{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "867bfa18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "419ed573",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_9819/1974911599.py:17: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
      "  x_rates.columns = x_rates.columns.str.replace(\"[\",\"\").str.replace(\"]\",\"\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: center;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>australian_dollar</th>\n",
       "      <th>bulgarian_lev</th>\n",
       "      <th>brazilian_real</th>\n",
       "      <th>canadian_dollar</th>\n",
       "      <th>swiss_franc</th>\n",
       "      <th>chinese_yuan</th>\n",
       "      <th>cypriot_pound</th>\n",
       "      <th>czech_koruna</th>\n",
       "      <th>danish_krone</th>\n",
       "      <th>estonian_kroon</th>\n",
       "      <th>uk_pound</th>\n",
       "      <th>greek_drachma</th>\n",
       "      <th>hong_kong_dollar</th>\n",
       "      <th>croatian_kuna</th>\n",
       "      <th>hungarian_forint</th>\n",
       "      <th>indonesian_rupiah</th>\n",
       "      <th>israeli_shekel</th>\n",
       "      <th>indian_rupee</th>\n",
       "      <th>iceland_krona</th>\n",
       "      <th>japanese_yen</th>\n",
       "      <th>korean_won</th>\n",
       "      <th>lithuanian_litas</th>\n",
       "      <th>latvian_lats</th>\n",
       "      <th>maltese_lira</th>\n",
       "      <th>mexican_peso</th>\n",
       "      <th>malaysian_ringgit</th>\n",
       "      <th>norwegian_krone</th>\n",
       "      <th>new_zealand_dollar</th>\n",
       "      <th>philippine_peso</th>\n",
       "      <th>polish_zloty</th>\n",
       "      <th>romanian_leu</th>\n",
       "      <th>russian_rouble</th>\n",
       "      <th>swedish_krona</th>\n",
       "      <th>singapore_dollar</th>\n",
       "      <th>slovenian_tolar</th>\n",
       "      <th>slovak_koruna</th>\n",
       "      <th>thai_baht</th>\n",
       "      <th>turkish_lira</th>\n",
       "      <th>us_dollar</th>\n",
       "      <th>south_african_rand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.9100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8004</td>\n",
       "      <td>1.6168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5823</td>\n",
       "      <td>35.1070</td>\n",
       "      <td>7.4501</td>\n",
       "      <td>15.6466</td>\n",
       "      <td>0.7111</td>\n",
       "      <td>327.1500</td>\n",
       "      <td>9.1332</td>\n",
       "      <td>NaN</td>\n",
       "      <td>251.4800</td>\n",
       "      <td>9,433.6100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.4800</td>\n",
       "      <td>133.7300</td>\n",
       "      <td>1,398.5900</td>\n",
       "      <td>4.7170</td>\n",
       "      <td>0.6668</td>\n",
       "      <td>0.4432</td>\n",
       "      <td>11.6446</td>\n",
       "      <td>4.4798</td>\n",
       "      <td>8.8550</td>\n",
       "      <td>2.2229</td>\n",
       "      <td>45.5100</td>\n",
       "      <td>4.0712</td>\n",
       "      <td>1.3111</td>\n",
       "      <td>25.2875</td>\n",
       "      <td>9.4696</td>\n",
       "      <td>1.9554</td>\n",
       "      <td>189.0450</td>\n",
       "      <td>42.9910</td>\n",
       "      <td>42.6799</td>\n",
       "      <td>0.3723</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>6.9358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.8944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7965</td>\n",
       "      <td>1.6123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5823</td>\n",
       "      <td>34.9170</td>\n",
       "      <td>7.4495</td>\n",
       "      <td>15.6466</td>\n",
       "      <td>0.7122</td>\n",
       "      <td>324.7000</td>\n",
       "      <td>9.1341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.8000</td>\n",
       "      <td>9,314.5100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.5300</td>\n",
       "      <td>130.9600</td>\n",
       "      <td>1,373.0100</td>\n",
       "      <td>4.7174</td>\n",
       "      <td>0.6657</td>\n",
       "      <td>0.4432</td>\n",
       "      <td>11.5960</td>\n",
       "      <td>4.4805</td>\n",
       "      <td>8.7745</td>\n",
       "      <td>2.2011</td>\n",
       "      <td>44.7450</td>\n",
       "      <td>4.0245</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>26.5876</td>\n",
       "      <td>9.4025</td>\n",
       "      <td>1.9655</td>\n",
       "      <td>188.7750</td>\n",
       "      <td>42.8480</td>\n",
       "      <td>42.5048</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>6.7975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date      australian_dollar   bulgarian_lev  brazilian_real    canadian_dollar        swiss_franc      chinese_yuan     cypriot_pound        czech_koruna         danish_krone        estonian_kroon          uk_pound           greek_drachma      hong_kong_dollar    croatian_kuna   hungarian_forint     indonesian_rupiah   israeli_shekel  indian_rupee     iceland_krona        japanese_yen          korean_won        lithuanian_litas       latvian_lats         maltese_lira         mexican_peso       malaysian_ringgit     norwegian_krone    new_zealand_dollar     philippine_peso       polish_zloty         romanian_leu        russian_rouble        swedish_krona      singapore_dollar      slovenian_tolar       slovak_koruna          thai_baht          turkish_lira           us_dollar       south_african_rand \n",
       "0 1999-01-04               1.9100      NaN             NaN                     1.8004               1.6168      NaN                    0.5823              35.1070               7.4501              15.6466               0.7111             327.1500               9.1332      NaN                   251.4800           9,433.6100       NaN            NaN                   81.4800             133.7300           1,398.5900               4.7170               0.6668               0.4432              11.6446               4.4798               8.8550               2.2229              45.5100               4.0712               1.3111              25.2875               9.4696               1.9554             189.0450              42.9910              42.6799               0.3723               1.1789               6.9358\n",
       "1 1999-01-05               1.8944      NaN             NaN                     1.7965               1.6123      NaN                    0.5823              34.9170               7.4495              15.6466               0.7122             324.7000               9.1341      NaN                   250.8000           9,314.5100       NaN            NaN                   81.5300             130.9600           1,373.0100               4.7174               0.6657               0.4432              11.5960               4.4805               8.7745               2.2011              44.7450               4.0245               1.3168              26.5876               9.4025               1.9655             188.7750              42.8480              42.5048               0.3728               1.1790               6.7975"
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     "output_type": "display_data"
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    {
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       "      <th>date</th>\n",
       "      <th>australian_dollar</th>\n",
       "      <th>bulgarian_lev</th>\n",
       "      <th>brazilian_real</th>\n",
       "      <th>canadian_dollar</th>\n",
       "      <th>swiss_franc</th>\n",
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       "      <th>cypriot_pound</th>\n",
       "      <th>czech_koruna</th>\n",
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       "      <th>estonian_kroon</th>\n",
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       "      <th>philippine_peso</th>\n",
       "      <th>polish_zloty</th>\n",
       "      <th>romanian_leu</th>\n",
       "      <th>russian_rouble</th>\n",
       "      <th>swedish_krona</th>\n",
       "      <th>singapore_dollar</th>\n",
       "      <th>slovenian_tolar</th>\n",
       "      <th>slovak_koruna</th>\n",
       "      <th>thai_baht</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.5836</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5172</td>\n",
       "      <td>1.5601</td>\n",
       "      <td>1.0833</td>\n",
       "      <td>7.9392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.1470</td>\n",
       "      <td>7.4392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9019</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>7.5660</td>\n",
       "      <td>357.7900</td>\n",
       "      <td>17,259.9900</td>\n",
       "      <td>3.9027</td>\n",
       "      <td>90.0455</td>\n",
       "      <td>155.3000</td>\n",
       "      <td>127.1300</td>\n",
       "      <td>1,342.2900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.2552</td>\n",
       "      <td>4.9570</td>\n",
       "      <td>10.3435</td>\n",
       "      <td>1.6907</td>\n",
       "      <td>59.0430</td>\n",
       "      <td>4.4998</td>\n",
       "      <td>4.8712</td>\n",
       "      <td>91.2000</td>\n",
       "      <td>10.0575</td>\n",
       "      <td>1.6253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8590</td>\n",
       "      <td>8.9987</td>\n",
       "      <td>1.2276</td>\n",
       "      <td>18.7919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.5758</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5748</td>\n",
       "      <td>1.5543</td>\n",
       "      <td>1.0827</td>\n",
       "      <td>7.9184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.1630</td>\n",
       "      <td>7.4369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.9013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.4982</td>\n",
       "      <td>7.5690</td>\n",
       "      <td>359.6200</td>\n",
       "      <td>17,247.3300</td>\n",
       "      <td>3.8981</td>\n",
       "      <td>89.7975</td>\n",
       "      <td>155.5000</td>\n",
       "      <td>127.2600</td>\n",
       "      <td>1,337.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.4718</td>\n",
       "      <td>4.9359</td>\n",
       "      <td>10.2863</td>\n",
       "      <td>1.6883</td>\n",
       "      <td>58.9470</td>\n",
       "      <td>4.5113</td>\n",
       "      <td>4.8708</td>\n",
       "      <td>90.8000</td>\n",
       "      <td>10.0510</td>\n",
       "      <td>1.6228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8480</td>\n",
       "      <td>9.0146</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>18.7212</td>\n",
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      ],
      "text/plain": [
       "        date      australian_dollar      bulgarian_lev       brazilian_real       canadian_dollar        swiss_franc         chinese_yuan      cypriot_pound     czech_koruna         danish_krone      estonian_kroon       uk_pound        greek_drachma   hong_kong_dollar       croatian_kuna      hungarian_forint     indonesian_rupiah     israeli_shekel        indian_rupee         iceland_krona        japanese_yen          korean_won       lithuanian_litas  latvian_lats  maltese_lira     mexican_peso       malaysian_ringgit     norwegian_krone    new_zealand_dollar     philippine_peso       polish_zloty         romanian_leu        russian_rouble        swedish_krona      singapore_dollar    slovenian_tolar  slovak_koruna       thai_baht          turkish_lira           us_dollar       south_african_rand \n",
       "5697 2021-01-07               1.5836               1.9558               6.5172               1.5601               1.0833               7.9392      NaN                    26.1470               7.4392       NaN                     0.9019      NaN                     9.5176               7.5660             357.7900          17,259.9900               3.9027              90.0455             155.3000             127.1300           1,342.2900        NaN             NaN           NaN                   24.2552               4.9570              10.3435               1.6907              59.0430               4.4998               4.8712              91.2000              10.0575               1.6253       NaN             NaN                    36.8590               8.9987               1.2276              18.7919\n",
       "5698 2021-01-08               1.5758               1.9558               6.5748               1.5543               1.0827               7.9184      NaN                    26.1630               7.4369       NaN                     0.9013      NaN                     9.4982               7.5690             359.6200          17,247.3300               3.8981              89.7975             155.5000             127.2600           1,337.9000        NaN             NaN           NaN                   24.4718               4.9359              10.2863               1.6883              58.9470               4.5113               4.8708              90.8000              10.0510               1.6228       NaN             NaN                    36.8480               9.0146               1.2250              18.7212"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pprint\n",
    "\n",
    "pd.options.display.float_format = '{:20,.4f}'.format\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('display.colheader_justify', 'center')\n",
    "pd.set_option('display.precision', 3)\n",
    "\n",
    "# open file\n",
    "x_rates = pd.read_csv(\"euro-daily-hist_1999_2020.csv\")\n",
    "\n",
    "# strip brackets and trailing space from country names\n",
    "# replace remaining space with underscore\n",
    "# lower case all column names\n",
    "x_rates.columns = x_rates.columns.str.replace(\"[\",\"\").str.replace(\"]\",\"\")\n",
    "x_rates.columns = x_rates.columns.str.rstrip()\n",
    "x_rates.columns = x_rates.columns.str.replace(\" \",\"_\")\n",
    "x_rates.columns = x_rates.columns.str.lower()\n",
    "\n",
    "# rename columns\n",
    "x_rates.rename(columns={\"period\\\\unit:\":\"date\",\n",
    "                        \"chinese_yuan_renminbi\":\"chinese_yuan\",\n",
    "                        \"uk_pound_sterling\":\"uk_pound\"}, inplace=True)\n",
    "\n",
    "# convert datetime\n",
    "x_rates[\"date\"] = pd.to_datetime(x_rates[\"date\"])\n",
    "\n",
    "# resort and reindex\n",
    "x_rates.sort_values(\"date\", inplace=True)\n",
    "x_rates.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# convert hyphens in currency columns to NaN\n",
    "import numpy as np\n",
    "x_rates = x_rates.replace(\"-\", np.nan)\n",
    "\n",
    "# convert exchange rate values to float\n",
    "x_rates.iloc[:,1:] = x_rates.iloc[:,1:].astype(float)\n",
    "\n",
    "display(x_rates.head(2))\n",
    "display(x_rates.tail(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e57164fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# miniaturize x_rates\n",
    "x_rates = x_rates.iloc[:,:6]\n",
    "\n",
    "# array with names of currencies\n",
    "currency_names = x_rates.columns.values\n",
    "currency_names = currency_names[1:]\n",
    "\n",
    "# create a list & dictionary of data frames for each currency with log rate of the exchange rate, 30 day rolling mean, and year\n",
    "col_names = [] # list with names of currencies\n",
    "df_list = [] # list of 40 dataframes\n",
    "df_dict = {} # dictionary with 40 currency:currency_dataframe pairs\n",
    "for currency in x_rates.columns[1:]:\n",
    "    df_name = currency\n",
    "    col_names.append(df_name)\n",
    "    df = x_rates[[\"date\", currency]].copy()\n",
    "    df = df[df[currency].notna()]\n",
    "    df[\"log_rate\"] = np.log(df.iloc[:,1]/df.iloc[:,1].shift()) # getting the log of the exchange rate # double check this is the correct way to get log\n",
    "    df[\"rolling_mean_30\"] = df[currency].rolling(30).mean()\n",
    "    df[\"year\"] = df[\"date\"].dt.year\n",
    "    df_dict[currency] = df\n",
    "    df_list.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27713ddc",
   "metadata": {},
   "outputs": [
    {
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       "    }\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: center;\">\n",
       "      <th></th>\n",
       "      <th>bulgarian_lev_annual_vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.0098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.0058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.0075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.0047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.0051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bulgarian_lev_annual_vol\n",
       "2000                  0.0098  \n",
       "2001                  0.0058  \n",
       "2002                  0.0075  \n",
       "2003                  0.0047  \n",
       "2004                  0.0051  \n",
       "2005                  0.0097  \n",
       "2006                  0.0004  \n",
       "2007                  0.0001  \n",
       "2008                  0.0000  \n",
       "2009                  0.0000  \n",
       "2010                  0.0001  \n",
       "2011                  0.0000  \n",
       "2012                  0.0000  \n",
       "2013                  0.0001  \n",
       "2014                  0.0000  \n",
       "2015                  0.0001  \n",
       "2016                  0.0000  \n",
       "2017                  0.0000  \n",
       "2018                  0.0000  \n",
       "2019                  0.0000  \n",
       "2020                  0.0000  \n",
       "2021                  0.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# function to create df with year and annual volatility for every row # i think i could use aggregation here but don't know how\n",
    "def volatizer(currency):\n",
    "    annual_df_list = [currency[currency['year'] == y] for y in currency['year'].unique()] # list of annual dfs\n",
    "    c_name = currency.columns[1]\n",
    "    row_dict = {}\n",
    "    for frame in annual_df_list:\n",
    "        year_name = frame.iat[0,4] # first cell of the \"year\" column, becomes the \"year\" key for row_dict\n",
    "        annual_volatility = frame[\"log_rate\"].std()*253**.5 # volatility measured by standard deviation * 253 trading days per year raised to the 0.5 power\n",
    "        row_dict[year_name] = annual_volatility\n",
    "    df = pd.DataFrame.from_dict(row_dict, orient=\"index\", columns=[c_name+\"_annual_vol\"]) # indexing on year, not sure if this is cool\n",
    "    return df\n",
    "\n",
    "# apply volatizer to each currency df\n",
    "for key in df_dict:\n",
    "    df_dict[key] = volatizer(df_dict[key])\n",
    "\n",
    "display(df_dict[\"bulgarian_lev\"])"
   ]
  }
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