CustomPortfolio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Portfolio Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pull in portfolio data from CSVs\n",
    "erii_returns = Path('erii_historical.csv')\n",
    "tpr_returns = Path('tpr_historical.csv')\n",
    "mrk_returns = Path('mrk_historical.csv')\n",
    "low_returns = Path('low_historical.csv')\n",
    "sp500_returns = Path('sp500_historical.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10/1/2019 16:00:00</td>\n",
       "      <td>2940.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10/2/2019 16:00:00</td>\n",
       "      <td>2887.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10/3/2019 16:00:00</td>\n",
       "      <td>2910.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10/4/2019 16:00:00</td>\n",
       "      <td>2952.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10/7/2019 16:00:00</td>\n",
       "      <td>2938.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>9/24/2020 16:00:00</td>\n",
       "      <td>3246.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>9/25/2020 16:00:00</td>\n",
       "      <td>3298.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>9/28/2020 16:00:00</td>\n",
       "      <td>3351.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>9/29/2020 16:00:00</td>\n",
       "      <td>3335.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>9/30/2020 16:00:00</td>\n",
       "      <td>3363.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date    Close\n",
       "0    10/1/2019 16:00:00  2940.25\n",
       "1    10/2/2019 16:00:00  2887.61\n",
       "2    10/3/2019 16:00:00  2910.63\n",
       "3    10/4/2019 16:00:00  2952.01\n",
       "4    10/7/2019 16:00:00  2938.79\n",
       "..                  ...      ...\n",
       "248  9/24/2020 16:00:00  3246.59\n",
       "249  9/25/2020 16:00:00  3298.46\n",
       "250  9/28/2020 16:00:00  3351.60\n",
       "251  9/29/2020 16:00:00  3335.47\n",
       "252  9/30/2020 16:00:00  3363.00\n",
       "\n",
       "[253 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read CSVs and Create DataFrames\n",
    "erii_dataframe = pd.read_csv(erii_returns, infer_datetime_format=True, parse_dates=True)\n",
    "tpr_dataframe = pd.read_csv(tpr_returns, infer_datetime_format=True, parse_dates=True)\n",
    "mrk_dataframe = pd.read_csv(mrk_returns, infer_datetime_format=True, parse_dates=True)\n",
    "low_dataframe = pd.read_csv(low_returns, infer_datetime_format=True, parse_dates=True)\n",
    "sp500_dataframe = pd.read_csv(sp500_returns, infer_datetime_format=True, parse_dates=True)\n",
    "sp500_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping Date column so that new data lines up with Whale Data as Whale Data (2015 - April 2019) does not line up with Custom Portfolio Data (October 2019 - October 2020)\n",
    "erii_dataframe = erii_dataframe.drop(['Date'], axis=1)\n",
    "tpr_dataframe = tpr_dataframe.drop(['Date'], axis=1)\n",
    "mrk_dataframe = mrk_dataframe.drop(['Date'], axis=1)\n",
    "low_dataframe = low_dataframe.drop(['Date'], axis=1)\n",
    "sp500_dataframe = sp500_dataframe.drop(['Date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Close\n",
      "0         NaN\n",
      "1    0.008811\n",
      "2   -0.007642\n",
      "3   -0.003300\n",
      "4   -0.012141\n",
      "..        ...\n",
      "248  0.011278\n",
      "249  0.000000\n",
      "250  0.021066\n",
      "251 -0.008495\n",
      "252  0.003672\n",
      "\n",
      "[253 rows x 1 columns]\n",
      "        Close\n",
      "0         NaN\n",
      "1   -0.033594\n",
      "2    0.021423\n",
      "3    0.008706\n",
      "4   -0.012162\n",
      "..        ...\n",
      "248 -0.015069\n",
      "249  0.001224\n",
      "250  0.013447\n",
      "251 -0.065139\n",
      "252  0.008387\n",
      "\n",
      "[253 rows x 1 columns]\n",
      "        Close\n",
      "0         NaN\n",
      "1   -0.023801\n",
      "2    0.013722\n",
      "3    0.027314\n",
      "4   -0.007176\n",
      "..        ...\n",
      "248  0.006293\n",
      "249 -0.002646\n",
      "250 -0.002050\n",
      "251 -0.010391\n",
      "252  0.012821\n",
      "\n",
      "[253 rows x 1 columns]\n",
      "        Close\n",
      "0         NaN\n",
      "1   -0.028944\n",
      "2    0.000188\n",
      "3    0.009871\n",
      "4    0.007540\n",
      "..        ...\n",
      "248 -0.000567\n",
      "249  0.006498\n",
      "250  0.019054\n",
      "251 -0.002030\n",
      "252  0.022187\n",
      "\n",
      "[253 rows x 1 columns]\n",
      "        Close\n",
      "0         NaN\n",
      "1   -0.017903\n",
      "2    0.007972\n",
      "3    0.014217\n",
      "4   -0.004478\n",
      "..        ...\n",
      "248  0.002987\n",
      "249  0.015977\n",
      "250  0.016111\n",
      "251 -0.004813\n",
      "252  0.008254\n",
      "\n",
      "[253 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate Returns For Each Stock\n",
    "erii_daily_return = erii_dataframe.pct_change()\n",
    "tpr_daily_return = tpr_dataframe.pct_change()\n",
    "mrk_daily_return = mrk_dataframe.pct_change()\n",
    "low_daily_return = low_dataframe.pct_change()\n",
    "sp500_daily_return = sp500_dataframe.pct_change()\n",
    "\n",
    "print(erii_daily_return)\n",
    "print(tpr_daily_return)\n",
    "print(mrk_daily_return)\n",
    "print(low_daily_return)\n",
    "print(sp500_daily_return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Project Cornerstone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56.9525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55.4675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.8675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.7425</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56.6875</td>\n",
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       "      <th>...</th>\n",
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       "      <th>248</th>\n",
       "      <td>66.5200</td>\n",
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       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>66.7275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>67.5425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>66.9575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>68.1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Project Cornerstone\n",
       "0                56.9525\n",
       "1                55.4675\n",
       "2                55.8675\n",
       "3                56.7425\n",
       "4                56.6875\n",
       "..                   ...\n",
       "248              66.5200\n",
       "249              66.7275\n",
       "250              67.5425\n",
       "251              66.9575\n",
       "252              68.1600\n",
       "\n",
       "[253 rows x 1 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate DataFrames into a single DataFrame. Excludes S&P 500 as it is an index\n",
    "\n",
    "combined_dataframe = pd.concat([erii_dataframe, tpr_dataframe, mrk_dataframe, low_dataframe], axis='columns', join='inner')\n",
    "combined_dataframe.columns = ['ERII','TPR','MRK','LOW']\n",
    "\n",
    "# Calculate weighted returns for portfolio with equal amount of each stock. Excludes S&P 500 as it is an index\n",
    "weights = [0.25,0.25,0.25,0.25]\n",
    "combined_portfolio_returns = combined_dataframe.dot(weights)\n",
    "\n",
    "# Make the weighted returns into a new DataFrame\n",
    "combined_portfolio_returns = pd.DataFrame(combined_portfolio_returns)\n",
    "\n",
    "#Change the column name\n",
    "combined_portfolio_returns.columns = ['Project Cornerstone']\n",
    "\n",
    "combined_portfolio_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Project Cornerstone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.026074</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.007211</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015662</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000969</td>\n",
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       "      <th>248</th>\n",
       "      <td>0.001016</td>\n",
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       "      <th>249</th>\n",
       "      <td>0.003119</td>\n",
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       "      <th>250</th>\n",
       "      <td>0.012214</td>\n",
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       "      <th>251</th>\n",
       "      <td>-0.008661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.017959</td>\n",
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       "<p>253 rows × 1 columns</p>\n",
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      "text/plain": [
       "     Project Cornerstone\n",
       "0                    NaN\n",
       "1              -0.026074\n",
       "2               0.007211\n",
       "3               0.015662\n",
       "4              -0.000969\n",
       "..                   ...\n",
       "248             0.001016\n",
       "249             0.003119\n",
       "250             0.012214\n",
       "251            -0.008661\n",
       "252             0.017959\n",
       "\n",
       "[253 rows x 1 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine Daily return based on the new DataFrame\n",
    "combined_portfolio_returns = combined_portfolio_returns.pct_change()\n",
    "combined_portfolio_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>SOROS FUND MANAGEMENT LLC</th>\n",
       "      <th>PAULSON &amp; CO.INC.</th>\n",
       "      <th>TIGER GLOBAL MANAGEMENT LLC</th>\n",
       "      <th>BERKSHIRE HATHAWAY INC</th>\n",
       "      <th>Algo 1</th>\n",
       "      <th>Algo 2</th>\n",
       "      <th>S&amp;P 500</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-03-03</td>\n",
       "      <td>-0.001266</td>\n",
       "      <td>-0.004981</td>\n",
       "      <td>-0.000496</td>\n",
       "      <td>-0.006569</td>\n",
       "      <td>-0.001942</td>\n",
       "      <td>-0.000949</td>\n",
       "      <td>-0.004539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-03-04</td>\n",
       "      <td>0.002230</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>-0.002534</td>\n",
       "      <td>0.004213</td>\n",
       "      <td>-0.008589</td>\n",
       "      <td>0.002416</td>\n",
       "      <td>-0.004389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-05</td>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.004076</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.006726</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.001196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-03-06</td>\n",
       "      <td>-0.007905</td>\n",
       "      <td>-0.003574</td>\n",
       "      <td>-0.008481</td>\n",
       "      <td>-0.013098</td>\n",
       "      <td>-0.004957</td>\n",
       "      <td>-0.011460</td>\n",
       "      <td>-0.014174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-03-09</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>-0.005447</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.003944</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>2019-04-16</td>\n",
       "      <td>0.002699</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>-0.000831</td>\n",
       "      <td>0.000837</td>\n",
       "      <td>-0.006945</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>0.000509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1039</th>\n",
       "      <td>2019-04-17</td>\n",
       "      <td>-0.002897</td>\n",
       "      <td>-0.006467</td>\n",
       "      <td>-0.004409</td>\n",
       "      <td>0.003222</td>\n",
       "      <td>-0.010301</td>\n",
       "      <td>-0.005228</td>\n",
       "      <td>-0.002274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>0.001448</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>-0.000588</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>0.001579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>-0.002586</td>\n",
       "      <td>-0.007333</td>\n",
       "      <td>-0.003640</td>\n",
       "      <td>-0.001088</td>\n",
       "      <td>0.000677</td>\n",
       "      <td>-0.001936</td>\n",
       "      <td>0.001012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>2019-04-23</td>\n",
       "      <td>0.007167</td>\n",
       "      <td>0.003485</td>\n",
       "      <td>0.006472</td>\n",
       "      <td>0.013278</td>\n",
       "      <td>0.004969</td>\n",
       "      <td>0.009622</td>\n",
       "      <td>0.008841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1043 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  SOROS FUND MANAGEMENT LLC  PAULSON & CO.INC.   \\\n",
       "0     2015-03-03                  -0.001266           -0.004981   \n",
       "1     2015-03-04                   0.002230            0.003241   \n",
       "2     2015-03-05                   0.004016            0.004076   \n",
       "3     2015-03-06                  -0.007905           -0.003574   \n",
       "4     2015-03-09                   0.000582            0.004225   \n",
       "...          ...                        ...                 ...   \n",
       "1038  2019-04-16                   0.002699            0.000388   \n",
       "1039  2019-04-17                  -0.002897           -0.006467   \n",
       "1040  2019-04-18                   0.001448            0.001222   \n",
       "1041  2019-04-22                  -0.002586           -0.007333   \n",
       "1042  2019-04-23                   0.007167            0.003485   \n",
       "\n",
       "      TIGER GLOBAL MANAGEMENT LLC  BERKSHIRE HATHAWAY INC    Algo 1    Algo 2  \\\n",
       "0                       -0.000496               -0.006569 -0.001942 -0.000949   \n",
       "1                       -0.002534                0.004213 -0.008589  0.002416   \n",
       "2                        0.002355                0.006726 -0.000955  0.004323   \n",
       "3                       -0.008481               -0.013098 -0.004957 -0.011460   \n",
       "4                        0.005843               -0.001652 -0.005447  0.001303   \n",
       "...                           ...                     ...       ...       ...   \n",
       "1038                    -0.000831                0.000837 -0.006945  0.002899   \n",
       "1039                    -0.004409                0.003222 -0.010301 -0.005228   \n",
       "1040                     0.000582                0.001916 -0.000588 -0.001229   \n",
       "1041                    -0.003640               -0.001088  0.000677 -0.001936   \n",
       "1042                     0.006472                0.013278  0.004969  0.009622   \n",
       "\n",
       "       S&P 500  \n",
       "0    -0.004539  \n",
       "1    -0.004389  \n",
       "2     0.001196  \n",
       "3    -0.014174  \n",
       "4     0.003944  \n",
       "...        ...  \n",
       "1038  0.000509  \n",
       "1039 -0.002274  \n",
       "1040  0.001579  \n",
       "1041  0.001012  \n",
       "1042  0.008841  \n",
       "\n",
       "[1043 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bring in Whale Returns to compare\n",
    "whale_returns = Path('whale_portfolio.csv')\n",
    "whale_dataframe = pd.read_csv(whale_returns, infer_datetime_format=True, parse_dates=True)\n",
    "whale_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SOROS FUND MANAGEMENT LLC</th>\n",
       "      <th>PAULSON &amp; CO.INC.</th>\n",
       "      <th>TIGER GLOBAL MANAGEMENT LLC</th>\n",
       "      <th>BERKSHIRE HATHAWAY INC</th>\n",
       "      <th>Algo 1</th>\n",
       "      <th>Algo 2</th>\n",
       "      <th>S&amp;P 500</th>\n",
       "      <th>Project Cornerstone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.001266</td>\n",
       "      <td>-0.004981</td>\n",
       "      <td>-0.000496</td>\n",
       "      <td>-0.006569</td>\n",
       "      <td>-0.001942</td>\n",
       "      <td>-0.000949</td>\n",
       "      <td>-0.004539</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002230</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>-0.002534</td>\n",
       "      <td>0.004213</td>\n",
       "      <td>-0.008589</td>\n",
       "      <td>0.002416</td>\n",
       "      <td>-0.004389</td>\n",
       "      <td>-0.026074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.004076</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.006726</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.007211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.007905</td>\n",
       "      <td>-0.003574</td>\n",
       "      <td>-0.008481</td>\n",
       "      <td>-0.013098</td>\n",
       "      <td>-0.004957</td>\n",
       "      <td>-0.011460</td>\n",
       "      <td>-0.014174</td>\n",
       "      <td>0.015662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>-0.005447</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.003944</td>\n",
       "      <td>-0.000969</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.008691</td>\n",
       "      <td>0.014958</td>\n",
       "      <td>0.022396</td>\n",
       "      <td>-0.011997</td>\n",
       "      <td>-0.005957</td>\n",
       "      <td>0.006605</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>0.026421</td>\n",
       "      <td>0.008685</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>-0.001870</td>\n",
       "      <td>0.003119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>-0.007371</td>\n",
       "      <td>-0.015972</td>\n",
       "      <td>-0.007554</td>\n",
       "      <td>-0.001977</td>\n",
       "      <td>0.004232</td>\n",
       "      <td>-0.000851</td>\n",
       "      <td>-0.008121</td>\n",
       "      <td>0.012214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.016906</td>\n",
       "      <td>0.009735</td>\n",
       "      <td>0.012634</td>\n",
       "      <td>0.030164</td>\n",
       "      <td>-0.005267</td>\n",
       "      <td>0.017115</td>\n",
       "      <td>0.023869</td>\n",
       "      <td>-0.008661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.005057</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>-0.000338</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.010729</td>\n",
       "      <td>0.004094</td>\n",
       "      <td>0.017959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SOROS FUND MANAGEMENT LLC  PAULSON & CO.INC.   \\\n",
       "0                    -0.001266           -0.004981   \n",
       "1                     0.002230            0.003241   \n",
       "2                     0.004016            0.004076   \n",
       "3                    -0.007905           -0.003574   \n",
       "4                     0.000582            0.004225   \n",
       "..                         ...                 ...   \n",
       "248                   0.008691            0.014958   \n",
       "249                   0.003480            0.002174   \n",
       "250                  -0.007371           -0.015972   \n",
       "251                   0.016906            0.009735   \n",
       "252                   0.005057            0.002188   \n",
       "\n",
       "     TIGER GLOBAL MANAGEMENT LLC  BERKSHIRE HATHAWAY INC    Algo 1    Algo 2  \\\n",
       "0                      -0.000496               -0.006569 -0.001942 -0.000949   \n",
       "1                      -0.002534                0.004213 -0.008589  0.002416   \n",
       "2                       0.002355                0.006726 -0.000955  0.004323   \n",
       "3                      -0.008481               -0.013098 -0.004957 -0.011460   \n",
       "4                       0.005843               -0.001652 -0.005447  0.001303   \n",
       "..                           ...                     ...       ...       ...   \n",
       "248                     0.022396               -0.011997 -0.005957  0.006605   \n",
       "249                     0.026421                0.008685  0.005786  0.005180   \n",
       "250                    -0.007554               -0.001977  0.004232 -0.000851   \n",
       "251                     0.012634                0.030164 -0.005267  0.017115   \n",
       "252                    -0.000338                0.000073  0.000264  0.010729   \n",
       "\n",
       "      S&P 500  Project Cornerstone  \n",
       "0   -0.004539                  NaN  \n",
       "1   -0.004389            -0.026074  \n",
       "2    0.001196             0.007211  \n",
       "3   -0.014174             0.015662  \n",
       "4    0.003944            -0.000969  \n",
       "..        ...                  ...  \n",
       "248  0.011348             0.001016  \n",
       "249 -0.001870             0.003119  \n",
       "250 -0.008121             0.012214  \n",
       "251  0.023869            -0.008661  \n",
       "252  0.004094             0.017959  \n",
       "\n",
       "[253 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove Date column per comments above as dates don't line up but data does\n",
    "compiled_dataframe = pd.concat([whale_dataframe, combined_portfolio_returns], axis='columns', join='inner')\n",
    "compiled_dataframe.drop(['Date'], axis=1, inplace=True)\n",
    "compiled_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SOROS FUND MANAGEMENT LLC</th>\n",
       "      <th>PAULSON &amp; CO.INC.</th>\n",
       "      <th>TIGER GLOBAL MANAGEMENT LLC</th>\n",
       "      <th>BERKSHIRE HATHAWAY INC</th>\n",
       "      <th>Algo 1</th>\n",
       "      <th>Algo 2</th>\n",
       "      <th>S&amp;P 500</th>\n",
       "      <th>Project Cornerstone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002230</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>-0.002534</td>\n",
       "      <td>0.004213</td>\n",
       "      <td>-0.008589</td>\n",
       "      <td>0.002416</td>\n",
       "      <td>-0.004389</td>\n",
       "      <td>-0.026074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004016</td>\n",
       "      <td>0.004076</td>\n",
       "      <td>0.002355</td>\n",
       "      <td>0.006726</td>\n",
       "      <td>-0.000955</td>\n",
       "      <td>0.004323</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>0.007211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.007905</td>\n",
       "      <td>-0.003574</td>\n",
       "      <td>-0.008481</td>\n",
       "      <td>-0.013098</td>\n",
       "      <td>-0.004957</td>\n",
       "      <td>-0.011460</td>\n",
       "      <td>-0.014174</td>\n",
       "      <td>0.015662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000582</td>\n",
       "      <td>0.004225</td>\n",
       "      <td>0.005843</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>-0.005447</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.003944</td>\n",
       "      <td>-0.000969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.010263</td>\n",
       "      <td>-0.005341</td>\n",
       "      <td>-0.012079</td>\n",
       "      <td>-0.009739</td>\n",
       "      <td>-0.001392</td>\n",
       "      <td>-0.012155</td>\n",
       "      <td>-0.016961</td>\n",
       "      <td>-0.014686</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.008691</td>\n",
       "      <td>0.014958</td>\n",
       "      <td>0.022396</td>\n",
       "      <td>-0.011997</td>\n",
       "      <td>-0.005957</td>\n",
       "      <td>0.006605</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0.003480</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>0.026421</td>\n",
       "      <td>0.008685</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.005180</td>\n",
       "      <td>-0.001870</td>\n",
       "      <td>0.003119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>-0.007371</td>\n",
       "      <td>-0.015972</td>\n",
       "      <td>-0.007554</td>\n",
       "      <td>-0.001977</td>\n",
       "      <td>0.004232</td>\n",
       "      <td>-0.000851</td>\n",
       "      <td>-0.008121</td>\n",
       "      <td>0.012214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.016906</td>\n",
       "      <td>0.009735</td>\n",
       "      <td>0.012634</td>\n",
       "      <td>0.030164</td>\n",
       "      <td>-0.005267</td>\n",
       "      <td>0.017115</td>\n",
       "      <td>0.023869</td>\n",
       "      <td>-0.008661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.005057</td>\n",
       "      <td>0.002188</td>\n",
       "      <td>-0.000338</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.010729</td>\n",
       "      <td>0.004094</td>\n",
       "      <td>0.017959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SOROS FUND MANAGEMENT LLC  PAULSON & CO.INC.   \\\n",
       "1                     0.002230            0.003241   \n",
       "2                     0.004016            0.004076   \n",
       "3                    -0.007905           -0.003574   \n",
       "4                     0.000582            0.004225   \n",
       "5                    -0.010263           -0.005341   \n",
       "..                         ...                 ...   \n",
       "248                   0.008691            0.014958   \n",
       "249                   0.003480            0.002174   \n",
       "250                  -0.007371           -0.015972   \n",
       "251                   0.016906            0.009735   \n",
       "252                   0.005057            0.002188   \n",
       "\n",
       "     TIGER GLOBAL MANAGEMENT LLC  BERKSHIRE HATHAWAY INC    Algo 1    Algo 2  \\\n",
       "1                      -0.002534                0.004213 -0.008589  0.002416   \n",
       "2                       0.002355                0.006726 -0.000955  0.004323   \n",
       "3                      -0.008481               -0.013098 -0.004957 -0.011460   \n",
       "4                       0.005843               -0.001652 -0.005447  0.001303   \n",
       "5                      -0.012079               -0.009739 -0.001392 -0.012155   \n",
       "..                           ...                     ...       ...       ...   \n",
       "248                     0.022396               -0.011997 -0.005957  0.006605   \n",
       "249                     0.026421                0.008685  0.005786  0.005180   \n",
       "250                    -0.007554               -0.001977  0.004232 -0.000851   \n",
       "251                     0.012634                0.030164 -0.005267  0.017115   \n",
       "252                    -0.000338                0.000073  0.000264  0.010729   \n",
       "\n",
       "      S&P 500  Project Cornerstone  \n",
       "1   -0.004389            -0.026074  \n",
       "2    0.001196             0.007211  \n",
       "3   -0.014174             0.015662  \n",
       "4    0.003944            -0.000969  \n",
       "5   -0.016961            -0.014686  \n",
       "..        ...                  ...  \n",
       "248  0.011348             0.001016  \n",
       "249 -0.001870             0.003119  \n",
       "250 -0.008121             0.012214  \n",
       "251  0.023869            -0.008661  \n",
       "252  0.004094             0.017959  \n",
       "\n",
       "[252 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Null values\n",
    "compiled_dataframe.dropna(inplace=True)\n",
    "compiled_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How did Project Cornerstone do against the rest of the portfolios?\n",
    "compiled_dataframe.plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "Project Cornerstone is made up of a consumer/home goods company(Lowe's), a Luxury/Lifestyle company (Tapestry), a pharmaceutical company (Merck), and an energy company (ERII). This portfolio went through a period of massive volatility during the coronavirus onset (March-April 2020) which is why we see the large spikes and valleys. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}