEbM3MDOqEQcHSy8eHN9BncI0/xqKbAxPeowcuZGLBbbCutztzc1DuFAXAo7oxLZcOg0Gw6dZuKi3VTxc6d7/SC61w+mbY2KuDk7mv0SpJRTaV0ApX231Ozs7Mv+wbRv3z5uvvlmqlevftXPUbFixat+rIiIiIiIiIgUjeV7Ynl81iYSUjMJ8HLhk3ta0Lamv9mxpJTp3agSU+9rwQdLI9kfm0zNQE9Gdw+nd6MQwLax51+7YvhrVwwr98ZxLD6V/60+xP9WH8LDxZFOtQPoXj+I6+oGEeTjZvKrkdKoXI0HMQyDlIysQn39tPkYI2duZHd0EulZVnZHJzFy5kZ+2nyswNco7EckunXrxqhRoxg1ahR+fn74+/vz4osv5lzn9OnT3H///VSoUAEPDw9uvPFGIiMjcx4/Y8YM/Pz8+OWXX2jQoAGurq4MGTKEr7/+mp9++gmLxYLFYmHZsmVYLBY2bNjAa6+9hsViYfz48QBs27aN66+/Hnd3d/z9/Rk+fDhnzpy5bOYLx4NcKaOIiIiIiIiIFB3DMPjk770Mnr6WhNRMmoX68fNjnVRYy1Xr3agSv4/uwu4JN/L76C45hTVAZT937mtXna8Gt2bzyzfwxf2tuLtNNYJ9XEnJyOaPiBM8O28bbd5Yym0f/8sHSyLZfiyh3I8RWbQ9it6T/6Hui7/Te/I/LNoeZXYku1WuVlqnZmbT4OXFV/VY46Lb0d9vLvBjI17rhYdL4f5Vf/311wwdOpT//vuP9evXM3z4cKpXr86wYcMYPHgwkZGRLFy4EB8fH5599lluuukmIiIickZ0pKSk8Oabb/LFF1/g7+9PSEgIaWlpJCYmMn36dMC2OjoqKooePXrQu3dvxo4di5eXFykpKfTu3Zt27dqxbt06YmJieOihhxg1ahQzZswoUP6CZBQRERERERGRa3cmPYun5mxm8Y4TANzdJpTxtzbE1UkjGqT4ubs40qNBMD0aBGMYjdhxPJGlO2P4a9cJthxNyPmatGQPwT6uXF8vmO71guhYOwB3l/Lz3+jCzcd4/PvNWLD1i+cWxk69r0WpmuhQUspVaV2ahIaGMmnSJCwWC3Xr1mXbtm1MmjSJbt26sXDhQlauXEmHDh0A+PbbbwkNDWXBggXcddddAGRmZvLpp5/StGnTnGu6u7uTnp5OSMj5d8ZCQkJwcnLCy8sr5/jnn39Oamoq//vf//D09ATg448/5pZbbuHtt98mODj4stnPldVXyigiIiIiIiIi12Zf7BmG/289+2KTcXF04NXbGnJ3m2pmx5JyymKx0KiKL42q+DK6Rx1iktJYtiuWJTtP8O/eOE4kpjNr7WFmrT2Mq5MDHWr5071+MNfXC6Kyn7vZ8a+Z1WpwPCGVA3HJ7I9Ntt3GJbM/9gxHT6cCuRfGWizwwdJIldb5KFeltbuzIxGv9SrUY27/ZCWRJ85w4YcXLBYID/bmx0c6FPh5C6tdu3a55lC3b9+e9957j4iICJycnGjbtm3Off7+/tStW5edO3fmHHNxcaFJkyaFfl6AnTt30rRp05zCGqBjx45YrVZ27959xdJ6586dBcooIiIiIiIiIlfvjx3RPDlnC2fSswjxcWPKfS1oXq2C2bFEcgR5u9G/dSj9W4eSlpnNfwdO8dfOEyzZGcOx+FT+3h3L37tjAahfyYfu9YLoXj+IplX9cHCw341D41My2HeulI49w4G45Jyv9Cxrga9jGLA/NrkYk5Ze5aq0tlgshR7T8WTP8Hx3S32iR3ihr1WcDMPIVXK7u7tf9a7AF1/rQgW55qXmE13uuiIiIiIiIiJSMNlWg8lL9vDRX3sBaFOjIp/c04JAb1eTk4lcmpuzI13DA+kaHsj4Ww32nDjD0l0n+GtnDBsPn2ZnVCI7oxL5+O+9BHi50K1uEN3rBdGpTgDebiU/ajYtM5tDJ1PYH3uG/XG5C+rTKZmXfJyzo4VqFT2oGehFzQBPagR4UjPQi+d/3Ma+mLwLY2sGel7yWuWZ/bSudupKu6UWlzVr1uT5vk6dOjRo0ICsrCz++++/nNEbJ0+eZM+ePdSvX/+y13RxcSE7O/uKz92gQQO+/vprkpOTc1Zbr1y5EgcHB8LDwwv0+KvNKCIiIiIiIiKXlpCSyejZm1h2dnXqkI5hPH9TfZwdHUxOJlJwFouFuiHe1A3x5pFutTmVnMGy3TEs3RXDP7tjiTuTwdwNR5m74SjOjhba1fTn+npBdK8XTDV/jyLLYbUaHItPzVVI7z872uN4QiqX2zeykq/b2ULakxoBtoK6ZqAnVfzcccrn/8exN+S/MHZ09yt3beWRSusC6N2oUonPljly5AhPPvkkI0aMYOPGjXz00Ue899571KlTh9tuu41hw4Yxbdo0vL29GTduHFWqVOG222677DXDwsJYvHgxu3fvxt/fH19f33w3Rbz33nt55ZVXeOCBBxg/fjyxsbE89thjDBo06IqjQYBryigiIiIiIiIi+dsVnciIbzZw6GQKbs4OvNmvMX2bVzU7lsg1q+jpQr8WVenXoiqZ2VbWHTjF0l0xLN15goMnU1gRGceKyDhe/TmCOkFeXF/fVmC3qObHkp0nmLwkkgNxydQI8GRMjzp5erzTyRnsjztzfs702dsDJ5PJuMw4D283p5wV0zUDPKkRaFs5XSPAs9ATGMxaGFtaqbS2U/fffz+pqam0adMGR0dHHnvsMYYPHw7A9OnTGT16NH369CEjI4MuXbrw22+/5VtAX2jYsGEsW7aMVq1acebMGf7++2+6deuW5zwPDw8WL17M6NGjad26NR4eHtxxxx28//77Bc5/tRlFREREREREJK+FW47z7NytpGZmU7WCO9MGtaRhZV+zY4kUOWdHBzrUDqBD7QBe6tOA/bFnWLozhqW7TrDu4GkiY84QGXOGacv34+HiSErG+akCu6OTGDlzI7c1q4yTgwP742yrp+MvM87DxdGB6v4etjI60JNaAV455bS/p0uRjro1Y2FsaWUxLjWAuBRITEzE19eXhIQEfHx8ct2XlpbGgQMHqFGjBm5ubiYlvDrdunWjWbNmTJ482ewoJaY0/3qJiIiIiIiIFJesbCtvL9rF5ysOANC5TgAfDmxOBU8Xk5OJlLyE1Ez+2RPL0p0nWLYn9rJl9MUq+7pRM9ArZ6V0zUBPagZ4UaWCO452vOljWXK5LvdiWmktIiIiIiIiImKHTp5JZ9R3m1i9/yQAj3SrxVM31FXBJuWWr7sztzStzC1NK5OVbaX+y4vIzM67HtfBAk/2DLfNmg70JMzfE3cXRxMSy9VSaS0iIiIiIiIiYme2Ho1n5DcbOJ6QhqeLI+/1b6qxAiIXcHJ0oFagF7ujk7iwtrZYoG6IN6Our2NaNrl2Kq3t0LJly8yOICIiIiIiIiImmbP+CC8u2E5GlpWaAZ5MG9SSOsHeZscSsTtjetRh5MyNWCxgGOTcju4ebnY0uUYOZgcQERERERERERHIyLLy4oJtPDN3KxlZVnrUD2bBqI4qrEUuoXejSky9rwX1QrxxdXKgXog3U+9rSe9GIWZHk2ukldYiIiIiIiIiIiY7kZjGI99uZMOh01gs8GSPcB69rjYOml8tclm9G1XS6JwySKW1iIiIiIiIiJQZi7ZHMXlJJAfikqkR4MmYHnXsstC6MGeIjxunUzJITMvCx82JDwY257p6QWZHFBExjanjQbKysnjxxRepUaMG7u7u1KxZk9deew2r1WpmLBERERERERG5yKLtUfSe/A91X/yd3pP/YdH2KLMj5bFoexQjZ25kd3QS6VlWdkcnMXLmRrvLenHOQ6dSSEzLooqfGwtHdVJhLSLlnqkrrd9++22mTp3K119/TcOGDVm/fj1DhgzB19eX0aNHmxlNRERERERERM46V7JaAANyyuCp97W44ipmwzDIzDbIyLaSkWUlPSubjKxz/2zNOZ7zfZaVjOzc55w/fv7cnGtlnz9v/cHTtuc899xnb0d/v5magXuL619Poe2PPQOcz3eOl5szYQGeJR9IRMTOmFpar169mttuu42bb74ZgLCwMGbNmsX69evNjCUiIiIiIiIiF5i0JDKnsOaC2yfnbOGzf/bnKZMzsq2kZ1pJP3vcbOlZVnZGJZod44oOxiWbHUFExC6YWlp36tSJqVOnsmfPHsLDw9myZQv//vsvkydPzvf89PR00tPTc75PTLT/P3BERERERERESiPDMNhyNIEfNx5ld3RSvuekZGSz8XB8oa7r5GDBxckBVycHXM59OTrg4uSYc9w159j5+12dHXBxdMw55nrhNc6e++4fu4mKT8u1gtkChFZ05/W+ja/630VRe37+No6eTs2d0wI1A7XKWkQETC6tn332WRISEqhXrx6Ojo5kZ2fz+uuvc/fdd+d7/ptvvsmrr75awilLXkxMDC+99BK///47J06coEKFCjRt2pTx48fTvn17wsLCOHToEEDOLPDHHnuM4cOH07NnTxwdHVm8eHGua3766ac899xzbNu2jWrVqpnxskRERERE5BJKy8ZxUHqylpac9uhYfCoLNh1j3saj7I+99MpfC1DZz52X+jTIKY/zFtEOuF5QRrs4OuDgYCm27B4ujrYxJhYwDHJun7+pAZ3rBBbb8xbWCzfXzzfn6O7hZkcTEbELFsMwLh6hVGK+//57nn76ad555x0aNmzI5s2bGTNmDO+//z4PPPBAnvPzW2kdGhpKQkICPj4+uc5NS0vjwIED1KhRAzc3t2sLGrEQlr8FJ/eCf23oOg4a3Hpt17yMzp07k5mZyZtvvknNmjU5ceIES5cupUmTJtx8882EhYUxdOhQhg0bxpkzZ5gxYwavv/4633//PR06dKBx48a8/fbbjBgxAoADBw7QpEkTPvroIwYPHlxsua9Fkf56iYiIiIiUIhfPCj53W5BZwSWttGQtLTntSVJaJr9vj2b+xqOs2X8q57ibswO9G4YQWtGDj/7am6dknXpfS3o3CjExeV6LtkfxwdJI9scmUzPQk9Hdw+0uI5SenCIiRSUxMRFfX998u9yLmVpah4aGMm7cOB599NGcYxMmTGDmzJns2rXrio+/3AvNtwQ1DMhMKVzIXb/B/Ifg4h93+n0B9W4q2DWcPWx/ohdAfHw8FSpUYNmyZXTt2jXfc8LCwhgzZgxjxozJORYeHk7Lli2ZNWsWX3/9NaNGjWLr1q2EhYXRvXt3fHx8WLBgQcHymkCltYiIiIiUV70n/8Pu6KQ8G7L5ujvTs0GwKZku5c+IaBJSs/Ict7es+eW0WKBeiDe/j+5iUir7k5Vt5d+9cczfeIw/IqJJy7TNnrZYoH1Nf/o2r8KNjSvh5Wr7kLZKVhERuRaFKa1NHQ+SkpKCg4NDrmOOjo5YrcW0SUNmCrxR+SoffNF2E/MfKvhDnz8OLgWbS+Xl5YWXlxcLFiygXbt2uLq6Fuhxbm5uZGZmAvDAAw/w448/MmTIEO644w62b9/O9u3bC55XRERERERKzIG45DyFNUBCaiZzNxwt8TxXozRkNQzYFZ3EFyv207lOIOHBXlgKuLiorIk4nsj8jUf5actxYpPOf5q5VqAn/VpU5fbmVaji557ncb0bVdJKdRERKRGmlta33HILr7/+OtWqVaNhw4Zs2rSJ999/nwcffNDMWKZycnJixowZDBs2jKlTp9KiRQu6du3KwIEDadKkSZ7zs7KymDlzJtu2bePhhx/OOf7ZZ5/RqFEjVqxYwdy5cwkKCirJlyEiIiIiIgXk7+XC8fi0PMeDvF15sFMNExJd2pf/HshVcp5jb1kvldMwYMKvO4GdBHm70ql2AJ3DA+hYO4Ag77L9ic+YxDR+2nyceRuPsuuCTRUrerpwa9PK9GtRhcZVfMttkS8iIvbF1PEgSUlJvPTSS/z444/ExMRQuXJl7r77bl5++WVcXFyu+PgSGQ/yRXeI2QUX7z0cVB8eWlKwaxRiPMg5aWlprFixgtWrV7No0SLWrl3LF198weDBgwkLCyMqKgpnZ2fS09NxcXHh0Ucf5a233sq1cv3FF19kwYIFpWKVtcaDiIiIiEh5YxgGX6w4wOu/7cx13N5nBee3eZy9Zb1UzjtbViE2KYP/DpzMGYVxTr0QbzrXCaBznUDa1KiIm7OjSemLTmpGNn9ERDNv4zH+jYzFevavtS6ODvRoEETf5lXpGh6Ii5PD5S8kIiJSBErNTOtrVejS+mpELIQ5g8gz03rATKh/yzWkL5yHHnqIP//8k0OHDhEWFsZ9993H4MGD8fDwoFKlSvm+Gz5+/HgWLFjA5s2bSyzn1VJpLSIiIiLlSbbVYMKvEUxfeRCA6+oFEh2fxv44+58VXFrmGl8uZ1pmNhsPnWbF3jhWRMay/Vhirse6ODnQJqwineoE0LlOAPVDfHBwKB0rkK1WgzUHTvLjxmP8ti2K5IzsnPtaVq9AvxZV6NO4Mr4eziamFBGR8qjUzLQuFRrcCv2/geVvw8lI8K8D3caVaGEN0KBBg1wbKQYEBFC7du0SzSAiIiIiItcuLTObJ+ds5rdt0QC8eHN9Hupc0+RUBVda5hpfLqebsyMdagfQoXYAz/aux8kz6azcd5J/I2NZERlHVEIa/+6N49+9cbz1O/h7utCpToBtnEidQEJ87W+hzd6YM/y46SgLNh3nWHxqzvHQiu70a16Vvs2rEBZQsL2WREREzKbSuiAa3Gr7KgEnT57krrvu4sEHH6RJkyZ4e3uzfv16Jk6cyG233VYiGUREREREpHgkpGQy7H/rWXvwFM6OFt7r34xbm17tZvFSVPy9XLm1aWVubVoZwzDYF5vMishY/o2MY/X+k5xMzuCnzcf5afNxAOoEedG5TiCd6wTQtmZFPFzM+av1qeQMft5ynPkbj7LlaELOcW83J/o0sc2pblW9guZUi4hIqaPS2s54eXnRtm1bJk2axL59+8jMzCQ0NJRhw4bx/PPPmx1PRERERESu0rH4VB74ai17Y87g7ebEtEEt6VArwOxYchGLxULtIC9qB3kxpGMNMrKsbDp8mhWRcazYG8fWo/FExpwhMuYMX608gLOjhZbVK+SU2A0r++JYjKNE0rOy+WtnDPM2HmPZ7hiyzg6qdnSw0C08kH4tqtK9flCZmMktIiLll2Zai13Qr5eIiIiIlGURxxMZMmMtJxLTCfFxY8aDrakXcvlZjmKf4lMyWLXvJCsiY/lnT1yuURwAfh7OdKwdQOfaAXQOD6SKn/s1P6dhGGw8fJp5G4/x69YoElIzc+5rXMWXfi2qcEvTygR4uV7zc4mIiBQXzbQWERERERGxEyv3xjHimw2cSc8iPNiLGUPaULkIikwxh5+HCzc1rsRNjSthGAYHT6bwb2Qs/0TGsWbfSeJTMvl1axS/bo0CoGaAJ53rBNCpTiDtalbE263gGyAePpnCj5uOMX/TUQ6dTMk5HuLjRt8WVejXvAp1gr2L/DWKiIiYTaW1iIiIiIhIMflp8zHG/rCFzGyDtjUq8tn9rfB1L3hpKfbNYrFQI8CTGgGeDGofRma2lS1H4m2jRCJj2XI0gf1xyeyPS+br1YdwcrDQvJofnesE0qlOAE2q+LJk5wkmL4nkQFwyNQI8GdalJhlZVuZvPMq6g6dznsvDxZHejUK4o0VV2tX0L9YRJCIiImbTeBCxC/r1EhEREZGyxDAMpv2zn7d+3wXAzU0q8X7/prg6ac5weZKQmsnqfSf5d28sKyLjcq2WBnB3diA103rJx1ss0Kl2AP1aVKFXwxDTNnwUEREpChoPIiIiIiIiYpJsq8H//RLBjFUHAXioUw2ev6k+DloZW+74ujvTu1EIvRuFALZxHyv2xvJvZBwr98aRmJaV7+NcnBx4qmc4tzWrQoivFvWIiEj5U+ZL61K8kLxc0a+TiIiIiJQFaZnZjPl+M4t2RGOxwAs31eehzjXNjiV2opq/B/f6V+fettXJthrUe+l3MrPz/l3IAozoWqvkA4qIiNgJB7MDFBdnZ9ucuJSUlCucKfYgIyMDAEdHfVxSREREREqn+JQM7vviPxbtiMbF0YGP7m6uwlouydHBQq1ALy5ef2+xQM1AT1MyiYiI2Isyu9La0dERPz8/YmJiAPDw8MBi0cfx7JHVaiU2NhYPDw+cnMrsf5IiIiIiUoYdPZ3CA1+tZV9sMt5uTnx+fyva1fQ3O5bYuTE96jBy5kYsFjAMcm5Hdw83O5qIiIipynRDGBJimxt2rrgW++Xg4EC1atX0xoKIiIiIlDo7jicwePo6YpPSqeTrxtcPtiE82NvsWFIK9G5Uian3teCDpZHsj02mZqAno7uH58zAFhERKa8sRikeJlzQHSezs7PJzMwswWRSWC4uLjg4lNlpNSIiIiJSRq2IjOXhmRs5k55FvRBvpg9pTSVfd7NjiYiIiNidgna5UMZXWp/j6OioWckiIiIiIlKk5m88yjNzt5JlNWhf059p97fEx83Z7FgiIiIipV65KK1FRERERESKimEYTFm+j4mLdgNwa9PKvHNXE1ydtFBGREREpCiotBYRERERESmgbKvB+IU7+GbNIQBGdKnJs73r4eCgvVlEREREiopKaxERERERkQJIy8zm8Vmb+CPiBBYLvNynAUM61jA7loiIiEiZo9JaRERERETkCk4nZzD063VsPByPi5MDHwxoxo2NK5kdS0RERKRMUmktIiIiIiJyGUdOpfDA9LXsj03Gx82JLx5oTZsaFc2OJSIiIlJmqbQWERERERG5hO3HEhg8fR1xZ9Kp4ufOjCGtqRPsbXYsERERkTJNpbWIiIiIiEg+lu+J5ZGZG0jOyKZeiDdfP9iGYB83s2OJiIiIlHkqrUVERERERC4yd8NRxs3bSpbVoGNtf6bc1xIfN2ezY4mIiIiUCyqtRUREREREzjIMg0+X7eOdxbsBuL1ZZSbe2RQXJweTk4mIiIiUHyqtRUREREREgKxsK68s3MG3/x0GYGTXWjzTqy4ODhaTk4mIiIiULyqtRURERESk3EvNyOaxWZtYsvMEFguMv6UhD3QIMzuWiIiISLmk0lpERERERMq1U8kZDP16HZsOx+Pq5MAHA5vRu1Els2OJiIiIlFsqrUVEREREpNw6fDKFB6av5UBcMr7uznz5QCtahVU0O5aIiIhIuabSWkREREREyqVtRxMYMmMtcWcyqOLnztcPtqZ2kLfZsURERETKPZXWIiJSZBZtj2LykkgOxCVTI8CTMT3q6OPVIiJil/7eHcOj324kJSObBpV8mDGkNUE+bmbHEhERERHAwewAIiJSNizaHsXImRvZHZ1EepaV3dFJjJy5kUXbo8yOJiIiksuc9Ud46Ov1pGRk07lOALNHtFNhLSIiImJHVFqLiEiRmLwkEgtgnP3eACwW+GBppImpREREzjMMgw+XRvLM3K1kWw36Na/Clw+0xtvN2exoIiIiInIBjQcREZEicSAuOaewPscwYH9ssil5RERE4Pzoqv1xyXg4OxKfmgnAI91q8XSvulgsFpMTioiIiMjFVFqLiEiRqFrBnX35FNShFd1NSCMiInJ+dNW5TwJlZFkBGNgmlGd61zM1m4iIiIhcmsaDiIjINbNajcusVLOQbb14DbaIiEjxu3h0FYAF2HIk3pxAIiIiIlIgKq1FROSafbXyAHtjzuDi5EDtQE9cnRyoFeiJi5MDe2PO8MWK/WZHFBGRcijf0VVodJWIiIiIvdN4EBERuSZ7Y5KYuHg3AONvacg9bavl3Pf92sOMm7+Nd//YTec6gTSo7GNWTBERKYcq+bpx8GRKrmMWC9QM9DQpkYiIiIgUhFZai4jIVcvKtvLUnC1kZFnpEh7I3W1Cc90/oHUoPeoHk5lt8MTszaRlZpuUVEREypu0zGzSM625jlkstk2CR3cPNymViIiIiBSESmsREblqU5fvY8vRBHzcnJh4R5M8c60tFgtv3dGYAC8Xdp9I4r0/dpuUVEREypu3ft9FVGIaPm5OhAd74erkQL0Qb6be15LejULMjiciIiIil6HxICIiclV2HE/gg6WRALx6W0NCfN3yPS/Ay5W3+jXhof+t54t/D3BdvSA61AooyagiIlLO/LMnlhmrDgLw4d3N6VY3yNxAIiIiIlIoWmktIiKFlp6VzVNztpCZbdC7YQi3N6ty2fN7NAjm7jahGAaMnbOFhNTMEkoqIiLlzenkDMb+sAWAB9pXV2EtIiIiUgqptBYRkUL7cGkku6KT8Pd0YULfRnnGguTnxZsbUN3fg+MJabzy0/YSSCkiIuWNYRg8/+M2YpLSqRXoybgb65sdSURERESugkprEREplI2HTzNl2T4AXu/biAAv1wI9ztPViff7N8PBAgs2H+fnLceLM6aIiJRD8zYe4/ft0Tg5WPhgYHPcXRzNjiQiIiIiV0GltYiIFFhqRjZj52zBakDf5lXo3ahSoR7fsnoFRl1XG4AXF2wnOiGtOGKKiEg5dPhkSs4neZ68IZxGVXxNTiQiIiIiV0ultYiIFNjExbvYH5dMsI8r429peFXXeKx7HZpU9SUhNZOn527BajWKOKWIiJQ32VaDJ+dsJjkjmzZhFRnRpZbZkURERETkGqi0FhGRAlm1L47pKw8C8PYdTfD1cL6q6zg7OjBpQDPcnB1YERnH16sPFl1IEREpl6Yu38f6Q6fxcnXivf5NcXS48l4LIiIiImK/VFqLiMgVnUnP4ukftgJwd5tqdKsbdE3XqxXoxfM32TbHeuv3XUSeSLrmjCIiUj5tPRrPpD/3APDqrQ0JrehhciIRERERuVYqrUVE5Ipe/zWCY/GphFZ054Wb6xfJNQe1q06X8EDSs6yMmb2ZjCxrkVxXRETKj9SMbMbM3kyW1eDmxpXo16KK2ZFEREREpAiotBYRkcv6e3cMs9YewWKBd+5siperU5Fc12Kx8M6dTfDzcGbH8UQ+WLqnSK4rIiLlxxu/7WR/rG2vhdf7NsJi0VgQERERkbJApbWIiFxSfEoGz861jQV5sGMN2tX0L9LrB/u48WbfxgBMWbaP9QdPFen1RUSk7Pp7VwzfrDkEwHt3NcPPw8XkRCIiIiJSVFRai4jIJb2ycAcxSenUDPTk6V51i+U5bmxciTtaVMVqwBNzNnMmPatYnkdERMqOk2fSefqCN1U71QkwOZGIiIiIFCWV1iIikq/ftkXx0+bjOFjg/f7NcHN2LLbneuXWBlTxc+fIqVRe+3lHsT2PiIiUfoZhMG7+NuLOpBMe7MUzvYvnTVURERERMY9KaxERySM2KZ0XF2wH4JFutWkW6lesz+fj5sz7/ZtiscCc9UdZvCO6WJ9PRERKr9nrjvBnxAlcHB2YPKB5sb6pKiIiIiLmUGktIiK5GIbBCz9u41RyBvVCvHm8e50Sed62Nf0Z3qUmAM/N30ZMUlqJPK+IiJQeB+OSee2XCADG9gqnQWUfkxOJiIiISHFQaS0iIrn8uOkYf0ScwNnRwvv9m+HiVHJ/VDzZM5z6lXw4lZzBuHnbMAyjxJ5bRETsW1a2lTGzN5OSkU27mhV5qFNNsyOJiIiISDFRaS0iIjmiElJ5ZaFtpvSYHiW/gs3VyZHJA2xF+V+7Yvhu7eESfX4REbFfn/y9j81H4vF2c+K9/s1wcLCYHUlEREREiolKaxERAWxjQZ6Zu5WktCyahfoxoos5K9jqhnjzTC/bploTftnJgbhkU3KIiIj92HT4NB/+FQnAhNsbUcXP3eREIiIiIlchYiFM6QATgmy3EQvNTmS3VFqLiAgA3609zIrIOFydHHivf1OcHM37I+LBjjXoUMuf1Mxsnpi9maxsq2lZRETEXMnpWTwxezPZVoNbm1bmtmZVzI4kIiIiUngRC2HOIDgRAVnptts5g1RcX4JKaxER4dDJZF7/dScAz/auR61AL1PzODhYePeupni7ObH5SDyf/L3P1DwiImKeCb/u5ODJFCr7uvF/tzUyO46IiIjI1Vn+1tl/MC64tcDyt00KZN9UWouIlHPZVoOnf9hKSkY2bWtUZHCHMLMjAVDZz50Jt9vKiQ//imTzkXhzA4mISIn7M+IEs9YexmKBd/s3xdfD2exIIiIiIoWXcgpiduZzhwEnI0s8Tmmg0lpEpJybvvIAaw+ewtPFkXfvampXG1vd1qwKtzStTLbV4InZm0nJyDI7koiIlJDYpHTGzdsKwLDONelQK8DkRCIiIiJX4eh6mNYVjPzGXlrAv06JRyoNVFqLiJRje2OSmLh4NwAv9mlAaEUPkxPlNeG2RoT4uHEgLpk3fsvvnWkRESlrDMPg2XlbOZmcQb0Qb566IdzsSCIiIiKFYxiw6mP4qhckHAbPoLN3WC64NaDbOJMC2jeV1iIi5VRWtpUn52whI8tK1/BABrYONTtSvnw9nHn3rqYAzFxzmL93x5icSEREitu3/x3mr10xuDg58MHA5rg6OZodSURERKTgUk7B9/fAHy+ANQsa3A6PrYf+30BwQ3Bytd0OmAn1bzE7rV1yMjuAiIiYY8qyfWw9moCPmxNv39EEi8V+xoJcrFOdAIZ0DGP6yoM8M3cri8d0oaKni9mxRESkGOyLPcOEXyMA2+bAdUO8TU4kIiIiUghH1sHcIZBwBBxdoPeb0GooWCzQ4Fbbl1yRVlqLiJRDO44n8MFS22YP/3d7I0J83UxOdGXP9q5HnSAvYpPSeW7+VgzDuPKDRESkVMnMtvLE7M2kZVrpWNufIXayObCIiIjIFZ0bBzK9t62wrlADhv4JrR+yFdZSKCqtRUTKmfSsbJ6as4Usq8GNjUK4tWllsyMViJuzI5MGNMPZ0cLiHSeYu+Go2ZFERKSIfbQ0kq1HE/B1d7a7zYFFRERELunicSAN+8KIf6ByM7OTlVoqrUVEypnJSyLZFZ2Ev6cLE25vZNdjQS7WqIovT/S0bcb16s8RHDmVYnIiEREpKhsOneLjv/cC8EbfxlTydTc5kYiIiEgBHFkH07rA7t9s40Bufg/unA5uPmYnK9VUWouIlCMbDp1m2vJ9ALzRrzH+Xq4mJyq8EV1q0TqsAmfSs3hyzmayrRoTIiJS2p1Jz+KJ2VuwGtCveRVublLJ7EgiIiIil2cYsOqj8+NAKtaEh5ZoHEgRUWktIlJOpGZkM/aH84VAr4YhZke6Ko4OFt7v3wxPF0fWHTzNZ//sNzuSiIhco9d+3sHhUylU8XNn/G0NzY4jIiIicnkpp2DW3fDHi+fHgQxfDpWamp2szFBpLSJSTry9aBcH4pIJ8XHjlVtKdyEQWtGDV261vYb3/9zN9mMJJicSEZGrtWh7FHPWH8VigUkDmuHj5mx2JBEREZFLOzcOZM/v4OgKN7+vcSDFQKW1iEg5sGpfHDNWHQTg7Tub4OtR+guBu1pWpVfDYDKzDZ6YvZm0zGyzI4mISCHFJKbx3PxtAIzsWos2NSqanEhERETkEvIdB/IntB6qcSDFQKW1iEgZl5SWydM/bAXgnrbV6BoeaHKiomGxWHijb2MCvFyJjDnDxEW7zY4kIiKFYBgGY+du5XRKJg0r+/BEj3CzI4mIiIjkL884kH4aB1LMVFqLiJRxr/+6k2PxqYRWdOeFm+qbHadI+Xu58s6dTQD4auUBVu6NMzmRiIgU1P9WH+KfPbG4OjnwwcBmuDjpryYiIiJih46szWccyFcaB1LM9JOhiEgZ9veuGL5fdwSLBd69symerk5mRypy19UL4t621QB4as4WElIyTU4kIiJXsjcmiTd+2wnA8zfVp3aQt8mJRERERC6SMw7kxgvGgSzROJASotJaRKSMik/J4Nl5trEgQzvWoG1Nf5MTFZ8Xbq5PjQBPohPTeOmn7WbHERGRy8jIsjL6+82kZ1npEh7I/e2rmx1JREREJLeUUzBrYD7jQJqYnazcUGktIlJGvfzTDmKS0qkV6MnYXnXNjlOsPFycmDSgGY4OFhZuOc5Pm4+ZHUlERC5h8pI97DieSAUPZ965swkWrVQSERERe3JkLUztDHsW2caB9JmkcSAmUGktIlIG/bo1ioVbjuPoYOG9/s1wc3Y0O1Kxaxbqx2PX1wbgpQXbOR6fanIiERG52NoDp5iyfB8Ab/ZrTLCPm8mJRERERM6yWmHlh7ZxIIlHoWIt2ziQVg9qHIgJVFqLiJQxsUnpvLhgGwCPdKtFs1A/cwOVoFHX1aZpqB+JaVmM/WELVqthdiQRETkrMS2TJ2ZvxjDgrpZV6d2oktmRRERERGxSTsH3d8OfL9nGgTS6A4Yv0zgQE6m0FhEpQwzD4Ln52zidkkn9Sj48dn0dsyOVKCdHByYPaIa7syOr9p1k+qqDZkcSEZGzxi/cwbH4VKpV9OCVWxuaHUdERETEJs84kMlwx5caB2IyldYiImXI/I3HWLLzBM6OFt7v3xQXp/L323yNAE9e7FMfgLcX7WJ3dJLJiURE5NetUczfeAwHC0wa0BQvVyezI4mIiEh5Z7XCyg/yGQcyRONA7ED5azNERMqo4/GpjP95BwBP9AynfqXy+67wPW2qcX29IDKyrIyZvZn0rGyzI4mIlFvRCWk8/6NtbNWj19WmZfWKJicSERGRci/lFMwaCH++fH4cyIjlGgdiR1Rai4iUAYZh8Oy8rSSlZdG8mh/DO9c0O5KpLBYLb93RmIqeLuyMSmTSn5FmRxIRKZesVoOxP2whITWTJlV9ebx7+RpbJSIiInbo8H+2cSCRi3OPA3H1NjuZXECltYhIGTDzv8OsiIzDzdmB9+5qipOjfnsP8nbjzX6NAZj2zz7+23/S5EQiIuXP9FUH+Xev7c+nSQOa4aw/n0RERMQs+Y0DGbZU40DslH5qFBEp5Q6dTOaNX3cC8GzvetQM9DI5kf3o1TCE/q2qYhjw5JwtJKVlmh1JRKTc2B2dxNuLdgHw4s0NqKU/n0RERMQsF44DMbKh0Z22cSAhjc1OJpeg0lpEpBTLPvux69TMbNrVrMgD7cPMjmR3Xr6lIaEV3TkWn8r4hRFmxxERKRfSs7IZ/f0mMrKsXF8viHvbVjM7koiIiJRXh/+DqZ3OjwO55QO44wuNA7FzKq1FREqxr/49wLqDp/F0ceSdO5vi4KCPNF3My9WJSf2b4WCBeRuP8vu2KLMjiYiUee//sYdd0Un4e7rw9h1NsOgjtyIiIlLSco0DOQb+tW3jQFoO1jiQUsD00vrYsWPcd999+Pv74+HhQbNmzdiwYYPZsURE7F7kiSTe+WM3AC/1aUBoRQ+TE9mvVmEVGdm1FgDP/7iNmMQ0kxOJiJRdq/bF8dmK/QC8dUcTAr1dTU4kIiIi5U7ySZg1IPc4kOHLNA6kFDG1tD59+jQdO3bE2dmZ33//nYiICN577z38/PzMjCUiYvcys6089cMWMrKsdKsbyIDWoWZHsntjeoTTsLIPp1MyeXruVgzDMDuSiEiZk5Caydg5WzAMuLtNKD0bBJsdSURERMqbw2tgWmeI/EPjQEoxJzOf/O233yY0NJTp06fnHAsLCzMvkIhIKTFl2T62Hk3A191ZH7suIBcnByYPaEafj/5l+Z5YZq45xCDNABcRKVIv/7Sd4wlphPl78OLNDcyOIyIiIuVBxEJY/hbE7QV3PzgTC1ht40DumqHV1aWUxTBxqVmDBg3o1asXR48eZfny5VSpUoVHHnmEYcOG5Xt+eno66enpOd8nJiYSGhpKQkICPj4+JRVbRMRU248lcPsnK8myGnwwsBm3NatidqRSZfrKA7z6cwTOjhZCK3hwLD6VGgGejOlRh96NKpkdT0Sk1Ppp8zFGf78ZRwcLc0e2p3m1CmZHEhERkbLEMCAzFdKTID3R9rXnD1thjQW4oOIMbQ/3/aDV1XYmMTERX1/fAnW5pq603r9/P1OmTOHJJ5/k+eefZ+3atTz++OO4urpy//335zn/zTff5NVXXzUhqYiIfUjPyuapOVvIshrc1DiEW5tWNjtSqfNA+zBmrzvCrugk9sclA7A7OomRMzcy9b4WKq5FRK7CsfhUXlywHYDHrq+twlpERKQsOLeC+eRe26rlruOgwa2Fv45hQFb62aL5bOGcdu6fLyig05PyOX7R+Ub2pZ7kgn+2QEaSCutSztSV1i4uLrRq1YpVq1blHHv88cdZt24dq1evznO+VlqLlB6LtkcxeUkkB+KS7XoVa2nLGXniDNmGgbebE8vGdsPfS5tbXY0e7y9nb8yZPMcr+brx9h1NqOznRiVfdzxdTX1vV0SkVLBaDe75Yg1r9p+iWagfc0e2x8nR9P3eRURE5FpELIQ5gzi/gvnsbbfnbeM2rlg2X3TcmlmE4Szg6gNuPpBwlNyF9VlOrvBiTBE+pxSFUrPSulKlSjRokHvWXf369Zk3b16+57u6uuLqqoJGxN4t2h7FyJkbc/5oO7eK9dHratGqekWz4+VYf+gUn/y9r9TkvFBSWhbrDp6yy4K9NDhyKiXf41EJadz/1dqc733dnans505lXzcq+7lTyc+NKn7uVPJ1p7KfG8E+bjirmBGRcirnDdWYM2RbjZy9A1RYi4iIlGLJJ+HYevj1ybMHjNy3y964hotbbKufc758zv+zm88F3/vkPs/NN/djXDzh3L5OUzrAiQjyrLT2r3MNOcUemFpad+zYkd27d+c6tmfPHqpXr25SIhEpCpOXRAJ5/mg7W7zuy+8hpiotOS9kscAHSyNVWl+lGgGe7I5OyvN+vKerI1X9PDiekEpSWhYJqZkkpGayMyox3+s4WCDI2822MtvP/Wyh7Xa26LYV2xU9XbRRpoiUORe/QQ2QkWVlV3QiYQGeZkYTERGRgspKh+htcHS9rag+uh5OH7jCgyxQpeUFRfO5kvniojmfEtrFCxyK+M3truMusSJ8XNE+j5Q4U0vrJ554gg4dOvDGG2/Qv39/1q5dy2effcZnn31mZiwRuUbn5gRfzAI0rupbsmEuY9vRhPw+RFQqchoG7I/N/9+zXNmYHnVsZYvF9u/y3O17dzWjd6MQAJLSMolKSONYfCrH41OJik/jeHwqxxNSOR6fRnRCGhnZVqIT04hOTIPD8fk+l6uTg22V9rky+4KV2wUZQ1JaRtiISNl2Jj2Lo6dTOHY6laOnU/lgyR7gojVNekNVRETEfhkGnD6Yu6CO3grZGXnP9a8DybGQFn/RHRYIbgjDlpZA4AJqcCv0/waWvw0nI23Zu42D+reYnUyukakzrQF++eUXnnvuOSIjI6lRowZPPvkkw4YNK9BjCzMHRURKTpvXlxCTlJ7rmMUC9UK8+X10F5NS5dV78j95VtsqZ/mxaHsUHyyNZH9sMjUDPRndPTynsC4Iq9UgLjn9gjLbdhuVkMqxs8diL/r/4FIuNYbk8MkU3vtzz8VrBrRhpIgUucS0zJxC+ujplJzbY/G2Y/EpBZtD6erkwO4JNxZzWhEREbmi1Hg4vtFWTp8rqlNO5j3PvSJUbQVVWp29bQHuFS4903rATBXCctUK0+WaXlpfC5XWIvYnOT2Ldm8uJSkt6/wfbWdXsU69r2WhSsHilvPR5otW2yqnFJX0rGxOJKSfXZ2dmrNyOyretlr73BiSwtAbFiJyNRJSM3PK6PzK6cQC/F7k5+FM1Qq2UUhrD5zi9EVFtn5/EhERMUl2FsTsyF1Qx+3Je56DM1Rqcr6grtoKKtQ4Px/6YhELtYJZilSp2YhRRMqeGasOkpSWRaCXC/5erhyIu7pVrCWhd6NKTL2vxTWtti0JpSWn5OXq5Eg1fw+q+Xtc8pzLjSFZs/9UnvM1Gkak7CvsWCDDMM6W0heX0alnV0qnFOgNsoqeLlTxc6dqhXNfHraS+mxR7e3mnCtjfm+oju4eXiT/DkREROQSDAMSj10w5mMDHN8EWal5z60QdsEK6lYQ0hic3Qr+XA1utX2JmEArrUWkyCSkZtL57b9ITMvig4HNuK1ZFbMjiZRq+Y2GAahfSSsZRcqqizc4PHf77p1NqBvic361dHzucvpM+pVLaX9Plzxl9Lnvq/hdfr7+pbLqDVUREZFiln4GojbD0XVni+oNkBSV9zxXX9tojwtHfXgGlHhckcvRSmsRMcXn/+wnMS2LusHe3NKkstlxREq9izeMPKdGgKd5oUSkWE1eEplTVHPB7di5W6/42AAv1zxldNUK7oRWsG0C6+FStD/6925USfP1RUTKg4iFsPwtOLkX/GtD13H2u/q2tGS9VE6rFeJ2nx3zsc5WUMdEgGHN/XiLo21DxAsLav864OBgzusRKQZaaS0iRSLuTDpdJv5NSkY20wa1pFdDrbQSKQoXrmSs6OlCVEIaAG/2a8zdbaqZnE5EiophGKw7eJqBn63GeomfzgO9XXOV0efmS59bKe3u4liyoUVEpOy71GZ8/b+xvzK4tGS9VM6gBhB/BDKS8j7Gp0rugrpSM3C59AhCEXtVbBsxJiQk8OOPP7JixQoOHjxISkoKgYGBNG/enF69etGhQ4drDl8YKq1F7MdrP0fw1coDNK3qy4JHO2K51EYOInJN3v9zDx8ujcTRwcIX97fiunpBZkcSkWtgtRos2XmCqcv3sfFwfL7nWIC6Id4sGqOxQCIiUsKmdIATEXDxwDqLA7h4mxLpkjKS8q5IBvvLeqmc5zh7QuXm5zdKrNIKfPTJJikbinw8SFRUFC+//DLffvstISEhtGnThmbNmuHu7s6pU6f4+++/effdd6levTqvvPIKAwYMKJIXIiKlw/H4VGauOQTA2F51VViLFKMnetTheHwqczcc5dHvNjJ7eHsaV/U1O5aIFFJ6VjY/bTrOtH/2se/s5qoujg60qVGRf/fG5dngcEwPbXAoIiImiIskT2ENttI1PaHE41yV0pLVwQmGL4fAeuCoab4iBfq/oGnTptx///2sXbuWRo0a5XtOamoqCxYs4P333+fIkSOMHTu2SIOKiP366K9IMrKttKtZkU61tdGDSHGyWCy82a8xJxLTWBEZx5AZ6/jxkQ6EVtTHA0VKg6S0TL777zBfrTzAicR0ALzdnLivXXWGdAwjyNtNGxyKiIh92PkzWPPb6NcCAXVg4KwSj3RZswbaZkTnKtntMOulcgbWg5D8OzeR8qhA40FiY2MJDAws8EULe/7V0ngQEfMdjEum+/vLybYazHu4PS2rVzQ7kki5kJSWSf9pa9gZlUjNQE/mjexABU8Xs2OJyCXEJKYxfdVBZq45RFKarQAI9nFlaKca3N2mGt5uziYnFBEROSsrHf54CdZOu+DgRfOXB8yE+reYk+9SLjUr2t6ylpacIsWg2GZa2xuV1iLmG/39Jn7afJzr6gYyfUgbs+OIlCsnEtPo+8lKjiek0ap6BWY+1BY3Z23EJmJP9see4fMV+5m34RgZ2bb5lbUCPRnRtRa3NauMq5P+nxURETtych/MHQJRW2zftx8FVVrCivfgZCT414Fu4+y3XI1YCMvftv+spSWnSBEr1tL666+/JiAggJtvvhmAZ555hs8++4wGDRowa9YsqlevfvXJC0mltYi5dkUncuMHKzAM+OWxTjSqorm6IiVtz4kk7piyiqS0LG5qHMLHd7fAwUFz5UXMtvlIPFOX7WNxRDTnftpuUc2PkV1r0aN+sP4/FRER+7N9Hiwcbdso0L0C3D4V6vY2O5WIlCGF6XIdCnvxN954A3d3dwBWr17Nxx9/zMSJEwkICOCJJ564usQiUiq998ceDANublxJhbWIScKDvflsUCtcHB34bVs0r/+20+xIIuWWYRj8vTuGgZ+t5vZPVrJoh62w7lE/iB9Gtmf+Ix25oWGICmsREbEvmanw82iY+6CtsK7WHkauVGEtIqYq9HakR44coXbt2gAsWLCAO++8k+HDh9OxY0e6detW1PlExE5tPhLPnxEncLDAEz3DzY4jUq61r+XPO3c1YfT3m/ny3wNU9nNnaKcaZscSKTcys638ujWKqcv3sSs6CQAnBwu3NavCiK41CQ/2NjmhiIjIJcTugR8GQ8wOwAKdn4Ruz4NjoesiEZEiVejfhby8vDh58iTVqlXjjz/+yFld7ebmRmpqapEHFBH79N4fuwHo16IqtYO8TE4jIrc1q0JUQhpv/b6LCb9GUNnXjRsbVzI7lkiZlpKRxex1R/hixQGOxdt+DvZ0ceTuNtV4sFMNKvu5m5xQRETkMrZ8D788CZnJ4BkIfadB7e5mpxIRAa6itO7ZsycPPfQQzZs3Z8+ePTmzrXfs2EFYWFhR5xMRO7R630lWRMbh7GhhdPc6ZscRkbNGdKnJsdOpfLPmEKNnbybQ25VWYRXNjiVS5pxKzmDGqoP8b/VB4lMyAfD3dGFIxzAGtQvD18PZ5IQiIiKXkZEMvz0Nm7+1fR/WGe74ArxDzM0lInKBQpfWn3zyCS+++CJHjhxh3rx5+Pv7A7BhwwbuvvvuIg8oIvbFMAzePbvK+u421Qit6GFyIhE5x2KxMP7WhkQnpvFnxAke+t965j3cgVqB+jSESFE4ciqFL1bsZ/b6I6RlWgGo7u/BsM41ubNlVdycHU1OKCIicgUnImzjQOJ2g8UBuo6DLmPBQX+GiYh9sRjGuf3MS5/C7DgpIkXjr10neHDGetycHfjn6esI8nEzO5KIXCQ1I5u7P1/D5iPxhFZ0Z/7DHQn0djU7lkipteN4AtOW7+fXbVFkW20/Ojeu4svIrrXo3SgER22sKCIi9s4wYOP/4PdnICsNvEJsq6trdDY7mYiUI4Xpcgu90josLIwHH3yQIUOGEBoaetUhRaT0sVoN3l28B4AH2oepsBaxU+4ujnz5QCv6TVnFoZMpPDhjHd8Pb4enqzbUESkowzBYve8kU5bvY0VkXM7xznUCGNm1Fh1q+WOxqKwWEZFSID0Jfh4D2+favq/V3Ta/2ivQ1FgiIpfjUNgHPPXUU/z000/UqFGDnj178v3335Oenl4c2UTEzvy+PZqIqES8XJ0Y2bWW2XFE5DL8vVz5ekgbKnq6sO1YAqO+20hWttXsWCJ2L9tq8Nu2KG77ZCX3fPEfKyLjcLDALU0r88tjnfhmaFs61g5QYS0iIqVD1BaY1sVWWFscofsrcO9cFdYiYveuejzIli1b+Oqrr5g1axZZWVncc889PPjgg7Ro0aKoM16SxoOIlJysbCs3TP6H/bHJjOlRhzE9ws2OJCIFsPHwae75fA1pmVbublONN/o2Utkmko+0zGzmbTzK5//s5+DJFADcnB3o3yqUYZ1rag8HERGAiIWw/C04uRf8a9vmITe41exUkh/DgHVfwOLnITsDfKrCnV9CtXZmJxORcqwwXe41z7TOzMzk008/5dlnnyUzM5NGjRoxevRohgwZUux/KVZpLVJyflh/hKfnbqWChzP/PHMd3m7OZkcSkQL6Y0c0I2duwGrA073q8uh1tc2OJGI3ElIymfnfIaavPEDcmQwA/Dycub99GA+0r46/l+bBi4gAtsJ6ziDAAhjnb/t/o+La3qTGw8LHYOdC2/fhN8Ltn4JHRVNjiYgU60zrczIzM/nxxx+ZPn06f/75J+3atWPo0KEcP36cF154gSVLlvDdd99d7eVFxI6kZ2UzeUkkAA93q6XCWqSUuaFhCONvbcjLP+3gncW7qeTrRr8WVc2OJVKiFm2PYvKSSA7EJVMjwJMHOlRnf2wy3/13mOSMbACq+LkztFMNBrQO1Qx4EZGLLXvr7D8YF9xaYPnbKq3tydENMHcwxB8GB2fo+Sq0ewT0STsRKWUK/dP4xo0bmT59OrNmzcLR0ZFBgwYxadIk6tWrl3PODTfcQJcuXYo0qIiYZ/a6IxyLTyXI25X724eZHUdErsL97cM4djqVaf/s55m5WwnydqNTnQCzY4mUiEXboxg5c2PO2sBd0Uk8N397zv31QrwZ0bUmfZpUxtmx0Fu+iIiUfdHbICYinzsMiNkJsXsgUOMDTWUYsOZT+PMVsGaCX3W4czpUbWl2MhGRq1Lo0rp169b07NmTKVOmcPvtt+PsnHfFZYMGDRg4cGCRBBQRc6VmZPPRX3sBeKx7HdycHU1OJCJX69ne9TiekMbPW44zcuYGfhjZnvqVNF5Lyr7JSyJzCusLebg48sm9LegWHqhZ7yIi+clItq2wXv0JeX8XPcvIhk9aQ+Xm0GQgNLpDm/yVtJRTsOBh2LPI9n39W+HWj8Ddz9RYIiLXotCl9f79+6levfplz/H09GT69OlXHUpE7MfXqw8Sm5RO1QruDGgVanYcEbkGDg4W3r2rCTGJafx34BRDpq/jx0c7UMnX3exoIsXqQFxyvlVLttXgurpBJZ5HRKRU2LMYfh0LCYdt31dtDUfXkWemdaXmcGIbHN9k+1r8PNTuAU0HQN2bwFk/ZxSrw2tg7lBIPAqOLtDrDWj9kMaBiEipV+jPP16psBaRsiMxLZOpy/cB8ESPcFyc9JFpkdLO1cmRzwa1ok6QF9GJaQz+ah2JaZlmxxIpVn4eeT8ZaLFAzUBPE9KIiNi5xCiYcz98199WWPtWg3vmwENLbJsuBjcEJ1fb7YCZMGIZPLUbbpwIlVvYVl5HLoa5D8K74fDTKDj4L1itZr+yssVqhRXvw/SbbIV1xVq2X6M2w1RYi0iZYDEM4xKf8clfdnY2kyZNYs6cORw+fJiMjIxc9586dapIA15OYXacFJHCm/TnHj5YGkntIC8Wj+mCo4N++BEpK47Fp9L3k5XEJKXTvqY/Xz/YRm9MSZk0b8NRnvphS65jFott9OfU+1rSu1GISclEROyMNRvWfQlLX4OMJLA4QvtHods4cCnEm3yxe2DrbNg65/wqbbCV303uso0Q0fzra3MmFn4cDvv+sn3f+C7oMwlcvc3NJSJyBYXpcgv9t9NXX32V999/n/79+5OQkMCTTz5Jv379cHBwYPz48VebWUTszKnkDL5YsR+AJ3uGq7AWKWOq+LkzfUhrPF0cWb3/JM/M3UIh38cWsXuLtkfz9FxbYX1dvUDqh3jj6uRAvRBvFdYiIheK2gJf9IDfn7YV1lVawYjlcMP/Fa6wBlsh3f0lGL0FBv8KzQeBq4+twF7xnm3+9WfdYM1UW/kqhXNgBUztZCusndxss6v7fa7CWkTKnEKvtK5VqxYffvghN998M97e3mzevDnn2Jo1a/juu++KK2seWmktUnze+G0nn/2zn0ZVfFj4aCccVFqLlEn/7InlwRnryLIaPNKtFs/0rmd2JJEisSIylqEz1pORbeXOllWZeEcT/VkmInKx9DOw7E1Y8ykYVnD1hR4vQ8sh4FCEG7BnpsLu320rsPcuAWuW7bjFEer0hCYDoO6Nmn99OdZs+OcdWP627dcqoC7cNQOCG5idTESkwArT5Ra6tPb09GTnzp1Uq1aNSpUq8euvv9KiRQv2799P8+bNSUhIuKbwhaHSWqR4nEhMo8vEv0nPsjJ9SGttUiVSxv2w/ghPz90KwOt9G3FvW+1fIaXbhkOnuO+LtaRmZnNjoxA+urs5To4afyMiksuu3+C3p23zkAEa9oPeb4J3MX8KJTkOts+DLd/D8Y3nj7v6QIPboOlAqNYBHPT7do6kaJj3EBxcYfu+2X1w08TCr4IXETFZYbpcp8JevGrVqkRFRVGtWjVq167NH3/8QYsWLVi3bh2urq5XHVpE7MdHf0WSnmWlVfUKdAsPNDuOiBSzu1qFciw+lclLInlpwXZCfNzoXj/Y7FgiV2XH8QQGT19HamY2XcIDmTywmQprEZELJRyF35+FXb/YvverDje/D3V6lMzzewZA2xG2r9g9sPX7s/Ovj8Cmb2xfmn993t6lMH84pMSBsyf0ed9W7IuIlHGFXmk9btw4fHx8eP7555k7dy533303YWFhHD58mCeeeIK33nqruLLmoZXWIkXv8MkUrn9vGVlWg9nD29G2pr/ZkUSkBBiGwbPztjJn/VHcnR35fng7mob6mR1LpFD2xZ6h/9TVnEzOoHVYBf73YFvcXYrw4+0iIqWZNRvWfgZ/TYCMM+DgBB0egy7PgIuHydmscHiVbfV1xE+Qnnj+vsrNbeV1ozvAqxwtqMnOgmVvwIr3AQOCG8Gd01Xii0ipVqzjQS62Zs0aVq1aRe3atbn11luv5VKFptJapOg9OWcz8zceo3OdAL4Z2tbsOCJSgjKzrQz9ej3/7InF39OF+Y90oLq/PnYqpcOx+FTumrKK4wlpNKzsw6zh7fBxczY7loiIfTi+CX4ebdtwESC0LfSZBMENzc2VH82/tq2GnzsUjqyxfd9yiG10S1l+zSJSLpRoaW0mldYiRSvyRBK9Jv+D1YCFozrSpKqf2ZFEpISdSc9iwLTV7DieSI0AT+Y93IGKni5mxxK5rNikdPpPW82BuGRqBXoyZ0R7/L00tk5EhPQk+Ot1WDvNtnmfmy/0eBVaPFA6ZkbnzL+eZSvezynL86/3LIYfR0DqaXDxhls/sK0yFxEpA4q8tF64cGGBn7wkV1urtBYpWg/P3MDv26Pp1TCYaYNamR1HREwSk5hG309XcSw+lRbV/PhuWDvcnDViQexTQkomAz5bza7oJKr4uTP34fZU8tVKNBEp5wzDNrP6t2cg6bjtWOO7oNcb4FVKN1m/eP71OWVl/nVWBix9FVZ/bPu+UjO48yvwr2VqLBGRolTkpbXDRe9aWiwWLn6YxWIBIDs7u7B5r5pKa5Gis/1YAn0++heLBRaP6UJ4sLfZkaQ0ilgIy9+Ck3vBvzZ0HQcNSnZ0lBSNvTFJ9Pt0FYlpWfRqGMyn97bE0cFidiyRXJLTs7jvy//YdDieQG9XfhjRnrAAjbQRkXIu/gj8/gzs/s32fYUacPN7ULu7ubmKymXnX7ewrb528YI1n9j/z6TnfnaOi7SNP8lKtR1vOxJ6vgZO+tSQiJQtxToeZMmSJTz77LO88cYbtG/fHovFwqpVq3jxxRd544036Nmz5zWFLwyV1iJFZ/D0tSzbHUvf5lWYNKCZ2XGkNIpYCHMGARbAOH/b/xv7/EuCXNF/+08y6Mu1ZGRbGdwhjFduaZDzJrWI2dIys3lwxjpW7TuJr7szc0a0p26I3nAVkXIsOwv+mwp/vwGZyeDgDB1HQ5exZXcWcmaqrZzfcnb+tXGZRXQNbocAO1qJHbcHIhbkPd5xDPR8taTTiIiUiGItrRs1asTUqVPp1KlTruMrVqxg+PDh7Ny5s/CJr5JKa5Gise7gKe6auhonBwtLn+qqjdfkyrLSIfE4JB6DhGO2j2iu/tg2ey8Xi22Dn4dXmhJTrt3PW47z2CzbDMkXb67PQ51rmpxIxLZp6CPfbuTPiBN4ujjy7bB2NAv1MzuWiIh5jm2wbbQYvc32fbX20GcyBNUzNVaJOhNrm3+95BXISjM7zVXSz84iUrYVpst1KuzF9+3bh6+vb57jvr6+HDx4sLCXExGTGYbBO4t2A9C/dagKawFrNpw5Ydu1POHo+WI68ejZgvooJMcU8GIGnNgBy962fVSzQvVijS5F75amlYlKSOWN33Yx4dedhPi60adJZbNjSTlmtRo8M3crf0acwMXJgS8eaK3CWkTKr7QE+GsCrP0cMMDND274P2h2X9nanLAgvAKh3UhY8nL+91scodWDJZvpctZ/lc/KcANORpoSR0TE3hS6tG7dujVjxoxh5syZVKpUCYDo6Gieeuop2rRpU+QBRaR4/RMZx9qDp3BxcuCx62ubHUeKm2FAysl8yuizhXTiMUiKAmvWla/l5AY+VcC3CvhUhb1/QnJsfk8Ky96wfYV1hqZ323Z7d/Uq8pcnxWNY55ocj09jxqqDPDl7C4FerrSt6W92LCmHDMPg5YXb+XHTMZwcLEy5twXta+m/RREphwzDNs950Tjbz25g24jwhgm28rY8868NJyKwjas7xwJB9eHmd81KldfhVfnn9K9jViIREbtS6NL6q6++om/fvlSvXp1q1aoBcPjwYcLDw1mwYEFR5xORYmQYBu/9YVtlPahddSr5FuGsu9KyIV9Zy5mWeIky+oJSuiAfl7Q4gk/ls6V01fPFtG+V88c8/OHC+caXmmnddiTE7IQD/8DBFbav38ZC/Vuh2d0Q1qX8rQQqZSwWCy/1aUBUQiqLd5xg2P/WM/+RDtQO0vxgKVkTF+9m5prDWCzw/oBmdK8fbHYkEZGSd/oQ/PY0RC62fV+xJvSZBDW7mRrLbnQdl//PpN3GmZvrYqUlp4iISQo90xpsRdeff/7Jrl27MAyDBg0a0KNHjxLfnEkzrUWuzaLt0YycuQFPF0f+eeY6/L2KaHfqS5WXt34EdW4omucoCpF/wMLHKLU5G91pW618bmRH4rHcu6dfjldw7lXSOWV0qO2fvYLBwbHwWSMWwvK3bR9r9K9j+6G7/i22++KPwNbZsPk7OLXv/GN8Q6HJAGh2D/jXKvxzSolJy8zmns/XsPFwPFX83PnxkQ4E+biZHUvKiU+X7WXi2XFWb/RtzD1tq5mcSESkhGVnwppPYdlbkJli22ix85PQ6Ulw1p/HuVzuZ1J7UlpyiogUkWLdiNGeqLQWuXrZVoPek/8hMuYMj11fm6duqFt0F/+olWaxmcnNz7YS+lKrpH0qg1MRvUFxNQwDjq6zldfb50N6wvn7qraxrb5u2A/c/UyLKJd2KjmDO6as4kBcMo2q+PD98PZ4uRb6g1sihfLNmkO8tGA7AM/dWI8RXfUGl4iUM0fWwS9j4ITt90Kqd7RttBgYbmYqERGRQlFpLSJX9OOmozwxewu+7s7888x1+Lo7X/tF4w/D8omw6ZtLn2O5itW7xSXPxicXKA05LQ7Q5RlbGe1b1VZM+1QuXbOiM9Ng92+wZRbsXQKG1Xbc0RXq3WxbfV3zOnBUKWpPDp1Mpt+nqziZnEHX8EC+eKAVzo4a8SLF48dNR3lyzhYMA0ZdV5uxvYrwTVYREXuXGg9LX7Nt2ocB7hVtc6ub3ZN7TJuIiEgpoNJaRC4rM9tK9/eWc/hUCs/0rssj3a5xA8bEKFjxHmyYAdbMS5xkgeCG8PDKa3uuojSlQ/6bnyinOZKiYescW4EdE3H+uFcwNOkPTe+B4Abm5ZNcNh+JZ+Bnq0nLtDKgVShv3dG4xMeESdn3x45oHv52I9lWg8Edwnjllgb670xEygfDgB3zYdFzcOaE7Vize6Hn/4GnNqAVEZHSqTBdrpZFiZRDc9Yf4fCpFAK8XBncIezqL5QcB3+8CB82g3Wf2wrrGl3g+pfPnmC54NYONxXpOo7zM6JBOU3mHQIdH4eHV8Hw5bYNHD38bX9RW/URTGkP07rAf9Mg+aTZacu9ZqF+fHx3CxwsMHv9ET5cutfsSFLGrNwbx6jvNpFtNejXogov91FhLSLlxOmD8O2dMPdB289B/rXhgZ/h9k9VWIuISLmhldYi5UxaZjbd3llGdGIa429pwOCONQp/kdR4WP0xrJkCGWdsx0LbwnUvQM2utu9Ly6YiymnfsjJsG1FumQV7FoE1y3bcwRnCe0HTu22bZjq5mJuzHLtw1nAlXzdOJWdQI8CTMT3q0LtRJZPTSWm18fBp7vviP1IysunVMJhP7mmBk0bQiEhZFbEQlr8FcXtte3qknAZrBji6QOex0GmMufuRiIiIFJFiHQ9y5swZNmzYQHR0NBaLheDgYFq2bImXV8nPUFVpLVJ4X6zYz4Rfd1LFz52/xnbF1akQs5vTk+C/qbZVr2lnN8+r1BSufwlq99BcPSleySdh+1zbBo5Rm88f9/CHxnfZCuxKTfXfoQlGfLOexTtO5Hx/9rMATL2vhYprKbSdUYkMmLaaxLQsOtcJ4IsHWhXuzyoRkdIkYiHMGcT5Pz3PCqwPA76BgDpmJRMRESlyhelyC7yzVVZWFk899RSff/45aWlpuLi4YBgGmZmZuLm5MXz4cN555x2cnYtgMzcRKRZn0rP4dNk+AB7vXrvgJUBGCqz/Ev6dBClnxzIE1ofrX4B6fVQSSsnw9Ie2I2xfJyJgy3e2GdhnTtjeTPlvKgQ1sJXXTfrbxo2UBedWX53ca/t4cNdx0OBWs1PlcuhkSq7vzw2z+WBppEprKZQDcckM+nItiWlZtKxegWmDWqqwFpGyKykafn3q7DcX7V3i4KjCWkREyrUCf87yqaeeYt68eUyfPp1Tp06RlpZGeno6p06dYvr06cyfP5+nn366OLOKyDX66t8DOR/dv6NF1Ss/ICsd1n4OHza3za5OOQkVa8EdX9o2AKx/iwprMUdwA7hhAjwRAffOhYb9wNHVtoHjny/B+/Vh5p2wfT5kppmd9uqdW311IsL2/+OJCNv3EQvNTpbLgbjkPMcMYM+JM8QmpZd8ICmVjsWnct8X/xF3Jp0GlXz4anBrPFwKvL5CRKR0sFph318w+z6Y1BCSY/I5ybCNhBMRESnHCjweJDAwkNmzZ3P99dfne//SpUsZOHAgsbGxRRrwcjQeRKTg4lMy6Pz23ySlZ/Hh3c25tWnlS5+cnWmbIbx8IiQcsR3zrQbdnoUmA8FRJYLYodR42PGjbXzI0bXnj7v52krtZvdA1db280aLYUBGMqQn2kbvpCVCesLZ20Tb7aoPIfniP1ctENzQ9saRneg9+R92RyeR3w8Urk4O9G8VyvAuNQmt6FHi2aR0iDuTTv+pq9kfl0zNAE/mjGxPgJfmt4pIGXImFjbPhA0zbBstnuPsAZmp5FlpbWd/1ouIiBSFYhkPkpqaSkBAwCXv9/f3JzU1teApRaRETftnP0npWdQL8aZP40t8XN+aDdvnwbI34dR+2zHvStBlLDS/X5vdiX1z94NWQ2xfcXttb7xs+R4Sj8KG6bYv/9rQdKDtzZfjm65+7IbVer5sPlcw59wmXFBCX3xf0gXFdBIY2VfxQu1v9dWYHnUYOXMjFoutiz93G+bvwcGTKXyz5hDfrT1MnyaVeLhbLeqF6I1mOS8hNZP7v1zL/rhkqvi5M/OhtiqsRaRsMAw4uALWT4edP4M103bc1ReaDoCWQ2w/h+SaaX32tts483KLiIjYgQKvtL7llltITU3l22+/JTg4ONd9J06cYNCgQbi5ubFwYcl9ZFkrrUUKJiYpjS4T/yYt08oX97eiR4Pc/w9jtcKun+HvNyB2l+2YRwB0fhJaPQjO7iUfWqQoWK22vyxu/g52LoTMlItOuOgviM3vhwrV8imaLyqfM5KKLqPFEVy9wc3H9pdYNx9w9bHdRv4JqafyPqZCDRi9uegyFIFF26P4YGkk+2OTqRnoyeju4fRqGMya/aeYsnwf/+w5v2L8+npBPNytFq3DKpqYWOxBSkYWg75cy4ZDpwnwcuWHke2pEeBpdiwRkWuTcsr2s8eG6bZS+pwqLW1FdaN+4HLB73URC2H527Y3pf3r2Arr+reUfG4REZFiVpgut8Cl9ZEjR7jpppvYtWsXjRo1Ijg4GIvFQnR0NNu3b6dBgwb8+uuvVK1agDm5RUSltUjBjF+4gxmrDtIs1I8fH+mA5dx4BMOAyD/grwkQvdV2zM0XOo6GNiPA1cu80CJFLT3J9pfCLbNsRXZRcHQ5XzBfeJvfMTcfWzl9cTHt7HHpkSXnZlrnlOvnWGwboXZ6ChwKvD2FqbYfS2DK8n38ti2Kcz95tA6rwMPdanFd3aDzvy9JuZGelc1DX69nRWQcPm5OzB7RnvqV9POciJRShgGH18D6ryDiJ8g+u6eDixc0vsv2SbBKTc3NKCIiYrJiKa0BrFYrixcvZs2aNURHRwMQEhJC+/btueGGG3Ao4b84q7QWubKjp1O47t1lZGYbfPdQWzrUDrD9UH1gua2sPrrOdqKLN7R/BNo9YhuzIFKW/V8gZGfkPW5xgGb32t68cfW5YAX0heWz79ny2Qec3Yo/64WrryrWAs9A2/+/AHVugL7TwKP0rFg+EJfMZ//sY96GY2RkWwGoF+LNw91qcXPjSjg5lo4SXq5NVraVR7/byOIdJ/BwcWTmQ21pUa2C2bFERAovNR62zraV1ec+sQgQ0sT2icXGd9p+bhAREZHiK63tjUprkSt7Zu4W5qw/Soda/nw3rJ1tBchfE86vNHVyh7bDocNo8PQ3N6xISZnSAU5EUGo3Pdo0E359CrLSbJuk9p9h+8hxKXIiMY0v/z3At2sOkZxhm+0dWtGd4V1qcVfLqrg5O5qcUIqL1Wowdu4W5m88houjA9OHtKZj7UvvmyIiYncMA45tsBXV2+dD1tm9nZw9oNEdtlXVlVvYz+bPIiIidqJYS+vs7GwcHc//RfK///4jPT2d9u3b4+zsfHWJr5JKa5HL2xd7hhsm/UO21WBRfy/qRXwIe5fY7nR0sa3+6PQkeAdf/kIiZU2esRtnbwfMLD0zJKO3wZz7bZumOrpA7zeh1dBS9xfkhJRMvllzkK9WHuRUsm31e4CXKw92CuO+dtXxcSvZny2keBmGwfiFO/h69SEcHSxMubcFNzQMMTuWiEjBpCXCtjmwfgac2Hb+eFAD28/VTfrbPpElIiIi+SqW0joqKoq77rqLNWvW0LFjRxYsWMCgQYP47bffAKhTpw7Lli2jUqVK1/4KCkiltcjljfpuI5Hb/uPNCj/TIuXs6lEHJ2h+H3R5GnxLbga9iN0pC5sepSXAT4/Czp9t3zfuD30mlcp59KkZ2cxZf4TP/tnPsXjbijVvVyfubVedBzuFEeRdAqNYpNi9u3g3H/+9F4sFJvVvxu3Nq5gdSUTkyo5vgvXTYdtcyEy2HXN0tW2o2HIIhLYpdW8ai4iImKFYSuv777+fffv2MW7cOL799luOHDmCo6Mjs2bNwmq1cu+999KkSRM+/vjjInkRBaHSWuTS9kZsImLWc/RxWIODxbDN6m0yALo+AxVrmh1PRIqKYcDqT+DPl8HIhsB60P9/EFjX7GRXJTPbys9bjjN1+T72nDgDgIuTA3e1rMrwLjWp7u9pckK5WtOW7+PN323zXifc3oj72lU3OZGIyGWkn4Ht82DDdFtpfU5AuK2objqwVO0pISIiYg+KpbSuXLky8+fPp127dpw6dYqAgAD+/PNPunfvDsDff//NQw89xL59+679FRSQSmuRfJw+CMsnYt08Cwdsm5zRsC90e67UllgiUgCHVsMPg+FMNDh7wq0f2jZ/KqWsVoO/dsXw6bK9bDwcD4CDBfo0qczIrrVoUFl/7pcm3/53iBd+3A7As73r8XC3WiYnEhG5hOjttqJ6y2zISLIdc3SB+rfaZlVX76hV1SIiIlepWEprd3d39uzZQ2hoKABeXl5s3ryZ2rVrA3D48GHq1atHSkrKNcYvOJXWIhdIOAYr3oWN/wNrFgB/WltS/+43qVq/rcnhRKREnImBeUPhwD+279sMhxsmgJOrubmugWEYrD1wik+X7WP5ntic49fVDeThbrVpHVYBi8oDu/bT5mOMmb0Zw4CHu9Xi2d71zI4kIpJbZirs+NG2seLRdeePV6wJLQdDs3vBUxvGioiIXKvCdLlOBb1oUFAQUVFROaX1qFGjqFjx/MehTp8+jaenPrIrUuLOxMC/k2Ddl5CdDsBW15a8lHgbdVt2o2f9piYHFJES4xUEgxbA32/Y3sRa+xkc2wB3fQ1+oWanuyoWi4W2Nf1pW9OfHccTmLp8P79uPc7fu2P5e3csLatX4JFutbiubhAODiqv7c3SnSd4as4WDAMGtavOM730iR8RsSMxu86uqp5l2ycCbPu/1LvZtrFiWBdwcDA3o4iISDlV4JXWt912G9dffz2jR4/O9/5PPvmE+fPns3Tp0iINeDlaaS3lTsRCWP4WnNwLFWqAf23Y9xdknv2EQ7UObKv7GLf8bODi6MBfY7tStYKHuZlFxBx7FsP84ZAWD+4VoN8XUKeH2amKxMG4ZD5bsZ+564+SkW0bg1Q32JuR3WrSp0llnB1VMNiDVfviGDx9HRlZVvo2r8J7dzXVGwsiYr6sdNvP1Ou/gsOrzh/3q3Z2VfV94B1sWjwREZGyrFjGg1zJunXrcHd3p1GjRkVxuQJRaS3lSsRCmDMIsAAX/W9bpSVc/yJGjW70nbKazUfiGdwhjPG3NjQhqIjYjdOH4IcHzm4gZbFtxNr1WXBwNDtZkYhJTOPLlQf4ds1hzqTbxiJVreDO8C416d8qFDfnsvE6S6NNh09z3xf/kZyRTc8GwUy5twVOejNB5PIuXJzgXxu6joMGt5qdKn+lJeuFOX2r2bIe+Q9ST9nutzhC3RttGyvWul6rqkVERIqZKaW1GVRaS7kypQOciCBPYe1XHUZvAYuFJREneOh/63F3dmT5M90I8nYzJaqI2JGsdFg0zraiDKDmdXDHF2VqNmdCaiYz1xziq38PcDI5AwB/Txce7FSD+9pVx9fd2eSE5cvu6CT6T1tNQmomHWv78+UDrfUGgsiVXGpxQpOBENzArFT5OxEBW7/Pe9zesl4qJ4BPFWjxALQYBD6VSzaXiIhIOVbkpfXq1atp3759gZ48OTmZgwcP0rBh8a/wVGkt5cr/BUJ2Rt7jTq7wYgxWq8FNH65gV3SSNroSkby2zIZfxtjGCXlXhv5fQ2gbs1MVqbTMbH5Yf4Rp/+zn6OlUALxcnbi3bTWGdqpBkI/eyCtuB+OSuWvaamKT0mlezY+ZQ9vi6VrgLVREyqczsTCtEyRFm52kfPCrDo9tBEf93iQiIlLSinwjxvvvv5+wsDCGDRvGTTfdhJeXV55zIiIimDlzJtOnT2fixIklUlqLlBtRW8Canc8dFvCvA8Av26LYFZ2Et6sTI7rULNl8ImL/mg6ASk1g9iA4GQnTb4QbJkDbkWApG3OG3ZwdGdQ+jLvbVOOXrVFMWbaP3SeSmPbPfqavPMgdLasyoktNdkUnMnlJJAfikqkR4MmYHnXo3aiS2fHzWLQ9qlTl3B+bjNUwyLIa1AvxZsbgNiqsRS4lMw32LLJtABj5Jxj5/ZwHWBygyYCSzXYlW2eDYc173N6yXirnmWgV1iIiIqVAgVZaZ2ZmMm3aND7++GP27dtHeHg4lStXxs3NjdOnT7Nr1y6Sk5Pp168fzz33XInNtdZKaykXDqyA7++B9MSzB859bPTs7YCZZIXfTM9J/3AgLpmneobzWPc65uUVEfuWngQLH4MdP9q+b3Ab3PoxuJW9P0cNw+CvXTF8umwfGw6dBvL8DppzO3lAM3o2sJ+Nt/6MOMGY2ZtLXc5z3r6jCQNah5oVS8Q+GQYcWWsrqnfMh7SE8/c5uUFW2kUPsEBwQ3h4ZYnGvKJ8R9bZYdbSklNERKQcKdaZ1hs3bmTFihUcPHiQ1NRUAgICaN68Oddddx0VK1a8puCFpdJayryIhTDvIchOh+qdbHP3Vn1kWyXpXwe6jYP6tzB73WGenbft/9u77/CoyrwP49+ZdEJIIFQp0qVXBQVBVAQbAoriothZUFTWtWHD1bV3XUUUVxfbq64VG0VUiq6CEGroLfSeBAKkzDzvH0MGAgESPJkzT+b+XBdXkjOT5J4QeMiPM89RlcRYTbvnbFXkzDYAx2KMNPNNaeIDkj8/cGGqK94N/CBfTs1as1Ojf1qhn5Zuczul3PNIalYrSd+P6O52ChAedq0JbNE07/+kXasPHq9UO3BmctsrpW1LD9vT+uDJCWrex5Xsozpi/+0wbbWlEwCACMKFGIHyYPZ/pG/uCDytsdnF0mX/lmKO3I81t8Cns5/9WRuz9uvBi5rrpm5sDQKghNbNkv57nZS9XopOkPq8FBielGNNHvhO+T5r/+ljjbhor5Y+doHbGYB79mdJi76U5n0kZfx68HhMYuAZLm2vlOp3k7zeg7elj5emPn3EyQlhyZZWWzoBAIgQju9pDSCEjJGmPy/9+M/A2x2ukS568ah77334e4Y2Zu1XzUrxuvr0k0MYCsB6dU+Thk6TPr9JWvmj9MVQKeN/0vlPF/ufZOVBo2oVtXTz7qJPFvdIp9RI0pfDu7rWdbi+r/6iZVvs7WxYLdG1JsA1vgJp1U+BM6qXfHvIdh8eqWEPqe1fpOYXS7FH+fPR4pLALxvY0mpLJwAAOAJDayCc+P3SxPul318PvN3tTumch456kbS9eQV67acVkqTbzm2s+JioUJUCKC8SU6WrPpWmPSv9/FTgWR4b0wLbhVSu73ad4/7Ws4mGvT9HHk/g/wgLX/6tZ9Ow+jv0jvPs7hxxblO304DQ2bwgcEb1/E+knK0Hj1c9RWr3F6n1FVJybff6AAAALMT2IEC4KMiTvrpFWvDfwNvnPyWdfvMx3+W1n1bo2YlLVa9KBU258yzFRHmPeX8AOKYVUwL76O/bKcUnS/3fkE4pf1s8TFi4SS9PWa5V23LUsFqiRpzbVOe3qul21hHoBMLY7i2Bf7PN+0jasuDg8QqpUuvLA9t/1Gp31BMPAAAAIhF7WgO2ycuRPh4srZwieaOlfq9Lba445rtk7ctXt6d/VPb+Ar04sK36t68TolgA5VrW+sA+1+tnBd4+8w7p7AePukURAESM/H2BbT/mfRT4N5vxB45HxQb+g6/tX6TGPaWoGHc7AQAAwlTI9rTev3+/4uPL556XQMjs3Sl9cLm04Q8ppoJ0xXtSk57Hfbe3pq9S9v4CNaleUZe05SmnABySXEe67jtp8qjAVkUzXpTW/xG4GGxSDbfrACC0/P7AXv/z/k9K/0rKzT54W51OgTOqW/aXKlRxrxEAAKAcKvXQ2u/36/HHH9eYMWO0ZcsWLVu2TA0bNtRDDz2k+vXr68YbbyyLTqB8ylovvXeptH2plFBZGvTfwIXRjmP7nlz9e8ZqSdKdvU5RlJenngJwUHSsdMFTUt1O0vjbpDXTpTe6SwPeluqHz0UAAaDM7Fgpzf84cFZ15tqDx5PrBQbVba+UUhu51wcAAFDOlXpo/dhjj2ncuHF65plnNGTIkODx1q1b68UXX2RoDZTUtqXSe/2l7A1SpdrS4C+kaqcc810mLNykl35YruVb9shnjE5OraDeLTnzEUAZaXWpVKOV9Mk10rbF0rg+Us+HpS63s08rgPJn3y5p0ReBQfW63w8ej02SWvaV2g6S6p0hebmGCAAAQFkr9Z7WjRs31htvvKFzzz1XSUlJmjdvnho2bKglS5bojDPO0K5du8qq9QjsaQ1rrf9D+mBA4Iejqk2lqz+XUuoe810mLNykYe/PkUfSoX9ox1zdQee3qlWmuQAiXF6O9M0dgbMOJemUi6R+o6WEFFezAOBP8+VLK34IbP+x9HvJlxc47vFKjc4J7FN9yoVSbAV3OwEAAMqBMt3TesOGDWrcuPERx/1+v/Lz80v74YDIs+KHwEUX8/dKtTsGtgRJTD3q3Y0xWrktRw9/tSjw9iG3eTzSy1OWM7QGULZiE6X+b0j1Tpe+v1da+q305lnSFe9Ktdq6XQcAx5Y+Xpr6lLRjhZTaWDrrXinl5MAZ1Qv+K+3dfvC+1VtK7f4itb5cSqrpXjMAAECEK/XQumXLlpo+fbpOPvnkIsf/+9//qn379o6FAeXSgk+lL4ZK/gKp0bmBgU9cxSPutm13rn5ZsV0zVmzXLyu2a1PW/mI/nDHSqm05ZV0NAIH/JTv1BqlWO+mTa6Vda6S3zpMuek7qcI3bdQBQvPTx0ieDpcLnqm1ZFNjy6FCJ1aTWVwSG1TVbu1EJAACAw5R6aP3www9r8ODB2rBhg/x+vz7//HMtXbpU7777rr755puyaATKh9/GSBPuDbzeaoDU7/XAxc4k7c0r0MzVOzVjeWBQvWTz7iLvGhvtVbTXo715viLHPR6pYbXEkOQDgCSpdgdp6FTpi2HS8omBCzVm/C5d+CxPnwcQfqY+JR2xuZoCx1r2D2z/0egcKarUPxYBAACgDJV6T2tJmjhxop544gnNnj1bfr9fHTp00KhRo9SrV6+yaDwq9rSGFYyRfnxMmv5c4O1OQ+Xr/aQWbNytGcu3acaK7ZqzNlN5Pn+Rd2t5UiWd2aSqzmxcVafVr6Kfl24N7GntCXzIwpdjru6o81vx9FUAIeb3S7+8GPj7zfil5LpSdLyUlXHg6fcjpRaXuF0JIJL5/dJj1QLPcDtcVJz00NbQNwEAAESw0sxyT2hoHS4YWiPs+X2Bi5fNGSdJmtvkVo3x9devq3Yoe3/RH6BqpyTozMZVdWaTqurSKFWpFeOO+HATFm7Sy1OWa9W2HDWslqgR5zZlYA3AXaumSh9fLeVmH3LwwFmNV7zH4BqAO7Ytk74eIWX8WsyNHqlGS+nmX0KeBQAAEMnK9EKMAEpmV1a29n98vWpt/EE+efVg/vX6vwVdJG2RJCXFR6tLo9QDg+pqqp9aQR6P55gf8/xWtbjoIoDw0vAsKanWYUNrI8kjTX2aoTWA0CrIlWa8KE1/XvLlBc6o9uXq4BYhB172GOluJwAAAI6p1EPrypUrFztY83g8io+PV+PGjXXdddfp+uuvdyQQsMX+fJ9mr92l6cu3a86yNfr7jn/odO9i5Zpo3Z5/q370dFanBpXVrXFVdW1SVW1qJys6yut2NgD8eZlrijlopB3LQ10CIJKt/V/g7OrtSwNvN+klXfS8tHFu4D/RdiyXUpsEBtbN+7iaCgAAgGMr9dB61KhRevzxx3XBBReoU6dOMsZo1qxZmjBhgoYPH67Vq1fr5ptvVkFBgYYMGVIWzUBY8PuN0jdl65cVgYsnzly9U7kFflVVlsbFPqWW3rXKUYI+O+VZXdmht15oUEWJcTy5AUA5lNpY2pKuIy50ltrElRwAEWZfpvTDP6TZ7wTeTqwuXfB04EKLHo+UUo9nfQAAAFim1BO0GTNm6LHHHtOwYcOKHH/jjTc0adIkffbZZ2rTpo1eeeWVUg2tn3zySd1///0aMWKEXnrppdJmASGxIXOfZizfpunLt+vXlTu0MyevyO3tKu7SW54nVDV/g/wVqipx8Oe6plZbl2oBIETOGil9MlgHn35/QNPz3SoCEAmMkdK/kr6/R9oT2H5NHa6RzntUSqjsbhsAAAD+lFJfiLFixYqaO3euGjduXOT4ihUr1K5dO+3Zs0crV65UmzZtlJOTU6KPOWvWLF1xxRWqVKmSzj777BIPrbkQI5wyYeEmvfTDcq3enqMGVRP1t55NdH6rWsral6//rdwRPJt69fai39OJsVE6vWGqujauqnMrb1W97wfLs2eLlHKyNPgLKbWRS48IAEIsffzBp9/HJkl7twf2ur7lNykhxe06AOVN1nrpu7ulpd8F3k5tLPV5Wap/prtdAAAAOKoyvRBjlSpV9PXXX+uOO+4ocvzrr79WlSpVJEk5OTlKSkoq0cfbs2ePrrrqKo0dO1aPPfZYaXOAP23Cwk0a9v6c4PmBSzfv1rD356hB1Qpau2Ov/If8t06U16O2dZJ1ZpNqOrNxVbWrm6LYaK+0Zob0f38JXIisRivp6s+kpJpuPSQACL0Wlxx8+n3eXmlMV2nnKmnSg1LfV91tA1B++H3SrLekKY9KeXskb4x05h1StzulmHi36wAAAOCQUg+tH3roId1888366aef1KlTJ3k8Hs2cOVPfffedxowZI0maPHmyzjrrrBJ9vOHDh+uiiy5Sz549jzu0zs3NVW5ubvDt7Ozs0uYDR3jph+VFntBe+HL19r2SpIbVEnVm46o6s3FVnd4oVZXiY4p+gCXfSv+9PnBl+npdpL/8H2cVAohssRWkvq9J71wgpb0ntbpUanSO21UAbLd5ofT17dKG2YG363YOnF1dvbm7XQAAAHBcqYfWQ4YMUYsWLfTqq6/q888/lzFGzZo109SpU9WlSxdJ0p133lmij/XRRx9pzpw5mjVrVonu/+STT+qRRx4pbTJwTKu35xx+6TBJUrTXo6n3nK3aKQlHf+c57wV+eDJ+6ZSLpAH/lmKOcX8AiBQnd5E6/VWa+aY0/nbplv9JcSV7FhYAFJG/T5r6jPTrK5K/QIqrJPX8h9TxesnrdbsOAAAAZaDUQ2tJ6tq1q7p27fqnPvG6des0YsQITZo0SfHxJXsq33333ae///3vwbezs7NVt27dP9UBNKiaqKWbdxcZXHs8UpMaFY8+sDZGmvGiNOXAf6K0v1q6+GUp6oT+SAFA+XTuw9KyCVJmhvTDP6SLnne7CIBtVv0sff03adfqwNvN+0gXPCtVquVmFQAAAMpYqS/EKEl+v18rVqzQ1q1b5ff7i9zWvXv3En2ML7/8Uv3791dUVFTwmM/nk8fjkdfrVW5ubpHbisOFGOGEwj2tC3k8gZn0mKs76vxWxexL7fcH9mj97bXA22feERjMeDwhKgYAi6z6WXq3b+D1a7+RGnRzNQeAJfbulCY+IM37MPB20knSRc9JzS5ytwsAAAAnrEwvxPjbb79p0KBBWrt2rQ6fd3s8Hvl8vhJ9nHPPPVcLFiwocuz6669Xs2bNdO+99x53YA045fxWtdS2TrLmrc9SlNejpjUqasS5TYsfWPvypa+GS/M/Drzd63Gpy62hDQYAmzTsIXW8Tpr9H2n8rdLNv0qxiS5HAQhbxkgL/itNGCnt3SHJI3UaIp3zkBTPSSoAAACRotRD62HDhunUU0/Vt99+q1q1aslzgmeXJiUlqVWrVkWOJSYmKjU19YjjQFkyxmhj1n5J0sd/PV2n1q9S/B3zcqRPrpVWTJa80YGLjLW9MoSlAGCp8x6Vlk+Wdq2RfnxMOv9Jt4sAhKOdq6Vv/y6t/DHwdvUWUp9XpLqnudsFAACAkCv10Hr58uX69NNP1bhx47LoAUJuQ+Y+bdudq2ivR61qJxd/p707pQ+vkNbPkqITpCvelZr2Cm0oANgqPlnq87L0wQDpt9elFv2kep3drgIQLnwFgW3XfnpSKtgnRcVJPe6VutwuRcW4XQcAAAAXlHpo3blzZ61YsaJMhtY///yz4x8TOJ45GZmSpBYnVVJ8TDHb0mRtkN6/VNq2RIpPka76r1S3U0gbAcB6Tc6T2g4K7E/71XBp2HQp5igXuwUQOTbMkb6+Xdp8YNvABt2li1+SUhu5mgUAAAB3lXpofdttt+nOO+/U5s2b1bp1a8XEFD37oU2bNo7FAaGQlrFLktShXuUjb9y2THqvv5S9PnABoMGfS9Wbh7gQAMqJ85+QVk6RdiyXfn5KOu8Rt4sAuCV3j/TTE9Lvr0vGLyVUDlwrpN0gLm4NAACA0g+tL7vsMknSDTfcEDzm8XhkjCnVhRiBcJF24Ezr9vVSit6wfnbgqez7dkqpTQID65R6Ie8DgHIjobJ08YvSR4OkX1+RWlwi1e7odhWAUFs2Sfr2TikrI/B26yuk3k9IFau52wUAAICwUeqh9erVq8uiA3BFboFP6RuzJUnt6x5ypvWKKdLHg6X8HOmkDtJVn0qJqS5VAkA50uwiqdUAaeGn0pfDpaFTpeg4t6sAhMKerdKEkdLCzwJvp9STLnpRatLT3S4AAACEnVIPrU8++eSy6ABcsXBDtvJ8fg2oMEd1P3lc2rFCSqwmZW+SjE9qeLY08H0prqLbqQBQflzwjLTqZ2nbYmnac9I5D7hdBKAsGSOlvSdNelDanyV5vNIZw6Ue90mxiW7XAQAAIAyVemhdKD09XRkZGcrLyyty/JJLLvnTUUCopGXsUm/vTD3nf0na4pFkpKz1gRvrdpYGfcwZgADgtMRU6aLnpP9eJ814QWreR6rFNTGAcmn7CunrEdLaGYG3a7WV+rwindTO1SwAAACEt1IPrVetWqX+/ftrwYIFwb2spcC+1pLY0xpWSVuXqb9Ffy4jjzwyRW/My2FgDQBlpWV/aeHn0uLx0le3SEN+kqJijv9+AOxQkCf98rI07VnJlyvFVJDOfkDqPEyKOuHzZgAAABAhvKV9hxEjRqhBgwbasmWLKlSooEWLFmnatGk69dRT9fPPP5dBIlB25mZkqoFn05EDa0nasTz0QQAQSS56PnBxxs0LpBkvuV0DwCkZv0tvdJN+eiwwsG7cU7rlN6nLrQysAQAAUCKlHlr/73//06OPPqpq1arJ6/XK6/XqzDPP1JNPPqnbb7+9LBqBMrEle782ZO7TalOrmJG1R0pt4kIVAESQitUD+1tL0tSnpa2L3e0B8Ofsz5K++bv0dm9p25LAdUIu+3fggtaVuS4OAAAASq7UQ2ufz6eKFQMXpatatao2btwoKXCBxqVLlzpbB5ShtIxdkqRPK14lT5FbDuxt3WOkC1UAEGF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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "for ticker in df_avgs.index:\n",
    "    # Select the data from the 3rd column onward\n",
    "    plt.plot(\n",
    "        df_avgs.columns[:-2],\n",
    "        df_avgs.loc[ticker].values[:-2],\n",
    "        label=ticker,\n",
    "        marker=\"o\",\n",
    "        markersize=4,\n",
    "    )\n",
    "plt.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=0.8)\n",
    "plt.legend(title=\"Ticker\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"% Change (30 days)\")\n",
    "plt.title(\"% Change portfolio vs SPY\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### YEARLY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight\n",
       "Ticker                                                         \n",
       "NEE                         NextEra Energy (NEE)       1.037676\n",
       "DUK                            Duke Energy (DUK)       0.974257\n",
       "ATO                           Atmos Energy (ATO)       1.040557\n",
       "AGR                               Avangrid (AGR)       0.847966\n",
       "OTTR                           Otter Tail (OTTR)       1.268206\n",
       "...                                          ...            ...\n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092\n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776\n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863\n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161\n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889\n",
       "\n",
       "[79 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_updated = df_updated[[\"Company Name\", \"Capped_weight\"]]\n",
    "df_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight  2023-12-01  \\\n",
       "Ticker                                                                        \n",
       "NEE                         NextEra Energy (NEE)       1.037676         NaN   \n",
       "DUK                            Duke Energy (DUK)       0.974257         NaN   \n",
       "ATO                           Atmos Energy (ATO)       1.040557         NaN   \n",
       "AGR                               Avangrid (AGR)       0.847966         NaN   \n",
       "OTTR                           Otter Tail (OTTR)       1.268206         NaN   \n",
       "...                                          ...            ...         ...   \n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092         NaN   \n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776         NaN   \n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863         NaN   \n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161         NaN   \n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889         NaN   \n",
       "\n",
       "        2024-01-01  2024-02-01  2024-03-01  2024-04-01  2024-05-01  \\\n",
       "Ticker                                                               \n",
       "NEE            NaN         NaN         NaN         NaN         NaN   \n",
       "DUK            NaN         NaN         NaN         NaN         NaN   \n",
       "ATO            NaN         NaN         NaN         NaN         NaN   \n",
       "AGR            NaN         NaN         NaN         NaN         NaN   \n",
       "OTTR           NaN         NaN         NaN         NaN         NaN   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST           NaN         NaN         NaN         NaN         NaN   \n",
       "DG             NaN         NaN         NaN         NaN         NaN   \n",
       "CLX            NaN         NaN         NaN         NaN         NaN   \n",
       "HSY            NaN         NaN         NaN         NaN         NaN   \n",
       "UL             NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        2024-06-01  2024-07-01  2024-08-01  2024-09-01  2024-10-01  \\\n",
       "Ticker                                                               \n",
       "NEE            NaN         NaN         NaN         NaN         NaN   \n",
       "DUK            NaN         NaN         NaN         NaN         NaN   \n",
       "ATO            NaN         NaN         NaN         NaN         NaN   \n",
       "AGR            NaN         NaN         NaN         NaN         NaN   \n",
       "OTTR           NaN         NaN         NaN         NaN         NaN   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST           NaN         NaN         NaN         NaN         NaN   \n",
       "DG             NaN         NaN         NaN         NaN         NaN   \n",
       "CLX            NaN         NaN         NaN         NaN         NaN   \n",
       "HSY            NaN         NaN         NaN         NaN         NaN   \n",
       "UL             NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "        2024-11-01  2024-12-01  \n",
       "Ticker                          \n",
       "NEE            NaN         NaN  \n",
       "DUK            NaN         NaN  \n",
       "ATO            NaN         NaN  \n",
       "AGR            NaN         NaN  \n",
       "OTTR           NaN         NaN  \n",
       "...            ...         ...  \n",
       "COST           NaN         NaN  \n",
       "DG             NaN         NaN  \n",
       "CLX            NaN         NaN  \n",
       "HSY            NaN         NaN  \n",
       "UL             NaN         NaN  \n",
       "\n",
       "[79 rows x 15 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = \"2023-12-01\"\n",
    "\n",
    "# Generate the first day of each month, starting from the start_date to the end of 2024\n",
    "dates = pd.date_range(\n",
    "    start=start_date,\n",
    "    end=\"2024-12-01\",\n",
    "    freq=\"MS\",  # 'MS' specifies the first day of each month\n",
    ")\n",
    "\n",
    "# Create a DataFrame with NaN values for each of these dates\n",
    "new_columns = pd.DataFrame(\n",
    "    np.nan, index=final_port.index, columns=dates.strftime(\"%Y-%m-%d\")\n",
    ")\n",
    "\n",
    "# Concatenate the existing DataFrame with the new columns\n",
    "df_updated = pd.concat([final_port, new_columns], axis=1)\n",
    "df_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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       "      <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>NEE</th>\n",
       "      <td>NextEra Energy (NEE)</td>\n",
       "      <td>1.037676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.867302</td>\n",
       "      <td>10.003371</td>\n",
       "      <td>15.270309</td>\n",
       "      <td>37.732272</td>\n",
       "      <td>21.879799</td>\n",
       "      <td>32.335923</td>\n",
       "      <td>39.473301</td>\n",
       "      <td>47.385885</td>\n",
       "      <td>38.179716</td>\n",
       "      <td>37.168419</td>\n",
       "      <td>30.101196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DUK</th>\n",
       "      <td>Duke Energy (DUK)</td>\n",
       "      <td>0.974257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.174059</td>\n",
       "      <td>2.063628</td>\n",
       "      <td>3.699435</td>\n",
       "      <td>9.303381</td>\n",
       "      <td>6.838083</td>\n",
       "      <td>16.474074</td>\n",
       "      <td>21.462636</td>\n",
       "      <td>24.047688</td>\n",
       "      <td>24.015404</td>\n",
       "      <td>25.930456</td>\n",
       "      <td>19.669103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATO</th>\n",
       "      <td>Atmos Energy (ATO)</td>\n",
       "      <td>1.040557</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.903977</td>\n",
       "      <td>5.067534</td>\n",
       "      <td>4.210159</td>\n",
       "      <td>2.460075</td>\n",
       "      <td>3.838613</td>\n",
       "      <td>13.835253</td>\n",
       "      <td>16.381158</td>\n",
       "      <td>24.244651</td>\n",
       "      <td>24.307339</td>\n",
       "      <td>35.539613</td>\n",
       "      <td>27.158501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGR</th>\n",
       "      <td>Avangrid (AGR)</td>\n",
       "      <td>0.847966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.468723</td>\n",
       "      <td>21.650353</td>\n",
       "      <td>21.950809</td>\n",
       "      <td>20.214849</td>\n",
       "      <td>18.612431</td>\n",
       "      <td>20.552812</td>\n",
       "      <td>20.620423</td>\n",
       "      <td>22.455489</td>\n",
       "      <td>22.181768</td>\n",
       "      <td>23.516152</td>\n",
       "      <td>23.204417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTTR</th>\n",
       "      <td>Otter Tail (OTTR)</td>\n",
       "      <td>1.268206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044247</td>\n",
       "      <td>-3.941820</td>\n",
       "      <td>-5.098068</td>\n",
       "      <td>0.583146</td>\n",
       "      <td>-2.123228</td>\n",
       "      <td>8.302515</td>\n",
       "      <td>-5.520235</td>\n",
       "      <td>-12.192018</td>\n",
       "      <td>-11.787585</td>\n",
       "      <td>-9.405893</td>\n",
       "      <td>-11.062731</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COST</th>\n",
       "      <td>COSTCO WHOLESALE CORPORATION (XNAS:COST)</td>\n",
       "      <td>1.611092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.053012</td>\n",
       "      <td>5.587584</td>\n",
       "      <td>4.185281</td>\n",
       "      <td>16.909737</td>\n",
       "      <td>22.698270</td>\n",
       "      <td>18.657845</td>\n",
       "      <td>29.000778</td>\n",
       "      <td>28.153666</td>\n",
       "      <td>26.369824</td>\n",
       "      <td>40.493156</td>\n",
       "      <td>45.805584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DG</th>\n",
       "      <td>DOLLAR GENERAL CORPORATION (XNYS:DG)</td>\n",
       "      <td>0.580776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.505240</td>\n",
       "      <td>18.680366</td>\n",
       "      <td>5.851085</td>\n",
       "      <td>4.503642</td>\n",
       "      <td>0.931389</td>\n",
       "      <td>-8.106100</td>\n",
       "      <td>-36.380384</td>\n",
       "      <td>-35.153543</td>\n",
       "      <td>-38.627045</td>\n",
       "      <td>-40.333427</td>\n",
       "      <td>-36.897209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLX</th>\n",
       "      <td>COLGATE-PALMOLIVE COMPANY (XNYS:CL)</td>\n",
       "      <td>1.142863</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.443912</td>\n",
       "      <td>6.305058</td>\n",
       "      <td>2.666881</td>\n",
       "      <td>-7.895719</td>\n",
       "      <td>-4.458261</td>\n",
       "      <td>-7.636686</td>\n",
       "      <td>10.831797</td>\n",
       "      <td>15.029288</td>\n",
       "      <td>11.950718</td>\n",
       "      <td>18.948941</td>\n",
       "      <td>19.112580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HSY</th>\n",
       "      <td>THE HERSHEY COMPANY (XNYS:HSY)</td>\n",
       "      <td>0.970161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.903790</td>\n",
       "      <td>1.216070</td>\n",
       "      <td>0.914250</td>\n",
       "      <td>2.948975</td>\n",
       "      <td>-3.694189</td>\n",
       "      <td>3.456834</td>\n",
       "      <td>1.141282</td>\n",
       "      <td>1.175528</td>\n",
       "      <td>-6.315830</td>\n",
       "      <td>-7.080789</td>\n",
       "      <td>-3.551317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UL</th>\n",
       "      <td>UNILEVER PLC (XBUE:UL)</td>\n",
       "      <td>0.978889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.575072</td>\n",
       "      <td>4.010747</td>\n",
       "      <td>7.450844</td>\n",
       "      <td>13.460631</td>\n",
       "      <td>14.919879</td>\n",
       "      <td>28.315698</td>\n",
       "      <td>35.400236</td>\n",
       "      <td>36.818057</td>\n",
       "      <td>28.288003</td>\n",
       "      <td>26.034373</td>\n",
       "      <td>25.382231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Company Name  Capped_weight  2024-01-01  \\\n",
       "Ticker                                                                        \n",
       "NEE                         NextEra Energy (NEE)       1.037676         NaN   \n",
       "DUK                            Duke Energy (DUK)       0.974257         NaN   \n",
       "ATO                           Atmos Energy (ATO)       1.040557         NaN   \n",
       "AGR                               Avangrid (AGR)       0.847966         NaN   \n",
       "OTTR                           Otter Tail (OTTR)       1.268206         NaN   \n",
       "...                                          ...            ...         ...   \n",
       "COST    COSTCO WHOLESALE CORPORATION (XNAS:COST)       1.611092         NaN   \n",
       "DG          DOLLAR GENERAL CORPORATION (XNYS:DG)       0.580776         NaN   \n",
       "CLX          COLGATE-PALMOLIVE COMPANY (XNYS:CL)       1.142863         NaN   \n",
       "HSY               THE HERSHEY COMPANY (XNYS:HSY)       0.970161         NaN   \n",
       "UL                        UNILEVER PLC (XBUE:UL)       0.978889         NaN   \n",
       "\n",
       "        2024-02-01  2024-03-01  2024-04-01  2024-05-01  2024-06-01  \\\n",
       "Ticker                                                               \n",
       "NEE      -5.867302   10.003371   15.270309   37.732272   21.879799   \n",
       "DUK      -4.174059    2.063628    3.699435    9.303381    6.838083   \n",
       "ATO      -0.903977    5.067534    4.210159    2.460075    3.838613   \n",
       "AGR       2.468723   21.650353   21.950809   20.214849   18.612431   \n",
       "OTTR      0.044247   -3.941820   -5.098068    0.583146   -2.123228   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "COST      7.053012    5.587584    4.185281   16.909737   22.698270   \n",
       "DG       10.505240   18.680366    5.851085    4.503642    0.931389   \n",
       "CLX       6.443912    6.305058    2.666881   -7.895719   -4.458261   \n",
       "HSY      -2.903790    1.216070    0.914250    2.948975   -3.694189   \n",
       "UL        0.575072    4.010747    7.450844   13.460631   14.919879   \n",
       "\n",
       "        2024-07-01  2024-08-01  2024-09-01  2024-10-01  2024-11-01  2024-12-01  \n",
       "Ticker                                                                          \n",
       "NEE      32.335923   39.473301   47.385885   38.179716   37.168419   30.101196  \n",
       "DUK      16.474074   21.462636   24.047688   24.015404   25.930456   19.669103  \n",
       "ATO      13.835253   16.381158   24.244651   24.307339   35.539613   27.158501  \n",
       "AGR      20.552812   20.620423   22.455489   22.181768   23.516152   23.204417  \n",
       "OTTR      8.302515   -5.520235  -12.192018  -11.787585   -9.405893  -11.062731  \n",
       "...            ...         ...         ...         ...         ...         ...  \n",
       "COST     18.657845   29.000778   28.153666   26.369824   40.493156   45.805584  \n",
       "DG       -8.106100  -36.380384  -35.153543  -38.627045  -40.333427  -36.897209  \n",
       "CLX      -7.636686   10.831797   15.029288   11.950718   18.948941   19.112580  \n",
       "HSY       3.456834    1.141282    1.175528   -6.315830   -7.080789   -3.551317  \n",
       "UL       28.315698   35.400236   36.818057   28.288003   26.034373   25.382231  \n",
       "\n",
       "[79 rows x 14 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date = \"2024-01-01\"\n",
    "\n",
    "# Generate the first day of each month, starting from the start_date to the end of 2024\n",
    "dates = pd.date_range(\n",
    "    start=start_date,\n",
    "    end=\"2024-12-01\",\n",
    "    freq=\"MS\",  # 'MS' specifies the first day of each month\n",
    ")\n",
    "\n",
    "# Create a DataFrame with NaN values for each of these dates\n",
    "new_columns = pd.DataFrame(\n",
    "    np.nan, index=final_port.index, columns=dates.strftime(\"%Y-%m-%d\")\n",
    ")\n",
    "\n",
    "# Concatenate the existing DataFrame with the new columns\n",
    "df_updated = pd.concat([final_port, new_columns], axis=1)\n",
    "df_updated\n",
    "\n",
    "for indx in df_updated.index:\n",
    "    for months in df_updated.columns[3:]:  # Skip the first column (e.g., Jan)\n",
    "        # Ensure `months` is handled as a datetime object\n",
    "        if isinstance(months, str):\n",
    "            month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "        else:\n",
    "            month = months  # Already a datetime object\n",
    "\n",
    "        # Update your DataFrame with the calculated return\n",
    "        if indx == \"PKST\":\n",
    "            break\n",
    "        else:\n",
    "            df_updated.at[indx, months] = get_returns(\n",
    "                indx, month, intvl=\"1mo\", start_of_year=\"2024-01-01\", mth=1, dys=0\n",
    "            ).values[0]\n",
    "\n",
    "df_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df_updated.columns[2:]:\n",
    "    df_updated[col] = df_updated[col] * (df_updated[\"Capped_weight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>2024-01-01</th>\n",
       "      <th>2024-02-01</th>\n",
       "      <th>2024-03-01</th>\n",
       "      <th>2024-04-01</th>\n",
       "      <th>2024-05-01</th>\n",
       "      <th>2024-06-01</th>\n",
       "      <th>2024-07-01</th>\n",
       "      <th>2024-08-01</th>\n",
       "      <th>2024-09-01</th>\n",
       "      <th>2024-10-01</th>\n",
       "      <th>2024-11-01</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>portfolio</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.998312</td>\n",
       "      <td>14.480697</td>\n",
       "      <td>10.163700</td>\n",
       "      <td>16.625609</td>\n",
       "      <td>19.748890</td>\n",
       "      <td>22.291656</td>\n",
       "      <td>26.559068</td>\n",
       "      <td>29.592693</td>\n",
       "      <td>30.877281</td>\n",
       "      <td>44.144350</td>\n",
       "      <td>43.093828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.218691</td>\n",
       "      <td>8.322977</td>\n",
       "      <td>4.278428</td>\n",
       "      <td>9.552792</td>\n",
       "      <td>13.053119</td>\n",
       "      <td>14.791228</td>\n",
       "      <td>17.473382</td>\n",
       "      <td>19.574102</td>\n",
       "      <td>18.870577</td>\n",
       "      <td>25.959242</td>\n",
       "      <td>27.039994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2024-01-01  2024-02-01  2024-03-01  2024-04-01  2024-05-01  \\\n",
       "portfolio         NaN    7.998312   14.480697   10.163700   16.625609   \n",
       "SPY               NaN    5.218691    8.322977    4.278428    9.552792   \n",
       "\n",
       "           2024-06-01  2024-07-01  2024-08-01  2024-09-01  2024-10-01  \\\n",
       "portfolio   19.748890   22.291656   26.559068   29.592693   30.877281   \n",
       "SPY         13.053119   14.791228   17.473382   19.574102   18.870577   \n",
       "\n",
       "           2024-11-01  2024-12-01  Company Name  Capped_weight  \n",
       "portfolio   44.144350   43.093828           NaN            NaN  \n",
       "SPY         25.959242   27.039994           NaN            NaN  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the values of each column (from the 3rd column onward)\n",
    "column_sums = df_updated.iloc[:, 2:].mean(axis=0)\n",
    "\n",
    "# Create a new DataFrame with the same columns\n",
    "df_avgs = pd.DataFrame(\n",
    "    column_sums\n",
    ").T  # Transpose to match the structure of the original DataFrame\n",
    "df_avgs.columns = df_updated.columns[\n",
    "    2:\n",
    "]  # Assign the column names from the 3rd column onward\n",
    "\n",
    "# Add the two new index values: 'portfolio name' and 'SPY'\n",
    "df_avgs.index = [\"portfolio\"]\n",
    "spy_values = [np.NaN, np.NaN, np.NaN]\n",
    "\n",
    "# Iterate over the columns from the 3rd column onward\n",
    "for months in df_updated.columns[3:]:\n",
    "    if isinstance(months, str):\n",
    "        month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "    else:\n",
    "        month = months  # Already a datetime object\n",
    "    month = datetime.strptime(months, \"%Y-%m-%d\")\n",
    "    spy_value = get_returns(\n",
    "        \"SPY\", month, intvl=\"1mo\", start_of_year=\"2024-01-01\", mth=1, dys=0\n",
    "    ).values[0]\n",
    "    spy_values.append(spy_value)\n",
    "\n",
    "spy_values = pd.DataFrame([spy_values])\n",
    "spy_values.columns = df_updated.columns\n",
    "spy_values.index = [\"SPY\"]\n",
    "df_avgs = pd.concat([df_avgs, spy_values])\n",
    "df_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2024-01-01</th>\n",
       "      <th>2024-02-01</th>\n",
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       "      <th>2024-08-01</th>\n",
       "      <th>2024-09-01</th>\n",
       "      <th>2024-10-01</th>\n",
       "      <th>2024-11-01</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>portfolio</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.998312</td>\n",
       "      <td>14.480697</td>\n",
       "      <td>10.163700</td>\n",
       "      <td>16.625609</td>\n",
       "      <td>19.748890</td>\n",
       "      <td>22.291656</td>\n",
       "      <td>26.559068</td>\n",
       "      <td>29.592693</td>\n",
       "      <td>30.877281</td>\n",
       "      <td>44.144350</td>\n",
       "      <td>43.093828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.218691</td>\n",
       "      <td>8.322977</td>\n",
       "      <td>4.278428</td>\n",
       "      <td>9.552792</td>\n",
       "      <td>13.053119</td>\n",
       "      <td>14.791228</td>\n",
       "      <td>17.473382</td>\n",
       "      <td>19.574102</td>\n",
       "      <td>18.870577</td>\n",
       "      <td>25.959242</td>\n",
       "      <td>27.039994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2024-01-01  2024-02-01  2024-03-01  2024-04-01  2024-05-01  \\\n",
       "portfolio         0.0    7.998312   14.480697   10.163700   16.625609   \n",
       "SPY               0.0    5.218691    8.322977    4.278428    9.552792   \n",
       "\n",
       "           2024-06-01  2024-07-01  2024-08-01  2024-09-01  2024-10-01  \\\n",
       "portfolio   19.748890   22.291656   26.559068   29.592693   30.877281   \n",
       "SPY         13.053119   14.791228   17.473382   19.574102   18.870577   \n",
       "\n",
       "           2024-11-01  2024-12-01  Company Name  Capped_weight  \n",
       "portfolio   44.144350   43.093828           0.0            0.0  \n",
       "SPY         25.959242   27.039994           0.0            0.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avgs = df_avgs.fillna(0)\n",
    "df_avgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_avgs = df_avgs.fillna(0)\n",
    "\n",
    "plt.figure(figsize=(18, 6))\n",
    "for ticker in df_avgs.index:\n",
    "    # Select the data from the 3rd column onward\n",
    "    plt.plot(\n",
    "        df_avgs.columns[:-2],\n",
    "        df_avgs.loc[ticker].values[:-2],\n",
    "        label=ticker,\n",
    "        marker=\"o\",\n",
    "        markersize=4,\n",
    "    )\n",
    "plt.axhline(y=0, color=\"black\", linestyle=\"--\", linewidth=0.8)\n",
    "plt.legend(title=\"Ticker\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"% Change (1 year)\")\n",
    "plt.title(\"% Change portfolio vs SPY (YTD)\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>2024-01-01</th>\n",
       "      <th>2024-02-01</th>\n",
       "      <th>2024-03-01</th>\n",
       "      <th>2024-04-01</th>\n",
       "      <th>2024-05-01</th>\n",
       "      <th>2024-06-01</th>\n",
       "      <th>2024-07-01</th>\n",
       "      <th>2024-08-01</th>\n",
       "      <th>2024-09-01</th>\n",
       "      <th>2024-10-01</th>\n",
       "      <th>2024-11-01</th>\n",
       "      <th>2024-12-01</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Capped_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>portfolio</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.998312</td>\n",
       "      <td>14.480697</td>\n",
       "      <td>10.163700</td>\n",
       "      <td>16.625609</td>\n",
       "      <td>19.748890</td>\n",
       "      <td>22.291656</td>\n",
       "      <td>26.559068</td>\n",
       "      <td>29.592693</td>\n",
       "      <td>30.877281</td>\n",
       "      <td>44.144350</td>\n",
       "      <td>43.093828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPY</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.218691</td>\n",
       "      <td>8.322977</td>\n",
       "      <td>4.278428</td>\n",
       "      <td>9.552792</td>\n",
       "      <td>13.053119</td>\n",
       "      <td>14.791228</td>\n",
       "      <td>17.473382</td>\n",
       "      <td>19.574102</td>\n",
       "      <td>18.870577</td>\n",
       "      <td>25.959242</td>\n",
       "      <td>27.039994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2024-01-01  2024-02-01  2024-03-01  2024-04-01  2024-05-01  \\\n",
       "portfolio         0.0    7.998312   14.480697   10.163700   16.625609   \n",
       "SPY               0.0    5.218691    8.322977    4.278428    9.552792   \n",
       "\n",
       "           2024-06-01  2024-07-01  2024-08-01  2024-09-01  2024-10-01  \\\n",
       "portfolio   19.748890   22.291656   26.559068   29.592693   30.877281   \n",
       "SPY         13.053119   14.791228   17.473382   19.574102   18.870577   \n",
       "\n",
       "           2024-11-01  2024-12-01  Company Name  Capped_weight  \n",
       "portfolio   44.144350   43.093828           0.0            0.0  \n",
       "SPY         25.959242   27.039994           0.0            0.0  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df_avgs\n",
    "df_avgs"
   ]
  }
 ],
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