sp500_by_date.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-16T14:29:50.731803Z",
     "start_time": "2019-11-16T14:29:50.715887Z"
    }
   },
   "source": [
    "# S&P 500 by date\n",
    "\n",
    "Get snapshot of S&P 500 components at a given date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:06.441094Z",
     "start_time": "2020-04-21T03:40:05.446579Z"
    }
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "import os\n",
    "import shutil\n",
    "import pandas as pd\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "pd.set_option('display.max_rows', 600)\n",
    "\n",
    "# -*- encoding: utf-8 -*-\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:06.475172Z",
     "start_time": "2020-04-21T03:40:06.452014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:06.495067Z",
     "start_time": "2020-04-21T03:40:06.483757Z"
    }
   },
   "outputs": [],
   "source": [
    "# Date to use for snapshot of S&P 500 components.\n",
    "snap_shot = '2018-10-23'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:06.513572Z",
     "start_time": "2020-04-21T03:40:06.498055Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_table(filename):\n",
    "\n",
    "    if os.path.isfile(filename):\n",
    "        df = pd.read_csv(filename, index_col='date')\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:06.749548Z",
     "start_time": "2020-04-21T03:40:06.521295Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tickers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-01</th>\n",
       "      <td>A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-04</th>\n",
       "      <td>A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-18</th>\n",
       "      <td>A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-25</th>\n",
       "      <td>A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-03</th>\n",
       "      <td>A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      tickers\n",
       "date                                                         \n",
       "2024-02-01  A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...\n",
       "2024-03-04  A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...\n",
       "2024-03-18  A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...\n",
       "2024-03-25  A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM...\n",
       "2024-04-03  A,AAL,AAPL,ABBV,ABNB,ABT,ACGL,ACN,ADBE,ADI,ADM..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'S&P 500 Historical Components & Changes(04-08-2024).csv'\n",
    "df = get_table(filename)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:07.098171Z",
     "start_time": "2020-04-21T03:40:06.759974Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tickers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-01</th>\n",
       "      <td>[A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-04</th>\n",
       "      <td>[A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-18</th>\n",
       "      <td>[A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-25</th>\n",
       "      <td>[A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-04-03</th>\n",
       "      <td>[A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                      tickers\n",
       "date                                                         \n",
       "2024-02-01  [A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...\n",
       "2024-03-04  [A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...\n",
       "2024-03-18  [A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...\n",
       "2024-03-25  [A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB...\n",
       "2024-04-03  [A, AAL, AAPL, ABBV, ABNB, ABT, ACGL, ACN, ADB..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert ticker column from csv to list, then sort.\n",
    "df['tickers'] = df['tickers'].apply(lambda x: sorted(x.split(',')))\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "487"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of symbols in the first row.\n",
    "l = list(df['tickers'].head(1))[0]\n",
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:07.144606Z",
     "start_time": "2020-04-21T03:40:07.110209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tickers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-15</th>\n",
       "      <td>[A, AAL, AAP, AAPL, ABBV, ABC, ABMD, ABT, ACN,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      tickers\n",
       "date                                                         \n",
       "2018-10-15  [A, AAL, AAP, AAPL, ABBV, ABC, ABMD, ABT, ACN,..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the synbols on snap_shot date by filtering df by rows before or on the snap_shot date,\n",
    "# then picking the last row.\n",
    "df2 = df[df.index <= snap_shot]\n",
    "last_row = df2.tail(1)\n",
    "last_row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:07.172402Z",
     "start_time": "2020-04-21T03:40:07.151600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************** S&P 500 on 2018-10-23 ****************************************\n",
      "['A', 'AAL', 'AAP', 'AAPL', 'ABBV', 'ABC', 'ABMD', 'ABT', 'ACN', 'ADBE', 'ADI', 'ADM', 'ADP', 'ADS', 'ADSK', 'AEE', 'AEP', 'AES', 'AET', 'AFL', 'AGN', 'AIG', 'AIV', 'AIZ', 'AJG', 'AKAM', 'ALB', 'ALGN', 'ALK', 'ALL', 'ALLE', 'ALXN', 'AMAT', 'AMD', 'AME', 'AMG', 'AMGN', 'AMP', 'AMT', 'AMZN', 'ANET', 'ANSS', 'ANTM', 'AON', 'AOS', 'APA', 'APC', 'APD', 'APH', 'APTV', 'ARE', 'ARNC', 'ATVI', 'AVB', 'AVGO', 'AVY', 'AWK', 'AXP', 'AZO', 'BA', 'BAC', 'BAX', 'BBT', 'BBY', 'BDX', 'BEN', 'BF.B', 'BHF', 'BHGE', 'BIIB', 'BK', 'BKNG', 'BLK', 'BLL', 'BMY', 'BR', 'BRK.B', 'BSX', 'BWA', 'BXP', 'C', 'CA', 'CAG', 'CAH', 'CAT', 'CB', 'CBOE', 'CBRE', 'CBS', 'CCI', 'CCL', 'CDNS', 'CELG', 'CERN', 'CF', 'CFG', 'CHD', 'CHRW', 'CHTR', 'CI', 'CINF', 'CL', 'CLX', 'CMA', 'CMCSA', 'CME', 'CMG', 'CMI', 'CMS', 'CNC', 'CNP', 'COF', 'COG', 'COL', 'COO', 'COP', 'COST', 'COTY', 'CPB', 'CPRI', 'CPRT', 'CRM', 'CSCO', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTXS', 'CVS', 'CVX', 'CXO', 'D', 'DAL', 'DE', 'DFS', 'DG', 'DGX', 'DHI', 'DHR', 'DIS', 'DISCA', 'DISCK', 'DISH', 'DLR', 'DLTR', 'DOV', 'DRE', 'DRI', 'DTE', 'DUK', 'DVA', 'DVN', 'DWDP', 'DXC', 'EA', 'EBAY', 'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMN', 'EMR', 'EOG', 'EQIX', 'EQR', 'EQT', 'ES', 'ESRX', 'ESS', 'ETFC', 'ETN', 'ETR', 'EVRG', 'EW', 'EXC', 'EXPD', 'EXPE', 'EXR', 'F', 'FAST', 'FB', 'FBHS', 'FCX', 'FDX', 'FE', 'FFIV', 'FIS', 'FISV', 'FITB', 'FL', 'FLIR', 'FLR', 'FLS', 'FLT', 'FMC', 'FOX', 'FOXA', 'FRT', 'FTI', 'FTNT', 'FTV', 'GD', 'GE', 'GILD', 'GIS', 'GLW', 'GM', 'GOOG', 'GOOGL', 'GPC', 'GPN', 'GPS', 'GRMN', 'GS', 'GT', 'GWW', 'HAL', 'HAS', 'HBAN', 'HBI', 'HCA', 'HCP', 'HD', 'HES', 'HFC', 'HIG', 'HII', 'HLT', 'HOG', 'HOLX', 'HON', 'HP', 'HPE', 'HPQ', 'HRB', 'HRL', 'HRS', 'HSIC', 'HST', 'HSY', 'HUM', 'IBM', 'ICE', 'IDXX', 'IFF', 'ILMN', 'INCY', 'INFO', 'INTC', 'INTU', 'IP', 'IPG', 'IPGP', 'IQV', 'IR', 'IRM', 'ISRG', 'IT', 'ITW', 'IVZ', 'JBHT', 'JCI', 'JEC', 'JEF', 'JNJ', 'JNPR', 'JPM', 'JWN', 'K', 'KEY', 'KHC', 'KIM', 'KLAC', 'KMB', 'KMI', 'KMX', 'KO', 'KR', 'KSS', 'KSU', 'L', 'LB', 'LEG', 'LEN', 'LH', 'LKQ', 'LLL', 'LLY', 'LMT', 'LNC', 'LNT', 'LOW', 'LRCX', 'LUV', 'LYB', 'M', 'MA', 'MAA', 'MAC', 'MAR', 'MAS', 'MAT', 'MCD', 'MCHP', 'MCK', 'MCO', 'MDLZ', 'MDT', 'MET', 'MGM', 'MHK', 'MKC', 'MLM', 'MMC', 'MMM', 'MNST', 'MO', 'MOS', 'MPC', 'MRK', 'MRO', 'MS', 'MSCI', 'MSFT', 'MSI', 'MTB', 'MTD', 'MU', 'MYL', 'NBL', 'NCLH', 'NDAQ', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NKTR', 'NLSN', 'NOC', 'NOV', 'NRG', 'NSC', 'NTAP', 'NTRS', 'NUE', 'NVDA', 'NWL', 'NWS', 'NWSA', 'O', 'OKE', 'OMC', 'ORCL', 'ORLY', 'OXY', 'PAYX', 'PBCT', 'PCAR', 'PCG', 'PEG', 'PEP', 'PFE', 'PFG', 'PG', 'PGR', 'PH', 'PHM', 'PKG', 'PKI', 'PLD', 'PM', 'PNC', 'PNR', 'PNW', 'PPG', 'PPL', 'PRGO', 'PRU', 'PSA', 'PSX', 'PVH', 'PWR', 'PX', 'PXD', 'PYPL', 'QCOM', 'QRVO', 'RCL', 'RE', 'REG', 'REGN', 'RF', 'RHI', 'RHT', 'RJF', 'RL', 'RMD', 'ROK', 'ROL', 'ROP', 'ROST', 'RSG', 'RTN', 'SBAC', 'SBUX', 'SCG', 'SCHW', 'SEE', 'SHW', 'SIVB', 'SJM', 'SLB', 'SLG', 'SNA', 'SNPS', 'SO', 'SPG', 'SPGI', 'SRCL', 'SRE', 'STI', 'STT', 'STX', 'STZ', 'SWK', 'SWKS', 'SYF', 'SYK', 'SYMC', 'SYY', 'T', 'TAP', 'TDG', 'TEL', 'TGT', 'TIF', 'TJX', 'TMK', 'TMO', 'TPR', 'TRIP', 'TROW', 'TRV', 'TSCO', 'TSN', 'TSS', 'TTWO', 'TWTR', 'TXN', 'TXT', 'UA', 'UAA', 'UAL', 'UDR', 'UHS', 'ULTA', 'UNH', 'UNM', 'UNP', 'UPS', 'URI', 'USB', 'UTX', 'V', 'VAR', 'VFC', 'VIAB', 'VLO', 'VMC', 'VNO', 'VRSK', 'VRSN', 'VRTX', 'VTR', 'VZ', 'WAT', 'WBA', 'WCG', 'WDC', 'WEC', 'WELL', 'WFC', 'WHR', 'WLTW', 'WM', 'WMB', 'WMT', 'WRK', 'WU', 'WY', 'WYNN', 'XEC', 'XEL', 'XLNX', 'XOM', 'XRAY', 'XRX', 'XYL', 'YUM', 'ZBH', 'ZION', 'ZTS']\n"
     ]
    }
   ],
   "source": [
    "past = last_row['tickers'].iloc[0]\n",
    "print('*'*40, f'S&P 500 on {snap_shot}', '*'*40)\n",
    "print(past)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:07.209243Z",
     "start_time": "2020-04-21T03:40:07.181234Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get current S&P500 list.\n",
    "filename = 'sp500.csv'\n",
    "current = pd.read_csv(filename)\n",
    "current = sorted(list(current['Symbol']))\n",
    "#print(current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-21T03:40:07.242625Z",
     "start_time": "2020-04-21T03:40:07.217534Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************** ADDED since 2018-10-23 ****************************************\n",
      "['GNRC', 'PODD', 'MTCH', 'LW', 'WAB', 'ODFL', 'MKTX', 'ZBRA', 'ACGL', 'GEV', 'WBD', 'NXPI', 'HUBB', 'WRB', 'BIO', 'ELV', 'HWM', 'UBER', 'BLDR', 'PANW', 'LULU', 'FICO', 'PTC', 'CTVA', 'CEG', 'WTW', 'EPAM', 'GEN', 'TDY', 'FDS', 'JBL', 'GEHC', 'J', 'TFC', 'INVH', 'KVUE', 'MPWR', 'ETSY', 'PAYC', 'NOW', 'TRMB', 'DPZ', 'VLTO', 'CDW', 'LDOS', 'NVR', 'IEX', 'TFX', 'DOW', 'BKR', 'CSGP', 'SMCI', 'CRL', 'RVTY', 'TSLA', 'NDSN', 'ABNB', 'ATO', 'COR', 'DECK', 'FANG', 'META', 'MRNA', 'LHX', 'EG', 'VTRS', 'CZR', 'FI', 'LVS', 'CTRA', 'AXON', 'LYV', 'ENPH', 'DOC', 'TRGP', 'BRO', 'TMUS', 'BX', 'OTIS', 'DD', 'KDP', 'GL', 'DAY', 'CPAY', 'PARA', 'MOH', 'CTLT', 'BBWI', 'CE', 'VICI', 'POOL', 'AMCR', 'JKHY', 'STE', 'ON', 'DXCM', 'CPT', 'TECH', 'TT', 'TER', 'BG', 'TYL', 'WST', 'CARR', 'BALL', 'RTX', 'FSLR', 'SOLV', 'KEYS', 'LIN', 'STLD']\n",
      "\n",
      "**************************************** REMOVED since 2018-10-23 ****************************************\n",
      "['XEC', 'CA', 'AIV', 'MAC', 'COG', 'XRAY', 'ETFC', 'DWDP', 'TSS', 'BLL', 'VFC', 'DISH', 'BBT', 'CBS', 'PVH', 'BHGE', 'LLL', 'ABMD', 'UA', 'HCP', 'DXC', 'FLT', 'NLSN', 'ZION', 'INFO', 'HRS', 'FB', 'HOG', 'KSU', 'SIVB', 'ABC', 'NBL', 'AET', 'RHT', 'FLS', 'RTN', 'RE', 'VIAB', 'XRX', 'COTY', 'HRB', 'KSS', 'AAP', 'SEE', 'NKTR', 'FBHS', 'AMG', 'VAR', 'PBCT', 'GT', 'HBI', 'CTL', 'NWL', 'XLNX', 'ADS', 'STI', 'ESRX', 'VNO', 'SYMC', 'APC', 'FLR', 'SLG', 'FISV', 'FLIR', 'LB', 'UTX', 'FTI', 'M', 'IPGP', 'TRIP', 'CERN', 'ALXN', 'DISCK', 'TMK', 'NFX', 'ATVI', 'LEG', 'DRE', 'SCG', 'ANTM', 'HP', 'TWTR', 'BHF', 'TIF', 'JWN', 'WLTW', 'WHR', 'MYL', 'CELG', 'FL', 'UNM', 'MAT', 'UAA', 'JEF', 'COL', 'CTXS', 'NOV', 'GPS', 'LNC', 'WCG', 'ALK', 'JEC', 'CXO', 'DISCA', 'HFC', 'ARNC', 'PRGO', 'WU', 'SRCL', 'AGN', 'PKI', 'PX', 'CPRI']\n"
     ]
    }
   ],
   "source": [
    "# Show what's been added and removed since snap_shot date.\n",
    "\n",
    "added = list(set(current) - set(past))\n",
    "print('*'*40, f'ADDED since {snap_shot}', '*'*40)\n",
    "print(added)\n",
    "print()\n",
    "\n",
    "removed = list(set(past) - set(current))\n",
    "print('*'*40, f'REMOVED since {snap_shot}', '*'*40)\n",
    "print(removed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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