stock_selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sectors = range(10, 65, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gvkey is unique identifier\n",
    "df_dict = {'gvkey':[], 'predicted_return':[], 'trade_date':[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=time.time()\n",
    "for sector in sectors:\n",
    "    os.system(f\"python3 fundamental_run_model.py -sector_name sector{sector} -tic_column gvkey -fundamental final_ratios.csv -sector sector{sector}.xlsx \")\n",
    "    df = pd.read_csv(f\"results/sector{sector}/df_predict_best.csv\", index_col=0)\n",
    "    for idx in df.index:\n",
    "        predicted_return = df.loc[idx]\n",
    "        top_q = predicted_return.quantile(0.75)\n",
    "        predicted_return = predicted_return[predicted_return >= top_q]\n",
    "        for gvkey in predicted_return.index:\n",
    "            df_dict[\"gvkey\"].append(gvkey)\n",
    "            df_dict[\"predicted_return\"].append(predicted_return[gvkey])\n",
    "            df_dict[\"trade_date\"].append(idx)\n",
    "end=time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it took  0.40141645272572835  minutes\n"
     ]
    }
   ],
   "source": [
    "print(\"it took \", (end-start)/60, ' minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result = pd.DataFrame(df_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result.to_csv(\"stock_selected.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gvkey</th>\n",
       "      <th>predicted_return</th>\n",
       "      <th>trade_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2991</td>\n",
       "      <td>-0.000871</td>\n",
       "      <td>2001-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4430</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>2001-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7017</td>\n",
       "      <td>-0.001656</td>\n",
       "      <td>2001-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7912</td>\n",
       "      <td>0.002009</td>\n",
       "      <td>2001-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8068</td>\n",
       "      <td>-0.000118</td>\n",
       "      <td>2001-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gvkey  predicted_return  trade_date\n",
       "0  2991         -0.000871  2001-03-01\n",
       "1  4430          0.001057  2001-03-01\n",
       "2  7017         -0.001656  2001-03-01\n",
       "3  7912          0.002009  2001-03-01\n",
       "4  8068         -0.000118  2001-03-01"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result.head()"
   ]
  }
 ],
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