{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99b92926",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".container {width:84% !important;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import libraries and set display options\n",
    "import pandas as pd\n",
    "import pprint\n",
    "\n",
    "# set frame width and center output\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"\"\"\n",
    "<style>\n",
    ".container {width:84% !important;\n",
    "}\n",
    "</style>\n",
    "\"\"\"))\n",
    "\n",
    "# pretty print options\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', 3000)\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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eed88132",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_10747/1632861684.py:4: 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",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.8820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>1.6116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5820</td>\n",
       "      <td>34.8500</td>\n",
       "      <td>7.4452</td>\n",
       "      <td>15.6466</td>\n",
       "      <td>0.7076</td>\n",
       "      <td>324.7200</td>\n",
       "      <td>9.1010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.6700</td>\n",
       "      <td>9,337.6800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.5400</td>\n",
       "      <td>131.4200</td>\n",
       "      <td>1,359.5400</td>\n",
       "      <td>4.6994</td>\n",
       "      <td>0.6649</td>\n",
       "      <td>0.4420</td>\n",
       "      <td>11.4705</td>\n",
       "      <td>4.4637</td>\n",
       "      <td>8.7335</td>\n",
       "      <td>2.1890</td>\n",
       "      <td>44.8720</td>\n",
       "      <td>4.0065</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>27.4315</td>\n",
       "      <td>9.3050</td>\n",
       "      <td>1.9699</td>\n",
       "      <td>188.7000</td>\n",
       "      <td>42.7780</td>\n",
       "      <td>42.6949</td>\n",
       "      <td>0.3722</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>6.7307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.8474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7602</td>\n",
       "      <td>1.6165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5819</td>\n",
       "      <td>34.8860</td>\n",
       "      <td>7.4431</td>\n",
       "      <td>15.6466</td>\n",
       "      <td>0.7058</td>\n",
       "      <td>324.4000</td>\n",
       "      <td>9.0131</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0900</td>\n",
       "      <td>9,218.7700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.0600</td>\n",
       "      <td>129.4300</td>\n",
       "      <td>1,337.1600</td>\n",
       "      <td>4.6548</td>\n",
       "      <td>0.6627</td>\n",
       "      <td>0.4413</td>\n",
       "      <td>11.5511</td>\n",
       "      <td>4.4203</td>\n",
       "      <td>8.6295</td>\n",
       "      <td>2.1531</td>\n",
       "      <td>44.4360</td>\n",
       "      <td>4.0165</td>\n",
       "      <td>1.3092</td>\n",
       "      <td>26.9876</td>\n",
       "      <td>9.1800</td>\n",
       "      <td>1.9436</td>\n",
       "      <td>188.8000</td>\n",
       "      <td>42.7650</td>\n",
       "      <td>42.1678</td>\n",
       "      <td>0.3701</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>6.8283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.8406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7643</td>\n",
       "      <td>1.6138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5819</td>\n",
       "      <td>34.9380</td>\n",
       "      <td>7.4433</td>\n",
       "      <td>15.6466</td>\n",
       "      <td>0.7094</td>\n",
       "      <td>324.0000</td>\n",
       "      <td>9.0302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.1500</td>\n",
       "      <td>9,321.6300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.9900</td>\n",
       "      <td>130.0900</td>\n",
       "      <td>1,366.7300</td>\n",
       "      <td>4.6643</td>\n",
       "      <td>0.6654</td>\n",
       "      <td>0.4419</td>\n",
       "      <td>11.4414</td>\n",
       "      <td>4.4295</td>\n",
       "      <td>8.5900</td>\n",
       "      <td>2.1557</td>\n",
       "      <td>44.2950</td>\n",
       "      <td>4.0363</td>\n",
       "      <td>1.3143</td>\n",
       "      <td>27.2075</td>\n",
       "      <td>9.1650</td>\n",
       "      <td>1.9537</td>\n",
       "      <td>188.8400</td>\n",
       "      <td>42.5600</td>\n",
       "      <td>42.5590</td>\n",
       "      <td>0.3718</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>6.7855</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\n",
       "2 1999-01-06               1.8820      NaN             NaN                     1.7711               1.6116      NaN                    0.5820              34.8500               7.4452              15.6466               0.7076             324.7200               9.1010      NaN                   250.6700           9,337.6800       NaN            NaN                   81.5400             131.4200           1,359.5400               4.6994               0.6649               0.4420              11.4705               4.4637               8.7335               2.1890              44.8720               4.0065               1.3168              27.4315               9.3050               1.9699             188.7000              42.7780              42.6949               0.3722               1.1743               6.7307\n",
       "3 1999-01-07               1.8474      NaN             NaN                     1.7602               1.6165      NaN                    0.5819              34.8860               7.4431              15.6466               0.7058             324.4000               9.0131      NaN                   250.0900           9,218.7700       NaN            NaN                   81.0600             129.4300           1,337.1600               4.6548               0.6627               0.4413              11.5511               4.4203               8.6295               2.1531              44.4360               4.0165               1.3092              26.9876               9.1800               1.9436             188.8000              42.7650              42.1678               0.3701               1.1632               6.8283\n",
       "4 1999-01-08               1.8406      NaN             NaN                     1.7643               1.6138      NaN                    0.5819              34.9380               7.4433              15.6466               0.7094             324.0000               9.0302      NaN                   250.1500           9,321.6300       NaN            NaN                   80.9900             130.0900           1,366.7300               4.6643               0.6654               0.4419              11.4414               4.4295               8.5900               2.1557              44.2950               4.0363               1.3143              27.2075               9.1650               1.9537             188.8400              42.5600              42.5590               0.3718               1.1659               6.7855"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "27c42da0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.pyplot import cm\n",
    "colors = iter([plt.cm.Set3(i) for i in range(20)])\n",
    "\n",
    "# create a list of data frame names\n",
    "# not sure this is neccessary but trying df = df_list.index(currency) in the framer function doesn't seem to work\n",
    "# create a list of data frames for each currency with log rate of the exchange rate, 30 day rolling mean, and year\n",
    "# col_names = []\n",
    "df_dict = {}\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",
    "\n",
    "# currencies for comparison\n",
    "high_freq = [df_dict[\"us_dollar\"], df_dict[\"japanese_yen\"], df_dict[\"uk_pound\"], df_dict[\"australian_dollar\"]]\n",
    "low_freq = [df_dict[\"israeli_shekel\"], df_dict[\"philippine_peso\"], df_dict[\"malaysian_ringgit\"], df_dict[\"romanian_leu\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "edeb8b57",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'ipympl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
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      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2349\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_local_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2350\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2351\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2352\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/magics/pylab.py\u001b[0m in \u001b[0;36mmatplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Available matplotlib backends: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbackends_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m             \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    100\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_matplotlib_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36menable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m   3531\u001b[0m                 \u001b[0mgui\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_gui_and_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpylab_gui_select\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3533\u001b[0;31m         \u001b[0mpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivate_matplotlib\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3534\u001b[0m         \u001b[0mconfigure_inline_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mactivate_matplotlib\u001b[0;34m(backend)\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswitch_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_needmain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mswitch_backend\u001b[0;34m(newbackend)\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0mbackend_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend_module_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m     \u001b[0;32mclass\u001b[0m \u001b[0mbackend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend_bases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_Backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m         \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbackend_mod\u001b[0;34m()\u001b[0m\n\u001b[1;32m    266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m     \u001b[0;32mclass\u001b[0m \u001b[0mbackend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend_bases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_Backend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m         \u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0mrequired_framework\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_required_interactive_framework\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend_mod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    125\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'ipympl'"
     ]
    }
   ],
   "source": [
    "# line graph showing Euro to four high frequency trading pairs overlapped\n",
    "# widget can be turned on and off to discover specific dates\n",
    "%matplotlib widget\n",
    "fig, ax = plt.subplots(figsize=(7.5,5))\n",
    "ax1 = ax.twinx()\n",
    "ax2 = ax.twinx()\n",
    "ax3 = ax.twinx()\n",
    "line = ax.plot(df_dict[\"us_dollar\"][\"date\"], df_dict[\"us_dollar\"][\"rolling_mean_30\"], 'tab:orange')\n",
    "line_1 = ax1.plot(df_dict[\"us_dollar\"][\"date\"], df_dict[\"japanese_yen\"][\"rolling_mean_30\"], 'tab:blue')\n",
    "line_2 = ax2.plot(df_dict[\"us_dollar\"][\"date\"], df_dict[\"uk_pound\"][\"rolling_mean_30\"], 'tab:green')\n",
    "line_3 = ax3.plot(df_dict[\"us_dollar\"][\"date\"], df_dict[\"australian_dollar\"][\"rolling_mean_30\"], 'tab:red')\n",
    "lines = [line, line_1, line_2, line_3]\n",
    "for ax in ax, ax1, ax2, ax3:\n",
    "    ax.set(yticks=[])\n",
    "plt.show()"
   ]
  }
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