ETF-3-A-XGB-V1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4beac85-78c6-464e-8b20-b17611cc6541",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40ff97f0-159e-4391-9416-dc572dd85ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>Closing Price</th>\n",
       "      <th>IBIT</th>\n",
       "      <th>FBTC</th>\n",
       "      <th>BITB</th>\n",
       "      <th>ARKB</th>\n",
       "      <th>BTCO</th>\n",
       "      <th>EZBC</th>\n",
       "      <th>BRRR</th>\n",
       "      <th>HODL</th>\n",
       "      <th>BTCW</th>\n",
       "      <th>GBTC</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11-01-2024</td>\n",
       "      <td>46368.58594</td>\n",
       "      <td>111.7</td>\n",
       "      <td>227.0</td>\n",
       "      <td>237.9</td>\n",
       "      <td>65.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>50.1</td>\n",
       "      <td>29.4</td>\n",
       "      <td>10.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-95.1</td>\n",
       "      <td>655.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12-01-2024</td>\n",
       "      <td>42853.16797</td>\n",
       "      <td>386.0</td>\n",
       "      <td>195.3</td>\n",
       "      <td>17.4</td>\n",
       "      <td>39.8</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-484.1</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16-01-2024</td>\n",
       "      <td>43154.94531</td>\n",
       "      <td>212.7</td>\n",
       "      <td>102.0</td>\n",
       "      <td>50.2</td>\n",
       "      <td>122.3</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-594.4</td>\n",
       "      <td>-52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17-01-2024</td>\n",
       "      <td>42742.65234</td>\n",
       "      <td>371.4</td>\n",
       "      <td>358.1</td>\n",
       "      <td>68.2</td>\n",
       "      <td>50.3</td>\n",
       "      <td>57.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-460.6</td>\n",
       "      <td>453.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18-01-2024</td>\n",
       "      <td>41262.05859</td>\n",
       "      <td>145.5</td>\n",
       "      <td>177.9</td>\n",
       "      <td>20.1</td>\n",
       "      <td>41.8</td>\n",
       "      <td>58.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-582.3</td>\n",
       "      <td>-126.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19-01-2024</td>\n",
       "      <td>41618.40625</td>\n",
       "      <td>201.5</td>\n",
       "      <td>222.3</td>\n",
       "      <td>56.7</td>\n",
       "      <td>62.6</td>\n",
       "      <td>63.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>-590.4</td>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22-01-2024</td>\n",
       "      <td>39507.36719</td>\n",
       "      <td>260.6</td>\n",
       "      <td>158.7</td>\n",
       "      <td>41.6</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>9.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-640.5</td>\n",
       "      <td>-87.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23-01-2024</td>\n",
       "      <td>39845.55078</td>\n",
       "      <td>160.1</td>\n",
       "      <td>157.7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-515.3</td>\n",
       "      <td>-106.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24-01-2024</td>\n",
       "      <td>40077.07422</td>\n",
       "      <td>66.2</td>\n",
       "      <td>125.7</td>\n",
       "      <td>19.1</td>\n",
       "      <td>24.9</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>9.1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-429.3</td>\n",
       "      <td>-158.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>25-01-2024</td>\n",
       "      <td>39933.80859</td>\n",
       "      <td>170.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-394.1</td>\n",
       "      <td>-79.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26-01-2024</td>\n",
       "      <td>41816.87109</td>\n",
       "      <td>87.1</td>\n",
       "      <td>100.1</td>\n",
       "      <td>30.9</td>\n",
       "      <td>46.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-255.1</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29-01-2024</td>\n",
       "      <td>43288.24609</td>\n",
       "      <td>198.4</td>\n",
       "      <td>208.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-191.7</td>\n",
       "      <td>255.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>30-01-2024</td>\n",
       "      <td>42952.60938</td>\n",
       "      <td>299.2</td>\n",
       "      <td>119.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>16.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>-220.7</td>\n",
       "      <td>247.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31-01-2024</td>\n",
       "      <td>42582.60547</td>\n",
       "      <td>116.2</td>\n",
       "      <td>232.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>14.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-187.7</td>\n",
       "      <td>197.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>01-02-2024</td>\n",
       "      <td>43075.77344</td>\n",
       "      <td>163.9</td>\n",
       "      <td>35.8</td>\n",
       "      <td>4.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-182.0</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>02-02-2024</td>\n",
       "      <td>43185.85938</td>\n",
       "      <td>105.8</td>\n",
       "      <td>78.9</td>\n",
       "      <td>11.5</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-144.6</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>05-02-2024</td>\n",
       "      <td>42658.66797</td>\n",
       "      <td>137.3</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-107.9</td>\n",
       "      <td>68.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>06-02-2024</td>\n",
       "      <td>43084.67188</td>\n",
       "      <td>45.2</td>\n",
       "      <td>37.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-72.7</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>07-02-2024</td>\n",
       "      <td>44318.22266</td>\n",
       "      <td>56.2</td>\n",
       "      <td>130.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>8.6</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-80.8</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>08-02-2024</td>\n",
       "      <td>45301.56641</td>\n",
       "      <td>204.1</td>\n",
       "      <td>128.3</td>\n",
       "      <td>60.5</td>\n",
       "      <td>86.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-101.6</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>09-02-2024</td>\n",
       "      <td>47147.19922</td>\n",
       "      <td>250.7</td>\n",
       "      <td>188.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>136.5</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-51.8</td>\n",
       "      <td>541.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12-02-2024</td>\n",
       "      <td>49958.22266</td>\n",
       "      <td>374.7</td>\n",
       "      <td>151.9</td>\n",
       "      <td>33.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-20.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-95.0</td>\n",
       "      <td>493.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13-02-2024</td>\n",
       "      <td>49742.44141</td>\n",
       "      <td>493.1</td>\n",
       "      <td>163.6</td>\n",
       "      <td>10.8</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-72.8</td>\n",
       "      <td>636.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14-02-2024</td>\n",
       "      <td>51826.69531</td>\n",
       "      <td>224.3</td>\n",
       "      <td>118.9</td>\n",
       "      <td>47.2</td>\n",
       "      <td>101.5</td>\n",
       "      <td>-37.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-131.2</td>\n",
       "      <td>339.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15-02-2024</td>\n",
       "      <td>51938.55469</td>\n",
       "      <td>330.9</td>\n",
       "      <td>97.4</td>\n",
       "      <td>120.2</td>\n",
       "      <td>88.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-174.6</td>\n",
       "      <td>477.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16-02-2024</td>\n",
       "      <td>52160.20313</td>\n",
       "      <td>191.4</td>\n",
       "      <td>116.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>-150.4</td>\n",
       "      <td>330.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20-02-2024</td>\n",
       "      <td>52284.87500</td>\n",
       "      <td>154.3</td>\n",
       "      <td>71.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-137.0</td>\n",
       "      <td>135.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>21-02-2024</td>\n",
       "      <td>51839.17969</td>\n",
       "      <td>96.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-199.3</td>\n",
       "      <td>-35.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22-02-2024</td>\n",
       "      <td>51304.97266</td>\n",
       "      <td>125.1</td>\n",
       "      <td>158.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-55.7</td>\n",
       "      <td>251.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23-02-2024</td>\n",
       "      <td>50731.94922</td>\n",
       "      <td>167.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-44.2</td>\n",
       "      <td>232.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>26-02-2024</td>\n",
       "      <td>54522.40234</td>\n",
       "      <td>111.8</td>\n",
       "      <td>243.3</td>\n",
       "      <td>37.2</td>\n",
       "      <td>130.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-22.4</td>\n",
       "      <td>519.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>27-02-2024</td>\n",
       "      <td>57085.37109</td>\n",
       "      <td>520.2</td>\n",
       "      <td>126.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>16.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-125.6</td>\n",
       "      <td>576.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>28-02-2024</td>\n",
       "      <td>62504.78906</td>\n",
       "      <td>612.1</td>\n",
       "      <td>245.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-216.4</td>\n",
       "      <td>673.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>29-02-2024</td>\n",
       "      <td>61198.38281</td>\n",
       "      <td>603.9</td>\n",
       "      <td>44.8</td>\n",
       "      <td>21.7</td>\n",
       "      <td>9.9</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-598.9</td>\n",
       "      <td>92.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>01-03-2024</td>\n",
       "      <td>62440.63281</td>\n",
       "      <td>202.5</td>\n",
       "      <td>49.3</td>\n",
       "      <td>42.3</td>\n",
       "      <td>55.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-492.4</td>\n",
       "      <td>-139.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>04-03-2024</td>\n",
       "      <td>68330.41406</td>\n",
       "      <td>420.1</td>\n",
       "      <td>404.6</td>\n",
       "      <td>90.9</td>\n",
       "      <td>38.2</td>\n",
       "      <td>-25.7</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>-5.7</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-368.0</td>\n",
       "      <td>562.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>05-03-2024</td>\n",
       "      <td>63801.19922</td>\n",
       "      <td>788.3</td>\n",
       "      <td>125.6</td>\n",
       "      <td>3.7</td>\n",
       "      <td>63.7</td>\n",
       "      <td>-14.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-332.5</td>\n",
       "      <td>648.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>06-03-2024</td>\n",
       "      <td>66106.80469</td>\n",
       "      <td>281.7</td>\n",
       "      <td>205.7</td>\n",
       "      <td>28.6</td>\n",
       "      <td>41.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-276.2</td>\n",
       "      <td>332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>07-03-2024</td>\n",
       "      <td>66925.48438</td>\n",
       "      <td>244.2</td>\n",
       "      <td>473.4</td>\n",
       "      <td>41.4</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-374.8</td>\n",
       "      <td>472.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>08-03-2024</td>\n",
       "      <td>68300.09375</td>\n",
       "      <td>336.3</td>\n",
       "      <td>130.3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>-7.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-302.9</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>11-03-2024</td>\n",
       "      <td>72123.90625</td>\n",
       "      <td>562.9</td>\n",
       "      <td>215.5</td>\n",
       "      <td>49.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.4</td>\n",
       "      <td>118.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>-494.1</td>\n",
       "      <td>505.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>12-03-2024</td>\n",
       "      <td>71481.28906</td>\n",
       "      <td>849.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>24.6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.6</td>\n",
       "      <td>82.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>1045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>13-03-2024</td>\n",
       "      <td>73083.50000</td>\n",
       "      <td>586.5</td>\n",
       "      <td>281.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>44.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-276.5</td>\n",
       "      <td>683.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>14-03-2024</td>\n",
       "      <td>71396.59375</td>\n",
       "      <td>345.4</td>\n",
       "      <td>13.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-257.1</td>\n",
       "      <td>132.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>15-03-2024</td>\n",
       "      <td>69403.77344</td>\n",
       "      <td>139.8</td>\n",
       "      <td>155.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>15.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-139.4</td>\n",
       "      <td>198.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>18-03-2024</td>\n",
       "      <td>67548.59375</td>\n",
       "      <td>451.5</td>\n",
       "      <td>5.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-642.5</td>\n",
       "      <td>-154.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>19-03-2024</td>\n",
       "      <td>61912.77344</td>\n",
       "      <td>75.2</td>\n",
       "      <td>39.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-443.5</td>\n",
       "      <td>-326.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>20-03-2024</td>\n",
       "      <td>67913.67188</td>\n",
       "      <td>49.3</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>-10.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-386.6</td>\n",
       "      <td>-261.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>21-03-2024</td>\n",
       "      <td>65491.39063</td>\n",
       "      <td>233.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-358.8</td>\n",
       "      <td>-94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>22-03-2024</td>\n",
       "      <td>63778.76172</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-169.9</td>\n",
       "      <td>-51.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Closing Price   IBIT   FBTC   BITB   ARKB  BTCO  EZBC  BRRR  \\\n",
       "0   11-01-2024    46368.58594  111.7  227.0  237.9   65.3  17.4  50.1  29.4   \n",
       "1   12-01-2024    42853.16797  386.0  195.3   17.4   39.8  28.4   0.0  20.2   \n",
       "2   16-01-2024    43154.94531  212.7  102.0   50.2  122.3  31.9   0.0  15.3   \n",
       "3   17-01-2024    42742.65234  371.4  358.1   68.2   50.3  57.6   1.2   1.2   \n",
       "4   18-01-2024    41262.05859  145.5  177.9   20.1   41.8  58.8   0.0   9.3   \n",
       "5   19-01-2024    41618.40625  201.5  222.3   56.7   62.6  63.4   0.0  10.4   \n",
       "6   22-01-2024    39507.36719  260.6  158.7   41.6   65.0   5.6   4.7   9.7   \n",
       "7   23-01-2024    39845.55078  160.1  157.7   26.3   61.8   0.0   1.1   0.0   \n",
       "8   24-01-2024    40077.07422   66.2  125.7   19.1   24.9  19.9   1.2   9.1   \n",
       "9   25-01-2024    39933.80859  170.7  101.0   20.0   16.1   0.0   0.0   6.5   \n",
       "10  26-01-2024    41816.87109   87.1  100.1   30.9   46.4   0.0   1.2   1.8   \n",
       "11  29-01-2024    43288.24609  198.4  208.2   20.0   17.2   3.0   0.0   0.0   \n",
       "12  30-01-2024    42952.60938  299.2  119.2   21.9   16.8   6.3   2.5   0.0   \n",
       "13  31-01-2024    42582.60547  116.2  232.0   17.8   14.8   1.5   0.0   0.6   \n",
       "14  01-02-2024    43075.77344  163.9   35.8    4.2   15.9   0.0   0.0   0.0   \n",
       "15  02-02-2024    43185.85938  105.8   78.9   11.5   22.6   0.0   2.5   0.0   \n",
       "16  05-02-2024    42658.66797  137.3   38.0    0.0    0.0   0.0   0.0   0.0   \n",
       "17  06-02-2024    43084.67188   45.2   37.7   11.3    8.6   0.0   0.0   0.0   \n",
       "18  07-02-2024    44318.22266   56.2  130.1   21.4    3.3   8.6   5.1   1.2   \n",
       "19  08-02-2024    45301.56641  204.1  128.3   60.5   86.4  13.4   0.0   1.9   \n",
       "20  09-02-2024    47147.19922  250.7  188.4   29.1  136.5 -17.4   1.4   1.4   \n",
       "21  12-02-2024    49958.22266  374.7  151.9   33.0   40.0 -20.8   0.0   1.1   \n",
       "22  13-02-2024    49742.44141  493.1  163.6   10.8   40.0   0.0   0.0   0.0   \n",
       "23  14-02-2024    51826.69531  224.3  118.9   47.2  101.5 -37.5   9.0   1.0   \n",
       "24  15-02-2024    51938.55469  330.9   97.4  120.2   88.9   1.3   3.0   7.4   \n",
       "25  16-02-2024    52160.20313  191.4  116.7   20.9  140.0   1.0   0.0   7.9   \n",
       "26  20-02-2024    52284.87500  154.3   71.7   11.1   27.4   0.0   0.0   0.0   \n",
       "27  21-02-2024    51839.17969   96.5   52.5    0.0   10.7   1.0   3.0   0.0   \n",
       "28  22-02-2024    51304.97266  125.1  158.9    7.9    6.7   0.0   0.0   1.2   \n",
       "29  23-02-2024    50731.94922  167.5   52.5   12.0   34.5   0.0   1.5   0.0   \n",
       "30  26-02-2024    54522.40234  111.8  243.3   37.2  130.6   4.4   7.9   0.0   \n",
       "31  27-02-2024    57085.37109  520.2  126.0   18.4    5.4   2.6  16.6   0.0   \n",
       "32  28-02-2024    62504.78906  612.1  245.2    9.9   23.8   0.0   0.0   0.0   \n",
       "33  29-02-2024    61198.38281  603.9   44.8   21.7    9.9  -1.5   5.4   0.0   \n",
       "34  01-03-2024    62440.63281  202.5   49.3   42.3   55.1   0.0   5.4   0.0   \n",
       "35  04-03-2024    68330.41406  420.1  404.6   90.9   38.2 -25.7   7.8   3.7   \n",
       "36  05-03-2024    63801.19922  788.3  125.6    3.7   63.7 -14.2   3.6   0.0   \n",
       "37  06-03-2024    66106.80469  281.7  205.7   28.6   41.3   3.0   5.8  40.7   \n",
       "38  07-03-2024    66925.48438  244.2  473.4   41.4   42.1   0.0   0.0  41.8   \n",
       "39  08-03-2024    68300.09375  336.3  130.3    8.0    1.7  -7.6   8.0  41.4   \n",
       "40  11-03-2024    72123.90625  562.9  215.5   49.8   13.0  -9.7   0.0  43.4   \n",
       "41  12-03-2024    71481.28906  849.0   51.6   24.6   93.0 -19.7   0.0  39.6   \n",
       "42  13-03-2024    73083.50000  586.5  281.5    5.6   44.6   0.0  19.1   4.1   \n",
       "43  14-03-2024    71396.59375  345.4   13.7    0.0    3.5   0.0   4.0   9.4   \n",
       "44  15-03-2024    69403.77344  139.8  155.6   20.5    0.0   0.0   2.0   1.2   \n",
       "45  18-03-2024    67548.59375  451.5    5.9   17.6    2.7   0.0   0.0   4.8   \n",
       "46  19-03-2024    61912.77344   75.2   39.6    2.5    0.0   0.0   0.0   0.0   \n",
       "47  20-03-2024    67913.67188   49.3   12.9   18.6   23.3 -10.2  19.0   2.9   \n",
       "48  21-03-2024    65491.39063  233.4    2.9   12.0    2.0   4.2   3.8   4.7   \n",
       "49  22-03-2024    63778.76172   18.9   18.1   16.3    5.4   4.5  29.6  25.5   \n",
       "\n",
       "     HODL  BTCW   GBTC   Total  \n",
       "0    10.6   1.0  -95.1   655.3  \n",
       "1     0.0   0.0 -484.1   203.0  \n",
       "2     7.3   0.0 -594.4   -52.7  \n",
       "3     4.8   1.6 -460.6   453.8  \n",
       "4     2.3   0.0 -582.3  -126.6  \n",
       "5    14.2   2.9 -590.4    43.6  \n",
       "6     6.8   0.4 -640.5   -87.4  \n",
       "7     2.2   0.0 -515.3  -106.1  \n",
       "8     4.5   0.4 -429.3  -158.3  \n",
       "9     0.0   0.0 -394.1   -79.8  \n",
       "10    2.4   0.0 -255.1    14.8  \n",
       "11    0.0   0.0 -191.7   255.1  \n",
       "12    0.0   2.1 -220.7   247.3  \n",
       "13    2.4   0.0 -187.7   197.6  \n",
       "14    0.0   0.7 -182.0    38.5  \n",
       "15    2.4   0.9 -144.6    80.0  \n",
       "16    0.0   0.7 -107.9    68.1  \n",
       "17    2.4   1.1  -72.7    33.6  \n",
       "18    0.0   0.9  -80.8   146.0  \n",
       "19   10.3   1.7 -101.6   405.0  \n",
       "20    2.7   0.5  -51.8   541.5  \n",
       "21    8.5   0.0  -95.0   493.4  \n",
       "22    0.0   1.6  -72.8   636.3  \n",
       "23    2.9   3.6 -131.2   339.7  \n",
       "24    2.9   0.0 -174.6   477.4  \n",
       "25    0.0   2.8 -150.4   330.3  \n",
       "26    5.9   2.2 -137.0   135.6  \n",
       "27    0.0   0.0 -199.3   -35.6  \n",
       "28    2.9   4.4  -55.7   251.4  \n",
       "29    8.7   0.0  -44.2   232.5  \n",
       "30    6.2   0.9  -22.4   519.9  \n",
       "31    9.7   3.6 -125.6   576.9  \n",
       "32   -3.4   2.2 -216.4   673.4  \n",
       "33    7.0   0.0 -598.9    92.3  \n",
       "34   -1.8   0.0 -492.4  -139.6  \n",
       "35   -5.7  -3.2 -368.0   562.7  \n",
       "36    3.5   6.6 -332.5   648.3  \n",
       "37    0.0   1.4 -276.2   332.0  \n",
       "38    1.9   2.5 -374.8   472.5  \n",
       "39    7.8   0.0 -302.9   223.0  \n",
       "40  118.8   5.8 -494.1   505.4  \n",
       "41   82.9   3.0  -79.0  1045.0  \n",
       "42   16.5   2.3 -276.5   683.7  \n",
       "43   13.8   0.0 -257.1   132.7  \n",
       "44   15.8   3.3 -139.4   198.8  \n",
       "45    5.7   0.0 -642.5  -154.3  \n",
       "46    0.0   0.0 -443.5  -326.2  \n",
       "47    9.3   0.0 -386.6  -261.5  \n",
       "48    1.8   0.0 -358.8   -94.0  \n",
       "49    0.0   0.0 -169.9   -51.6  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('BETF Final.csv')\n",
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3871e794",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Closing Price</th>\n",
       "      <th>IBIT</th>\n",
       "      <th>FBTC</th>\n",
       "      <th>BITB</th>\n",
       "      <th>ARKB</th>\n",
       "      <th>BTCO</th>\n",
       "      <th>EZBC</th>\n",
       "      <th>BRRR</th>\n",
       "      <th>HODL</th>\n",
       "      <th>BTCW</th>\n",
       "      <th>GBTC</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>20-03-2024</td>\n",
       "      <td>67913.67188</td>\n",
       "      <td>49.3</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>-10.2</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-386.6</td>\n",
       "      <td>-261.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>21-03-2024</td>\n",
       "      <td>65491.39063</td>\n",
       "      <td>233.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-358.8</td>\n",
       "      <td>-94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>22-03-2024</td>\n",
       "      <td>63778.76172</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.1</td>\n",
       "      <td>16.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29.6</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-169.9</td>\n",
       "      <td>-51.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>25-03-2024</td>\n",
       "      <td>69958.81250</td>\n",
       "      <td>35.5</td>\n",
       "      <td>261.8</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-350.1</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>26-03-2024</td>\n",
       "      <td>69987.83594</td>\n",
       "      <td>162.2</td>\n",
       "      <td>279.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>73.6</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.9</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-212.3</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>27-03-2024</td>\n",
       "      <td>69455.34375</td>\n",
       "      <td>323.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-299.8</td>\n",
       "      <td>243.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>28-03-2024</td>\n",
       "      <td>70744.95313</td>\n",
       "      <td>95.1</td>\n",
       "      <td>68.1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-104.9</td>\n",
       "      <td>182.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>01-04-2024</td>\n",
       "      <td>69702.14844</td>\n",
       "      <td>165.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-302.6</td>\n",
       "      <td>-85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>02-04-2024</td>\n",
       "      <td>65446.97266</td>\n",
       "      <td>150.5</td>\n",
       "      <td>44.8</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-87.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-81.9</td>\n",
       "      <td>39.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>03-04-2024</td>\n",
       "      <td>65980.81250</td>\n",
       "      <td>42.0</td>\n",
       "      <td>116.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-75.1</td>\n",
       "      <td>113.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>04-04-2024</td>\n",
       "      <td>68508.84375</td>\n",
       "      <td>144.0</td>\n",
       "      <td>106.6</td>\n",
       "      <td>11.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-79.3</td>\n",
       "      <td>213.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>05-04-2024</td>\n",
       "      <td>67837.64063</td>\n",
       "      <td>308.8</td>\n",
       "      <td>83.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-198.9</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>08-04-2024</td>\n",
       "      <td>71631.35938</td>\n",
       "      <td>21.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-303.3</td>\n",
       "      <td>-223.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>09-04-2024</td>\n",
       "      <td>69139.01563</td>\n",
       "      <td>128.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-154.9</td>\n",
       "      <td>-19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>10-04-2024</td>\n",
       "      <td>70587.88281</td>\n",
       "      <td>33.3</td>\n",
       "      <td>76.3</td>\n",
       "      <td>24.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-17.5</td>\n",
       "      <td>123.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>11-04-2024</td>\n",
       "      <td>70060.60938</td>\n",
       "      <td>192.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-124.9</td>\n",
       "      <td>91.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>12-04-2024</td>\n",
       "      <td>67195.86719</td>\n",
       "      <td>111.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-166.2</td>\n",
       "      <td>-55.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>15-04-2024</td>\n",
       "      <td>63426.21094</td>\n",
       "      <td>73.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-110.1</td>\n",
       "      <td>-36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>16-04-2024</td>\n",
       "      <td>63811.86328</td>\n",
       "      <td>25.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-12.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-79.4</td>\n",
       "      <td>-58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>17-04-2024</td>\n",
       "      <td>61276.69141</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.3</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-133.1</td>\n",
       "      <td>-165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>18-04-2024</td>\n",
       "      <td>63512.75391</td>\n",
       "      <td>18.8</td>\n",
       "      <td>37.4</td>\n",
       "      <td>12.8</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-90.0</td>\n",
       "      <td>-4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>19-04-2024</td>\n",
       "      <td>63843.57031</td>\n",
       "      <td>29.3</td>\n",
       "      <td>54.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-45.8</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>22-04-2024</td>\n",
       "      <td>66837.67969</td>\n",
       "      <td>19.7</td>\n",
       "      <td>34.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>62.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>23-04-2024</td>\n",
       "      <td>66407.27344</td>\n",
       "      <td>37.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>33.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-66.9</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>24-04-2024</td>\n",
       "      <td>64276.89844</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-130.4</td>\n",
       "      <td>-120.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>25-04-2024</td>\n",
       "      <td>64481.70703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-31.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>-20.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-139.4</td>\n",
       "      <td>-217.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>26-04-2024</td>\n",
       "      <td>63755.32031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-82.4</td>\n",
       "      <td>-83.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>29-04-2024</td>\n",
       "      <td>63841.12109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.9</td>\n",
       "      <td>6.8</td>\n",
       "      <td>-31.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-24.7</td>\n",
       "      <td>-51.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>30-04-2024</td>\n",
       "      <td>60636.85547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-35.3</td>\n",
       "      <td>-34.3</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-93.2</td>\n",
       "      <td>-161.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>01-05-2024</td>\n",
       "      <td>58254.01172</td>\n",
       "      <td>-36.9</td>\n",
       "      <td>-191.1</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>-98.1</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-13.4</td>\n",
       "      <td>-9.7</td>\n",
       "      <td>-6.5</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-167.4</td>\n",
       "      <td>-563.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>02-05-2024</td>\n",
       "      <td>59123.43359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-54.9</td>\n",
       "      <td>-34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>03-05-2024</td>\n",
       "      <td>62889.83594</td>\n",
       "      <td>12.7</td>\n",
       "      <td>102.6</td>\n",
       "      <td>33.5</td>\n",
       "      <td>28.1</td>\n",
       "      <td>33.2</td>\n",
       "      <td>60.9</td>\n",
       "      <td>35.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>378.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>06-05-2024</td>\n",
       "      <td>63161.94922</td>\n",
       "      <td>21.5</td>\n",
       "      <td>99.2</td>\n",
       "      <td>2.1</td>\n",
       "      <td>75.6</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>07-05-2024</td>\n",
       "      <td>62334.81641</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-28.6</td>\n",
       "      <td>-15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>08-05-2024</td>\n",
       "      <td>61187.94141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>09-05-2024</td>\n",
       "      <td>63049.96094</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-43.4</td>\n",
       "      <td>-11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>10-05-2024</td>\n",
       "      <td>60792.77734</td>\n",
       "      <td>12.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>-84.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>13-05-2024</td>\n",
       "      <td>62901.44922</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>14-05-2024</td>\n",
       "      <td>61552.78906</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-50.9</td>\n",
       "      <td>100.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>15-05-2024</td>\n",
       "      <td>66267.49219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>131.3</td>\n",
       "      <td>86.3</td>\n",
       "      <td>38.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3.7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>16-05-2024</td>\n",
       "      <td>65231.58203</td>\n",
       "      <td>93.7</td>\n",
       "      <td>67.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>18.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>257.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>17-05-2024</td>\n",
       "      <td>67051.87500</td>\n",
       "      <td>38.1</td>\n",
       "      <td>99.4</td>\n",
       "      <td>20.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.6</td>\n",
       "      <td>221.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>20-05-2024</td>\n",
       "      <td>71448.19531</td>\n",
       "      <td>66.4</td>\n",
       "      <td>64.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>68.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>237.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>21-05-2024</td>\n",
       "      <td>70136.53125</td>\n",
       "      <td>290.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>-4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>305.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>22-05-2024</td>\n",
       "      <td>69122.33594</td>\n",
       "      <td>92.0</td>\n",
       "      <td>74.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-16.1</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23-05-2024</td>\n",
       "      <td>67929.56250</td>\n",
       "      <td>89.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-13.7</td>\n",
       "      <td>107.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>24-05-2024</td>\n",
       "      <td>68526.10156</td>\n",
       "      <td>182.1</td>\n",
       "      <td>43.7</td>\n",
       "      <td>6.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>251.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>28-05-2024</td>\n",
       "      <td>68296.21875</td>\n",
       "      <td>102.5</td>\n",
       "      <td>34.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-105.2</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>29-05-2024</td>\n",
       "      <td>67578.09375</td>\n",
       "      <td>24.6</td>\n",
       "      <td>17.7</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>-31.1</td>\n",
       "      <td>28.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>30-05-2024</td>\n",
       "      <td>68364.99219</td>\n",
       "      <td>1.6</td>\n",
       "      <td>119.1</td>\n",
       "      <td>25.9</td>\n",
       "      <td>-99.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Closing Price   IBIT   FBTC  BITB   ARKB  BTCO  EZBC  BRRR  \\\n",
       "47  20-03-2024    67913.67188   49.3   12.9  18.6   23.3 -10.2  19.0   2.9   \n",
       "48  21-03-2024    65491.39063  233.4    2.9  12.0    2.0   4.2   3.8   4.7   \n",
       "49  22-03-2024    63778.76172   18.9   18.1  16.3    5.4   4.5  29.6  25.5   \n",
       "50  25-03-2024    69958.81250   35.5  261.8  14.0    0.0  18.5  20.5  11.2   \n",
       "51  26-03-2024    69987.83594  162.2  279.1  16.7   73.6  26.7  26.3  29.9   \n",
       "52  27-03-2024    69455.34375  323.8    1.5   0.0  200.7   4.8   4.0   5.1   \n",
       "53  28-03-2024    70744.95313   95.1   68.1  67.0   27.6   3.9   0.0   6.0   \n",
       "54  01-04-2024    69702.14844  165.9   44.0   1.1   -0.3   4.2   0.0   0.0   \n",
       "55  02-04-2024    65446.97266  150.5   44.8   4.3  -87.5   0.0   0.0   3.7   \n",
       "56  03-04-2024    65980.81250   42.0  116.7  23.0    0.0   0.0   3.8   0.0   \n",
       "57  04-04-2024    68508.84375  144.0  106.6  11.2   12.0   0.0   0.0   3.4   \n",
       "58  05-04-2024    67837.64063  308.8   83.0   7.4    0.0   2.7   0.0   0.0   \n",
       "59  08-04-2024    71631.35938   21.3    6.3  40.3    9.3   0.0   0.0   0.0   \n",
       "60  09-04-2024    69139.01563  128.7    3.0   3.8    0.0   0.0   0.0   0.0   \n",
       "61  10-04-2024    70587.88281   33.3   76.3  24.3    7.3   0.0   0.0   0.0   \n",
       "62  11-04-2024    70060.60938  192.1    4.6  11.1    0.0   0.0   0.0   8.4   \n",
       "63  12-04-2024    67195.86719  111.1    0.0   0.0    0.0   0.0   0.0   0.0   \n",
       "64  15-04-2024    63426.21094   73.4    0.0   0.0    0.0   0.0   0.0   0.0   \n",
       "65  16-04-2024    63811.86328   25.8    1.4   0.0  -12.9   0.0   1.8   1.7   \n",
       "66  17-04-2024    61276.69141   18.1    0.0  -7.3  -42.7   0.0   0.0   0.0   \n",
       "67  18-04-2024    63512.75391   18.8   37.4  12.8    9.5   0.0   0.0   0.0   \n",
       "68  19-04-2024    63843.57031   29.3   54.8   4.9   12.5   3.9   1.9   0.0   \n",
       "69  22-04-2024    66837.67969   19.7   34.8   2.2   22.6   2.7   7.7   0.0   \n",
       "70  23-04-2024    66407.27344   37.9    4.4  23.2   33.3  -0.3   1.9   0.0   \n",
       "71  24-04-2024    64276.89844    0.0    5.6   0.0    4.2   0.0   0.0   0.0   \n",
       "72  25-04-2024    64481.70703    0.0  -22.6  -6.0  -31.3   0.0   1.9 -20.2   \n",
       "73  26-04-2024    63755.32031    0.0   -2.8  -3.8    5.4   0.0   0.0   0.0   \n",
       "74  29-04-2024    63841.12109    0.0   -6.9   6.8  -31.3   0.0   1.8   2.7   \n",
       "75  30-04-2024    60636.85547    0.0  -35.3 -34.3    3.6  -2.4   0.0   0.0   \n",
       "76  01-05-2024    58254.01172  -36.9 -191.1 -29.0  -98.1  -5.4 -13.4  -9.7   \n",
       "77  02-05-2024    59123.43359    0.0    0.0   0.0   13.3   1.5   3.4   2.3   \n",
       "78  03-05-2024    62889.83594   12.7  102.6  33.5   28.1  33.2  60.9  35.6   \n",
       "79  06-05-2024    63161.94922   21.5   99.2   2.1   75.6  11.1   1.8   0.0   \n",
       "80  07-05-2024    62334.81641    0.0    4.1   0.0    2.8   6.0   0.0   0.0   \n",
       "81  08-05-2024    61187.94141    0.0    0.0  11.5    0.0   0.0   0.0   0.0   \n",
       "82  09-05-2024    63049.96094   14.2    2.7   6.8    4.4   2.2   1.8   0.0   \n",
       "83  10-05-2024    60792.77734   12.4    5.3   0.0    0.0   0.0   0.0   0.0   \n",
       "84  13-05-2024    62901.44922    0.0   38.6  20.3    0.0   0.0   0.0   0.0   \n",
       "85  14-05-2024    61552.78906    0.0    8.1   0.0  133.1   5.5   1.8   1.2   \n",
       "86  15-05-2024    66267.49219    0.0  131.3  86.3   38.6   4.6   1.9   3.7   \n",
       "87  16-05-2024    65231.58203   93.7   67.1   1.4   62.0   6.2   3.8  18.5   \n",
       "88  17-05-2024    67051.87500   38.1   99.4  20.8   10.0   5.7   0.0   6.4   \n",
       "89  20-05-2024    71448.19531   66.4   64.0  24.0   68.3   0.0   0.0   0.0   \n",
       "90  21-05-2024    70136.53125  290.0   25.8  -4.2    0.0   0.0   0.0   0.0   \n",
       "91  22-05-2024    69122.33594   92.0   74.6   0.0    3.5   0.0   0.0   0.0   \n",
       "92  23-05-2024    67929.56250   89.0   19.1   0.0    2.0   2.0   0.0   0.0   \n",
       "93  24-05-2024    68526.10156  182.1   43.7   6.4    4.1   0.0   0.0   0.0   \n",
       "94  28-05-2024    68296.21875  102.5   34.3   3.3    4.1   3.4   0.0   1.2   \n",
       "95  29-05-2024    67578.09375   24.6   17.7  11.0    4.0   1.0   0.0   0.0   \n",
       "96  30-05-2024    68364.99219    1.6  119.1  25.9  -99.9   2.1   0.0   0.0   \n",
       "\n",
       "    HODL  BTCW   GBTC  Total  \n",
       "47   9.3   0.0 -386.6 -261.5  \n",
       "48   1.8   0.0 -358.8  -94.0  \n",
       "49   0.0   0.0 -169.9  -51.6  \n",
       "50   4.0   0.0 -350.1   15.4  \n",
       "51  15.8   0.0 -212.3  418.0  \n",
       "52   1.9   1.5 -299.8  243.5  \n",
       "53  20.0   0.0 -104.9  182.8  \n",
       "54   2.0   0.0 -302.6  -85.7  \n",
       "55   5.6   0.0  -81.9   39.5  \n",
       "56   0.0   3.1  -75.1  113.5  \n",
       "57  15.5   0.0  -79.3  213.4  \n",
       "58   0.0   0.0 -198.9  203.0  \n",
       "59   0.0   2.3 -303.3 -223.8  \n",
       "60   0.0   0.0 -154.9  -19.4  \n",
       "61   0.0   0.0  -17.5  123.7  \n",
       "62   0.0   0.0 -124.9   91.3  \n",
       "63   0.0   0.0 -166.2  -55.1  \n",
       "64   0.0   0.0 -110.1  -36.7  \n",
       "65   3.6   0.0  -79.4  -58.0  \n",
       "66   0.0   0.0 -133.1 -165.0  \n",
       "67   7.2   0.0  -90.0   -4.3  \n",
       "68  -1.8   0.0  -45.8   59.7  \n",
       "69   7.5   0.0  -35.0   62.2  \n",
       "70  -1.9   0.0  -66.9   31.6  \n",
       "71   0.0   0.0 -130.4 -120.6  \n",
       "72   0.0   0.0 -139.4 -217.6  \n",
       "73   0.0   0.0  -82.4  -83.6  \n",
       "74   0.0   0.0  -24.7  -51.6  \n",
       "75   0.0   0.0  -93.2 -161.6  \n",
       "76  -6.5  -6.2 -167.4 -563.7  \n",
       "77   0.0   0.0  -54.9  -34.4  \n",
       "78   8.7   0.0   63.0  378.3  \n",
       "79   1.8   0.0    3.9  217.0  \n",
       "80   0.0   0.0  -28.6  -15.7  \n",
       "81   0.0   0.0    0.0   11.5  \n",
       "82   0.0   0.0  -43.4  -11.3  \n",
       "83   0.0   0.6 -103.0  -84.7  \n",
       "84   7.1   0.0    0.0   66.0  \n",
       "85   1.7   0.0  -50.9  100.5  \n",
       "86   7.5   2.1   27.0  303.0  \n",
       "87   0.0   0.0    4.6  257.3  \n",
       "88   9.5   0.0   31.6  221.5  \n",
       "89   0.0   5.2    9.3  237.2  \n",
       "90  -5.9   0.0    0.0  305.7  \n",
       "91   0.0   0.0  -16.1  154.0  \n",
       "92   9.5   0.0  -13.7  107.9  \n",
       "93  15.6   0.0    0.0  251.9  \n",
       "94   0.0   1.4 -105.2   45.0  \n",
       "95   0.0   1.1  -31.1   28.3  \n",
       "96   0.0   0.0    0.0   48.8  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21afe173-1314-4c1e-92ec-675b255c6e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Closing Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bfbf16ce-edd0-4f00-b37d-c9478dbe2a85",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['IBIT', 'FBTC', 'BITB', 'ARKB', 'BTCO',\n",
    "       'EZBC', 'BRRR', 'HODL', 'BTCW',\n",
    "       'GBTC', ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "49eb3045-46b9-4712-a441-df17f9dd4faa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=410)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "106492f1-1cae-4212-b255-7bda2c3766de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#lm = LinearRegression()\n",
    "#lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8649d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=410, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=410, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=410, ...)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the XGBoost model\n",
    "\n",
    "\n",
    "xgb_model = xgb.XGBRegressor(n_estimators=100, random_state=410)\n",
    "xgb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5209097e-4b05-415c-b802-4cebc0de3b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([57026.3  , 57109.89 , 68121.38 , 46540.348, 61123.418, 61416.383,\n",
       "       52103.418, 62789.984, 61848.367, 62689.957, 61015.64 , 62924.355,\n",
       "       56717.832, 61962.96 , 59422.047, 51762.324, 62287.754, 66328.1  ,\n",
       "       45815.62 , 68623.61 , 65385.88 , 65655.62 , 44231.605, 60906.49 ,\n",
       "       62002.926, 40723.25 , 59347.02 , 68672.375, 62167.168, 68079.04 ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = xgb_model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f9ef6a4f-4ce7-4394-959a-c53de1076992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24170710b10>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d254c308-f6eb-4898-aa7c-5cb90bbb3f08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 4354.513281416666\n",
      "MSE: 38604668.01492843\n",
      "RMSE: 6213.265487240057\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1fd8c400-7e3e-4eec-bc69-750a96e8266d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.02715891645452"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, predictions)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8baea92f-9789-422e-9437-7d7b0f93c596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted and Actual Values:\n",
      "         Actual     Predicted\n",
      "18  44318.22266  57026.300781\n",
      "10  41816.87109  57109.890625\n",
      "70  66407.27344  68121.382812\n",
      "2   43154.94531  46540.347656\n",
      "22  49742.44141  61123.417969\n",
      "33  61198.38281  61416.382812\n",
      "23  51826.69531  52103.417969\n",
      "66  61276.69141  62789.984375\n",
      "72  64481.70703  61848.367188\n",
      "90  70136.53125  62689.957031\n",
      "77  59123.43359  61015.640625\n",
      "80  62334.81641  62924.355469\n",
      "40  72123.90625  56717.832031\n",
      "75  60636.85547  61962.960938\n",
      "38  66925.48438  59422.046875\n",
      "26  52284.87500  51762.324219\n",
      "71  64276.89844  62287.753906\n",
      "87  65231.58203  66328.101562\n",
      "15  43185.85938  45815.621094\n",
      "39  68300.09375  68623.609375\n",
      "61  70587.88281  65385.878906\n",
      "92  67929.56250  65655.617188\n",
      "7   39845.55078  44231.605469\n",
      "63  67195.86719  60906.488281\n",
      "73  63755.32031  62002.925781\n",
      "5   41618.40625  40723.250000\n",
      "50  69958.81250  59347.019531\n",
      "62  70060.60938  68672.375000\n",
      "69  66837.67969  62167.167969\n",
      "43  71396.59375  68079.039062\n"
     ]
    }
   ],
   "source": [
    "results_df = pd.DataFrame({'Actual': y_test, 'Predicted': predictions})\n",
    "print(\"Predicted and Actual Values:\")\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "388f56e2-5d56-44d9-9eea-f0e037afeb37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Closing Price', ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "sns.histplot(x= 'Closing Price',data = df,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4c963ce9-cea3-46d8-8e97-505e69e48cbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Closing Price', ylabel='Count'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['expenses'] = np.log(df['Closing Price'])\n",
    "plt.figure(figsize = (12,8))\n",
    "sns.histplot(x= 'Closing Price',data = df,kde = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a40264c3-7161-44d4-96ca-e6f5cbf9f459",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[56899.145]\n"
     ]
    }
   ],
   "source": [
    "new_data = pd.DataFrame({\n",
    "    'IBIT': [120],\n",
    "    'FBTC': [175],\n",
    "    'BITB': [5],\n",
    "    'ARKB': [50],\n",
    "    'BTCO': [1],\n",
    "    'EZBC': [2],\n",
    "    'BRRR': [3],\n",
    "    'HODL': [4],\n",
    "    'BTCW': [5],\n",
    "    'GBTC': [-20]\n",
    "})\n",
    "\n",
    "# Predicting the expenses for the new data\n",
    "predicted_btc_closing_price = xgb_model.predict(new_data)\n",
    "\n",
    "print(predicted_btc_closing_price )\n",
    "\n",
    "#print(f\"Predicted btc closing price: {predicted_btc_closing_price:.2f}\")\n",
    "\n",
    "# Convert the predicted log expenses back to the original scale\n",
    "#predicted_value = np.exp(predicted_btc_closing_price[0])\n",
    "                      \n",
    "#print(f\"Predicted value: {predicted_value:.2f}\")"
   ]
  }
 ],
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