example-stablecoin-analysis/stablecoin comparison.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook to analyze stablecoin metrics\n",
    "\n",
    "This notebook provides some functions to visualize and analize the behavior of stablecoins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import san\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we specify the stableoins we want to look at:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "coin_list = [\"tether\", \"usd-coin\", \"paxos-standard-token\", \"gemini-dollar\", \"nusd\", \"trueusd\", \n",
    "             \"dai\", \"ckusd\", \"bitusd\"] # , \"steem-dollars\" removed because it moved extremely strong within the year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function accepts the list of coins and some specifications about the selected time and returns some descriptive statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_sd(coinlist, from_date, to_date, interval):\n",
    "    data = san.get(\"ohlcv/bitcoin\", from_date=from_date, to_date=to_date, interval=interval)\n",
    "    for coin in coinlist: # Download all the projects in the list\n",
    "        project = san.get(\"ohlcv/\" + coin, from_date=from_date, to_date=to_date, interval=interval)\n",
    "        data[coin + \"close\"] = project.closePriceUsd\n",
    "        data[coin + \"close_change\"] = project.closePriceUsd.pct_change()\n",
    "        data[coin + \"high\"] = project.highPriceUsd\n",
    "        data[coin + \"low\"] = project.lowPriceUsd\n",
    "        data[coin + \"volume\"] = project.volume\n",
    "        data[coin + \"volume_change\"] = project.volume.pct_change()\n",
    "\n",
    "    standard_deviations = []\n",
    "    all_days_above = []\n",
    "    volume_mean = []\n",
    "\n",
    "    for coin in coinlist:  # Iterate over the projects to collect standard deviations and count outliers\n",
    "        days_above = sum(i > 1.05 for i in data[coin + \"high\"]) + sum(i < 0.95 for i in data[coin + \"low\"])\n",
    "        sd = data[coin + \"close_change\"].std() * 100\n",
    "        #print(\"The standard deviation for {} is {} percent\".format(coin, round(sd, 4)), \"with {} days over 1.05 or under 0.95\".format(days_above))\n",
    "        standard_deviations.append(sd)\n",
    "        all_days_above.append(days_above)\n",
    "        volume_mean.append(int(data[coin + \"volume\"].mean()))\n",
    "    \n",
    "    results = pd.DataFrame(standard_deviations,\n",
    "                           columns=[\"standard_deviation_percent\"], index=coin_list)\n",
    "    results[\"outliers above 1.05 or below 0.95\"] = all_days_above\n",
    "    results[\"average daily volume\"] = volume_mean\n",
    "    \n",
    "    display(results)\n",
    "    \n",
    "    print(\"Plotting the standard deviations of stablecoins for the last year:\")\n",
    "    #plt.boxplot(data=)\n",
    "    plt.figure(figsize=(10, 5)) \n",
    "    plt.barh(coinlist, standard_deviations)\n",
    "    plt.gca().set_xlim([0,2.6])\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    plt.xlabel(\"daily standard deviation\")\n",
    "    plt.show()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's execute our function:\n",
    "\n",
    "(beware that some stablecoins only exist for a short time and therefore can only be analysed for that period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "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: right;\">\n",
       "      <th></th>\n",
       "      <th>standard_deviation_percent</th>\n",
       "      <th>outliers above 1.05 or below 0.95</th>\n",
       "      <th>average daily volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>0.394601</td>\n",
       "      <td>3</td>\n",
       "      <td>2742563518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usd-coin</th>\n",
       "      <td>0.505783</td>\n",
       "      <td>1</td>\n",
       "      <td>1593539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paxos-standard-token</th>\n",
       "      <td>0.642558</td>\n",
       "      <td>1</td>\n",
       "      <td>23382452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gemini-dollar</th>\n",
       "      <td>0.695926</td>\n",
       "      <td>4</td>\n",
       "      <td>2590611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nusd</th>\n",
       "      <td>0.763107</td>\n",
       "      <td>11</td>\n",
       "      <td>106483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trueusd</th>\n",
       "      <td>0.949821</td>\n",
       "      <td>7</td>\n",
       "      <td>11785356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dai</th>\n",
       "      <td>1.198680</td>\n",
       "      <td>28</td>\n",
       "      <td>3623593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ckusd</th>\n",
       "      <td>1.450638</td>\n",
       "      <td>52</td>\n",
       "      <td>122362102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitusd</th>\n",
       "      <td>2.531675</td>\n",
       "      <td>99</td>\n",
       "      <td>575039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      standard_deviation_percent  \\\n",
       "tether                                  0.394601   \n",
       "usd-coin                                0.505783   \n",
       "paxos-standard-token                    0.642558   \n",
       "gemini-dollar                           0.695926   \n",
       "nusd                                    0.763107   \n",
       "trueusd                                 0.949821   \n",
       "dai                                     1.198680   \n",
       "ckusd                                   1.450638   \n",
       "bitusd                                  2.531675   \n",
       "\n",
       "                      outliers above 1.05 or below 0.95  average daily volume  \n",
       "tether                                                3            2742563518  \n",
       "usd-coin                                              1               1593539  \n",
       "paxos-standard-token                                  1              23382452  \n",
       "gemini-dollar                                         4               2590611  \n",
       "nusd                                                 11                106483  \n",
       "trueusd                                               7              11785356  \n",
       "dai                                                  28               3623593  \n",
       "ckusd                                                52             122362102  \n",
       "bitusd                                               99                575039  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting the standard deviations of stablecoins for the last year:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = calculate_sd(coin_list, \"2018-01-01\", \"2018-11-08\", \"1d\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about a shorter time frame? Let's look at the results for one week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "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: right;\">\n",
       "      <th></th>\n",
       "      <th>standard_deviation_percent</th>\n",
       "      <th>outliers above 1.05 or below 0.95</th>\n",
       "      <th>average daily volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>0.598609</td>\n",
       "      <td>1</td>\n",
       "      <td>2654239110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usd-coin</th>\n",
       "      <td>0.449076</td>\n",
       "      <td>0</td>\n",
       "      <td>1929935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paxos-standard-token</th>\n",
       "      <td>1.014411</td>\n",
       "      <td>0</td>\n",
       "      <td>48457077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gemini-dollar</th>\n",
       "      <td>0.932422</td>\n",
       "      <td>1</td>\n",
       "      <td>4673166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nusd</th>\n",
       "      <td>0.735024</td>\n",
       "      <td>2</td>\n",
       "      <td>61947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trueusd</th>\n",
       "      <td>1.119276</td>\n",
       "      <td>0</td>\n",
       "      <td>24353398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dai</th>\n",
       "      <td>1.086330</td>\n",
       "      <td>2</td>\n",
       "      <td>4793461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ckusd</th>\n",
       "      <td>0.787420</td>\n",
       "      <td>0</td>\n",
       "      <td>144387950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitusd</th>\n",
       "      <td>0.747310</td>\n",
       "      <td>0</td>\n",
       "      <td>77437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      standard_deviation_percent  \\\n",
       "tether                                  0.598609   \n",
       "usd-coin                                0.449076   \n",
       "paxos-standard-token                    1.014411   \n",
       "gemini-dollar                           0.932422   \n",
       "nusd                                    0.735024   \n",
       "trueusd                                 1.119276   \n",
       "dai                                     1.086330   \n",
       "ckusd                                   0.787420   \n",
       "bitusd                                  0.747310   \n",
       "\n",
       "                      outliers above 1.05 or below 0.95  average daily volume  \n",
       "tether                                                1            2654239110  \n",
       "usd-coin                                              0               1929935  \n",
       "paxos-standard-token                                  0              48457077  \n",
       "gemini-dollar                                         1               4673166  \n",
       "nusd                                                  2                 61947  \n",
       "trueusd                                               0              24353398  \n",
       "dai                                                   2               4793461  \n",
       "ckusd                                                 0             144387950  \n",
       "bitusd                                                0                 77437  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting the standard deviations of stablecoins for the last year:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calculate_sd(coin_list, \"2018-11-01\", \"2018-11-08\", \"1d\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also create a function that displays boxplots for the prices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TnE6nVq1aVe9L+apVq9iZeQAfwgBqHTx4UBs3bqy3P9i4caMOHjzo7dL8Hr8VY52EhAQlJibWm049MTFRCQkJ3i4NPo7AA58yZMgQpaWlKT4+XoWFhYqPj1daWpqGDBni7dL8Hkd0AdRq06aNbrnllnr7g1tuuYUfHvUARipYh8k20FIEHviUHTt2aOrUqdqwYYMiIiK0YcMGTZ06VTt27PB2aX6PI7oAalVUVGjTpk31Di5t2rRJFRUV3i7N7zHsCvA9BB74lNzcXD311FP629/+pnPnzulvf/ubnnrqKY6MeUCbNm00YcKEesMsJkyYwBFdoBUKDg5WRESEFi5cqLCwMC1cuFAREREKDg72dml+j2FX1mFoNlqKwAOfwpEx67hcLqWnp9f7EE5PT5fL5fJ2aQAus4qKCuXn52v48OHKy8vT8OHDlZ+fzxkeD4iLi9O1116r+Ph4hYaGKj4+Xtdeey3DrjyA66PQUgQe+BSOjFmnb9++DV7D07dvX2+XBsALunXrpu3btys6Olrbt29Xt27dvF2SLcybN0/vvfeekpOTderUKSUnJ+u9997TvHnzvF2a3+P6KLQUgQc+hQsSrZOQkKDMzMx6QwEyMzMJk0Ar9c0332jWrFk6deqUZs2apW+++cbbJdnC+vXrNXfuXE2cOFEhISGaOHGi5s6dq/Xr13u7NL/HKBC0lMMYY7xdREOOHz/u7RKapfaXfuF59PbiREdHW7Le5v7oJvjhUSuxP/Cs6OhoXX/99aqoqHD/OGZwcLD27NnDe/4SRUdHKzc3VyEhIe7ttqysTE6nk95eosZ+MLe1Hhi14jPHqn2tVZ+PUVFRzVouyOOPDMArLuaDlC/mAPbs2aOOHTtKkk6cOKEzZ854uSJ7CA4OVkZGhiZOnOi+LSMjgwkhPKA21CQnJ2vs2LFyOp2tNuzg4hB4WigzM1OpqanuI2MJCQm84QAAfqFr1671Qs6ZM2fkcDjUpUsXL1fm+5pzNn3hwoVauHDhRf9bzgA1LS4uTnFxcZz1xUUh8LRAY6dUJRF6mmDVsCuJDwoAaK4zZ87IGKNx48Zp5cqVmjFjhjIyMjjL0wzN+ayZN2+e1q9fr4qKCgUHB+unP/2pFi9efBmq828MzYZVCDwt0Ni0iMnJyQSeJjDsCgC8r6ysTPfdd5927typLl26yOl06r777tOmTZu8XZotLF68WIsXL+Zz7CLxHQFWIfC0ANMiAgB8WXOOlNcNNwcOHNCBAwea9W85Wg7A3xB4WsDpdOr555/Xli1b3NfwjBw5kmkRAQA+oalQcs011yg0NFSvvPKKbn59qT7+2Sw9+eSTKi0t1VdffXWZqgSAy4PA0wJDhgzR6tWrNXfuXD311FN6/vnntWTJEo0fP97bpXlF1f88LJWVWrPuJ0Z7doUhoQr81VueXSf8klXbrce3WYntFh43btw4vfHGG5oyZYoKvv1WkR9PUXFxsR599FFvlwbgInj6M+ekR9dWR0ioVWtuFgJPC+zYsUNTpkzRhg0btHjxYjmdTk2ZMkVbt271dmneUVZqyThaK2ZgseTLKPyTBdutlb9fYEdMYuI9tRfQr1+/XkY1kxg8+uijXFgP+BErvnvZ9dooAk8L5ObmauvWrUpKSnJ/wXG5XFq9erW3S4PNcPYMdsYFyt7FhfUAWgsCTws4nU7t3LlTMTEx7tt27tzZaq/hyYp9U9pw2oI1W7DO2Dd1r+fXah3OnlnGmu3WiveB/G+7BQDAhxB4GtCcYRZjxoxp0b+14zCLUdnj/etLeTxHMmHNdmvpkDa2WwAAWoTA04DmhJLMzEylpqYq98ABOfv1U0JCAr/BAwAAAPgYAk8LxcXFKS4ujrHPsBTDBQEAAC4NgQfwYQwXBFDLryYxkZjIBIDPIPDAI6z4sLRkLngvzwMPAC3mR5OYSP43kQkA+2pW4FmzZo12796tjh07auXKlefdb4xRenq6PvnkE7Vt21aTJ09Wr169JEnbtm3Txo0bJUkPPPCAhg4d6rnq4ROsGtLHcMEahEkAsJZfnT3jzBlw0ZoVeIYOHaqRI0cqLS2twfs/+eQTnThxouYi/txcvfrqq3r22WdVWlqqP/zhD1q6dKkkadasWRo0aJBCQ/liBDQHYRJALb+6pk/yr+v6/OjsGWfOgIvXrMAzYMAA5efnN3r/Rx99pDvvvFMOh0N9+/bV2bNnderUKe3bt0833HCDO+DccMMN+vTTT3X77bd7pvqL4FdHbySO4ACXgaffu5acOZM4ewZJ/nVNn8R1fQB8h0eu4SkqKlJkZKT774iICBUVFamoqEgRERHu28PDw1VUVOSJh7x4fnT0RuIIDmA1K/YHnDkDAMD3eCTwGGPOu83hcDS4bGO3Z2dnKzs7W5K0dOnSegHKE9L9cCjABA/3wN+clDy+HaAGvbUGfZXyx/1IprTEknVbcSDIEdpenTO2eny9VjgpP7qmTzW99Zf3g1Xv3aCgII+vl/1MDfpgDbv21SOBJyIiot5ZjsLCQoWFhSk8PFz79+93315UVKQBAwY0uI7Y2FjFxsa6//b0WRN/HApQwFAAS3qLGvTWGq29r6a0xP/2tX7ymvnjNX3+0tus2DeltEPeLqN5Yt/UvX7SV6v5y/blb/ypr1FRUc1aziOBZ9CgQdqyZYtiYmKUm5urkJAQhYWF6cYbb9Rvf/tblZbWXDvz2Wef6eGHH/bEQ7aIPx0ZY8w+AACXhz8dFOXaKODiNSvwvPDCC9q/f79KSko0adIkjRkzRpWVlZKkESNG6KabbtLu3buVkJCg4OBgTZ48WZIUGhqqn/zkJ5o9e7Yk6cEHH/TaDG3+eGTMjqKjoy/2HzR70by8vIusBgAAAHbXrMAzffr0C97vcDj0+OOPN3jfsGHDNGzYsIuvDLZ0MaHEqiEsdmVVmCRIAgBayq9myWWGXNvyyJA2wJMyMzPdv+nkdDqVkJCguLg4b5fl8wiT8AX8Vgz8ld8Me/e3Ie9+NEsuM+TaF4EHPiUzM1PLli3TihUrNGrUKGVlZSkxMVGSCD2AH/CnayEkrodADYa9A/ZG4IFPSU1N1YoVKxQTE6M2bdooJiZGK1asUHJyMoEHAJqJ6yUB4N8IPA3gg8J7cnNzNXjw4Hq3DR48WLm5uV6qCAD8T1OfNfPmzdMbb7yhiIgIffvtt7rqqqtUWFioRx99VIsXL75MVQLA5RHg7QJ8UV5e3gX/19ic31FRUU3+W1yY0+nUzp076922c+dOOZ1OL1UEAPaTkZGhDh06KC0tTaWlpUpLS1OHDh2UkZHh7dIAwOM4w9MCx48flyQNHz5cv/71r/XYY4/pr3/9q/t2tFxCQoISExPd1/Dk5OQoMTFRSUlJ3i4NQDP5zcXfkv9dAO4hlZWVSk1NrTd8ODU1VePHj/d2aQDgcQSeFrrjjjv061//WpGRkfr1r3+tsWPH6h//+Ie3y/J7tdfpJCcna+zYsXI6nUpKSuL6HcBPcPG3/zhw4IDuueeeen8DgB0ReFqouLj4gn+j5eLi4hQXF8fUyQBgkU6dOiklJUWBgYF66qmn9PLLLyslJUWdOnXydmmwGb+aqp5p6m2LwNNCn332mXr37q3y8nK1a9dO5eXl3i4JgEUuaiITJjGBH1iyZImSkpL07LPPauHChQoKClJISIiWLFni7dJgM/40VT3T1NsXgacFoqKidPz4cXfIqf3/xiYzAODfmhtMOCsJfxEXF6ePPvpI69evlyQFBATooYceYvgwAFtilrYWKCsru6jbAQDwJZmZmXrnnXf0m9/8RqWlpfrNb36jd955R5mZmd4uDQA8jsDTAqdPn9bcuXOVl5en7777Tnl5eZo7d65On7ZijCrgOZmZmRo2bJiuuOIKDRs2jC83QCvV2I88p6amers0APA4Ak8LnTp1qt4Xx1OnTnm7JOCCMjMztWzZMi1atEjFxcVatGiRli1bRugBWiF+5BlAa8I1PC0QEBCgtWvXKjIyUtXV1SoqKtLatWsVEEB+hO9q7IhucnIy4/aBVqb2R55jYmLct/Ejz4B9tfbJdwg8LdCuXTuVlZWpoqJCAQEBqqiokDFGV1xxhbdLAxrFEV0AtfiR55a7qC+ONf+g2Yv6y5dH+J/WPvkOgacFysrKNGLECG3btk3V1dU6d+6cRowYob/85S/eLg1oFEd0AdTiR55brqkvjrXDh2vDZFZWljtM0l/AOwg8LfTYY48pPT3dnYS3b99O4IFP44gufA1Hyr2LH3m2BsOHz1f1xGiPr/Okx9coKSTUirXCBxB4WqBr166aPn26Vq9e7f7iOH36dHXt2tXbpQGN4ogufM3FhBK+lMNfMHy4Pit+dFSqCVFWrRv2Q+BpgXnz5ikpKUkPP/ywKisrFRQUpHbt2mnZsmXeLg24II7oAoC1GD4M+B6mFWuhtm3bqmvXrnI4HOratavatm3r7ZIAAICX1Q4fzsnJkcvlcg8fTkhI8HZpQKvFGZ4WSE1N1dq1axUTE+M+Up6Tk9Oqx+cCAACGDwO+iMDTAozPBQAAjWH4MOBbCDwt4HQ6tWrVKm3dulW5ublyOp360Y9+xPhcAAAAwMdwDU8LDBkyRGlpaYqPj1dhYaHi4+OVlpamIUOGeLs0AAAAAHUQeFpgx44dmjp1qjZs2KCIiAht2LBBU6dO1Y4dO7xdGgAAAIA6GNLWArm5udq6dauefvpp9/hcl8ulF1980dulAQAAAKiDMzwtUDvHfl3MsQ8AAAD4HgJPCzDHPgAAAOAfGNLWAsyxDwAAAPgHAk8LMcc+AAAA4PsY0gYAAADAtgg8AAAAAGyLwAMAAADAtgg8AAAAAGyLwAMAAADAtgg8AAAAAGyLwAMAAADAtgg8AAAAAGyLwAMAAADAtgg8AAAAAGyLwAMAAAC/kJmZqWHDhqln1scaNmyYMjMzvV0S/ECQtwsAAAAAoqOjL2r5AwcOaMqUKZoyZcoFl8vLy7uUsmADBB4AAAB4XVPBZNiwYRo5cqS2bNmi3NxcOZ1O999/+9vfLlOV8EcEHgAAAPi8gwcPqqysTCtXrtSoUaOUlZWlGTNm6NixY94uDT6uWYHn008/VXp6uqqrq3XPPfcoLi6u3v3ffvut1q5dq+LiYoWGhmratGmKiIiQJMXHx6tHjx6SpMjISCUlJXn4KQAAAMDu2rRpowkTJigmJkZt2rRRTEyMJkyYoKVLl3q7NPi4JgNPdXW1XnvtNc2bN08RERGaPXu2Bg0apG7durmXycjI0J133qmhQ4dq7969euuttzRt2jRJUnBwsJYvX27dMwAAAIDtuVwupaen6/vf/75GjRqlnJwcpaeny+Vyebs0+LgmZ2k7dOiQunTpoquvvlpBQUEaMmSIdu3aVW+ZY8eO6frrr5ckfe9739NHH31kTbUAAABolfr27auwsDDFx8crNDRU8fHxCgsLU9++fb1dGnxck2d4ioqK3MPTJCkiIkK5ubn1lrnmmmv04YcfatSoUdq5c6fOnTunkpIStW/fXi6XS7NmzVJgYKDuu+8+DR48uMHHyc7OVnZ2tiRp6dKlioyMvJTnddkEBQX5Ta3+ht5ah95ag75ah95ah95ah956Vvfu3ZWdna0nn3xSS5cu1axZs/TKK68oNjaWPnuIXbfZJgOPMea82xwOR72/x40bp9dff13btm1T//79FR4ersDAQEnSmjVrFB4erpMnT2rhwoXq0aOHunTpct46Y2NjFRsb6/67oKDgop+MN0RGRvpNrf6G3lqH3lqDvlqH3lqH3lqH3nrWu+++qwceeEDbt29XZGSknE6nHnjgAf35z3+mzx7ib9tsVFRUs5ZrMvBERESosLDQ/XdhYaHCwsLqLRMeHq7ExERJUnl5uT788EOFhIS475Okq6++WgMGDNDRo0cbDDwAAABAYyoqKrRs2TKFhIS4v5iXlZVp48aN3i4NPq7Ja3h69+6tb775Rvn5+aqsrNSOHTs0aNCgessUFxerurpakvSnP/1Jd999tySptLTUfSFZcXGxDhw4UG+yAwAAAKA5goODlZGRUe+2jIwMBQcHe6ki+Ismz/AEBgbqZz/7mZYsWaLq6mrdfffd6t69uzZs2KDevXtr0KBB2r9omBmkAAAVk0lEQVR/v9566y05HA71799fP//5zyXV/IDUK6+8ooCAAFVXVysuLo7AAwAAgIv205/+VEuWLJEkPfXUU3r55Ze1ZMkSjR8/3suVwdc5TEMX6fiA48ePe7uEZvG3sY7+hN5ah95ag75ah95ah95ah9563rx587R+/XpVVFQoODhYP/3pT7V48WJvl2Ub/rbNNvcaniaHtAEAAAC+YNCgQbr22msVEBCga6+99rzLLICGNDmkDQAAAPC2zMxMJSUlqby8XNXV1Tp8+LCSkpIkSXFxcV6uDr6MMzwAAADweXPnzlVZWZnmzJmjU6dOac6cOSorK9PcuXO9XRp8HGd4AAAA4PNOnz6tuXPnauLEiQoJCdHEiRNVVVXlnsgAaAxneAAAAOAX+vXrd8G/gYYQeAAAAODzgoKClJCQoJycHLlcLuXk5CghIUFBQQxYwoUReAAAAODzxo0bp+LiYk2ZMkXt27fXlClTVFxcrHHjxnm7NPg4IjEAAAB8Xu3v7axfv17GGJ05c0aPPvoov8ODJhF4AAAA4BcWL16sxYsX+90PZMK7GNIGAAAAwLYIPAAAAABsi8ADAAAAwLYIPAAAAABsi8ADAAAAwLYIPAAAAABsi8ADAAAAwLYIPAAAAABsy2GMMd4uAgAAAACswBmeSzRr1ixvl2Bb9NY69NYa9NU69NY69NY69NY69NYadu0rgQcAAACAbRF4AAAAANhW4IIFCxZ4uwh/16tXL2+XYFv01jr01hr01Tr01jr01jr01jr01hp27CuTFgAAAACwLYa0AQAAALAtAg8AAAAA2yLwSDp79qy2bt16Ucvs27dPS5cutbq0VmHKlCkqLi6+5PWkpKTo7NmzHqjI923btk1FRUUeW9+CBQt0+PDhFv/7/Px8zZgxo8H7mlurp7YDKzRn29qwYYM+//zzJteVlpamDz744ILL1H09fLkvvuj3v/+9Nm/e7O0yLNecz63LZdu2bXrttde8XYbPaGob/Mtf/qLt27dfxor8S3P2kb6wTl/X2OfySy+9pGPHjkmSNm7c6LHH8/XPKgKPaj44/vKXv1zyMhejqqrKY+tCjdmzZ+vKK6/0dhmXxbZt23Tq1CmvPX51dXWzl/V2rZ7QnG0rPj5eN9xww2WqqHEX89rAfzX2mcTr7/tGjBihu+66y9tloJWaNGmSunXrJkn605/+5OVqLp8gbxfgC9566y2dOHFCM2fO1A033KCOHTvq/fffl8vl0uDBgzVmzJjzlhk4cKDKy8u1cuVKff311+rVq5emTZsmh8OhL7/8Um+88YbKy8vVoUMHTZ48WWFhYVqwYIH69u2rAwcOaNCgQbr33nu9/dQ9Jj8/X8uWLdPKlSslSZs3b1Z5eblCQ0P117/+VYGBgerWrZumT5+ukpIS/epXv1JxcbH69OmjxubNKC8v1+uvv67Dhw/L4XDowQcf1G233ab33nvP/Sa96aab9Mgjj0iqObqQkpKi8vJypaSkqF+/fjp48KDCw8P19NNPKzg4uEXP69lnn1WfPn109OhRde3aVVOnTtXbb7+tjz/+WBUVFerbt6+efPJJVVdXa968eXrkkUf0ve99T2+99ZYcDof++7//W3v27FFGRoaqqqrUu3dvPfHEE2rTpo3Wr1+vjz76SIGBgbrhhhs0fvz4eo9fXV2ttWvX6ssvv5Qk3X333YqMjNThw4eVmpqq4OBgLVmyRJs3bz6vHofDoQULFqhPnz7at2+fysrKNGnSJPXv318VFRVas2aNjh07pujoaFVUVLgfc926dTp8+LAqKip02223acyYMe7+3n333frss880cuRIde3aVWvXrlVwcLCuu+66Bvv3wQcfnFfrgQMHGuxFrYqKCi1fvly33nqrYmNj9e677+r//u//VFlZKafTqccff1wBAQEaN26cRo0apd27dys4OFgzZ85Up06d9Ic//EHvvfeeIiIi1L59e/Xq1UuDBw/Wa6+9puLiYrVt21YTJ05UdHS00tLSFBwcrOPHj+vbb7/V5MmTtW3bNuXm5qpPnz6aMmVKs7ettLQ03Xzzzbrtttvq9cAYo9dff1179+5V586d693X2HbRmOeee06FhYVyuVwaNWqUYmNjJUnjxo3Tf/3Xf+mzzz7T+PHjG309fE1+fn6D/Xz22Wc1btw49e7dW8XFxZo9e7bS0tL09ddfa82aNaqsrJQxRjNmzFDXrl21ceNGbd++XZGRke7X3O7qfiYFBQWpXbt26tSpk44eParZs2c3uD8eM2aMTpw40eh7oe72O27cOGVkZOjUqVN64YUXVFZWpurqaj3++OPq37+//v73vyszM1OdOnVS165dL7jdtgYNbYPZ2dl65513VFlZqauvvlrTpk1T27Zt9fvf/17t2rXT6NGjvV22T9i+fbvefvttORwO9ejRQwEB/z4W/7vf/U6FhYX6xS9+oWnTpiklJUUdOnTQ4cOHlZGRoQULFmj//v1KT0+XJDkcDj3zzDNq165do/vd1qSqqkqrV6+u9/0lJSVF48aN0wcffKCKigrNnDlT3bt319ixYxvdb2RlZbX4u5zPMDAnT540v/zlL40xxnz66afmpZdeMtXV1aaqqsqkpKSYffv21VvGGGP27t1rxo8fbwoKCkxVVZWZM2eO+eKLL4zL5TJz5841Z86cMcYYk5OTY9LS0owxxsyfP9+sW7fu8j/By+A/+7Np0yazYcMG8+STT5qKigpjjDGlpaXGGGNee+0187//+7/GGGM+/vhj89BDD7n7VVdGRoZJT093/11SUmIKCwvNpEmTzJkzZ0xlZaVZsGCB+fDDD40xxkyePNmcOXPGnDx50sTHx5sjR44YY4xZuXKl2b59e4uf10MPPWS++OILY4wxaWlpZtOmTaakpMS9TGpqqtm1a5cxxph//etfZvr06eazzz4zM2fONC6Xy3z33Xdm0qRJJi8vzxhjzIsvvmj+/Oc/m5KSEpOQkGCqq6vr9aeuw4cPm4ULF7r/rl1m/vz55tChQ/V601A98+fPN2+88YYxpqbXtet6++233dvl0aNHTXx8vHt9teuqqqoy8+fPN0ePHjXG1PQ3MzPT/TgzZsww+/btM8YY8+abb9Z7/euqW2tjvahd/8mTJ83ChQvNtm3bjDHGfP311yYlJcW4XC5jjDHr1q1z3/fQQw+5n2dGRob5wx/+YA4dOmQSExPNd999Z8rKysy0adPMpk2bzDPPPGOOHz9ujDHm4MGDZsGCBcYYY1avXm2ef/55U11dbXbu3GnGjx9vvvrqK1NVVWWefvpp9zbUnG1r9erV5v333z/v+X/wwQdm4cKFpqqqyhQWFppHH33UvP/++xfsRd2e1T523dfmu+++M7/85S9NcXGxuxc5OTkN9t+XNdbPus//zJkzZvLkycaYmn3Hu+++a4wx7vfW4cOHzS9/+UtTXl5uzp49a6ZOnWo2bdrkledzOdXd5+7du9c88sgj5uTJk+fdZ8y/98fGmAu+F+puv4888ogxxpjNmzebP/7xj8aYmn1CWVmZKSoqcu+HXS6XmTdvnnn11Vctfsa+q7FtsPb9aYwxv/3tb01WVpYxxpgNGza0im20Of71r3+ZhISEevu42m0xIyPDvPzyy+7PyLr7wkOHDpn58+cbY4xJSUlxf0afO3fOVFZWNrrfbU0a+/5Sd/9a+z6vXb6x/calfJfzFZzh+Q+fffaZPv/8cz399NOSas4ynDhxQpGRkect26dPH0VEREiSevbsqfz8fIWEhOjrr7/WokWLJNUcoQ8LC3P/myFDhlyGZ+E7evToodTUVN1yyy0aPHiwJOmLL75QYmKiJGngwIGNDhXas2ePpk+f7v47NDRUu3bt0ve+9z116NBBknTHHXfoiy++cK+7VufOndWzZ09JNfPJf/vtty1+DhEREe4j5nfeeaeysrLUuXNnbd68Wd99951KS0vVvXt3DRo0SN27d9cdd9yhZcuWafHixQoKCtLRo0fVuXNnRUVFSZLuuusubd26VSNHjlRwcLBeeuklDRw4UDfffPN5j925c2fl5+fr9ddf18CBAxsdMrV3794G65Hk7k2vXr2Un58vSdq/f79GjRolSbrmmmt0zTXXuNe1Y8cOvfPOO6qqqtKpU6d07Ngx9/21229ZWZnOnj2rAQMGuPvy6aefNtnL48ePN9iLH//4x5Kk5cuXa/To0brjjjvcz+vIkSOaPXu2pJqzP7WvfVBQkLtnvXr10ueff65//vOfuuWWW9xn826++Wa5XC4dOHBAq1atctdRWVnp/u+bb77ZfWSxY8eO6tGjhySpe/fuys/Pd29HdV+Ti9m2vvjiC8XExCggIEDh4eH6/ve/36xeNCQrK0u7du2SJBUUFOibb75R+/btFRAQcN6ZJX9xMf3s27evNm7cqMLCQt16663q2rWr+/3ftm1bSXJv961Nnz59mjySXV5efsH3QkN69+6ttWvXqrKyUoMHD1bPnj21Z8+eevvhH/7wh/rmm28u/Un4qca2wa+//lq/+93vdPbsWZWXl+sHP/iBN8v0SXv37tVtt93m3pZCQ0MlSX/84x/Vp08fTZw4scl1XHfddXrzzTd1++2369Zbb1VERESj+93WpqHvLy1xKd/lfAWBpwFxcXEaPnx4vdtqvyjWVfcUfkBAgHvsdLdu3bRkyZIG1127Q7SbwMDAemPHXS6XpJprH/bv36+PPvpIf/zjH90ftA6H47x1bNmyRe+884773zW0nGnmKdP/fG3qDtm6WP9Zg8Ph0GuvvaaUlBRFRkbq97//fb31/+tf/1JISIjOnDlzwfUGBgbq2Wef1Z49e7Rjxw5t2bJFycnJSkpKklTzoRkfH6/ly5fr008/1ZYtW7Rjxw5Nnjy53noqKiouWE9tL+puo43Jz8/X22+/rZSUFIWGhiotLc39Wkr/3n6NMQ2+hpK0Zs0aHTlyROHh4e7Xsbn69eunTz75RLfffrscDoeMMbrrrrv08MMPn7dsYGCgu4aAgABVVVU1uH0YY3TllVdq+fLlDT5mbX8cDke97cbhcDTYr6a2rdzcXL3yyiuSaq7rqV3Xpdq3b5/27NmjxYsXq23btlqwYIH7tWnTpk29YSD+pKF+BgYGul/Lutvf7bffrj59+mj37t1asmSJJk2aJMkz/fV3dT9bGtsfV1dXN/peqPtvjDHuIDRgwAA988wz2r17t1588UWNHj1aV1xxhZVPxS81tA2mpaVp5syZ6tmzp7Zt26Z9+/Z5oTLf1thnSe/evfXll1+qtLTUHYICAgIa3C/ExcVp4MCB2r17t+bOnavk5GRJ7Bekhr+/NKax/YZ0cd/lfJV/fkJ62BVXXKFz585Jkn7wgx/o73//u8rLyyVJRUVFOnPmTL1lLiQqKkrFxcU6ePCgpJqjZ19//bV1xfuIjh07qri4WCUlJXK5XNq9e7eMMSooKND3v/99PfLIIyorK1N5ebn69++vf/zjH5KkTz75xD371ciRI7V8+XItX75c4eHhuuGGG7Rlyxb3Y5SWlsrpdGr//v0qLi5WdXW1cnJy3GcZrFJQUOB+Pd977z330ZIOHTqovLxcH374oXvZDz/8UKWlpXrmmWeUnp6us2fPKioqSvn5+Tpx4oQk6d1339WAAQNUXl6usrIyDRw4UI899piOHj2qgIAAdw/i4+Pdz/O2227T2LFjdeTIEUlSu3bt3Ntj7U6poXoaM2DAAL333nuSagLaV199JanmzE27du0UEhKi06dPN3rW5sorr1RISIj++c9/SpL79ZSkyZMna/ny5e6wU7fWxnpRa8yYMWrfvr1effVVSdL111+vDz74wB0eS0tLL3gG4LrrrnNfy1ReXu6+vqdz5856//33JdV8wB49erTJHrWU0+l0v4aDBg1S//79tWPHDlVXV+vUqVPuLz1N9eI/lZWV6corr1Tbtm2Vl5en3Nxcy56Dt1111VXu69bqzqx08uRJXX311Ro1apQGDRqkr776Sv3799fOnTtVUVGhc+fO6eOPP/ZW2ZfVhT6TGtofS1JISEij74W6Pd+1a5d7Yp1vv/1WHTt2VGxsrIYNG6YjR46498MlJSWqrKxsdbNf/afGtsHy8nKFhYWpsrKy3j4S/3b99dfr/fffV0lJiaSafbwk3XjjjYqLi1NKSop7O+/cuXOD+4UTJ06oR48eiouLU69evZSXl9fofre1aez7S62goCD3wY3G9hvV1dUX9V3OV3GGR1L79u3Vr18/zZgxQzfeeKNuv/12zZ07V1LNl7Vp06apS5cu9ZYZOHBgg+sKCgrSjBkzlJ6errKyMlVVVWnUqFHq3r375XxKl11QUJB+8pOfaM6cOe5hOtXV1XrxxRdVVlYmSfrxj3+sK6+8Ug899JB+9atfKSkpSf37929wuKAk/eQnP9Grr76qGTNmKCAgQA8++KBuvfVWPfzww3rmmWck1UxacMstt1j63KKjo7Vt2za98sor6tKli0aMGKGzZ89qxowZ6ty5s3r37i1JKi4u1ltvvaXk5GRFRkbqRz/6kdLT0zV16lRNnjxZq1atcl+cPnz4cJWWluq5556Ty+WSMUaPPvroeY9dVFSktWvXuo+61J7pGDp0qNatW+eeCOCee+45r54LGTFihNasWaPExET17NlTffr0kVQzNLNnz57udfXr16/RdUyePNk9acGFhmr8Z60N9aKuxx57TGvXrtVvfvMbPfLIIxo7dqwWL14sY4wCAwP185//XFdddVWDj9WnTx/dfPPNmjlzpq666ir17t1bISEhSkhI0Lp167Rx40ZVVlYqJibmvKFqVhk8eLD27t3rvsC+f//+kqTg4OAme1HXjTfeqL/+9a9KTExUVFSUnE7nZanfG+699149//zzevfdd+sNRdmxY4f+8Y9/KDAwUJ06ddKDDz6o0NBQDRkyxP2a+8uEDZeq7udWcHCwOnbs6L6vof1xrcbeC/fcc4/7QMX111/vPmO0b98+vf322woMDFS7du00depUhYWF6aGHHtK8efPUqVMnXXvtta16drhevXo1uA3Gx8drzpw5uuqqq9SjR49mHTRtbbp37677779fCxYsUEBAQL398g9/+EOdO3dOzz33nGbPnq0HH3xQL730kv70pz+5P7OkmqG++/btU0BAgKKjo3XTTTcpKCiowf1ua9PQ95e6B4XuuecezZw5U9dee60SEhIa3G9c6nc5X+EwzR0jBLRC/zn7HHxfeXm52rVrp++++07z58/Xk08+2Spm7QIAAA3jDA8AW3n55Zd17NgxuVwu3XXXXYQdAABaOc7wAAAAALAtJi0AAAAAYFsEHgAAAAC2ReABAAAAYFsEHgAAAAC2ReABAAAAYFv/D2kzX2Ke35SqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_boxplot(coinlist, from_date, to_date, interval):\n",
    "    pricerange = pd.DataFrame()\n",
    "    for coin in coinlist:\n",
    "        price = san.get(\"ohlcv/\" + coin, from_date=from_date, to_date=to_date, interval=interval).closePriceUsd\n",
    "        pricerange[coin] = price\n",
    "    \n",
    "    plt.figure(figsize=(14, 5))\n",
    "    pricerange.boxplot()\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "do_boxplot(coin_list, \"2018-01-01\", \"2018-11-06\", \"1d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "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: right;\">\n",
       "      <th></th>\n",
       "      <th>openPriceUsd</th>\n",
       "      <th>closePriceUsd</th>\n",
       "      <th>highPriceUsd</th>\n",
       "      <th>lowPriceUsd</th>\n",
       "      <th>volume</th>\n",
       "      <th>marketcap</th>\n",
       "      <th>tetherclose</th>\n",
       "      <th>tetherclose_change</th>\n",
       "      <th>tetherhigh</th>\n",
       "      <th>tetherlow</th>\n",
       "      <th>...</th>\n",
       "      <th>ckusdhigh</th>\n",
       "      <th>ckusdlow</th>\n",
       "      <th>ckusdvolume</th>\n",
       "      <th>ckusdvolume_change</th>\n",
       "      <th>bitusdclose</th>\n",
       "      <th>bitusdclose_change</th>\n",
       "      <th>bitusdhigh</th>\n",
       "      <th>bitusdlow</th>\n",
       "      <th>bitusdvolume</th>\n",
       "      <th>bitusdvolume_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:00+00:00</th>\n",
       "      <td>6319.162887</td>\n",
       "      <td>6377.78</td>\n",
       "      <td>6547.140000</td>\n",
       "      <td>6311.830000</td>\n",
       "      <td>3789400000</td>\n",
       "      <td>1.107549e+11</td>\n",
       "      <td>0.991679</td>\n",
       "      <td>0.003442</td>\n",
       "      <td>1.016448</td>\n",
       "      <td>0.987329</td>\n",
       "      <td>...</td>\n",
       "      <td>1.036990</td>\n",
       "      <td>0.995124</td>\n",
       "      <td>104650000.0</td>\n",
       "      <td>0.182721</td>\n",
       "      <td>1.010007</td>\n",
       "      <td>0.009392</td>\n",
       "      <td>1.034340</td>\n",
       "      <td>0.996988</td>\n",
       "      <td>72077.0</td>\n",
       "      <td>-0.716256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02 00:00:00+00:00</th>\n",
       "      <td>6379.505304</td>\n",
       "      <td>6388.44</td>\n",
       "      <td>6396.860252</td>\n",
       "      <td>6324.851064</td>\n",
       "      <td>4234870000</td>\n",
       "      <td>1.104961e+11</td>\n",
       "      <td>0.993217</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.996308</td>\n",
       "      <td>0.977024</td>\n",
       "      <td>...</td>\n",
       "      <td>1.006958</td>\n",
       "      <td>0.993864</td>\n",
       "      <td>136737180.0</td>\n",
       "      <td>0.306614</td>\n",
       "      <td>1.000781</td>\n",
       "      <td>-0.009135</td>\n",
       "      <td>1.010007</td>\n",
       "      <td>0.997028</td>\n",
       "      <td>49787.0</td>\n",
       "      <td>-0.309253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03 00:00:00+00:00</th>\n",
       "      <td>6384.789501</td>\n",
       "      <td>6361.26</td>\n",
       "      <td>6400.070000</td>\n",
       "      <td>6342.370000</td>\n",
       "      <td>3658640000</td>\n",
       "      <td>1.105626e+11</td>\n",
       "      <td>0.999432</td>\n",
       "      <td>0.006257</td>\n",
       "      <td>1.005739</td>\n",
       "      <td>0.993098</td>\n",
       "      <td>...</td>\n",
       "      <td>1.023280</td>\n",
       "      <td>1.001200</td>\n",
       "      <td>144078000.0</td>\n",
       "      <td>0.053686</td>\n",
       "      <td>0.994567</td>\n",
       "      <td>-0.006209</td>\n",
       "      <td>1.001274</td>\n",
       "      <td>0.994311</td>\n",
       "      <td>51001.0</td>\n",
       "      <td>0.024384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04 00:00:00+00:00</th>\n",
       "      <td>6365.473018</td>\n",
       "      <td>6376.13</td>\n",
       "      <td>6389.260698</td>\n",
       "      <td>6294.570000</td>\n",
       "      <td>4390020000</td>\n",
       "      <td>1.102561e+11</td>\n",
       "      <td>0.988642</td>\n",
       "      <td>-0.010796</td>\n",
       "      <td>1.001563</td>\n",
       "      <td>0.945107</td>\n",
       "      <td>...</td>\n",
       "      <td>1.016600</td>\n",
       "      <td>0.978337</td>\n",
       "      <td>163009697.0</td>\n",
       "      <td>0.131399</td>\n",
       "      <td>0.988005</td>\n",
       "      <td>-0.006598</td>\n",
       "      <td>1.003662</td>\n",
       "      <td>0.986482</td>\n",
       "      <td>87722.0</td>\n",
       "      <td>0.720005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-05 00:00:00+00:00</th>\n",
       "      <td>6375.517587</td>\n",
       "      <td>6419.66</td>\n",
       "      <td>6482.283210</td>\n",
       "      <td>6363.620000</td>\n",
       "      <td>4174800000</td>\n",
       "      <td>1.116830e+11</td>\n",
       "      <td>0.993176</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>1.007206</td>\n",
       "      <td>0.990898</td>\n",
       "      <td>...</td>\n",
       "      <td>1.014585</td>\n",
       "      <td>0.999589</td>\n",
       "      <td>153702000.0</td>\n",
       "      <td>-0.057099</td>\n",
       "      <td>0.999569</td>\n",
       "      <td>0.011704</td>\n",
       "      <td>1.005096</td>\n",
       "      <td>0.987099</td>\n",
       "      <td>145447.0</td>\n",
       "      <td>0.658045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           openPriceUsd  closePriceUsd  highPriceUsd  \\\n",
       "datetime                                                               \n",
       "2018-11-01 00:00:00+00:00   6319.162887        6377.78   6547.140000   \n",
       "2018-11-02 00:00:00+00:00   6379.505304        6388.44   6396.860252   \n",
       "2018-11-03 00:00:00+00:00   6384.789501        6361.26   6400.070000   \n",
       "2018-11-04 00:00:00+00:00   6365.473018        6376.13   6389.260698   \n",
       "2018-11-05 00:00:00+00:00   6375.517587        6419.66   6482.283210   \n",
       "\n",
       "                           lowPriceUsd      volume     marketcap  tetherclose  \\\n",
       "datetime                                                                        \n",
       "2018-11-01 00:00:00+00:00  6311.830000  3789400000  1.107549e+11     0.991679   \n",
       "2018-11-02 00:00:00+00:00  6324.851064  4234870000  1.104961e+11     0.993217   \n",
       "2018-11-03 00:00:00+00:00  6342.370000  3658640000  1.105626e+11     0.999432   \n",
       "2018-11-04 00:00:00+00:00  6294.570000  4390020000  1.102561e+11     0.988642   \n",
       "2018-11-05 00:00:00+00:00  6363.620000  4174800000  1.116830e+11     0.993176   \n",
       "\n",
       "                           tetherclose_change  tetherhigh  tetherlow  \\\n",
       "datetime                                                               \n",
       "2018-11-01 00:00:00+00:00            0.003442    1.016448   0.987329   \n",
       "2018-11-02 00:00:00+00:00            0.001551    0.996308   0.977024   \n",
       "2018-11-03 00:00:00+00:00            0.006257    1.005739   0.993098   \n",
       "2018-11-04 00:00:00+00:00           -0.010796    1.001563   0.945107   \n",
       "2018-11-05 00:00:00+00:00            0.004586    1.007206   0.990898   \n",
       "\n",
       "                                  ...           ckusdhigh  ckusdlow  \\\n",
       "datetime                          ...                                 \n",
       "2018-11-01 00:00:00+00:00         ...            1.036990  0.995124   \n",
       "2018-11-02 00:00:00+00:00         ...            1.006958  0.993864   \n",
       "2018-11-03 00:00:00+00:00         ...            1.023280  1.001200   \n",
       "2018-11-04 00:00:00+00:00         ...            1.016600  0.978337   \n",
       "2018-11-05 00:00:00+00:00         ...            1.014585  0.999589   \n",
       "\n",
       "                           ckusdvolume  ckusdvolume_change  bitusdclose  \\\n",
       "datetime                                                                  \n",
       "2018-11-01 00:00:00+00:00  104650000.0            0.182721     1.010007   \n",
       "2018-11-02 00:00:00+00:00  136737180.0            0.306614     1.000781   \n",
       "2018-11-03 00:00:00+00:00  144078000.0            0.053686     0.994567   \n",
       "2018-11-04 00:00:00+00:00  163009697.0            0.131399     0.988005   \n",
       "2018-11-05 00:00:00+00:00  153702000.0           -0.057099     0.999569   \n",
       "\n",
       "                           bitusdclose_change  bitusdhigh  bitusdlow  \\\n",
       "datetime                                                               \n",
       "2018-11-01 00:00:00+00:00            0.009392    1.034340   0.996988   \n",
       "2018-11-02 00:00:00+00:00           -0.009135    1.010007   0.997028   \n",
       "2018-11-03 00:00:00+00:00           -0.006209    1.001274   0.994311   \n",
       "2018-11-04 00:00:00+00:00           -0.006598    1.003662   0.986482   \n",
       "2018-11-05 00:00:00+00:00            0.011704    1.005096   0.987099   \n",
       "\n",
       "                           bitusdvolume  bitusdvolume_change  \n",
       "datetime                                                      \n",
       "2018-11-01 00:00:00+00:00       72077.0            -0.716256  \n",
       "2018-11-02 00:00:00+00:00       49787.0            -0.309253  \n",
       "2018-11-03 00:00:00+00:00       51001.0             0.024384  \n",
       "2018-11-04 00:00:00+00:00       87722.0             0.720005  \n",
       "2018-11-05 00:00:00+00:00      145447.0             0.658045  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the connection between the volatility and the volume of our stablecoins:\n",
    "\n",
    "(Be aware that this code only works right if you have staked SAN-tokens an got an api-key. Otherwise there is a limit of the amount of data you can pull per request.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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: right;\">\n",
       "      <th></th>\n",
       "      <th>rsquared volume</th>\n",
       "      <th>rsquared market</th>\n",
       "      <th>rsquared combined model</th>\n",
       "      <th>beta volume</th>\n",
       "      <th>beta market</th>\n",
       "      <th>p-value volume</th>\n",
       "      <th>p-value market</th>\n",
       "      <th>p-value combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>tether</th>\n",
       "      <td>0.687889</td>\n",
       "      <td>0.419178</td>\n",
       "      <td>0.786135</td>\n",
       "      <td>0.028368</td>\n",
       "      <td>0.268809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.000000, 0.000000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usd-coin</th>\n",
       "      <td>0.543923</td>\n",
       "      <td>0.871401</td>\n",
       "      <td>0.873446</td>\n",
       "      <td>0.011492</td>\n",
       "      <td>0.576379</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.484380, 0.000000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paxos-standard-token</th>\n",
       "      <td>0.206543</td>\n",
       "      <td>0.846186</td>\n",
       "      <td>0.855128</td>\n",
       "      <td>0.008059</td>\n",
       "      <td>0.582723</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.114881, 0.000000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gemini-dollar</th>\n",
       "      <td>0.458460</td>\n",
       "      <td>0.549527</td>\n",
       "      <td>0.549551</td>\n",
       "      <td>0.017136</td>\n",
       "      <td>0.986389</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.966718, 0.014406]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nusd</th>\n",
       "      <td>0.019832</td>\n",
       "      <td>0.054679</td>\n",
       "      <td>0.058133</td>\n",
       "      <td>0.013861</td>\n",
       "      <td>1.457358</td>\n",
       "      <td>0.123393</td>\n",
       "      <td>0.009841</td>\n",
       "      <td>[0.511918, 0.030451]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trueusd</th>\n",
       "      <td>0.797595</td>\n",
       "      <td>0.351784</td>\n",
       "      <td>0.798111</td>\n",
       "      <td>0.055292</td>\n",
       "      <td>0.378098</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.000000, 0.427860]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dai</th>\n",
       "      <td>0.161062</td>\n",
       "      <td>0.396975</td>\n",
       "      <td>0.407573</td>\n",
       "      <td>0.017621</td>\n",
       "      <td>0.516150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.018958, 0.000000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ckusd</th>\n",
       "      <td>0.615831</td>\n",
       "      <td>0.797175</td>\n",
       "      <td>0.820788</td>\n",
       "      <td>0.166500</td>\n",
       "      <td>0.599857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.000000, 0.000000]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitusd</th>\n",
       "      <td>0.027086</td>\n",
       "      <td>0.791379</td>\n",
       "      <td>0.801534</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>1.428085</td>\n",
       "      <td>0.035214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.004654, 0.000000]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      rsquared volume  rsquared market  \\\n",
       "tether                       0.687889         0.419178   \n",
       "usd-coin                     0.543923         0.871401   \n",
       "paxos-standard-token         0.206543         0.846186   \n",
       "gemini-dollar                0.458460         0.549527   \n",
       "nusd                         0.019832         0.054679   \n",
       "trueusd                      0.797595         0.351784   \n",
       "dai                          0.161062         0.396975   \n",
       "ckusd                        0.615831         0.797175   \n",
       "bitusd                       0.027086         0.791379   \n",
       "\n",
       "                      rsquared combined model  beta volume  beta market  \\\n",
       "tether                               0.786135     0.028368     0.268809   \n",
       "usd-coin                             0.873446     0.011492     0.576379   \n",
       "paxos-standard-token                 0.855128     0.008059     0.582723   \n",
       "gemini-dollar                        0.549551     0.017136     0.986389   \n",
       "nusd                                 0.058133     0.013861     1.457358   \n",
       "trueusd                              0.798111     0.055292     0.378098   \n",
       "dai                                  0.407573     0.017621     0.516150   \n",
       "ckusd                                0.820788     0.166500     0.599857   \n",
       "bitusd                               0.801534     0.000293     1.428085   \n",
       "\n",
       "                     p-value volume p-value market      p-value combined  \n",
       "tether                     0.000000       0.000000  [0.000000, 0.000000]  \n",
       "usd-coin                   0.000001       0.000000  [0.484380, 0.000000]  \n",
       "paxos-standard-token       0.001712       0.000000  [0.114881, 0.000000]  \n",
       "gemini-dollar              0.000006       0.000000  [0.966718, 0.014406]  \n",
       "nusd                       0.123393       0.009841  [0.511918, 0.030451]  \n",
       "trueusd                    0.000000       0.000000  [0.000000, 0.427860]  \n",
       "dai                        0.000000       0.000000  [0.018958, 0.000000]  \n",
       "ckusd                      0.000000       0.000000  [0.000000, 0.000000]  \n",
       "bitusd                     0.035214       0.000000  [0.004654, 0.000000]  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2376 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_rsquared(coinlist, from_date, to_date, interval):\n",
    "    volume = san.get(\"ohlcv/bitcoin\", from_date=from_date, to_date=to_date, interval=interval)\n",
    "    volatility = san.get(\"ohlcv/bitcoin\", from_date=from_date, to_date=to_date, interval=interval)\n",
    "    volatility[\"bitcoin_change\"] = volatility.closePriceUsd.pct_change()\n",
    "    \n",
    "    for coin in coinlist:\n",
    "        project = san.get(\"prices/\" + coin, from_date=from_date, to_date=to_date, interval=interval)\n",
    "        volume[coin + \"volume_change\"] = project.volume.pct_change()\n",
    "        volatility[coin + \"volatility\"] = project.priceUsd.pct_change()\n",
    "\n",
    "    volume = volume.resample(\"1d\").std()\n",
    "    volatility = volatility.resample(\"1d\").std()\n",
    "\n",
    "    \n",
    "    rsquared_volume = []\n",
    "    beta_volume = []\n",
    "    sig_volume = []\n",
    "    \n",
    "    rsquared_combined = []\n",
    "    sig_combined = []\n",
    "    \n",
    "    rsquared_bench = []\n",
    "    beta_bench = []\n",
    "    sig_bench  = []\n",
    "    \n",
    "    for coin in coinlist:\n",
    "        predictors = pd.DataFrame()\n",
    "        predictors[\"volume\"] = volume[coin + \"volume_change\"]\n",
    "        predictors[\"market\"] = volatility.bitcoin_change\n",
    "        predictors = predictors.dropna()\n",
    "        \n",
    "        # Calculating the three models:\n",
    "        model_volume = sm.OLS(volatility[coin + \"volatility\"].dropna(), predictors[\"volume\"]).fit()\n",
    "        model_benchmark = sm.OLS(volatility[coin + \"volatility\"].dropna(), predictors[\"market\"]).fit()\n",
    "        model_combined = sm.OLS(volatility[coin + \"volatility\"].dropna(), predictors).fit()\n",
    "        \n",
    "        rsquared_volume.append(model_volume.rsquared)\n",
    "        rsquared_bench.append(model_benchmark.rsquared)\n",
    "        rsquared_combined.append(model_combined.rsquared)\n",
    "        beta_volume.append(model_volume.params[0])\n",
    "        beta_bench.append(model_benchmark.params[0])\n",
    "        sig_volume.append('{:f}'.format(model_volume.pvalues[0]))\n",
    "        sig_bench.append('{:f}'.format(model_benchmark.pvalues[0]))\n",
    "        sig_combined.append(['{:f}'.format(model_combined.pvalues[0]), \n",
    "                             '{:f}'.format(model_combined.pvalues[1])])\n",
    "        #print(model_combined.summary())  # Here you could optionally display the complete results of all regression analyses\n",
    "        \n",
    "    # Pepare and display a table of our results:\n",
    "    results = pd.DataFrame(rsquared_volume,\n",
    "                           columns=[\"rsquared volume\"], index=coin_list)\n",
    "    results[\"rsquared market\"] = rsquared_bench\n",
    "    results[\"rsquared combined model\"] = rsquared_combined\n",
    "    \n",
    "    results[\"beta volume\"] = beta_volume\n",
    "    results[\"beta market\"] = beta_bench\n",
    "    \n",
    "    results[\"p-value volume\"] = sig_volume\n",
    "    results[\"p-value market\"] = sig_bench\n",
    "    results[\"p-value combined\"] = sig_combined\n",
    "    display(results)\n",
    "    \n",
    "    \n",
    "    # Doing a lot of plotting:\n",
    "    \n",
    "    plt.figure(figsize=(10,33))\n",
    "               \n",
    "    plt.subplot(6, 1, 1) \n",
    "    plt.gca().set_xlim([0,1])\n",
    "    pos = np.arange(len(coin_list))\n",
    "    bar_width = 0.2\n",
    "    plt.barh(pos + bar_width + 0.05, rsquared_volume, bar_width, label=\"r^2 volume\")\n",
    "    plt.barh(pos , rsquared_bench, bar_width, label=\"r^2 market\")\n",
    "    plt.barh(pos - bar_width - 0.05, rsquared_combined, bar_width, label=\"r^2 combined\")\n",
    "    plt.yticks(pos, coin_list)\n",
    "    plt.xlabel(\"variance explained (R^2)\")\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.subplot(6, 1, 2)\n",
    "    plt.gca().set_xlim([0,1])\n",
    "    plt.barh(coin_list, rsquared_volume)\n",
    "    plt.xlabel(\"variance explained (R^2) by volume\")\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    \n",
    "    plt.subplot(6, 1, 3)\n",
    "    plt.gca().set_xlim([0,1])\n",
    "    plt.barh(coin_list, rsquared_bench)\n",
    "    plt.xlabel(\"variance explained (R^2) by the market\")\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    \n",
    "    plt.subplot(6, 1, 4)\n",
    "    plt.gca().set_xlim([0,1])\n",
    "    plt.barh(coin_list, rsquared_combined)\n",
    "    plt.xlabel(\"variance explained (R^2) by volume and market\")\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    \n",
    "    plt.subplot(6, 1, 5)\n",
    "    plt.gca().set_xlim([0,1])\n",
    "    plt.barh(coin_list, beta_volume)\n",
    "    plt.xlabel(\"beta volume\")\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    \n",
    "    plt.subplot(6, 1, 6)\n",
    "    plt.gca().set_xlim([0,1])\n",
    "    plt.barh(coin_list, beta_bench)\n",
    "    plt.xlabel(\"beta market\")\n",
    "    plt.ylabel(\"stablecoins\")\n",
    "    \n",
    "    \n",
    "calculate_rsquared(coin_list, \"2018-01-01\", \"2018-11-10\", \"1h\");"
   ]
  },
  {
   "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",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}