Data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bb77bd1",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "Strategy:\n",
    "pick a company\n",
    "run time series for kp,kf days from past, future on volume, high/low, open/close.\n",
    "Do I want to buy or sell?\n",
    "calculate target variable = percent difference between today's high and the max low on the next kf future days--so I can't predicty daily top/bottom, but I can say that I should buy today so I can sell some time soon.\n",
    "\n",
    "Problems so far:\n",
    "random forest is being a pain because of the data being continuous. SVM or NN? temporary solution: round target, send all negatives to 0, define a hit as predict positive actual positive or predict 0 actual 0.\n",
    "\n",
    "How to normalize the data?\n",
    "\n",
    "Using analysis of indicators seems very tricky since there seem to be 8529 unique indicators but not every company has a value for them. Use the financial data csv?\n",
    "\n",
    "No idea how to use headlines\n",
    "\n",
    "Can I put macroeconomic data? Is that even feasible given international companies?\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea4466a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "85e63198",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2a45d862",
   "metadata": {},
   "outputs": [],
   "source": [
    "path1 = \"./data/1/\"\n",
    "path2 = \"./data/2/\"\n",
    "path3 = \"./data/3/\"\n",
    "path4 = \"./data/4/\"\n",
    "path5 = \"./data/5/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9a6bc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "companies_df = pd.read_csv(path1+\"companies.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b8a9b53",
   "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>company_id</th>\n",
       "      <th>name_latest</th>\n",
       "      <th>names_previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000045</td>\n",
       "      <td>Nicholas Financial Inc</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000180</td>\n",
       "      <td>Sandisk Corp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000209</td>\n",
       "      <td>Medallion Financial Corp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000228</td>\n",
       "      <td>Henry Schein Inc</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000229</td>\n",
       "      <td>Core Laboratories N V</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12168</th>\n",
       "      <td>99313</td>\n",
       "      <td>Transnet Corp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12169</th>\n",
       "      <td>99359</td>\n",
       "      <td>Breeze-Eastern Corp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12170</th>\n",
       "      <td>99771</td>\n",
       "      <td>Trinity Capital Corp</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12171</th>\n",
       "      <td>99780</td>\n",
       "      <td>Trinity Industries Inc</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12172</th>\n",
       "      <td>9984</td>\n",
       "      <td>Barnes Group Inc</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12173 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       company_id               name_latest names_previous\n",
       "0         1000045    Nicholas Financial Inc            NaN\n",
       "1         1000180              Sandisk Corp            NaN\n",
       "2         1000209  Medallion Financial Corp            NaN\n",
       "3         1000228          Henry Schein Inc            NaN\n",
       "4         1000229     Core Laboratories N V            NaN\n",
       "...           ...                       ...            ...\n",
       "12168       99313             Transnet Corp            NaN\n",
       "12169       99359       Breeze-Eastern Corp            NaN\n",
       "12170       99771      Trinity Capital Corp            NaN\n",
       "12171       99780    Trinity Industries Inc            NaN\n",
       "12172        9984          Barnes Group Inc            NaN\n",
       "\n",
       "[12173 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e98ae807",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = companies_df['name_latest'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fbe75438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Apple Reit SIX Inc\n",
      "Apple Reit Seven, Inc.\n",
      "Apple Reit Eight, Inc.\n",
      "Apple Hospitality Reit, Inc.\n",
      "DR Pepper Snapple Group, Inc.\n",
      "Apple Reit Ten, Inc.\n",
      "Apple Green Holding, Inc.\n",
      "Apple Inc\n",
      "Maui Land & Pineapple CO Inc\n"
     ]
    }
   ],
   "source": [
    "for x in k:\n",
    "    if 'apple' in x.lower():\n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9a3945f",
   "metadata": {},
   "outputs": [],
   "source": [
    "apple_id = companies_df[companies_df['name_latest']=='Apple Inc']['company_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3c79ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "indicators_df = pd.read_csv(path1+\"indicators_by_company.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a32cbf56",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>company_id</th>\n",
       "      <th>indicator_id</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
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       "      <td>257236034.0</td>\n",
       "      <td>263835468.0</td>\n",
       "      <td>283429579.0</td>\n",
       "      <td>302528591.0</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000045</td>\n",
       "      <td>AssetsHeldForSaleAtCarryingValue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1373001.0</td>\n",
       "      <td>1203664.0</td>\n",
       "      <td>1696330.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1907878</th>\n",
       "      <td>9984</td>\n",
       "      <td>UnrecognizedTaxBenefitsInterestOnIncomeTaxesAc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1031000.0</td>\n",
       "      <td>1031000.0</td>\n",
       "      <td>1923000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907879</th>\n",
       "      <td>9984</td>\n",
       "      <td>UnrecognizedTaxBenefitsReductionsResultingFrom...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>177000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907880</th>\n",
       "      <td>9984</td>\n",
       "      <td>ValuationAllowanceDeferredTaxAssetChangeInAmount</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13355000.0</td>\n",
       "      <td>8255000.0</td>\n",
       "      <td>-6063000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907881</th>\n",
       "      <td>9984</td>\n",
       "      <td>WeightedAverageNumberOfDilutedSharesOutstanding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55931882.0</td>\n",
       "      <td>55224457.0</td>\n",
       "      <td>54973344.0</td>\n",
       "      <td>55723267.0</td>\n",
       "      <td>55513219.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1907882</th>\n",
       "      <td>9984</td>\n",
       "      <td>WeightedAverageNumberOfSharesOutstandingBasic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55214586.0</td>\n",
       "      <td>54626453.0</td>\n",
       "      <td>53860308.0</td>\n",
       "      <td>54791030.0</td>\n",
       "      <td>55028063.0</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "<p>1907883 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "         company_id                                       indicator_id  2010  \\\n",
       "0           1000045  AccountsPayableAndAccruedLiabilitiesCurrentAnd...   NaN   \n",
       "1           1000045  AccumulatedDepreciationDepletionAndAmortizatio...   NaN   \n",
       "2           1000045                          AdjustmentForAmortization   NaN   \n",
       "3           1000045                                             Assets   NaN   \n",
       "4           1000045                   AssetsHeldForSaleAtCarryingValue   NaN   \n",
       "...             ...                                                ...   ...   \n",
       "1907878        9984  UnrecognizedTaxBenefitsInterestOnIncomeTaxesAc...   NaN   \n",
       "1907879        9984  UnrecognizedTaxBenefitsReductionsResultingFrom...   NaN   \n",
       "1907880        9984   ValuationAllowanceDeferredTaxAssetChangeInAmount   NaN   \n",
       "1907881        9984    WeightedAverageNumberOfDilutedSharesOutstanding   NaN   \n",
       "1907882        9984      WeightedAverageNumberOfSharesOutstandingBasic   NaN   \n",
       "\n",
       "                2011         2012         2013         2014         2015  2016  \n",
       "0          6612429.0    7405579.0    8924919.0    7841070.0    5839000.0   NaN  \n",
       "1                NaN    2111343.0    2242703.0    2236449.0    2462000.0   NaN  \n",
       "2                NaN  -11482251.0  -13490892.0  -13852305.0  -13811000.0   NaN  \n",
       "3        257236034.0  263835468.0  283429579.0  302528591.0  325309000.0   NaN  \n",
       "4          1373001.0    1203664.0    1696330.0          NaN          NaN   NaN  \n",
       "...              ...          ...          ...          ...          ...   ...  \n",
       "1907878          0.0          0.0    1031000.0    1031000.0    1923000.0   NaN  \n",
       "1907879     177000.0          0.0          0.0          NaN     215000.0   NaN  \n",
       "1907880  -13355000.0    8255000.0   -6063000.0          NaN          NaN   NaN  \n",
       "1907881   55931882.0   55224457.0   54973344.0   55723267.0   55513219.0   NaN  \n",
       "1907882   55214586.0   54626453.0   53860308.0   54791030.0   55028063.0   NaN  \n",
       "\n",
       "[1907883 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "33405ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8529"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(indicators_df['indicator_id'].unique())#could there possibly typos?8529!=302(see apple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "341ed179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "320193"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_id=np.int64(apple_id)[0]\n",
    "apple_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "28a0fb85",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>company_id</th>\n",
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       "      <th>2010</th>\n",
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       "    <tr>\n",
       "      <th>1273367</th>\n",
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       "      <td>AccountsPayableCurrent</td>\n",
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       "      <td>1.093000e+10</td>\n",
       "      <td>1.310200e+10</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1273369</th>\n",
       "      <td>320193</td>\n",
       "      <td>AccruedLiabilitiesCurrent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.247000e+09</td>\n",
       "      <td>1.141400e+10</td>\n",
       "      <td>1.385600e+10</td>\n",
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       "    <tr>\n",
       "      <th>1273370</th>\n",
       "      <td>320193</td>\n",
       "      <td>AccruedMarketingCostsCurrent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.100000e+08</td>\n",
       "      <td>1.291000e+09</td>\n",
       "      <td>2.321000e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273371</th>\n",
       "      <td>320193</td>\n",
       "      <td>AccumulatedDepreciationDepletionAndAmortizatio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.435000e+09</td>\n",
       "      <td>1.192200e+10</td>\n",
       "      <td>1.839100e+10</td>\n",
       "      <td>2.678600e+10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273664</th>\n",
       "      <td>320193</td>\n",
       "      <td>UnrecognizedTaxBenefitsThatWouldImpactEffectiv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.630000e+08</td>\n",
       "      <td>8.890000e+08</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>2.500000e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273665</th>\n",
       "      <td>320193</td>\n",
       "      <td>UnrecordedUnconditionalPurchaseObligationBalan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.400000e+09</td>\n",
       "      <td>9.880000e+08</td>\n",
       "      <td>1.300000e+09</td>\n",
       "      <td>3.400000e+09</td>\n",
       "      <td>7.300000e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273666</th>\n",
       "      <td>320193</td>\n",
       "      <td>WeightedAverageNumberDilutedSharesOutstandingA...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.238700e+07</td>\n",
       "      <td>1.053700e+07</td>\n",
       "      <td>6.331000e+06</td>\n",
       "      <td>3.709100e+07</td>\n",
       "      <td>3.964800e+07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273667</th>\n",
       "      <td>320193</td>\n",
       "      <td>WeightedAverageNumberOfDilutedSharesOutstanding</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.366450e+08</td>\n",
       "      <td>9.453550e+08</td>\n",
       "      <td>9.316620e+08</td>\n",
       "      <td>6.122663e+09</td>\n",
       "      <td>5.793069e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273668</th>\n",
       "      <td>320193</td>\n",
       "      <td>WeightedAverageNumberOfSharesOutstandingBasic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.242580e+08</td>\n",
       "      <td>9.348180e+08</td>\n",
       "      <td>9.253310e+08</td>\n",
       "      <td>6.085572e+09</td>\n",
       "      <td>5.753421e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>302 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         company_id                                       indicator_id  2010  \\\n",
       "1273367      320193                             AccountsPayableCurrent   NaN   \n",
       "1273368      320193                       AccountsReceivableNetCurrent   NaN   \n",
       "1273369      320193                          AccruedLiabilitiesCurrent   NaN   \n",
       "1273370      320193                       AccruedMarketingCostsCurrent   NaN   \n",
       "1273371      320193  AccumulatedDepreciationDepletionAndAmortizatio...   NaN   \n",
       "...             ...                                                ...   ...   \n",
       "1273664      320193  UnrecognizedTaxBenefitsThatWouldImpactEffectiv...   NaN   \n",
       "1273665      320193  UnrecordedUnconditionalPurchaseObligationBalan...   NaN   \n",
       "1273666      320193  WeightedAverageNumberDilutedSharesOutstandingA...   NaN   \n",
       "1273667      320193    WeightedAverageNumberOfDilutedSharesOutstanding   NaN   \n",
       "1273668      320193      WeightedAverageNumberOfSharesOutstandingBasic   NaN   \n",
       "\n",
       "                 2011          2012          2013          2014          2015  \\\n",
       "1273367  1.463200e+10  2.117500e+10  2.236700e+10  3.019600e+10  3.549000e+10   \n",
       "1273368  5.369000e+09  1.093000e+10  1.310200e+10  1.746000e+10  1.684900e+10   \n",
       "1273369  9.247000e+09  1.141400e+10  1.385600e+10  1.845300e+10  2.518100e+10   \n",
       "1273370           NaN  9.100000e+08  1.291000e+09  2.321000e+09           NaN   \n",
       "1273371           NaN  6.435000e+09  1.192200e+10  1.839100e+10  2.678600e+10   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "1273664  5.630000e+08  8.890000e+08  1.400000e+09  1.400000e+09  2.500000e+09   \n",
       "1273665  2.400000e+09  9.880000e+08  1.300000e+09  3.400000e+09  7.300000e+09   \n",
       "1273666  1.238700e+07  1.053700e+07  6.331000e+06  3.709100e+07  3.964800e+07   \n",
       "1273667  9.366450e+08  9.453550e+08  9.316620e+08  6.122663e+09  5.793069e+09   \n",
       "1273668  9.242580e+08  9.348180e+08  9.253310e+08  6.085572e+09  5.753421e+09   \n",
       "\n",
       "         2016  \n",
       "1273367   NaN  \n",
       "1273368   NaN  \n",
       "1273369   NaN  \n",
       "1273370   NaN  \n",
       "1273371   NaN  \n",
       "...       ...  \n",
       "1273664   NaN  \n",
       "1273665   NaN  \n",
       "1273666   NaN  \n",
       "1273667   NaN  \n",
       "1273668   NaN  \n",
       "\n",
       "[302 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_indicators = indicators_df[indicators_df['company_id']==apple_id]\n",
    "apple_indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a39b7487",
   "metadata": {},
   "source": [
    "\"\"\"\n",
    "The company ID's of the above two dataframes match.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c91d466e",
   "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>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Stocks That Hit 52-Week Highs On Friday</td>\n",
       "      <td>2020-06-05 10:30:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Stocks That Hit 52-Week Highs On Wednesday</td>\n",
       "      <td>2020-06-03 10:45:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>71 Biggest Movers From Friday</td>\n",
       "      <td>2020-05-26 04:30:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>46 Stocks Moving In Friday's Mid-Day Session</td>\n",
       "      <td>2020-05-22 12:45:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>B of A Securities Maintains Neutral on Agilent...</td>\n",
       "      <td>2020-05-22 11:38:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1400464</th>\n",
       "      <td>1413844.0</td>\n",
       "      <td>Top Narrow Based Indexes For August 29</td>\n",
       "      <td>2011-08-29 10:41:00-04:00</td>\n",
       "      <td>ZX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400465</th>\n",
       "      <td>1413845.0</td>\n",
       "      <td>Recap: Wednesday's Top Percentage Gainers and ...</td>\n",
       "      <td>2011-06-22 16:44:00-04:00</td>\n",
       "      <td>ZX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400466</th>\n",
       "      <td>1413846.0</td>\n",
       "      <td>UPDATE: Oppenheimer Color on China Zenix Auto ...</td>\n",
       "      <td>2011-06-21 08:26:00-04:00</td>\n",
       "      <td>ZX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400467</th>\n",
       "      <td>1413847.0</td>\n",
       "      <td>Oppenheimer Initiates China Zenix At Outperfor...</td>\n",
       "      <td>2011-06-21 05:59:00-04:00</td>\n",
       "      <td>ZX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400468</th>\n",
       "      <td>1413848.0</td>\n",
       "      <td>China Zenix Auto International Opens For Tradi...</td>\n",
       "      <td>2011-05-12 09:36:00-04:00</td>\n",
       "      <td>ZX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1400469 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0                                              title  \\\n",
       "0               0.0            Stocks That Hit 52-Week Highs On Friday   \n",
       "1               1.0         Stocks That Hit 52-Week Highs On Wednesday   \n",
       "2               2.0                      71 Biggest Movers From Friday   \n",
       "3               3.0       46 Stocks Moving In Friday's Mid-Day Session   \n",
       "4               4.0  B of A Securities Maintains Neutral on Agilent...   \n",
       "...             ...                                                ...   \n",
       "1400464   1413844.0             Top Narrow Based Indexes For August 29   \n",
       "1400465   1413845.0  Recap: Wednesday's Top Percentage Gainers and ...   \n",
       "1400466   1413846.0  UPDATE: Oppenheimer Color on China Zenix Auto ...   \n",
       "1400467   1413847.0  Oppenheimer Initiates China Zenix At Outperfor...   \n",
       "1400468   1413848.0  China Zenix Auto International Opens For Tradi...   \n",
       "\n",
       "                              date stock  \n",
       "0        2020-06-05 10:30:00-04:00     A  \n",
       "1        2020-06-03 10:45:00-04:00     A  \n",
       "2        2020-05-26 04:30:00-04:00     A  \n",
       "3        2020-05-22 12:45:00-04:00     A  \n",
       "4        2020-05-22 11:38:00-04:00     A  \n",
       "...                            ...   ...  \n",
       "1400464  2011-08-29 10:41:00-04:00    ZX  \n",
       "1400465  2011-06-22 16:44:00-04:00    ZX  \n",
       "1400466  2011-06-21 08:26:00-04:00    ZX  \n",
       "1400467  2011-06-21 05:59:00-04:00    ZX  \n",
       "1400468  2011-05-12 09:36:00-04:00    ZX  \n",
       "\n",
       "[1400469 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyst_ratings_processed = pd.read_csv(path2+\"analyst_ratings_processed.csv\")\n",
    "analyst_ratings_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2c5051c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Revenue Growth</th>\n",
       "      <th>Cost of Revenue</th>\n",
       "      <th>Gross Profit</th>\n",
       "      <th>R&amp;D Expenses</th>\n",
       "      <th>SG&amp;A Expense</th>\n",
       "      <th>Operating Expenses</th>\n",
       "      <th>Operating Income</th>\n",
       "      <th>Interest Expense</th>\n",
       "      <th>...</th>\n",
       "      <th>Receivables growth</th>\n",
       "      <th>Inventory Growth</th>\n",
       "      <th>Asset Growth</th>\n",
       "      <th>Book Value per Share Growth</th>\n",
       "      <th>Debt Growth</th>\n",
       "      <th>R&amp;D Expense Growth</th>\n",
       "      <th>SG&amp;A Expenses Growth</th>\n",
       "      <th>Sector</th>\n",
       "      <th>2015 PRICE VAR [%]</th>\n",
       "      <th>Class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PG</td>\n",
       "      <td>7.440100e+10</td>\n",
       "      <td>-0.0713</td>\n",
       "      <td>3.903000e+10</td>\n",
       "      <td>3.537100e+10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.146100e+10</td>\n",
       "      <td>2.146100e+10</td>\n",
       "      <td>1.391000e+10</td>\n",
       "      <td>7.090000e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0187</td>\n",
       "      <td>-0.0217</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.1228</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.1746</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>-9.323276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VIPS</td>\n",
       "      <td>3.734148e+09</td>\n",
       "      <td>1.1737</td>\n",
       "      <td>2.805625e+09</td>\n",
       "      <td>9.285226e+08</td>\n",
       "      <td>1.083303e+08</td>\n",
       "      <td>3.441414e+08</td>\n",
       "      <td>7.939267e+08</td>\n",
       "      <td>1.345959e+08</td>\n",
       "      <td>1.214869e+07</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>1.6484</td>\n",
       "      <td>1.7313</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>-25.512193</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KR</td>\n",
       "      <td>9.837500e+10</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>7.813800e+10</td>\n",
       "      <td>2.023700e+10</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.519600e+10</td>\n",
       "      <td>1.751200e+10</td>\n",
       "      <td>2.725000e+09</td>\n",
       "      <td>4.430000e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>0.0981</td>\n",
       "      <td>0.1886</td>\n",
       "      <td>0.3268</td>\n",
       "      <td>0.2738</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0234</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>33.118297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RAD</td>\n",
       "      <td>2.552641e+10</td>\n",
       "      <td>0.0053</td>\n",
       "      <td>1.820268e+10</td>\n",
       "      <td>7.323734e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.561162e+09</td>\n",
       "      <td>6.586482e+09</td>\n",
       "      <td>7.372520e+08</td>\n",
       "      <td>4.245910e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0211</td>\n",
       "      <td>-0.0510</td>\n",
       "      <td>-0.0189</td>\n",
       "      <td>0.1963</td>\n",
       "      <td>-0.0458</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0060</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>2.752291</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GIS</td>\n",
       "      <td>1.790960e+10</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>1.153980e+10</td>\n",
       "      <td>6.369800e+09</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.474300e+09</td>\n",
       "      <td>3.412400e+09</td>\n",
       "      <td>2.957400e+09</td>\n",
       "      <td>3.024000e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0257</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0274</td>\n",
       "      <td>0.1025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0220</td>\n",
       "      <td>Consumer Defensive</td>\n",
       "      <td>12.897715</td>\n",
       "      <td>1</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>3803</th>\n",
       "      <td>TSRI</td>\n",
       "      <td>4.952987e+07</td>\n",
       "      <td>0.1028</td>\n",
       "      <td>4.125164e+07</td>\n",
       "      <td>8.278229e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.253061e+06</td>\n",
       "      <td>8.253061e+06</td>\n",
       "      <td>2.516800e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0384</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>Technology</td>\n",
       "      <td>29.362884</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3804</th>\n",
       "      <td>TZOO</td>\n",
       "      <td>1.532400e+08</td>\n",
       "      <td>-0.1019</td>\n",
       "      <td>1.917400e+07</td>\n",
       "      <td>1.340660e+08</td>\n",
       "      <td>1.132600e+07</td>\n",
       "      <td>1.125130e+08</td>\n",
       "      <td>1.162560e+08</td>\n",
       "      <td>1.781000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1529</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.1872</td>\n",
       "      <td>0.1823</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2830</td>\n",
       "      <td>-0.0637</td>\n",
       "      <td>Technology</td>\n",
       "      <td>-31.167763</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3805</th>\n",
       "      <td>USATP</td>\n",
       "      <td>4.200000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.700000e+07</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.400000e+07</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</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>Technology</td>\n",
       "      <td>-23.558900</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3806</th>\n",
       "      <td>WSTG</td>\n",
       "      <td>3.407580e+08</td>\n",
       "      <td>0.1344</td>\n",
       "      <td>3.159480e+08</td>\n",
       "      <td>2.481000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.651300e+07</td>\n",
       "      <td>1.651300e+07</td>\n",
       "      <td>8.297000e+06</td>\n",
       "      <td>-4.720000e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0333</td>\n",
       "      <td>0.1338</td>\n",
       "      <td>0.0023</td>\n",
       "      <td>0.0890</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0650</td>\n",
       "      <td>Technology</td>\n",
       "      <td>7.779579</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3807</th>\n",
       "      <td>WTT</td>\n",
       "      <td>4.033737e+07</td>\n",
       "      <td>0.1925</td>\n",
       "      <td>2.129368e+07</td>\n",
       "      <td>1.904369e+07</td>\n",
       "      <td>3.379920e+06</td>\n",
       "      <td>1.098477e+07</td>\n",
       "      <td>1.436469e+07</td>\n",
       "      <td>4.679002e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0469</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>-0.1646</td>\n",
       "      <td>-0.0226</td>\n",
       "      <td>-0.4594</td>\n",
       "      <td>0.2778</td>\n",
       "      <td>-0.0268</td>\n",
       "      <td>Technology</td>\n",
       "      <td>-34.099613</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3808 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0       Revenue  Revenue Growth  Cost of Revenue  Gross Profit  \\\n",
       "0            PG  7.440100e+10         -0.0713     3.903000e+10  3.537100e+10   \n",
       "1          VIPS  3.734148e+09          1.1737     2.805625e+09  9.285226e+08   \n",
       "2            KR  9.837500e+10          0.0182     7.813800e+10  2.023700e+10   \n",
       "3           RAD  2.552641e+10          0.0053     1.820268e+10  7.323734e+09   \n",
       "4           GIS  1.790960e+10          0.0076     1.153980e+10  6.369800e+09   \n",
       "...         ...           ...             ...              ...           ...   \n",
       "3803       TSRI  4.952987e+07          0.1028     4.125164e+07  8.278229e+06   \n",
       "3804       TZOO  1.532400e+08         -0.1019     1.917400e+07  1.340660e+08   \n",
       "3805      USATP  4.200000e+07             NaN     2.700000e+07  1.500000e+07   \n",
       "3806       WSTG  3.407580e+08          0.1344     3.159480e+08  2.481000e+07   \n",
       "3807        WTT  4.033737e+07          0.1925     2.129368e+07  1.904369e+07   \n",
       "\n",
       "      R&D Expenses  SG&A Expense  Operating Expenses  Operating Income  \\\n",
       "0     0.000000e+00  2.146100e+10        2.146100e+10      1.391000e+10   \n",
       "1     1.083303e+08  3.441414e+08        7.939267e+08      1.345959e+08   \n",
       "2     0.000000e+00  1.519600e+10        1.751200e+10      2.725000e+09   \n",
       "3     0.000000e+00  6.561162e+09        6.586482e+09      7.372520e+08   \n",
       "4     0.000000e+00  3.474300e+09        3.412400e+09      2.957400e+09   \n",
       "...            ...           ...                 ...               ...   \n",
       "3803  0.000000e+00  8.253061e+06        8.253061e+06      2.516800e+04   \n",
       "3804  1.132600e+07  1.125130e+08        1.162560e+08      1.781000e+07   \n",
       "3805           NaN  1.400000e+07        1.500000e+07      0.000000e+00   \n",
       "3806  0.000000e+00  1.651300e+07        1.651300e+07      8.297000e+06   \n",
       "3807  3.379920e+06  1.098477e+07        1.436469e+07      4.679002e+06   \n",
       "\n",
       "      Interest Expense  ...  Receivables growth  Inventory Growth  \\\n",
       "0         7.090000e+08  ...             -0.0187           -0.0217   \n",
       "1         1.214869e+07  ...                 NaN               NaN   \n",
       "2         4.430000e+08  ...              0.0618            0.0981   \n",
       "3         4.245910e+08  ...              0.0211           -0.0510   \n",
       "4         3.024000e+08  ...              0.0257            0.0090   \n",
       "...                ...  ...                 ...               ...   \n",
       "3803      0.000000e+00  ...             -0.0384            0.0000   \n",
       "3804      0.000000e+00  ...              0.1529            0.0000   \n",
       "3805      0.000000e+00  ...                 NaN               NaN   \n",
       "3806     -4.720000e+05  ...             -0.0333            0.1338   \n",
       "3807      0.000000e+00  ...             -0.0469            0.0455   \n",
       "\n",
       "      Asset Growth  Book Value per Share Growth  Debt Growth  \\\n",
       "0           0.0359                       0.0316       0.1228   \n",
       "1              NaN                          NaN          NaN   \n",
       "2           0.1886                       0.3268       0.2738   \n",
       "3          -0.0189                       0.1963      -0.0458   \n",
       "4           0.0215                       0.0274       0.1025   \n",
       "...            ...                          ...          ...   \n",
       "3803       -0.0041                      -0.0049       0.0000   \n",
       "3804       -0.1872                       0.1823       0.0000   \n",
       "3805           NaN                          NaN          NaN   \n",
       "3806        0.0023                       0.0890       0.0000   \n",
       "3807       -0.1646                      -0.0226      -0.4594   \n",
       "\n",
       "      R&D Expense Growth  SG&A Expenses Growth              Sector  \\\n",
       "0                 0.0000               -0.1746  Consumer Defensive   \n",
       "1                 1.6484                1.7313  Consumer Defensive   \n",
       "2                 0.0000                0.0234  Consumer Defensive   \n",
       "3                 0.0000               -0.0060  Consumer Defensive   \n",
       "4                 0.0000               -0.0220  Consumer Defensive   \n",
       "...                  ...                   ...                 ...   \n",
       "3803              0.0000                0.0213          Technology   \n",
       "3804              0.2830               -0.0637          Technology   \n",
       "3805                 NaN                   NaN          Technology   \n",
       "3806              0.0000                0.0650          Technology   \n",
       "3807              0.2778               -0.0268          Technology   \n",
       "\n",
       "      2015 PRICE VAR [%]  Class  \n",
       "0              -9.323276      0  \n",
       "1             -25.512193      0  \n",
       "2              33.118297      1  \n",
       "3               2.752291      1  \n",
       "4              12.897715      1  \n",
       "...                  ...    ...  \n",
       "3803           29.362884      1  \n",
       "3804          -31.167763      0  \n",
       "3805          -23.558900      0  \n",
       "3806            7.779579      1  \n",
       "3807          -34.099613      0  \n",
       "\n",
       "[3808 rows x 225 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2014_financial_data = pd.read_csv(path4+\"2014_financial_data.csv\")\n",
    "df_2014_financial_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "95ea6b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 0',\n",
       " 'Revenue',\n",
       " 'Revenue Growth',\n",
       " 'Cost of Revenue',\n",
       " 'Gross Profit',\n",
       " 'R&D Expenses',\n",
       " 'SG&A Expense',\n",
       " 'Operating Expenses',\n",
       " 'Operating Income',\n",
       " 'Interest Expense',\n",
       " 'Earnings before Tax',\n",
       " 'Income Tax Expense',\n",
       " 'Net Income - Non-Controlling int',\n",
       " 'Net Income - Discontinued ops',\n",
       " 'Net Income',\n",
       " 'Preferred Dividends',\n",
       " 'Net Income Com',\n",
       " 'EPS',\n",
       " 'EPS Diluted',\n",
       " 'Weighted Average Shs Out',\n",
       " 'Weighted Average Shs Out (Dil)',\n",
       " 'Dividend per Share',\n",
       " 'Gross Margin',\n",
       " 'EBITDA Margin',\n",
       " 'EBIT Margin',\n",
       " 'Profit Margin',\n",
       " 'Free Cash Flow margin',\n",
       " 'EBITDA',\n",
       " 'EBIT',\n",
       " 'Consolidated Income',\n",
       " 'Earnings Before Tax Margin',\n",
       " 'Net Profit Margin',\n",
       " 'Cash and cash equivalents',\n",
       " 'Short-term investments',\n",
       " 'Cash and short-term investments',\n",
       " 'Receivables',\n",
       " 'Inventories',\n",
       " 'Total current assets',\n",
       " 'Property, Plant & Equipment Net',\n",
       " 'Goodwill and Intangible Assets',\n",
       " 'Long-term investments',\n",
       " 'Tax assets',\n",
       " 'Total non-current assets',\n",
       " 'Total assets',\n",
       " 'Payables',\n",
       " 'Short-term debt',\n",
       " 'Total current liabilities',\n",
       " 'Long-term debt',\n",
       " 'Total debt',\n",
       " 'Deferred revenue',\n",
       " 'Tax Liabilities',\n",
       " 'Deposit Liabilities',\n",
       " 'Total non-current liabilities',\n",
       " 'Total liabilities',\n",
       " 'Other comprehensive income',\n",
       " 'Retained earnings (deficit)',\n",
       " 'Total shareholders equity',\n",
       " 'Investments',\n",
       " 'Net Debt',\n",
       " 'Other Assets',\n",
       " 'Other Liabilities',\n",
       " 'Depreciation & Amortization',\n",
       " 'Stock-based compensation',\n",
       " 'Operating Cash Flow',\n",
       " 'Capital Expenditure',\n",
       " 'Acquisitions and disposals',\n",
       " 'Investment purchases and sales',\n",
       " 'Investing Cash flow',\n",
       " 'Issuance (repayment) of debt',\n",
       " 'Issuance (buybacks) of shares',\n",
       " 'Dividend payments',\n",
       " 'Financing Cash Flow',\n",
       " 'Effect of forex changes on cash',\n",
       " 'Net cash flow / Change in cash',\n",
       " 'Free Cash Flow',\n",
       " 'Net Cash/Marketcap',\n",
       " 'priceBookValueRatio',\n",
       " 'priceToBookRatio',\n",
       " 'priceToSalesRatio',\n",
       " 'priceEarningsRatio',\n",
       " 'priceToFreeCashFlowsRatio',\n",
       " 'priceToOperatingCashFlowsRatio',\n",
       " 'priceCashFlowRatio',\n",
       " 'priceEarningsToGrowthRatio',\n",
       " 'priceSalesRatio',\n",
       " 'dividendYield',\n",
       " 'enterpriseValueMultiple',\n",
       " 'priceFairValue',\n",
       " 'ebitperRevenue',\n",
       " 'ebtperEBIT',\n",
       " 'niperEBT',\n",
       " 'grossProfitMargin',\n",
       " 'operatingProfitMargin',\n",
       " 'pretaxProfitMargin',\n",
       " 'netProfitMargin',\n",
       " 'effectiveTaxRate',\n",
       " 'returnOnAssets',\n",
       " 'returnOnEquity',\n",
       " 'returnOnCapitalEmployed',\n",
       " 'nIperEBT',\n",
       " 'eBTperEBIT',\n",
       " 'eBITperRevenue',\n",
       " 'payablesTurnover',\n",
       " 'inventoryTurnover',\n",
       " 'fixedAssetTurnover',\n",
       " 'assetTurnover',\n",
       " 'currentRatio',\n",
       " 'quickRatio',\n",
       " 'cashRatio',\n",
       " 'daysOfSalesOutstanding',\n",
       " 'daysOfInventoryOutstanding',\n",
       " 'operatingCycle',\n",
       " 'daysOfPayablesOutstanding',\n",
       " 'cashConversionCycle',\n",
       " 'debtRatio',\n",
       " 'debtEquityRatio',\n",
       " 'longtermDebtToCapitalization',\n",
       " 'totalDebtToCapitalization',\n",
       " 'interestCoverage',\n",
       " 'cashFlowToDebtRatio',\n",
       " 'companyEquityMultiplier',\n",
       " 'operatingCashFlowPerShare',\n",
       " 'freeCashFlowPerShare',\n",
       " 'cashPerShare',\n",
       " 'payoutRatio',\n",
       " 'operatingCashFlowSalesRatio',\n",
       " 'freeCashFlowOperatingCashFlowRatio',\n",
       " 'cashFlowCoverageRatios',\n",
       " 'shortTermCoverageRatios',\n",
       " 'capitalExpenditureCoverageRatios',\n",
       " 'dividendpaidAndCapexCoverageRatios',\n",
       " 'dividendPayoutRatio',\n",
       " 'Revenue per Share',\n",
       " 'Net Income per Share',\n",
       " 'Operating Cash Flow per Share',\n",
       " 'Free Cash Flow per Share',\n",
       " 'Cash per Share',\n",
       " 'Book Value per Share',\n",
       " 'Tangible Book Value per Share',\n",
       " 'Shareholders Equity per Share',\n",
       " 'Interest Debt per Share',\n",
       " 'Market Cap',\n",
       " 'Enterprise Value',\n",
       " 'PE ratio',\n",
       " 'Price to Sales Ratio',\n",
       " 'POCF ratio',\n",
       " 'PFCF ratio',\n",
       " 'PB ratio',\n",
       " 'PTB ratio',\n",
       " 'EV to Sales',\n",
       " 'Enterprise Value over EBITDA',\n",
       " 'EV to Operating cash flow',\n",
       " 'EV to Free cash flow',\n",
       " 'Earnings Yield',\n",
       " 'Free Cash Flow Yield',\n",
       " 'Debt to Equity',\n",
       " 'Debt to Assets',\n",
       " 'Net Debt to EBITDA',\n",
       " 'Current ratio',\n",
       " 'Interest Coverage',\n",
       " 'Income Quality',\n",
       " 'Dividend Yield',\n",
       " 'Payout Ratio',\n",
       " 'SG&A to Revenue',\n",
       " 'R&D to Revenue',\n",
       " 'Intangibles to Total Assets',\n",
       " 'Capex to Operating Cash Flow',\n",
       " 'Capex to Revenue',\n",
       " 'Capex to Depreciation',\n",
       " 'Stock-based compensation to Revenue',\n",
       " 'Graham Number',\n",
       " 'ROIC',\n",
       " 'Return on Tangible Assets',\n",
       " 'Graham Net-Net',\n",
       " 'Working Capital',\n",
       " 'Tangible Asset Value',\n",
       " 'Net Current Asset Value',\n",
       " 'Invested Capital',\n",
       " 'Average Receivables',\n",
       " 'Average Payables',\n",
       " 'Average Inventory',\n",
       " 'Days Sales Outstanding',\n",
       " 'Days Payables Outstanding',\n",
       " 'Days of Inventory on Hand',\n",
       " 'Receivables Turnover',\n",
       " 'Payables Turnover',\n",
       " 'Inventory Turnover',\n",
       " 'ROE',\n",
       " 'Capex per Share',\n",
       " 'Gross Profit Growth',\n",
       " 'EBIT Growth',\n",
       " 'Operating Income Growth',\n",
       " 'Net Income Growth',\n",
       " 'EPS Growth',\n",
       " 'EPS Diluted Growth',\n",
       " 'Weighted Average Shares Growth',\n",
       " 'Weighted Average Shares Diluted Growth',\n",
       " 'Dividends per Share Growth',\n",
       " 'Operating Cash Flow growth',\n",
       " 'Free Cash Flow growth',\n",
       " '10Y Revenue Growth (per Share)',\n",
       " '5Y Revenue Growth (per Share)',\n",
       " '3Y Revenue Growth (per Share)',\n",
       " '10Y Operating CF Growth (per Share)',\n",
       " '5Y Operating CF Growth (per Share)',\n",
       " '3Y Operating CF Growth (per Share)',\n",
       " '10Y Net Income Growth (per Share)',\n",
       " '5Y Net Income Growth (per Share)',\n",
       " '3Y Net Income Growth (per Share)',\n",
       " '10Y Shareholders Equity Growth (per Share)',\n",
       " '5Y Shareholders Equity Growth (per Share)',\n",
       " '3Y Shareholders Equity Growth (per Share)',\n",
       " '10Y Dividend per Share Growth (per Share)',\n",
       " '5Y Dividend per Share Growth (per Share)',\n",
       " '3Y Dividend per Share Growth (per Share)',\n",
       " 'Receivables growth',\n",
       " 'Inventory Growth',\n",
       " 'Asset Growth',\n",
       " 'Book Value per Share Growth',\n",
       " 'Debt Growth',\n",
       " 'R&D Expense Growth',\n",
       " 'SG&A Expenses Growth',\n",
       " 'Sector',\n",
       " '2015 PRICE VAR [%]',\n",
       " 'Class']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_2014_financial_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29a65cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "apple = pd.read_csv(path5+\"aapl_historical.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "25dfb888",
   "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>Date</th>\n",
       "      <th>Close/Last</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/15/2023</td>\n",
       "      <td>$172.07</td>\n",
       "      <td>37266660</td>\n",
       "      <td>$173.16</td>\n",
       "      <td>$173.21</td>\n",
       "      <td>$171.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>$172.57</td>\n",
       "      <td>45533140</td>\n",
       "      <td>$173.62</td>\n",
       "      <td>$174.06</td>\n",
       "      <td>$171.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>$173.75</td>\n",
       "      <td>49514680</td>\n",
       "      <td>$173.85</td>\n",
       "      <td>$174.59</td>\n",
       "      <td>$172.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>$173.555</td>\n",
       "      <td>53724500</td>\n",
       "      <td>$173.02</td>\n",
       "      <td>$174.03</td>\n",
       "      <td>$171.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>$171.77</td>\n",
       "      <td>45326870</td>\n",
       "      <td>$173.05</td>\n",
       "      <td>$173.54</td>\n",
       "      <td>$171.60</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>$15.7626</td>\n",
       "      <td>442730783</td>\n",
       "      <td>$15.8589</td>\n",
       "      <td>$16.0125</td>\n",
       "      <td>$15.6507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>$15.7021</td>\n",
       "      <td>455670978</td>\n",
       "      <td>$15.6482</td>\n",
       "      <td>$15.91</td>\n",
       "      <td>$15.5071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>$15.8189</td>\n",
       "      <td>450118020</td>\n",
       "      <td>$15.4254</td>\n",
       "      <td>$15.9214</td>\n",
       "      <td>$15.3607</td>\n",
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       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>$15.4736</td>\n",
       "      <td>427864749</td>\n",
       "      <td>$15.6804</td>\n",
       "      <td>$15.7175</td>\n",
       "      <td>$15.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>05/16/2013</td>\n",
       "      <td>$15.5206</td>\n",
       "      <td>602950359</td>\n",
       "      <td>$15.1157</td>\n",
       "      <td>$15.6375</td>\n",
       "      <td>$14.9607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2517 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Close/Last     Volume      Open      High       Low\n",
       "0     05/15/2023    $172.07   37266660   $173.16   $173.21   $171.47\n",
       "1     05/12/2023    $172.57   45533140   $173.62   $174.06   $171.00\n",
       "2     05/11/2023    $173.75   49514680   $173.85   $174.59   $172.17\n",
       "3     05/10/2023   $173.555   53724500   $173.02   $174.03   $171.90\n",
       "4     05/09/2023    $171.77   45326870   $173.05   $173.54   $171.60\n",
       "...          ...        ...        ...       ...       ...       ...\n",
       "2512  05/22/2013   $15.7626  442730783  $15.8589  $16.0125  $15.6507\n",
       "2513  05/21/2013   $15.7021  455670978  $15.6482    $15.91  $15.5071\n",
       "2514  05/20/2013   $15.8189  450118020  $15.4254  $15.9214  $15.3607\n",
       "2515  05/17/2013   $15.4736  427864749  $15.6804  $15.7175  $15.3932\n",
       "2516  05/16/2013   $15.5206  602950359  $15.1157  $15.6375  $14.9607\n",
       "\n",
       "[2517 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd13ff1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close/Last</th>\n",
       "      <th>Volume</th>\n",
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       "      <th>High</th>\n",
       "      <th>Low</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>05/15/2023</td>\n",
       "      <td>$172.07</td>\n",
       "      <td>37266660</td>\n",
       "      <td>$173.16</td>\n",
       "      <td>$173.21</td>\n",
       "      <td>$171.47</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>$172.57</td>\n",
       "      <td>45533140</td>\n",
       "      <td>$173.62</td>\n",
       "      <td>$174.06</td>\n",
       "      <td>$171.00</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>$173.75</td>\n",
       "      <td>49514680</td>\n",
       "      <td>$173.85</td>\n",
       "      <td>$174.59</td>\n",
       "      <td>$172.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>$173.555</td>\n",
       "      <td>53724500</td>\n",
       "      <td>$173.02</td>\n",
       "      <td>$174.03</td>\n",
       "      <td>$171.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>$171.77</td>\n",
       "      <td>45326870</td>\n",
       "      <td>$173.05</td>\n",
       "      <td>$173.54</td>\n",
       "      <td>$171.60</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>$15.7626</td>\n",
       "      <td>442730783</td>\n",
       "      <td>$15.8589</td>\n",
       "      <td>$16.0125</td>\n",
       "      <td>$15.6507</td>\n",
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       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>$15.7021</td>\n",
       "      <td>455670978</td>\n",
       "      <td>$15.6482</td>\n",
       "      <td>$15.91</td>\n",
       "      <td>$15.5071</td>\n",
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       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>$15.8189</td>\n",
       "      <td>450118020</td>\n",
       "      <td>$15.4254</td>\n",
       "      <td>$15.9214</td>\n",
       "      <td>$15.3607</td>\n",
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       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>$15.4736</td>\n",
       "      <td>427864749</td>\n",
       "      <td>$15.6804</td>\n",
       "      <td>$15.7175</td>\n",
       "      <td>$15.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>05/16/2013</td>\n",
       "      <td>$15.5206</td>\n",
       "      <td>602950359</td>\n",
       "      <td>$15.1157</td>\n",
       "      <td>$15.6375</td>\n",
       "      <td>$14.9607</td>\n",
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       "  </tbody>\n",
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       "<p>2517 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Close/Last     Volume      Open      High       Low\n",
       "0     05/15/2023    $172.07   37266660   $173.16   $173.21   $171.47\n",
       "1     05/12/2023    $172.57   45533140   $173.62   $174.06   $171.00\n",
       "2     05/11/2023    $173.75   49514680   $173.85   $174.59   $172.17\n",
       "3     05/10/2023   $173.555   53724500   $173.02   $174.03   $171.90\n",
       "4     05/09/2023    $171.77   45326870   $173.05   $173.54   $171.60\n",
       "...          ...        ...        ...       ...       ...       ...\n",
       "2512  05/22/2013   $15.7626  442730783  $15.8589  $16.0125  $15.6507\n",
       "2513  05/21/2013   $15.7021  455670978  $15.6482    $15.91  $15.5071\n",
       "2514  05/20/2013   $15.8189  450118020  $15.4254  $15.9214  $15.3607\n",
       "2515  05/17/2013   $15.4736  427864749  $15.6804  $15.7175  $15.3932\n",
       "2516  05/16/2013   $15.5206  602950359  $15.1157  $15.6375  $14.9607\n",
       "\n",
       "[2517 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple2 = apple.copy(deep=True)\n",
    "apple2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b3a107c6",
   "metadata": {},
   "outputs": [
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       " ...]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_open = []\n",
    "new_close = []\n",
    "new_high = []\n",
    "new_low = []\n",
    "for i in range(len(apple2['Open'])):\n",
    "    new_open.append(float(apple2['Open'][i][1:]))\n",
    "    new_close.append(float(apple2['Close/Last'][i][1:]))\n",
    "    new_high.append(float(apple2['High'][i][1:]))\n",
    "    new_low.append(float(apple2['Low'][i][1:]))\n",
    "new_open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d9220e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "            Date     Volume\n",
       "0     05/15/2023   37266660\n",
       "1     05/12/2023   45533140\n",
       "2     05/11/2023   49514680\n",
       "3     05/10/2023   53724500\n",
       "4     05/09/2023   45326870\n",
       "...          ...        ...\n",
       "2512  05/22/2013  442730783\n",
       "2513  05/21/2013  455670978\n",
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       "2515  05/17/2013  427864749\n",
       "2516  05/16/2013  602950359\n",
       "\n",
       "[2517 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple2.drop('Open',axis=1,inplace=True)\n",
    "apple2.drop('Close/Last',axis=1,inplace=True)\n",
    "apple2.drop('High',axis=1,inplace=True)\n",
    "apple2.drop('Low',axis=1,inplace=True)\n",
    "apple2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "76ce3d43",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>05/15/2023</td>\n",
       "      <td>37266660</td>\n",
       "      <td>172.0700</td>\n",
       "      <td>173.1600</td>\n",
       "      <td>173.2100</td>\n",
       "      <td>171.4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>45533140</td>\n",
       "      <td>172.5700</td>\n",
       "      <td>173.6200</td>\n",
       "      <td>174.0600</td>\n",
       "      <td>171.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>49514680</td>\n",
       "      <td>173.7500</td>\n",
       "      <td>173.8500</td>\n",
       "      <td>174.5900</td>\n",
       "      <td>172.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>53724500</td>\n",
       "      <td>173.5550</td>\n",
       "      <td>173.0200</td>\n",
       "      <td>174.0300</td>\n",
       "      <td>171.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.7700</td>\n",
       "      <td>173.0500</td>\n",
       "      <td>173.5400</td>\n",
       "      <td>171.6000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>442730783</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</td>\n",
       "      <td>16.0125</td>\n",
       "      <td>15.6507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>455670978</td>\n",
       "      <td>15.7021</td>\n",
       "      <td>15.6482</td>\n",
       "      <td>15.9100</td>\n",
       "      <td>15.5071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>450118020</td>\n",
       "      <td>15.8189</td>\n",
       "      <td>15.4254</td>\n",
       "      <td>15.9214</td>\n",
       "      <td>15.3607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>427864749</td>\n",
       "      <td>15.4736</td>\n",
       "      <td>15.6804</td>\n",
       "      <td>15.7175</td>\n",
       "      <td>15.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>05/16/2013</td>\n",
       "      <td>602950359</td>\n",
       "      <td>15.5206</td>\n",
       "      <td>15.1157</td>\n",
       "      <td>15.6375</td>\n",
       "      <td>14.9607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2517 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date     Volume     Close      Open      High       Low\n",
       "0     05/15/2023   37266660  172.0700  173.1600  173.2100  171.4700\n",
       "1     05/12/2023   45533140  172.5700  173.6200  174.0600  171.0000\n",
       "2     05/11/2023   49514680  173.7500  173.8500  174.5900  172.1700\n",
       "3     05/10/2023   53724500  173.5550  173.0200  174.0300  171.9000\n",
       "4     05/09/2023   45326870  171.7700  173.0500  173.5400  171.6000\n",
       "...          ...        ...       ...       ...       ...       ...\n",
       "2512  05/22/2013  442730783   15.7626   15.8589   16.0125   15.6507\n",
       "2513  05/21/2013  455670978   15.7021   15.6482   15.9100   15.5071\n",
       "2514  05/20/2013  450118020   15.8189   15.4254   15.9214   15.3607\n",
       "2515  05/17/2013  427864749   15.4736   15.6804   15.7175   15.3932\n",
       "2516  05/16/2013  602950359   15.5206   15.1157   15.6375   14.9607\n",
       "\n",
       "[2517 rows x 6 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple2['Close'] = new_close\n",
    "apple2['Open'] = new_open\n",
    "apple2['High'] = new_high\n",
    "apple2['Low'] = new_low\n",
    "apple2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "d36e221f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t-5Date</th>\n",
       "      <th>t-5Volume</th>\n",
       "      <th>t-5Close</th>\n",
       "      <th>t-5Open</th>\n",
       "      <th>t-5High</th>\n",
       "      <th>t-5Low</th>\n",
       "      <th>t-4Date</th>\n",
       "      <th>t-4Volume</th>\n",
       "      <th>t-4Close</th>\n",
       "      <th>t-4Open</th>\n",
       "      <th>...</th>\n",
       "      <th>t--4Close</th>\n",
       "      <th>t--4Open</th>\n",
       "      <th>t--4High</th>\n",
       "      <th>t--4Low</th>\n",
       "      <th>t--5Date</th>\n",
       "      <th>t--5Volume</th>\n",
       "      <th>t--5Close</th>\n",
       "      <th>t--5Open</th>\n",
       "      <th>t--5High</th>\n",
       "      <th>t--5Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>05/08/2023</td>\n",
       "      <td>55962790</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>173.85</td>\n",
       "      <td>172.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>...</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>173.85</td>\n",
       "      <td>172.11</td>\n",
       "      <td>05/05/2023</td>\n",
       "      <td>113453200</td>\n",
       "      <td>173.57</td>\n",
       "      <td>170.975</td>\n",
       "      <td>174.3</td>\n",
       "      <td>170.76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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>...</td>\n",
       "      <td>173.57</td>\n",
       "      <td>170.975</td>\n",
       "      <td>174.3</td>\n",
       "      <td>170.76</td>\n",
       "      <td>05/04/2023</td>\n",
       "      <td>81235430</td>\n",
       "      <td>165.79</td>\n",
       "      <td>164.89</td>\n",
       "      <td>167.04</td>\n",
       "      <td>164.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>...</td>\n",
       "      <td>165.79</td>\n",
       "      <td>164.89</td>\n",
       "      <td>167.04</td>\n",
       "      <td>164.31</td>\n",
       "      <td>05/03/2023</td>\n",
       "      <td>65136020</td>\n",
       "      <td>167.45</td>\n",
       "      <td>169.5</td>\n",
       "      <td>170.92</td>\n",
       "      <td>167.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>05/15/2023</td>\n",
       "      <td>37266660</td>\n",
       "      <td>172.07</td>\n",
       "      <td>173.16</td>\n",
       "      <td>...</td>\n",
       "      <td>167.45</td>\n",
       "      <td>169.5</td>\n",
       "      <td>170.92</td>\n",
       "      <td>167.16</td>\n",
       "      <td>05/02/2023</td>\n",
       "      <td>48425700</td>\n",
       "      <td>168.54</td>\n",
       "      <td>170.09</td>\n",
       "      <td>170.35</td>\n",
       "      <td>167.54</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>05/30/2013</td>\n",
       "      <td>353419499</td>\n",
       "      <td>16.1279</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.2321</td>\n",
       "      <td>15.8754</td>\n",
       "      <td>05/29/2013</td>\n",
       "      <td>330212828</td>\n",
       "      <td>15.8911</td>\n",
       "      <td>15.7143</td>\n",
       "      <td>...</td>\n",
       "      <td>15.5206</td>\n",
       "      <td>15.1157</td>\n",
       "      <td>15.6375</td>\n",
       "      <td>14.9607</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>2513</th>\n",
       "      <td>05/29/2013</td>\n",
       "      <td>330212828</td>\n",
       "      <td>15.8911</td>\n",
       "      <td>15.7143</td>\n",
       "      <td>15.9821</td>\n",
       "      <td>15.6929</td>\n",
       "      <td>05/28/2013</td>\n",
       "      <td>385245966</td>\n",
       "      <td>15.7657</td>\n",
       "      <td>16.0679</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>2514</th>\n",
       "      <td>05/28/2013</td>\n",
       "      <td>385245966</td>\n",
       "      <td>15.7657</td>\n",
       "      <td>16.0679</td>\n",
       "      <td>16.1111</td>\n",
       "      <td>15.7446</td>\n",
       "      <td>05/24/2013</td>\n",
       "      <td>276035930</td>\n",
       "      <td>15.8982</td>\n",
       "      <td>15.7446</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>2515</th>\n",
       "      <td>05/24/2013</td>\n",
       "      <td>276035930</td>\n",
       "      <td>15.8982</td>\n",
       "      <td>15.7446</td>\n",
       "      <td>15.9164</td>\n",
       "      <td>15.7271</td>\n",
       "      <td>05/23/2013</td>\n",
       "      <td>352959179</td>\n",
       "      <td>15.7907</td>\n",
       "      <td>15.5696</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>2516</th>\n",
       "      <td>05/23/2013</td>\n",
       "      <td>352959179</td>\n",
       "      <td>15.7907</td>\n",
       "      <td>15.5696</td>\n",
       "      <td>15.9343</td>\n",
       "      <td>15.5639</td>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>442730783</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</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>2517 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         t-5Date  t-5Volume t-5Close  t-5Open  t-5High   t-5Low     t-4Date  \\\n",
       "0            NaN        NaN      NaN      NaN      NaN      NaN         NaN   \n",
       "1            NaN        NaN      NaN      NaN      NaN      NaN         NaN   \n",
       "2            NaN        NaN      NaN      NaN      NaN      NaN         NaN   \n",
       "3            NaN        NaN      NaN      NaN      NaN      NaN         NaN   \n",
       "4            NaN        NaN      NaN      NaN      NaN      NaN  05/15/2023   \n",
       "...          ...        ...      ...      ...      ...      ...         ...   \n",
       "2512  05/30/2013  353419499  16.1279  15.9161  16.2321  15.8754  05/29/2013   \n",
       "2513  05/29/2013  330212828  15.8911  15.7143  15.9821  15.6929  05/28/2013   \n",
       "2514  05/28/2013  385245966  15.7657  16.0679  16.1111  15.7446  05/24/2013   \n",
       "2515  05/24/2013  276035930  15.8982  15.7446  15.9164  15.7271  05/23/2013   \n",
       "2516  05/23/2013  352959179  15.7907  15.5696  15.9343  15.5639  05/22/2013   \n",
       "\n",
       "      t-4Volume t-4Close  t-4Open  ... t--4Close t--4Open t--4High  t--4Low  \\\n",
       "0           NaN      NaN      NaN  ...    171.77   173.05   173.54    171.6   \n",
       "1           NaN      NaN      NaN  ...     173.5   172.48   173.85   172.11   \n",
       "2           NaN      NaN      NaN  ...    173.57  170.975    174.3   170.76   \n",
       "3           NaN      NaN      NaN  ...    165.79   164.89   167.04   164.31   \n",
       "4      37266660   172.07   173.16  ...    167.45    169.5   170.92   167.16   \n",
       "...         ...      ...      ...  ...       ...      ...      ...      ...   \n",
       "2512  330212828  15.8911  15.7143  ...   15.5206  15.1157  15.6375  14.9607   \n",
       "2513  385245966  15.7657  16.0679  ...       NaN      NaN      NaN      NaN   \n",
       "2514  276035930  15.8982  15.7446  ...       NaN      NaN      NaN      NaN   \n",
       "2515  352959179  15.7907  15.5696  ...       NaN      NaN      NaN      NaN   \n",
       "2516  442730783  15.7626  15.8589  ...       NaN      NaN      NaN      NaN   \n",
       "\n",
       "        t--5Date t--5Volume t--5Close t--5Open t--5High t--5Low  \n",
       "0     05/08/2023   55962790     173.5   172.48   173.85  172.11  \n",
       "1     05/05/2023  113453200    173.57  170.975    174.3  170.76  \n",
       "2     05/04/2023   81235430    165.79   164.89   167.04  164.31  \n",
       "3     05/03/2023   65136020    167.45    169.5   170.92  167.16  \n",
       "4     05/02/2023   48425700    168.54   170.09   170.35  167.54  \n",
       "...          ...        ...       ...      ...      ...     ...  \n",
       "2512         NaN        NaN       NaN      NaN      NaN     NaN  \n",
       "2513         NaN        NaN       NaN      NaN      NaN     NaN  \n",
       "2514         NaN        NaN       NaN      NaN      NaN     NaN  \n",
       "2515         NaN        NaN       NaN      NaN      NaN     NaN  \n",
       "2516         NaN        NaN       NaN      NaN      NaN     NaN  \n",
       "\n",
       "[2517 rows x 66 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Close','Volume','Open','High','Low']\n",
    "cases = pd.DataFrame(apple2.values)\n",
    "cases\n",
    "df = pd.concat([cases.shift(5),cases.shift(4),cases.shift(3),cases.shift(2),cases.shift(1),cases,cases.shift(-1),cases.shift(-2),cases.shift(-3),cases.shift(-4),cases.shift(-5)],axis=1)\n",
    "df.columns = ['t-'+str(i)+x for i in range(5,-6,-1) for x in ['Date','Volume','Close','Open','High','Low'] ]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "52182dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t-5Date</th>\n",
       "      <th>t-5Volume</th>\n",
       "      <th>t-5Close</th>\n",
       "      <th>t-5Open</th>\n",
       "      <th>t-5High</th>\n",
       "      <th>t-5Low</th>\n",
       "      <th>t-4Date</th>\n",
       "      <th>t-4Volume</th>\n",
       "      <th>t-4Close</th>\n",
       "      <th>t-4Open</th>\n",
       "      <th>...</th>\n",
       "      <th>t--4Close</th>\n",
       "      <th>t--4Open</th>\n",
       "      <th>t--4High</th>\n",
       "      <th>t--4Low</th>\n",
       "      <th>t--5Date</th>\n",
       "      <th>t--5Volume</th>\n",
       "      <th>t--5Close</th>\n",
       "      <th>t--5Open</th>\n",
       "      <th>t--5High</th>\n",
       "      <th>t--5Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05/15/2023</td>\n",
       "      <td>37266660</td>\n",
       "      <td>172.07</td>\n",
       "      <td>173.16</td>\n",
       "      <td>173.21</td>\n",
       "      <td>171.47</td>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>45533140</td>\n",
       "      <td>172.57</td>\n",
       "      <td>173.62</td>\n",
       "      <td>...</td>\n",
       "      <td>168.54</td>\n",
       "      <td>170.09</td>\n",
       "      <td>170.35</td>\n",
       "      <td>167.54</td>\n",
       "      <td>05/01/2023</td>\n",
       "      <td>52472940</td>\n",
       "      <td>169.59</td>\n",
       "      <td>169.28</td>\n",
       "      <td>170.45</td>\n",
       "      <td>168.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05/12/2023</td>\n",
       "      <td>45533140</td>\n",
       "      <td>172.57</td>\n",
       "      <td>173.62</td>\n",
       "      <td>174.06</td>\n",
       "      <td>171.0</td>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>49514680</td>\n",
       "      <td>173.75</td>\n",
       "      <td>173.85</td>\n",
       "      <td>...</td>\n",
       "      <td>169.59</td>\n",
       "      <td>169.28</td>\n",
       "      <td>170.45</td>\n",
       "      <td>168.64</td>\n",
       "      <td>04/28/2023</td>\n",
       "      <td>55275850</td>\n",
       "      <td>169.68</td>\n",
       "      <td>168.49</td>\n",
       "      <td>169.85</td>\n",
       "      <td>167.8801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05/11/2023</td>\n",
       "      <td>49514680</td>\n",
       "      <td>173.75</td>\n",
       "      <td>173.85</td>\n",
       "      <td>174.59</td>\n",
       "      <td>172.17</td>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>53724500</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>...</td>\n",
       "      <td>169.68</td>\n",
       "      <td>168.49</td>\n",
       "      <td>169.85</td>\n",
       "      <td>167.8801</td>\n",
       "      <td>04/27/2023</td>\n",
       "      <td>64902330</td>\n",
       "      <td>168.41</td>\n",
       "      <td>165.19</td>\n",
       "      <td>168.56</td>\n",
       "      <td>165.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05/10/2023</td>\n",
       "      <td>53724500</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>174.03</td>\n",
       "      <td>171.9</td>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>...</td>\n",
       "      <td>168.41</td>\n",
       "      <td>165.19</td>\n",
       "      <td>168.56</td>\n",
       "      <td>165.19</td>\n",
       "      <td>04/26/2023</td>\n",
       "      <td>45498800</td>\n",
       "      <td>163.76</td>\n",
       "      <td>163.055</td>\n",
       "      <td>165.28</td>\n",
       "      <td>162.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/09/2023</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>05/08/2023</td>\n",
       "      <td>55962790</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>...</td>\n",
       "      <td>163.76</td>\n",
       "      <td>163.055</td>\n",
       "      <td>165.28</td>\n",
       "      <td>162.8</td>\n",
       "      <td>04/25/2023</td>\n",
       "      <td>48714060</td>\n",
       "      <td>163.77</td>\n",
       "      <td>165.19</td>\n",
       "      <td>166.305</td>\n",
       "      <td>163.73</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2502</th>\n",
       "      <td>06/06/2013</td>\n",
       "      <td>416669513</td>\n",
       "      <td>15.6593</td>\n",
       "      <td>15.9096</td>\n",
       "      <td>15.9643</td>\n",
       "      <td>15.5018</td>\n",
       "      <td>06/05/2013</td>\n",
       "      <td>290097524</td>\n",
       "      <td>15.8968</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7907</td>\n",
       "      <td>15.5696</td>\n",
       "      <td>15.9343</td>\n",
       "      <td>15.5639</td>\n",
       "      <td>05/22/2013</td>\n",
       "      <td>442730783</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</td>\n",
       "      <td>16.0125</td>\n",
       "      <td>15.6507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503</th>\n",
       "      <td>06/05/2013</td>\n",
       "      <td>290097524</td>\n",
       "      <td>15.8968</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>15.8468</td>\n",
       "      <td>06/04/2013</td>\n",
       "      <td>290301084</td>\n",
       "      <td>16.0468</td>\n",
       "      <td>16.1864</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7626</td>\n",
       "      <td>15.8589</td>\n",
       "      <td>16.0125</td>\n",
       "      <td>15.6507</td>\n",
       "      <td>05/21/2013</td>\n",
       "      <td>455670978</td>\n",
       "      <td>15.7021</td>\n",
       "      <td>15.6482</td>\n",
       "      <td>15.91</td>\n",
       "      <td>15.5071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2504</th>\n",
       "      <td>06/04/2013</td>\n",
       "      <td>290301084</td>\n",
       "      <td>16.0468</td>\n",
       "      <td>16.1864</td>\n",
       "      <td>16.2296</td>\n",
       "      <td>15.9782</td>\n",
       "      <td>06/03/2013</td>\n",
       "      <td>371602131</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>16.0975</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7021</td>\n",
       "      <td>15.6482</td>\n",
       "      <td>15.91</td>\n",
       "      <td>15.5071</td>\n",
       "      <td>05/20/2013</td>\n",
       "      <td>450118020</td>\n",
       "      <td>15.8189</td>\n",
       "      <td>15.4254</td>\n",
       "      <td>15.9214</td>\n",
       "      <td>15.3607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2505</th>\n",
       "      <td>06/03/2013</td>\n",
       "      <td>371602131</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>16.0975</td>\n",
       "      <td>16.1557</td>\n",
       "      <td>15.8029</td>\n",
       "      <td>05/31/2013</td>\n",
       "      <td>382752007</td>\n",
       "      <td>16.062</td>\n",
       "      <td>16.1607</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8189</td>\n",
       "      <td>15.4254</td>\n",
       "      <td>15.9214</td>\n",
       "      <td>15.3607</td>\n",
       "      <td>05/17/2013</td>\n",
       "      <td>427864749</td>\n",
       "      <td>15.4736</td>\n",
       "      <td>15.6804</td>\n",
       "      <td>15.7175</td>\n",
       "      <td>15.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2506</th>\n",
       "      <td>05/31/2013</td>\n",
       "      <td>382752007</td>\n",
       "      <td>16.062</td>\n",
       "      <td>16.1607</td>\n",
       "      <td>16.325</td>\n",
       "      <td>16.0536</td>\n",
       "      <td>05/30/2013</td>\n",
       "      <td>353419499</td>\n",
       "      <td>16.1279</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>...</td>\n",
       "      <td>15.4736</td>\n",
       "      <td>15.6804</td>\n",
       "      <td>15.7175</td>\n",
       "      <td>15.3932</td>\n",
       "      <td>05/16/2013</td>\n",
       "      <td>602950359</td>\n",
       "      <td>15.5206</td>\n",
       "      <td>15.1157</td>\n",
       "      <td>15.6375</td>\n",
       "      <td>14.9607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2507 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         t-5Date  t-5Volume t-5Close  t-5Open  t-5High   t-5Low     t-4Date  \\\n",
       "0     05/15/2023   37266660   172.07   173.16   173.21   171.47  05/12/2023   \n",
       "1     05/12/2023   45533140   172.57   173.62   174.06    171.0  05/11/2023   \n",
       "2     05/11/2023   49514680   173.75   173.85   174.59   172.17  05/10/2023   \n",
       "3     05/10/2023   53724500  173.555   173.02   174.03    171.9  05/09/2023   \n",
       "4     05/09/2023   45326870   171.77   173.05   173.54    171.6  05/08/2023   \n",
       "...          ...        ...      ...      ...      ...      ...         ...   \n",
       "2502  06/06/2013  416669513  15.6593  15.9096  15.9643  15.5018  06/05/2013   \n",
       "2503  06/05/2013  290097524  15.8968  15.9161  16.0971  15.8468  06/04/2013   \n",
       "2504  06/04/2013  290301084  16.0468  16.1864  16.2296  15.9782  06/03/2013   \n",
       "2505  06/03/2013  371602131  16.0971  16.0975  16.1557  15.8029  05/31/2013   \n",
       "2506  05/31/2013  382752007   16.062  16.1607   16.325  16.0536  05/30/2013   \n",
       "\n",
       "      t-4Volume t-4Close  t-4Open  ... t--4Close t--4Open t--4High   t--4Low  \\\n",
       "0      45533140   172.57   173.62  ...    168.54   170.09   170.35    167.54   \n",
       "1      49514680   173.75   173.85  ...    169.59   169.28   170.45    168.64   \n",
       "2      53724500  173.555   173.02  ...    169.68   168.49   169.85  167.8801   \n",
       "3      45326870   171.77   173.05  ...    168.41   165.19   168.56    165.19   \n",
       "4      55962790    173.5   172.48  ...    163.76  163.055   165.28     162.8   \n",
       "...         ...      ...      ...  ...       ...      ...      ...       ...   \n",
       "2502  290097524  15.8968  15.9161  ...   15.7907  15.5696  15.9343   15.5639   \n",
       "2503  290301084  16.0468  16.1864  ...   15.7626  15.8589  16.0125   15.6507   \n",
       "2504  371602131  16.0971  16.0975  ...   15.7021  15.6482    15.91   15.5071   \n",
       "2505  382752007   16.062  16.1607  ...   15.8189  15.4254  15.9214   15.3607   \n",
       "2506  353419499  16.1279  15.9161  ...   15.4736  15.6804  15.7175   15.3932   \n",
       "\n",
       "        t--5Date t--5Volume t--5Close t--5Open t--5High   t--5Low  \n",
       "0     05/01/2023   52472940    169.59   169.28   170.45    168.64  \n",
       "1     04/28/2023   55275850    169.68   168.49   169.85  167.8801  \n",
       "2     04/27/2023   64902330    168.41   165.19   168.56    165.19  \n",
       "3     04/26/2023   45498800    163.76  163.055   165.28     162.8  \n",
       "4     04/25/2023   48714060    163.77   165.19  166.305    163.73  \n",
       "...          ...        ...       ...      ...      ...       ...  \n",
       "2502  05/22/2013  442730783   15.7626  15.8589  16.0125   15.6507  \n",
       "2503  05/21/2013  455670978   15.7021  15.6482    15.91   15.5071  \n",
       "2504  05/20/2013  450118020   15.8189  15.4254  15.9214   15.3607  \n",
       "2505  05/17/2013  427864749   15.4736  15.6804  15.7175   15.3932  \n",
       "2506  05/16/2013  602950359   15.5206  15.1157  15.6375   14.9607  \n",
       "\n",
       "[2507 rows x 66 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df = df.reset_index(drop=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "530d4b0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5620415886837684"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_percent_inc = []\n",
    "target_best_day = []\n",
    "options = ['t--'+str(i)+'Low' for i in range(1,6)]\n",
    "for i in range(len(df['t-4Date'])):\n",
    "    today = df['t-0High'][i]\n",
    "    best = np.max([df['t--'+str(j)+'Low'][i] for j in range(1,6)])\n",
    "    target_percent_inc.append((best-today)*100/today)\n",
    "    \n",
    "np.average(target_percent_inc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c58fc0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['5dayinc'] = target_percent_inc\n",
    "for i in range(0,-6,-1):\n",
    "    for x in ['Date','Volume','Close','Open','High','Low']:\n",
    "        df.drop('t-'+str(i)+x,axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "90184412",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t-5Volume</th>\n",
       "      <th>t-5Close</th>\n",
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       "      <th>t-5High</th>\n",
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       "      <th>t-4Low</th>\n",
       "      <th>...</th>\n",
       "      <th>t-2Close</th>\n",
       "      <th>t-2Open</th>\n",
       "      <th>t-2High</th>\n",
       "      <th>t-2Low</th>\n",
       "      <th>t-1Volume</th>\n",
       "      <th>t-1Close</th>\n",
       "      <th>t-1Open</th>\n",
       "      <th>t-1High</th>\n",
       "      <th>t-1Low</th>\n",
       "      <th>5dayinc</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37266660</td>\n",
       "      <td>172.07</td>\n",
       "      <td>173.16</td>\n",
       "      <td>173.21</td>\n",
       "      <td>171.47</td>\n",
       "      <td>45533140</td>\n",
       "      <td>172.57</td>\n",
       "      <td>173.62</td>\n",
       "      <td>174.06</td>\n",
       "      <td>171.0</td>\n",
       "      <td>...</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>174.03</td>\n",
       "      <td>171.9</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>-1.777394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45533140</td>\n",
       "      <td>172.57</td>\n",
       "      <td>173.62</td>\n",
       "      <td>174.06</td>\n",
       "      <td>171.0</td>\n",
       "      <td>49514680</td>\n",
       "      <td>173.75</td>\n",
       "      <td>173.85</td>\n",
       "      <td>174.59</td>\n",
       "      <td>172.17</td>\n",
       "      <td>...</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>55962790</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>173.85</td>\n",
       "      <td>172.11</td>\n",
       "      <td>-3.247275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49514680</td>\n",
       "      <td>173.75</td>\n",
       "      <td>173.85</td>\n",
       "      <td>174.59</td>\n",
       "      <td>172.17</td>\n",
       "      <td>53724500</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>174.03</td>\n",
       "      <td>171.9</td>\n",
       "      <td>...</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>173.85</td>\n",
       "      <td>172.11</td>\n",
       "      <td>113453200</td>\n",
       "      <td>173.57</td>\n",
       "      <td>170.975</td>\n",
       "      <td>174.3</td>\n",
       "      <td>170.76</td>\n",
       "      <td>0.957854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53724500</td>\n",
       "      <td>173.555</td>\n",
       "      <td>173.02</td>\n",
       "      <td>174.03</td>\n",
       "      <td>171.9</td>\n",
       "      <td>45326870</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>...</td>\n",
       "      <td>173.57</td>\n",
       "      <td>170.975</td>\n",
       "      <td>174.3</td>\n",
       "      <td>170.76</td>\n",
       "      <td>81235430</td>\n",
       "      <td>165.79</td>\n",
       "      <td>164.89</td>\n",
       "      <td>167.04</td>\n",
       "      <td>164.31</td>\n",
       "      <td>-1.333957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45326870</td>\n",
       "      <td>171.77</td>\n",
       "      <td>173.05</td>\n",
       "      <td>173.54</td>\n",
       "      <td>171.6</td>\n",
       "      <td>55962790</td>\n",
       "      <td>173.5</td>\n",
       "      <td>172.48</td>\n",
       "      <td>173.85</td>\n",
       "      <td>172.11</td>\n",
       "      <td>...</td>\n",
       "      <td>165.79</td>\n",
       "      <td>164.89</td>\n",
       "      <td>167.04</td>\n",
       "      <td>164.31</td>\n",
       "      <td>65136020</td>\n",
       "      <td>167.45</td>\n",
       "      <td>169.5</td>\n",
       "      <td>170.92</td>\n",
       "      <td>167.16</td>\n",
       "      <td>-1.003816</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>2502</th>\n",
       "      <td>416669513</td>\n",
       "      <td>15.6593</td>\n",
       "      <td>15.9096</td>\n",
       "      <td>15.9643</td>\n",
       "      <td>15.5018</td>\n",
       "      <td>290097524</td>\n",
       "      <td>15.8968</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>15.8468</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>16.0975</td>\n",
       "      <td>16.1557</td>\n",
       "      <td>15.8029</td>\n",
       "      <td>382752007</td>\n",
       "      <td>16.062</td>\n",
       "      <td>16.1607</td>\n",
       "      <td>16.325</td>\n",
       "      <td>16.0536</td>\n",
       "      <td>-3.003308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2503</th>\n",
       "      <td>290097524</td>\n",
       "      <td>15.8968</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>15.8468</td>\n",
       "      <td>290301084</td>\n",
       "      <td>16.0468</td>\n",
       "      <td>16.1864</td>\n",
       "      <td>16.2296</td>\n",
       "      <td>15.9782</td>\n",
       "      <td>...</td>\n",
       "      <td>16.062</td>\n",
       "      <td>16.1607</td>\n",
       "      <td>16.325</td>\n",
       "      <td>16.0536</td>\n",
       "      <td>353419499</td>\n",
       "      <td>16.1279</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.2321</td>\n",
       "      <td>15.8754</td>\n",
       "      <td>-1.486038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2504</th>\n",
       "      <td>290301084</td>\n",
       "      <td>16.0468</td>\n",
       "      <td>16.1864</td>\n",
       "      <td>16.2296</td>\n",
       "      <td>15.9782</td>\n",
       "      <td>371602131</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>16.0975</td>\n",
       "      <td>16.1557</td>\n",
       "      <td>15.8029</td>\n",
       "      <td>...</td>\n",
       "      <td>16.1279</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.2321</td>\n",
       "      <td>15.8754</td>\n",
       "      <td>330212828</td>\n",
       "      <td>15.8911</td>\n",
       "      <td>15.7143</td>\n",
       "      <td>15.9821</td>\n",
       "      <td>15.6929</td>\n",
       "      <td>-2.383450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2505</th>\n",
       "      <td>371602131</td>\n",
       "      <td>16.0971</td>\n",
       "      <td>16.0975</td>\n",
       "      <td>16.1557</td>\n",
       "      <td>15.8029</td>\n",
       "      <td>382752007</td>\n",
       "      <td>16.062</td>\n",
       "      <td>16.1607</td>\n",
       "      <td>16.325</td>\n",
       "      <td>16.0536</td>\n",
       "      <td>...</td>\n",
       "      <td>15.8911</td>\n",
       "      <td>15.7143</td>\n",
       "      <td>15.9821</td>\n",
       "      <td>15.6929</td>\n",
       "      <td>385245966</td>\n",
       "      <td>15.7657</td>\n",
       "      <td>16.0679</td>\n",
       "      <td>16.1111</td>\n",
       "      <td>15.7446</td>\n",
       "      <td>-1.669347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2506</th>\n",
       "      <td>382752007</td>\n",
       "      <td>16.062</td>\n",
       "      <td>16.1607</td>\n",
       "      <td>16.325</td>\n",
       "      <td>16.0536</td>\n",
       "      <td>353419499</td>\n",
       "      <td>16.1279</td>\n",
       "      <td>15.9161</td>\n",
       "      <td>16.2321</td>\n",
       "      <td>15.8754</td>\n",
       "      <td>...</td>\n",
       "      <td>15.7657</td>\n",
       "      <td>16.0679</td>\n",
       "      <td>16.1111</td>\n",
       "      <td>15.7446</td>\n",
       "      <td>276035930</td>\n",
       "      <td>15.8982</td>\n",
       "      <td>15.7446</td>\n",
       "      <td>15.9164</td>\n",
       "      <td>15.7271</td>\n",
       "      <td>-1.779808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2507 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      t-5Volume t-5Close  t-5Open  t-5High   t-5Low  t-4Volume t-4Close  \\\n",
       "0      37266660   172.07   173.16   173.21   171.47   45533140   172.57   \n",
       "1      45533140   172.57   173.62   174.06    171.0   49514680   173.75   \n",
       "2      49514680   173.75   173.85   174.59   172.17   53724500  173.555   \n",
       "3      53724500  173.555   173.02   174.03    171.9   45326870   171.77   \n",
       "4      45326870   171.77   173.05   173.54    171.6   55962790    173.5   \n",
       "...         ...      ...      ...      ...      ...        ...      ...   \n",
       "2502  416669513  15.6593  15.9096  15.9643  15.5018  290097524  15.8968   \n",
       "2503  290097524  15.8968  15.9161  16.0971  15.8468  290301084  16.0468   \n",
       "2504  290301084  16.0468  16.1864  16.2296  15.9782  371602131  16.0971   \n",
       "2505  371602131  16.0971  16.0975  16.1557  15.8029  382752007   16.062   \n",
       "2506  382752007   16.062  16.1607   16.325  16.0536  353419499  16.1279   \n",
       "\n",
       "      t-4Open  t-4High   t-4Low  ... t-2Close  t-2Open  t-2High   t-2Low  \\\n",
       "0      173.62   174.06    171.0  ...  173.555   173.02   174.03    171.9   \n",
       "1      173.85   174.59   172.17  ...   171.77   173.05   173.54    171.6   \n",
       "2      173.02   174.03    171.9  ...    173.5   172.48   173.85   172.11   \n",
       "3      173.05   173.54    171.6  ...   173.57  170.975    174.3   170.76   \n",
       "4      172.48   173.85   172.11  ...   165.79   164.89   167.04   164.31   \n",
       "...       ...      ...      ...  ...      ...      ...      ...      ...   \n",
       "2502  15.9161  16.0971  15.8468  ...  16.0971  16.0975  16.1557  15.8029   \n",
       "2503  16.1864  16.2296  15.9782  ...   16.062  16.1607   16.325  16.0536   \n",
       "2504  16.0975  16.1557  15.8029  ...  16.1279  15.9161  16.2321  15.8754   \n",
       "2505  16.1607   16.325  16.0536  ...  15.8911  15.7143  15.9821  15.6929   \n",
       "2506  15.9161  16.2321  15.8754  ...  15.7657  16.0679  16.1111  15.7446   \n",
       "\n",
       "      t-1Volume t-1Close  t-1Open  t-1High   t-1Low   5dayinc  \n",
       "0      45326870   171.77   173.05   173.54    171.6 -1.777394  \n",
       "1      55962790    173.5   172.48   173.85   172.11 -3.247275  \n",
       "2     113453200   173.57  170.975    174.3   170.76  0.957854  \n",
       "3      81235430   165.79   164.89   167.04   164.31 -1.333957  \n",
       "4      65136020   167.45    169.5   170.92   167.16 -1.003816  \n",
       "...         ...      ...      ...      ...      ...       ...  \n",
       "2502  382752007   16.062  16.1607   16.325  16.0536 -3.003308  \n",
       "2503  353419499  16.1279  15.9161  16.2321  15.8754 -1.486038  \n",
       "2504  330212828  15.8911  15.7143  15.9821  15.6929 -2.383450  \n",
       "2505  385245966  15.7657  16.0679  16.1111  15.7446 -1.669347  \n",
       "2506  276035930  15.8982  15.7446  15.9164  15.7271 -1.779808  \n",
       "\n",
       "[2507 rows x 26 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1,6):#change date dropping to month/year later\n",
    "    for x in ['Date']:\n",
    "        df.drop('t-'+str(i)+x,axis=1,inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "80e08b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(target):#expected no trades on losses, so all loss is 0\n",
    "    if target<=0:\n",
    "        return 0\n",
    "    else:\n",
    "        return round(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11914f51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d73e5ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y= df['5dayinc']\n",
    "X= df.drop('5dayinc',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "450eb52f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -1.777394\n",
       "1      -3.247275\n",
       "2       0.957854\n",
       "3      -1.333957\n",
       "4      -1.003816\n",
       "          ...   \n",
       "2502   -3.003308\n",
       "2503   -1.486038\n",
       "2504   -2.383450\n",
       "2505   -1.669347\n",
       "2506   -1.779808\n",
       "Name: 5dayinc, Length: 2507, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "50b2e16f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\phill\\AppData\\Local\\Temp\\ipykernel_14908\\4003270644.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  y[i] = preprocess(y[i])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       0.0\n",
       "1       0.0\n",
       "2       1.0\n",
       "3       0.0\n",
       "4       0.0\n",
       "       ... \n",
       "2502    0.0\n",
       "2503    0.0\n",
       "2504    0.0\n",
       "2505    0.0\n",
       "2506    0.0\n",
       "Name: 5dayinc, Length: 2507, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(y)):\n",
    "    y[i] = preprocess(y[i])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2d878a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d8f2b5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "19e20bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "ab9b830b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d64ab2ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3baefb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score(pred,actual):\n",
    "    s = 0\n",
    "    for i in range(len(pred)):\n",
    "        if (actual[i] == 0 and pred[i] != 0 ) or (actual[i] != 0 and pred[i] ==0):\n",
    "            continue\n",
    "        else:\n",
    "            s+=1\n",
    "    return s/len(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6ccf5e2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "637ff1f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.765702891326022"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score(y_pred,y_test)"
   ]
  }
 ],
 "metadata": {
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