eth_dominant_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ETH Dominant Features - Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import os\n",
    "import requests\n",
    "import datetime \n",
    "\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in annual coin metrics from csv\n",
    "metrics_2021 = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_annual_metrics/eth_metrics_1.csv', parse_dates=True)\n",
    "metrics_2020 = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_annual_metrics/eth_metrics_2.csv', parse_dates=True)\n",
    "metrics_2019 = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_annual_metrics/eth_metrics_3.csv', parse_dates=True)\n",
    "metrics_2018 = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_annual_metrics/eth_metrics_4.csv', parse_dates=True)\n",
    "metrics_2017 = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_annual_metrics/eth_metrics_5.csv', parse_dates=True)\n",
    "metrics_2016 = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_annual_metrics/eth_metrics_6.csv', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ETH_Trends</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-12-27</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ETH_Trends\n",
       "time                  \n",
       "2021-12-27          57\n",
       "2021-12-28          56\n",
       "2021-12-29          59\n",
       "2021-12-30          57\n",
       "2021-12-31          53"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in combined ETH Google trends data\n",
    "eth_google_trends = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/eth_google_trends/eth_trends_combined.csv', parse_dates=True)\n",
    "eth_google_trends.reset_index(inplace=True)\n",
    "eth_google_trends.rename(columns={'date':'time', 'ethereum':'ETH_Trends'},inplace=True)\n",
    "eth_google_trends = eth_google_trends.drop(columns='index')\n",
    "eth_google_trends['time'] = pd.to_datetime(eth_google_trends['time']).dt.date\n",
    "eth_google_trends = eth_google_trends.set_index('time')\n",
    "eth_google_trends.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>asset</th>\n",
       "      <th>AdrActCnt</th>\n",
       "      <th>CapMVRVCur</th>\n",
       "      <th>CapMrktCurUSD</th>\n",
       "      <th>HashRate</th>\n",
       "      <th>NVTAdj</th>\n",
       "      <th>PriceUSD</th>\n",
       "      <th>SplyCur</th>\n",
       "      <th>TxCnt</th>\n",
       "      <th>TxTfrValAdjUSD</th>\n",
       "      <th>VelCur1yr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>2021-12-13 00:00:00+00:00</td>\n",
       "      <td>eth</td>\n",
       "      <td>610120</td>\n",
       "      <td>1.698278</td>\n",
       "      <td>4.452681e+11</td>\n",
       "      <td>877.486094</td>\n",
       "      <td>37.563468</td>\n",
       "      <td>3788.082337</td>\n",
       "      <td>1.175445e+08</td>\n",
       "      <td>1242978</td>\n",
       "      <td>1.185375e+10</td>\n",
       "      <td>18.892499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>2021-12-14 00:00:00+00:00</td>\n",
       "      <td>eth</td>\n",
       "      <td>617852</td>\n",
       "      <td>1.719872</td>\n",
       "      <td>4.526614e+11</td>\n",
       "      <td>878.316915</td>\n",
       "      <td>39.143196</td>\n",
       "      <td>3850.763308</td>\n",
       "      <td>1.175511e+08</td>\n",
       "      <td>1214900</td>\n",
       "      <td>1.156424e+10</td>\n",
       "      <td>18.881284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>2021-12-15 00:00:00+00:00</td>\n",
       "      <td>eth</td>\n",
       "      <td>607024</td>\n",
       "      <td>1.773629</td>\n",
       "      <td>4.720652e+11</td>\n",
       "      <td>878.531504</td>\n",
       "      <td>47.901188</td>\n",
       "      <td>4015.642312</td>\n",
       "      <td>1.175566e+08</td>\n",
       "      <td>1258462</td>\n",
       "      <td>9.854978e+09</td>\n",
       "      <td>18.863606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>2021-12-16 00:00:00+00:00</td>\n",
       "      <td>eth</td>\n",
       "      <td>601544</td>\n",
       "      <td>1.758465</td>\n",
       "      <td>4.671211e+11</td>\n",
       "      <td>857.226757</td>\n",
       "      <td>48.596247</td>\n",
       "      <td>3973.451095</td>\n",
       "      <td>1.175605e+08</td>\n",
       "      <td>1155456</td>\n",
       "      <td>9.612287e+09</td>\n",
       "      <td>18.833360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>2021-12-17 00:00:00+00:00</td>\n",
       "      <td>eth</td>\n",
       "      <td>615024</td>\n",
       "      <td>1.731316</td>\n",
       "      <td>4.567951e+11</td>\n",
       "      <td>853.830893</td>\n",
       "      <td>51.870505</td>\n",
       "      <td>3885.430082</td>\n",
       "      <td>1.175662e+08</td>\n",
       "      <td>1270355</td>\n",
       "      <td>8.806451e+09</td>\n",
       "      <td>18.772575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                          time asset  AdrActCnt  CapMVRVCur  CapMrktCurUSD  \\\n",
       "346  2021-12-13 00:00:00+00:00   eth     610120    1.698278   4.452681e+11   \n",
       "347  2021-12-14 00:00:00+00:00   eth     617852    1.719872   4.526614e+11   \n",
       "348  2021-12-15 00:00:00+00:00   eth     607024    1.773629   4.720652e+11   \n",
       "349  2021-12-16 00:00:00+00:00   eth     601544    1.758465   4.671211e+11   \n",
       "350  2021-12-17 00:00:00+00:00   eth     615024    1.731316   4.567951e+11   \n",
       "\n",
       "       HashRate     NVTAdj     PriceUSD       SplyCur    TxCnt  \\\n",
       "346  877.486094  37.563468  3788.082337  1.175445e+08  1242978   \n",
       "347  878.316915  39.143196  3850.763308  1.175511e+08  1214900   \n",
       "348  878.531504  47.901188  4015.642312  1.175566e+08  1258462   \n",
       "349  857.226757  48.596247  3973.451095  1.175605e+08  1155456   \n",
       "350  853.830893  51.870505  3885.430082  1.175662e+08  1270355   \n",
       "\n",
       "     TxTfrValAdjUSD  VelCur1yr  \n",
       "346    1.185375e+10  18.892499  \n",
       "347    1.156424e+10  18.881284  \n",
       "348    9.854978e+09  18.863606  \n",
       "349    9.612287e+09  18.833360  \n",
       "350    8.806451e+09  18.772575  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat all csv files  and print combined csv\n",
    "combined = pd.concat([metrics_2016,metrics_2017,metrics_2018,metrics_2019,metrics_2020,metrics_2021], axis=0)\n",
    "combined_df = pd.DataFrame(combined)\n",
    "combined_df = combined_df.drop_duplicates(keep='first')\n",
    "combined_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "time              0\n",
       "asset             0\n",
       "AdrActCnt         0\n",
       "CapMVRVCur        0\n",
       "CapMrktCurUSD     0\n",
       "HashRate          0\n",
       "NVTAdj            0\n",
       "PriceUSD          0\n",
       "SplyCur           0\n",
       "TxCnt             0\n",
       "TxTfrValAdjUSD    0\n",
       "VelCur1yr         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove timestamp in time column\n",
    "combined_df['time'] = pd.to_datetime(combined_df['time']).dt.date\n",
    "combined_df = combined_df.set_index('time')\n",
    "combined.to_csv('resources/eth_annual_metrics/combined.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "      <th>AdrActCnt</th>\n",
       "      <th>CapMVRVCur</th>\n",
       "      <th>CapMrktCurUSD</th>\n",
       "      <th>HashRate</th>\n",
       "      <th>NVTAdj</th>\n",
       "      <th>PriceUSD</th>\n",
       "      <th>SplyCur</th>\n",
       "      <th>TxCnt</th>\n",
       "      <th>TxTfrValAdjUSD</th>\n",
       "      <th>VelCur1yr</th>\n",
       "      <th>ETH_Trends</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>eth</td>\n",
       "      <td>2433.0</td>\n",
       "      <td>2.166056</td>\n",
       "      <td>7.010116e+07</td>\n",
       "      <td>0.483217</td>\n",
       "      <td>264.814266</td>\n",
       "      <td>0.920370</td>\n",
       "      <td>7.616628e+07</td>\n",
       "      <td>8233.0</td>\n",
       "      <td>264718.210777</td>\n",
       "      <td>1.362126</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>eth</td>\n",
       "      <td>2486.0</td>\n",
       "      <td>2.226491</td>\n",
       "      <td>7.238322e+07</td>\n",
       "      <td>0.480767</td>\n",
       "      <td>370.221897</td>\n",
       "      <td>0.949990</td>\n",
       "      <td>7.619366e+07</td>\n",
       "      <td>9164.0</td>\n",
       "      <td>195513.065884</td>\n",
       "      <td>1.365054</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>eth</td>\n",
       "      <td>2528.0</td>\n",
       "      <td>2.238090</td>\n",
       "      <td>7.294862e+07</td>\n",
       "      <td>0.494175</td>\n",
       "      <td>234.211669</td>\n",
       "      <td>0.957063</td>\n",
       "      <td>7.622134e+07</td>\n",
       "      <td>9258.0</td>\n",
       "      <td>311464.489292</td>\n",
       "      <td>1.369430</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>eth</td>\n",
       "      <td>2623.0</td>\n",
       "      <td>2.222385</td>\n",
       "      <td>7.243632e+07</td>\n",
       "      <td>0.491670</td>\n",
       "      <td>626.141010</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>7.624876e+07</td>\n",
       "      <td>9474.0</td>\n",
       "      <td>115686.914897</td>\n",
       "      <td>1.370961</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>eth</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>2.207573</td>\n",
       "      <td>7.189832e+07</td>\n",
       "      <td>0.492788</td>\n",
       "      <td>111.469298</td>\n",
       "      <td>0.942600</td>\n",
       "      <td>7.627660e+07</td>\n",
       "      <td>16430.0</td>\n",
       "      <td>645005.606026</td>\n",
       "      <td>1.384049</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           asset  AdrActCnt  CapMVRVCur  CapMrktCurUSD  HashRate      NVTAdj  \\\n",
       "time                                                                           \n",
       "2016-01-01   eth     2433.0    2.166056   7.010116e+07  0.483217  264.814266   \n",
       "2016-01-02   eth     2486.0    2.226491   7.238322e+07  0.480767  370.221897   \n",
       "2016-01-03   eth     2528.0    2.238090   7.294862e+07  0.494175  234.211669   \n",
       "2016-01-04   eth     2623.0    2.222385   7.243632e+07  0.491670  626.141010   \n",
       "2016-01-05   eth     2620.0    2.207573   7.189832e+07  0.492788  111.469298   \n",
       "\n",
       "            PriceUSD       SplyCur    TxCnt  TxTfrValAdjUSD  VelCur1yr  \\\n",
       "time                                                                     \n",
       "2016-01-01  0.920370  7.616628e+07   8233.0   264718.210777   1.362126   \n",
       "2016-01-02  0.949990  7.619366e+07   9164.0   195513.065884   1.365054   \n",
       "2016-01-03  0.957063  7.622134e+07   9258.0   311464.489292   1.369430   \n",
       "2016-01-04  0.950000  7.624876e+07   9474.0   115686.914897   1.370961   \n",
       "2016-01-05  0.942600  7.627660e+07  16430.0   645005.606026   1.384049   \n",
       "\n",
       "            ETH_Trends  \n",
       "time                    \n",
       "2016-01-01           8  \n",
       "2016-01-02           4  \n",
       "2016-01-03           4  \n",
       "2016-01-04           5  \n",
       "2016-01-05           8  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concat metrics and google trends data\n",
    "combined_df = pd.concat([combined_df,eth_google_trends],axis=1).dropna()\n",
    "combined_df = combined_df.reset_index()\n",
    "combined_df = combined_df.rename(columns={'index':'time'})\n",
    "combined_df = combined_df.set_index('time')\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RSI_14</th>\n",
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       "      <th>time</th>\n",
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       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>84.969870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>73.199592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>77.421415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>82.161744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>70.537567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RSI_14\n",
       "time                 \n",
       "2016-01-15  84.969870\n",
       "2016-01-16  73.199592\n",
       "2016-01-17  77.421415\n",
       "2016-01-18  82.161744\n",
       "2016-01-19  70.537567"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in Relative Strength Index (RSI) data\n",
    "rsi_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/rsi_eth.csv', parse_dates=True, index_col='time')\n",
    "rsi_eth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROC_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>40.257836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>27.558185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>36.974993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>54.341493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>43.544091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               ROC_14\n",
       "time                 \n",
       "2016-01-15  40.257836\n",
       "2016-01-16  27.558185\n",
       "2016-01-17  36.974993\n",
       "2016-01-18  54.341493\n",
       "2016-01-19  43.544091"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in Rate of Change (ROC) data\n",
    "roc_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/roc_eth.csv', parse_dates=True, index_col='time')\n",
    "roc_eth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MFI_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-23</th>\n",
       "      <td>71.061607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24</th>\n",
       "      <td>79.997218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-25</th>\n",
       "      <td>81.373298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26</th>\n",
       "      <td>80.565557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27</th>\n",
       "      <td>81.611830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MFI_14\n",
       "time                 \n",
       "2017-11-23  71.061607\n",
       "2017-11-24  79.997218\n",
       "2017-11-25  81.373298\n",
       "2017-11-26  80.565557\n",
       "2017-11-27  81.611830"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in Money Flow Index (MFI) data\n",
    "mfi_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/mfi_eth.csv', parse_dates=True)\n",
    "mfi_eth = mfi_eth.rename(columns={'Date':'time'})\n",
    "mfi_eth['time'] = pd.to_datetime(mfi_eth['time']).dt.date\n",
    "mfi_eth = mfi_eth.set_index('time').dropna()\n",
    "mfi_eth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>TSI_13_25_13</th>\n",
       "      <th>TSIs_13_25_13</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-19</th>\n",
       "      <td>36.723593</td>\n",
       "      <td>55.155104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-20</th>\n",
       "      <td>33.186631</td>\n",
       "      <td>52.016751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-21</th>\n",
       "      <td>31.275368</td>\n",
       "      <td>49.053696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-22</th>\n",
       "      <td>32.410383</td>\n",
       "      <td>46.676080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-23</th>\n",
       "      <td>33.287523</td>\n",
       "      <td>44.763429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSI_13_25_13  TSIs_13_25_13\n",
       "time                                   \n",
       "2016-02-19     36.723593      55.155104\n",
       "2016-02-20     33.186631      52.016751\n",
       "2016-02-21     31.275368      49.053696\n",
       "2016-02-22     32.410383      46.676080\n",
       "2016-02-23     33.287523      44.763429"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in True Strength (TSI) data\n",
    "tsi_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/tsi_eth.csv', parse_dates=True)\n",
    "tsi_eth = tsi_eth.rename(columns={'Date':'time'})\n",
    "tsi_eth['time'] = pd.to_datetime(tsi_eth['time']).dt.date\n",
    "tsi_eth = tsi_eth.set_index('time').dropna()\n",
    "tsi_eth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STOCHk_14_3_3</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-26</th>\n",
       "      <td>76.290256</td>\n",
       "      <td>68.788673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27</th>\n",
       "      <td>89.010118</td>\n",
       "      <td>78.137467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28</th>\n",
       "      <td>88.764255</td>\n",
       "      <td>84.688210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29</th>\n",
       "      <td>81.392237</td>\n",
       "      <td>86.388870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>65.975886</td>\n",
       "      <td>78.710793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STOCHk_14_3_3  STOCHd_14_3_3\n",
       "time                                    \n",
       "2017-11-26      76.290256      68.788673\n",
       "2017-11-27      89.010118      78.137467\n",
       "2017-11-28      88.764255      84.688210\n",
       "2017-11-29      81.392237      86.388870\n",
       "2017-11-30      65.975886      78.710793"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in True Stochastic Oscilator (TSI) data\n",
    "stoch_eth = pd.read_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/stoch_eth.csv', parse_dates=True)\n",
    "stoch_eth = stoch_eth.rename(columns={'Date':'time'})\n",
    "stoch_eth['time'] = pd.to_datetime(stoch_eth['time']).dt.date\n",
    "stoch_eth = stoch_eth.set_index('time').dropna()\n",
    "stoch_eth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "      <th>AdrActCnt</th>\n",
       "      <th>CapMVRVCur</th>\n",
       "      <th>CapMrktCurUSD</th>\n",
       "      <th>HashRate</th>\n",
       "      <th>NVTAdj</th>\n",
       "      <th>PriceUSD</th>\n",
       "      <th>SplyCur</th>\n",
       "      <th>TxCnt</th>\n",
       "      <th>TxTfrValAdjUSD</th>\n",
       "      <th>VelCur1yr</th>\n",
       "      <th>ETH_Trends</th>\n",
       "      <th>RSI_14</th>\n",
       "      <th>ROC_14</th>\n",
       "      <th>MFI_14</th>\n",
       "      <th>TSI_13_25_13</th>\n",
       "      <th>TSIs_13_25_13</th>\n",
       "      <th>STOCHk_14_3_3</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-26</th>\n",
       "      <td>eth</td>\n",
       "      <td>252960.0</td>\n",
       "      <td>2.136961</td>\n",
       "      <td>4.532666e+10</td>\n",
       "      <td>110.937699</td>\n",
       "      <td>26.683421</td>\n",
       "      <td>472.277975</td>\n",
       "      <td>9.597453e+07</td>\n",
       "      <td>542484.0</td>\n",
       "      <td>1.698682e+09</td>\n",
       "      <td>43.383654</td>\n",
       "      <td>42.0</td>\n",
       "      <td>81.329664</td>\n",
       "      <td>53.747922</td>\n",
       "      <td>80.565557</td>\n",
       "      <td>48.205372</td>\n",
       "      <td>27.038302</td>\n",
       "      <td>76.290256</td>\n",
       "      <td>68.788673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-27</th>\n",
       "      <td>eth</td>\n",
       "      <td>277958.0</td>\n",
       "      <td>2.096252</td>\n",
       "      <td>4.585175e+10</td>\n",
       "      <td>108.314055</td>\n",
       "      <td>18.637635</td>\n",
       "      <td>477.648247</td>\n",
       "      <td>9.599480e+07</td>\n",
       "      <td>608695.0</td>\n",
       "      <td>2.460170e+09</td>\n",
       "      <td>43.578673</td>\n",
       "      <td>52.0</td>\n",
       "      <td>81.878157</td>\n",
       "      <td>51.376284</td>\n",
       "      <td>81.611830</td>\n",
       "      <td>51.049354</td>\n",
       "      <td>30.468452</td>\n",
       "      <td>89.010118</td>\n",
       "      <td>78.137467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-28</th>\n",
       "      <td>eth</td>\n",
       "      <td>309063.0</td>\n",
       "      <td>2.042647</td>\n",
       "      <td>4.492780e+10</td>\n",
       "      <td>106.374294</td>\n",
       "      <td>16.739206</td>\n",
       "      <td>467.924270</td>\n",
       "      <td>9.601511e+07</td>\n",
       "      <td>638989.0</td>\n",
       "      <td>2.683986e+09</td>\n",
       "      <td>43.817570</td>\n",
       "      <td>52.0</td>\n",
       "      <td>77.441800</td>\n",
       "      <td>40.007181</td>\n",
       "      <td>72.748642</td>\n",
       "      <td>51.825378</td>\n",
       "      <td>33.519442</td>\n",
       "      <td>88.764255</td>\n",
       "      <td>84.688210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-29</th>\n",
       "      <td>eth</td>\n",
       "      <td>315598.0</td>\n",
       "      <td>1.873957</td>\n",
       "      <td>4.120373e+10</td>\n",
       "      <td>107.542913</td>\n",
       "      <td>14.874535</td>\n",
       "      <td>429.046691</td>\n",
       "      <td>9.603553e+07</td>\n",
       "      <td>663579.0</td>\n",
       "      <td>2.770085e+09</td>\n",
       "      <td>44.086424</td>\n",
       "      <td>74.0</td>\n",
       "      <td>62.792792</td>\n",
       "      <td>29.705962</td>\n",
       "      <td>60.135562</td>\n",
       "      <td>47.088466</td>\n",
       "      <td>35.457874</td>\n",
       "      <td>81.392237</td>\n",
       "      <td>86.388870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-30</th>\n",
       "      <td>eth</td>\n",
       "      <td>299340.0</td>\n",
       "      <td>1.907781</td>\n",
       "      <td>4.212616e+10</td>\n",
       "      <td>109.821141</td>\n",
       "      <td>18.072286</td>\n",
       "      <td>438.558600</td>\n",
       "      <td>9.605595e+07</td>\n",
       "      <td>655457.0</td>\n",
       "      <td>2.330981e+09</td>\n",
       "      <td>44.336848</td>\n",
       "      <td>56.0</td>\n",
       "      <td>64.559194</td>\n",
       "      <td>33.032779</td>\n",
       "      <td>55.936900</td>\n",
       "      <td>44.023660</td>\n",
       "      <td>36.681557</td>\n",
       "      <td>65.975886</td>\n",
       "      <td>78.710793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           asset  AdrActCnt  CapMVRVCur  CapMrktCurUSD    HashRate     NVTAdj  \\\n",
       "time                                                                            \n",
       "2017-11-26   eth   252960.0    2.136961   4.532666e+10  110.937699  26.683421   \n",
       "2017-11-27   eth   277958.0    2.096252   4.585175e+10  108.314055  18.637635   \n",
       "2017-11-28   eth   309063.0    2.042647   4.492780e+10  106.374294  16.739206   \n",
       "2017-11-29   eth   315598.0    1.873957   4.120373e+10  107.542913  14.874535   \n",
       "2017-11-30   eth   299340.0    1.907781   4.212616e+10  109.821141  18.072286   \n",
       "\n",
       "              PriceUSD       SplyCur     TxCnt  TxTfrValAdjUSD  VelCur1yr  \\\n",
       "time                                                                        \n",
       "2017-11-26  472.277975  9.597453e+07  542484.0    1.698682e+09  43.383654   \n",
       "2017-11-27  477.648247  9.599480e+07  608695.0    2.460170e+09  43.578673   \n",
       "2017-11-28  467.924270  9.601511e+07  638989.0    2.683986e+09  43.817570   \n",
       "2017-11-29  429.046691  9.603553e+07  663579.0    2.770085e+09  44.086424   \n",
       "2017-11-30  438.558600  9.605595e+07  655457.0    2.330981e+09  44.336848   \n",
       "\n",
       "            ETH_Trends     RSI_14     ROC_14     MFI_14  TSI_13_25_13  \\\n",
       "time                                                                    \n",
       "2017-11-26        42.0  81.329664  53.747922  80.565557     48.205372   \n",
       "2017-11-27        52.0  81.878157  51.376284  81.611830     51.049354   \n",
       "2017-11-28        52.0  77.441800  40.007181  72.748642     51.825378   \n",
       "2017-11-29        74.0  62.792792  29.705962  60.135562     47.088466   \n",
       "2017-11-30        56.0  64.559194  33.032779  55.936900     44.023660   \n",
       "\n",
       "            TSIs_13_25_13  STOCHk_14_3_3  STOCHd_14_3_3  \n",
       "time                                                     \n",
       "2017-11-26      27.038302      76.290256      68.788673  \n",
       "2017-11-27      30.468452      89.010118      78.137467  \n",
       "2017-11-28      33.519442      88.764255      84.688210  \n",
       "2017-11-29      35.457874      81.392237      86.388870  \n",
       "2017-11-30      36.681557      65.975886      78.710793  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create model features dataframe\n",
    "features_eth = pd.concat([combined_df,rsi_eth,roc_eth,mfi_eth,tsi_eth, stoch_eth],axis=1).dropna()\n",
    "features_eth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_eth.to_csv('/Users/draganbogatic/Documents/Fin_Tech/crypto-analysis/resources/features_eth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'EMA ETH'}, xlabel='time', ylabel='ETH Price ($)'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set short and long windows for EMA (Exponential Moving Average) to create trading signals feature\n",
    "short_window = 20\n",
    "long_window = 50\n",
    "\n",
    "# Construct a `Fast` and `Slow` Exponential Moving Average from short and long windows, respectively\n",
    "features_eth['Fast_EMA'] = features_eth['PriceUSD'].ewm(halflife=short_window).mean()\n",
    "features_eth['Slow_EMA'] = features_eth['PriceUSD'].ewm(halflife=long_window).mean()\n",
    "\n",
    "# Plot the EMA of ETH closing prices\n",
    "features_eth[['PriceUSD', 'Fast_EMA', 'Slow_EMA']].plot(figsize=(12,7), title='EMA ETH', ylabel='ETH Price ($)', rot=45) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct a crossover trading signal\n",
    "features_eth['Crossover_Long'] = np.where(features_eth['Fast_EMA'] > features_eth['Slow_EMA'], 1.0, 0.0)\n",
    "features_eth['Crossover_Short'] = np.where(features_eth['Fast_EMA'] < features_eth['Slow_EMA'],-1.0, 0.0)\n",
    "features_eth['Crossover_Signal'] = features_eth['Crossover_Long'] + features_eth['Crossover_Short']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate daily return\n",
    "features_eth['Daily_Return'] = features_eth['PriceUSD'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>asset</th>\n",
       "      <th>AdrActCnt</th>\n",
       "      <th>CapMVRVCur</th>\n",
       "      <th>CapMrktCurUSD</th>\n",
       "      <th>HashRate</th>\n",
       "      <th>NVTAdj</th>\n",
       "      <th>PriceUSD</th>\n",
       "      <th>SplyCur</th>\n",
       "      <th>TxCnt</th>\n",
       "      <th>TxTfrValAdjUSD</th>\n",
       "      <th>...</th>\n",
       "      <th>TSIs_13_25_13</th>\n",
       "      <th>STOCHk_14_3_3</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "      <th>Fast_EMA</th>\n",
       "      <th>Slow_EMA</th>\n",
       "      <th>Crossover_Long</th>\n",
       "      <th>Crossover_Short</th>\n",
       "      <th>Crossover_Signal</th>\n",
       "      <th>Daily_Return</th>\n",
       "      <th>Positive_Return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>eth</td>\n",
       "      <td>610120.0</td>\n",
       "      <td>1.698278</td>\n",
       "      <td>4.452681e+11</td>\n",
       "      <td>877.486094</td>\n",
       "      <td>37.563468</td>\n",
       "      <td>3788.082337</td>\n",
       "      <td>1.175445e+08</td>\n",
       "      <td>1242978.0</td>\n",
       "      <td>1.185375e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.941841</td>\n",
       "      <td>-47.728137</td>\n",
       "      <td>-45.630063</td>\n",
       "      <td>4157.750783</td>\n",
       "      <td>3689.560213</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.085223</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14</th>\n",
       "      <td>eth</td>\n",
       "      <td>617852.0</td>\n",
       "      <td>1.719872</td>\n",
       "      <td>4.526614e+11</td>\n",
       "      <td>878.316915</td>\n",
       "      <td>39.143196</td>\n",
       "      <td>3850.763308</td>\n",
       "      <td>1.175511e+08</td>\n",
       "      <td>1214900.0</td>\n",
       "      <td>1.156424e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.986558</td>\n",
       "      <td>-39.907077</td>\n",
       "      <td>-43.773283</td>\n",
       "      <td>4147.293663</td>\n",
       "      <td>3691.779544</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016547</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-15</th>\n",
       "      <td>eth</td>\n",
       "      <td>607024.0</td>\n",
       "      <td>1.773629</td>\n",
       "      <td>4.720652e+11</td>\n",
       "      <td>878.531504</td>\n",
       "      <td>47.901188</td>\n",
       "      <td>4015.642312</td>\n",
       "      <td>1.175566e+08</td>\n",
       "      <td>1258462.0</td>\n",
       "      <td>9.854978e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.874639</td>\n",
       "      <td>-43.965116</td>\n",
       "      <td>-43.866777</td>\n",
       "      <td>4142.809134</td>\n",
       "      <td>3696.238258</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.042817</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-16</th>\n",
       "      <td>eth</td>\n",
       "      <td>601544.0</td>\n",
       "      <td>1.758465</td>\n",
       "      <td>4.671211e+11</td>\n",
       "      <td>857.226757</td>\n",
       "      <td>48.596247</td>\n",
       "      <td>3973.451095</td>\n",
       "      <td>1.175605e+08</td>\n",
       "      <td>1155456.0</td>\n",
       "      <td>9.612287e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.668715</td>\n",
       "      <td>-12.941491</td>\n",
       "      <td>-32.271228</td>\n",
       "      <td>4137.040178</td>\n",
       "      <td>3700.054729</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.010507</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-17</th>\n",
       "      <td>eth</td>\n",
       "      <td>615024.0</td>\n",
       "      <td>1.731316</td>\n",
       "      <td>4.567951e+11</td>\n",
       "      <td>853.830893</td>\n",
       "      <td>51.870505</td>\n",
       "      <td>3885.430082</td>\n",
       "      <td>1.175662e+08</td>\n",
       "      <td>1270355.0</td>\n",
       "      <td>8.806451e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.457071</td>\n",
       "      <td>-14.269018</td>\n",
       "      <td>-23.725209</td>\n",
       "      <td>4128.469414</td>\n",
       "      <td>3702.606847</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.022152</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           asset  AdrActCnt  CapMVRVCur  CapMrktCurUSD    HashRate     NVTAdj  \\\n",
       "time                                                                            \n",
       "2021-12-13   eth   610120.0    1.698278   4.452681e+11  877.486094  37.563468   \n",
       "2021-12-14   eth   617852.0    1.719872   4.526614e+11  878.316915  39.143196   \n",
       "2021-12-15   eth   607024.0    1.773629   4.720652e+11  878.531504  47.901188   \n",
       "2021-12-16   eth   601544.0    1.758465   4.671211e+11  857.226757  48.596247   \n",
       "2021-12-17   eth   615024.0    1.731316   4.567951e+11  853.830893  51.870505   \n",
       "\n",
       "               PriceUSD       SplyCur      TxCnt  TxTfrValAdjUSD  ...  \\\n",
       "time                                                              ...   \n",
       "2021-12-13  3788.082337  1.175445e+08  1242978.0    1.185375e+10  ...   \n",
       "2021-12-14  3850.763308  1.175511e+08  1214900.0    1.156424e+10  ...   \n",
       "2021-12-15  4015.642312  1.175566e+08  1258462.0    9.854978e+09  ...   \n",
       "2021-12-16  3973.451095  1.175605e+08  1155456.0    9.612287e+09  ...   \n",
       "2021-12-17  3885.430082  1.175662e+08  1270355.0    8.806451e+09  ...   \n",
       "\n",
       "            TSIs_13_25_13  STOCHk_14_3_3  STOCHd_14_3_3     Fast_EMA  \\\n",
       "time                                                                   \n",
       "2021-12-13      -1.941841     -47.728137     -45.630063  4157.750783   \n",
       "2021-12-14      -2.986558     -39.907077     -43.773283  4147.293663   \n",
       "2021-12-15      -3.874639     -43.965116     -43.866777  4142.809134   \n",
       "2021-12-16      -4.668715     -12.941491     -32.271228  4137.040178   \n",
       "2021-12-17      -5.457071     -14.269018     -23.725209  4128.469414   \n",
       "\n",
       "               Slow_EMA  Crossover_Long  Crossover_Short  Crossover_Signal  \\\n",
       "time                                                                         \n",
       "2021-12-13  3689.560213             1.0              0.0               1.0   \n",
       "2021-12-14  3691.779544             1.0              0.0               1.0   \n",
       "2021-12-15  3696.238258             1.0              0.0               1.0   \n",
       "2021-12-16  3700.054729             1.0              0.0               1.0   \n",
       "2021-12-17  3702.606847             1.0              0.0               1.0   \n",
       "\n",
       "            Daily_Return  Positive_Return  \n",
       "time                                       \n",
       "2021-12-13     -0.085223              0.0  \n",
       "2021-12-14      0.016547              1.0  \n",
       "2021-12-15      0.042817              1.0  \n",
       "2021-12-16     -0.010507              0.0  \n",
       "2021-12-17     -0.022152              0.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct the dependent variable where if daily return is greater than 0, then 1, else, 0.\n",
    "features_eth['Positive_Return'] = np.where(features_eth['Daily_Return'] > 0, 1.0, 0.0)\n",
    "\n",
    "features_eth = features_eth.dropna()\n",
    "\n",
    "# Convert date to str to set test and train windows \n",
    "features_eth.index = pd.to_datetime(features_eth.index, format = '%Y/%m/%d')\n",
    "features_eth.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select model features and timeframe for train data\n",
    "X = features_eth[['AdrActCnt','CapMrktCurUSD','CapMVRVCur','HashRate','NVTAdj','TxCnt','TxTfrValAdjUSD','VelCur1yr','Crossover_Signal','ETH_Trends','RSI_14', 'ROC_14','MFI_14','TSI_13_25_13','TSIs_13_25_13','STOCHk_14_3_3','STOCHd_14_3_3']]\n",
    "X_train = X.loc['2016':'2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select prediction variable and timeframe for train data\n",
    "y = features_eth['Positive_Return']\n",
    "y_train = y['2016':'2020']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define timeframe for testing data\n",
    "X_test = X['2021':]\n",
    "y_test = y['2021':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the StandardScaler instance\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the Standard Scaler with the training data\n",
    "X_scaler = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale the training data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.665723 using {'criterion': 'entropy', 'max_depth': 10, 'n_estimators': 500}\n"
     ]
    }
   ],
   "source": [
    "# Create the decision tree classifier instance and calculate the optimal tree parameters\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "n_estimators = [20,500]\n",
    "max_depth= [5,10]\n",
    "num_folds = 10\n",
    "scoring = 'accuracy'\n",
    "\n",
    "criterion = [\"gini\",\"entropy\"]\n",
    "param_grid = dict(n_estimators=n_estimators, max_depth=max_depth, \\\n",
    "  criterion = criterion )\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=-1)\n",
    "\n",
    "kfold = KFold(n_splits=num_folds, random_state=None)\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, \\\n",
    "  scoring=scoring, cv=kfold)\n",
    "grid_result = grid.fit(X_train, y_train)\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_,\\\n",
    "  grid_result.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model using optimized settings (also use if you want to run without optimizer above)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=-1)\n",
    "model = RandomForestClassifier(criterion='entropy', max_depth=10,n_estimators=500, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the model\n",
    "model = model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making predictions using the testing data\n",
    "predictions = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the confusion matrix\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "cm_df = pd.DataFrame(cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "# Calculating the accuracy score\n",
    "acc_score = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>71</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>23</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           71           87\n",
       "Actual 1           23          170"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.6866096866096866\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.76      0.45      0.56       158\n",
      "         1.0       0.66      0.88      0.76       193\n",
      "\n",
      "    accuracy                           0.69       351\n",
      "   macro avg       0.71      0.67      0.66       351\n",
      "weighted avg       0.70      0.69      0.67       351\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix\")\n",
    "display(cm_df)\n",
    "print(f\"Accuracy Score : {acc_score}\")\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Positive_Return</th>\n",
       "      <th>Predicted Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-28</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-08</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Positive_Return  Predicted Value\n",
       "time                                        \n",
       "2021-11-28              1.0              1.0\n",
       "2021-11-29              1.0              1.0\n",
       "2021-11-30              1.0              1.0\n",
       "2021-12-01              0.0              1.0\n",
       "2021-12-02              0.0              1.0\n",
       "2021-12-03              0.0              0.0\n",
       "2021-12-04              0.0              0.0\n",
       "2021-12-05              1.0              1.0\n",
       "2021-12-06              1.0              1.0\n",
       "2021-12-07              0.0              1.0\n",
       "2021-12-08              1.0              1.0\n",
       "2021-12-09              0.0              0.0\n",
       "2021-12-10              0.0              1.0\n",
       "2021-12-11              1.0              1.0\n",
       "2021-12-12              1.0              1.0\n",
       "2021-12-13              0.0              1.0\n",
       "2021-12-14              1.0              1.0\n",
       "2021-12-15              1.0              1.0\n",
       "2021-12-16              0.0              1.0\n",
       "2021-12-17              0.0              1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assemble actual y data (Y_test) with predicted y data (from just above) into two columns in a dataframe\n",
    "Results = y_test.to_frame()\n",
    "Results[\"Predicted Value\"] = predictions\n",
    "Results[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.14633939363478252, 'RSI_14'),\n",
       " (0.07705838120044532, 'STOCHd_14_3_3'),\n",
       " (0.07512699866732267, 'STOCHk_14_3_3'),\n",
       " (0.07485895034022658, 'TSI_13_25_13'),\n",
       " (0.07417755815959254, 'ROC_14'),\n",
       " (0.06838352076073637, 'MFI_14'),\n",
       " (0.05742918746870959, 'TSIs_13_25_13'),\n",
       " (0.052616541363613586, 'NVTAdj'),\n",
       " (0.052452693033977794, 'TxCnt'),\n",
       " (0.04850211634020613, 'AdrActCnt'),\n",
       " (0.047660834551045035, 'HashRate'),\n",
       " (0.04634294251951032, 'TxTfrValAdjUSD'),\n",
       " (0.045571556973226346, 'CapMVRVCur'),\n",
       " (0.044149661825309335, 'CapMrktCurUSD'),\n",
       " (0.04406234584667865, 'ETH_Trends'),\n",
       " (0.04285315249670168, 'VelCur1yr'),\n",
       " (0.0024141648179156656, 'Crossover_Signal')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the features sorted in descending order by feature importance\n",
    "importances = model.feature_importances_\n",
    "sorted(zip(model.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the features by importance\n",
    "importances_df = pd.DataFrame(sorted(zip(model.feature_importances_,    X.columns), reverse=True))\n",
    "importances_df.set_index(importances_df[1], inplace=True)\n",
    "importances_df.drop(columns=1, inplace=True)\n",
    "importances_df.rename(columns={0: 'Feature Importances'}, inplace=True)\n",
    "\n",
    "importances_sorted = importances_df.sort_values(by=\n",
    "'Feature Importances')\n",
    "\n",
    "importances_plot = importances_sorted.plot(kind='barh', color='lightgreen', title='Features Importances', legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RSI is the most dominant feature which shows that ETH price is primarily determined by momentum."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "1d1365bc7bea9ca53bbaaff2a498c5cc15b63a6fcc47bade387e2edfe7e7b068"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 64-bit ('algotrading': conda)",
   "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": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}