Random Forest for Continuous Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ad54e369",
   "metadata": {},
   "source": [
    "# Inch to USDT Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a2de6ee",
   "metadata": {},
   "source": [
    "<p>Download required packages:</p>\n",
    "<p>Numpy</p>\n",
    "<p>Pandas</p>\n",
    "<p>Seaborn</p>\n",
    "<p>Plotly</p>\n",
    "<p>Matplotlib</p>\n",
    "<p>Pandas_ta or talib</p>\n",
    "<p>Sklearn</p>\n",
    "<p>Scikeras</p>\n",
    "<p>Tensorflow</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fead1a3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.2' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pandas_ta as ta\n",
    "import numpy as np\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import matplotlib as mpl\n",
    "from matplotlib import rcParams\n",
    "import warnings\n",
    "import datetime as dt\n",
    "import time as t\n",
    "import math\n",
    "import os\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, Lasso\n",
    "from sklearn.feature_selection import RFE, SelectKBest, f_regression\n",
    "from sklearn.model_selection import TimeSeriesSplit, learning_curve, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score, confusion_matrix\n",
    "from sklearn.metrics import mean_poisson_deviance, mean_gamma_deviance, accuracy_score, roc_curve, roc_auc_score, classification_report\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.datasets import fetch_openml\n",
    "from scikeras.wrappers import KerasRegressor\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Input, Dense, LSTM, Conv1D, MaxPooling1D, Flatten, Dropout, concatenate, Reshape\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc663d12",
   "metadata": {},
   "source": [
    "## Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14373293",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>Volume USDT</th>\n",
       "      <th>tradecount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2023-10-19 23:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.2442</td>\n",
       "      <td>0.2433</td>\n",
       "      <td>0.2436</td>\n",
       "      <td>253236.8</td>\n",
       "      <td>61696.30641</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 22:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2432</td>\n",
       "      <td>0.2439</td>\n",
       "      <td>0.2429</td>\n",
       "      <td>0.2434</td>\n",
       "      <td>101621.2</td>\n",
       "      <td>24736.31761</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 21:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.2427</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>324836.5</td>\n",
       "      <td>79392.19887</td>\n",
       "      <td>780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 20:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2456</td>\n",
       "      <td>0.2478</td>\n",
       "      <td>0.2448</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>359589.1</td>\n",
       "      <td>88541.75689</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 19:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2443</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.2442</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>124860.2</td>\n",
       "      <td>30589.04366</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 18:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2446</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.2438</td>\n",
       "      <td>0.2444</td>\n",
       "      <td>197413.8</td>\n",
       "      <td>48278.53129</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 17:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.2433</td>\n",
       "      <td>0.2448</td>\n",
       "      <td>306781.2</td>\n",
       "      <td>75094.33596</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 16:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2470</td>\n",
       "      <td>0.2488</td>\n",
       "      <td>0.2455</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>1011725.9</td>\n",
       "      <td>250049.69081</td>\n",
       "      <td>1374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 15:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.2478</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>472579.3</td>\n",
       "      <td>116591.70086</td>\n",
       "      <td>888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 14:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2469</td>\n",
       "      <td>0.2470</td>\n",
       "      <td>0.2443</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>762689.5</td>\n",
       "      <td>187408.37667</td>\n",
       "      <td>1268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Symbol    Open    High     Low   Close  Volume 1INCH  \\\n",
       "Date                                                                           \n",
       "2023-10-19 23:00:00  1INCHUSDT  0.2435  0.2442  0.2433  0.2436      253236.8   \n",
       "2023-10-19 22:00:00  1INCHUSDT  0.2432  0.2439  0.2429  0.2434      101621.2   \n",
       "2023-10-19 21:00:00  1INCHUSDT  0.2472  0.2473  0.2427  0.2431      324836.5   \n",
       "2023-10-19 20:00:00  1INCHUSDT  0.2456  0.2478  0.2448  0.2472      359589.1   \n",
       "2023-10-19 19:00:00  1INCHUSDT  0.2443  0.2458  0.2442  0.2454      124860.2   \n",
       "2023-10-19 18:00:00  1INCHUSDT  0.2446  0.2454  0.2438  0.2444      197413.8   \n",
       "2023-10-19 17:00:00  1INCHUSDT  0.2458  0.2458  0.2433  0.2448      306781.2   \n",
       "2023-10-19 16:00:00  1INCHUSDT  0.2470  0.2488  0.2455  0.2458     1011725.9   \n",
       "2023-10-19 15:00:00  1INCHUSDT  0.2467  0.2478  0.2454  0.2472      472579.3   \n",
       "2023-10-19 14:00:00  1INCHUSDT  0.2469  0.2470  0.2443  0.2467      762689.5   \n",
       "\n",
       "                      Volume USDT  tradecount  \n",
       "Date                                           \n",
       "2023-10-19 23:00:00   61696.30641         352  \n",
       "2023-10-19 22:00:00   24736.31761         299  \n",
       "2023-10-19 21:00:00   79392.19887         780  \n",
       "2023-10-19 20:00:00   88541.75689         890  \n",
       "2023-10-19 19:00:00   30589.04366         316  \n",
       "2023-10-19 18:00:00   48278.53129         407  \n",
       "2023-10-19 17:00:00   75094.33596         680  \n",
       "2023-10-19 16:00:00  250049.69081        1374  \n",
       "2023-10-19 15:00:00  116591.70086         888  \n",
       "2023-10-19 14:00:00  187408.37667        1268  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Datas\\Binance_1INCHUSDT_1h (1).csv\", delimiter=\",\", index_col='Date', parse_dates=['Date'])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbbb0ef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Symbol', 'Open', 'High', 'Low', 'Close', 'Volume 1INCH', 'Volume USDT',\n",
       "       'tradecount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e9c24090",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24672 entries, 2023-10-19 23:00:00 to 2020-12-25 05:00:00\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Symbol        24672 non-null  object \n",
      " 1   Open          24672 non-null  float64\n",
      " 2   High          24672 non-null  float64\n",
      " 3   Low           24672 non-null  float64\n",
      " 4   Close         24672 non-null  float64\n",
      " 5   Volume 1INCH  24672 non-null  float64\n",
      " 6   Volume USDT   24672 non-null  float64\n",
      " 7   tradecount    24672 non-null  int64  \n",
      "dtypes: float64(6), int64(1), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aee6cf81",
   "metadata": {},
   "source": [
    "There are no null value so we can continue working on examining our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4137187d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>Volume USDT</th>\n",
       "      <th>tradecount</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24672.000000</td>\n",
       "      <td>24672.000000</td>\n",
       "      <td>24672.000000</td>\n",
       "      <td>24672.000000</td>\n",
       "      <td>2.467200e+04</td>\n",
       "      <td>2.467200e+04</td>\n",
       "      <td>24672.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.728424</td>\n",
       "      <td>1.748911</td>\n",
       "      <td>1.706915</td>\n",
       "      <td>1.728408</td>\n",
       "      <td>4.415206e+05</td>\n",
       "      <td>9.315547e+05</td>\n",
       "      <td>3092.458414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.575206</td>\n",
       "      <td>1.598287</td>\n",
       "      <td>1.550845</td>\n",
       "      <td>1.575185</td>\n",
       "      <td>8.714524e+05</td>\n",
       "      <td>2.901046e+06</td>\n",
       "      <td>7679.835670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.223500</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.221700</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.513000</td>\n",
       "      <td>0.516000</td>\n",
       "      <td>0.509000</td>\n",
       "      <td>0.513000</td>\n",
       "      <td>1.317910e+05</td>\n",
       "      <td>7.772265e+04</td>\n",
       "      <td>458.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.926000</td>\n",
       "      <td>0.936000</td>\n",
       "      <td>0.917000</td>\n",
       "      <td>0.926000</td>\n",
       "      <td>2.453122e+05</td>\n",
       "      <td>2.710018e+05</td>\n",
       "      <td>1176.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.707325</td>\n",
       "      <td>2.742925</td>\n",
       "      <td>2.669050</td>\n",
       "      <td>2.707550</td>\n",
       "      <td>4.684321e+05</td>\n",
       "      <td>9.993602e+05</td>\n",
       "      <td>3543.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.762900</td>\n",
       "      <td>7.874800</td>\n",
       "      <td>7.593200</td>\n",
       "      <td>7.768400</td>\n",
       "      <td>3.597447e+07</td>\n",
       "      <td>2.341467e+08</td>\n",
       "      <td>596071.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open          High           Low         Close  Volume 1INCH  \\\n",
       "count  24672.000000  24672.000000  24672.000000  24672.000000  2.467200e+04   \n",
       "mean       1.728424      1.748911      1.706915      1.728408  4.415206e+05   \n",
       "std        1.575206      1.598287      1.550845      1.575185  8.714524e+05   \n",
       "min        0.200000      0.223500      0.200000      0.221700  0.000000e+00   \n",
       "25%        0.513000      0.516000      0.509000      0.513000  1.317910e+05   \n",
       "50%        0.926000      0.936000      0.917000      0.926000  2.453122e+05   \n",
       "75%        2.707325      2.742925      2.669050      2.707550  4.684321e+05   \n",
       "max        7.762900      7.874800      7.593200      7.768400  3.597447e+07   \n",
       "\n",
       "        Volume USDT     tradecount  \n",
       "count  2.467200e+04   24672.000000  \n",
       "mean   9.315547e+05    3092.458414  \n",
       "std    2.901046e+06    7679.835670  \n",
       "min    0.000000e+00       0.000000  \n",
       "25%    7.772265e+04     458.000000  \n",
       "50%    2.710018e+05    1176.500000  \n",
       "75%    9.993602e+05    3543.500000  \n",
       "max    2.341467e+08  596071.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b6e594",
   "metadata": {},
   "source": [
    "## Preprocessing Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7bacc6d2",
   "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>Symbol</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>Volume USDT</th>\n",
       "      <th>tradecount</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2023-10-19 23:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2435</td>\n",
       "      <td>0.2442</td>\n",
       "      <td>0.2433</td>\n",
       "      <td>0.2436</td>\n",
       "      <td>253236.8</td>\n",
       "      <td>61696.30641</td>\n",
       "      <td>352</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 22:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2432</td>\n",
       "      <td>0.2439</td>\n",
       "      <td>0.2429</td>\n",
       "      <td>0.2434</td>\n",
       "      <td>101621.2</td>\n",
       "      <td>24736.31761</td>\n",
       "      <td>299</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 21:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>0.2473</td>\n",
       "      <td>0.2427</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>324836.5</td>\n",
       "      <td>79392.19887</td>\n",
       "      <td>780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 20:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2456</td>\n",
       "      <td>0.2478</td>\n",
       "      <td>0.2448</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>359589.1</td>\n",
       "      <td>88541.75689</td>\n",
       "      <td>890</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 19:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2443</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.2442</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>124860.2</td>\n",
       "      <td>30589.04366</td>\n",
       "      <td>316</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 18:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2446</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.2438</td>\n",
       "      <td>0.2444</td>\n",
       "      <td>197413.8</td>\n",
       "      <td>48278.53129</td>\n",
       "      <td>407</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 17:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>0.2433</td>\n",
       "      <td>0.2448</td>\n",
       "      <td>306781.2</td>\n",
       "      <td>75094.33596</td>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 16:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2470</td>\n",
       "      <td>0.2488</td>\n",
       "      <td>0.2455</td>\n",
       "      <td>0.2458</td>\n",
       "      <td>1011725.9</td>\n",
       "      <td>250049.69081</td>\n",
       "      <td>1374</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 15:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.2478</td>\n",
       "      <td>0.2454</td>\n",
       "      <td>0.2472</td>\n",
       "      <td>472579.3</td>\n",
       "      <td>116591.70086</td>\n",
       "      <td>888</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-19 14:00:00</th>\n",
       "      <td>1INCHUSDT</td>\n",
       "      <td>0.2469</td>\n",
       "      <td>0.2470</td>\n",
       "      <td>0.2443</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>762689.5</td>\n",
       "      <td>187408.37667</td>\n",
       "      <td>1268</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Symbol    Open    High     Low   Close  Volume 1INCH  \\\n",
       "Date                                                                           \n",
       "2023-10-19 23:00:00  1INCHUSDT  0.2435  0.2442  0.2433  0.2436      253236.8   \n",
       "2023-10-19 22:00:00  1INCHUSDT  0.2432  0.2439  0.2429  0.2434      101621.2   \n",
       "2023-10-19 21:00:00  1INCHUSDT  0.2472  0.2473  0.2427  0.2431      324836.5   \n",
       "2023-10-19 20:00:00  1INCHUSDT  0.2456  0.2478  0.2448  0.2472      359589.1   \n",
       "2023-10-19 19:00:00  1INCHUSDT  0.2443  0.2458  0.2442  0.2454      124860.2   \n",
       "2023-10-19 18:00:00  1INCHUSDT  0.2446  0.2454  0.2438  0.2444      197413.8   \n",
       "2023-10-19 17:00:00  1INCHUSDT  0.2458  0.2458  0.2433  0.2448      306781.2   \n",
       "2023-10-19 16:00:00  1INCHUSDT  0.2470  0.2488  0.2455  0.2458     1011725.9   \n",
       "2023-10-19 15:00:00  1INCHUSDT  0.2467  0.2478  0.2454  0.2472      472579.3   \n",
       "2023-10-19 14:00:00  1INCHUSDT  0.2469  0.2470  0.2443  0.2467      762689.5   \n",
       "\n",
       "                      Volume USDT  tradecount  Target  \n",
       "Date                                                   \n",
       "2023-10-19 23:00:00   61696.30641         352      -1  \n",
       "2023-10-19 22:00:00   24736.31761         299      -1  \n",
       "2023-10-19 21:00:00   79392.19887         780       1  \n",
       "2023-10-19 20:00:00   88541.75689         890      -1  \n",
       "2023-10-19 19:00:00   30589.04366         316      -1  \n",
       "2023-10-19 18:00:00   48278.53129         407       1  \n",
       "2023-10-19 17:00:00   75094.33596         680       1  \n",
       "2023-10-19 16:00:00  250049.69081        1374       1  \n",
       "2023-10-19 15:00:00  116591.70086         888      -1  \n",
       "2023-10-19 14:00:00  187408.37667        1268       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the target column\n",
    "df['Target'] = df['Close'].shift(-1) - df['Close']\n",
    "\n",
    "df['Target'] = df['Target'].apply(lambda x: 1 if x > 0 else -1)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fdbc91a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove unnecessary columns\n",
    "df.drop(columns=['Symbol', 'Volume USDT'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b35f8f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "32451f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>tradecount</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2020-12-25 05:00:00</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>3.0885</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>2.5826</td>\n",
       "      <td>13125130.77</td>\n",
       "      <td>48768</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 06:00:00</th>\n",
       "      <td>2.5824</td>\n",
       "      <td>2.6900</td>\n",
       "      <td>2.2249</td>\n",
       "      <td>2.5059</td>\n",
       "      <td>9270672.93</td>\n",
       "      <td>31099</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 07:00:00</th>\n",
       "      <td>2.5152</td>\n",
       "      <td>2.8870</td>\n",
       "      <td>2.3609</td>\n",
       "      <td>2.6237</td>\n",
       "      <td>7923760.56</td>\n",
       "      <td>33001</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 08:00:00</th>\n",
       "      <td>2.6318</td>\n",
       "      <td>2.8247</td>\n",
       "      <td>2.4650</td>\n",
       "      <td>2.6134</td>\n",
       "      <td>6625565.00</td>\n",
       "      <td>30459</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 09:00:00</th>\n",
       "      <td>2.6104</td>\n",
       "      <td>2.7498</td>\n",
       "      <td>2.5629</td>\n",
       "      <td>2.6365</td>\n",
       "      <td>3739132.73</td>\n",
       "      <td>21023</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close  Volume 1INCH  tradecount  \\\n",
       "Date                                                                            \n",
       "2020-12-25 05:00:00  0.2000  3.0885  0.2000  2.5826   13125130.77       48768   \n",
       "2020-12-25 06:00:00  2.5824  2.6900  2.2249  2.5059    9270672.93       31099   \n",
       "2020-12-25 07:00:00  2.5152  2.8870  2.3609  2.6237    7923760.56       33001   \n",
       "2020-12-25 08:00:00  2.6318  2.8247  2.4650  2.6134    6625565.00       30459   \n",
       "2020-12-25 09:00:00  2.6104  2.7498  2.5629  2.6365    3739132.73       21023   \n",
       "\n",
       "                     Target  \n",
       "Date                         \n",
       "2020-12-25 05:00:00      -1  \n",
       "2020-12-25 06:00:00       1  \n",
       "2020-12-25 07:00:00      -1  \n",
       "2020-12-25 08:00:00       1  \n",
       "2020-12-25 09:00:00      -1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ea9acc0",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2160x2592 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_new['2021':'2023'].plot(subplots=True, figsize=(30, 36))\n",
    "plt.title('Bınance stock attributes from 2021 to 2023')\n",
    "plt.savefig('stocks.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c625e20e",
   "metadata": {},
   "source": [
    "## Feature Adding\n",
    "\n",
    "1- Looking for what kind of features available in pandas_ta\n",
    "2- Add lots of technical indicators based upon articles' choices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bdf9401a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas TA - Technical Analysis Indicators - v0.3.14b0\n",
      "Total Indicators & Utilities: 205\n",
      "Abbreviations:\n",
      "    aberration, above, above_value, accbands, ad, adosc, adx, alma, amat, ao, aobv, apo, aroon, atr, bbands, below, below_value, bias, bop, brar, cci, cdl_pattern, cdl_z, cfo, cg, chop, cksp, cmf, cmo, coppock, cross, cross_value, cti, decay, decreasing, dema, dm, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, increasing, inertia, jma, kama, kc, kdj, kst, kurtosis, kvo, linreg, log_return, long_run, macd, mad, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, squeeze, squeeze_pro, ssf, stc, stdev, stoch, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, tos_stdevall, trima, trix, true_range, tsi, tsignals, ttm_trend, ui, uo, variance, vhf, vidya, vortex, vp, vwap, vwma, wcp, willr, wma, xsignals, zlma, zscore\n",
      "\n",
      "Candle Patterns:\n",
      "    2crows, 3blackcrows, 3inside, 3linestrike, 3outside, 3starsinsouth, 3whitesoldiers, abandonedbaby, advanceblock, belthold, breakaway, closingmarubozu, concealbabyswall, counterattack, darkcloudcover, doji, dojistar, dragonflydoji, engulfing, eveningdojistar, eveningstar, gapsidesidewhite, gravestonedoji, hammer, hangingman, harami, haramicross, highwave, hikkake, hikkakemod, homingpigeon, identical3crows, inneck, inside, invertedhammer, kicking, kickingbylength, ladderbottom, longleggeddoji, longline, marubozu, matchinglow, mathold, morningdojistar, morningstar, onneck, piercing, rickshawman, risefall3methods, separatinglines, shootingstar, shortline, spinningtop, stalledpattern, sticksandwich, takuri, tasukigap, thrusting, tristar, unique3river, upsidegap2crows, xsidegap3methods\n"
     ]
    }
   ],
   "source": [
    "df.ta.indicators()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "95729fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>tradecount</th>\n",
       "      <th>Target</th>\n",
       "      <th>SMA_10</th>\n",
       "      <th>SMA_20</th>\n",
       "      <th>SMA_100</th>\n",
       "      <th>...</th>\n",
       "      <th>RVI_90</th>\n",
       "      <th>MACD_12_26_9</th>\n",
       "      <th>MACDh_12_26_9</th>\n",
       "      <th>MACDs_12_26_9</th>\n",
       "      <th>WILLR_5</th>\n",
       "      <th>CCI_14_0.015</th>\n",
       "      <th>MOM_10</th>\n",
       "      <th>ATRr_14</th>\n",
       "      <th>AMATe_LR_8_21_2</th>\n",
       "      <th>AMATe_SR_8_21_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
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       "      <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-25 05:00:00</th>\n",
       "      <td>0.2000</td>\n",
       "      <td>3.0885</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>2.5826</td>\n",
       "      <td>13125130.77</td>\n",
       "      <td>48768</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 06:00:00</th>\n",
       "      <td>2.5824</td>\n",
       "      <td>2.6900</td>\n",
       "      <td>2.2249</td>\n",
       "      <td>2.5059</td>\n",
       "      <td>9270672.93</td>\n",
       "      <td>31099</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 07:00:00</th>\n",
       "      <td>2.5152</td>\n",
       "      <td>2.8870</td>\n",
       "      <td>2.3609</td>\n",
       "      <td>2.6237</td>\n",
       "      <td>7923760.56</td>\n",
       "      <td>33001</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 08:00:00</th>\n",
       "      <td>2.6318</td>\n",
       "      <td>2.8247</td>\n",
       "      <td>2.4650</td>\n",
       "      <td>2.6134</td>\n",
       "      <td>6625565.00</td>\n",
       "      <td>30459</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25 09:00:00</th>\n",
       "      <td>2.6104</td>\n",
       "      <td>2.7498</td>\n",
       "      <td>2.5629</td>\n",
       "      <td>2.6365</td>\n",
       "      <td>3739132.73</td>\n",
       "      <td>21023</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-15.648260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29 04:00:00</th>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.9448</td>\n",
       "      <td>0.9010</td>\n",
       "      <td>0.9426</td>\n",
       "      <td>2630370.46</td>\n",
       "      <td>6883</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.04374</td>\n",
       "      <td>1.105370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.888091</td>\n",
       "      <td>-0.072614</td>\n",
       "      <td>-0.014164</td>\n",
       "      <td>-0.058450</td>\n",
       "      <td>-78.096948</td>\n",
       "      <td>-158.409167</td>\n",
       "      <td>-0.1764</td>\n",
       "      <td>0.067725</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29 05:00:00</th>\n",
       "      <td>0.9426</td>\n",
       "      <td>0.9426</td>\n",
       "      <td>0.9100</td>\n",
       "      <td>0.9370</td>\n",
       "      <td>1414056.08</td>\n",
       "      <td>4999</td>\n",
       "      <td>1</td>\n",
       "      <td>1.02393</td>\n",
       "      <td>1.091100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.814886</td>\n",
       "      <td>-0.075344</td>\n",
       "      <td>-0.013516</td>\n",
       "      <td>-0.061829</td>\n",
       "      <td>-75.648253</td>\n",
       "      <td>-123.340387</td>\n",
       "      <td>-0.1981</td>\n",
       "      <td>0.065214</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29 06:00:00</th>\n",
       "      <td>0.9370</td>\n",
       "      <td>0.9372</td>\n",
       "      <td>0.8737</td>\n",
       "      <td>0.8964</td>\n",
       "      <td>2162981.47</td>\n",
       "      <td>5446</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00385</td>\n",
       "      <td>1.076885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.744054</td>\n",
       "      <td>-0.079863</td>\n",
       "      <td>-0.014428</td>\n",
       "      <td>-0.065436</td>\n",
       "      <td>-86.694021</td>\n",
       "      <td>-118.412037</td>\n",
       "      <td>-0.2008</td>\n",
       "      <td>0.065092</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29 07:00:00</th>\n",
       "      <td>0.8962</td>\n",
       "      <td>0.9050</td>\n",
       "      <td>0.8502</td>\n",
       "      <td>0.8814</td>\n",
       "      <td>2485976.71</td>\n",
       "      <td>9144</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98247</td>\n",
       "      <td>1.061635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3.675185</td>\n",
       "      <td>-0.083690</td>\n",
       "      <td>-0.014604</td>\n",
       "      <td>-0.069087</td>\n",
       "      <td>-69.471624</td>\n",
       "      <td>-112.154296</td>\n",
       "      <td>-0.2138</td>\n",
       "      <td>0.064356</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29 08:00:00</th>\n",
       "      <td>0.8814</td>\n",
       "      <td>0.9054</td>\n",
       "      <td>0.8647</td>\n",
       "      <td>0.8905</td>\n",
       "      <td>1458057.89</td>\n",
       "      <td>4829</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.96246</td>\n",
       "      <td>1.047410</td>\n",
       "      <td>1.554049</td>\n",
       "      <td>...</td>\n",
       "      <td>5.450809</td>\n",
       "      <td>-0.085009</td>\n",
       "      <td>-0.012738</td>\n",
       "      <td>-0.072271</td>\n",
       "      <td>-57.399577</td>\n",
       "      <td>-90.179453</td>\n",
       "      <td>-0.2001</td>\n",
       "      <td>0.062665</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close  Volume 1INCH  tradecount  \\\n",
       "Date                                                                            \n",
       "2020-12-25 05:00:00  0.2000  3.0885  0.2000  2.5826   13125130.77       48768   \n",
       "2020-12-25 06:00:00  2.5824  2.6900  2.2249  2.5059    9270672.93       31099   \n",
       "2020-12-25 07:00:00  2.5152  2.8870  2.3609  2.6237    7923760.56       33001   \n",
       "2020-12-25 08:00:00  2.6318  2.8247  2.4650  2.6134    6625565.00       30459   \n",
       "2020-12-25 09:00:00  2.6104  2.7498  2.5629  2.6365    3739132.73       21023   \n",
       "...                     ...     ...     ...     ...           ...         ...   \n",
       "2020-12-29 04:00:00  0.9100  0.9448  0.9010  0.9426    2630370.46        6883   \n",
       "2020-12-29 05:00:00  0.9426  0.9426  0.9100  0.9370    1414056.08        4999   \n",
       "2020-12-29 06:00:00  0.9370  0.9372  0.8737  0.8964    2162981.47        5446   \n",
       "2020-12-29 07:00:00  0.8962  0.9050  0.8502  0.8814    2485976.71        9144   \n",
       "2020-12-29 08:00:00  0.8814  0.9054  0.8647  0.8905    1458057.89        4829   \n",
       "\n",
       "                     Target   SMA_10    SMA_20   SMA_100  ...    RVI_90  \\\n",
       "Date                                                      ...             \n",
       "2020-12-25 05:00:00      -1      NaN       NaN       NaN  ...       NaN   \n",
       "2020-12-25 06:00:00       1      NaN       NaN       NaN  ...       NaN   \n",
       "2020-12-25 07:00:00      -1      NaN       NaN       NaN  ...       NaN   \n",
       "2020-12-25 08:00:00       1      NaN       NaN       NaN  ...       NaN   \n",
       "2020-12-25 09:00:00      -1      NaN       NaN       NaN  ...       NaN   \n",
       "...                     ...      ...       ...       ...  ...       ...   \n",
       "2020-12-29 04:00:00      -1  1.04374  1.105370       NaN  ...  3.888091   \n",
       "2020-12-29 05:00:00       1  1.02393  1.091100       NaN  ...  3.814886   \n",
       "2020-12-29 06:00:00       1  1.00385  1.076885       NaN  ...  3.744054   \n",
       "2020-12-29 07:00:00       1  0.98247  1.061635       NaN  ...  3.675185   \n",
       "2020-12-29 08:00:00      -1  0.96246  1.047410  1.554049  ...  5.450809   \n",
       "\n",
       "                     MACD_12_26_9  MACDh_12_26_9  MACDs_12_26_9    WILLR_5  \\\n",
       "Date                                                                         \n",
       "2020-12-25 05:00:00           NaN            NaN            NaN        NaN   \n",
       "2020-12-25 06:00:00           NaN            NaN            NaN        NaN   \n",
       "2020-12-25 07:00:00           NaN            NaN            NaN        NaN   \n",
       "2020-12-25 08:00:00           NaN            NaN            NaN        NaN   \n",
       "2020-12-25 09:00:00           NaN            NaN            NaN -15.648260   \n",
       "...                           ...            ...            ...        ...   \n",
       "2020-12-29 04:00:00     -0.072614      -0.014164      -0.058450 -78.096948   \n",
       "2020-12-29 05:00:00     -0.075344      -0.013516      -0.061829 -75.648253   \n",
       "2020-12-29 06:00:00     -0.079863      -0.014428      -0.065436 -86.694021   \n",
       "2020-12-29 07:00:00     -0.083690      -0.014604      -0.069087 -69.471624   \n",
       "2020-12-29 08:00:00     -0.085009      -0.012738      -0.072271 -57.399577   \n",
       "\n",
       "                     CCI_14_0.015  MOM_10   ATRr_14  AMATe_LR_8_21_2  \\\n",
       "Date                                                                   \n",
       "2020-12-25 05:00:00           NaN     NaN       NaN                0   \n",
       "2020-12-25 06:00:00           NaN     NaN       NaN                0   \n",
       "2020-12-25 07:00:00           NaN     NaN       NaN                0   \n",
       "2020-12-25 08:00:00           NaN     NaN       NaN                0   \n",
       "2020-12-25 09:00:00           NaN     NaN       NaN                0   \n",
       "...                           ...     ...       ...              ...   \n",
       "2020-12-29 04:00:00   -158.409167 -0.1764  0.067725                0   \n",
       "2020-12-29 05:00:00   -123.340387 -0.1981  0.065214                0   \n",
       "2020-12-29 06:00:00   -118.412037 -0.2008  0.065092                0   \n",
       "2020-12-29 07:00:00   -112.154296 -0.2138  0.064356                0   \n",
       "2020-12-29 08:00:00    -90.179453 -0.2001  0.062665                0   \n",
       "\n",
       "                     AMATe_SR_8_21_2  \n",
       "Date                                  \n",
       "2020-12-25 05:00:00                0  \n",
       "2020-12-25 06:00:00                0  \n",
       "2020-12-25 07:00:00                0  \n",
       "2020-12-25 08:00:00                0  \n",
       "2020-12-25 09:00:00                0  \n",
       "...                              ...  \n",
       "2020-12-29 04:00:00                1  \n",
       "2020-12-29 05:00:00                1  \n",
       "2020-12-29 06:00:00                1  \n",
       "2020-12-29 07:00:00                1  \n",
       "2020-12-29 08:00:00                1  \n",
       "\n",
       "[100 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.ta.sma(length=10, append=True)\n",
    "df_new.ta.sma(length=20, append=True)\n",
    "df_new.ta.sma(length=100, append=True)\n",
    "df_new.ta.ema(length=5, append=True)\n",
    "df_new.ta.ema(length=10, append=True)\n",
    "df_new.ta.rsi(length=9, append=True)\n",
    "df_new.ta.log_return(length=1, append=True)\n",
    "df_new.ta.log_return(length=3, append=True)\n",
    "df_new.ta.log_return(length=5, append=True)\n",
    "df_new.ta.roc(length=5, append=True)\n",
    "df_new.ta.log_return(length=1, append=True)\n",
    "df_new.ta.rvi(length=30, append=True)\n",
    "df_new.ta.rvi(length=180, append=True)\n",
    "df_new.ta.rvi(length=270, append=True)\n",
    "df_new.ta.rvi(length=360, append=True)\n",
    "df_new.ta.rvi(length=60, append=True)\n",
    "df_new.ta.rvi(length=90, append=True)\n",
    "df_new.ta.macd(append=True)\n",
    "df_new.ta.willr(length=5, append=True)\n",
    "df_new.ta.cci(append=True)\n",
    "df_new.ta.mom(append=True)\n",
    "df_new.ta.atr(append=True)\n",
    "df_new.ta.amat(append=True)\n",
    "df_new.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b89eaa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24672 entries, 2020-12-25 05:00:00 to 2023-10-19 23:00:00\n",
      "Data columns (total 32 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Open             24672 non-null  float64\n",
      " 1   High             24672 non-null  float64\n",
      " 2   Low              24672 non-null  float64\n",
      " 3   Close            24672 non-null  float64\n",
      " 4   Volume 1INCH     24672 non-null  float64\n",
      " 5   tradecount       24672 non-null  int64  \n",
      " 6   Target           24672 non-null  int64  \n",
      " 7   SMA_10           24663 non-null  float64\n",
      " 8   SMA_20           24653 non-null  float64\n",
      " 9   SMA_100          24573 non-null  float64\n",
      " 10  EMA_5            24668 non-null  float64\n",
      " 11  EMA_10           24663 non-null  float64\n",
      " 12  RSI_9            24663 non-null  float64\n",
      " 13  LOGRET_1         24671 non-null  float64\n",
      " 14  LOGRET_3         24669 non-null  float64\n",
      " 15  LOGRET_5         24667 non-null  float64\n",
      " 16  ROC_5            24667 non-null  float64\n",
      " 17  RVI_30           24643 non-null  float64\n",
      " 18  RVI_180          24493 non-null  float64\n",
      " 19  RVI_270          24403 non-null  float64\n",
      " 20  RVI_360          24313 non-null  float64\n",
      " 21  RVI_60           24613 non-null  float64\n",
      " 22  RVI_90           24583 non-null  float64\n",
      " 23  MACD_12_26_9     24647 non-null  float64\n",
      " 24  MACDh_12_26_9    24639 non-null  float64\n",
      " 25  MACDs_12_26_9    24639 non-null  float64\n",
      " 26  WILLR_5          24668 non-null  float64\n",
      " 27  CCI_14_0.015     24659 non-null  float64\n",
      " 28  MOM_10           24662 non-null  float64\n",
      " 29  ATRr_14          24658 non-null  float64\n",
      " 30  AMATe_LR_8_21_2  24672 non-null  int32  \n",
      " 31  AMATe_SR_8_21_2  24672 non-null  int32  \n",
      "dtypes: float64(28), int32(2), int64(2)\n",
      "memory usage: 6.5 MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4e7f8d",
   "metadata": {},
   "source": [
    "## Handling Missing Values\n",
    "\n",
    "When we look at info values, it can be derived that from 24672 row, at least 24313 rows are non-null. Because this dataset is time series dataset, dropping NaN values is necessary. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7dec3f2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new1 = df_new.iloc[400:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "feece3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>tradecount</th>\n",
       "      <th>Target</th>\n",
       "      <th>SMA_10</th>\n",
       "      <th>SMA_20</th>\n",
       "      <th>SMA_100</th>\n",
       "      <th>...</th>\n",
       "      <th>RVI_90</th>\n",
       "      <th>MACD_12_26_9</th>\n",
       "      <th>MACDh_12_26_9</th>\n",
       "      <th>MACDs_12_26_9</th>\n",
       "      <th>WILLR_5</th>\n",
       "      <th>CCI_14_0.015</th>\n",
       "      <th>MOM_10</th>\n",
       "      <th>ATRr_14</th>\n",
       "      <th>AMATe_LR_8_21_2</th>\n",
       "      <th>AMATe_SR_8_21_2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2021-01-10 21:00:00</th>\n",
       "      <td>1.1890</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>1.1825</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>487164.76</td>\n",
       "      <td>2338</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.22839</td>\n",
       "      <td>1.277765</td>\n",
       "      <td>1.308747</td>\n",
       "      <td>...</td>\n",
       "      <td>49.426063</td>\n",
       "      <td>-0.018193</td>\n",
       "      <td>-0.011751</td>\n",
       "      <td>-0.006442</td>\n",
       "      <td>-14.050822</td>\n",
       "      <td>-61.305665</td>\n",
       "      <td>-0.0022</td>\n",
       "      <td>0.060792</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10 22:00:00</th>\n",
       "      <td>1.2241</td>\n",
       "      <td>1.2400</td>\n",
       "      <td>1.1980</td>\n",
       "      <td>1.2029</td>\n",
       "      <td>429493.72</td>\n",
       "      <td>1994</td>\n",
       "      <td>1</td>\n",
       "      <td>1.22290</td>\n",
       "      <td>1.271475</td>\n",
       "      <td>1.306123</td>\n",
       "      <td>...</td>\n",
       "      <td>48.475670</td>\n",
       "      <td>-0.019292</td>\n",
       "      <td>-0.010280</td>\n",
       "      <td>-0.009012</td>\n",
       "      <td>-30.568012</td>\n",
       "      <td>-48.908559</td>\n",
       "      <td>-0.0549</td>\n",
       "      <td>0.059450</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10 23:00:00</th>\n",
       "      <td>1.2029</td>\n",
       "      <td>1.2326</td>\n",
       "      <td>1.2001</td>\n",
       "      <td>1.2058</td>\n",
       "      <td>243153.82</td>\n",
       "      <td>1181</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.21452</td>\n",
       "      <td>1.266435</td>\n",
       "      <td>1.304418</td>\n",
       "      <td>...</td>\n",
       "      <td>49.456218</td>\n",
       "      <td>-0.019703</td>\n",
       "      <td>-0.008552</td>\n",
       "      <td>-0.011150</td>\n",
       "      <td>-26.307692</td>\n",
       "      <td>-40.502095</td>\n",
       "      <td>-0.0838</td>\n",
       "      <td>0.057525</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11 00:00:00</th>\n",
       "      <td>1.2032</td>\n",
       "      <td>1.2126</td>\n",
       "      <td>1.1650</td>\n",
       "      <td>1.1729</td>\n",
       "      <td>511019.91</td>\n",
       "      <td>2751</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20820</td>\n",
       "      <td>1.257280</td>\n",
       "      <td>1.302158</td>\n",
       "      <td>...</td>\n",
       "      <td>48.523527</td>\n",
       "      <td>-0.022424</td>\n",
       "      <td>-0.009019</td>\n",
       "      <td>-0.013405</td>\n",
       "      <td>-51.615385</td>\n",
       "      <td>-103.503155</td>\n",
       "      <td>-0.0632</td>\n",
       "      <td>0.056816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11 01:00:00</th>\n",
       "      <td>1.1725</td>\n",
       "      <td>1.1993</td>\n",
       "      <td>1.1500</td>\n",
       "      <td>1.1683</td>\n",
       "      <td>479973.16</td>\n",
       "      <td>2141</td>\n",
       "      <td>1</td>\n",
       "      <td>1.19943</td>\n",
       "      <td>1.249270</td>\n",
       "      <td>1.299938</td>\n",
       "      <td>...</td>\n",
       "      <td>47.609410</td>\n",
       "      <td>-0.024668</td>\n",
       "      <td>-0.009010</td>\n",
       "      <td>-0.015657</td>\n",
       "      <td>-79.666667</td>\n",
       "      <td>-118.686406</td>\n",
       "      <td>-0.0877</td>\n",
       "      <td>0.056279</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close  Volume 1INCH  tradecount  \\\n",
       "Date                                                                            \n",
       "2021-01-10 21:00:00  1.1890  1.2250  1.1825  1.2250     487164.76        2338   \n",
       "2021-01-10 22:00:00  1.2241  1.2400  1.1980  1.2029     429493.72        1994   \n",
       "2021-01-10 23:00:00  1.2029  1.2326  1.2001  1.2058     243153.82        1181   \n",
       "2021-01-11 00:00:00  1.2032  1.2126  1.1650  1.1729     511019.91        2751   \n",
       "2021-01-11 01:00:00  1.1725  1.1993  1.1500  1.1683     479973.16        2141   \n",
       "\n",
       "                     Target   SMA_10    SMA_20   SMA_100  ...     RVI_90  \\\n",
       "Date                                                      ...              \n",
       "2021-01-10 21:00:00      -1  1.22839  1.277765  1.308747  ...  49.426063   \n",
       "2021-01-10 22:00:00       1  1.22290  1.271475  1.306123  ...  48.475670   \n",
       "2021-01-10 23:00:00      -1  1.21452  1.266435  1.304418  ...  49.456218   \n",
       "2021-01-11 00:00:00       1  1.20820  1.257280  1.302158  ...  48.523527   \n",
       "2021-01-11 01:00:00       1  1.19943  1.249270  1.299938  ...  47.609410   \n",
       "\n",
       "                     MACD_12_26_9  MACDh_12_26_9  MACDs_12_26_9    WILLR_5  \\\n",
       "Date                                                                         \n",
       "2021-01-10 21:00:00     -0.018193      -0.011751      -0.006442 -14.050822   \n",
       "2021-01-10 22:00:00     -0.019292      -0.010280      -0.009012 -30.568012   \n",
       "2021-01-10 23:00:00     -0.019703      -0.008552      -0.011150 -26.307692   \n",
       "2021-01-11 00:00:00     -0.022424      -0.009019      -0.013405 -51.615385   \n",
       "2021-01-11 01:00:00     -0.024668      -0.009010      -0.015657 -79.666667   \n",
       "\n",
       "                     CCI_14_0.015  MOM_10   ATRr_14  AMATe_LR_8_21_2  \\\n",
       "Date                                                                   \n",
       "2021-01-10 21:00:00    -61.305665 -0.0022  0.060792                0   \n",
       "2021-01-10 22:00:00    -48.908559 -0.0549  0.059450                0   \n",
       "2021-01-10 23:00:00    -40.502095 -0.0838  0.057525                0   \n",
       "2021-01-11 00:00:00   -103.503155 -0.0632  0.056816                0   \n",
       "2021-01-11 01:00:00   -118.686406 -0.0877  0.056279                0   \n",
       "\n",
       "                     AMATe_SR_8_21_2  \n",
       "Date                                  \n",
       "2021-01-10 21:00:00                1  \n",
       "2021-01-10 22:00:00                1  \n",
       "2021-01-10 23:00:00                1  \n",
       "2021-01-11 00:00:00                1  \n",
       "2021-01-11 01:00:00                1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f08576d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24272 entries, 2021-01-10 21:00:00 to 2023-10-19 23:00:00\n",
      "Data columns (total 32 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Open             24272 non-null  float64\n",
      " 1   High             24272 non-null  float64\n",
      " 2   Low              24272 non-null  float64\n",
      " 3   Close            24272 non-null  float64\n",
      " 4   Volume 1INCH     24272 non-null  float64\n",
      " 5   tradecount       24272 non-null  int64  \n",
      " 6   Target           24272 non-null  int64  \n",
      " 7   SMA_10           24272 non-null  float64\n",
      " 8   SMA_20           24272 non-null  float64\n",
      " 9   SMA_100          24272 non-null  float64\n",
      " 10  EMA_5            24272 non-null  float64\n",
      " 11  EMA_10           24272 non-null  float64\n",
      " 12  RSI_9            24272 non-null  float64\n",
      " 13  LOGRET_1         24272 non-null  float64\n",
      " 14  LOGRET_3         24272 non-null  float64\n",
      " 15  LOGRET_5         24272 non-null  float64\n",
      " 16  ROC_5            24272 non-null  float64\n",
      " 17  RVI_30           24272 non-null  float64\n",
      " 18  RVI_180          24272 non-null  float64\n",
      " 19  RVI_270          24272 non-null  float64\n",
      " 20  RVI_360          24272 non-null  float64\n",
      " 21  RVI_60           24272 non-null  float64\n",
      " 22  RVI_90           24272 non-null  float64\n",
      " 23  MACD_12_26_9     24272 non-null  float64\n",
      " 24  MACDh_12_26_9    24272 non-null  float64\n",
      " 25  MACDs_12_26_9    24272 non-null  float64\n",
      " 26  WILLR_5          24272 non-null  float64\n",
      " 27  CCI_14_0.015     24272 non-null  float64\n",
      " 28  MOM_10           24272 non-null  float64\n",
      " 29  ATRr_14          24272 non-null  float64\n",
      " 30  AMATe_LR_8_21_2  24272 non-null  int32  \n",
      " 31  AMATe_SR_8_21_2  24272 non-null  int32  \n",
      "dtypes: float64(28), int32(2), int64(2)\n",
      "memory usage: 5.9 MB\n"
     ]
    }
   ],
   "source": [
    "df_new1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0ad4c1",
   "metadata": {},
   "source": [
    "Info shows us that there are no missing values in our newly constructed dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e3631f",
   "metadata": {},
   "source": [
    "## Normalizing for Easier Feature Selection Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e8e8a280",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Min-Max Normalized Data:\n",
      "                         Open      High       Low     Close  Volume 1INCH  \\\n",
      "Date                                                                        \n",
      "2021-01-10 21:00:00  0.128269  0.130893  0.132763  0.132946      0.013542   \n",
      "2021-01-10 22:00:00  0.132923  0.132853  0.134860  0.130017      0.011939   \n",
      "2021-01-10 23:00:00  0.130112  0.131886  0.135144  0.130401      0.006759   \n",
      "2021-01-11 00:00:00  0.130152  0.129272  0.130396  0.126042      0.014205   \n",
      "2021-01-11 01:00:00  0.126081  0.127534  0.128367  0.125432      0.013342   \n",
      "...                       ...       ...       ...       ...           ...   \n",
      "2023-10-19 19:00:00  0.002997  0.002915  0.005831  0.003140      0.003471   \n",
      "2023-10-19 20:00:00  0.003169  0.003176  0.005912  0.003379      0.009996   \n",
      "2023-10-19 21:00:00  0.003381  0.003111  0.005628  0.002836      0.009030   \n",
      "2023-10-19 22:00:00  0.002851  0.002666  0.005655  0.002875      0.002825   \n",
      "2023-10-19 23:00:00  0.002891  0.002705  0.005709  0.002902      0.007039   \n",
      "\n",
      "                     tradecount  Target    SMA_10    SMA_20   SMA_100  ...  \\\n",
      "Date                                                                   ...   \n",
      "2021-01-10 21:00:00    0.003922     0.0  0.138465  0.148647  0.160000  ...   \n",
      "2021-01-10 22:00:00    0.003345     1.0  0.137708  0.147759  0.159610  ...   \n",
      "2021-01-10 23:00:00    0.001981     0.0  0.136553  0.147047  0.159357  ...   \n",
      "2021-01-11 00:00:00    0.004615     1.0  0.135682  0.145753  0.159021  ...   \n",
      "2021-01-11 01:00:00    0.003592     1.0  0.134473  0.144621  0.158691  ...   \n",
      "...                         ...     ...       ...       ...       ...  ...   \n",
      "2023-10-19 19:00:00    0.000530     0.0  0.003303  0.003690  0.002739  ...   \n",
      "2023-10-19 20:00:00    0.001493     0.0  0.003204  0.003676  0.002740  ...   \n",
      "2023-10-19 21:00:00    0.001309     1.0  0.003067  0.003592  0.002739  ...   \n",
      "2023-10-19 22:00:00    0.000502     0.0  0.002971  0.003524  0.002739  ...   \n",
      "2023-10-19 23:00:00    0.000591     0.0  0.002924  0.003434  0.002738  ...   \n",
      "\n",
      "                       RVI_90  MACD_12_26_9  MACDh_12_26_9  MACDs_12_26_9  \\\n",
      "Date                                                                        \n",
      "2021-01-10 21:00:00  0.514430      0.401585       0.328154       0.454849   \n",
      "2021-01-10 22:00:00  0.490999      0.400433       0.331436       0.451602   \n",
      "2021-01-10 23:00:00  0.515174      0.400003       0.335292       0.448902   \n",
      "2021-01-11 00:00:00  0.492179      0.397151       0.334250       0.446053   \n",
      "2021-01-11 01:00:00  0.469643      0.394801       0.334270       0.443208   \n",
      "...                       ...           ...            ...            ...   \n",
      "2023-10-19 19:00:00  0.347694      0.418386       0.352053       0.461577   \n",
      "2023-10-19 20:00:00  0.378458      0.418465       0.352654       0.461333   \n",
      "2023-10-19 21:00:00  0.354583      0.418209       0.352563       0.461076   \n",
      "2023-10-19 22:00:00  0.386611      0.418062       0.352675       0.460835   \n",
      "2023-10-19 23:00:00  0.418802      0.417993       0.352898       0.460625   \n",
      "\n",
      "                      WILLR_5  CCI_14_0.015    MOM_10   ATRr_14  \\\n",
      "Date                                                              \n",
      "2021-01-10 21:00:00  0.859492      0.429640  0.400465  0.128122   \n",
      "2021-01-10 22:00:00  0.694320      0.443033  0.389109  0.125242   \n",
      "2021-01-10 23:00:00  0.736923      0.452114  0.382882  0.121112   \n",
      "2021-01-11 00:00:00  0.483846      0.384055  0.387321  0.119591   \n",
      "2021-01-11 01:00:00  0.203333      0.367653  0.382041  0.118439   \n",
      "...                       ...           ...       ...       ...   \n",
      "2023-10-19 19:00:00  0.381818      0.411009  0.399302  0.004289   \n",
      "2023-10-19 20:00:00  0.709091      0.442243  0.399388  0.004276   \n",
      "2023-10-19 21:00:00  0.078431      0.412178  0.398806  0.004510   \n",
      "2023-10-19 22:00:00  0.137255      0.395962  0.399431  0.004176   \n",
      "2023-10-19 23:00:00  0.176471      0.405181  0.400207  0.003850   \n",
      "\n",
      "                     AMATe_LR_8_21_2  AMATe_SR_8_21_2  \n",
      "Date                                                   \n",
      "2021-01-10 21:00:00              0.0              1.0  \n",
      "2021-01-10 22:00:00              0.0              1.0  \n",
      "2021-01-10 23:00:00              0.0              1.0  \n",
      "2021-01-11 00:00:00              0.0              1.0  \n",
      "2021-01-11 01:00:00              0.0              1.0  \n",
      "...                              ...              ...  \n",
      "2023-10-19 19:00:00              0.0              1.0  \n",
      "2023-10-19 20:00:00              0.0              1.0  \n",
      "2023-10-19 21:00:00              0.0              1.0  \n",
      "2023-10-19 22:00:00              0.0              1.0  \n",
      "2023-10-19 23:00:00              0.0              1.0  \n",
      "\n",
      "[24272 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "feature_columns = ['Open', 'High', 'Low', 'Close', 'Volume 1INCH', 'tradecount',\n",
    "       'SMA_10', 'SMA_20', 'SMA_100', 'EMA_5', 'EMA_10', 'RSI_9', 'LOGRET_1',\n",
    "       'LOGRET_3', 'LOGRET_5', 'ROC_5', 'RVI_30', 'RVI_180', 'RVI_270',\n",
    "       'RVI_360', 'RVI_60', 'RVI_90', 'MACD_12_26_9', 'MACDh_12_26_9',\n",
    "       'MACDs_12_26_9', 'WILLR_5', 'CCI_14_0.015', 'MOM_10', 'ATRr_14',\n",
    "       'AMATe_LR_8_21_2', 'AMATe_SR_8_21_2']\n",
    "target_column = 'Target'\n",
    "# Min-Max Normalization\n",
    "min_max_scaler = MinMaxScaler()\n",
    "X = df_new1[feature_columns]\n",
    "y = df_new1[target_column]\n",
    "data_normalized_minmax = df_new1.copy()\n",
    "data_normalized_minmax[feature_columns] = min_max_scaler.fit_transform(X)\n",
    "data_normalized_minmax['Target'] = (df_new1['Target'] - df_new1['Target'].min()) / (df_new1['Target'].max() - df_new1['Target'].min())\n",
    "\n",
    "X = data_normalized_minmax[feature_columns]\n",
    "y = data_normalized_minmax['Target']\n",
    "print(\"\\nMin-Max Normalized Data:\")\n",
    "print(data_normalized_minmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "57386aeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24272 entries, 2021-01-10 21:00:00 to 2023-10-19 23:00:00\n",
      "Data columns (total 32 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Open             24272 non-null  float64\n",
      " 1   High             24272 non-null  float64\n",
      " 2   Low              24272 non-null  float64\n",
      " 3   Close            24272 non-null  float64\n",
      " 4   Volume 1INCH     24272 non-null  float64\n",
      " 5   tradecount       24272 non-null  float64\n",
      " 6   Target           24272 non-null  float64\n",
      " 7   SMA_10           24272 non-null  float64\n",
      " 8   SMA_20           24272 non-null  float64\n",
      " 9   SMA_100          24272 non-null  float64\n",
      " 10  EMA_5            24272 non-null  float64\n",
      " 11  EMA_10           24272 non-null  float64\n",
      " 12  RSI_9            24272 non-null  float64\n",
      " 13  LOGRET_1         24272 non-null  float64\n",
      " 14  LOGRET_3         24272 non-null  float64\n",
      " 15  LOGRET_5         24272 non-null  float64\n",
      " 16  ROC_5            24272 non-null  float64\n",
      " 17  RVI_30           24272 non-null  float64\n",
      " 18  RVI_180          24272 non-null  float64\n",
      " 19  RVI_270          24272 non-null  float64\n",
      " 20  RVI_360          24272 non-null  float64\n",
      " 21  RVI_60           24272 non-null  float64\n",
      " 22  RVI_90           24272 non-null  float64\n",
      " 23  MACD_12_26_9     24272 non-null  float64\n",
      " 24  MACDh_12_26_9    24272 non-null  float64\n",
      " 25  MACDs_12_26_9    24272 non-null  float64\n",
      " 26  WILLR_5          24272 non-null  float64\n",
      " 27  CCI_14_0.015     24272 non-null  float64\n",
      " 28  MOM_10           24272 non-null  float64\n",
      " 29  ATRr_14          24272 non-null  float64\n",
      " 30  AMATe_LR_8_21_2  24272 non-null  float64\n",
      " 31  AMATe_SR_8_21_2  24272 non-null  float64\n",
      "dtypes: float64(32)\n",
      "memory usage: 6.1 MB\n"
     ]
    }
   ],
   "source": [
    "data_normalized_minmax.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f564987d",
   "metadata": {},
   "source": [
    "## Feature Selection with SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96765115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Scores:\n",
      "            Feature         Score\n",
      "12         LOGRET_1  15764.510894\n",
      "25          WILLR_5   9937.446794\n",
      "16           RVI_30   3297.663717\n",
      "13         LOGRET_3   2967.516822\n",
      "11            RSI_9   2721.591781\n",
      "26     CCI_14_0.015   1732.295753\n",
      "20           RVI_60   1636.636952\n",
      "14         LOGRET_5   1476.463354\n",
      "15            ROC_5   1440.958592\n",
      "21           RVI_90   1049.658614\n",
      "29  AMATe_LR_8_21_2    749.117789\n",
      "30  AMATe_SR_8_21_2    749.117789\n",
      "17          RVI_180    517.339362\n",
      "27           MOM_10    340.797961\n",
      "18          RVI_270    215.275870\n",
      "23    MACDh_12_26_9     75.418355\n",
      "19          RVI_360     73.132548\n",
      "0              Open     49.857067\n",
      "6            SMA_10     43.871001\n",
      "7            SMA_20     43.555661\n",
      "10           EMA_10     42.368826\n",
      "8           SMA_100     42.318676\n",
      "9             EMA_5     39.954405\n",
      "1              High     37.150433\n",
      "2               Low     35.741271\n",
      "28          ATRr_14     35.630392\n",
      "3             Close     26.291251\n",
      "4      Volume 1INCH      9.438350\n",
      "24    MACDs_12_26_9      4.367778\n",
      "22     MACD_12_26_9      0.630972\n",
      "5        tradecount      0.026033\n",
      "\n",
      "Top 6 Features Selected: ['LOGRET_1', 'WILLR_5', 'RVI_30', 'LOGRET_3', 'RSI_9', 'CCI_14_0.015']\n"
     ]
    }
   ],
   "source": [
    "k = 6  # Number of top features to select\n",
    "selector = SelectKBest(score_func=f_regression, k=k)\n",
    "X_selected = selector.fit_transform(X, y)\n",
    "\n",
    "# Get the scores and feature names\n",
    "scores = selector.scores_\n",
    "features = X.columns\n",
    "\n",
    "# Create a DataFrame for visualization\n",
    "feature_scores = pd.DataFrame({'Feature': features, 'Score': scores})\n",
    "feature_scores = feature_scores.sort_values(by='Score', ascending=False)\n",
    "\n",
    "print(\"Feature Scores:\")\n",
    "print(feature_scores)\n",
    "\n",
    "# Selected features\n",
    "selected_features = feature_scores['Feature'].head(k).tolist()\n",
    "print(f\"\\nTop {k} Features Selected: {selected_features}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a20f532",
   "metadata": {},
   "source": [
    "Top 6 Features are : LOGRET_1, WILLR_5, RVI_30, LOGRET_3, RSI_9, CCI_14_0.015"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ac462d",
   "metadata": {},
   "source": [
    "##  Feature Selection with Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f88b6fd2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Highly Correlated Feature Pairs:\n",
      "Open and High: 1.00\n",
      "Open and Low: 1.00\n",
      "Open and Close: 1.00\n",
      "Open and SMA_10: 1.00\n",
      "Open and SMA_20: 1.00\n",
      "Open and SMA_100: 0.99\n",
      "Open and EMA_5: 1.00\n",
      "Open and EMA_10: 1.00\n",
      "Open and ATRr_14: 0.86\n",
      "High and Open: 1.00\n",
      "High and Low: 1.00\n",
      "High and Close: 1.00\n",
      "High and SMA_10: 1.00\n",
      "High and SMA_20: 1.00\n",
      "High and SMA_100: 0.99\n",
      "High and EMA_5: 1.00\n",
      "High and EMA_10: 1.00\n",
      "High and ATRr_14: 0.87\n",
      "Low and Open: 1.00\n",
      "Low and High: 1.00\n",
      "Low and Close: 1.00\n",
      "Low and SMA_10: 1.00\n",
      "Low and SMA_20: 1.00\n",
      "Low and SMA_100: 0.99\n",
      "Low and EMA_5: 1.00\n",
      "Low and EMA_10: 1.00\n",
      "Low and ATRr_14: 0.86\n",
      "Close and Open: 1.00\n",
      "Close and High: 1.00\n",
      "Close and Low: 1.00\n",
      "Close and SMA_10: 1.00\n",
      "Close and SMA_20: 1.00\n",
      "Close and SMA_100: 0.99\n",
      "Close and EMA_5: 1.00\n",
      "Close and EMA_10: 1.00\n",
      "Close and ATRr_14: 0.86\n",
      "SMA_10 and Open: 1.00\n",
      "SMA_10 and High: 1.00\n",
      "SMA_10 and Low: 1.00\n",
      "SMA_10 and Close: 1.00\n",
      "SMA_10 and SMA_20: 1.00\n",
      "SMA_10 and SMA_100: 0.99\n",
      "SMA_10 and EMA_5: 1.00\n",
      "SMA_10 and EMA_10: 1.00\n",
      "SMA_10 and ATRr_14: 0.86\n",
      "SMA_20 and Open: 1.00\n",
      "SMA_20 and High: 1.00\n",
      "SMA_20 and Low: 1.00\n",
      "SMA_20 and Close: 1.00\n",
      "SMA_20 and SMA_10: 1.00\n",
      "SMA_20 and SMA_100: 0.99\n",
      "SMA_20 and EMA_5: 1.00\n",
      "SMA_20 and EMA_10: 1.00\n",
      "SMA_20 and ATRr_14: 0.86\n",
      "SMA_100 and Open: 0.99\n",
      "SMA_100 and High: 0.99\n",
      "SMA_100 and Low: 0.99\n",
      "SMA_100 and Close: 0.99\n",
      "SMA_100 and SMA_10: 0.99\n",
      "SMA_100 and SMA_20: 0.99\n",
      "SMA_100 and EMA_5: 0.99\n",
      "SMA_100 and EMA_10: 0.99\n",
      "SMA_100 and ATRr_14: 0.86\n",
      "EMA_5 and Open: 1.00\n",
      "EMA_5 and High: 1.00\n",
      "EMA_5 and Low: 1.00\n",
      "EMA_5 and Close: 1.00\n",
      "EMA_5 and SMA_10: 1.00\n",
      "EMA_5 and SMA_20: 1.00\n",
      "EMA_5 and SMA_100: 0.99\n",
      "EMA_5 and EMA_10: 1.00\n",
      "EMA_5 and ATRr_14: 0.86\n",
      "EMA_10 and Open: 1.00\n",
      "EMA_10 and High: 1.00\n",
      "EMA_10 and Low: 1.00\n",
      "EMA_10 and Close: 1.00\n",
      "EMA_10 and SMA_10: 1.00\n",
      "EMA_10 and SMA_20: 1.00\n",
      "EMA_10 and SMA_100: 0.99\n",
      "EMA_10 and EMA_5: 1.00\n",
      "EMA_10 and ATRr_14: 0.86\n",
      "RSI_9 and CCI_14_0.015: 0.88\n",
      "RSI_9 and AMATe_LR_8_21_2: 0.75\n",
      "RSI_9 and AMATe_SR_8_21_2: -0.75\n",
      "LOGRET_3 and LOGRET_5: 0.76\n",
      "LOGRET_3 and ROC_5: 0.76\n",
      "LOGRET_5 and LOGRET_3: 0.76\n",
      "LOGRET_5 and ROC_5: 1.00\n",
      "ROC_5 and LOGRET_3: 0.76\n",
      "ROC_5 and LOGRET_5: 1.00\n",
      "RVI_30 and RVI_60: 0.90\n",
      "RVI_30 and RVI_90: 0.81\n",
      "RVI_180 and RVI_60: 0.80\n",
      "RVI_180 and RVI_90: 0.89\n",
      "RVI_60 and RVI_30: 0.90\n",
      "RVI_60 and RVI_180: 0.80\n",
      "RVI_60 and RVI_90: 0.95\n",
      "RVI_90 and RVI_30: 0.81\n",
      "RVI_90 and RVI_180: 0.89\n",
      "RVI_90 and RVI_60: 0.95\n",
      "MACD_12_26_9 and MACDs_12_26_9: 0.95\n",
      "MACDh_12_26_9 and MOM_10: 0.86\n",
      "MACDs_12_26_9 and MACD_12_26_9: 0.95\n",
      "CCI_14_0.015 and RSI_9: 0.88\n",
      "CCI_14_0.015 and AMATe_LR_8_21_2: 0.80\n",
      "CCI_14_0.015 and AMATe_SR_8_21_2: -0.80\n",
      "MOM_10 and MACDh_12_26_9: 0.86\n",
      "ATRr_14 and Open: 0.86\n",
      "ATRr_14 and High: 0.87\n",
      "ATRr_14 and Low: 0.86\n",
      "ATRr_14 and Close: 0.86\n",
      "ATRr_14 and SMA_10: 0.86\n",
      "ATRr_14 and SMA_20: 0.86\n",
      "ATRr_14 and SMA_100: 0.86\n",
      "ATRr_14 and EMA_5: 0.86\n",
      "ATRr_14 and EMA_10: 0.86\n",
      "AMATe_LR_8_21_2 and RSI_9: 0.75\n",
      "AMATe_LR_8_21_2 and CCI_14_0.015: 0.80\n",
      "AMATe_LR_8_21_2 and AMATe_SR_8_21_2: -1.00\n",
      "AMATe_SR_8_21_2 and RSI_9: -0.75\n",
      "AMATe_SR_8_21_2 and CCI_14_0.015: -0.80\n",
      "AMATe_SR_8_21_2 and AMATe_LR_8_21_2: -1.00\n"
     ]
    }
   ],
   "source": [
    "# Visualize the correlation matrix as a heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(X.corr(), annot=True, fmt=\".2f\", cmap=\"coolwarm\")\n",
    "plt.title(\"Correlation Matrix Heatmap\")\n",
    "plt.show()\n",
    "\n",
    "# Identify highly correlated features\n",
    "threshold = 0.7  # Set a threshold for high correlation\n",
    "high_corr_pairs = [\n",
    "    (col1, col2, X.corr().loc[col1, col2])\n",
    "    for col1 in X.corr().columns\n",
    "    for col2 in X.corr().columns\n",
    "    if col1 != col2 and abs(X.corr().loc[col1, col2]) > threshold\n",
    "]\n",
    "\n",
    "# Display highly correlated feature pairs\n",
    "print(\"\\nHighly Correlated Feature Pairs:\")\n",
    "for col1, col2, corr_value in high_corr_pairs:\n",
    "    print(f\"{col1} and {col2}: {corr_value:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0fc274",
   "metadata": {},
   "source": [
    "When we look at correlation matrix, SMA_10, SMA_20, EMA_5, EMA_10, and ATRr_14 have high correlation with closing price. With 6 features that are mentioned in SelectKBest part, these would be quite useful for lstm and lstm+cnn training process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968b6de2",
   "metadata": {},
   "source": [
    "## Dropping Unnecessary Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9d7010b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_normalized_minmax.drop(columns=['tradecount', 'SMA_100','LOGRET_5', 'ROC_5', 'RVI_180', 'RVI_270', 'RVI_360', 'RVI_60', 'RVI_90', 'MACD_12_26_9', 'MACDh_12_26_9', 'MACDs_12_26_9', 'MOM_10','AMATe_LR_8_21_2', 'AMATe_SR_8_21_2'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ebe0ce6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new1.drop(columns=['tradecount', 'SMA_100','LOGRET_5', 'ROC_5', 'RVI_180', 'RVI_270', 'RVI_360', 'RVI_60', 'RVI_90', 'MACD_12_26_9', 'MACDh_12_26_9', 'MACDs_12_26_9', 'MOM_10','AMATe_LR_8_21_2', 'AMATe_SR_8_21_2'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9879e5ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "d_n_m = data_normalized_minmax\n",
    "df_new2 = df_new1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f397a0ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>Target</th>\n",
       "      <th>SMA_10</th>\n",
       "      <th>SMA_20</th>\n",
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       "      <th>RVI_30</th>\n",
       "      <th>WILLR_5</th>\n",
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       "      <th>ATRr_14</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2021-01-10 21:00:00</th>\n",
       "      <td>0.128269</td>\n",
       "      <td>0.130893</td>\n",
       "      <td>0.132763</td>\n",
       "      <td>0.132946</td>\n",
       "      <td>0.013542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.138465</td>\n",
       "      <td>0.148647</td>\n",
       "      <td>0.133721</td>\n",
       "      <td>0.138873</td>\n",
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       "      <td>0.564003</td>\n",
       "      <td>0.501184</td>\n",
       "      <td>0.423968</td>\n",
       "      <td>0.859492</td>\n",
       "      <td>0.429640</td>\n",
       "      <td>0.128122</td>\n",
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       "    <tr>\n",
       "      <th>2021-01-10 22:00:00</th>\n",
       "      <td>0.132923</td>\n",
       "      <td>0.132853</td>\n",
       "      <td>0.134860</td>\n",
       "      <td>0.130017</td>\n",
       "      <td>0.011939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137708</td>\n",
       "      <td>0.147759</td>\n",
       "      <td>0.133364</td>\n",
       "      <td>0.138250</td>\n",
       "      <td>0.397937</td>\n",
       "      <td>0.479168</td>\n",
       "      <td>0.500109</td>\n",
       "      <td>0.383162</td>\n",
       "      <td>0.694320</td>\n",
       "      <td>0.443033</td>\n",
       "      <td>0.125242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10 23:00:00</th>\n",
       "      <td>0.130112</td>\n",
       "      <td>0.131886</td>\n",
       "      <td>0.135144</td>\n",
       "      <td>0.130401</td>\n",
       "      <td>0.006759</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136553</td>\n",
       "      <td>0.147047</td>\n",
       "      <td>0.133258</td>\n",
       "      <td>0.137813</td>\n",
       "      <td>0.406820</td>\n",
       "      <td>0.514342</td>\n",
       "      <td>0.495273</td>\n",
       "      <td>0.436127</td>\n",
       "      <td>0.736923</td>\n",
       "      <td>0.452114</td>\n",
       "      <td>0.121112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11 00:00:00</th>\n",
       "      <td>0.130152</td>\n",
       "      <td>0.129272</td>\n",
       "      <td>0.130396</td>\n",
       "      <td>0.126042</td>\n",
       "      <td>0.014205</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.135682</td>\n",
       "      <td>0.145753</td>\n",
       "      <td>0.131702</td>\n",
       "      <td>0.136627</td>\n",
       "      <td>0.337494</td>\n",
       "      <td>0.463028</td>\n",
       "      <td>0.429544</td>\n",
       "      <td>0.393190</td>\n",
       "      <td>0.483846</td>\n",
       "      <td>0.384055</td>\n",
       "      <td>0.119591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11 01:00:00</th>\n",
       "      <td>0.126081</td>\n",
       "      <td>0.127534</td>\n",
       "      <td>0.128367</td>\n",
       "      <td>0.125432</td>\n",
       "      <td>0.013342</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.134473</td>\n",
       "      <td>0.144621</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>0.135541</td>\n",
       "      <td>0.328421</td>\n",
       "      <td>0.503527</td>\n",
       "      <td>0.445401</td>\n",
       "      <td>0.351492</td>\n",
       "      <td>0.203333</td>\n",
       "      <td>0.367653</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Open      High       Low     Close  Volume 1INCH  \\\n",
       "Date                                                                        \n",
       "2021-01-10 21:00:00  0.128269  0.130893  0.132763  0.132946      0.013542   \n",
       "2021-01-10 22:00:00  0.132923  0.132853  0.134860  0.130017      0.011939   \n",
       "2021-01-10 23:00:00  0.130112  0.131886  0.135144  0.130401      0.006759   \n",
       "2021-01-11 00:00:00  0.130152  0.129272  0.130396  0.126042      0.014205   \n",
       "2021-01-11 01:00:00  0.126081  0.127534  0.128367  0.125432      0.013342   \n",
       "\n",
       "                     Target    SMA_10    SMA_20     EMA_5    EMA_10     RSI_9  \\\n",
       "Date                                                                            \n",
       "2021-01-10 21:00:00     0.0  0.138465  0.148647  0.133721  0.138873  0.445365   \n",
       "2021-01-10 22:00:00     1.0  0.137708  0.147759  0.133364  0.138250  0.397937   \n",
       "2021-01-10 23:00:00     0.0  0.136553  0.147047  0.133258  0.137813  0.406820   \n",
       "2021-01-11 00:00:00     1.0  0.135682  0.145753  0.131702  0.136627  0.337494   \n",
       "2021-01-11 01:00:00     1.0  0.134473  0.144621  0.130457  0.135541  0.328421   \n",
       "\n",
       "                     LOGRET_1  LOGRET_3    RVI_30   WILLR_5  CCI_14_0.015  \\\n",
       "Date                                                                        \n",
       "2021-01-10 21:00:00  0.564003  0.501184  0.423968  0.859492      0.429640   \n",
       "2021-01-10 22:00:00  0.479168  0.500109  0.383162  0.694320      0.443033   \n",
       "2021-01-10 23:00:00  0.514342  0.495273  0.436127  0.736923      0.452114   \n",
       "2021-01-11 00:00:00  0.463028  0.429544  0.393190  0.483846      0.384055   \n",
       "2021-01-11 01:00:00  0.503527  0.445401  0.351492  0.203333      0.367653   \n",
       "\n",
       "                      ATRr_14  \n",
       "Date                           \n",
       "2021-01-10 21:00:00  0.128122  \n",
       "2021-01-10 22:00:00  0.125242  \n",
       "2021-01-10 23:00:00  0.121112  \n",
       "2021-01-11 00:00:00  0.119591  \n",
       "2021-01-11 01:00:00  0.118439  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_n_m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9de7b933",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume 1INCH</th>\n",
       "      <th>Target</th>\n",
       "      <th>SMA_10</th>\n",
       "      <th>SMA_20</th>\n",
       "      <th>EMA_5</th>\n",
       "      <th>EMA_10</th>\n",
       "      <th>RSI_9</th>\n",
       "      <th>LOGRET_1</th>\n",
       "      <th>LOGRET_3</th>\n",
       "      <th>RVI_30</th>\n",
       "      <th>WILLR_5</th>\n",
       "      <th>CCI_14_0.015</th>\n",
       "      <th>ATRr_14</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-10 21:00:00</th>\n",
       "      <td>1.1890</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>1.1825</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>487164.76</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.22839</td>\n",
       "      <td>1.277765</td>\n",
       "      <td>1.210797</td>\n",
       "      <td>1.227666</td>\n",
       "      <td>45.609685</td>\n",
       "      <td>0.031512</td>\n",
       "      <td>0.021036</td>\n",
       "      <td>44.686570</td>\n",
       "      <td>-14.050822</td>\n",
       "      <td>-61.305665</td>\n",
       "      <td>0.060792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10 22:00:00</th>\n",
       "      <td>1.2241</td>\n",
       "      <td>1.2400</td>\n",
       "      <td>1.1980</td>\n",
       "      <td>1.2029</td>\n",
       "      <td>429493.72</td>\n",
       "      <td>1</td>\n",
       "      <td>1.22290</td>\n",
       "      <td>1.271475</td>\n",
       "      <td>1.208165</td>\n",
       "      <td>1.223163</td>\n",
       "      <td>41.329467</td>\n",
       "      <td>-0.018206</td>\n",
       "      <td>0.020069</td>\n",
       "      <td>41.817151</td>\n",
       "      <td>-30.568012</td>\n",
       "      <td>-48.908559</td>\n",
       "      <td>0.059450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-10 23:00:00</th>\n",
       "      <td>1.2029</td>\n",
       "      <td>1.2326</td>\n",
       "      <td>1.2001</td>\n",
       "      <td>1.2058</td>\n",
       "      <td>243153.82</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.21452</td>\n",
       "      <td>1.266435</td>\n",
       "      <td>1.207377</td>\n",
       "      <td>1.220006</td>\n",
       "      <td>42.131167</td>\n",
       "      <td>0.002408</td>\n",
       "      <td>0.015714</td>\n",
       "      <td>45.541537</td>\n",
       "      <td>-26.307692</td>\n",
       "      <td>-40.502095</td>\n",
       "      <td>0.057525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11 00:00:00</th>\n",
       "      <td>1.2032</td>\n",
       "      <td>1.2126</td>\n",
       "      <td>1.1650</td>\n",
       "      <td>1.1729</td>\n",
       "      <td>511019.91</td>\n",
       "      <td>1</td>\n",
       "      <td>1.20820</td>\n",
       "      <td>1.257280</td>\n",
       "      <td>1.195884</td>\n",
       "      <td>1.211441</td>\n",
       "      <td>35.874684</td>\n",
       "      <td>-0.027664</td>\n",
       "      <td>-0.043462</td>\n",
       "      <td>42.522324</td>\n",
       "      <td>-51.615385</td>\n",
       "      <td>-103.503155</td>\n",
       "      <td>0.056816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-11 01:00:00</th>\n",
       "      <td>1.1725</td>\n",
       "      <td>1.1993</td>\n",
       "      <td>1.1500</td>\n",
       "      <td>1.1683</td>\n",
       "      <td>479973.16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.19943</td>\n",
       "      <td>1.249270</td>\n",
       "      <td>1.186690</td>\n",
       "      <td>1.203597</td>\n",
       "      <td>35.055839</td>\n",
       "      <td>-0.003930</td>\n",
       "      <td>-0.029186</td>\n",
       "      <td>39.590217</td>\n",
       "      <td>-79.666667</td>\n",
       "      <td>-118.686406</td>\n",
       "      <td>0.056279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Open    High     Low   Close  Volume 1INCH  Target  \\\n",
       "Date                                                                        \n",
       "2021-01-10 21:00:00  1.1890  1.2250  1.1825  1.2250     487164.76      -1   \n",
       "2021-01-10 22:00:00  1.2241  1.2400  1.1980  1.2029     429493.72       1   \n",
       "2021-01-10 23:00:00  1.2029  1.2326  1.2001  1.2058     243153.82      -1   \n",
       "2021-01-11 00:00:00  1.2032  1.2126  1.1650  1.1729     511019.91       1   \n",
       "2021-01-11 01:00:00  1.1725  1.1993  1.1500  1.1683     479973.16       1   \n",
       "\n",
       "                      SMA_10    SMA_20     EMA_5    EMA_10      RSI_9  \\\n",
       "Date                                                                    \n",
       "2021-01-10 21:00:00  1.22839  1.277765  1.210797  1.227666  45.609685   \n",
       "2021-01-10 22:00:00  1.22290  1.271475  1.208165  1.223163  41.329467   \n",
       "2021-01-10 23:00:00  1.21452  1.266435  1.207377  1.220006  42.131167   \n",
       "2021-01-11 00:00:00  1.20820  1.257280  1.195884  1.211441  35.874684   \n",
       "2021-01-11 01:00:00  1.19943  1.249270  1.186690  1.203597  35.055839   \n",
       "\n",
       "                     LOGRET_1  LOGRET_3     RVI_30    WILLR_5  CCI_14_0.015  \\\n",
       "Date                                                                          \n",
       "2021-01-10 21:00:00  0.031512  0.021036  44.686570 -14.050822    -61.305665   \n",
       "2021-01-10 22:00:00 -0.018206  0.020069  41.817151 -30.568012    -48.908559   \n",
       "2021-01-10 23:00:00  0.002408  0.015714  45.541537 -26.307692    -40.502095   \n",
       "2021-01-11 00:00:00 -0.027664 -0.043462  42.522324 -51.615385   -103.503155   \n",
       "2021-01-11 01:00:00 -0.003930 -0.029186  39.590217 -79.666667   -118.686406   \n",
       "\n",
       "                      ATRr_14  \n",
       "Date                           \n",
       "2021-01-10 21:00:00  0.060792  \n",
       "2021-01-10 22:00:00  0.059450  \n",
       "2021-01-10 23:00:00  0.057525  \n",
       "2021-01-11 00:00:00  0.056816  \n",
       "2021-01-11 01:00:00  0.056279  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5179bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24272, 17)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c23cc93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Volume 1INCH', 'Target', 'SMA_10',\n",
       "       'SMA_20', 'EMA_5', 'EMA_10', 'RSI_9', 'LOGRET_1', 'LOGRET_3', 'RVI_30',\n",
       "       'WILLR_5', 'CCI_14_0.015', 'ATRr_14'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new2.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f0b2c8",
   "metadata": {},
   "source": [
    "## Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "964aeefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_feature_columns = ['SMA_10', 'SMA_20', 'EMA_5', 'EMA_10', 'ATRr_14', 'Close']\n",
    "X = df_new2[n_feature_columns]\n",
    "y1 = df_new2[target_column]         #For predicting (binary) target value \n",
    "y3 = df_new2['Close']\n",
    "\n",
    "\n",
    "# Splitting the data into train and test sets\n",
    "train_size = int(X.shape[0] * 0.8)\n",
    "X_train, y3_train = X[:train_size], y3[:train_size]\n",
    "X_test, y3_test = X[train_size:], y3[train_size:]\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Splitting the train set into train and validation sets\n",
    "tscv = TimeSeriesSplit(n_splits=5)  # You can adjust the number of splits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3c7b6a8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19417, 6), (4855, 6))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0915f9c4",
   "metadata": {},
   "source": [
    "# Examining Closing Price Estimation (Continuous)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5966b644",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2731bec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr_normal = RandomForestRegressor(n_estimators=50,          \n",
    "    max_depth=10,               \n",
    "    min_samples_split=3,       \n",
    "    min_samples_leaf=5,         \n",
    "    max_features='sqrt',        \n",
    "    random_state=42,           \n",
    "    bootstrap=True  )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a93803",
   "metadata": {},
   "source": [
    "## Overfit Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a43193f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation MSE Scores: [0.0007170510776273134, 0.0007897460754241639, 0.037135128303902504, 3.6816216506929844e-05, 0.0028395389431165184]\n",
      "Mean CV MSE: 0.0083\n",
      "Test Set MSE: 0.0068\n"
     ]
    }
   ],
   "source": [
    "cv_scores = []\n",
    "for train_index, val_index in tscv.split(X_train_scaled):\n",
    "    X_train_fold, X_val_fold = X_train_scaled[train_index], X_train_scaled[val_index]\n",
    "    y3_train_fold, y3_val_fold = y3_train[train_index], y3_train[val_index]\n",
    "\n",
    "    rfr_normal.fit(X_train_fold, y3_train_fold)\n",
    "\n",
    "    # Validate the model\n",
    "    y3_val_pred = rfr_normal.predict(X_val_fold)\n",
    "    mse = mean_squared_error(y3_val_fold, y3_val_pred)\n",
    "    cv_scores.append(mse)\n",
    "\n",
    "print(f\"Cross-Validation MSE Scores: {cv_scores}\")\n",
    "print(f\"Mean CV MSE: {np.mean(cv_scores):.4f}\")\n",
    "\n",
    "# Train the model on the full training set\n",
    "rfr_normal.fit(X_train_scaled, y3_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y3_test_pred = rfr_normal.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate performance on the test set\n",
    "test_mse = mean_squared_error(y3_test, y3_test_pred)\n",
    "print(f\"Test Set MSE: {test_mse:.4f}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c22185b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMA_10: 0.14330630469424108\n",
      "SMA_20: 0.04127007659944573\n",
      "EMA_5: 0.2096151142363854\n",
      "EMA_10: 0.27972963222661046\n",
      "ATRr_14: 5.748154803358693e-05\n",
      "Close: 0.32602139069528385\n"
     ]
    }
   ],
   "source": [
    "importances = rfr_normal.feature_importances_\n",
    "for feature, importance in zip(n_feature_columns, importances):\n",
    "    print(f\"{feature}: {importance}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5575fd63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e5WjbY489VqUdSOU7TsXHGnqINJgNGzbM1dSHpjFlZmYyfvz4gG6jOQvn/MM5dwi//DfmnOC6/1lK27goTpwpAeD/u74vM0c3/JE1oSDc9jvAli1buOSSS4DwfuxTOOcOjZ9/5e+rcma2xjk3rKbxOuImInIO5i/LolVMJB//5ArmjvOdHj12qsTjqEQkXKhwExFpoIMnC3h9wz5uHpZKm9hourSK4PJenXj6k10UlQTubkERkXIq3EREGmjxyl2UlDlmj0mvmDZ7TDoH8wp5a1Oud4GJSNhQ4SYi0gAFxaU8/cluJvbpTHrHVhXTL+/VifQOLVm4PNu74EQkbKhwExFpgNc+28eRU0XMGVv1JoSICGPm6HTW7j7Ohpzj3gQnImFDhZuISD2cc8xbmkWfLvGM7tHhrPk3DUuhVUwkC3TUTUQCTIWbiEg9Vuw8wtb9ecwZ273G7vFtYqP5t6EpvPFZLofzz344t4hIY1HhJiJSj3lLs2nfKoYpg7rWOmbm6HSKSst45pOaHyAu0hjMjBkzZlR8LikpoVOnTlx77bUB3/bvfvc7+vTpw8iRIxk4cCCLFi3ioYce4sEHH6wybv369Wf1JQMYP3483bp1q/KIralTp9K6dWsAysrK+MEPfkD//v0ZMGAAw4cPr3g4fXp6OgMGDKhozPuDH/ygxhj/8Ic/sGjRIgCOHj3KVVddRc+ePbnqqqs4duxYjcu8/fbb9O7dm4svvpi5c+dWTK9t+eLiYmbNmsWAAQO45JJLePjhhyuWmTRpEgMHDqRfv37cddddlJb6nmv8P//zP8yfP7/uL3ADqXATEanDriOnWLL1ALeN7EZstUcYVXZx59aM69mRxZ/sojiADxKX8NaqVSs2bdrEmTO+56C+9957JCef/9MYGuqJJ57gvffeY9WqVXzyySd89NFHOOeYPn06zz77bJWxGRkZfPOb36xxPQkJCSxbtgyA48ePk5v71d3Yzz77LPv27WPDhg1s3LiRl19+mYSEhIr5H3zwQcUD6B9//PGz1l1SUsK8efMqtj137lwmTpzIF198wcSJE6sUZeVKS0u5++67eeutt9i8eTPPPPMMmzdvrnP5l19+mcLCQjZu3MiaNWv4y1/+UvGEh+eee47PPvuMTZs2cejQoYpHg82ZM6fGmM+HCjcRkTosWJ5NVIRx+6j6n6c4e0w6B04W8vam/U0QmXhu/PizX//7v755p0/XPL/88U6HD589r4EmT57MP//5TwCeeeaZKs8lPXXqFHPmzGH48OEMHjyYV199FYDs7GzGjRvHkCFDGDJkCMuXLwe+egLGTTfdRJ8+fbjttttqfOj8r3/9a/73f/+XNm3aANC2bVtmzZpF7969SUhI4JNPPqkY+9xzzzFt2rQaY582bRoZGRkAvPTSS9x4440V83Jzc0lKSiIiwleapKSk0K5duwZ/Xd5//32GDBlCVFQUAK+++iqzZs0CYNasWbzyyitnLbNq1SouvvhiLrroImJiYpg2bVrF16y25c2MU6dOUVJSwpkzZ4iJian4upT/W1JSQlFRUcWlFS1btiQ9PZ1Vq1Y1OJ/aBLRwM7NJZrbNzHaY2QM1zDcze9w/f4OZDfFPjzWzVWb2mZl9bmb/VWmZh8xsr5mt97+uCWQOIhK+8gqKeX51Dtde2pXENrH1jp/QuzNpHVrqJgUJqPLip6CggA0bNjBy5MiKeb/61a+44oor+PTTT/nggw/48Y9/zKlTp+jcuTPvvfcea9eu5dlnn61yqnHdunX84Q9/YPPmzezcubPiiFi5vLw88vLy6NGjR43xTJ8+vaIYW7lyJR06dKBnz541jp04cSIfffQRpaWlZGRkcOutt1bMu+WWW3j99dcZNGgQP/rRj1i3bl2VZSdMmFBxqvSxxx47a93Lli1j6NChFZ8PHDhAUlIS4HvW6sGDB89aZu/evaSmplZ8TklJYe/evXUuP3XqVFq1akVSUhLdunXj/vvvp3379hXruPrqq+ncuTPx8fHcdNNNFdOHDRvGxx9/XOPX5VxEXfAaamFmkcCfgKuAHOBTM3vNObe50rDJQE//ayTwZ/+/hcAVzrl8M4sGlprZW865lf7lHnPO/S5QsYuIADy3Oof8whLuGJveoPEREcaMUWn88p9b2JhzggEpbQMboHgrM7P2eS1b1j2/Y8e659fh0ksvJTs7m2eeeYZrrql67OLdd9/ltdde43e/8/2KLCgoYPfu3XTt2pV77rmH9evXExkZyfbt2yuWGTFiBCkpKQAVD3a/7LLLKuY752q8KafctGnTGDNmDI8++igZGRlVjgBWFxkZyWWXXcazzz7LmTNnSE9Pr5iXkpLCtm3beP/993n//feZOHEizz//PBMnTgR8p0o7duxY67pzc3NrvLauLjUdXawrV4A1a9YQGRnJvn37OHbsGOPGjePKK6/koosuAuCdd96hoKCA2267jffff5+rrroKgM6dO7N169Zziq8mgTziNgLY4Zzb6ZwrAjKA66uNuR5Y5HxWAglmluT/nO8fE+1/nf3VFREJkNIyx8Ll2QxLa8elKQkNXu7mYam0VGsQCbApU6Zw//33n1UkOed48cUXK64F2717N5dccgmPPfYYiYmJfPbZZ6xevZqioqKKZVq0aFHxPjIykpKSqs/ebdOmDa1atWLnzp01xpKamkp6ejoffvghL774IrfcckudsU+bNo3vf//7NY5r0aIFkydP5re//S0//elPazy9WZu4uDgKCgoqPicmJlZcQ5ebm0vnzp3PWiYlJYU9e/ZUfM7JyaFr1651Lv/cc88xadIkoqOj6dy5M2PHjmX16tVV1hsbG8uUKVMqTruCr4iOi4trcD61CWThlgzsqfQ5xz+tQWPMLNLM1gMHgfecc59UGneP/9TqPDNr+AlwEZEGWrLlALuPnmbOZd3rH1xJ27hobhySzOuf7VNrEAmYOXPm8P/+3/9jwIABVaZfffXV/PGPf6w4klR+uvHEiRMV14899dRTFXc7NtSDDz7I3XffzcmTJwE4efIkTz75ZMX86dOnc99999GjR4+Ko3e1GTduHA8++OBZRefatWvZt28f4LvDdMOGDaSl1X9tablLLrmEHTt2VHyeMmUKCxcuBGDhwoVcf331Y0cwfPhwvvjiC7KysigqKiIjI4MpU6bUuXxqairvv/8+zjlOnTrFypUr6dOnD/n5+RWFXklJCW+++SZ9+vSp2Nb27dvp379/g/OpTcBOlQI1HWusftSs1jHOuVJgkJklAC+bWX/n3CZ8p1N/4R/3C+BRYM5ZGze7E7gTfFVz5nkekm6o/Pz8gG+jOQvn/MM5dwjd/H+/6gwdYo0Wh7aSmbmtxjG15X5JVBlFpWX8+tkPmdIjJsCReiNU93td2rZtS15eHuC7G7H8fVPLy8ujbdu2zJkzh7y8PE6fPk1JSQl5eXnce++9PPDAA/Tv3x/nHN26deP5559n5syZzJgxg4yMDL72ta/RqlWrs5YFKCoqoqCg4Kzcbr/9do4cOcLQoUOJiooiJiaGe+65p2Lc5MmT+fd//3d++9vf1vp1KS0t5dSpU+Tn5/Pd7363Ipfyf7Ozs/nWt75FYaHvD56hQ4cya9Ys8vLycM5x+eWXExnpu7O7X79+VQpH8BWEd955Z8U67777bmbPns1f//pXUlNTWbhwIXl5eeTm5nLPPffw4osvAvDII49w1VVXUVpayowZM+jWrRt5eXm1Lj9nzhy+//3v07dvX5xz3H777XTv3p0DBw5w8803U1RURGlpKV/72te47bbbKuL5+OOP+dGPfnTW16egoODc/i855wLyAkYD71T6/CDwYLUxfwGmV/q8DUiqYV0/B+6vYXo6sKm+WIYOHeoC7YMPPgj4NpqzcM4/nHN3LjTz37T3uEv7yRvuicwddY6rK/fb/rrSjfzVv1xRSWkjR9c8hOJ+r8/mzZsr3p88edLDSLzVnHOfOnWq2759e0C3cT75r1271t1+++01zqv8fVUOWO1qqWkCear0U6CnmXU3sxhgGvBatTGvATP9d5eOAk4453LNrJP/SBtmFgdcCWz1f06qtPwNwKYA5iAiYWjBsmzioiOZNrzbea9j9ph09p8s4N3PDzRiZCJSl7lz51bpDddcHD58mF/84heNsq6AnSp1zpWY2T3AO0AkMM8597mZ3eWf/wTwJnANsAM4DdzhXzwJWOi/MzUCeM4594Z/3iNmNgjfqdJs4LuBykFEws/h/EJeXb+PW4en0rZl9HmvZ0KfzqS2j2PB8iy+cWlS/QuIyAXr3bs3vXv39jqMs5TfWdoYAnmNG865N/EVZ5WnPVHpvQPurmG5DcDgWtY5o6bpIiKN4emVuykqLWN2A1uA1CYywpg1Op1f/nMLm/aeoH+yWoOIyIXTkxNERPwKS0p5auUuxvfuRI9OrS94fTcPSyUuOpKFag0iIo1EhZuIiN8/N+RyOL+QOWPPrQVIbcpbg7z62T6OniqqfwERkXqocBMRwXeH/d+XZlU8LL6xzBqTTlFJGc+s2t1o6xSR8KXCTUQE+DT7GJ/vO8mcsd3rfeTNueiVGM/YizuweOUuSkrLGm29El6OHDlS8ZzOLl26kJycXPG58lMQapOZmVnxYPnqFixYgJmxZMmSimkvv/wyZsYLL7wAwBtvvMHgwYMZOHAgw4cP5y9/+QsADz30UJVYBg0axPHjx8/aRm5uLtdee23F54cffpiLL76Y3r17884779QY19GjR7nqqqvo2bMnV111FceOHat3+aKiIu6880569epFnz59Knq17d69mwkTJjB48GAuvfRS3nzTd/n9oUOHmDRpUr1fv+ZEhZuICDBvaRYJLaO5YXD1B7xcuFmj08k9UcC7m9UaRM5Phw4dKh5jddddd3HfffdVfI6Jqb/Jc12FG8CAAQN45plnKj5nZGQwcOBAAIqLi7nzzjt5/fXX+eyzz1i6dCnjx4+vGFs5lvXr15OQkHDW+n//+9/zne98B4DNmzeTkZHB559/zttvv833vve9Gp/kMHfuXCZOnMgXX3zBxIkTmTt3br3L/+pXv6Jz585s376dzZs3c/nllwPwy1/+kltuuYV169aRkZHB9773PQA6depEUlISy5Ytq/dr2FwE9K5SEZFgsOfoad7dvJ/vXt6DuJjIRl//xEsSSWkXx4Ll2VwzQK1BQsFPPvgJm49ubtR1DuoyiD9M+kODx69Zs4Yf/vCH5Ofn07FjRxYsWEBSUhKPP/44TzzxBFFRUfTt25e5c+fyxBNPEBkZyeLFi/njH//IuHHjqqxr3LhxfPzxxxQXF1NYWMiOHTsYNGgQ4HuqQUlJCR06dAB8zxM915YbL774Ir/85S8BePXVV5k2bRotWrSge/fuXHzxxaxatYrRo0dXWebVV1+teKLArFmzGD9+PL/5zW/qXH7evHkVD3KPiIioeCi9mVU8ruvEiRMVzyMFmDp1Kk8//TRjx449p5y8oiNuIhL2Fq3IxsyYObrhz0U8F5ERvnWvyjrK5n0nA7INCS/OOb7//e/zwgsvsGbNGubMmcPPfvYzwHekat26dWzYsIEnnniC9PT0Kkfpqhdt4CtsrrzySt555x1effXViud1ArRv354pU6aQlpbG9OnTefbZZykr++q0/2OPPVZxmnTChAlnrTsrK4t27dpVPMx+7969pKamVsxPSUlh7969Zy134MABkpJ8f+gkJSVx8ODBOpcvP0X7n//5nwwZMoSbb76ZAwd8R7kfeughFi9eTEpKCtdccw1//OMfK5YfNmwYH3/8cT1f8eZDR9xEJKzlF5aQ8ekerhmQRFLbuIBt59Zh3XjsvS9YuDyb39x0acC2I03jNxN+Q3x8vGfbLywsZNOmTRWNXUtLSyuKnEsvvZTbbruNqVOnMnXq1Aavc9q0aTz++OOcOHGCRx99lF//+tcV8/72t7+xceNG/vWvf/HHP/6RpUuXsmDBAsB3qvT++++vdb25ubl06tSp4rOvhWtV53JdaW3Ll5SUkJOTw9ixY/n973/P73//e+6//36eeuopnnnmGWbPns2PfvQjVqxYwYwZM9i0aRMRERF07ty54uH2wUBH3EQkrL24Joe8ghLuuMCGu/Vp2zKaqYOTeWX9Xo6pNYhcIOcc/fr1q7iubOPGjbz77rsA/POf/+Tuu+9mzZo1DB06lJKSkgatc8SIEWzatInDhw/Tq1evs+YPGDCA++67j1dffbXiov+GiIuLo6CgoOJzSkoKe/bsqfick5NT5dRlucTExIrHV+Xm5tK5c+c6l+/QoQMtW7bkhhtuAODmm29m7dq1APz973/nlltuAWD06NEUFBRw+PBhwPeQ97i4wP3R1thUuIlI2CorcyxYns2g1ASGdGsX8O3NHpNOYUkZGZ/uqX+wSB1atGjBoUOHWLFiBeC7geDzzz+nrKyMPXv2MGHCBB555BGOHz9Ofn4+8fHx5OXl1bvehx9+uMqRNoD8/PyKa80ANmzYQFpawy8r6NWrF9nZ2RWfp0yZQkZGBoWFhWRlZfHFF18wYsSIs5abMmUKCxcuBGDhwoVcf/31dS5vZlx33XUVsS5ZsoS+ffsC0K1bt4q7Zrds2UJBQUHFUcDt27fTv3//BufjNZ0qFZGwlbn9IFmHT/H49BqfsNfoeneJZ/RFHXhqRTbfGdedqEj97SznJyIighdeeIEf/OAHnDhxgpKSEu6991569erF7bffzokTJ3DOcd9995GQkMB1113HTTfdxKuvvlrjzQnlJk+efNY05xyPPPII3/3ud4mLiyM2NrbiNCn4rnFbvHhxxedXXnmF9PT0is+tWrWiR48e7Nixg4svvph+/fpxyy230LdvX6KiovjTn/5EZKTvpqBvf/vb3HXXXQwbNowHHniAW265hb///e9069aN559/HqDO5X/zm98wY8YM7r33Xjp16sT8+fMBePTRR/nOd77DY489hplVtEAB+OCDD/jGN75x/jujqTnnQv41dOhQF2gffPBBwLfRnIVz/uGcu3PBnf9tf13pRv7qX66opPS8lj+f3N/elOvSfvKGe2vjvvPaZnMRzPv9fG3evLni/cmTJz2MxFvnk/tLL73kfvaznwUgmgs3btw4d/To0QaPb+x9X/n7qhyw2tVS0+jPPREJS9v257F0x2FmjkkjugmPfF15SSLJCXHMX5bdZNsU8doNN9xQ5Shcc3Ho0CF++MMf0q5d4C+VaCwq3EQkLC1YnkVsdATTh3dr0u2Wtwb5JOsoW3LVGkTCx7e//W2vQzhLp06dzunO2+ZAhZuIhJ2jp4p4ae1ebhicQrtW9Xedb2y3Dk8lNjqChcuzm3zbcmFcDa0oRM7X+Xw/qXATkbDzzKrdFJaUMSfALUBqk9AyhhvUGiToxMbGcuTIERVv0iiccxw5coTY2NhzWk53lYpIWCkqKWPRimzG9exIz0TvGqjOGpPOM6v28OzqPdx1eQ/P4pCGS0lJIScnh0OHDlFQUHDOv3BDRTjnDo2bf2xsLCkpKee0jAo3EQkrb23K5cDJQube6O3TC/p0acOoi9rz1IpdfPsytQYJBtHR0XTv3h3wPbR98OCmaSPT3IRz7uB9/vpJISJhwznHvKVZXNSxFZf36lT/AgE2e0w6e4+f4V9bDnodiogECRVuIhI21u4+zmc5J7hjbDoREQ1/NmKglLcG0U0KItJQKtxEJGzMW5ZFm9gobhxybteUBEpUZAS3j0pjxc4jbN2v1iAiUj8VbiISFvYdP8Pbm/YzbUQ3WrVoPpf3ThueSouoCBYu3+V1KCISBFS4iUhYWLRiF845Zo5u+MOxm0K7VjFMHZTMy+tyOH5arUFEpG4q3EQk5J0uKuGZVbuZ1L8LKe1aeh3OWWaNSaeguIznVu/xOhQRaeZUuIlIyHtp7V5OnClmztjuXodSo75d2zCie3sWrdhFaZmau4pI7VS4iUhIKytzzF+WxYDktgxNa74Pkr5jTDo5x86wZMsBr0MRkWZMhZuIhLSPdxzmy0OnmHNZOmbetwCpzVV9E+naNpYFag0iInVQ4SYiIW3e0iw6xbfgGwO6eh1KnaIiI7h9dBrLvzzC9gN5XocjIs2UCjcRCVk7Dubx4fZDzByVRkxU8/9xN214N2KiInTUTURq1fx/komInKf5y7KJiYrgmyO7eR1Kg7RvFcPUQV15ee1eTpwu9jocEWmGVLiJSEg6frqIl9buZeqgrnRo3cLrcBps1ph0zhSXqjWIiNQooIWbmU0ys21mtsPMHqhhvpnZ4/75G8xsiH96rJmtMrPPzOxzM/uvSsu0N7P3zOwL/7/N9zYxEfFMxqd7OFNcyh3NtAVIbfp1bcuI9PYsWpmt1iAicpaAFW5mFgn8CZgM9AWmm1nfasMmAz39rzuBP/unFwJXOOcGAoOASWY2yj/vAWCJc64nsMT/WUSkQnFpGQuXZzOmRwcuSWrjdTjnbNaYdPYcPcP7Ww96HYqINDOBPOI2AtjhnNvpnCsCMoDrq425HljkfFYCCWaW5P+c7x8T7X+5Ssss9L9fCEwNYA4iEoTe+Xw/uScKgu5oW7mv90skqW0sC3WTgohUY84F5lC8md0ETHLOfdv/eQYw0jl3T6UxbwBznXNL/Z+XAD9xzq32H7FbA1wM/Mk59xP/mOPOuYRK6zjmnDvrdKmZ3YnvKB6JiYlDMzIyApJnufz8fFq3bh3QbTRn4Zx/OOcOzTP/X648w8kix9xxcUQEsHdbIHN//csiXvyimF9dFkdy6+Z3OXJz3O9NKZzzD+fcoWnynzBhwhrn3LCa5kUFcLs1/bSsXiXWOsY5VwoMMrME4GUz6++c29TQjTvnngSeBBg2bJgbP358Qxc9L5mZmQR6G81ZOOcfzrlD88t//Z7j7Hh7GT+/ri9XBPiIWyBzHzCskNfnvs+Wkk7cNn5AQLZxIZrbfm9q4Zx/OOcO3ucfyD/jcoDUSp9TgH3nOsY5dxzIBCb5Jx0wsyQA/7+6CEREKsxflkV8iyhuHpZa/+BmrEPrFkwZ2JUX1/iesyoiAoEt3D4FeppZdzOLAaYBr1Ub8xow03936SjghHMu18w6+Y+0YWZxwJXA1krLzPK/nwW8GsAcRCSI7D9RwD835HLzsFRatwjkCYWmMdvfGuR5tQYREb+AFW7OuRLgHuAdYAvwnHPuczO7y8zu8g97E9gJ7AD+CnzPPz0J+MDMNuArAN9zzr3hnzcXuMrMvgCu8n8WEWHxyl2UOsfsMeleh9Io+ie3ZVhaOxat2KXWICICBPYaN5xzb+IrzipPe6LSewfcXcNyG4DBtazzCDCxcSMVkWBXUFzK05/s4qpLEunWoaXX4TSa2WPTuecf68jcdpCJlyR6HY6IeKz53aokInIeXlm3l2Oni5lzWXC2AKnN1f260KVNrJ5fKiKACjcRCQHOOeYty+KSpDaM7N7e63AaVXRkBLeP6sbHXxxmx8E8r8MREY+pcBORoLdsxxG2H8hnzth0LIB927wybUQ3YiIjWLh8l9ehiIjHVLiJSNCbvyyLjq1juG5gV69DCYiOrVtw3cCuvLg2h5MFag0iEs5UuIlIUMs6fIolWw9y28g0YqMjvQ4nYGaPSed0USnPr87xOhQR8ZAKNxEJaguWZRETGcFto7p5HUpADUhpy9C0dixakU2ZWoOIhC0VbiIStE6cKeb5NTlcOzCJzvGxXocTcLPGpLPryGkyt+uBMSLhSoWbiASt51fv4XRRKXMC/EzS5mJy/y4ktmnBAt2kIBK2VLiJSFAqKS1j/rJsRnRvT//ktl6H0ySiIyO4bWQaH20/xJeH8r0OR0Q8oMJNRILSv7YcYO/xM2FztK3cdH9rkEVqyCsSllS4iUhQmrc0m5R2cVzVN7weA9UpvgXXXprEC2tyyFNrEJGwo8JNRILOpr0nWJV9lNlj0omMCL2Gu/WZNSadU0WlvLBGrUFEwo0KNxEJOvOWZdEqJpJbhqd6HYonBqYmMLhbAotW7FJrEJEwo8JNRILKwbwCXv9sHzcPS6VNbLTX4Xhm9ph0sg6f4sMvDnkdiog0IRVuIhJUFq/cTUmZY9aYdK9D8dTk/kl0im/BQt2kIBJWVLiJSNAoKC7lH5/s4orenenesZXX4XgqJiqC20emkbntEDvVGkQkbKhwE5Gg8fpn+zicX8Scy8KrBUhtpo9MJTrSWLRCDXlFwoUKNxEJCs455i3LpndiPGN6dPA6nGahc3ws117alRfW5JBfWOJ1OCLSBFS4iUhQWLnzKFtyT3LH2HTMwq8FSG1mjUknv7CEF9UaRCQsqHATkaAwf1kW7VpGM3VwstehNCuDUhMYlJrAwuXZag0iEgZUuIlIs7f7yGne23KA20amERsd6XU4zc7sMensPHyKj3cc9joUEQkwFW4i0uwtWJ5NpBkzRqd5HUqzdM2AJDq2bsGCZVlehyIiAabCTUSatbyCYp5bvYdvXJpEYptYr8NplmKiIrhtZDc+2HaIrMOnvA5HRAJIhZuINGvld0zeMVYtQOpy28huREUYi1Zkex2KiASQCjcRabZKyxwLlmczNK0dg1ITvA6nWevcJpZvXJrEC6vVGkQklKlwE5Fm6/2tB9l15DRzdLStQWaNSSevsISX1qo1iEioUuEmIs3WvKVZdG0by9X9Er0OJSgMTk1gYEpbtQYRCWEq3ESkWdqSe5IVO48wc0w6UZH6UdUQZsasMel8eegUS9UaRCQk6aehiDRL85dlERcdybThqV6HElS+cWkSHVvHsHB5ttehiEgAqHATkWbncH4hr6zfx78NTSahZYzX4QSVFlGRfHNEN97fdpBdR9QaRCTUqHATkWbnH5/spqikjNljdFPC+bhtVBqRZixascvrUESkkQW0cDOzSWa2zcx2mNkDNcw3M3vcP3+DmQ3xT081sw/MbIuZfW5m/15pmYfMbK+Zrfe/rglkDiLStIpKynhq5S4u79WJizu39jqcoJTYJpbJA5J47tM9nFJrEJGQErDCzcwigT8Bk4G+wHQz61tt2GSgp/91J/Bn//QS4EfOuUuAUcDd1ZZ9zDk3yP96M1A5iEjT++fGfRzKK2TOZTradiFml7cGWbfX61BEpBEF8ojbCGCHc26nc64IyACurzbmemCR81kJJJhZknMu1zm3FsA5lwdsAZIDGKuINAPOOf6+NIuLO7fmaz07eh1OUBvSLYEByb7WIM6pNYhIqLBA/Yc2s5uASc65b/s/zwBGOufuqTTmDWCuc26p//MS4CfOudWVxqQDHwH9nXMnzewhYDZwEliN78jcsRq2fye+o3gkJiYOzcjICESaFfLz82ndOnxP64Rz/uGcOzRu/tuPlfLrTwqY2TeGK7pFN8o6A6m57/tle4v568Yifjwsln4dIxt13c0990AL5/zDOXdomvwnTJiwxjk3rKZ5UQHcrtUwrXqVWOcYM2sNvAjc65w76Z/8Z+AX/nG/AB4F5py1EueeBJ4EGDZsmBs/fvw5hn9uMjMzCfQ2mrNwzj+cc4fGzf/ZxWtoG3eEB6ZNoGVMIH88NY7mvu9Hl5Ty0s73WXeqDXffNLxR193ccw+0cM4/nHMH7/MP5KnSHKByA6YUYF9Dx5hZNL6i7Wnn3EvlA5xzB5xzpc65MuCv+E7JikiQyzl2mnc+38/0Ed2ComgLBi2iIvnmyG4s2XqQ3UdOex2OiDSCQBZunwI9zay7mcUA04DXqo15DZjpv7t0FHDCOZdrZgb8HdjinPt95QXMLKnSxxuATYFLQUSayqIVuzAzZo5O8zqUkHLbyPLWINlehyIijSBghZtzrgS4B3gH380FzznnPjezu8zsLv+wN4GdwA58R8++558+FpgBXFFD249HzGyjmW0AJgD3BSoHEWkapwpLeGbVbib370LXhDivwwkpXdrGMql/F55drdYgIqEgoOcj/K063qw27YlK7x1wdw3LLaXm699wzs1o5DBFxGMvrs0hr6CEO8aqBUggzB6Tzhsbcnl53V5uH6UjmiLBTE9OEBFPlZU5FizLZmBqAkO6JXgdTkgamtaO/slt1BpEJASocBMRT324/RA7D59izth0fJe3SmMzM2aNTueLg/ks//KI1+GIyAVQ4SYinpq3LIvENi24ZkBS/YPlvF03sCvtW8WwYHm216GIyAVQ4SYintl+II+PvzjMzNHpREfqx1EgxUZHMn1EKv/acoA9R9UaRCRY6SeliHhm/rJsWkRFMH1EN69DCQu3j0ojwoynVu7yOhQROU8q3ETEE8dOFfHS2hxuHJJM+1YxXocTFpLaxjGpXxcyVu3mdJFag4gEIxVuIuKJf6zaTWFJmVqANLHZY9M5WVDCK+uqP8hGRIKBCjcRaXLFpWU8tWIXl13ckV6J8V6HE1aGpbWjb1IbFizPUmsQkSCkwk1Emtxbm/az/2QBcy5L9zqUsGNmzB6bzvYD+azYqdYgIsFGhZuINLl5S7Po3rEV43t19jqUsDRlYFfatYxmwbJsr0MRkXOkwk1EmtTa3cdYv+c4d4xNJyJCDXe94GsN0k2tQUSCkAo3EWlS85ZmER8bxb8NSfE6lLB2+6g0zIzFag0iElRUuIlIk8k9cYa3Nu1n2vBUWrWI8jqcsNY1IY6r+yWS8ekezhSVeh2OiDSQCjcRaTKLVuzCOcfM0elehyLArNHpnDhTzCvr93odiog0kAo3EWkSZ4pK+ccnu7m6XxdS27f0OhwBRnRvzyVJbVi4PFutQUSChAo3EWkSL63L4cSZYjXcbUbMjNlj0ti6P4+VO496HY6INIAKNxEJOOcc85dl0z+5DcPT23kdjlRy/aBkElpGs3B5ttehiEgDqHATkYD7+IvD7DiYz5yx3TFTC5DmJDY6kmnDu/Hu5v3kHFNrEJHmToWbiATcvGVZdIpvwTcuTfI6FKnBjNFpACxeudvjSESkPircRCSgdhzMJ3PbIW4fmUaLqEivw5EaJCfE8fW+Xcj4dDcFxWoNItKcqXATkYBasDyLmMgIbhvVzetQpA6zx6Zz/HQxr6o1iEizpsJNRALmxOliXlyzl+sHdaVj6xZehyN1GNm9PX26xDN/mVqDiDRnKtxEJGAyPt3NmeJStQAJAr7WIOls3Z/Hqiy1BhFprlS4iUhAlJSWsXB5NqMuak/frm28Dkca4PpBybSNi2aBWoOINFsq3EQkIN75/AD7ThQwR0fbgkZcTCTTRqTy7uYD7D1+xutwRKQGKtxEJCDmL8uiW/uWTLwk0etQ5BzMGJWGc47FK3d5HYqI1ECFm4g0us/2HGf1rmPMHpNOZIQa7gaTlHYtuapvIhmr1BpEpDlS4SYijW7+sixat4ji5mEpXoci52HWmHSOnS7mtc/2eR2KiFSjwk1EGtWBkwW8sSGXm4elEB8b7XU4ch5GX9SB3onxLFBrEJFmR4WbiDSqxSt3Ueocs8ekex2KnCczY9aYdDbnnmT1rmNehyMilahwE5FGU1BcytOf7ObKSxJJ69DK63DkAkwd3NXXGmRZttehiEglAS3czGySmW0zsx1m9kAN883MHvfP32BmQ/zTU83sAzPbYmafm9m/V1qmvZm9Z2Zf+P9tF8gcRKThXl2/l6OnitQCJAS0jIni1uGpvP35fnJPqDWISHMRsMLNzCKBPwGTgb7AdDPrW23YZKCn/3Un8Gf/9BLgR865S4BRwN2Vln0AWOKc6wks8X8WEY8555i3NJs+XeIZdVF7r8ORRqDWICLNTyCPuI0AdjjndjrnioAM4PpqY64HFjmflUCCmSU553Kdc2sBnHN5wBYgudIyC/3vFwJTA5iDiDTQii+PsO1AHnMu646ZWoCEglR/H75nVu1RaxCRZsICdceQmd0ETHLOfdv/eQYw0jl3T6UxbwBznXNL/Z+XAD9xzq2uNCYd+Ajo75w7aWbHnXMJleYfc86ddbrUzO7EdxSPxMTEoRkZGQHI8iv5+fm0bt06oNtozsI5/3DOHb7K/w9rCvjyRCmPXt6SmMjwKNzCYd9vPlLKI58W8K3+MYxL+eou4XDIvS7hnH845w5Nk/+ECRPWOOeG1TQvKoDbreknd/Uqsc4xZtYaeBG41zl38lw27px7EngSYNiwYW78+PHnsvg5y8zMJNDbaM7COf9wzh18+af1H85n72Ty/St68vWJvbwOqcmEw76/3Dle2f0RK49G8H9vu6ziaGo45F6XcM4/nHMH7/MP5KnSHCC10ucUoHo3x1rHmFk0vqLtaefcS5XGHDCzJP+YJOBgI8ctIudo4fJsoiKM20d18zoUaWTlrUE+33eSNWoNIuK5QBZunwI9zay7mcUA04DXqo15DZjpv7t0FHDCOZdrvj/p/g5scc79voZlZvnfzwJeDVwKIlKf08WO51fv4bpLu9I5PtbrcCQAbhicTJvYKBYsz/Y6FJGwV2fhZma3V3o/ttq8e85e4ivOuRLgHuAdfDcXPOec+9zM7jKzu/zD3gR2AjuAvwLf808fC8wArjCz9f7XNf55c4GrzOwL4Cr/ZxHxyMd7SzhVVModagESsspbg7y1aT/7TxR4HY5IWKvvGrcfAov97/8IDKk0bw7wP3Ut7Jx7E19xVnnaE5XeO+DuGpZbSs3Xv+GcOwJMrCduEWkCpWWO93YVMyK9PQNS2nodjgTQjFHp/G1pFk9/sosffb231+GIhK36TpVaLe9r+iwiYea9zQc4fMZxx9h0r0ORAOvWoSUT+yTyj092qzWIiIfqK9xcLe9r+iwiYWbesiw6xBpX9U30OhRpArPHpHPkVBH/3JDrdSgiYau+wq2P/1FUGyu9L/+sY+UiYWzT3hOsyjrKlWnRREXqscfhYOzFHbi4c2sWLM8mUD1ARaRu9V3jdkmTRCEiQWf+smxaxkTytZRAtoOU5qS8Nch/vrKJL1NimeB1QCJhqM4/k51zuyq/gHx8Nyh09H8WkTB0MK+A1z/bx01DU2gVrctdw8mNg5OJj43ivV3FXociEpbqawfyhpn1979PAjbhu5v0KTO7N/DhiUhz9PTK3RSVljF7TLrXoUgTa9UiiluGpbL6QCkHTqo1iEhTq+/ClO7OuU3+93cA7znnrgNG4ivgRCTMFJaU8vQnu7iiT2cu6hS+zysMZzNHp1Hm4OmVOvEi0tTqK9wqHwufiL8nm3MuDygLVFAi0ny9/lkuh/OLmKOGu2ErrUMrLu0UyT9W7aawRK1BRJpSfYXbHjP7vpndgO/atrcBzCwOiA50cCLSvDjnmLc0i16JrRl7cQevwxEPXZUWzeF8tQYRaWr1FW7fAvoBs4FbnXPH/dNHAfMDF5aINEefZB1lc+5J7hjbHd8jhSVc9esQQY9OrdQaRKSJ1XdX6UHn3F3Oueudc+9Wmv6Bc+53gQ9PRJqT+cuyaNcymhsGJ3sdinjMzJg9Jp0NOSdYt+e41+GIhI06GzCZ2Wt1zXfOTWnccESkudp95DTvbj7A98b3IDY60utwpBm4cUgKj7y9jYXLsxnSrZ3X4YiEhfo6Z44G9gDPAJ+g55OKhK2FK7KJNGPGqHSvQ5FmolWLKG4alsJTK3bxs2suoXObWK9DEgl59V3j1gX4KdAf+G/gKuCwc+5D59yHgQ5ORJqH/MISnvt0D9cMSKJLW/1ylq/MGp1OqXM8/clur0MRCQv1XeNW6px72zk3C98NCTuATDP7fpNEJyLNwgur95BXWMKcy9QCRKpK79iK8b068fQnuykqUZcokUCr98nQZtbCzG4EFgN3A48DLwU6MBFpHsrKHPOXZzOkWwKDUhO8Dkeaodlju3M4v5A3N6o1iEig1ffIq4XAcnw93P7LOTfcOfcL59zeJolORDz3/taD7DpymjvUcFdqMe7ijlzUsRXzl2d7HYpIyKvviNsMoBfw78ByMzvpf+WZ2cnAhyciXpu3LIuktrFM6t/F61CkmYqIMGaNSeezPcdZt/uY1+GIhLT6rnGLcM7F+19tKr3inXNtmipIEfHG1v0nWf7lEWaOTic6st4rKySM/dvQFFq3iGKhjrqJBJR+EotIreYvzSY2OoLpI1K9DkWaudYtorhpaAr/3JjLwbwCr8MRCVkq3ESkRkfyC3l5/V5uHJJCQssYr8ORIDBzdBrFpY5/qDWISMCocBORGv3D397hjjHpXociQeKiTq0Z31utQUQCSYWbiJylqKSMp1bu4mu9OtEzMd7rcCSIzBqTzqG8Qt7apNYgIoGgwk1EzvLmxlwO5hUyZ2y616FIkLm8Zye6d2zFAt2kIBIQKtxEpArnHPOWZdGjUyu+1rOT1+FIkImIMGaOTmPd7uN8tue41+GIhBwVbiJSxZpdx9iQc4LZY7sTEWFehyNB6KahKbSKiVRrEJEAUOEmIlXMX5ZNm9go/m1IstehSJCKj43mpqEpvL5hH4fyCr0ORySkqHATkQo5x07z1qZcpo/sRsuYKK/DkSA2c0w6xaWOZ1apNYhIY1LhJiIVnlqxCzNj5uh0r0ORINejU2u+1qsTi1fuUmsQkUakwk1EADhVWMIzq3YzqV8XkhPivA5HQsAdY9I5mFfI25/v9zoUkZChwk1EAHhpbQ4nC0qYc1m616FIiLi8VyfSO7RkwbIsr0MRCRkBLdzMbJKZbTOzHWb2QA3zzcwe98/fYGZDKs2bZ2YHzWxTtWUeMrO9Zrbe/7omkDmIhIOyMsf85dkMTGnLkG7tvA5HQoSvNUg6a3cfZ0POca/DEQkJASvczCwS+BMwGegLTDezvtWGTQZ6+l93An+uNG8BMKmW1T/mnBvkf73ZqIGLhKEPvzjEzkOnmHNZd8zUAkQaz03DUmgZE6mGvCKNJJBH3EYAO5xzO51zRUAGcH21MdcDi5zPSiDBzJIAnHMfAUcDGJ+I+M1bmkXn+BZM7p/kdSgSYtr4W4O88Vkuh/PVGkTkQgXyfv9kYE+lzznAyAaMSQbqe8jdPWY2E1gN/Mg5d6z6ADO7E99RPBITE8nMzDyn4M9Vfn5+wLfRnIVz/sGe+978Mj7+4gw39oxm+dKPznn5YM//Qij3zAaN7RNZRlFpGb9+9kOm9IgJbGBNRPs+0+swPON1/oEs3Go63+LOY0x1fwZ+4R/3C+BRYM5ZK3HuSeBJgGHDhrnx48fXs9oLk5mZSaC30ZyFc/7BnvtPX95Ii6gcfnbr5XRo3eKclw/2/C+Ech/f4PFvHfiEZQfy+M3srxEdGfz3xWnfj/c6DM94nX8g//fkAKmVPqcA+85jTBXOuQPOuVLnXBnwV3ynZEXkPBw7VcRLa3O4YXDyeRVtIg01e0w6B04W8vYmtQYRuRCBLNw+BXqaWXcziwGmAa9VG/MaMNN/d+ko4IRzrs7TpOXXwPndAGyqbayI1O2ZT3dTUFzG7LHpXociIW5C786kdWip55eKXKCAFW7OuRLgHuAdYAvwnHPuczO7y8zu8g97E9gJ7MB39Ox75cub2TPACqC3meWY2bf8sx4xs41mtgGYANwXqBxEQllxaRmLlu9i7MUd6NOljdfhSIiLiDBmjEpj9a5jbNp7wutwRIJWQB9G6G/V8Wa1aU9Ueu+Au2tZdnot02c0Zowi4ertTfvZf7KAX93Q3+tQJEzcPCyV37+3nQXLs/ndzQO9DkckKAX/FaIicl7mLcsivUNLJvTu7HUoEibaxkVz45BkXvtsH0fUGkTkvKhwEwlDa3cfY93u48wek05EhBruStOZNTqdopIyMj7dU/9gETmLCjeRMDR/WTbxLaK4aVhq/YNFGlHPxHguu7gji1fuori0zOtwRIKOCjeRMJN74gxvbczl1uGptG4R0MtcRWo0e0w6uScKePfzA16HIhJ0VLiJhJmnVuyizDlmjUn3OhQJUxP6dCa1fZxag4icBxVuImHkTFEp/1i1m6v6JpLavqXX4UiYiowwZo1OZ1X2UT7fp9YgIudChZtIGHl53V6Ony5mztjuXociYe7mYanERUfqqJvIOVLhJhImnHPMX5ZFv65tGNG9vdfhSJgrbw3yyvp9HD1V5HU4IkFDhZtImFi64zBfHMxnztjumKkFiHhv1pjy1iC7vQ5FJGiocBMJE/OWZtGxdQuuHZhU/2CRJtArMZ6xF3dg8YpdlKg1iEiDqHATCQNfHsrng22HuH1UN1pERXodjkiFWaPT2XeigPc2qzWISEOocBMJAwuXZxMTGcFtI9O8DkWkiomXJJLSLo75uklBpEFUuImEuBOni3l+dQ5TBnWlU3wLr8MRqSIywpg5Oo1VWUfZvO+k1+GINHsq3ERC3LOrd3OmuJQ7xqZ7HYpIjW4d1k2tQUQaSIWbSAgrKS1j4fJdjOzenn5d23odjkiN2raMZurgZF5Zv5djag0iUicVbiIh7N3NB9h7/AxzLlPDXWneZo9Jp7CkjIxP93gdikizpsJNJITNX5ZFavs4rrwk0etQROrUu0s8oy/qwOKVag0iUhcVbiIhakPOcT7NPsbsMd2JjFDDXWn+Zo9NZ+/xM/xri1qDiNRGhZtIiJq/LJtWMZHcPCzF61BEGuTKSxJJTohjgW5SEKmVCjeREHTwZAFvbNjHzcNSaRMb7XU4Ig1S3hpk5c6jbMlVaxCRmqhwEwlBi1fuoqTMMXtMutehiJyTW4enEhsdwaIV2V6HItIsqXATCTEFxaUs/mQ3E/skkt6xldfhiJyThJYx3DA4mZfX7eX4abUGEalOhZtIiHlt/T6OnipijhruSpCaNSadguIynlVrEJGzqHATCSHOOeYty6JPl3hG9+jgdTgi56VPlzaMuqg9i1bsorTMeR2OSLOiwk0khKzYeYSt+/OYM7Y7ZmoBIsFr9hi1BhGpiQo3kRAyb2k27VvFMGVQV69DEbkgFa1BlmV7HYpIs6LCTSREZB8+xZKtB7htZDdioyO9DkfkgkRFRnD7qDRW7DzCtv15Xocj0myocBMJEQuWZxMVYdw+Ks3rUEQaxbThqbSIilBDXpFKVLiJhIC8gmJeWJPDtZd2JbFNrNfhiDSKdq1imDoomZfX5XDidLHX4Yg0CyrcRELAc6tzyC8sYc7Y7l6HItKoKlqDrN7tdSgizYIKN5EgV1rmWLA8i2Fp7RiQ0tbrcEQaVd+ubRjRXa1BRMoFtHAzs0lmts3MdpjZAzXMNzN73D9/g5kNqTRvnpkdNLNN1ZZpb2bvmdkX/n/bBTIHkebuX1sOsOfoGeZcpqNtEpruGJNOzrEzLFFrEJHAFW5mFgn8CZgM9AWmm1nfasMmAz39rzuBP1eatwCYVMOqHwCWOOd6Akv8n0XC1vxlWSQnxPH1volehyISEFf1TaRr21gW6vmlIgE94jYC2OGc2+mcKwIygOurjbkeWOR8VgIJZpYE4Jz7CDhaw3qvBxb63y8EpgYieJFg8Pm+E6zceZRZY9KIitSVDxKaoiIjuH10Gst2HGH7AbUGkfBmzgXmmgEzuwmY5Jz7tv/zDGCkc+6eSmPeAOY655b6Py8BfuKcW+3/nA684ZzrX2mZ4865hEqfjznnzjpdamZ34juKR2Ji4tCMjIzGT7KS/Px8WrduHdBtNGfhnL+Xuf9tYyGr9pfw2PiWtIr25kkJ2vfKvSnkFTnuyzzNuOQoZvVr0WTbrY32fXjmDk2T/4QJE9Y454bVNC8qgNut6bdI9SqxIWPOi3PuSeBJgGHDhrnx48c3xmprlZmZSaC30ZyFc/5e5X4or5BV773PrcPT+MZV/etfIEC078d7HYYnvMj945Of8fpnufzhjrG0bRndpNuuTvt+vNdheMbr/AN5biUHSK30OQXYdx5jqjtQfjrV/+/BC4xTJCj945PdFJWWMXtsutehiDSJWWPSOVNcyvNr9ngdiohnAlm4fQr0NLPuZhYDTANeqzbmNWCm/+7SUcAJ51xuPet9DZjlfz8LeLUxgxYJBoUlpTy1chcTeneiR6fwPWUh4aVf17aMSG/PwhXZag0iYStghZtzrgS4B3gH2AI855z73MzuMrO7/MPeBHYCO4C/At8rX97MngFWAL3NLMfMvuWfNRe4ysy+AK7yfxYJK298lsvh/ELuUMNdCTOzxqSz5+gZPtiqky0SngJ5jRvOuTfxFWeVpz1R6b0D7q5l2em1TD8CTGzEMEWCinOOecuyuLhza8b17Oh1OCJN6uv9EklqG8uC5dlcqRY4EobUP0AkyHyafYzP951kztjumHlzJ6mIV6IjI7h9VBpLdxzmC7UGkTCkwk0kyMxbmkVCy2huGJzsdSginpg2PJWYqAg15JWwpMJNJIjsOXqadzfvZ/qIbsTFRHodjognOrRuwZSBXXlp7V5OnCn2OhyRJqXCTSSILFyejZkxc3Sa16GIeGr2mHROF5Xy/Gq1BpHwosJNJEjkF5bw7Kd7uGZAEklt47wOR8RT/ZPbMiytHYtW7FJrEAkrKtxEgsSLa3LIKyxhjhruigC+1iC7j54mc5tag0j4UOEmEgTKyhzzl2UxKDWBwd3OejSvSFia1L8LiW1asGB5ttehiDSZgD1kvjkZFh/vVg8dWnXiLbfA974Hp0/DNdecvdDs2b7X4cNw001nz/8//wduvRX27IEZMzh+/DgJCQlfzf/Rj+C662DbNvjud89e/v/+X7jySli/Hu699+z5v/41jBkDy5fDT3969vw//AEGDYJ//Qt++cuz5//lL9C7N7z+Ojz66Nnzn3oKUlPh2Wfhz38+e/4LL0DHjrBgge9V3ZtvQsuW8L//C889d3b+mZm+f3/3O3jjjarLxsXBW2/53v/iF7BkSdX5HTrAiy/63j/4IKxYUXV+SgosXux7f++9vq9hZb16wZNP+t7feSds3151/qBBvq8fwO23Q05O1fmjR8PDD/ve/9u/wZEjVedPnAj/+Z++95Mnczw3t2ru114L99/ve1/T8+zO43vv2Okitu3P4+LO8XT88b9X+d47SxN/752175v4e+8sTfi9dzwzs2ruTfy9x5kzVecH4Huviko/945fd13V3MGTn3s5x86Qc+w0A1MTiJv3tyb73jv+5JNn5x8mP/eOjBxJh7hql2s04fee1z/3jt9//9n7vpF/7tmHH9b6kHkdcRMJArknCoiJiqB9qxivQxFpVhLbtMDM2H+iwOtQRJpEeBxxGzbMrV69OqDbyMzMZHxNf2WEiXDOP9C5b9ufx9V/+Ij/mNSb742/OGDbOV/a9+O9DsMTzSn3Hz63nrc37WflTyfSJja6SbbZnPJvauGcOzRN/mamI24iwWr+sixioyOYPryb16GINEt3jOnO6aJSXlidU/9gkSCnwk2kGTuSX8hL6/Zyw+AU2uk0qUiNBqS0ZUi3BBatyKZMrUEkxKlwE2nGnlm1m6KSMrUAEanH7LHdyT5ymg+3H/I6FJGAUuEm0kwVlZTx1MpdjOvZkZ6J8V6HI9KsTe7fhc7xLZiv1iAS4lS4iTRTb23K5cDJQuZc1t3rUESavejICG4flcZH2w/x5aF8r8MRCRgVbiLNkHOOvy/N4qKOrbi8ZyevwxEJCtNHdCMmMoKnVuzyOhSRgFHhJtIMrd19jA05J7hjbDoREeZ1OCJBoVN8C669NInnV+8hr6DY63BEAkKFm0gzNG9ZNm1io7hxSIrXoYgElVlj0jlVVMqLa9QaREKTCjeRZmbv8TO8vWk/00d0o1WLKK/DEQkqA1MTGNwtgYUrdqk1iISksHhyQnz3eDf050PrH3gBznpeY5gJ5/wbO/fdR0+Te+IMg1Lb0SKq+f9tpX2f4HUYnmjOuR/OL2THwXz6dGlDQsvAPEmhOecfaOGcOzRN/h/eoWeVigSFMuc4eLKA9i1jgqJoE2mOOrRqQXRkBPtP6vmlEoKccyH/Gjp0qAu0Dz74IODbaM7COf/GzP2pFdku7SdvuE+zjjTaOgNN+z48NffcH3tvm0v7yRtu56H8gKy/uecfSOGcu3NNkz+w2tVS0+hPepFmoqzMMX9ZFpemtGVoWjuvwxEJat8c2Y3oSGOhGvJKiFHhJtJMfPTFIb48dIo7xqZjphYgIheic3ws3xiQxAtrcsgvLPE6HJFGo8JNpJmYtyybTvEt+MaArl6HIhISZo/tTn5hiVqDSEhR4SbSDOw4mMdH2w8xc1QaMbopQaRRDEpNYGBqAgtXZKs1iIQM/YYQaQbmL8smJiqCb47s5nUoIiHljjHp7Dx0io93HPY6FJFGocJNxGPHTxfx4tocpg7qSofWLbwORySkXDMgiY6tW+gmBQkZKtxEPPbMqj0UFJdxx9juXociEnJioiK4bWQ3Pth2kOzDp7wOR+SCqXAT8VBxaRmLVmQzpkcHLklq43U4IiHptpHdiDRj0YpdXocicsFUuIl46J3P95N7ooA5OtomEjCd28TyjUuTeH71Hk6pNYgEuYAWbmY2ycy2mdkOM3ughvlmZo/7528wsyH1LWtmD5nZXjNb739dE8gcRAJp3tIs0jq05Io+nb0ORSSkzRqTTl5hCS+tVWsQCW4BK9zMLBL4EzAZ6AtMN7O+1YZNBnr6X3cCf27gso855wb5X28GKgeRQFq3+xhrdx9n9ph0IiLUcFckkAanJjAwpS0Llmfje6KQSHAK5BG3EcAO59xO51wRkAFcX23M9cAi/6O5VgIJZpbUwGVFgtr8ZdnEt4ji5mGpXociEvLMjFlj0vny0CmWqjWIBLGoAK47GdhT6XMOMLIBY5IbsOw9ZjYTWA38yDl3rPrGzexOfEfxSExMJDMz8/yyaKD8/PyAb6M5C+f8zyf3YwVl/HPDGa7sFsXqFUsDE1gT0b7P9DoMTwRj7vFljjYx8LvX1lA6NPaC1hWM+TeWcM4dvM8/kIVbTed+qh+frm1MXcv+GfiF//MvgEeBOWcNdu5J4EmAYcOGufHjxzco6POVmZlJoLfRnIVz/ueT+2/f2UoZX/KzW8bRrUPLwATWRLTvx3sdhieCNffZJdv44wc76D5gOGkdWp33eoI1/8YQzrmD9/kH8lRpDlD5HFAKsK+BY2pd1jl3wDlX6pwrA/6K77SqSNA4U1TKPz7ZzVWXJAZ90SYSbG4blabWIBLUAlm4fQr0NLPuZhYDTANeqzbmNWCm/+7SUcAJ51xuXcv6r4ErdwOwKYA5iDS6V9bv5djpYuZcphYgIk0tsU0skwck8Zxag0iQCljh5pwrAe4B3gG2AM855z43s7vM7C7/sDeBncAOfEfPvlfXsv5lHjGzjWa2AZgA3BeoHEQam3OO+cuy6JvUhpHd23sdjkhYmj0mnbyCEl5at9frUETOWSCvccPfquPNatOeqPTeAXc3dFn/9BmNHKZIk1m24wjbD+Tz25suxUwtQES8MKRbAgOS27JweTa3j+ym/4sSVPTkBJEmNG9ZFh1bx3DdwK5ehyIStsyM2WPS2XEwn2U7jngdjsg5UeEm0kR2Hsrn/a0HuW1kGrHRkV6HIxLWrh2YRIdWMSxYnu11KCLnRIWbSBNZuDybmMgIbhvVzetQRMJei6hIvjmyG0u2HmD3kdNehyPSYCrcRJrAiTPFPL8mh+sGdqVz/IU1/hSRxnHbSF9rkKdWZnsdikiDqXATaQLPfbqH00Wl3DE23etQRMSvS9tYJvXvwrOf7uF0kVqDSHBQ4SYSYCWlZSxYns2I7u3pn9zW63BEpJLZY9I5WVDCy2oNIkFChZtIgP1rywH2Hj/DnLFquCvS3AxNa0f/5DYsXJ6Nr0OVSPOmwk0kwOYtzSalXRxX9U30OhQRqcbMmDU6ne0H8lnxpVqDSPOnwk0kgDbmnGBV9lFmj0knMkJNPkWao+sGdqV9qxjmqzWIBAEVbiIBNH9ZFq1iIrlleKrXoYhILWKjI5k+IpUlWw6w56hag0jNXlm3l7Fz32f226cYO/d9XvHoukgVbiIBcjCvgNc37OPmYam0iY32OhwRqcPto9IwM55aucvrUKQZemXdXh54aQN7j58BYO/xMzz40kZPireAPqtUJJwtXrmbkjLHrDHpXociIvVIahvHpH5dyFi1m3uv7EnLGP16DFfOOfadKGDb/pNsyc1j2/483tyYS0lZ1ZtXzhSX8tt3tjF1cHKTxqfvTJEAKCgu5emVu7iid2e6d2zldTgi0gCzx6bzz425vLJuH98cqSechIOTBcVs25/H1v15bM09ybb9eWw7kEdewVd9/bq2jT2raCu3z38ErimpcBMJgNc+28eRU0XMuUwtQESCxbC0dvRN8rUGmT4iFTPdUBQqikrK2Hk4v6JI2+Yv1PadKKgYE98iit5d4pkysCt9ktrQp0s8vRLjaRsXzdi571ecJq2sa0JcU6YBqHATaXTOOeYvy6Z3YjxjenTwOhwRaSAzY/bYdP7jhQ2s2HmEMT06eh2SnKPKpznLC7Rt+/P48lA+xaW+o2ZREUaPTq0Zlt6e3l3i6dMlnj5JbejaNrbWYv3HV/fmwZc2cqa4tGJaXHQkP766d5PkVZkKN5FGtnLnUbbknuQ3/zZAf7GLBJkpA7vy8JtbWLg8W4VbM3eyoJjt+/PYsj+PbftPVhxNq36as3eXeMb37kyfLvH07hJPj06tiYk6t3szy69j++0729h7/AzJCXH8+OreTX59G6hwE2l085Zl0a5lNNcPavr/0CJyYXytQbrxxIdfknPsNCntWnodUtgrLi1j56FTbK1UnG3bn1fl1GV8iyh6lZ/m7BJP7y5t6J0YT9uWjXdH/9TByUwdnExmZibjx49vtPWeKxVuIo1o15FT/GvLAe4efzGx0ZFehyMi5+H2UWn85aOdPLVyFw9OvsTrcMKGc47cEwWVijPf6c7qpzkv6tSKIWnt+ObIbhVH0ZIT4sLmDIcKN5FGtGB5NpFmzBid5nUoInKeuibEcXW/RDJW7eHeib2Ii9EfYY0tr6CY7QfyKtpt+Iq1k5ysdJozyX+a8/LenXzXoXVpw0WdWtEiKrz3hwo3kUaSV1DM86tzuPbSJBLbxHodjohcgFmj03lz435eXb+XaSPUGuR8FZeWkXX4VJV2G1urneZs7b+b89ry05yJviKtMU9zhhIVbiKN5PnVOeQXlnDHWLUAEQl2I7q355KkNixYns2tw9UapD7OOfafLKjSamPr/jx2HjpFUWkZAJERxkUdvzrN2TvRd5ozpV34nOZsDCrcRBpBaZljwfJshqa1Y2BqgtfhiMgFMjNmj0njJy9u5JOso4y6SK19yp0pcazZdfSrIs3/74kzxRVjurSpepqzd2IbenTWac7GoMJNpBEs2XKA3UdP85NJfbwORUQayfWDknn4ra0sWJYdloVbif80Z/V2GznHzsC/VgC+05y9EltzzYAkLkmKrziKltAyxuPoQ5cKN5FGMH9ZNl3bxnJ1v0SvQxGRRhIbHcm04d148qMvK3p3hSLnHAdOFlZpt7F1fx5fHsw/6zTnoNQERnQsYfLogfTRaU5PqHATuUC7T5ayYucRHpjch6jIc2vqKCLN24zRaTz50Zc8tWIXD0wO/iPq+YUlFXdxbtt/0n80reppzsQ2LejTpQ1f69mR3v52Gxd3bl1xmjMzM5PxffVHqldUuImcp1fW7fV30S7AgDax+u8kEmqSE+L4et8uZHy6m3uv7Bk0/RlLKt3NWXEd2oGT7Dn61d2crWIi6dUlnmsGJFX0Q+uj05zNnn7TXKCvfnmfIXnl+549AkOa1ivr9lZ5bp0DfvHGFlrGRGn/i4SY2WPTeftzX2uQW4c3r9YgzjkO5hWyxd9qo7xI23Eon6KSr05zdu/YiktTErhlaGrFA9STE+KIiNBpzmCjwu0CVP/lvff4GX7y4gZ2Hs5nXM9OOOf7T+Xg7Pc4nIMy/zQqTas83fln+qZVGoNvfeAf66gy3Z011ve5zP+hekxl7qv1VR5beT341119ugOysotYXbjt7G1WbOur6eXxlq+vfGzNOZ+dA9XG1phzla/xV+tz1JNzWdW8yvdLmasa6+f7TlR08i53priU376zTYWbSIgZ2b09fbrEs2D5Lm4Z5l1rkFOFJWw7ULXdxrYDeRw/XfU0Z+8ubbisZ0dfP7Qk37M5g+VIodRPhdsF+O072yqKtnKFJWU8vmQHjy/Z4VFU3onYuQMzwwAzMAwMIvzvfdN8t9nX9t73x1/lab5lI4yKH5ZmvldExbZ8//o355teaZtUWrfVF0vF+v3vI8CIqLJN4Kyirdy+Sk0lRSQ0+FqDpPPASxtZlXWUkQG+w7SktIzsI1VPc27dX/U0Z8uYSHp3iWdy/y7+Ozl9R9HatdJpzlCnwu0C1PVL+qlvjWhwsVK9cCgvPKBqoRFhVQsaKk+vNrZK8VF9evW4/NukYn6lQoezY4movm4zzx+629TGzn2/Sufvcl1D9K4zkXBX3hpk4YrsRivcnHMcyiusaLdRXqh9cfCr05wRRpXTnL39j35KaafTnOFKhdsF6JoQV+Mv7+SEOMb17ORBRNJUfnx17yqnyQHioiP58dW9PYxKRAIlLiaSaSNS+dvHWed1ZP1UYQnbD+RVOormuybtWKXTnJ3jW9C7SzyzRqfRp0ubirs5dZpTKgto4WZmk4D/BiKBvznn5labb/751wCngdnOubV1LWtm7YFngXQgG7jFOXcskHnURr+8w1f5dWwVN6YkxOnGFJEQN2NUGn/5cCdff+wj8gtLarwhzXea83RFcVZeqO0+erpiTMuYSHolxnN1vy4VR9B0mlMaKmCFm5lFAn8CrgJygE/N7DXn3OZKwyYDPf2vkcCfgZH1LPsAsMQ5N9fMHvB//kmg8qiLfnmHt6mDk5k6ODnsThOLhKvV2ceIMF8vNPDdkPYfL2zgg60HiYqMYOv+kzWe5hyQ3JabhqZUtNtIbddSpznlvAXyiNsIYIdzbieAmWUA1wOVC7frgUXOd2vfSjNLMLMkfEfTalv2emC8f/mFQCYeFW6gX94iIuHit+9s89+l/pWi0jJe/WwfneJb0Md/mrP8RgGd5pRACGThlgzsqfQ5B99RtfrGJNezbKJzLhfAOZdrZp1r2riZ3QncCZCYmEhmZub5ZdFA+fn5Ad9GcxbO+Ydz7hDe+Sv3TK/DaFI1XdNc7rdjo4AzvlfeQQ7nweEvmiy0JhWO+74yr/MPZOFW03Hg6j0UahvTkGXr5Jx7EngSYNiwYS7QR8PC/YhbOOcfzrlDeOev3Md7HUaTSl5Z893kyQlxYfW1CMd9X5nX+QfywYo5QGqlzynAvgaOqWvZA/7Tqfj/PdiIMYuIiNTox1f3Jq7aqU/dkCZNLZCF26dATzPrbmYxwDTgtWpjXgNmms8o4IT/NGhdy74GzPK/nwW8GsAcREREAN81zQ/fOIBkf7/G5IQ4Hr5xgG5IkyYVsFOlzrkSM7sHeAdfS495zrnPzewu//wngDfxtQLZga8dyB11Letf9VzgOTP7FrAbuDlQOYiIiFSmG9LEawHt4+acexNfcVZ52hOV3jvg7oYu659+BJjYuJGKiIiINH+BPFUqIiIiIo1IhZuIiIhIkFDhJiIiIhIkVLiJiIiIBAkVbiIiIiJBQoWbiIiISJBQ4SYiIiISJFS4iYiIiAQJFW4iIiIiQcJ8Dy8IbWZ2CNgV4M10BA4HeBvNWTjnH865Q3jnr9zDVzjnH865Q9Pkn+ac61TTjLAo3JqCma12zg3zOg6vhHP+4Zw7hHf+yj08c4fwzj+ccwfv89epUhEREZEgocJNREREJEiocGs8T3odgMfCOf9wzh3CO3/lHr7COf9wzh08zl/XuImIiIgECR1xExEREQkSKtzOgZnNM7ODZraplvlmZo+b2Q4z22BmQ5o6xkBqQP7jzeyEma33v/5fU8cYKGaWamYfmNkWM/vczP69hjEhuf8bmHso7/tYM1tlZp/58/+vGsaE6r5vSO4hu+8BzCzSzNaZ2Rs1zAvJ/V5ZPfmH7L43s2wz2+jPa3UN8z3b91FNtaEQsQD4H2BRLfMnAz39r5HAn/3/hooF1J0/wMfOuWubJpwmVQL8yDm31szigTVm9p5zbnOlMaG6/xuSO4Tuvi8ErnDO5ZtZNLDUzN5yzq2sNCZU931DcofQ3fcA/w5sAdrUMC9U93tldeUPob3vJzjnauvX5tm+1xG3c+Cc+wg4WseQ64FFzmclkGBmSU0TXeA1IP+Q5ZzLdc6t9b/Pw/eDLLnasJDc/w3MPWT592e+/2O0/1X94uBQ3fcNyT1kmVkK8A3gb7UMCcn9Xq4B+Yczz/a9CrfGlQzsqfQ5hzD6Bec32n9a5S0z6+d1MIFgZunAYOCTarNCfv/XkTuE8L73ny5aDxwE3nPOhc2+b0DuELr7/g/AfwBltcwP2f3u9wfqzh9Cd9874F0zW2Nmd9Yw37N9r8KtcVkN08Lmr1NgLb7HdAwE/gi84m04jc/MWgMvAvc6505Wn13DIiGz/+vJPaT3vXOu1Dk3CEgBRphZ/2pDQnbfNyD3kNz3ZnYtcNA5t6auYTVMC4n93sD8Q3Lf+411zg3Bd0r0bjP7WrX5nu17FW6NKwdIrfQ5BdjnUSxNzjl3svy0inPuTSDazDp6HFaj8V/j8yLwtHPupRqGhOz+ry/3UN/35Zxzx4FMYFK1WSG778vVlnsI7/uxwBQzywYygCvMbHG1MaG83+vNP4T3Pc65ff5/DwIvAyOqDfFs36twa1yvATP9d5uMAk4453K9DqqpmFkXMzP/+xH4vr+OeBtV4/Dn9Xdgi3Pu97UMC8n935DcQ3zfdzKzBP/7OOBKYGu1YaG67+vNPVT3vXPuQedcinMuHZgGvO+cu73asJDc79Cw/EN135tZK/+NWJhZK+DrQPVuCp7te91Veg7M7BlgPNDRzHKAn+O7WBfn3BPAm8A1wA7gNHCHN5EGRgPyvwn4P2ZWApwBprnQ6fA8FpgBbPRf7wPwU6AbhPz+b0juobzvk4CFZhaJ7xfTc865N8zsLgj5fd+Q3EN5358lTPZ7rcJk3ycCL/tr0ijgH865t5vLvteTE0RERESChE6VioiIiAQJFW4iIiIiQUKFm4iIiEiQUOEmIiIiEiRUuImIiIgECRVuIiI1MLNSM1tf6ZVex9gFZnZTDdPHm9kbAQ1URMKK+riJiNTsjP9RTyIizYaOuImINJCZDTKzlWa2wcxeNrN2NYyZZGZbzWwpcKMHYYpICFPhJiJSs7hKp0lf9k9bBPzEOXcpsBHf00MqmFks8FfgOmAc0KUpAxaR0KdTpSIiNatyqtTM2gIJzrkP/ZMWAs9XW6YPkOWc+8K/zGLgziaIVUTChI64iYg0Lj1HUEQCRoWbiEgDOOdOAMfMbJx/0gzgw2rDtgLdzayH//P0popPRMKDTpWKiDTcLOAJM2sJ7ATuqDzTOVdgZncC/zSzw8BSoH/Thykiocqc01F9ERERkWCgU6UiIiIiQUKFm4iIiEiQUOEmIiIiEiRUuImIiIgECRVuIiIiIkFChZuIiIhIkFDhJiIiIhIkVLiJiIiIBIn/HwPLtIJROirxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(range(1, len(cv_scores) + 1), cv_scores, label='CV Fold MSE', marker='o')\n",
    "\n",
    "\n",
    "plt.axhline(np.mean(cv_scores), color='red', linestyle='--', label=f'Mean CV MSE ({np.mean(cv_scores):.4f})')\n",
    "\n",
    "plt.axhline(test_mse, color='green', linestyle='-', label=f'Test MSE ({test_mse:.4f})')\n",
    "\n",
    "plt.title('Cross-Validation and Test MSE')\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('MSE')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66cde88",
   "metadata": {},
   "source": [
    "## Random Forest Gird Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1e7469",
   "metadata": {},
   "source": [
    "I applied what was came out of result. I intended to restart however my computer's capacity is not enough for restarting all grid searches That I've done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0de5b0fd",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15916/2339820827.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mgrid_search\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my3_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Best Parameters:\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1387\u001b[0m                 )\n\u001b[0;32m   1388\u001b[0m             ):\n\u001b[1;32m-> 1389\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1391\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m   1022\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1023\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1024\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1025\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1026\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1569\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1570\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1571\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1572\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    968\u001b[0m                     )\n\u001b[0;32m    969\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 970\u001b[1;33m                 out = parallel(\n\u001b[0m\u001b[0;32m    971\u001b[0m                     delayed(_fit_and_score)(\n\u001b[0;32m    972\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     75\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         )\n\u001b[1;32m---> 77\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m             \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_sequential_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1917\u001b[0m             \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1918\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1920\u001b[0m         \u001b[1;31m# Let's create an ID that uniquely identifies the current call. If the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_dispatched_batches\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1846\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1847\u001b[1;33m                 \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1848\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1849\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_progress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    137\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    864\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    865\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 866\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    867\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    868\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1387\u001b[0m                 )\n\u001b[0;32m   1388\u001b[0m             ):\n\u001b[1;32m-> 1389\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1391\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    485\u001b[0m             \u001b[1;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m             \u001b[1;31m# since correctness does not rely on using threads.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 487\u001b[1;33m             trees = Parallel(\n\u001b[0m\u001b[0;32m    488\u001b[0m                 \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    489\u001b[0m                 \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     75\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mdelayed_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m         )\n\u001b[1;32m---> 77\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterable_with_config\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     78\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m             \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_sequential_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1917\u001b[0m             \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1918\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1920\u001b[0m         \u001b[1;31m# Let's create an ID that uniquely identifies the current call. If the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_dispatched_batches\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1846\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_dispatched_tasks\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1847\u001b[1;33m                 \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1848\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_completed_tasks\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1849\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_progress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    137\u001b[0m             \u001b[0mconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 139\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap, missing_values_in_feature_mask)\u001b[0m\n\u001b[0;32m    174\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    175\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 176\u001b[1;33m         indices = _generate_sample_indices(\n\u001b[0m\u001b[0;32m    177\u001b[0m             \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_samples_bootstrap\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    178\u001b[0m         )\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_generate_sample_indices\u001b[1;34m(random_state, n_samples, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m     \u001b[0mrandom_instance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m     sample_indices = random_instance.randint(\n\u001b[0m\u001b[0;32m    131\u001b[0m         \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_samples_bootstrap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m     )\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "param_grid = {\n",
    "    'max_depth': [10, 20, 30],\n",
    "    'min_samples_split': [2, 10, 20],\n",
    "    'min_samples_leaf': [1, 5, 10],\n",
    "    'max_features': ['sqrt', 'log2'],\n",
    "    'n_estimators': [100, 200, 300]\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(RandomForestRegressor(random_state=42), param_grid, cv=5)\n",
    "grid_search.fit(X_train, y3_train)\n",
    "\n",
    "print(\"Best Parameters:\", grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d57e84",
   "metadata": {},
   "source": [
    "References\n",
    "\n",
    "<a>1- https://github.com/MShahabSepehri/CryptoMamba/blob/main/models/lstm.py<br>\n",
    "2- https://colab.research.google.com/drive/1Bk4zPQwAfzoSHZokKUefKL1s6lqmam6S?usp=sharing#scrollTo=uD17zIbaIZ6n<br>\n",
    "3- https://www.kaggle.com/code/melissamonfared/google-s-stock-price-prediction-lstm<br>\n",
    "4- https://www.kaggle.com/code/faressayah/stock-market-analysis-prediction-using-lstm<br>\n",
    "5- https://www.kaggle.com/code/carlmcbrideellis/lstm-time-series-stock-price-prediction-fail<br>\n",
    "6- https://www.kaggle.com/code/meetnagadia/bitcoin-price-prediction-using-lstm<br>\n",
    "7- https://www.kaggle.com/code/thebrownviking20/everything-you-can-do-with-a-time-series<br>\n",
    "8- https://github.com/AhmedTolba36996/Short-Term-Solar-Irradiance-Forecasting-using-a-Hybridized-CNN-LSTM-MLP-KN-Model/blob/main/karachi-v4.ipynb<br>\n",
    "9- https://www.kaggle.com/code/prashant111/svm-classifier-tutorial#Support-Vector-Machines-Classifier-Tutorial-with-Python<br>\n",
    "10- https://www.kaggle.com/code/prashant111/random-forest-classifier-tutorial#Random-Forest-Classifier-Tutorial-with-Python<br>\n",
    "11- https://www.kaggle.com/code/kanncaa1/convolutional-neural-network-cnn-tutorial<br>\n",
    "12- ChatGpt<br>\n",
    "13- https://www.sciencedirect.com/science/article/abs/pii/S0957417414004473<br>\n",
    "14- https://doi.org/10.1016/j.frl.2024.105874<br>\n",
    "15- https://ieeexplore.ieee.org/abstract/document/9165760<br>\n",
    "16- https://doi.org/10.54216/FPA.170114<br>\n",
    "17- https://arxiv.org/abs/2501.01010<br>\n",
    "18- https://doi.org/10.61356/SMIJ.2025.10448<br>\n",
    "19- https://etasr.com/index.php/ETASR/article/view/9363<br>\n",
    "20- https://doi:10.20944/preprints202409.1904.v1<br>\n",
    "21- https://doi.org/10.3390/fractalfract7020203<br>\n",
    "22- https://www.cryptoarchive.com.au/downloads (for data)<br>\n",
    "23- stackoverflow</a>"
   ]
  }
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