Argentina - Dólar CCL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as wb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#review of Grupo Financiero Galicia ($GGAL)\n",
    "\n",
    "tickers=[\"GGAL\", \"GGAL.BA\"]\n",
    "CCL_data = pd.DataFrame()\n",
    "for t in tickers:\n",
    "    CCL_data[t]= wb.DataReader(t, data_source=\"yahoo\", start=\"01-01-2003\")[(\"Adj Close\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GGAL</th>\n",
       "      <th>GGAL.BA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>1.952801</td>\n",
       "      <td>0.666931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>2.076637</td>\n",
       "      <td>0.702539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>2.400516</td>\n",
       "      <td>0.765094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>2.381465</td>\n",
       "      <td>0.765094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>2.381465</td>\n",
       "      <td>0.784342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-24</th>\n",
       "      <td>10.040000</td>\n",
       "      <td>110.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-25</th>\n",
       "      <td>10.670000</td>\n",
       "      <td>116.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-26</th>\n",
       "      <td>10.210000</td>\n",
       "      <td>110.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>10.330000</td>\n",
       "      <td>109.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>9.970000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4404 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GGAL     GGAL.BA\n",
       "Date                             \n",
       "2003-01-02   1.952801    0.666931\n",
       "2003-01-03   2.076637    0.702539\n",
       "2003-01-06   2.400516    0.765094\n",
       "2003-01-07   2.381465    0.765094\n",
       "2003-01-08   2.381465    0.784342\n",
       "...               ...         ...\n",
       "2020-06-24  10.040000  110.500000\n",
       "2020-06-25  10.670000  116.250000\n",
       "2020-06-26  10.210000  110.400002\n",
       "2020-06-29  10.330000  109.800003\n",
       "2020-06-30   9.970000         NaN\n",
       "\n",
       "[4404 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe\n",
    "\n",
    "CCL_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "GGAL_CCL = CCL_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GGAL</th>\n",
       "      <th>GGAL.BA</th>\n",
       "      <th>Dólar CCL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4202.000000</td>\n",
       "      <td>4202.000000</td>\n",
       "      <td>4202.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.074543</td>\n",
       "      <td>25.640470</td>\n",
       "      <td>13.024448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.303909</td>\n",
       "      <td>38.960298</td>\n",
       "      <td>19.459834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.389264</td>\n",
       "      <td>0.550483</td>\n",
       "      <td>2.667486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.643287</td>\n",
       "      <td>2.097993</td>\n",
       "      <td>3.107777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.082202</td>\n",
       "      <td>3.413712</td>\n",
       "      <td>4.449153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.775762</td>\n",
       "      <td>36.736938</td>\n",
       "      <td>14.224320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>71.444939</td>\n",
       "      <td>174.550003</td>\n",
       "      <td>125.788149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GGAL      GGAL.BA    Dólar CCL\n",
       "count  4202.000000  4202.000000  4202.000000\n",
       "mean     14.074543    25.640470    13.024448\n",
       "std      13.303909    38.960298    19.459834\n",
       "min       1.389264     0.550483     2.667486\n",
       "25%       5.643287     2.097993     3.107777\n",
       "50%       8.082202     3.413712     4.449153\n",
       "75%      17.775762    36.736938    14.224320\n",
       "max      71.444939   174.550003   125.788149"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Resume of Dataframe\n",
    "GGAL_CCL.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Juan Ignacio\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "GGAL_CCL[\"Dólar CCL\"] = ((GGAL_CCL[\"GGAL.BA\"]*10)/(GGAL_CCL[\"GGAL\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GGAL</th>\n",
       "      <th>GGAL.BA</th>\n",
       "      <th>Dólar CCL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-02</th>\n",
       "      <td>1.952801</td>\n",
       "      <td>0.666931</td>\n",
       "      <td>3.415254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-03</th>\n",
       "      <td>2.076637</td>\n",
       "      <td>0.702539</td>\n",
       "      <td>3.383061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-06</th>\n",
       "      <td>2.400516</td>\n",
       "      <td>0.765094</td>\n",
       "      <td>3.187206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-07</th>\n",
       "      <td>2.381465</td>\n",
       "      <td>0.765094</td>\n",
       "      <td>3.212703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-08</th>\n",
       "      <td>2.381465</td>\n",
       "      <td>0.784342</td>\n",
       "      <td>3.293526</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-23</th>\n",
       "      <td>10.240000</td>\n",
       "      <td>111.849998</td>\n",
       "      <td>109.228517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-24</th>\n",
       "      <td>10.040000</td>\n",
       "      <td>110.500000</td>\n",
       "      <td>110.059761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-25</th>\n",
       "      <td>10.670000</td>\n",
       "      <td>116.250000</td>\n",
       "      <td>108.950327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-26</th>\n",
       "      <td>10.210000</td>\n",
       "      <td>110.400002</td>\n",
       "      <td>108.129286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>10.330000</td>\n",
       "      <td>109.800003</td>\n",
       "      <td>106.292356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4202 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GGAL     GGAL.BA   Dólar CCL\n",
       "Date                                         \n",
       "2003-01-02   1.952801    0.666931    3.415254\n",
       "2003-01-03   2.076637    0.702539    3.383061\n",
       "2003-01-06   2.400516    0.765094    3.187206\n",
       "2003-01-07   2.381465    0.765094    3.212703\n",
       "2003-01-08   2.381465    0.784342    3.293526\n",
       "...               ...         ...         ...\n",
       "2020-06-23  10.240000  111.849998  109.228517\n",
       "2020-06-24  10.040000  110.500000  110.059761\n",
       "2020-06-25  10.670000  116.250000  108.950327\n",
       "2020-06-26  10.210000  110.400002  108.129286\n",
       "2020-06-29  10.330000  109.800003  106.292356\n",
       "\n",
       "[4202 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GGAL_CCL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4202.000000\n",
       "mean       13.024448\n",
       "std        19.459834\n",
       "min         2.667486\n",
       "25%         3.107777\n",
       "50%         4.449153\n",
       "75%        14.224320\n",
       "max       125.788149\n",
       "Name: Dólar CCL, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GGAL_CCL[\"Dólar CCL\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dólar CCL [ARS/USD]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GGAL_CCL[\"Dólar CCL\"].plot(figsize=(20,12), color=\"g\")\n",
    "plt.title(\"DÓLAR CCL\")\n",
    "plt.ylabel(\"Pesos ARS\")\n",
    "plt.plot(label=\"Fecha\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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