homework5.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37906557",
   "metadata": {},
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d893550b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2011    0.028026\n",
       "2012    0.068209\n",
       "2013    0.591654\n",
       "2014    0.155168\n",
       "2015    0.030221\n",
       "2016   -0.046093\n",
       "2017    0.149957\n",
       "2018    0.026543\n",
       "2019    0.090765\n",
       "2020    0.842812\n",
       "2021    0.160252\n",
       "2022   -0.418615\n",
       "Name: ln_Returns, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'data/TSLA.csv'\n",
    "\n",
    "df = pd.read_csv(file, decimal = \".\")\n",
    "\n",
    "df = df[['Date', 'Close']]\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'], format = '%Y-%m-%d')\n",
    "\n",
    "df = df[(df['Date'].dt.year >= 2011) & (df['Date'].dt.year <= 2022)]\n",
    "\n",
    "df['ln_Returns'] = (np.log(df['Close']) - np.log(df['Close'].shift(1)))*100\n",
    "\n",
    "df_avg = df['ln_Returns'].groupby(df['Date'].dt.year).mean()\n",
    "\n",
    "df_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c63c4b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18763373982748632"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Current value - old value\n",
    "(np.log(1.778000) - np.log(1.774667))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f5aebba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2011: 0.028,\n",
       " 2012: 0.068,\n",
       " 2013: 0.592,\n",
       " 2014: 0.155,\n",
       " 2015: 0.03,\n",
       " 2016: -0.046,\n",
       " 2017: 0.15,\n",
       " 2018: 0.027,\n",
       " 2019: 0.091,\n",
       " 2020: 0.843,\n",
       " 2021: 0.16,\n",
       " 2022: -0.419}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = list(df_avg.index)\n",
    "values = list(np.round(df_avg.values, 3))\n",
    "\n",
    "zipped = zip(years, values)\n",
    "\n",
    "my_dict = dict(zipped)\n",
    "\n",
    "my_dict"
   ]
  }
 ],
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