code/data_cleaning/clean_prices.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "sys.path.append(os.path.abspath(os.path.join('../../../virtualenv/lib/python3.8/site-packages')))\n",
    "sys.path.append(os.path.abspath(os.path.join('../../../virtualenv/lib/python3.9/site-packages')))\n",
    "sys.path.append(os.path.abspath(os.path.join('../../../.local/lib/python3.9/site-packages')))\n",
    "sys.path.append('/usr/local/sas/grid/python3-3.9.1/lib/python3.9/site-packages')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "import wrds\n",
    "from multiprocessing import Pool\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow.dataset as ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identify files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get list of TAQ files\n",
    "taq_price_folder = '../../data/taq/prices/'\n",
    "taq_price_files = glob.glob(taq_price_folder + '*.parquet')\n",
    "taq_price_files_dates = [x.split('/')[-1].split('_')[0].split('.')[0] for x in taq_price_files]\n",
    "taq_price_files_dates = list(set(taq_price_files_dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get list of CRSP files\n",
    "crsp_price_folder = '../../../HFZoo/data/crsp/daily/'\n",
    "crsp_price_files = glob.glob(crsp_price_folder + '*.parquet')\n",
    "crsp_price_files_dates = [x.split('/')[-1].split('.')[0] for x in crsp_price_files]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Misc Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading library list...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "## Delisting returns\n",
    "conn = wrds.Connection(**{\"wrds_username\": \"sa400\"})\n",
    "crspmsedelist_df = conn.raw_sql(\"\"\"\n",
    "                    select DLSTDT, PERMNO, dlret\n",
    "                   from crsp.msedelist\n",
    "                    \"\"\")\n",
    "crspmsedelist_df['date'] = pd.to_datetime(crspmsedelist_df['dlstdt'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_times = ['9:30:00', '9:35:00', '9:40:00', '9:45:00', '9:50:00', '9:55:00',\n",
    "       '10:00:00', '10:05:00', '10:10:00', '10:15:00', '10:20:00',\n",
    "       '10:25:00', '10:30:00', '10:35:00', '10:40:00', '10:45:00',\n",
    "       '10:50:00', '10:55:00', '11:00:00', '11:05:00', '11:10:00',\n",
    "       '11:15:00', '11:20:00', '11:25:00', '11:30:00', '11:35:00',\n",
    "       '11:40:00', '11:45:00', '11:50:00', '11:55:00', '12:00:00',\n",
    "       '12:05:00', '12:10:00', '12:15:00', '12:20:00', '12:25:00',\n",
    "       '12:30:00', '12:35:00', '12:40:00', '12:45:00', '12:50:00',\n",
    "       '12:55:00', '13:00:00', '13:05:00', '13:10:00', '13:15:00',\n",
    "       '13:20:00', '13:25:00', '13:30:00', '13:35:00', '13:40:00',\n",
    "       '13:45:00', '13:50:00', '13:55:00', '14:00:00', '14:05:00',\n",
    "       '14:10:00', '14:15:00', '14:20:00', '14:25:00', '14:30:00',\n",
    "       '14:35:00', '14:40:00', '14:45:00', '14:50:00', '14:55:00',\n",
    "       '15:00:00', '15:05:00', '15:10:00', '15:15:00', '15:20:00',\n",
    "       '15:25:00', '15:30:00', '15:35:00', '15:40:00', '15:45:00',\n",
    "       '15:50:00', '15:55:00', '16:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_taq(date):\n",
    "\n",
    "    # Get TAQ files\n",
    "    taq_price_files_date = glob.glob(taq_price_folder + date + '*.parquet')\n",
    "    taq_df = pd.concat([pd.read_parquet(x) for x in taq_price_files_date])\n",
    "\n",
    "    # Clean up TAQ df columns\n",
    "    taq_df.columns = [x.lower() for x in taq_df.columns]\n",
    "\n",
    "    # Handle any missing times\n",
    "    index = pd.MultiIndex.from_product(\n",
    "        [taq_df[\"permno\"].unique(), all_times], names=[\"permno\", \"time\"]\n",
    "    )\n",
    "    index_df = pd.DataFrame(index=index).reset_index()\n",
    "    taq_df = (\n",
    "        taq_df.merge(index_df, on=[\"permno\", \"time\"], how=\"right\")\n",
    "        .sort_values(by=[\"permno\"])\n",
    "        .astype({'time': 'category'})\n",
    "    )\n",
    "    taq_df = taq_df.sort_values(by = ['permno', 'time'])\n",
    "\n",
    "    # Forward fill in entries\n",
    "    taq_df[['price', 'cusip9', 'symbol', 'ticker_identifier']] = taq_df.groupby(['permno'])[\n",
    "        ['price', 'cusip9', 'symbol', 'ticker_identifier']].ffill()\n",
    "    taq_df['date'] = int(date)\n",
    "\n",
    "    # Add date\n",
    "    taq_df['datetime'] = pd.to_datetime(taq_df['date'], format = '%Y%m%d') + pd.to_timedelta(taq_df['time'])\n",
    "    taq_df['time'] = taq_df['time'].astype(str)\n",
    "\n",
    "    # Add returns\n",
    "    taq_df = taq_df.sort_values(by = ['permno', 'datetime']).reset_index(drop = True)\n",
    "    taq_df['return'] = taq_df.groupby(['permno'])['price'].pct_change()\n",
    "\n",
    "    return taq_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_crsp(date):\n",
    "\n",
    "    # Get CRSP file\n",
    "    crsp_df = pd.read_parquet(crsp_price_folder + date + \".parquet\")\n",
    "\n",
    "    # Fix columns\n",
    "    crsp_df.columns = [x.lower() for x in crsp_df]\n",
    "    crsp_df[['ret', 'retx']] = crsp_df[['ret', 'retx']].apply(pd.to_numeric, errors = 'coerce')\n",
    "    crsp_df['date'] = pd.to_datetime(crsp_df['date'], format = '%Y%m%d')\n",
    "\n",
    "    # Infer close-to-open adjusted overnight returns \n",
    "    crsp_df['prc'] = np.abs(crsp_df['prc'])\n",
    "    crsp_df['openprc'] = np.abs(crsp_df['openprc'])\n",
    "    crsp_df['ret_open_close_intraday'] = (crsp_df['prc']-crsp_df['openprc'])/crsp_df['openprc']\n",
    "    crsp_df['ret_close_open_adj'] = (1+crsp_df['ret'])/(1+crsp_df['ret_open_close_intraday']) - 1\n",
    "    crsp_df['retx_close_open_adj'] = (1+crsp_df['retx'])/(1+crsp_df['ret_open_close_intraday']) - 1\n",
    "\n",
    "    # Create dataframes for start and end of the day\n",
    "    crsp_df_start = crsp_df.copy()\n",
    "    crsp_df_end = crsp_df.copy()\n",
    "    crsp_df_start['time'] = '09:30:00'\n",
    "    crsp_df_end['time'] = '16:00:00'\n",
    "\n",
    "    # Add prices and returns\n",
    "    crsp_df_start['price'] = crsp_df_start['openprc']\n",
    "    crsp_df_start['return'] = crsp_df_start['ret_close_open_adj']\n",
    "    crsp_df_start['returnx'] = crsp_df_start['retx_close_open_adj']\n",
    "    crsp_df_end['price'] = crsp_df_end['prc']\n",
    "    crsp_df_end['return'] = crsp_df_end['ret_open_close_intraday'] \n",
    "    \n",
    "    # Delisting return to end of day CRSP\n",
    "    crsp_df_end = crsp_df_end.merge(crspmsedelist_df, on = ['date', 'permno'], how = 'left')     \n",
    "\n",
    "    # Add dates\n",
    "    crsphf_df = pd.concat([crsp_df_start, crsp_df_end], ignore_index = True)\n",
    "    crsphf_df['datetime'] = crsphf_df['date'] + pd.to_timedelta(crsphf_df['time'])\n",
    "    \n",
    "    return crsphf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_crsp_taq(taq_df, crsphf_df):\n",
    "    \n",
    "    # Combine CRSP with TAQ\n",
    "    merge_df = taq_df.astype({'permno': 'str'}).merge(\n",
    "        crsphf_df.astype({'permno': 'str'}), on=[\"permno\", \"datetime\"], how=\"outer\").sort_values(\n",
    "        by=[\"permno\", \"datetime\"]\n",
    "    )\n",
    "\n",
    "    # Combined prices and returns - prefer CRSP prices (first/last)\n",
    "    merge_df[\"price\"] = merge_df[\"price_y\"].fillna(merge_df[\"price_x\"])\n",
    "    \n",
    "    # Drop permnos where CRSP and TAQ price differ to much (mismatch)\n",
    "    mismatches = merge_df.query('abs(log(price_y/price_x)) > 0.5 & time_y == \"09:30:00\"')\n",
    "    if len(mismatches):\n",
    "        permno_drops = mismatches['permno'].values\n",
    "        merge_df = merge_df.query('permno not in @permno_drops').copy()\n",
    "        print(f'Dropping ({\", \".join(mismatches[\"symbol\"].unique())}) \\\n",
    "            due to match errors (date = {merge_df[\"datetime\"].iloc[0].date()})')\n",
    "\n",
    "    # First return should use CRSP to include dividends\n",
    "    merge_df['return'] = merge_df.groupby(['permno'])['price'].pct_change()\n",
    "    merge_df[\"return\"] = np.where(\n",
    "        merge_df[\"time_y\"] == \"09:30:00\",\n",
    "        merge_df[\"return_y\"],\n",
    "        merge_df[\"return\"]\n",
    "    )\n",
    "\n",
    "    # Last return will be CRSP if we are missing TAQ data\n",
    "    merge_df['return'] = np.where(\n",
    "        (merge_df['time_y'] == \"16:00:00\") & (merge_df['return_x'].isna()),\n",
    "        merge_df['return_y'],\n",
    "        merge_df['return']\n",
    "    )\n",
    "\n",
    "    # Adjust last return to handle delisting\n",
    "    merge_df['return'] = np.where(\n",
    "        (merge_df['time_y'] == \"16:00:00\"),\n",
    "        (1+merge_df['return'])*(1+merge_df['dlret'].fillna(0))-1,\n",
    "        merge_df['return']\n",
    "    )\n",
    "\n",
    "    # Dividend unadjusted returns - just have retx for first observation\n",
    "    merge_df['returnx'] = np.where(\n",
    "        merge_df[\"time_y\"] == \"09:30:00\",\n",
    "        merge_df[\"returnx\"],\n",
    "        merge_df[\"return\"]\n",
    "    )\n",
    "\n",
    "    # Fill in missing data\n",
    "    merge_df[[\"permco\", \"shrout\"]] = merge_df.groupby([\"permno\"])[[\"permco\", \"shrout\"]].ffill()\n",
    "    merge_df[\"date\"] = pd.to_datetime(merge_df[\"datetime\"].iloc[0].date())\n",
    "    merge_df['time'] = merge_df['datetime'].dt.time.astype(str)\n",
    "\n",
    "    # Fix dtypes\n",
    "    merge_df[\"permno\"] = pd.Categorical(merge_df[\"permno\"])\n",
    "    merge_df[\"permco\"] = pd.Categorical(merge_df[\"permco\"])\n",
    "\n",
    "    # Add market equity info\n",
    "    merge_df[\"meq\"] = (\n",
    "        merge_df[\"shrout\"] * merge_df[\"price\"]\n",
    "    )  # / merge_df['cfacpr'] * merge_df['cfacshr']\n",
    "    merge_df[\"ME\"] = merge_df.groupby([\"datetime\", \"permco\"])[\"meq\"].transform(\"sum\")\n",
    "    merge_df[\"meq_day_max_permno\"] = merge_df.groupby([\"permno\"])[\"meq\"].transform(\"max\")\n",
    "    merge_df[\"meq_day_max_permco\"] = merge_df.groupby([\"permco\"])[\"meq\"].transform(\"max\")\n",
    "    merge_df = (\n",
    "        merge_df.query(\"meq_day_max_permno == meq_day_max_permco\")\n",
    "        .drop([\"meq_day_max_permno\", \"meq_day_max_permco\"], axis=1)\n",
    "        .copy()\n",
    "    )\n",
    "    \n",
    "    # Subset\n",
    "    merge_df = merge_df[['datetime', 'date', 'time', 'permco', 'permno', 'symbol', 'price', 'shrout', \n",
    "                         'return', 'returnx', 'ME']].reset_index(drop = True)\n",
    "    \n",
    "    return merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resample_merged(merge_df):\n",
    "    ## Resample so every permno has 79 observations\n",
    "\n",
    "    # Ensure there are 79 unique times in the data\n",
    "    if merge_df[\"datetime\"].nunique() < 79:\n",
    "        raise Exception(\"Missing datetimes\")\n",
    "\n",
    "    # Construct multiindex and merge to get dataframe with all permnos * 79 times\n",
    "    index = pd.MultiIndex.from_product(\n",
    "        [merge_df[\"permno\"].unique(), merge_df[\"datetime\"].unique()], names=[\"permno\", \"datetime\"]\n",
    "    )\n",
    "    index_df = pd.DataFrame(index=index).reset_index()\n",
    "    resample_df = (\n",
    "        merge_df.merge(index_df, on=[\"permno\", \"datetime\"], how=\"right\")\n",
    "        .sort_values(by=[\"permno\", \"datetime\"])\n",
    "        .drop([\"time\"], axis=1)\n",
    "    )\n",
    "\n",
    "    # Fix missing data\n",
    "    resample_df[\"date\"] = merge_df[\"date\"].iloc[0]\n",
    "    ffill_cols = [\"permco\", \"symbol\", \"shrout\", 'ME']\n",
    "    resample_df[ffill_cols] = resample_df.groupby(['permno'])[ffill_cols].ffill()\n",
    "\n",
    "    # Interpolate log prices and fill in missing prices/returns\n",
    "    resample_df['log_price'] = np.log(resample_df['price'])\n",
    "    resample_df['log_price_last'] = resample_df.groupby(['permno'])['log_price'].transform('last')\n",
    "    resample_df['log_price_first'] = resample_df.groupby(['permno'])['log_price'].transform('first')\n",
    "    resample_df['interp_beta'] = (resample_df['log_price_last'] - resample_df['log_price_first'])/78\n",
    "    resample_df['count'] = resample_df.groupby(['permno'])['datetime'].cumcount()\n",
    "    resample_df['log_price_interp'] = resample_df['log_price_first'] + resample_df['count']*resample_df['interp_beta']\n",
    "    resample_df['price'] = resample_df['price'].fillna(np.exp(resample_df['log_price_interp']))\n",
    "    resample_df['return'] = np.where(resample_df['datetime'].dt.time.astype(str) == '09:30:00', \n",
    "                                     resample_df['return'], resample_df.groupby(['permno'])['price'].pct_change())\n",
    "    resample_df['returnx'] = np.where(resample_df['datetime'].dt.time.astype(str) == '09:30:00', \n",
    "                                     resample_df['returnx'], resample_df.groupby(['permno'])['price'].pct_change())\n",
    "    \n",
    "    resample_df = resample_df.drop(['log_price', 'log_price_last', 'log_price_first', \n",
    "                                    'interp_beta', 'count', 'log_price_interp'], axis = 1)\n",
    "\n",
    "    return resample_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_folder = '../../data/proc/clean_prices/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5285/5285 [00:02<00:00, 1843.17it/s]\n"
     ]
    }
   ],
   "source": [
    "for x in tqdm(glob.glob(output_folder + '*')):\n",
    "    os.remove(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 1347/5284 [02:31<07:59,  8.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping (UTXWI)             due to match errors (date = 2005-06-01)Dropping (UTXWI)             due to match errors (date = 2005-06-06)\n",
      "\n",
      "Dropping (UTXWI)             due to match errors (date = 2005-06-02)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 1351/5284 [02:31<07:26,  8.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dropping (UTXWI)             due to match errors (date = 2005-06-07)\n",
      "Dropping (UTXWI)             due to match errors (date = 2005-06-03)"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 1353/5284 [02:31<07:22,  8.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping (UTXWI)             due to match errors (date = 2005-06-08)\n",
      "\n",
      "Dropping (UTXWI)             due to match errors (date = 2005-06-09)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5284/5284 [09:27<00:00,  9.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.1 s, sys: 4.18 s, total: 23.3 s\n",
      "Wall time: 9min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def clean_data(date):\n",
    "    \n",
    "    # Clean data\n",
    "    try:\n",
    "        \n",
    "        taq_df = clean_taq(date)\n",
    "        crsphf_df = clean_crsp(date)\n",
    "        merge_df = merge_crsp_taq(taq_df, crsphf_df)\n",
    "        \n",
    "        # Filter to just taq permnos\n",
    "        taq_permnos = taq_df['permno'].astype(str).unique()\n",
    "        merge_df = merge_df.query('permno in @taq_permnos')\n",
    "        \n",
    "        final_df = resample_merged(merge_df)\n",
    "        \n",
    "    except:\n",
    "        \n",
    "        print(date)\n",
    "        raise Exception()\n",
    "    \n",
    "    return final_df\n",
    "\n",
    "def process_date(date):\n",
    "    \n",
    "    # Get clean data\n",
    "    final_df = clean_data(date)\n",
    "    \n",
    "    # Save\n",
    "    mc = []\n",
    "    table = pa.Table.from_pandas(final_df)\n",
    "    filename = date + '.parquet'\n",
    "    pq.write_table(table, output_folder + filename, metadata_collector=mc)\n",
    "    mc[-1].set_file_path(filename)\n",
    "    \n",
    "    return mc\n",
    "\n",
    "# Pyarrow \n",
    "metadata_collector = []\n",
    "def cb(value):\n",
    "    filename_str = value[0].strftime('%Y%m%d')\n",
    "    return filename_str\n",
    "\n",
    "with Pool(24) as p:\n",
    "    for mc in tqdm(p.imap_unordered(process_date, np.sort(taq_price_files_dates)), \n",
    "                      total = len(taq_price_files_dates),\n",
    "                      smoothing = 0.1):\n",
    "        \n",
    "        # Add to metadata\n",
    "        metadata_collector.append(mc[0])\n",
    "        \n",
    "        continue\n",
    "\n",
    "# Write the ``_metadata`` parquet file with row groups statistics of all files\n",
    "table_schema = pa.Table.from_pandas(clean_data(taq_price_files_dates[0])).schema\n",
    "pq.write_metadata(\n",
    "    table_schema, output_folder + '_metadata',\n",
    "    metadata_collector=metadata_collector\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.7 s, sys: 4.48 s, total: 23.2 s\n",
      "Wall time: 22.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Folder with clean prices for all stocks\n",
    "data_folder = '../../data/proc/clean_prices/'\n",
    "\n",
    "# Read all files in data folder\n",
    "filter_ds = pq.ParquetDataset(\n",
    "    glob.glob(data_folder + '*.parquet'),\n",
    "    metadata = pq.read_metadata(data_folder + '_metadata')\n",
    ")\n",
    "rawdata_df = filter_ds.read_pandas().to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = '20000929'\n",
    "taq_df = clean_taq(date)\n",
    "crsphf_df = clean_crsp(date)\n",
    "temp_df = merge_crsp_taq(taq_df, crsphf_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "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>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>permco</th>\n",
       "      <th>permno</th>\n",
       "      <th>symbol</th>\n",
       "      <th>price</th>\n",
       "      <th>shrout</th>\n",
       "      <th>return</th>\n",
       "      <th>returnx</th>\n",
       "      <th>ME</th>\n",
       "      <th>abslret</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>2010-10-07 16:00:00</td>\n",
       "      <td>2010-10-07</td>\n",
       "      <td>20431.0</td>\n",
       "      <td>71685</td>\n",
       "      <td>CHP</td>\n",
       "      <td>0.2510</td>\n",
       "      <td>26478.0</td>\n",
       "      <td>-0.474401</td>\n",
       "      <td>-0.474401</td>\n",
       "      <td>6.645978e+03</td>\n",
       "      <td>0.643217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2000-09-29 09:30:00</td>\n",
       "      <td>2000-09-29</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14593</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>28.1875</td>\n",
       "      <td>335677.0</td>\n",
       "      <td>-0.473131</td>\n",
       "      <td>-0.473131</td>\n",
       "      <td>9.461895e+06</td>\n",
       "      <td>0.640804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>2005-02-28 09:30:00</td>\n",
       "      <td>2005-02-28</td>\n",
       "      <td>11112.0</td>\n",
       "      <td>76841</td>\n",
       "      <td>BIIB</td>\n",
       "      <td>37.7900</td>\n",
       "      <td>336700.0</td>\n",
       "      <td>-0.438317</td>\n",
       "      <td>-0.438317</td>\n",
       "      <td>1.272389e+07</td>\n",
       "      <td>0.576818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>2004-10-15 09:30:00</td>\n",
       "      <td>2004-10-15</td>\n",
       "      <td>43145.0</td>\n",
       "      <td>89393</td>\n",
       "      <td>NFLX</td>\n",
       "      <td>10.0200</td>\n",
       "      <td>52303.0</td>\n",
       "      <td>-0.425129</td>\n",
       "      <td>-0.425129</td>\n",
       "      <td>5.240761e+05</td>\n",
       "      <td>0.553610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>2002-11-21 09:30:00</td>\n",
       "      <td>2002-11-21</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>RTX</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>21076.0</td>\n",
       "      <td>-0.411765</td>\n",
       "      <td>-0.411765</td>\n",
       "      <td>2.107600e+03</td>\n",
       "      <td>0.530628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>2001-09-26 11:20:00</td>\n",
       "      <td>2001-09-26</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>RTX</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>17783.0</td>\n",
       "      <td>-0.411765</td>\n",
       "      <td>-0.411765</td>\n",
       "      <td>3.556600e+03</td>\n",
       "      <td>0.530628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1153</th>\n",
       "      <td>2008-11-25 13:25:00</td>\n",
       "      <td>2008-11-25</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>12892.0</td>\n",
       "      <td>-0.403509</td>\n",
       "      <td>-0.403509</td>\n",
       "      <td>4.383280e+03</td>\n",
       "      <td>0.516691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>2001-09-17 09:30:00</td>\n",
       "      <td>2001-09-17</td>\n",
       "      <td>16454.0</td>\n",
       "      <td>86783</td>\n",
       "      <td>PCLN</td>\n",
       "      <td>3.0200</td>\n",
       "      <td>220313.0</td>\n",
       "      <td>-0.396000</td>\n",
       "      <td>-0.396000</td>\n",
       "      <td>6.653453e+05</td>\n",
       "      <td>0.504181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>2000-09-27 09:30:00</td>\n",
       "      <td>2000-09-27</td>\n",
       "      <td>16454.0</td>\n",
       "      <td>86783</td>\n",
       "      <td>PCLN</td>\n",
       "      <td>11.3125</td>\n",
       "      <td>166633.0</td>\n",
       "      <td>-0.393127</td>\n",
       "      <td>-0.393127</td>\n",
       "      <td>1.885036e+06</td>\n",
       "      <td>0.499436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>2020-03-09 09:30:00</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>21322.0</td>\n",
       "      <td>34833</td>\n",
       "      <td>OXY.</td>\n",
       "      <td>15.5800</td>\n",
       "      <td>895225.0</td>\n",
       "      <td>-0.383326</td>\n",
       "      <td>-0.419956</td>\n",
       "      <td>1.394761e+07</td>\n",
       "      <td>0.483415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>2009-03-18 15:00:00</td>\n",
       "      <td>2009-03-18</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>12730.0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.092000e+03</td>\n",
       "      <td>0.470004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>2008-11-11 09:30:00</td>\n",
       "      <td>2008-11-11</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>12892.0</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>3.223000e+03</td>\n",
       "      <td>0.470004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>2008-10-09 15:35:00</td>\n",
       "      <td>2008-10-09</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.5200</td>\n",
       "      <td>12768.0</td>\n",
       "      <td>-0.373494</td>\n",
       "      <td>-0.373494</td>\n",
       "      <td>6.639360e+03</td>\n",
       "      <td>0.467597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>2011-10-25 09:30:00</td>\n",
       "      <td>2011-10-25</td>\n",
       "      <td>43145.0</td>\n",
       "      <td>89393</td>\n",
       "      <td>NFLX</td>\n",
       "      <td>74.9000</td>\n",
       "      <td>52504.0</td>\n",
       "      <td>-0.369741</td>\n",
       "      <td>-0.369741</td>\n",
       "      <td>3.932550e+06</td>\n",
       "      <td>0.461625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>2003-01-24 11:25:00</td>\n",
       "      <td>2003-01-24</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>RTX</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>21076.0</td>\n",
       "      <td>-0.363636</td>\n",
       "      <td>-0.363636</td>\n",
       "      <td>2.950640e+03</td>\n",
       "      <td>0.451985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>2009-03-18 13:55:00</td>\n",
       "      <td>2009-03-18</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>12730.0</td>\n",
       "      <td>-0.342105</td>\n",
       "      <td>-0.342105</td>\n",
       "      <td>3.182500e+03</td>\n",
       "      <td>0.418710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>2009-02-24 16:00:00</td>\n",
       "      <td>2009-02-24</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.4400</td>\n",
       "      <td>12730.0</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>5.601200e+03</td>\n",
       "      <td>0.416894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>2009-04-23 15:30:00</td>\n",
       "      <td>2009-04-23</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>12733.0</td>\n",
       "      <td>-0.340000</td>\n",
       "      <td>-0.340000</td>\n",
       "      <td>4.201890e+03</td>\n",
       "      <td>0.415515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>2009-02-12 13:05:00</td>\n",
       "      <td>2009-02-12</td>\n",
       "      <td>17322.0</td>\n",
       "      <td>87378</td>\n",
       "      <td>CHTR</td>\n",
       "      <td>0.0399</td>\n",
       "      <td>411738.0</td>\n",
       "      <td>-0.335000</td>\n",
       "      <td>-0.335000</td>\n",
       "      <td>1.642835e+04</td>\n",
       "      <td>0.407968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>2008-11-25 14:10:00</td>\n",
       "      <td>2008-11-25</td>\n",
       "      <td>37905.0</td>\n",
       "      <td>88409</td>\n",
       "      <td>MERR</td>\n",
       "      <td>0.5100</td>\n",
       "      <td>12892.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.574920e+03</td>\n",
       "      <td>0.405465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                datetime       date   permco permno symbol    price    shrout  \\\n",
       "631  2010-10-07 16:00:00 2010-10-07  20431.0  71685    CHP   0.2510   26478.0   \n",
       "158  2000-09-29 09:30:00 2000-09-29      7.0  14593   AAPL  28.1875  335677.0   \n",
       "632  2005-02-28 09:30:00 2005-02-28  11112.0  76841   BIIB  37.7900  336700.0   \n",
       "1264 2004-10-15 09:30:00 2004-10-15  43145.0  89393   NFLX  10.0200   52303.0   \n",
       "1264 2002-11-21 09:30:00 2002-11-21  37905.0  88409    RTX   0.1000   21076.0   \n",
       "1286 2001-09-26 11:20:00 2001-09-26  37905.0  88409    RTX   0.2000   17783.0   \n",
       "1153 2008-11-25 13:25:00 2008-11-25  37905.0  88409   MERR   0.3400   12892.0   \n",
       "1027 2001-09-17 09:30:00 2001-09-17  16454.0  86783   PCLN   3.0200  220313.0   \n",
       "1027 2000-09-27 09:30:00 2000-09-27  16454.0  86783   PCLN  11.3125  166633.0   \n",
       "553  2020-03-09 09:30:00 2020-03-09  21322.0  34833   OXY.  15.5800  895225.0   \n",
       "1172 2009-03-18 15:00:00 2009-03-18  37905.0  88409   MERR   0.4000   12730.0   \n",
       "1106 2008-11-11 09:30:00 2008-11-11  37905.0  88409   MERR   0.2500   12892.0   \n",
       "1179 2008-10-09 15:35:00 2008-10-09  37905.0  88409   MERR   0.5200   12768.0   \n",
       "1027 2011-10-25 09:30:00 2011-10-25  43145.0  89393   NFLX  74.9000   52504.0   \n",
       "1287 2003-01-24 11:25:00 2003-01-24  37905.0  88409    RTX   0.1400   21076.0   \n",
       "1159 2009-03-18 13:55:00 2009-03-18  37905.0  88409   MERR   0.2500   12730.0   \n",
       "1184 2009-02-24 16:00:00 2009-02-24  37905.0  88409   MERR   0.4400   12730.0   \n",
       "1099 2009-04-23 15:30:00 2009-04-23  37905.0  88409   MERR   0.3300   12733.0   \n",
       "1070 2009-02-12 13:05:00 2009-02-12  17322.0  87378   CHTR   0.0399  411738.0   \n",
       "1162 2008-11-25 14:10:00 2008-11-25  37905.0  88409   MERR   0.5100   12892.0   \n",
       "\n",
       "        return   returnx            ME   abslret  \n",
       "631  -0.474401 -0.474401  6.645978e+03  0.643217  \n",
       "158  -0.473131 -0.473131  9.461895e+06  0.640804  \n",
       "632  -0.438317 -0.438317  1.272389e+07  0.576818  \n",
       "1264 -0.425129 -0.425129  5.240761e+05  0.553610  \n",
       "1264 -0.411765 -0.411765  2.107600e+03  0.530628  \n",
       "1286 -0.411765 -0.411765  3.556600e+03  0.530628  \n",
       "1153 -0.403509 -0.403509  4.383280e+03  0.516691  \n",
       "1027 -0.396000 -0.396000  6.653453e+05  0.504181  \n",
       "1027 -0.393127 -0.393127  1.885036e+06  0.499436  \n",
       "553  -0.383326 -0.419956  1.394761e+07  0.483415  \n",
       "1172  0.600000  0.600000  5.092000e+03  0.470004  \n",
       "1106 -0.375000 -0.375000  3.223000e+03  0.470004  \n",
       "1179 -0.373494 -0.373494  6.639360e+03  0.467597  \n",
       "1027 -0.369741 -0.369741  3.932550e+06  0.461625  \n",
       "1287 -0.363636 -0.363636  2.950640e+03  0.451985  \n",
       "1159 -0.342105 -0.342105  3.182500e+03  0.418710  \n",
       "1184  0.517241  0.517241  5.601200e+03  0.416894  \n",
       "1099 -0.340000 -0.340000  4.201890e+03  0.415515  \n",
       "1070 -0.335000 -0.335000  1.642835e+04  0.407968  \n",
       "1162  0.500000  0.500000  6.574920e+03  0.405465  "
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for giant returns\n",
    "rawdata_df['abslret'] = abs(np.log(1+rawdata_df['return']))\n",
    "rawdata_df.sort_values(by = 'abslret', ascending = False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rawdata_df.query('permno == \"84398\"').plot('datetime', 'price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rawdata_df.query('permno == \"14593\"').plot('datetime', 'log_price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "permno\n",
       "12308           [CHTR.]\n",
       "13407             [FB.]\n",
       "14277            [SLB.]\n",
       "14542           [GOOG.]\n",
       "14593           [AAPL.]\n",
       "17830      [UTX., RTX.]\n",
       "19561             [BA.]\n",
       "24942            [RTN.]\n",
       "34833            [OXY.]\n",
       "76841           [BIIB.]\n",
       "77274           [GILD.]\n",
       "80100            [SPG.]\n",
       "84398            [SPY.]\n",
       "84788           [AMZN.]\n",
       "86783    [BKNG., PCLN.]\n",
       "89393           [NFLX.]\n",
       "90319          [GOOG.L]\n",
       "14849            [BNK.]\n",
       "                 [UTX.]\n",
       "Name: symbol, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawdata_df.groupby(['permno'])['symbol'].apply(lambda x: x.astype(str).unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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