06-04_analysts_output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyst Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAME = '06-04_analysts_output'\r\n",
    "PROJECT = 'conference-calls-sentiment'\r\n",
    "PYTHON_VERSION = '3.7.0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\r\n",
    "import re\r\n",
    "import numpy as np\r\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "workdir = re.sub(\"(?<={})[\\w\\W]*\".format(PROJECT), \"\", os.getcwd())\r\n",
    "os.chdir(workdir)\r\n",
    "\r\n",
    "pipeline = os.path.join('2_pipeline', NAME)\r\n",
    "if not os.path.exists(pipeline):\r\n",
    "    os.makedirs(pipeline)\r\n",
    "    for folder in ['out', 'store', 'tmp']:\r\n",
    "        os.makedirs(os.path.join(pipeline, folder))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Main code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "recommendations = pd.read_feather(os.path.join('2_pipeline', '06-01_analysts_recommendations', 'out', 'recommendations_analyst.feather'))\r\n",
    "recommendations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_target = pd.read_feather(os.path.join('2_pipeline', '06-02_analysts_price_target', 'out', 'price_level_analyst.feather'))\r\n",
    "price_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_forecast = pd.read_feather(os.path.join('2_pipeline', '06-03_analysts_eps_forecast', 'out', 'eps_forecast_analyst.feather'))\r\n",
    "eps_forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysts_output = (recommendations\r\n",
    "                   .merge(price_target, on=['gvkey', 'event_date', 'analyst'], how='outer')\r\n",
    "                   .merge(eps_forecast, on=['gvkey', 'event_date', 'analyst'], how='outer')\r\n",
    "                   .sort_values(['gvkey', 'event_date', 'analyst'])\r\n",
    "                   .reset_index(drop=True))\r\n",
    "\r\n",
    "analysts_output              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysts_output.to_feather(os.path.join(pipeline, 'out', 'analyst_outputs.feather'))"
   ]
  }
 ],
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