crypto_v2.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d9407b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "874b8f6a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2013-04-29 23:59:59</td>\n",
       "      <td>147.488007</td>\n",
       "      <td>134.000000</td>\n",
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       "      <td>144.539993</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2013-04-30 23:59:59</td>\n",
       "      <td>146.929993</td>\n",
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       "      <td>144.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.542813e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2013-05-01 23:59:59</td>\n",
       "      <td>139.889999</td>\n",
       "      <td>107.720001</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>116.989998</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.298955e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2013-05-02 23:59:59</td>\n",
       "      <td>125.599998</td>\n",
       "      <td>92.281898</td>\n",
       "      <td>116.379997</td>\n",
       "      <td>105.209999</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.168517e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2013-05-03 23:59:59</td>\n",
       "      <td>108.127998</td>\n",
       "      <td>79.099998</td>\n",
       "      <td>106.250000</td>\n",
       "      <td>97.750000</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>2987</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2021-07-02 23:59:59</td>\n",
       "      <td>33939.588699</td>\n",
       "      <td>32770.680780</td>\n",
       "      <td>33549.600177</td>\n",
       "      <td>33897.048590</td>\n",
       "      <td>3.872897e+10</td>\n",
       "      <td>6.354508e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>2988</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2021-07-03 23:59:59</td>\n",
       "      <td>34909.259899</td>\n",
       "      <td>33402.696536</td>\n",
       "      <td>33854.421362</td>\n",
       "      <td>34668.548402</td>\n",
       "      <td>2.438396e+10</td>\n",
       "      <td>6.499397e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>2989</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2021-07-04 23:59:59</td>\n",
       "      <td>35937.567147</td>\n",
       "      <td>34396.477458</td>\n",
       "      <td>34665.564866</td>\n",
       "      <td>35287.779766</td>\n",
       "      <td>2.492431e+10</td>\n",
       "      <td>6.615748e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>2990</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2021-07-05 23:59:59</td>\n",
       "      <td>35284.344430</td>\n",
       "      <td>33213.661034</td>\n",
       "      <td>35284.344430</td>\n",
       "      <td>33746.002456</td>\n",
       "      <td>2.672155e+10</td>\n",
       "      <td>6.326962e+11</td>\n",
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       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>2991</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>BTC</td>\n",
       "      <td>2021-07-06 23:59:59</td>\n",
       "      <td>35038.536363</td>\n",
       "      <td>33599.916169</td>\n",
       "      <td>33723.509655</td>\n",
       "      <td>34235.193451</td>\n",
       "      <td>2.650126e+10</td>\n",
       "      <td>6.418992e+11</td>\n",
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       "</table>\n",
       "<p>2991 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SNo     Name Symbol                 Date          High           Low  \\\n",
       "0        1  Bitcoin    BTC  2013-04-29 23:59:59    147.488007    134.000000   \n",
       "1        2  Bitcoin    BTC  2013-04-30 23:59:59    146.929993    134.050003   \n",
       "2        3  Bitcoin    BTC  2013-05-01 23:59:59    139.889999    107.720001   \n",
       "3        4  Bitcoin    BTC  2013-05-02 23:59:59    125.599998     92.281898   \n",
       "4        5  Bitcoin    BTC  2013-05-03 23:59:59    108.127998     79.099998   \n",
       "...    ...      ...    ...                  ...           ...           ...   \n",
       "2986  2987  Bitcoin    BTC  2021-07-02 23:59:59  33939.588699  32770.680780   \n",
       "2987  2988  Bitcoin    BTC  2021-07-03 23:59:59  34909.259899  33402.696536   \n",
       "2988  2989  Bitcoin    BTC  2021-07-04 23:59:59  35937.567147  34396.477458   \n",
       "2989  2990  Bitcoin    BTC  2021-07-05 23:59:59  35284.344430  33213.661034   \n",
       "2990  2991  Bitcoin    BTC  2021-07-06 23:59:59  35038.536363  33599.916169   \n",
       "\n",
       "              Open         Close        Volume     Marketcap  \n",
       "0       134.444000    144.539993  0.000000e+00  1.603769e+09  \n",
       "1       144.000000    139.000000  0.000000e+00  1.542813e+09  \n",
       "2       139.000000    116.989998  0.000000e+00  1.298955e+09  \n",
       "3       116.379997    105.209999  0.000000e+00  1.168517e+09  \n",
       "4       106.250000     97.750000  0.000000e+00  1.085995e+09  \n",
       "...            ...           ...           ...           ...  \n",
       "2986  33549.600177  33897.048590  3.872897e+10  6.354508e+11  \n",
       "2987  33854.421362  34668.548402  2.438396e+10  6.499397e+11  \n",
       "2988  34665.564866  35287.779766  2.492431e+10  6.615748e+11  \n",
       "2989  35284.344430  33746.002456  2.672155e+10  6.326962e+11  \n",
       "2990  33723.509655  34235.193451  2.650126e+10  6.418992e+11  \n",
       "\n",
       "[2991 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc = pd.read_csv('./data/coin_Bitcoin.csv')\n",
    "btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e9fb941d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2991, 10)"
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6267ccee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2991 entries, 0 to 2990\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   SNo        2991 non-null   int64  \n",
      " 1   Name       2991 non-null   object \n",
      " 2   Symbol     2991 non-null   object \n",
      " 3   Date       2991 non-null   object \n",
      " 4   High       2991 non-null   float64\n",
      " 5   Low        2991 non-null   float64\n",
      " 6   Open       2991 non-null   float64\n",
      " 7   Close      2991 non-null   float64\n",
      " 8   Volume     2991 non-null   float64\n",
      " 9   Marketcap  2991 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 233.8+ KB\n"
     ]
    }
   ],
   "source": [
    "btc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "512b1b7f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2991 entries, 0 to 2990\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   SNo        2991 non-null   int64         \n",
      " 1   Name       2991 non-null   object        \n",
      " 2   Symbol     2991 non-null   object        \n",
      " 3   Date       2991 non-null   datetime64[ns]\n",
      " 4   High       2991 non-null   float64       \n",
      " 5   Low        2991 non-null   float64       \n",
      " 6   Open       2991 non-null   float64       \n",
      " 7   Close      2991 non-null   float64       \n",
      " 8   Volume     2991 non-null   float64       \n",
      " 9   Marketcap  2991 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(6), int64(1), object(2)\n",
      "memory usage: 233.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# we see that 'Date' column is an object (likely string), better to convert to datetime for this time series analysis\n",
    "btc.Date = pd.to_datetime(btc.Date)\n",
    "btc.info() # we see now it's datetime64 variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef6fcb60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
       "      <td>2013-05-02 23:59:59</td>\n",
       "      <td>105.209999</td>\n",
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       "      <th>4</th>\n",
       "      <td>2013-05-03 23:59:59</td>\n",
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       "      <th>2986</th>\n",
       "      <td>2021-07-02 23:59:59</td>\n",
       "      <td>33897.048590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>2021-07-03 23:59:59</td>\n",
       "      <td>34668.548402</td>\n",
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       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>2021-07-04 23:59:59</td>\n",
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       "      <th>2989</th>\n",
       "      <td>2021-07-05 23:59:59</td>\n",
       "      <td>33746.002456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>2021-07-06 23:59:59</td>\n",
       "      <td>34235.193451</td>\n",
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       "<p>2991 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date         Close\n",
       "0    2013-04-29 23:59:59    144.539993\n",
       "1    2013-04-30 23:59:59    139.000000\n",
       "2    2013-05-01 23:59:59    116.989998\n",
       "3    2013-05-02 23:59:59    105.209999\n",
       "4    2013-05-03 23:59:59     97.750000\n",
       "...                  ...           ...\n",
       "2986 2021-07-02 23:59:59  33897.048590\n",
       "2987 2021-07-03 23:59:59  34668.548402\n",
       "2988 2021-07-04 23:59:59  35287.779766\n",
       "2989 2021-07-05 23:59:59  33746.002456\n",
       "2990 2021-07-06 23:59:59  34235.193451\n",
       "\n",
       "[2991 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_graph1 = btc[['Date', 'Close']]\n",
    "btc_graph1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7cf4484e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_graph1 = btc_graph1.set_index('Date') # set_index() does not do things \"in place\", so must assign it to variable\n",
    "type(btc_graph1) # still df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b60b5d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_graph1.Close # this is a series\n",
    "type(btc_graph1.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61393b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize = (17,10))\n",
    "ax = btc_graph1.Close.plot() # because it's a series, uses index as x-axis\n",
    "# ax.set_xticklabels(labels=keys, rotation=90)\n",
    "# ax.set_yticklabels(labels=keys, rotation=0)\n",
    "ax.set_title('Historic Bitcoin Price (USD)', size = 20)\n",
    "ax.set_ylabel('USD')\n",
    "plt.savefig('./images/BTC.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "64a3fe33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2015-08-08 23:59:59</td>\n",
       "      <td>2.798810</td>\n",
       "      <td>0.714725</td>\n",
       "      <td>2.793760</td>\n",
       "      <td>0.753325</td>\n",
       "      <td>6.741880e+05</td>\n",
       "      <td>4.548689e+07</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2015-08-09 23:59:59</td>\n",
       "      <td>0.879810</td>\n",
       "      <td>0.629191</td>\n",
       "      <td>0.706136</td>\n",
       "      <td>0.701897</td>\n",
       "      <td>5.321700e+05</td>\n",
       "      <td>4.239957e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2015-08-10 23:59:59</td>\n",
       "      <td>0.729854</td>\n",
       "      <td>0.636546</td>\n",
       "      <td>0.713989</td>\n",
       "      <td>0.708448</td>\n",
       "      <td>4.052830e+05</td>\n",
       "      <td>4.281836e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2015-08-11 23:59:59</td>\n",
       "      <td>1.131410</td>\n",
       "      <td>0.663235</td>\n",
       "      <td>0.708087</td>\n",
       "      <td>1.067860</td>\n",
       "      <td>1.463100e+06</td>\n",
       "      <td>6.456929e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2015-08-12 23:59:59</td>\n",
       "      <td>1.289940</td>\n",
       "      <td>0.883608</td>\n",
       "      <td>1.058750</td>\n",
       "      <td>1.217440</td>\n",
       "      <td>2.150620e+06</td>\n",
       "      <td>7.364501e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>2156</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2021-07-02 23:59:59</td>\n",
       "      <td>2155.596496</td>\n",
       "      <td>2021.824808</td>\n",
       "      <td>2109.892677</td>\n",
       "      <td>2150.040364</td>\n",
       "      <td>3.179621e+10</td>\n",
       "      <td>2.505527e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>2157</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2021-07-03 23:59:59</td>\n",
       "      <td>2237.567155</td>\n",
       "      <td>2117.590013</td>\n",
       "      <td>2150.835025</td>\n",
       "      <td>2226.114282</td>\n",
       "      <td>1.743336e+10</td>\n",
       "      <td>2.594475e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>2158</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2021-07-04 23:59:59</td>\n",
       "      <td>2384.286857</td>\n",
       "      <td>2190.837703</td>\n",
       "      <td>2226.550382</td>\n",
       "      <td>2321.724112</td>\n",
       "      <td>1.878711e+10</td>\n",
       "      <td>2.706217e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>2159</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2021-07-05 23:59:59</td>\n",
       "      <td>2321.922836</td>\n",
       "      <td>2163.041394</td>\n",
       "      <td>2321.922836</td>\n",
       "      <td>2198.582464</td>\n",
       "      <td>2.010379e+10</td>\n",
       "      <td>2.562978e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>2160</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>ETH</td>\n",
       "      <td>2021-07-06 23:59:59</td>\n",
       "      <td>2346.294874</td>\n",
       "      <td>2197.919385</td>\n",
       "      <td>2197.919385</td>\n",
       "      <td>2324.679449</td>\n",
       "      <td>2.089186e+10</td>\n",
       "      <td>2.710286e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SNo      Name Symbol                 Date         High          Low  \\\n",
       "0        1  Ethereum    ETH  2015-08-08 23:59:59     2.798810     0.714725   \n",
       "1        2  Ethereum    ETH  2015-08-09 23:59:59     0.879810     0.629191   \n",
       "2        3  Ethereum    ETH  2015-08-10 23:59:59     0.729854     0.636546   \n",
       "3        4  Ethereum    ETH  2015-08-11 23:59:59     1.131410     0.663235   \n",
       "4        5  Ethereum    ETH  2015-08-12 23:59:59     1.289940     0.883608   \n",
       "...    ...       ...    ...                  ...          ...          ...   \n",
       "2155  2156  Ethereum    ETH  2021-07-02 23:59:59  2155.596496  2021.824808   \n",
       "2156  2157  Ethereum    ETH  2021-07-03 23:59:59  2237.567155  2117.590013   \n",
       "2157  2158  Ethereum    ETH  2021-07-04 23:59:59  2384.286857  2190.837703   \n",
       "2158  2159  Ethereum    ETH  2021-07-05 23:59:59  2321.922836  2163.041394   \n",
       "2159  2160  Ethereum    ETH  2021-07-06 23:59:59  2346.294874  2197.919385   \n",
       "\n",
       "             Open        Close        Volume     Marketcap  \n",
       "0        2.793760     0.753325  6.741880e+05  4.548689e+07  \n",
       "1        0.706136     0.701897  5.321700e+05  4.239957e+07  \n",
       "2        0.713989     0.708448  4.052830e+05  4.281836e+07  \n",
       "3        0.708087     1.067860  1.463100e+06  6.456929e+07  \n",
       "4        1.058750     1.217440  2.150620e+06  7.364501e+07  \n",
       "...           ...          ...           ...           ...  \n",
       "2155  2109.892677  2150.040364  3.179621e+10  2.505527e+11  \n",
       "2156  2150.835025  2226.114282  1.743336e+10  2.594475e+11  \n",
       "2157  2226.550382  2321.724112  1.878711e+10  2.706217e+11  \n",
       "2158  2321.922836  2198.582464  2.010379e+10  2.562978e+11  \n",
       "2159  2197.919385  2324.679449  2.089186e+10  2.710286e+11  \n",
       "\n",
       "[2160 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# eth = pd.read_csv('C://Code/Galvanize/Capstone1/data/coin_Ethereum.csv')\n",
    "eth = pd.read_csv('./data/coin_Ethereum.csv')\n",
    "eth # note only 2160 rows (bth has 2991) <-- because btc has been around longer and has been tracked since 2013. eth tracked starting 2015.08.08\n",
    "# start date of these daily rows is later for eth compared to btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f84dd899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2160, 10)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9eb45399",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2160 entries, 0 to 2159\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   SNo        2160 non-null   int64  \n",
      " 1   Name       2160 non-null   object \n",
      " 2   Symbol     2160 non-null   object \n",
      " 3   Date       2160 non-null   object \n",
      " 4   High       2160 non-null   float64\n",
      " 5   Low        2160 non-null   float64\n",
      " 6   Open       2160 non-null   float64\n",
      " 7   Close      2160 non-null   float64\n",
      " 8   Volume     2160 non-null   float64\n",
      " 9   Marketcap  2160 non-null   float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 168.9+ KB\n"
     ]
    }
   ],
   "source": [
    "eth.info() # no missing data, like btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ec313698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2160 entries, 0 to 2159\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   SNo        2160 non-null   int64         \n",
      " 1   Name       2160 non-null   object        \n",
      " 2   Symbol     2160 non-null   object        \n",
      " 3   Date       2160 non-null   datetime64[ns]\n",
      " 4   High       2160 non-null   float64       \n",
      " 5   Low        2160 non-null   float64       \n",
      " 6   Open       2160 non-null   float64       \n",
      " 7   Close      2160 non-null   float64       \n",
      " 8   Volume     2160 non-null   float64       \n",
      " 9   Marketcap  2160 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(6), int64(1), object(2)\n",
      "memory usage: 168.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# we see that 'Date' column is an object (likely string), better to convert to datetime for this time series analysis\n",
    "eth.Date = pd.to_datetime(btc.Date)\n",
    "eth.info() # we see now it's datetime64 variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8724c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-04-29 23:59:59</td>\n",
       "      <td>0.753325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-04-30 23:59:59</td>\n",
       "      <td>0.701897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-01 23:59:59</td>\n",
       "      <td>0.708448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-02 23:59:59</td>\n",
       "      <td>1.067860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-03 23:59:59</td>\n",
       "      <td>1.217440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>2019-03-24 23:59:59</td>\n",
       "      <td>2150.040364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>2019-03-25 23:59:59</td>\n",
       "      <td>2226.114282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>2019-03-26 23:59:59</td>\n",
       "      <td>2321.724112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>2019-03-27 23:59:59</td>\n",
       "      <td>2198.582464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>2019-03-28 23:59:59</td>\n",
       "      <td>2324.679449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date        Close\n",
       "0    2013-04-29 23:59:59     0.753325\n",
       "1    2013-04-30 23:59:59     0.701897\n",
       "2    2013-05-01 23:59:59     0.708448\n",
       "3    2013-05-02 23:59:59     1.067860\n",
       "4    2013-05-03 23:59:59     1.217440\n",
       "...                  ...          ...\n",
       "2155 2019-03-24 23:59:59  2150.040364\n",
       "2156 2019-03-25 23:59:59  2226.114282\n",
       "2157 2019-03-26 23:59:59  2321.724112\n",
       "2158 2019-03-27 23:59:59  2198.582464\n",
       "2159 2019-03-28 23:59:59  2324.679449\n",
       "\n",
       "[2160 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth_graph1 = eth[['Date', 'Close']]\n",
    "eth_graph1 # this is a df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "10c93cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eth_graph1.Close.plot() # because we call the individual column 'Close' via the attribute .Close , it is now a series (not df), hence it uses index as x-axis and the series value as y-axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "88054fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# functionalize import csv, add Win/lose column (up or down during the day, and graph (closing price)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def import_data(filename):\n",
    "    df = pd.read_csv(filename)\n",
    "    df.Date = pd.to_datetime(df.Date)\n",
    "    df['Diff'] = df['Close'] - df['Open']\n",
    "    df['Percent'] = df['Diff']/df['Open']\n",
    "    df['Up'] = df['Diff'] >= 0\n",
    "    # convert to 1 and 0, 1 is 0\n",
    "    df['Up'] = df['Up'].apply(lambda x: 1 if x == True else 0)\n",
    "    df['Standard'] = (df['Close'] - df['Close'].mean()) / df['Close'].std() # Standardization of closing prices\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "\n",
    "def plot_df(df): #filename is the full relative (or absolute) path + filename + extension\n",
    "    df_graph1 = df[['Date', 'Standard']]\n",
    "    df_graph1 = df_graph1.set_index('Date')\n",
    "    df_graph1.Standard.plot()\n",
    "\n",
    "# if I'm plotting graph, cannot return a df in the same function\n",
    "\n",
    "# Create separate function for plot, this function to return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "94b9dce9",
   "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>SNo</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Percent</th>\n",
       "      <th>Up</th>\n",
       "      <th>Standard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2013-04-29 23:59:59</td>\n",
       "      <td>4.573600</td>\n",
       "      <td>4.225640</td>\n",
       "      <td>4.366760</td>\n",
       "      <td>4.383900</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.538896e+07</td>\n",
       "      <td>0.017140</td>\n",
       "      <td>0.003925</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.709911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2013-04-30 23:59:59</td>\n",
       "      <td>4.572380</td>\n",
       "      <td>4.168960</td>\n",
       "      <td>4.403520</td>\n",
       "      <td>4.296490</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.402092e+07</td>\n",
       "      <td>-0.107030</td>\n",
       "      <td>-0.024306</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.711293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2013-05-01 23:59:59</td>\n",
       "      <td>4.356860</td>\n",
       "      <td>3.520290</td>\n",
       "      <td>4.289540</td>\n",
       "      <td>3.801010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.560460e+07</td>\n",
       "      <td>-0.488530</td>\n",
       "      <td>-0.113889</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.719128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2013-05-02 23:59:59</td>\n",
       "      <td>4.039300</td>\n",
       "      <td>3.007170</td>\n",
       "      <td>3.780020</td>\n",
       "      <td>3.371980</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.828798e+07</td>\n",
       "      <td>-0.408040</td>\n",
       "      <td>-0.107947</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.725912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2013-05-03 23:59:59</td>\n",
       "      <td>3.453610</td>\n",
       "      <td>2.395940</td>\n",
       "      <td>3.390440</td>\n",
       "      <td>3.044910</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.269485e+07</td>\n",
       "      <td>-0.345530</td>\n",
       "      <td>-0.101913</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.731084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2986</th>\n",
       "      <td>2987</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2021-07-02 23:59:59</td>\n",
       "      <td>138.787700</td>\n",
       "      <td>130.935471</td>\n",
       "      <td>137.299274</td>\n",
       "      <td>136.943696</td>\n",
       "      <td>1.418981e+09</td>\n",
       "      <td>9.141322e+09</td>\n",
       "      <td>-0.355578</td>\n",
       "      <td>-0.002590</td>\n",
       "      <td>0</td>\n",
       "      <td>1.386212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2987</th>\n",
       "      <td>2988</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2021-07-03 23:59:59</td>\n",
       "      <td>141.356011</td>\n",
       "      <td>134.945288</td>\n",
       "      <td>136.930584</td>\n",
       "      <td>140.279688</td>\n",
       "      <td>1.236494e+09</td>\n",
       "      <td>9.364008e+09</td>\n",
       "      <td>3.349104</td>\n",
       "      <td>0.024458</td>\n",
       "      <td>1</td>\n",
       "      <td>1.438963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>2989</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2021-07-04 23:59:59</td>\n",
       "      <td>147.836059</td>\n",
       "      <td>137.096427</td>\n",
       "      <td>140.317998</td>\n",
       "      <td>144.905849</td>\n",
       "      <td>1.431657e+09</td>\n",
       "      <td>9.672815e+09</td>\n",
       "      <td>4.587852</td>\n",
       "      <td>0.032696</td>\n",
       "      <td>1</td>\n",
       "      <td>1.512115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2989</th>\n",
       "      <td>2990</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2021-07-05 23:59:59</td>\n",
       "      <td>144.849333</td>\n",
       "      <td>134.960263</td>\n",
       "      <td>144.849333</td>\n",
       "      <td>138.073246</td>\n",
       "      <td>1.338246e+09</td>\n",
       "      <td>9.216723e+09</td>\n",
       "      <td>-6.776088</td>\n",
       "      <td>-0.046780</td>\n",
       "      <td>0</td>\n",
       "      <td>1.404073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>2991</td>\n",
       "      <td>Litecoin</td>\n",
       "      <td>LTC</td>\n",
       "      <td>2021-07-06 23:59:59</td>\n",
       "      <td>142.703568</td>\n",
       "      <td>135.924837</td>\n",
       "      <td>137.951668</td>\n",
       "      <td>138.985636</td>\n",
       "      <td>1.504907e+09</td>\n",
       "      <td>9.277627e+09</td>\n",
       "      <td>1.033968</td>\n",
       "      <td>0.007495</td>\n",
       "      <td>1</td>\n",
       "      <td>1.418500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2991 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       SNo      Name Symbol                Date        High         Low  \\\n",
       "0        1  Litecoin    LTC 2013-04-29 23:59:59    4.573600    4.225640   \n",
       "1        2  Litecoin    LTC 2013-04-30 23:59:59    4.572380    4.168960   \n",
       "2        3  Litecoin    LTC 2013-05-01 23:59:59    4.356860    3.520290   \n",
       "3        4  Litecoin    LTC 2013-05-02 23:59:59    4.039300    3.007170   \n",
       "4        5  Litecoin    LTC 2013-05-03 23:59:59    3.453610    2.395940   \n",
       "...    ...       ...    ...                 ...         ...         ...   \n",
       "2986  2987  Litecoin    LTC 2021-07-02 23:59:59  138.787700  130.935471   \n",
       "2987  2988  Litecoin    LTC 2021-07-03 23:59:59  141.356011  134.945288   \n",
       "2988  2989  Litecoin    LTC 2021-07-04 23:59:59  147.836059  137.096427   \n",
       "2989  2990  Litecoin    LTC 2021-07-05 23:59:59  144.849333  134.960263   \n",
       "2990  2991  Litecoin    LTC 2021-07-06 23:59:59  142.703568  135.924837   \n",
       "\n",
       "            Open       Close        Volume     Marketcap      Diff   Percent  \\\n",
       "0       4.366760    4.383900  0.000000e+00  7.538896e+07  0.017140  0.003925   \n",
       "1       4.403520    4.296490  0.000000e+00  7.402092e+07 -0.107030 -0.024306   \n",
       "2       4.289540    3.801010  0.000000e+00  6.560460e+07 -0.488530 -0.113889   \n",
       "3       3.780020    3.371980  0.000000e+00  5.828798e+07 -0.408040 -0.107947   \n",
       "4       3.390440    3.044910  0.000000e+00  5.269485e+07 -0.345530 -0.101913   \n",
       "...          ...         ...           ...           ...       ...       ...   \n",
       "2986  137.299274  136.943696  1.418981e+09  9.141322e+09 -0.355578 -0.002590   \n",
       "2987  136.930584  140.279688  1.236494e+09  9.364008e+09  3.349104  0.024458   \n",
       "2988  140.317998  144.905849  1.431657e+09  9.672815e+09  4.587852  0.032696   \n",
       "2989  144.849333  138.073246  1.338246e+09  9.216723e+09 -6.776088 -0.046780   \n",
       "2990  137.951668  138.985636  1.504907e+09  9.277627e+09  1.033968  0.007495   \n",
       "\n",
       "      Up  Standard  \n",
       "0      1 -0.709911  \n",
       "1      0 -0.711293  \n",
       "2      0 -0.719128  \n",
       "3      0 -0.725912  \n",
       "4      0 -0.731084  \n",
       "...   ..       ...  \n",
       "2986   0  1.386212  \n",
       "2987   1  1.438963  \n",
       "2988   1  1.512115  \n",
       "2989   0  1.404073  \n",
       "2990   1  1.418500  \n",
       "\n",
       "[2991 rows x 14 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = './data/coin_Litecoin.csv'\n",
    "\n",
    "df_Litecoin = import_data(filename)\n",
    "\n",
    "df_Litecoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d2b2e0e8",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-92ba10a80d83>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'./data/coin_Dogecoin.csv'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf_Doge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# filename = './data/coin_Cardano.csv'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-33-cf1da4991a4b>\u001b[0m in \u001b[0;36mplot_df\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mplot_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m#filename is the full relative (or absolute) path + filename + extension\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m     \u001b[0mdf_graph1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Standard'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m     \u001b[0mdf_graph1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_graph1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m     \u001b[0mdf_graph1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStandard\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: string indices must be integers"
     ]
    }
   ],
   "source": [
    "filename = './data/coin_Dogecoin.csv'\n",
    "\n",
    "df_Doge = plot_df(filename)\n",
    "\n",
    "# filename = './data/coin_Cardano.csv'\n",
    "# plot_ts(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "04ee04a7",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_Doge' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-0314ad184752>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_Doge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df_Doge' is not defined"
     ]
    }
   ],
   "source": [
    "df_Doge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2f38eb56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n- extract date of the week, week of the year, month.\\n--> then groupby \\'average\\', day of the week, month,\\n\\nAre there any weekly patterns over time? extract week and year (single column), then group by week, or year, then plot and see pattern over the entire year.\\n\\nfunction called resample that can resample.\\n\\nlook up \"resample\" <-- pandas function; resample the data by year, quarter, week.\\naggregate data by whatever frequency that I want.\\nWhat is resample? aggregateing data by a different interval (but it\\'s still in order)\\n\\n- two pairs of time in the dataset after stationizing data.\\n- does the even impact the data before or after event\\nhypothesis before\\n\\n- even if we stationize the data, it might mess up the correlation, because regular T-test may not account for it.\\n\\n- Causal Impact <-- package bayesian hypothesis testing on time series data.\\n\\n'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    df = pd.read_csv(filename)\n",
    "    df.Date = pd.to_datetime(df.Date)\n",
    "    df['Diff'] = df['Close'] - df['Open']\n",
    "    df['Percent'] = df['Diff']/df['Open']\n",
    "    df['Up'] = df['Diff'] >= 0\n",
    "    # convert to 1 and 0, 1 is 0\n",
    "    df['Up'] = df['Up'].apply(lambda x: 1 if x == True else 0)\n",
    "df\n",
    "\n",
    "# ideas for further steps\n",
    "'''\n",
    "- extract date of the week, week of the year, month.\n",
    "--> then groupby 'average', day of the week, month,\n",
    "\n",
    "Are there any weekly patterns over time? extract week and year (single column), then group by week, or year, then plot and see pattern over the entire year.\n",
    "\n",
    "function called resample that can resample.\n",
    "\n",
    "look up \"resample\" <-- pandas function; resample the data by year, quarter, week.\n",
    "aggregate data by whatever frequency that I want.\n",
    "What is resample? aggregateing data by a different interval (but it's still in order)\n",
    "\n",
    "- two pairs of time in the dataset after stationizing data.\n",
    "- does the even impact the data before or after event\n",
    "hypothesis before\n",
    "\n",
    "- even if we stationize the data, it might mess up the correlation, because regular T-test may not account for it.\n",
    "\n",
    "- Causal Impact <-- package bayesian hypothesis testing on time series data.\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "856bdd55",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-d72f87239e19>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mjay\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Open'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Close'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mjay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Daily'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Close'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mjay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mjay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Daily'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mjay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Win'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mjay\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Daily'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1440\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1441\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1442\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m   1443\u001b[0m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1444\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "jay = pd.DataFrame({'Open': [1], 'Close':[0]})\n",
    "jay['Daily'] = jay['Close'] - jay['Open']\n",
    "if jay['Daily'] < 0:\n",
    "    jay['Win'] = 0\n",
    "if jay['Daily'] >= 0:\n",
    "    jay['Win'] = 1\n",
    "jay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a5010553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['coin_Aave.csv',\n",
       " 'coin_BinanceCoin.csv',\n",
       " 'coin_Bitcoin.csv',\n",
       " 'coin_Cardano.csv',\n",
       " 'coin_ChainLink.csv',\n",
       " 'coin_Cosmos.csv',\n",
       " 'coin_CryptocomCoin.csv',\n",
       " 'coin_Dogecoin.csv',\n",
       " 'coin_EOS.csv',\n",
       " 'coin_Ethereum.csv',\n",
       " 'coin_Iota.csv',\n",
       " 'coin_Litecoin.csv',\n",
       " 'coin_Monero.csv',\n",
       " 'coin_NEM.csv',\n",
       " 'coin_Polkadot.csv',\n",
       " 'coin_Solana.csv',\n",
       " 'coin_Stellar.csv',\n",
       " 'coin_Tether.csv',\n",
       " 'coin_Tron.csv',\n",
       " 'coin_Uniswap.csv',\n",
       " 'coin_USDCoin.csv',\n",
       " 'coin_WrappedBitcoin.csv',\n",
       " 'coin_XRP.csv']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "file_list = os.listdir('./data')\n",
    "file_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "91f05a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'df_Aave':      SNo  Name Symbol                Date        High         Low        Open  \\\n",
       " 0      1  Aave   AAVE 2020-10-05 23:59:59   55.112358   49.787900   52.675035   \n",
       " 1      2  Aave   AAVE 2020-10-06 23:59:59   53.402270   40.734578   53.291969   \n",
       " 2      3  Aave   AAVE 2020-10-07 23:59:59   42.408314   35.970690   42.399947   \n",
       " 3      4  Aave   AAVE 2020-10-08 23:59:59   44.902511   36.696057   39.885262   \n",
       " 4      5  Aave   AAVE 2020-10-09 23:59:59   47.569533   43.291776   43.764463   \n",
       " ..   ...   ...    ...                 ...         ...         ...         ...   \n",
       " 270  271  Aave   AAVE 2021-07-02 23:59:59  234.483957  215.951539  230.814119   \n",
       " 271  272  Aave   AAVE 2021-07-03 23:59:59  265.107342  228.485206  234.208478   \n",
       " 272  273  Aave   AAVE 2021-07-04 23:59:59  289.001124  248.285491  259.399426   \n",
       " 273  274  Aave   AAVE 2021-07-05 23:59:59  317.387234  263.433881  277.110533   \n",
       " 274  275  Aave   AAVE 2021-07-06 23:59:59  346.714780  307.997525  307.997525   \n",
       " \n",
       "           Close        Volume     Marketcap       Diff   Percent  Up  Standard  \n",
       " 0     53.219243  0.000000e+00  8.912813e+07   0.544208  0.010331   1 -1.251511  \n",
       " 1     42.401599  5.830915e+05  7.101144e+07 -10.890371 -0.204353   0 -1.318431  \n",
       " 2     40.083976  6.828342e+05  6.713004e+07  -2.315971 -0.054622   0 -1.332768  \n",
       " 3     43.764463  1.658817e+06  2.202651e+08   3.879201  0.097259   1 -1.310000  \n",
       " 4     46.817744  8.155377e+05  2.356322e+08   3.053281  0.069766   1 -1.291112  \n",
       " ..          ...           ...           ...        ...       ...  ..       ...  \n",
       " 270  233.948437  2.566395e+08  3.001542e+09   3.134318  0.013579   1 -0.133482  \n",
       " 271  259.655225  3.853778e+08  3.331546e+09  25.446747  0.108650   1  0.025545  \n",
       " 272  277.038792  4.275719e+08  3.555054e+09  17.639366  0.068001   1  0.133084  \n",
       " 273  307.829079  7.931409e+08  3.950269e+09  30.718546  0.110853   1  0.323559  \n",
       " 274  316.898507  9.887055e+08  4.066776e+09   8.900982  0.028900   1  0.379664  \n",
       " \n",
       " [275 rows x 14 columns],\n",
       " 'df_BinanceCoin':        SNo          Name Symbol                Date        High         Low  \\\n",
       " 0        1  Binance Coin    BNB 2017-07-26 23:59:59    0.109013    0.099266   \n",
       " 1        2  Binance Coin    BNB 2017-07-27 23:59:59    0.108479    0.100888   \n",
       " 2        3  Binance Coin    BNB 2017-07-28 23:59:59    0.109019    0.101473   \n",
       " 3        4  Binance Coin    BNB 2017-07-29 23:59:59    0.111264    0.101108   \n",
       " 4        5  Binance Coin    BNB 2017-07-30 23:59:59    0.108138    0.103162   \n",
       " ...    ...           ...    ...                 ...         ...         ...   \n",
       " 1437  1438  Binance Coin    BNB 2021-07-02 23:59:59  290.621683  277.350302   \n",
       " 1438  1439  Binance Coin    BNB 2021-07-03 23:59:59  302.605867  283.434010   \n",
       " 1439  1440  Binance Coin    BNB 2021-07-04 23:59:59  314.713019  292.787370   \n",
       " 1440  1441  Binance Coin    BNB 2021-07-05 23:59:59  307.684855  293.523465   \n",
       " 1441  1442  Binance Coin    BNB 2021-07-06 23:59:59  321.520965  302.195584   \n",
       " \n",
       "             Open       Close        Volume     Marketcap       Diff   Percent  \\\n",
       " 0       0.105893    0.105138  2.003950e+05  1.051380e+07  -0.000755 -0.007130   \n",
       " 1       0.105108    0.107737  3.444990e+05  1.077370e+07   0.002629  0.025012   \n",
       " 2       0.107632    0.104067  3.425680e+05  1.040670e+07  -0.003565 -0.033122   \n",
       " 3       0.104782    0.107811  3.402180e+05  1.078110e+07   0.003029  0.028908   \n",
       " 4       0.107935    0.106414  2.242610e+05  1.064140e+07  -0.001521 -0.014092   \n",
       " ...          ...         ...           ...           ...        ...       ...   \n",
       " 1437  287.754456  287.423094  1.133633e+09  4.410016e+10  -0.331363 -0.001152   \n",
       " 1438  287.215592  298.237117  1.113777e+09  4.575938e+10  11.021525  0.038374   \n",
       " 1439  298.113557  307.732096  1.387396e+09  4.721623e+10   9.618539  0.032265   \n",
       " 1440  307.684855  302.377980  1.504870e+09  4.639473e+10  -5.306875 -0.017248   \n",
       " 1441  302.195584  320.934802  2.203265e+09  4.924196e+10  18.739217  0.062010   \n",
       " \n",
       "       Up  Standard  \n",
       " 0      0 -0.451900  \n",
       " 1      1 -0.451878  \n",
       " 2      0 -0.451909  \n",
       " 3      1 -0.451877  \n",
       " 4      0 -0.451889  \n",
       " ...   ..       ...  \n",
       " 1437   0  2.038053  \n",
       " 1438   1  2.131769  \n",
       " 1439   1  2.214055  \n",
       " 1440   0  2.167655  \n",
       " 1441   1  2.328472  \n",
       " \n",
       " [1442 rows x 14 columns],\n",
       " 'df_Bitcoin':        SNo     Name Symbol                Date          High           Low  \\\n",
       " 0        1  Bitcoin    BTC 2013-04-29 23:59:59    147.488007    134.000000   \n",
       " 1        2  Bitcoin    BTC 2013-04-30 23:59:59    146.929993    134.050003   \n",
       " 2        3  Bitcoin    BTC 2013-05-01 23:59:59    139.889999    107.720001   \n",
       " 3        4  Bitcoin    BTC 2013-05-02 23:59:59    125.599998     92.281898   \n",
       " 4        5  Bitcoin    BTC 2013-05-03 23:59:59    108.127998     79.099998   \n",
       " ...    ...      ...    ...                 ...           ...           ...   \n",
       " 2986  2987  Bitcoin    BTC 2021-07-02 23:59:59  33939.588699  32770.680780   \n",
       " 2987  2988  Bitcoin    BTC 2021-07-03 23:59:59  34909.259899  33402.696536   \n",
       " 2988  2989  Bitcoin    BTC 2021-07-04 23:59:59  35937.567147  34396.477458   \n",
       " 2989  2990  Bitcoin    BTC 2021-07-05 23:59:59  35284.344430  33213.661034   \n",
       " 2990  2991  Bitcoin    BTC 2021-07-06 23:59:59  35038.536363  33599.916169   \n",
       " \n",
       "               Open         Close        Volume     Marketcap         Diff  \\\n",
       " 0       134.444000    144.539993  0.000000e+00  1.603769e+09    10.095993   \n",
       " 1       144.000000    139.000000  0.000000e+00  1.542813e+09    -5.000000   \n",
       " 2       139.000000    116.989998  0.000000e+00  1.298955e+09   -22.010002   \n",
       " 3       116.379997    105.209999  0.000000e+00  1.168517e+09   -11.169998   \n",
       " 4       106.250000     97.750000  0.000000e+00  1.085995e+09    -8.500000   \n",
       " ...            ...           ...           ...           ...          ...   \n",
       " 2986  33549.600177  33897.048590  3.872897e+10  6.354508e+11   347.448412   \n",
       " 2987  33854.421362  34668.548402  2.438396e+10  6.499397e+11   814.127040   \n",
       " 2988  34665.564866  35287.779766  2.492431e+10  6.615748e+11   622.214900   \n",
       " 2989  35284.344430  33746.002456  2.672155e+10  6.326962e+11 -1538.341974   \n",
       " 2990  33723.509655  34235.193451  2.650126e+10  6.418992e+11   511.683796   \n",
       " \n",
       "        Percent  Up  Standard  \n",
       " 0     0.075094   1 -0.581224  \n",
       " 1    -0.034722   0 -0.581714  \n",
       " 2    -0.158345   0 -0.583662  \n",
       " 3    -0.095979   0 -0.584705  \n",
       " 4    -0.080000   0 -0.585365  \n",
       " ...        ...  ..       ...  \n",
       " 2986  0.010356   1  2.406215  \n",
       " 2987  0.024048   1  2.474501  \n",
       " 2988  0.017949   1  2.529309  \n",
       " 2989 -0.043598   0  2.392846  \n",
       " 2990  0.015173   1  2.436144  \n",
       " \n",
       " [2991 rows x 14 columns],\n",
       " 'df_Cardano':        SNo     Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  Cardano    ADA 2017-10-02 23:59:59  0.030088  0.019969  0.024607   \n",
       " 1        2  Cardano    ADA 2017-10-03 23:59:59  0.027425  0.020690  0.025757   \n",
       " 2        3  Cardano    ADA 2017-10-04 23:59:59  0.022806  0.020864  0.020864   \n",
       " 3        4  Cardano    ADA 2017-10-05 23:59:59  0.022154  0.020859  0.021951   \n",
       " 4        5  Cardano    ADA 2017-10-06 23:59:59  0.021542  0.018360  0.021359   \n",
       " ...    ...      ...    ...                 ...       ...       ...       ...   \n",
       " 1369  1370  Cardano    ADA 2021-07-02 23:59:59  1.394397  1.286607  1.332942   \n",
       " 1370  1371  Cardano    ADA 2021-07-03 23:59:59  1.441714  1.359664  1.394152   \n",
       " 1371  1372  Cardano    ADA 2021-07-04 23:59:59  1.493717  1.382153  1.404008   \n",
       " 1372  1373  Cardano    ADA 2021-07-05 23:59:59  1.461221  1.379284  1.461221   \n",
       " 1373  1374  Cardano    ADA 2021-07-06 23:59:59  1.456887  1.393282  1.404712   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.025932  5.764130e+07  6.288991e+08  0.001325  0.053838   1 -0.562328  \n",
       " 1     0.020816  1.699780e+07  5.396927e+08 -0.004941 -0.191827   0 -0.574815  \n",
       " 2     0.021931  9.000050e+06  5.686195e+08  0.001067  0.051160   1 -0.572092  \n",
       " 3     0.021489  5.562510e+06  5.571390e+08 -0.000463 -0.021074   0 -0.573173  \n",
       " 4     0.018539  7.780710e+06  4.806646e+08 -0.002820 -0.132020   0 -0.580372  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 1369  1.394397  2.159410e+09  4.454587e+10  0.061455  0.046105   1  2.777907  \n",
       " 1370  1.406836  2.028094e+09  4.494324e+10  0.012684  0.009098   1  2.808268  \n",
       " 1371  1.458184  1.806362e+09  4.658364e+10  0.054176  0.038587   1  2.933603  \n",
       " 1372  1.404898  1.759461e+09  4.488134e+10 -0.056323 -0.038545   0  2.803538  \n",
       " 1373  1.418053  1.477700e+09  4.530158e+10  0.013341  0.009497   1  2.835647  \n",
       " \n",
       " [1374 rows x 14 columns],\n",
       " 'df_ChainLink':        SNo       Name Symbol                Date       High        Low  \\\n",
       " 0        1  Chainlink   LINK 2017-09-21 23:59:59   0.207892   0.155292   \n",
       " 1        2  Chainlink   LINK 2017-09-22 23:59:59   0.181675   0.139869   \n",
       " 2        3  Chainlink   LINK 2017-09-23 23:59:59   0.157440   0.126297   \n",
       " 3        4  Chainlink   LINK 2017-09-24 23:59:59   0.190250   0.130275   \n",
       " 4        5  Chainlink   LINK 2017-09-25 23:59:59   0.182637   0.158580   \n",
       " ...    ...        ...    ...                 ...        ...        ...   \n",
       " 1380  1381  Chainlink   LINK 2021-07-02 23:59:59  18.520256  17.305501   \n",
       " 1381  1382  Chainlink   LINK 2021-07-03 23:59:59  18.886003  17.898075   \n",
       " 1382  1383  Chainlink   LINK 2021-07-04 23:59:59  19.894503  18.028957   \n",
       " 1383  1384  Chainlink   LINK 2021-07-05 23:59:59  19.246891  17.814752   \n",
       " 1384  1385  Chainlink   LINK 2021-07-06 23:59:59  20.240997  18.351877   \n",
       " \n",
       "            Open      Close        Volume     Marketcap      Diff   Percent  \\\n",
       " 0      0.189132   0.169680  2.126270e+06  5.938800e+07 -0.019452 -0.102849   \n",
       " 1      0.169826   0.150503  1.423220e+06  5.267605e+07 -0.019323 -0.113781   \n",
       " 2      0.149562   0.131129  1.186670e+06  4.589515e+07 -0.018433 -0.123247   \n",
       " 3      0.131110   0.170278  1.025740e+06  5.959730e+07  0.039168  0.298742   \n",
       " 4      0.170636   0.182198  1.021370e+06  6.376930e+07  0.011562  0.067758   \n",
       " ...         ...        ...           ...           ...       ...       ...   \n",
       " 1380  18.220669  18.283561  6.865419e+08  7.990091e+09  0.062892  0.003452   \n",
       " 1381  18.285458  18.538818  5.090763e+08  8.101641e+09  0.253360  0.013856   \n",
       " 1382  18.548805  19.262838  6.802681e+08  8.418044e+09  0.714032  0.038495   \n",
       " 1383  19.246891  18.371589  6.986191e+08  8.028560e+09 -0.875302 -0.045478   \n",
       " 1384  18.351877  20.080466  1.156986e+09  8.775356e+09  1.728589  0.094191   \n",
       " \n",
       "       Up  Standard  \n",
       " 0      0 -0.620184  \n",
       " 1      0 -0.622121  \n",
       " 2      0 -0.624079  \n",
       " 3      1 -0.620124  \n",
       " 4      1 -0.618919  \n",
       " ...   ..       ...  \n",
       " 1380   1  1.209775  \n",
       " 1381   1  1.235562  \n",
       " 1382   1  1.308707  \n",
       " 1383   0  1.218668  \n",
       " 1384   1  1.391308  \n",
       " \n",
       " [1385 rows x 14 columns],\n",
       " 'df_Cosmos':      SNo    Name Symbol                Date       High        Low       Open  \\\n",
       " 0      1  Cosmos   ATOM 2019-03-15 23:59:59   7.715249   6.432468   6.633174   \n",
       " 1      2  Cosmos   ATOM 2019-03-16 23:59:59   8.305615   6.694531   7.507990   \n",
       " 2      3  Cosmos   ATOM 2019-03-17 23:59:59   7.357443   4.727895   7.357443   \n",
       " 3      4  Cosmos   ATOM 2019-03-18 23:59:59   5.229982   4.828242   4.828242   \n",
       " 4      5  Cosmos   ATOM 2019-03-19 23:59:59   5.206172   4.794501   5.099978   \n",
       " ..   ...     ...    ...                 ...        ...        ...        ...   \n",
       " 840  841  Cosmos   ATOM 2021-07-02 23:59:59  11.764925  10.543935  11.048080   \n",
       " 841  842  Cosmos   ATOM 2021-07-03 23:59:59  12.198276  11.540298  11.791034   \n",
       " 842  843  Cosmos   ATOM 2021-07-04 23:59:59  12.433965  11.520693  11.799601   \n",
       " 843  844  Cosmos   ATOM 2021-07-05 23:59:59  13.968271  11.955753  12.320437   \n",
       " 844  845  Cosmos   ATOM 2021-07-06 23:59:59  14.020420  12.823983  13.558099   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     7.504351  6.057301e+06  0.000000e+00  0.871177  0.131336   1  0.124030  \n",
       " 1     7.383882  3.477393e+06  0.000000e+00 -0.124108 -0.016530   0  0.103735  \n",
       " 2     4.776164  2.653565e+06  0.000000e+00 -2.581279 -0.350839   0 -0.335563  \n",
       " 3     5.110341  2.567201e+06  0.000000e+00  0.282099  0.058427   1 -0.279267  \n",
       " 4     4.821883  3.891084e+06  0.000000e+00 -0.278094 -0.054529   0 -0.327861  \n",
       " ..         ...           ...           ...       ...       ...  ..       ...  \n",
       " 840  11.764925  1.891418e+08  2.559039e+09  0.716845  0.064884   1  0.841770  \n",
       " 841  11.817927  1.551317e+08  2.571184e+09  0.026893  0.002281   1  0.850699  \n",
       " 842  12.263548  1.449177e+08  2.668775e+09  0.463948  0.039319   1  0.925768  \n",
       " 843  13.548475  4.489358e+08  2.949102e+09  1.228038  0.099675   1  1.142228  \n",
       " 844  13.159371  2.537822e+08  2.865093e+09 -0.398728 -0.029409   0  1.076679  \n",
       " \n",
       " [845 rows x 14 columns],\n",
       " 'df_CryptocomCoin':      SNo             Name Symbol                Date      High       Low  \\\n",
       " 0      1  Crypto.com Coin    CRO 2018-12-15 23:59:59  0.019845  0.016150   \n",
       " 1      2  Crypto.com Coin    CRO 2018-12-16 23:59:59  0.017496  0.014127   \n",
       " 2      3  Crypto.com Coin    CRO 2018-12-17 23:59:59  0.016723  0.011487   \n",
       " 3      4  Crypto.com Coin    CRO 2018-12-18 23:59:59  0.017215  0.013553   \n",
       " 4      5  Crypto.com Coin    CRO 2018-12-19 23:59:59  0.031509  0.015609   \n",
       " ..   ...              ...    ...                 ...       ...       ...   \n",
       " 930  931  Crypto.com Coin    CRO 2021-07-02 23:59:59  0.114202  0.108968   \n",
       " 931  932  Crypto.com Coin    CRO 2021-07-03 23:59:59  0.119593  0.112773   \n",
       " 932  933  Crypto.com Coin    CRO 2021-07-04 23:59:59  0.125262  0.117165   \n",
       " 933  934  Crypto.com Coin    CRO 2021-07-05 23:59:59  0.122467  0.113482   \n",
       " 934  935  Crypto.com Coin    CRO 2021-07-06 23:59:59  0.124989  0.115725   \n",
       " \n",
       "          Open     Close        Volume     Marketcap      Diff   Percent  Up  \\\n",
       " 0    0.019542  0.017243  9.897096e+05  0.000000e+00 -0.002299 -0.117623   0   \n",
       " 1    0.017213  0.015301  7.171740e+05  0.000000e+00 -0.001912 -0.111083   0   \n",
       " 2    0.015186  0.014448  6.156226e+05  1.547062e+07 -0.000738 -0.048619   0   \n",
       " 3    0.014308  0.016260  7.644745e+05  1.785590e+07  0.001951  0.136372   1   \n",
       " 4    0.016087  0.020790  1.025196e+06  2.340088e+07  0.004704  0.292397   1   \n",
       " ..        ...       ...           ...           ...       ...       ...  ..   \n",
       " 930  0.112168  0.113914  4.495990e+07  2.877801e+09  0.001745  0.015561   1   \n",
       " 931  0.113903  0.118407  4.578038e+07  2.991307e+09  0.004503  0.039538   1   \n",
       " 932  0.118389  0.122427  4.174486e+07  3.092883e+09  0.004039  0.034115   1   \n",
       " 933  0.122467  0.115814  4.728913e+07  2.925805e+09 -0.006653 -0.054326   0   \n",
       " 934  0.115725  0.122856  1.520764e+08  3.103718e+09  0.007131  0.061620   1   \n",
       " \n",
       "      Standard  \n",
       " 0   -1.204417  \n",
       " 1   -1.240598  \n",
       " 2   -1.256478  \n",
       " 3   -1.222738  \n",
       " 4   -1.138359  \n",
       " ..        ...  \n",
       " 930  0.596000  \n",
       " 931  0.679678  \n",
       " 932  0.754561  \n",
       " 933  0.631389  \n",
       " 934  0.762549  \n",
       " \n",
       " [935 rows x 14 columns],\n",
       " 'df_Dogecoin':        SNo      Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  Dogecoin   DOGE 2013-12-16 23:59:59  0.000866  0.000150  0.000299   \n",
       " 1        2  Dogecoin   DOGE 2013-12-17 23:59:59  0.000289  0.000116  0.000207   \n",
       " 2        3  Dogecoin   DOGE 2013-12-18 23:59:59  0.000362  0.000205  0.000267   \n",
       " 3        4  Dogecoin   DOGE 2013-12-19 23:59:59  0.001520  0.000328  0.000395   \n",
       " 4        5  Dogecoin   DOGE 2013-12-20 23:59:59  0.001143  0.000662  0.001143   \n",
       " ...    ...       ...    ...                 ...       ...       ...       ...   \n",
       " 2755  2756  Dogecoin   DOGE 2021-07-02 23:59:59  0.247997  0.238848  0.243982   \n",
       " 2756  2757  Dogecoin   DOGE 2021-07-03 23:59:59  0.250214  0.242454  0.245106   \n",
       " 2757  2758  Dogecoin   DOGE 2021-07-04 23:59:59  0.252567  0.243425  0.246425   \n",
       " 2758  2759  Dogecoin   DOGE 2021-07-05 23:59:59  0.246419  0.227838  0.246419   \n",
       " 2759  2760  Dogecoin   DOGE 2021-07-06 23:59:59  0.241910  0.229842  0.231216   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.000205  0.000000e+00  1.509085e+06 -0.000095 -0.315995   0 -0.216723  \n",
       " 1     0.000269  0.000000e+00  2.169688e+06  0.000062  0.297991   1 -0.215703  \n",
       " 2     0.000362  0.000000e+00  3.188943e+06  0.000094  0.352817   1 -0.214215  \n",
       " 3     0.001162  0.000000e+00  1.115034e+07  0.000767  1.942610   1 -0.201413  \n",
       " 4     0.000704  0.000000e+00  7.284337e+06 -0.000439 -0.384151   0 -0.208744  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 2755  0.245264  1.321471e+09  3.194925e+10  0.001282  0.005253   1  3.700541  \n",
       " 2756  0.246411  9.170158e+08  3.210491e+10  0.001305  0.005326   1  3.718879  \n",
       " 2757  0.246483  9.735115e+08  3.211767e+10  0.000058  0.000236   1  3.720030  \n",
       " 2758  0.231614  1.267949e+09  3.018344e+10 -0.014805 -0.060079   0  3.482356  \n",
       " 2759  0.234422  1.265920e+09  3.055252e+10  0.003206  0.013864   1  3.527231  \n",
       " \n",
       " [2760 rows x 14 columns],\n",
       " 'df_EOS':        SNo Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  EOS    EOS 2017-07-02 23:59:59  2.877510  0.822648  0.996521   \n",
       " 1        2  EOS    EOS 2017-07-03 23:59:59  5.395970  2.632310  2.717390   \n",
       " 2        3  EOS    EOS 2017-07-04 23:59:59  4.191240  2.933450  4.098010   \n",
       " 3        4  EOS    EOS 2017-07-05 23:59:59  3.523500  2.730130  3.356000   \n",
       " 4        5  EOS    EOS 2017-07-06 23:59:59  3.867470  3.007830  3.014260   \n",
       " ...    ...  ...    ...                 ...       ...       ...       ...   \n",
       " 1461  1462  EOS    EOS 2021-07-02 23:59:59  4.000508  3.751986  3.930561   \n",
       " 1462  1463  EOS    EOS 2021-07-03 23:59:59  4.060837  3.859586  3.926595   \n",
       " 1463  1464  EOS    EOS 2021-07-04 23:59:59  4.181861  3.952532  4.048645   \n",
       " 1464  1465  EOS    EOS 2021-07-05 23:59:59  4.061888  3.773130  4.061888   \n",
       " 1465  1466  EOS    EOS 2021-07-06 23:59:59  4.004299  3.797596  3.814236   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     2.710050  3.204520e+08  0.000000e+00  1.713529  1.719511   1 -0.609280  \n",
       " 1     4.086640  4.149500e+08  6.549307e+08  1.369250  0.503884   1 -0.171082  \n",
       " 2     3.372000  2.185590e+08  5.501552e+08 -0.726010 -0.177162   0 -0.398567  \n",
       " 3     3.003230  1.243390e+08  5.006264e+08 -0.352770 -0.105116   0 -0.515955  \n",
       " 4     3.361330  1.774630e+08  5.683615e+08  0.347070  0.115143   1 -0.401964  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 1461  3.934325  1.014258e+09  3.756672e+09  0.003764  0.000958   1 -0.219567  \n",
       " 1462  4.046421  7.302703e+08  3.863820e+09  0.119827  0.030517   1 -0.183884  \n",
       " 1463  4.059930  8.086025e+08  3.876834e+09  0.011285  0.002787   1 -0.179584  \n",
       " 1464  3.819266  8.004033e+08  3.647131e+09 -0.242622 -0.059731   0 -0.256192  \n",
       " 1465  3.882325  6.967875e+08  3.707457e+09  0.068088  0.017851   1 -0.236120  \n",
       " \n",
       " [1466 rows x 14 columns],\n",
       " 'df_Ethereum':        SNo      Name Symbol                Date         High          Low  \\\n",
       " 0        1  Ethereum    ETH 2015-08-08 23:59:59     2.798810     0.714725   \n",
       " 1        2  Ethereum    ETH 2015-08-09 23:59:59     0.879810     0.629191   \n",
       " 2        3  Ethereum    ETH 2015-08-10 23:59:59     0.729854     0.636546   \n",
       " 3        4  Ethereum    ETH 2015-08-11 23:59:59     1.131410     0.663235   \n",
       " 4        5  Ethereum    ETH 2015-08-12 23:59:59     1.289940     0.883608   \n",
       " ...    ...       ...    ...                 ...          ...          ...   \n",
       " 2155  2156  Ethereum    ETH 2021-07-02 23:59:59  2155.596496  2021.824808   \n",
       " 2156  2157  Ethereum    ETH 2021-07-03 23:59:59  2237.567155  2117.590013   \n",
       " 2157  2158  Ethereum    ETH 2021-07-04 23:59:59  2384.286857  2190.837703   \n",
       " 2158  2159  Ethereum    ETH 2021-07-05 23:59:59  2321.922836  2163.041394   \n",
       " 2159  2160  Ethereum    ETH 2021-07-06 23:59:59  2346.294874  2197.919385   \n",
       " \n",
       "              Open        Close        Volume     Marketcap        Diff  \\\n",
       " 0        2.793760     0.753325  6.741880e+05  4.548689e+07   -2.040435   \n",
       " 1        0.706136     0.701897  5.321700e+05  4.239957e+07   -0.004239   \n",
       " 2        0.713989     0.708448  4.052830e+05  4.281836e+07   -0.005541   \n",
       " 3        0.708087     1.067860  1.463100e+06  6.456929e+07    0.359773   \n",
       " 4        1.058750     1.217440  2.150620e+06  7.364501e+07    0.158690   \n",
       " ...           ...          ...           ...           ...         ...   \n",
       " 2155  2109.892677  2150.040364  3.179621e+10  2.505527e+11   40.147687   \n",
       " 2156  2150.835025  2226.114282  1.743336e+10  2.594475e+11   75.279257   \n",
       " 2157  2226.550382  2321.724112  1.878711e+10  2.706217e+11   95.173729   \n",
       " 2158  2321.922836  2198.582464  2.010379e+10  2.562978e+11 -123.340372   \n",
       " 2159  2197.919385  2324.679449  2.089186e+10  2.710286e+11  126.760064   \n",
       " \n",
       "        Percent  Up  Standard  \n",
       " 0    -0.730354   0 -0.637450  \n",
       " 1    -0.006003   0 -0.637535  \n",
       " 2    -0.007761   0 -0.637524  \n",
       " 3     0.508091   1 -0.636926  \n",
       " 4     0.149884   1 -0.636677  \n",
       " ...        ...  ..       ...  \n",
       " 2155  0.019028   1  2.938267  \n",
       " 2156  0.035000   1  3.064829  \n",
       " 2157  0.042745   1  3.223893  \n",
       " 2158 -0.053120   0  3.019025  \n",
       " 2159  0.057673   1  3.228809  \n",
       " \n",
       " [2160 rows x 14 columns],\n",
       " 'df_Iota':        SNo  Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  IOTA  MIOTA 2017-06-14 23:59:59  0.606196  0.495745  0.592347   \n",
       " 1        2  IOTA  MIOTA 2017-06-15 23:59:59  0.543165  0.300365  0.528284   \n",
       " 2        3  IOTA  MIOTA 2017-06-16 23:59:59  0.448249  0.309852  0.353285   \n",
       " 3        4  IOTA  MIOTA 2017-06-17 23:59:59  0.444205  0.414139  0.426762   \n",
       " 4        5  IOTA  MIOTA 2017-06-18 23:59:59  0.426069  0.393790  0.420597   \n",
       " ...    ...   ...    ...                 ...       ...       ...       ...   \n",
       " 1479  1480  IOTA  MIOTA 2021-07-02 23:59:59  0.832226  0.763102  0.802916   \n",
       " 1480  1481  IOTA  MIOTA 2021-07-03 23:59:59  0.847473  0.799677  0.827826   \n",
       " 1481  1482  IOTA  MIOTA 2021-07-04 23:59:59  0.877730  0.797663  0.830209   \n",
       " 1482  1483  IOTA  MIOTA 2021-07-05 23:59:59  0.868300  0.810015  0.844715   \n",
       " 1483  1484  IOTA  MIOTA 2021-07-06 23:59:59  0.882165  0.820371  0.820371   \n",
       " \n",
       "          Close       Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.528916  14194900.00  1.470138e+09 -0.063431 -0.107084   0 -0.251318  \n",
       " 1     0.363661  10300400.00  1.010807e+09 -0.164623 -0.311618   0 -0.458505  \n",
       " 2     0.410757   6920690.00  1.141712e+09  0.057472  0.162679   1 -0.399459  \n",
       " 3     0.419906   3100660.00  1.167141e+09 -0.006856 -0.016065   0 -0.387988  \n",
       " 4     0.405862   2514450.00  1.128106e+09 -0.014735 -0.035033   0 -0.405596  \n",
       " ...        ...          ...           ...       ...       ...  ..       ...  \n",
       " 1479  0.828754  33929219.23  2.303546e+09  0.025837  0.032179   1  0.124602  \n",
       " 1480  0.829169  28133181.35  2.304699e+09  0.001343  0.001622   1  0.125122  \n",
       " 1481  0.844145  37850311.04  2.346327e+09  0.013937  0.016787   1  0.143899  \n",
       " 1482  0.820022  39787635.09  2.279275e+09 -0.024693 -0.029233   0  0.113654  \n",
       " 1483  0.851735  38663873.89  2.367423e+09  0.031364  0.038231   1  0.153414  \n",
       " \n",
       " [1484 rows x 14 columns],\n",
       " 'df_Litecoin':        SNo      Name Symbol                Date        High         Low  \\\n",
       " 0        1  Litecoin    LTC 2013-04-29 23:59:59    4.573600    4.225640   \n",
       " 1        2  Litecoin    LTC 2013-04-30 23:59:59    4.572380    4.168960   \n",
       " 2        3  Litecoin    LTC 2013-05-01 23:59:59    4.356860    3.520290   \n",
       " 3        4  Litecoin    LTC 2013-05-02 23:59:59    4.039300    3.007170   \n",
       " 4        5  Litecoin    LTC 2013-05-03 23:59:59    3.453610    2.395940   \n",
       " ...    ...       ...    ...                 ...         ...         ...   \n",
       " 2986  2987  Litecoin    LTC 2021-07-02 23:59:59  138.787700  130.935471   \n",
       " 2987  2988  Litecoin    LTC 2021-07-03 23:59:59  141.356011  134.945288   \n",
       " 2988  2989  Litecoin    LTC 2021-07-04 23:59:59  147.836059  137.096427   \n",
       " 2989  2990  Litecoin    LTC 2021-07-05 23:59:59  144.849333  134.960263   \n",
       " 2990  2991  Litecoin    LTC 2021-07-06 23:59:59  142.703568  135.924837   \n",
       " \n",
       "             Open       Close        Volume     Marketcap      Diff   Percent  \\\n",
       " 0       4.366760    4.383900  0.000000e+00  7.538896e+07  0.017140  0.003925   \n",
       " 1       4.403520    4.296490  0.000000e+00  7.402092e+07 -0.107030 -0.024306   \n",
       " 2       4.289540    3.801010  0.000000e+00  6.560460e+07 -0.488530 -0.113889   \n",
       " 3       3.780020    3.371980  0.000000e+00  5.828798e+07 -0.408040 -0.107947   \n",
       " 4       3.390440    3.044910  0.000000e+00  5.269485e+07 -0.345530 -0.101913   \n",
       " ...          ...         ...           ...           ...       ...       ...   \n",
       " 2986  137.299274  136.943696  1.418981e+09  9.141322e+09 -0.355578 -0.002590   \n",
       " 2987  136.930584  140.279688  1.236494e+09  9.364008e+09  3.349104  0.024458   \n",
       " 2988  140.317998  144.905849  1.431657e+09  9.672815e+09  4.587852  0.032696   \n",
       " 2989  144.849333  138.073246  1.338246e+09  9.216723e+09 -6.776088 -0.046780   \n",
       " 2990  137.951668  138.985636  1.504907e+09  9.277627e+09  1.033968  0.007495   \n",
       " \n",
       "       Up  Standard  \n",
       " 0      1 -0.709911  \n",
       " 1      0 -0.711293  \n",
       " 2      0 -0.719128  \n",
       " 3      0 -0.725912  \n",
       " 4      0 -0.731084  \n",
       " ...   ..       ...  \n",
       " 2986   0  1.386212  \n",
       " 2987   1  1.438963  \n",
       " 2988   1  1.512115  \n",
       " 2989   0  1.404073  \n",
       " 2990   1  1.418500  \n",
       " \n",
       " [2991 rows x 14 columns],\n",
       " 'df_Monero':        SNo    Name Symbol                Date        High         Low  \\\n",
       " 0        1  Monero    XMR 2014-05-22 23:59:59    2.188880    1.359090   \n",
       " 1        2  Monero    XMR 2014-05-23 23:59:59    3.434370    2.051040   \n",
       " 2        3  Monero    XMR 2014-05-24 23:59:59    4.014240    2.617780   \n",
       " 3        4  Monero    XMR 2014-05-25 23:59:59    4.043030    2.803300   \n",
       " 4        5  Monero    XMR 2014-05-26 23:59:59    3.762350    2.312020   \n",
       " ...    ...     ...    ...                 ...         ...         ...   \n",
       " 2597  2598  Monero    XMR 2021-07-02 23:59:59  211.666077  199.608531   \n",
       " 2598  2599  Monero    XMR 2021-07-03 23:59:59  216.327967  207.042192   \n",
       " 2599  2600  Monero    XMR 2021-07-04 23:59:59  224.909012  213.023069   \n",
       " 2600  2601  Monero    XMR 2021-07-05 23:59:59  220.354191  206.502607   \n",
       " 2601  2602  Monero    XMR 2021-07-06 23:59:59  222.137136  212.296827   \n",
       " \n",
       "             Open       Close        Volume     Marketcap      Diff   Percent  \\\n",
       " 0       1.589400    2.095580  1.329180e+05  1.855608e+06  0.506180  0.318472   \n",
       " 1       2.051040    2.963020  2.668520e+05  2.692180e+06  0.911980  0.444643   \n",
       " 2       2.920390    3.702450  2.480280e+05  3.456658e+06  0.782060  0.267793   \n",
       " 3       4.043030    3.138560  2.835450e+05  2.993339e+06 -0.904470 -0.223711   \n",
       " 4       3.218970    3.021860  2.201480e+05  2.946133e+06 -0.197110 -0.061234   \n",
       " ...          ...         ...           ...           ...       ...       ...   \n",
       " 2597  206.252403  211.345143  1.519434e+08  3.792466e+09  5.092741  0.024692   \n",
       " 2598  211.458163  214.752778  1.364141e+08  3.853768e+09  3.294615  0.015580   \n",
       " 2599  214.839958  219.841418  1.697858e+08  3.945232e+09  5.001460  0.023280   \n",
       " 2600  219.961957  214.464993  1.709852e+08  3.848898e+09 -5.496964 -0.024991   \n",
       " 2601  214.470724  222.137136  1.678620e+08  3.986738e+09  7.666412  0.035746   \n",
       " \n",
       "       Up  Standard  \n",
       " 0      1 -0.790072  \n",
       " 1      1 -0.780558  \n",
       " 2      1 -0.772449  \n",
       " 3      0 -0.778633  \n",
       " 4      0 -0.779913  \n",
       " ...   ..       ...  \n",
       " 2597   1  1.504821  \n",
       " 2598   1  1.542193  \n",
       " 2599   1  1.598001  \n",
       " 2600   0  1.539037  \n",
       " 2601   1  1.623179  \n",
       " \n",
       " [2602 rows x 14 columns],\n",
       " 'df_NEM':        SNo Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  NEM    XEM 2015-04-02 23:59:59  0.000323  0.000227  0.000242   \n",
       " 1        2  NEM    XEM 2015-04-03 23:59:59  0.000330  0.000291  0.000309   \n",
       " 2        3  NEM    XEM 2015-04-04 23:59:59  0.000318  0.000251  0.000310   \n",
       " 3        4  NEM    XEM 2015-04-05 23:59:59  0.000283  0.000218  0.000272   \n",
       " 4        5  NEM    XEM 2015-04-06 23:59:59  0.000299  0.000229  0.000232   \n",
       " ...    ...  ...    ...                 ...       ...       ...       ...   \n",
       " 2283  2284  NEM    XEM 2021-07-02 23:59:59  0.131455  0.123406  0.128181   \n",
       " 2284  2285  NEM    XEM 2021-07-03 23:59:59  0.136359  0.128350  0.131542   \n",
       " 2285  2286  NEM    XEM 2021-07-04 23:59:59  0.140061  0.129912  0.134751   \n",
       " 2286  2287  NEM    XEM 2021-07-05 23:59:59  0.137633  0.126915  0.137633   \n",
       " 2287  2288  NEM    XEM 2021-07-06 23:59:59  0.136318  0.128804  0.130591   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.000314  2.854940e+04  2.823534e+06  0.000072  0.295601   1 -0.628055  \n",
       " 1     0.000310  2.067790e+04  2.792457e+06  0.000002  0.004933   1 -0.628072  \n",
       " 2     0.000277  2.355020e+04  2.488770e+06 -0.000034 -0.108643   0 -0.628243  \n",
       " 3     0.000232  2.680020e+04  2.087388e+06 -0.000040 -0.145814   0 -0.628468  \n",
       " 4     0.000289  2.251150e+04  2.598354e+06  0.000057  0.244197   1 -0.628181  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 2283  0.131455  3.983899e+07  1.183097e+09  0.003274  0.025545   1  0.034310  \n",
       " 2284  0.134756  4.164779e+07  1.212805e+09  0.003214  0.024434   1  0.050982  \n",
       " 2285  0.137617  4.953784e+07  1.238554e+09  0.002866  0.021272   1  0.065432  \n",
       " 2286  0.130602  5.999584e+07  1.175421e+09 -0.007030 -0.051081   0  0.030002  \n",
       " 2287  0.133591  6.635341e+07  1.202315e+09  0.002999  0.022966   1  0.045095  \n",
       " \n",
       " [2288 rows x 14 columns],\n",
       " 'df_Polkadot':      SNo      Name Symbol                Date       High        Low  \\\n",
       " 0      1  Polkadot    DOT 2020-08-21 23:59:59   3.090093   2.730919   \n",
       " 1      2  Polkadot    DOT 2020-08-22 23:59:59   4.510613   2.816762   \n",
       " 2      3  Polkadot    DOT 2020-08-23 23:59:59   4.487058   3.716271   \n",
       " 3      4  Polkadot    DOT 2020-08-24 23:59:59   4.783158   3.812670   \n",
       " 4      5  Polkadot    DOT 2020-08-25 23:59:59   5.760775   4.501223   \n",
       " ..   ...       ...    ...                 ...        ...        ...   \n",
       " 315  316  Polkadot    DOT 2021-07-02 23:59:59  15.469187  14.675115   \n",
       " 316  317  Polkadot    DOT 2021-07-03 23:59:59  15.776978  15.068263   \n",
       " 317  318  Polkadot    DOT 2021-07-04 23:59:59  16.579445  15.221813   \n",
       " 318  319  Polkadot    DOT 2021-07-05 23:59:59  15.996540  14.788740   \n",
       " 319  320  Polkadot    DOT 2021-07-06 23:59:59  16.162344  15.210810   \n",
       " \n",
       "           Open      Close        Volume     Marketcap      Diff   Percent  Up  \\\n",
       " 0     2.896923   2.875028  4.931314e+07  0.000000e+00 -0.021895 -0.007558   0   \n",
       " 1     2.878570   4.484690  4.858011e+08  0.000000e+00  1.606120  0.557958   1   \n",
       " 2     4.487058   3.967066  3.207495e+08  0.000000e+00 -0.519993 -0.115887   0   \n",
       " 3     3.987413   4.602614  4.076902e+08  0.000000e+00  0.615201  0.154286   1   \n",
       " 4     4.584953   5.523393  8.988475e+08  0.000000e+00  0.938440  0.204678   1   \n",
       " ..         ...        ...           ...           ...       ...       ...  ..   \n",
       " 315  15.219061  15.340774  7.531948e+08  1.468349e+10  0.121714  0.007997   1   \n",
       " 316  15.321934  15.553716  7.112351e+08  1.489035e+10  0.231782  0.015127   1   \n",
       " 317  15.553584  16.014026  7.520759e+08  1.533547e+10  0.460442  0.029604   1   \n",
       " 318  15.996540  15.235582  8.448446e+08  1.459323e+10 -0.760957 -0.047570   0   \n",
       " 319  15.240785  16.143564  1.001573e+09  1.546772e+10  0.902779  0.059234   1   \n",
       " \n",
       "      Standard  \n",
       " 0   -1.111559  \n",
       " 1   -0.994371  \n",
       " 2   -1.032056  \n",
       " 3   -0.985786  \n",
       " 4   -0.918750  \n",
       " ..        ...  \n",
       " 315 -0.204016  \n",
       " 316 -0.188513  \n",
       " 317 -0.155001  \n",
       " 318 -0.211674  \n",
       " 319 -0.145571  \n",
       " \n",
       " [320 rows x 14 columns],\n",
       " 'df_Solana':      SNo    Name Symbol                Date       High        Low       Open  \\\n",
       " 0      1  Solana    SOL 2020-04-11 23:59:59   1.049073   0.765020   0.951054   \n",
       " 1      2  Solana    SOL 2020-04-12 23:59:59   0.956670   0.762426   0.785448   \n",
       " 2      3  Solana    SOL 2020-04-13 23:59:59   0.891603   0.773976   0.890760   \n",
       " 3      4  Solana    SOL 2020-04-14 23:59:59   0.796472   0.628169   0.777832   \n",
       " 4      5  Solana    SOL 2020-04-15 23:59:59   0.704964   0.621531   0.669289   \n",
       " ..   ...     ...    ...                 ...        ...        ...        ...   \n",
       " 447  448  Solana    SOL 2021-07-02 23:59:59  34.031786  31.479924  33.306310   \n",
       " 448  449  Solana    SOL 2021-07-03 23:59:59  35.404770  33.298475  34.015575   \n",
       " 449  450  Solana    SOL 2021-07-04 23:59:59  35.502372  33.555737  34.495117   \n",
       " 450  451  Solana    SOL 2021-07-05 23:59:59  34.461824  32.482692  34.282550   \n",
       " 451  452  Solana    SOL 2021-07-06 23:59:59  34.978319  32.930307  32.930307   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.776819  4.386244e+07  0.000000e+00 -0.174235 -0.183202   0 -0.686855  \n",
       " 1     0.882507  3.873690e+07  0.000000e+00  0.097059  0.123572   1 -0.679367  \n",
       " 2     0.777832  1.821129e+07  0.000000e+00 -0.112928 -0.126777   0 -0.686783  \n",
       " 3     0.661925  1.674761e+07  0.000000e+00 -0.115907 -0.149013   0 -0.694995  \n",
       " 4     0.646651  1.307528e+07  0.000000e+00 -0.022638 -0.033824   0 -0.696077  \n",
       " ..         ...           ...           ...       ...       ...  ..       ...  \n",
       " 447  34.020482  4.402988e+08  9.275257e+09  0.714172  0.021443   1  1.668439  \n",
       " 448  34.478816  3.270200e+08  9.400216e+09  0.463241  0.013618   1  1.700912  \n",
       " 449  34.310601  3.034205e+08  9.354354e+09 -0.184516 -0.005349   0  1.688994  \n",
       " 450  32.984588  3.138393e+08  8.992833e+09 -1.297963 -0.037861   0  1.595047  \n",
       " 451  34.269140  3.653360e+08  9.343050e+09  1.338833  0.040657   1  1.686057  \n",
       " \n",
       " [452 rows x 14 columns],\n",
       " 'df_Stellar':        SNo     Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  Stellar    XLM 2014-08-06 23:59:00  0.003402  0.002266  0.002373   \n",
       " 1        2  Stellar    XLM 2014-08-07 23:59:00  0.003042  0.002455  0.002686   \n",
       " 2        3  Stellar    XLM 2014-08-08 23:59:00  0.003243  0.002493  0.002493   \n",
       " 3        4  Stellar    XLM 2014-08-09 23:59:00  0.003710  0.002873  0.002884   \n",
       " 4        5  Stellar    XLM 2014-08-10 23:59:00  0.007620  0.003363  0.003497   \n",
       " ...    ...      ...    ...                 ...       ...       ...       ...   \n",
       " 2522  2523  Stellar    XLM 2021-07-02 23:59:00  0.273359  0.252874  0.269148   \n",
       " 2523  2524  Stellar    XLM 2021-07-03 23:59:00  0.270331  0.259710  0.263304   \n",
       " 2524  2525  Stellar    XLM 2021-07-04 23:59:00  0.272314  0.259118  0.264254   \n",
       " 2525  2526  Stellar    XLM 2021-07-05 23:59:00  0.268272  0.252300  0.268272   \n",
       " 2526  2527  Stellar    XLM 2021-07-06 23:59:00  0.267757  0.253547  0.253931   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.002657  3.581980e+04  9.663695e+05  0.000284  0.119655   1 -0.700457  \n",
       " 1     0.002501  1.428640e+05  1.079148e+06 -0.000185 -0.069022   0 -0.701564  \n",
       " 2     0.002881  9.370780e+04  1.260109e+06  0.000388  0.155636   1 -0.698870  \n",
       " 3     0.003481  2.335790e+05  1.645185e+06  0.000597  0.207049   1 -0.694620  \n",
       " 4     0.004303  3.527370e+05  2.216012e+06  0.000806  0.230399   1 -0.688797  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 2522  0.263427  3.487793e+08  6.118631e+09 -0.005720 -0.021253   0  1.147338  \n",
       " 2523  0.264324  3.310069e+08  6.139464e+09  0.001020  0.003875   1  1.153693  \n",
       " 2524  0.268128  3.376196e+08  6.227819e+09  0.003874  0.014659   1  1.180648  \n",
       " 2525  0.254456  3.359242e+08  5.910264e+09 -0.013816 -0.051499   0  1.083771  \n",
       " 2526  0.260190  3.604261e+08  6.049985e+09  0.006259  0.024647   1  1.124395  \n",
       " \n",
       " [2527 rows x 14 columns],\n",
       " 'df_Tether':        SNo    Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  Tether   USDT 2015-02-26 23:59:59  1.212320  1.194710  1.210420   \n",
       " 1        2  Tether   USDT 2015-03-02 23:59:59  0.607890  0.568314  0.571249   \n",
       " 2        3  Tether   USDT 2015-03-03 23:59:59  0.606229  0.604416  0.605129   \n",
       " 3        4  Tether   USDT 2015-03-06 23:59:59  1.000000  1.000000  1.000000   \n",
       " 4        5  Tether   USDT 2015-03-07 23:59:59  1.000000  1.000000  1.000000   \n",
       " ...    ...     ...    ...                 ...       ...       ...       ...   \n",
       " 2313  2314  Tether   USDT 2021-07-02 23:59:59  1.000830  0.999991  1.000301   \n",
       " 2314  2315  Tether   USDT 2021-07-03 23:59:59  1.000674  0.999914  1.000046   \n",
       " 2315  2316  Tether   USDT 2021-07-04 23:59:59  1.000165  0.998902  0.999987   \n",
       " 2316  2317  Tether   USDT 2021-07-05 23:59:59  1.001040  0.999426  0.999426   \n",
       " 2317  2318  Tether   USDT 2021-07-06 23:59:59  1.000879  0.999825  1.000728   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up   Standard  \n",
       " 0     1.205740  5.955460e+00  3.033642e+05 -0.004680 -0.003866   0  13.714815  \n",
       " 1     0.606502  3.032500e+00  1.525959e+05  0.035253  0.061712   1 -26.366446  \n",
       " 2     0.606229  3.031130e+00  1.525272e+05  0.001100  0.001818   1 -26.384705  \n",
       " 3     1.000000  9.264720e+01  2.516000e+05  0.000000  0.000000   1  -0.046525  \n",
       " 4     1.000000  5.819680e+04  2.516000e+05  0.000000  0.000000   1  -0.046525  \n",
       " ...        ...           ...           ...       ...       ...  ..        ...  \n",
       " 2313  1.000033  4.562659e+10  6.237592e+10 -0.000268 -0.000268   0  -0.044337  \n",
       " 2314  0.999956  4.119499e+10  6.237115e+10 -0.000090 -0.000090   0  -0.049450  \n",
       " 2315  0.999501  4.306666e+10  6.234274e+10 -0.000486 -0.000486   0  -0.079909  \n",
       " 2316  1.000667  4.721585e+10  6.241550e+10  0.001241  0.001242   1  -0.001883  \n",
       " 2317  1.000090  5.105419e+10  6.233384e+10 -0.000638 -0.000638   0  -0.040499  \n",
       " \n",
       " [2318 rows x 14 columns],\n",
       " 'df_Tron':        SNo  Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  TRON    TRX 2017-09-14 23:59:59  0.002479  0.001298  0.002006   \n",
       " 1        2  TRON    TRX 2017-09-15 23:59:59  0.001831  0.001091  0.001419   \n",
       " 2        3  TRON    TRX 2017-09-16 23:59:59  0.017671  0.001371  0.001815   \n",
       " 3        4  TRON    TRX 2017-09-17 23:59:59  0.003879  0.001781  0.001836   \n",
       " 4        5  TRON    TRX 2017-09-18 23:59:59  0.003438  0.002534  0.003324   \n",
       " ...    ...   ...    ...                 ...       ...       ...       ...   \n",
       " 1387  1388  TRON    TRX 2021-07-02 23:59:59  0.066593  0.063369  0.064808   \n",
       " 1388  1389  TRON    TRX 2021-07-03 23:59:59  0.067337  0.065282  0.066566   \n",
       " 1389  1390  TRON    TRX 2021-07-04 23:59:59  0.068182  0.065113  0.066834   \n",
       " 1390  1391  TRON    TRX 2021-07-05 23:59:59  0.067344  0.063691  0.067330   \n",
       " 1391  1392  TRON    TRX 2021-07-06 23:59:59  0.066369  0.064010  0.064549   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.001427  5.876970e+04  0.000000e+00 -0.000579 -0.288729   0 -1.130468  \n",
       " 1     0.001801  2.647480e+04  0.000000e+00  0.000382  0.268908   1 -1.116898  \n",
       " 2     0.001831  5.453470e+04  0.000000e+00  0.000016  0.008759   1 -1.115800  \n",
       " 3     0.003320  1.249980e+05  0.000000e+00  0.001484  0.808570   1 -1.061784  \n",
       " 4     0.003263  1.591580e+05  0.000000e+00 -0.000061 -0.018387   0 -1.063867  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 1387  0.066512  8.494136e+08  4.766239e+09  0.001704  0.026295   1  1.230939  \n",
       " 1388  0.066813  7.328898e+08  4.787777e+09  0.000246  0.003699   1  1.241844  \n",
       " 1389  0.067298  8.447663e+08  4.822577e+09  0.000464  0.006942   1  1.259463  \n",
       " 1390  0.064574  7.981849e+08  4.627367e+09 -0.002755 -0.040924   0  1.160628  \n",
       " 1391  0.064935  6.995041e+08  4.653234e+09  0.000387  0.005990   1  1.173724  \n",
       " \n",
       " [1392 rows x 14 columns],\n",
       " 'df_Uniswap':      SNo     Name Symbol                Date       High        Low       Open  \\\n",
       " 0      1  Uniswap    UNI 2020-09-18 23:59:59   8.438537   3.261757   3.488153   \n",
       " 1      2  Uniswap    UNI 2020-09-19 23:59:59   7.443275   5.321795   6.945577   \n",
       " 2      3  Uniswap    UNI 2020-09-20 23:59:59   6.050326   4.720210   5.720515   \n",
       " 3      4  Uniswap    UNI 2020-09-21 23:59:59   5.622155   4.160743   5.244195   \n",
       " 4      5  Uniswap    UNI 2020-09-22 23:59:59   4.554092   3.755040   4.275014   \n",
       " ..   ...      ...    ...                 ...        ...        ...        ...   \n",
       " 287  288  Uniswap    UNI 2021-07-02 23:59:59  18.243571  16.983818  17.833072   \n",
       " 288  289  Uniswap    UNI 2021-07-03 23:59:59  19.524757  17.849987  18.227339   \n",
       " 289  290  Uniswap    UNI 2021-07-04 23:59:59  21.481411  18.947371  19.479936   \n",
       " 290  291  Uniswap    UNI 2021-07-05 23:59:59  20.867785  19.180908  20.767419   \n",
       " 291  292  Uniswap    UNI 2021-07-06 23:59:59  23.188558  20.075544  20.088390   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     6.897257  6.061110e+09  6.681116e+08  3.409104  0.977338   1 -0.796820  \n",
       " 1     5.724464  2.447471e+09  5.545076e+08 -1.221113 -0.175812   0 -0.888618  \n",
       " 2     5.244195  1.554144e+09  5.079857e+08 -0.476319 -0.083265   0 -0.926210  \n",
       " 3     4.266079  1.329464e+09  4.132392e+08 -0.978117 -0.186514   0 -1.002770  \n",
       " 4     3.905138  8.162827e+08  3.782762e+08 -0.369876 -0.086520   0 -1.031022  \n",
       " ..         ...           ...           ...       ...       ...  ..       ...  \n",
       " 287  18.241726  3.070457e+08  1.049364e+10  0.408654  0.022916   1  0.091147  \n",
       " 288  19.524757  3.448554e+08  1.146609e+10  1.297418  0.071180   1  0.191573  \n",
       " 289  20.771874  5.404909e+08  1.219861e+10  1.291939  0.066321   1  0.289189  \n",
       " 290  20.095181  5.452744e+08  1.180130e+10 -0.672238 -0.032370   0  0.236222  \n",
       " 291  22.400186  8.151239e+08  1.315501e+10  2.311796  0.115081   1  0.416642  \n",
       " \n",
       " [292 rows x 14 columns],\n",
       " 'df_USDCoin':        SNo      Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  USD Coin   USDC 2018-10-09 23:59:59  1.008330  1.000030  1.004270   \n",
       " 1        2  USD Coin   USDC 2018-10-10 23:59:59  1.019080  1.003970  1.004940   \n",
       " 2        3  USD Coin   USDC 2018-10-11 23:59:59  1.031230  1.001730  1.009940   \n",
       " 3        4  USD Coin   USDC 2018-10-12 23:59:59  1.018710  1.001140  1.008610   \n",
       " 4        5  USD Coin   USDC 2018-10-13 23:59:59  1.020290  1.003550  1.014450   \n",
       " ...    ...       ...    ...                 ...       ...       ...       ...   \n",
       " 997    998  USD Coin   USDC 2021-07-02 23:59:59  1.000916  0.999966  1.000177   \n",
       " 998    999  USD Coin   USDC 2021-07-03 23:59:59  1.000670  0.999925  1.000048   \n",
       " 999   1000  USD Coin   USDC 2021-07-04 23:59:59  1.000187  0.998901  0.999956   \n",
       " 1000  1001  USD Coin   USDC 2021-07-05 23:59:59  1.000839  0.999459  0.999565   \n",
       " 1001  1002  USD Coin   USDC 2021-07-06 23:59:59  1.000731  0.999662  1.000501   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     1.006860  1.088030e+05  0.000000e+00  0.002590  0.002579   1  0.449836  \n",
       " 1     1.009620  7.117830e+05  0.000000e+00  0.004680  0.004657   1  0.854427  \n",
       " 2     1.009780  4.177290e+06  0.000000e+00 -0.000160 -0.000158   0  0.877882  \n",
       " 3     1.012720  1.322240e+06  0.000000e+00  0.004110  0.004075   1  1.308859  \n",
       " 4     1.007430  6.985070e+05  0.000000e+00 -0.007020 -0.006920   0  0.533393  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 997   1.000035  1.787896e+09  2.539775e+10 -0.000142 -0.000142   0 -0.550670  \n",
       " 998   0.999984  1.491017e+09  2.550437e+10 -0.000064 -0.000064   0 -0.558129  \n",
       " 999   0.999500  1.578667e+09  2.551172e+10 -0.000456 -0.000456   0 -0.629017  \n",
       " 1000  1.000528  1.887496e+09  2.554724e+10  0.000962  0.000963   1 -0.478408  \n",
       " 1001  1.000059  2.312602e+09  2.567322e+10 -0.000442 -0.000442   0 -0.547196  \n",
       " \n",
       " [1002 rows x 14 columns],\n",
       " 'df_WrappedBitcoin':      SNo             Name Symbol                Date          High  \\\n",
       " 0      1  Wrapped Bitcoin   WBTC 2019-01-31 23:59:59   3535.421222   \n",
       " 1      2  Wrapped Bitcoin   WBTC 2019-02-01 23:59:59   3517.770253   \n",
       " 2      3  Wrapped Bitcoin   WBTC 2019-02-02 23:59:59   3546.404225   \n",
       " 3      4  Wrapped Bitcoin   WBTC 2019-02-03 23:59:59   3544.667094   \n",
       " 4      5  Wrapped Bitcoin   WBTC 2019-02-04 23:59:59   3500.410630   \n",
       " ..   ...              ...    ...                 ...           ...   \n",
       " 883  884  Wrapped Bitcoin   WBTC 2021-07-02 23:59:59  33841.008754   \n",
       " 884  885  Wrapped Bitcoin   WBTC 2021-07-03 23:59:59  34871.130346   \n",
       " 885  886  Wrapped Bitcoin   WBTC 2021-07-04 23:59:59  35914.361323   \n",
       " 886  887  Wrapped Bitcoin   WBTC 2021-07-05 23:59:59  35294.411111   \n",
       " 887  888  Wrapped Bitcoin   WBTC 2021-07-06 23:59:59  35004.772491   \n",
       " \n",
       "               Low          Open         Close        Volume     Marketcap  \\\n",
       " 0     3435.290530   3496.839832   3466.506533  1.928907e+04  0.000000e+00   \n",
       " 1     3422.825563   3469.392973   3480.772314  1.364047e+04  0.000000e+00   \n",
       " 2     3459.112772   3476.335119   3542.095361  1.894967e+03  0.000000e+00   \n",
       " 3     3436.177705   3540.562788   3477.866026  6.524925e+03  0.000000e+00   \n",
       " 4     3445.643244   3481.734248   3481.219346  3.540939e+03  0.000000e+00   \n",
       " ..            ...           ...           ...           ...           ...   \n",
       " 883  32761.826975  33529.259522  33824.263339  1.609147e+08  6.625345e+09   \n",
       " 884  33381.245301  33806.037094  34676.985915  1.659959e+08  6.792373e+09   \n",
       " 885  34382.512697  34672.651028  35289.762931  1.583060e+08  6.912401e+09   \n",
       " 886  33288.379267  35294.411111  33806.661764  1.711673e+08  6.621898e+09   \n",
       " 887  33700.560335  33772.552729  34189.372826  2.035448e+08  6.713947e+09   \n",
       " \n",
       "             Diff   Percent  Up  Standard  \n",
       " 0     -30.333299 -0.008674   0 -0.862112  \n",
       " 1      11.379341  0.003280   1 -0.861209  \n",
       " 2      65.760242  0.018917   1 -0.857327  \n",
       " 3     -62.696761 -0.017708   0 -0.861393  \n",
       " 4      -0.514903 -0.000148   0 -0.861181  \n",
       " ..           ...       ...  ..       ...  \n",
       " 883   295.003817  0.008798   1  1.059449  \n",
       " 884   870.948820  0.025763   1  1.113423  \n",
       " 885   617.111903  0.017798   1  1.152211  \n",
       " 886 -1487.749347 -0.042153   0  1.058334  \n",
       " 887   416.820097  0.012342   1  1.082559  \n",
       " \n",
       " [888 rows x 14 columns],\n",
       " 'df_XRP':        SNo Name Symbol                Date      High       Low      Open  \\\n",
       " 0        1  XRP    XRP 2013-08-05 23:59:59  0.005980  0.005613  0.005875   \n",
       " 1        2  XRP    XRP 2013-08-06 23:59:59  0.005661  0.004629  0.005637   \n",
       " 2        3  XRP    XRP 2013-08-07 23:59:59  0.004682  0.004333  0.004669   \n",
       " 3        4  XRP    XRP 2013-08-08 23:59:59  0.004424  0.004175  0.004397   \n",
       " 4        5  XRP    XRP 2013-08-09 23:59:59  0.004367  0.004253  0.004257   \n",
       " ...    ...  ...    ...                 ...       ...       ...       ...   \n",
       " 2888  2889  XRP    XRP 2021-07-02 23:59:59  0.667287  0.634726  0.659890   \n",
       " 2889  2890  XRP    XRP 2021-07-03 23:59:59  0.683677  0.644653  0.655639   \n",
       " 2890  2891  XRP    XRP 2021-07-04 23:59:59  0.707783  0.665802  0.673218   \n",
       " 2891  2892  XRP    XRP 2021-07-05 23:59:59  0.695653  0.648492  0.695653   \n",
       " 2892  2893  XRP    XRP 2021-07-06 23:59:59  0.679923  0.652676  0.653055   \n",
       " \n",
       "          Close        Volume     Marketcap      Diff   Percent  Up  Standard  \n",
       " 0     0.005613  0.000000e+00  4.387916e+07 -0.000262 -0.044618   0 -0.676779  \n",
       " 1     0.004680  0.000000e+00  3.659101e+07 -0.000956 -0.169661   0 -0.679532  \n",
       " 2     0.004417  0.000000e+00  3.453412e+07 -0.000252 -0.053951   0 -0.680309  \n",
       " 3     0.004254  0.000000e+00  3.325863e+07 -0.000142 -0.032406   0 -0.680791  \n",
       " 4     0.004291  0.000000e+00  3.354750e+07  0.000034  0.007956   1 -0.680682  \n",
       " ...        ...           ...           ...       ...       ...  ..       ...  \n",
       " 2888  0.656763  2.061607e+09  3.030759e+10 -0.003127 -0.004739   0  1.246121  \n",
       " 2889  0.672888  1.872820e+09  3.105172e+10  0.017249  0.026309   1  1.293741  \n",
       " 2890  0.694945  1.885242e+09  3.206960e+10  0.021728  0.032275   1  1.358878  \n",
       " 2891  0.654300  2.076373e+09  3.019395e+10 -0.041353 -0.059445   0  1.238849  \n",
       " 2892  0.665402  1.938959e+09  3.072284e+10  0.012347  0.018907   1  1.271635  \n",
       " \n",
       " [2893 rows x 14 columns]}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "file_list = os.listdir('./data')\n",
    "# lst = list()\n",
    "dct = dict()\n",
    "\n",
    "for i in file_list:\n",
    "    filename = './data/' + i\n",
    "    df = import_data(filename)\n",
    "    j = 'df' + i[4:-4]\n",
    "    dct[j] = df\n",
    "dct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a939a86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['df_Aave', 'df_BinanceCoin', 'df_Bitcoin', 'df_Cardano', 'df_ChainLink', 'df_Cosmos', 'df_CryptocomCoin', 'df_Dogecoin', 'df_EOS', 'df_Ethereum', 'df_Iota', 'df_Litecoin', 'df_Monero', 'df_NEM', 'df_Polkadot', 'df_Solana', 'df_Stellar', 'df_Tether', 'df_Tron', 'df_Uniswap', 'df_USDCoin', 'df_WrappedBitcoin', 'df_XRP'])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9ea797cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,16))\n",
    "plt.title('Standardized Movements of 23 Coins')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Standardized Closing Price')\n",
    "\n",
    "for df_name, df in dct.items():\n",
    "    plot_df(df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "97fcbf09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# figure out correlation between 2 time series. and figure out how to do it for each time series\n",
    "# numpy for correlation (regular python)\n",
    "\n",
    "# seaborn to visualize (heatmap.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eeb5ac0",
   "metadata": {},
   "source": [
    "# going to create a matrix of coins (all combinations) and fill it with corresponding correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "94202826",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "correlation_matrix = np.array((len(dct),len(dct)))\n",
    "# correlation_matrix.columns = \n",
    "\n",
    "keys = list(dct.keys())\n",
    "keys\n",
    "\n",
    "for i in range(len(keys)):\n",
    "    for j in range(len(keys)):\n",
    "        df = pd.DataFrame()\n",
    "        start_date_i = dct[keys[i]].iloc[0]['Date']\n",
    "        start_date_j = dct[keys[i]].iloc[0]['Date']\n",
    "        \n",
    "        \n",
    "#         len(dct[keys[i]]['Close']) > len(dct[keys[j]]['Close'])\n",
    "        \n",
    "#         len(dct[keys[i]]['Close']) < len(dct[keys[j]]['Close'])\n",
    "        \n",
    "#         np.corrcoef ((dct[keys[i]]['Close'], dct[keys[j]]['Close']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "45fda9dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7741582904068568"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date_i = dct[keys[0]].iloc[0]['Date']\n",
    "start_date_j = dct[keys[1]].iloc[0]['Date']\n",
    "start = start_date_i\n",
    "if start_date_j > start_date_i:\n",
    "    start = start_date_j\n",
    "\n",
    "df1 = dct[keys[0]].set_index('Date') # Aave coin\n",
    "df2 = dct[keys[1]].set_index('Date') # Binance coin\n",
    "ts1 = df1.loc[start:,'Close']\n",
    "ts2 = df2.loc[start:,'Close']\n",
    "\n",
    "ts1\n",
    "\n",
    "# create df out of 2 timeseries... ts1 pd.concat([series1,series2], axis = 1) <-- matches up by index <-- yes\n",
    "df3 = pd.concat([ts1,ts2], axis = 1)\n",
    "\n",
    "corr = df3.corr().iloc[1,0]\n",
    "corr\n",
    "# start_date_i \n",
    "# start_date_j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aeeb673d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00,  7.74158290e-01,  8.77041331e-01,\n",
       "         8.39792035e-01,  9.19572304e-01,  9.07633791e-01,\n",
       "         6.06599690e-01,  6.09484660e-01,  7.37519473e-01,\n",
       "         8.68677384e-01,  8.49440857e-01,  8.73240544e-01,\n",
       "         7.78799625e-01,  6.31753627e-01,  9.15371985e-01,\n",
       "         6.96013870e-01,             nan,  6.72032532e-02,\n",
       "         7.31029706e-01,  9.26131097e-01,  1.04429301e-01,\n",
       "         8.77759672e-01,  6.60442548e-01],\n",
       "       [ 7.74158290e-01,  1.00000000e+00,  8.56401363e-01,\n",
       "         8.93321800e-01,  8.69329421e-01,  9.02092493e-01,\n",
       "         6.25583664e-01,  8.89386817e-01,  1.52797654e-01,\n",
       "         9.04259873e-01,  3.00178739e-01,  6.43627824e-01,\n",
       "         5.95745083e-01,  1.87591048e-01,  8.46351367e-01,\n",
       "         9.48030983e-01,             nan, -8.12121030e-02,\n",
       "         7.27637858e-01,  9.16157655e-01, -2.17446935e-01,\n",
       "         8.50716181e-01,  4.84219543e-01],\n",
       "       [ 8.77041331e-01,  8.56401363e-01,  1.00000000e+00,\n",
       "         8.55017103e-01,  9.33914831e-01,  9.37843861e-01,\n",
       "         6.69825341e-01,  6.48205644e-01,  1.17229730e-01,\n",
       "         9.04147329e-01,  3.47407578e-01,  8.12678693e-01,\n",
       "         7.47431732e-01,  4.65713154e-01,  9.51767927e-01,\n",
       "         7.16856588e-01,             nan,  3.64008055e-02,\n",
       "         6.50913646e-01,  9.05691805e-01, -3.23815194e-01,\n",
       "         9.99966511e-01,  6.20505196e-01],\n",
       "       [ 8.39792035e-01,  8.93321800e-01,  8.55017103e-01,\n",
       "         1.00000000e+00,  8.10503621e-01,  8.99815108e-01,\n",
       "         6.16035506e-01,  8.30918878e-01,  3.06933035e-01,\n",
       "         9.60793129e-01,  5.09285954e-01,  7.66619661e-01,\n",
       "         7.54559293e-01,  4.80136160e-01,  8.48057721e-01,\n",
       "         9.30088788e-01,             nan, -6.93473533e-02,\n",
       "         7.95992816e-01,  8.95403524e-01, -2.36764625e-01,\n",
       "         8.81593644e-01,  6.49165426e-01],\n",
       "       [ 9.19572304e-01,  8.69329421e-01,  9.33914831e-01,\n",
       "         8.10503621e-01,  1.00000000e+00,  9.26709163e-01,\n",
       "         7.39517725e-01,  7.30575126e-01, -2.94769743e-02,\n",
       "         8.62913287e-01,  1.72498427e-01,  5.70215935e-01,\n",
       "         5.02060770e-01,  1.85082401e-01,  9.35553855e-01,\n",
       "         7.93001578e-01,             nan, -1.06306896e-01,\n",
       "         6.00367434e-01,  9.43349017e-01, -3.43126361e-01,\n",
       "         9.40722211e-01,  3.22276490e-01],\n",
       "       [ 9.07633791e-01,  9.02092493e-01,  9.37843861e-01,\n",
       "         8.99815108e-01,  9.26709163e-01,  1.00000000e+00,\n",
       "         6.88901954e-01,  7.06765875e-01,  5.91837609e-01,\n",
       "         9.07322456e-01,  9.65168832e-01,  9.25760040e-01,\n",
       "         9.24819619e-01,  8.58713338e-01,  9.70497786e-01,\n",
       "         7.77762939e-01,             nan, -1.55052713e-01,\n",
       "         9.00177656e-01,  9.48876078e-01, -1.31103983e-01,\n",
       "         9.37911304e-01,  8.22956935e-01],\n",
       "       [ 6.06599690e-01,  6.25583664e-01,  6.69825341e-01,\n",
       "         6.16035506e-01,  7.39517725e-01,  6.88901954e-01,\n",
       "         1.00000000e+00,  4.27782493e-01,  3.28248514e-01,\n",
       "         6.08806537e-01,  6.68015961e-01,  5.73539974e-01,\n",
       "         6.65716828e-01,  6.08402896e-01,  6.17935240e-01,\n",
       "         4.22298385e-01,             nan, -3.25817929e-01,\n",
       "         6.37737588e-01,  7.19355366e-01, -3.43432675e-01,\n",
       "         6.55398183e-01,  5.00697425e-01],\n",
       "       [ 6.09484660e-01,  8.89386817e-01,  6.48205644e-01,\n",
       "         8.30918878e-01,  7.30575126e-01,  7.06765875e-01,\n",
       "         4.27782493e-01,  1.00000000e+00,  1.73599866e-01,\n",
       "         8.37740591e-01,  2.69609935e-01,  5.40726728e-01,\n",
       "         5.34048227e-01,  1.81335113e-01,  5.68371349e-01,\n",
       "         9.29961732e-01,             nan,  8.33262415e-04,\n",
       "         6.40298645e-01,  6.98276879e-01, -1.68839568e-01,\n",
       "         6.42438124e-01,  4.88873047e-01],\n",
       "       [ 7.37519473e-01,  1.52797654e-01,  1.17229730e-01,\n",
       "         3.06933035e-01, -2.94769743e-02,  5.91837609e-01,\n",
       "         3.28248514e-01,  1.73599866e-01,  1.00000000e+00,\n",
       "         3.21867855e-01,  6.81802362e-01,  5.94466862e-01,\n",
       "         6.19067689e-01,  5.29658824e-01,  7.66643881e-01,\n",
       "         8.41603265e-01,             nan, -8.73714706e-02,\n",
       "         6.37617252e-01,  8.06822060e-01, -6.59782338e-02,\n",
       "         4.26185853e-01,  6.63072861e-01],\n",
       "       [ 8.68677384e-01,  9.04259873e-01,  9.04147329e-01,\n",
       "         9.60793129e-01,  8.62913287e-01,  9.07322456e-01,\n",
       "         6.08806537e-01,  8.37740591e-01,  3.21867855e-01,\n",
       "         1.00000000e+00,  4.98746042e-01,  8.31273719e-01,\n",
       "         8.15673431e-01,  5.27981881e-01,  8.40835190e-01,\n",
       "         9.27281472e-01,             nan,  3.15010762e-02,\n",
       "         7.87882012e-01,  9.03782653e-01, -2.74301331e-01,\n",
       "         9.11057706e-01,  6.99698390e-01],\n",
       "       [ 8.49440857e-01,  3.00178739e-01,  3.47407578e-01,\n",
       "         5.09285954e-01,  1.72498427e-01,  9.65168832e-01,\n",
       "         6.68015961e-01,  2.69609935e-01,  6.81802362e-01,\n",
       "         4.98746042e-01,  1.00000000e+00,  7.97739168e-01,\n",
       "         8.66110425e-01,  8.41162598e-01,  9.42804857e-01,\n",
       "         8.44180566e-01,             nan,  1.22325880e-01,\n",
       "         6.36676356e-01,  9.52461422e-01, -1.68117928e-01,\n",
       "         9.03256425e-01,  7.75316556e-01],\n",
       "       [ 8.73240544e-01,  6.43627824e-01,  8.12678693e-01,\n",
       "         7.66619661e-01,  5.70215935e-01,  9.25760040e-01,\n",
       "         5.73539974e-01,  5.40726728e-01,  5.94466862e-01,\n",
       "         8.31273719e-01,  7.97739168e-01,  1.00000000e+00,\n",
       "         9.59586522e-01,  7.64839248e-01,  9.15111769e-01,\n",
       "         8.05545572e-01,             nan,  8.25599265e-02,\n",
       "         7.93911890e-01,  9.09397367e-01, -2.92388732e-01,\n",
       "         9.07005305e-01,  8.75296662e-01],\n",
       "       [ 7.78799625e-01,  5.95745083e-01,  7.47431732e-01,\n",
       "         7.54559293e-01,  5.02060770e-01,  9.24819619e-01,\n",
       "         6.65716828e-01,  5.34048227e-01,  6.19067689e-01,\n",
       "         8.15673431e-01,  8.66110425e-01,  9.59586522e-01,\n",
       "         1.00000000e+00,  8.14404140e-01,  8.44322108e-01,\n",
       "         8.96629084e-01,             nan,  7.21976679e-02,\n",
       "         7.97224052e-01,  8.97607628e-01, -2.86088326e-01,\n",
       "         9.15890639e-01,  9.07294196e-01],\n",
       "       [ 6.31753627e-01,  1.87591048e-01,  4.65713154e-01,\n",
       "         4.80136160e-01,  1.85082401e-01,  8.58713338e-01,\n",
       "         6.08402896e-01,  1.81335113e-01,  5.29658824e-01,\n",
       "         5.27981881e-01,  8.41162598e-01,  7.64839248e-01,\n",
       "         8.14404140e-01,  1.00000000e+00,  7.75467999e-01,\n",
       "         4.35057194e-01,             nan,  1.18407429e-01,\n",
       "         5.99621975e-01,  6.31194038e-01, -2.39529510e-01,\n",
       "         8.92692477e-01,  8.28128376e-01],\n",
       "       [ 9.15371985e-01,  8.46351367e-01,  9.51767927e-01,\n",
       "         8.48057721e-01,  9.35553855e-01,  9.70497786e-01,\n",
       "         6.17935240e-01,  5.68371349e-01,  7.66643881e-01,\n",
       "         8.40835190e-01,  9.42804857e-01,  9.15111769e-01,\n",
       "         8.44322108e-01,  7.75467999e-01,  1.00000000e+00,\n",
       "         7.00051404e-01,             nan, -8.32809204e-02,\n",
       "         8.19355641e-01,  9.53401723e-01,  2.19410836e-02,\n",
       "         9.52044637e-01,  7.08196906e-01],\n",
       "       [ 6.96013870e-01,  9.48030983e-01,  7.16856588e-01,\n",
       "         9.30088788e-01,  7.93001578e-01,  7.77762939e-01,\n",
       "         4.22298385e-01,  9.29961732e-01,  8.41603265e-01,\n",
       "         9.27281472e-01,  8.44180566e-01,  8.05545572e-01,\n",
       "         8.96629084e-01,  4.35057194e-01,  7.00051404e-01,\n",
       "         1.00000000e+00,             nan, -1.35149696e-01,\n",
       "         8.85968090e-01,  8.12441471e-01, -4.84643585e-02,\n",
       "         7.17632934e-01,  8.85654023e-01],\n",
       "       [            nan,             nan,             nan,\n",
       "                    nan,             nan,             nan,\n",
       "                    nan,             nan,             nan,\n",
       "                    nan,             nan,             nan,\n",
       "                    nan,             nan,             nan,\n",
       "                    nan,  1.00000000e+00,             nan,\n",
       "                    nan,             nan,             nan,\n",
       "                    nan,             nan],\n",
       "       [ 6.72032532e-02, -8.12121030e-02,  3.64008055e-02,\n",
       "        -6.93473533e-02, -1.06306896e-01, -1.55052713e-01,\n",
       "        -3.25817929e-01,  8.33262415e-04, -8.73714706e-02,\n",
       "         3.15010762e-02,  1.22325880e-01,  8.25599265e-02,\n",
       "         7.21976679e-02,  1.18407429e-01, -8.32809204e-02,\n",
       "        -1.35149696e-01,             nan,  1.00000000e+00,\n",
       "        -7.24455288e-02, -6.16697292e-04,  2.27682612e-01,\n",
       "        -2.33051235e-01,  7.09332906e-02],\n",
       "       [ 7.31029706e-01,  7.27637858e-01,  6.50913646e-01,\n",
       "         7.95992816e-01,  6.00367434e-01,  9.00177656e-01,\n",
       "         6.37737588e-01,  6.40298645e-01,  6.37617252e-01,\n",
       "         7.87882012e-01,  6.36676356e-01,  7.93911890e-01,\n",
       "         7.97224052e-01,  5.99621975e-01,  8.19355641e-01,\n",
       "         8.85968090e-01,             nan, -7.24455288e-02,\n",
       "         1.00000000e+00,  8.62617664e-01, -1.92471100e-01,\n",
       "         8.33935911e-01,  8.43500511e-01],\n",
       "       [ 9.26131097e-01,  9.16157655e-01,  9.05691805e-01,\n",
       "         8.95403524e-01,  9.43349017e-01,  9.48876078e-01,\n",
       "         7.19355366e-01,  6.98276879e-01,  8.06822060e-01,\n",
       "         9.03782653e-01,  9.52461422e-01,  9.09397367e-01,\n",
       "         8.97607628e-01,  6.31194038e-01,  9.53401723e-01,\n",
       "         8.12441471e-01,             nan, -6.16697292e-04,\n",
       "         8.62617664e-01,  1.00000000e+00,  1.40206598e-01,\n",
       "         9.06401532e-01,  7.78981195e-01],\n",
       "       [ 1.04429301e-01, -2.17446935e-01, -3.23815194e-01,\n",
       "        -2.36764625e-01, -3.43126361e-01, -1.31103983e-01,\n",
       "        -3.43432675e-01, -1.68839568e-01, -6.59782338e-02,\n",
       "        -2.74301331e-01, -1.68117928e-01, -2.92388732e-01,\n",
       "        -2.86088326e-01, -2.39529510e-01,  2.19410836e-02,\n",
       "        -4.84643585e-02,             nan,  2.27682612e-01,\n",
       "        -1.92471100e-01,  1.40206598e-01,  1.00000000e+00,\n",
       "        -2.55963376e-01, -1.11257611e-01],\n",
       "       [ 8.77759672e-01,  8.50716181e-01,  9.99966511e-01,\n",
       "         8.81593644e-01,  9.40722211e-01,  9.37911304e-01,\n",
       "         6.55398183e-01,  6.42438124e-01,  4.26185853e-01,\n",
       "         9.11057706e-01,  9.03256425e-01,  9.07005305e-01,\n",
       "         9.15890639e-01,  8.92692477e-01,  9.52044637e-01,\n",
       "         7.17632934e-01,             nan, -2.33051235e-01,\n",
       "         8.33935911e-01,  9.06401532e-01, -2.55963376e-01,\n",
       "         1.00000000e+00,  7.46434529e-01],\n",
       "       [ 6.60442548e-01,  4.84219543e-01,  6.20505196e-01,\n",
       "         6.49165426e-01,  3.22276490e-01,  8.22956935e-01,\n",
       "         5.00697425e-01,  4.88873047e-01,  6.63072861e-01,\n",
       "         6.99698390e-01,  7.75316556e-01,  8.75296662e-01,\n",
       "         9.07294196e-01,  8.28128376e-01,  7.08196906e-01,\n",
       "         8.85654023e-01,             nan,  7.09332906e-02,\n",
       "         8.43500511e-01,  7.78981195e-01, -1.11257611e-01,\n",
       "         7.46434529e-01,  1.00000000e+00]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now find correlation for all combinations and write it into the correlation matrix\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "correlation_matrix = np.zeros((len(dct),len(dct)))\n",
    "# correlation_matrix.columns = \n",
    "\n",
    "keys = list(dct.keys())\n",
    "keys\n",
    "\n",
    "correlation_matrix.shape\n",
    "for i in range(len(keys)):\n",
    "    for j in range(len(keys)):\n",
    "        start_date_i = dct[keys[i]].iloc[0]['Date']\n",
    "        start_date_j = dct[keys[j]].iloc[0]['Date']\n",
    "        start = start_date_i\n",
    "        if start_date_j > start_date_i:\n",
    "            start = start_date_j\n",
    "        df1 = dct[keys[i]].set_index('Date') # [0] = Aave coin\n",
    "        df2 = dct[keys[j]].set_index('Date') # [1] = Binance coin\n",
    "        ts1 = df1.loc[start:,'Close']\n",
    "        ts2 = df2.loc[start:,'Close']\n",
    "\n",
    "        df3 = pd.concat([ts1,ts2], axis = 1)\n",
    "\n",
    "        corr = df3.corr().iloc[1,0]\n",
    "        correlation_matrix[j,i] = corr\n",
    "\n",
    "correlation_matrix  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e9bc66",
   "metadata": {},
   "source": [
    "for bitcoin, correlations:\n",
    "\n",
    "[ 8.77041331e-01,  8.56401363e-01,  1.00000000e+00,\n",
    "         8.55017103e-01,  9.33914831e-01,  9.37843861e-01,\n",
    "         6.69825341e-01,  6.48205644e-01,  1.17229730e-01,\n",
    "         9.04147329e-01,  3.47407578e-01,  8.12678693e-01,\n",
    "         7.47431732e-01,  4.65713154e-01,  9.51767927e-01,\n",
    "         7.16856588e-01,  7.61317873e-01,  3.64008055e-02,\n",
    "         6.50913646e-01,  9.05691805e-01, -3.23815194e-01,\n",
    "         9.99966511e-01,  6.20505196e-01],\n",
    "  \n",
    "  \n",
    "  ['df_Aave', 'df_BinanceCoin', 'df_Bitcoin', 'df_Cardano', 'df_ChainLink', 'df_Cosmos', 'df_CryptocomCoin', 'df_Dogecoin', 'df_EOS', 'df_Ethereum', 'df_Iota', 'df_Litecoin', 'df_Monero', 'df_NEM', 'df_Polkadot', 'df_Solana', 'df_Stellar', 'df_Tether', 'df_Tron', 'df_Uniswap', 'df_USDCoin', 'df_WrappedBitcoin', 'df_XRP']\n",
    "  \n",
    "  \n",
    "Bitcoin (blue, 2013): 1.00000000e+00\n",
    "Ethereum (green, 2015): 9.04147329e-01\n",
    "Cosmos (orange, 2019): 9.37843861e-01\n",
    "Uniswap (red, 2020): 9.05691805e-01\n",
    "Polkadot (purple, 2020): 9.51767927e-01\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "84c8ccfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import numpy as np; np.random.seed(0)\n",
    "import seaborn as sns; sns.set_theme()\n",
    "# uniform_data = np.random.rand(10, 12)\n",
    "\n",
    "plt.figure(figsize = (17,10))\n",
    "ax = sns.heatmap(correlation_matrix) #, annot=True) # dataframe or nparray\n",
    "ax.set_xticklabels(labels=keys, rotation=90)\n",
    "ax.set_yticklabels(labels=keys, rotation=0)\n",
    "ax.set_title('Correlation Matrix of Coin Prices', size = 20)\n",
    "plt.savefig('./images/correlation.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "be40ef61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ndct.keys...\\n['df_Aave', 'df_BinanceCoin', 'df_Bitcoin', 'df_Cardano', 'df_ChainLink', 'df_Cosmos', 'df_CryptocomCoin', 'df_Dogecoin', 'df_EOS', 'df_Ethereum', 'df_Iota', 'df_Litecoin', 'df_Monero', 'df_NEM', 'df_Polkadot', 'df_Solana', 'df_Stellar', 'df_Tether', 'df_Tron', 'df_Uniswap', 'df_USDCoin', 'df_WrappedBitcoin', 'df_XRP']\\n\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot standardized BTC with top 4 correlated coins: Cosmos, Ethereum, Pokadot, Uniswap\n",
    "plt.figure(figsize = (20,15))\n",
    "plt.title('Standardized Movements of 5 most correlated Coins', size=20)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Standardized Closing Price')\n",
    "# plt.xlim([2017, ])\n",
    "\n",
    "for df_name, df in dct.items():\n",
    "    if df_name in ['df_Bitcoin', 'df_Cosmos', 'df_Ethereum', 'df_Polkadot', 'df_Uniswap']:\n",
    "#     if df_name in ['df_Ethereum', 'df_Polkadot', 'df_Uniswap']:\n",
    "        plot_df(df)\n",
    "#         plt.legend()\n",
    "    \n",
    "plt.savefig('./images/standardized.png')\n",
    "'''\n",
    "dct.keys...\n",
    "['df_Aave', 'df_BinanceCoin', 'df_Bitcoin', 'df_Cardano', 'df_ChainLink', 'df_Cosmos', 'df_CryptocomCoin', 'df_Dogecoin', 'df_EOS', 'df_Ethereum', 'df_Iota', 'df_Litecoin', 'df_Monero', 'df_NEM', 'df_Polkadot', 'df_Solana', 'df_Stellar', 'df_Tether', 'df_Tron', 'df_Uniswap', 'df_USDCoin', 'df_WrappedBitcoin', 'df_XRP']\n",
    "'''\n",
    "\n",
    "# Order of beginning dates: Bitcoin: Blue, Ethereum: Green, Cosmos 2019, Unswap first in 2020, PokaDot sedond in 2020\n",
    "\n",
    "# Bitcoin (blue, 2013), Ethereum (green, 2015), Cosmos (orange, 2019), Uniswap (red, 2020), Polkadot (purple, 2020)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a4b92501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot only the last 500 days of the 5 coins\n",
    "\n",
    "\n",
    "\n",
    "def plot_df2(df): #filename is the full relative (or absolute) path + filename + extension\n",
    "    df_graph1 = df.iloc[-500:][['Date', 'Standard']] # only last 500 days\n",
    "    df_graph1 = df_graph1.set_index('Date')\n",
    "    df_graph1.Standard.plot()\n",
    "\n",
    "plt.figure(figsize = (20,15))\n",
    "plt.title('Standardized Movements of 5 most correlated Coins (last 500 days)', size=20)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Standardized Closing Price')\n",
    "# plt.xlim([2017, ])\n",
    "\n",
    "for df_name, df in dct.items():\n",
    "    if df_name in ['df_Bitcoin', 'df_Cosmos', 'df_Ethereum', 'df_Polkadot', 'df_Uniswap']:\n",
    "#     if df_name in ['df_Ethereum', 'df_Polkadot', 'df_Uniswap']:\n",
    "        plot_df2(df)\n",
    "#         plt.legend()\n",
    "plt.savefig('./images/standardized_500.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "56296ca9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-50-54918c114beb>:13: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(df['Coin'], rotation=90)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart of market cap by coin (descending) #23 bars\n",
    "\n",
    "lst_coin = keys\n",
    "lst_marketcap = list()\n",
    "\n",
    "for i in range(len(keys)):\n",
    "    lst_marketcap.append(dct[keys[i]].iloc[-1]['Marketcap'])\n",
    "df = pd.DataFrame({'Coin':lst_coin, 'Marketcap':lst_marketcap})\n",
    "df = df.sort_values('Marketcap', ascending = False)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "ax.set_xticklabels(df['Coin'], rotation=90)\n",
    "ax.bar(df.iloc[:,0], df.iloc[:,1]) #or can just pass in column nmaes\n",
    "# plt.yscale('log') # change scale to log\n",
    "\n",
    "ax.set_title('Marketcap of Cryptocurrencies', size = 20)\n",
    "ax.set_ylabel('100 billion USD')\n",
    "plt.tight_layout()\n",
    "plt.savefig('./images/marketcap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4a35e289",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-51-eec6c3c48935>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(df['Coin'], rotation=90)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart of coins ranked by volume traded in the past 365 days\n",
    "\n",
    "lst_coin = keys\n",
    "lst_volume = list()\n",
    "\n",
    "for i in range(len(keys)):\n",
    "#     one_year = dct[keys['Date'].max() - pd.DateOffset(years=1)\n",
    "#     lst_volume.append(df[one_year : keys['Date'].max()])['Volume'].mean()\n",
    "    lst_volume.append(dct[keys[i]].iloc[-365:]['Volume'].mean())\n",
    "df = pd.DataFrame({'Coin':lst_coin, 'Volume':lst_volume})\n",
    "df = df.sort_values('Volume', ascending = False)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (6,6))\n",
    "ax.set_xticklabels(df['Coin'], rotation=90)\n",
    "ax.bar(df.iloc[:,0], df.iloc[:,1]) #or can just pass in column nmaes\n",
    "# plt.yscale('log') # change scale to log\n",
    "\n",
    "ax.set_title('Average Daily Volume of Trades (last 365 days)', size = 20)\n",
    "ax.set_ylabel('10 billion trades')\n",
    "plt.tight_layout()\n",
    "plt.savefig('./images/volume.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4ee80dd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-52-25af5424b0b0>:15: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(df['Coin'], rotation=90)\n",
      "<ipython-input-52-25af5424b0b0>:38: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(df['Coin'], rotation=90)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# log-scale of above 2 barcharts\n",
    "\n",
    "# bar chart of market cap by coin (descending) #23 bars\n",
    "\n",
    "lst_coin = keys\n",
    "lst_marketcap = list()\n",
    "\n",
    "for i in range(len(keys)):\n",
    "    lst_marketcap.append(dct[keys[i]].iloc[-1]['Marketcap'])\n",
    "df = pd.DataFrame({'Coin':lst_coin, 'Marketcap':lst_marketcap})\n",
    "df = df.sort_values('Marketcap', ascending = False)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (20,10))\n",
    "ax.set_xticklabels(df['Coin'], rotation=90)\n",
    "ax.bar(df.iloc[:,0], df.iloc[:,1]) #or can just pass in column nmaes\n",
    "plt.yscale('log') # change scale to log\n",
    "\n",
    "ax.set_title('log Marketcap of Cryptocurrencies', size = 20)\n",
    "ax.set_ylabel('log(USD)')\n",
    "plt.savefig('./images/log_marketcap.png')\n",
    "\n",
    "\n",
    "# bar chart of coins ranked by volume traded in the past 365 days\n",
    "\n",
    "lst_coin = keys\n",
    "lst_volume = list()\n",
    "\n",
    "for i in range(len(keys)):\n",
    "#     one_year = dct[keys['Date'].max() - pd.DateOffset(years=1)\n",
    "#     lst_volume.append(df[one_year : keys['Date'].max()])['Volume'].mean()\n",
    "    lst_volume.append(dct[keys[i]].iloc[-365:]['Volume'].mean())\n",
    "df = pd.DataFrame({'Coin':lst_coin, 'Volume':lst_volume})\n",
    "df = df.sort_values('Volume', ascending = False)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (20,10))\n",
    "ax.set_xticklabels(df['Coin'], rotation=90)\n",
    "ax.bar(df.iloc[:,0], df.iloc[:,1]) #or can just pass in column nmaes\n",
    "plt.yscale('log') # change scale to log\n",
    "\n",
    "ax.set_title('log Average Daily Volume of Trades (last 365 days)', size = 20)\n",
    "ax.set_ylabel('log(trades)')\n",
    "plt.savefig('./images/log_volume.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34c028e3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}