Q3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import yfinance as yf\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from xgboost import XGBClassifier, XGBRFRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold, cross_val_predict, cross_val_score\n",
    "from sklearn.metrics import f1_score, precision_score, recall_score, roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pytz\n",
    "import plotly.io as pio\n",
    "import plotly.graph_objects as go\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "xmr = yf.Ticker(\"XMR-USD XMR-BTC XMR-EUR XMR-CNY\")\n",
    "df = yf.download(tickers = \"XMR-USD\",\n",
    "                     period = \"max\",\n",
    "                     interval = \"1d\")\n",
    "df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get Gold, Silver and Copper closing prices in our dataframe's date range.\n",
    "\n",
    "### Define date range\n",
    "start_Date = df['Date'].min().date()\n",
    "end_Date = df['Date'].max().date()\n",
    "\n",
    "def get_precious_metals(tickers: list):\n",
    "    p_metals_close = []\n",
    "    for ticker in tickers:\n",
    "        p_metal = yf.Ticker(f\"{ticker}\")\n",
    "        p_metal_hist = p_metal.history(start=df['Date'].min(), end=df['Date'].max(), actions=False)\n",
    "        p_metal_hist.drop(columns=['Open', 'High', 'Low', 'Volume'], inplace=True)\n",
    "        p_metal_hist.reset_index(inplace=True)\n",
    "        p_metal_hist['Date'] = p_metal_hist['Date'].dt.tz_convert(pytz.UTC)\n",
    "        p_metal_close=p_metal_hist[['Close']]    \n",
    "        p_metals_close.append(p_metal_close)\n",
    "    return p_metals_close\n",
    "\n",
    "### in order of : Gold >> Silver >> Copper\n",
    "metals_tickers = ['GC=F', 'SI=F', 'HG=F']\n",
    "pMetals_closePriceList = get_precious_metals(metals_tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gold_close</th>\n",
       "      <th>silver_close</th>\n",
       "      <th>copper_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1285.599976</td>\n",
       "      <td>16.943001</td>\n",
       "      <td>3.0800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1272.400024</td>\n",
       "      <td>16.844000</td>\n",
       "      <td>3.0695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1277.300049</td>\n",
       "      <td>17.023001</td>\n",
       "      <td>3.1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1281.500000</td>\n",
       "      <td>17.052000</td>\n",
       "      <td>3.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1276.500000</td>\n",
       "      <td>16.950001</td>\n",
       "      <td>3.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>1869.300049</td>\n",
       "      <td>21.792000</td>\n",
       "      <td>3.5940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>1927.400024</td>\n",
       "      <td>22.726999</td>\n",
       "      <td>3.5800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>1921.099976</td>\n",
       "      <td>22.610001</td>\n",
       "      <td>3.5870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>1922.699951</td>\n",
       "      <td>22.863001</td>\n",
       "      <td>3.5755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>1955.300049</td>\n",
       "      <td>22.944000</td>\n",
       "      <td>3.5820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1493 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gold_close  silver_close  copper_close\n",
       "0     1285.599976     16.943001        3.0800\n",
       "1     1272.400024     16.844000        3.0695\n",
       "2     1277.300049     17.023001        3.1115\n",
       "3     1281.500000     17.052000        3.0600\n",
       "4     1276.500000     16.950001        3.0490\n",
       "...           ...           ...           ...\n",
       "1488  1869.300049     21.792000        3.5940\n",
       "1489  1927.400024     22.726999        3.5800\n",
       "1490  1921.099976     22.610001        3.5870\n",
       "1491  1922.699951     22.863001        3.5755\n",
       "1492  1955.300049     22.944000        3.5820\n",
       "\n",
       "[1493 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Combine all 3 precious metals closing prices into a single dataframe named metal_df \n",
    "\n",
    "gold_close = pMetals_closePriceList[0]\n",
    "silver_close = pMetals_closePriceList[1]\n",
    "copper_close = pMetals_closePriceList[2]\n",
    "\n",
    "metal_df  = pd.concat([gold_close, silver_close,copper_close], axis=1)\n",
    "metal_df.columns = ['gold_close', 'silver_close', 'copper_close']\n",
    "metal_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get other crypto coins close price to improve Model performance\n",
    "\n",
    "def get_other_coins(tickers: list):\n",
    "    other_coins_close = []\n",
    "    for ticker in tickers:\n",
    "        coin = yf.Ticker(f\"{ticker}\")\n",
    "        coin_hist = coin.history(start=df['Date'].min(), end=df['Date'].max(), actions=False)\n",
    "        coin_hist.drop(columns=['Open', 'High', 'Low', 'Volume'], inplace=True)\n",
    "        coin_hist.reset_index(inplace=True)\n",
    "        coin_hist['Date'] = coin_hist['Date'].dt.tz_convert(pytz.UTC)\n",
    "        coin_close = coin_hist[['Close']]\n",
    "        other_coins_close.append(coin_close)\n",
    "    return other_coins_close\n",
    "\n",
    "### Coins close prices (as a df) will be returned in the order spedified in the coins_tickers list.  \n",
    "coins_tickers = ['BTC-USD', 'ETH-USD', 'USDT-USD', 'BNB-USD', 'XRP-USD']\n",
    "coins_close_list = get_other_coins(coins_tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bitcoin_close</th>\n",
       "      <th>ethereum_close</th>\n",
       "      <th>tether_close</th>\n",
       "      <th>bnb_close</th>\n",
       "      <th>xrp_close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7143.580078</td>\n",
       "      <td>320.884003</td>\n",
       "      <td>1.008180</td>\n",
       "      <td>1.990770</td>\n",
       "      <td>0.217488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6618.140137</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>1.006010</td>\n",
       "      <td>1.796840</td>\n",
       "      <td>0.206483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6357.600098</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>1.008990</td>\n",
       "      <td>1.670470</td>\n",
       "      <td>0.210430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5950.069824</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>1.012470</td>\n",
       "      <td>1.519690</td>\n",
       "      <td>0.197339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6559.490234</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>1.009350</td>\n",
       "      <td>1.686620</td>\n",
       "      <td>0.203442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2165</th>\n",
       "      <td>26861.707031</td>\n",
       "      <td>1555.256836</td>\n",
       "      <td>1.000106</td>\n",
       "      <td>206.601898</td>\n",
       "      <td>0.486775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>27159.652344</td>\n",
       "      <td>1558.069824</td>\n",
       "      <td>1.000373</td>\n",
       "      <td>209.742508</td>\n",
       "      <td>0.487846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2167</th>\n",
       "      <td>28519.466797</td>\n",
       "      <td>1600.534302</td>\n",
       "      <td>1.000548</td>\n",
       "      <td>214.823959</td>\n",
       "      <td>0.497977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2168</th>\n",
       "      <td>28415.748047</td>\n",
       "      <td>1565.439575</td>\n",
       "      <td>1.000404</td>\n",
       "      <td>211.643234</td>\n",
       "      <td>0.491694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2169</th>\n",
       "      <td>28328.341797</td>\n",
       "      <td>1563.749878</td>\n",
       "      <td>1.000227</td>\n",
       "      <td>210.501038</td>\n",
       "      <td>0.488319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2170 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bitcoin_close  ethereum_close  tether_close   bnb_close  xrp_close\n",
       "0       7143.580078      320.884003      1.008180    1.990770   0.217488\n",
       "1       6618.140137      299.252991      1.006010    1.796840   0.206483\n",
       "2       6357.600098      314.681000      1.008990    1.670470   0.210430\n",
       "3       5950.069824      307.907990      1.012470    1.519690   0.197339\n",
       "4       6559.490234      316.716003      1.009350    1.686620   0.203442\n",
       "...             ...             ...           ...         ...        ...\n",
       "2165   26861.707031     1555.256836      1.000106  206.601898   0.486775\n",
       "2166   27159.652344     1558.069824      1.000373  209.742508   0.487846\n",
       "2167   28519.466797     1600.534302      1.000548  214.823959   0.497977\n",
       "2168   28415.748047     1565.439575      1.000404  211.643234   0.491694\n",
       "2169   28328.341797     1563.749878      1.000227  210.501038   0.488319\n",
       "\n",
       "[2170 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create a df for other crypto coins\n",
    "\n",
    "crypto_df = pd.concat(coins_close_list, ignore_index=True, axis=1)\n",
    "crypto_df.columns = ['bitcoin_close', 'ethereum_close', 'tether_close', 'bnb_close', 'xrp_close']\n",
    "crypto_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get 'S&P 500' stock market index\n",
    "\n",
    "sp = yf.Ticker(\"^GSPC\")\n",
    "sp_hist = sp.history(start=df['Date'].min(), end=df['Date'].max(), actions=False)\n",
    "sp_hist.drop(columns=['Open', 'High', 'Low', 'Volume'], inplace=True)\n",
    "sp_hist.reset_index(inplace=True)\n",
    "sp_hist['Date'] = sp_hist['Date'].dt.tz_convert(pytz.UTC)\n",
    "sp_close=sp_hist[['Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Combine Other Crypto coins and precious metals' dataframes\n",
    "crypto_metal_df = pd.concat([crypto_df, metal_df, sp_close ], axis=1)\n",
    "\n",
    "### Assign column names\n",
    "crypto_metal_df.columns = ['bitcoin_close', 'ethereum_close', 'tether_close', 'bnb_close', 'xrp_close',\n",
    "                     'gold_close', 'silver_close', 'copper_close','sp_close']\n",
    "### Fill missing values using interpolate technique\n",
    "crypto_metal_df.interpolate(inplace=True)\n",
    "\n",
    "### Combine to main df\n",
    "crypto_metal_df1=pd.concat([df, crypto_metal_df], axis=1)\n",
    "\n",
    "df = crypto_metal_df1.copy()\n",
    "\n",
    "### Fill missing values using interpolate technique\n",
    "df.interpolate(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_hash_difficulty_files(files: list):\n",
    "    final_files = []\n",
    "    for file in files:\n",
    "        \n",
    "        ### Read csv file\n",
    "        file = pd.read_csv(f'./data_phase2/{file}', index_col=None)\n",
    "        file.drop(columns=['Unnamed: 0'], inplace=True)\n",
    "\n",
    "        ### Convert hourly difficulty rates to daily\n",
    "        \n",
    "        ### First, ensure that the 'time' column is of datetime type\n",
    "        file['time'] = pd.to_datetime(file['time'])\n",
    "\n",
    "        ### Set 'time' as the index for the DataFrame\n",
    "        file.set_index('time', inplace=True)\n",
    "\n",
    "        ### Use the resample method to aggregate hourly data to daily data\n",
    "        rate = ''\n",
    "        if 'difficulty' in file.columns:\n",
    "            rate_col = 'difficulty'\n",
    "        elif 'hash' in file.columns:\n",
    "            rate_col = 'hash'\n",
    "        converted_rate = file[rate_col].resample('D').mean()\n",
    "        converted_rate = pd.DataFrame(converted_rate)\n",
    "        converted_rate.reset_index(inplace=True)\n",
    "        \n",
    "        ### Remove UTC time format from its time column\n",
    "        converted_rate['time'] = converted_rate['time'].dt.tz_localize(None)\n",
    "\n",
    "        ### Filter converted_rate date range to correspond to date range in df( aka XMR-Monero)\n",
    "        start_date = df['Date'].min()\n",
    "        ### converted_rate ends in 2023-10-08 so forcibly we have to cut df short to 2023-10-08 \n",
    "        end_date = df['Date'].max()\n",
    "\n",
    "        ### Final file named With daily rates instead of hourly \n",
    "        ### And Correct date range\n",
    "        ### Note : There are still some rows missing in converted_rate_with_range which has to be dropped from df  \n",
    "        converted_rate_with_range = converted_rate[\n",
    "            (converted_rate['time'] >= start_date) & (converted_rate['time'] <= end_date)\n",
    "        ]\n",
    "        converted_rate_with_range.reset_index(drop=True, inplace=True)\n",
    "        converted_rate_with_range = converted_rate_with_range.rename(columns={'time': 'Date'})\n",
    "        final_files.append(converted_rate_with_range)\n",
    "    return final_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### difficulty_hash_final_files contains a list with 2 elements \n",
    "### At index 0 it has final xmr_difficulty file\n",
    "### At index 1 it has final xmr_hash file \n",
    "difficulty_hash_final_files = clean_hash_difficulty_files(files=['XMR_difficulty.csv', 'XMR_hash.csv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>bitcoin_close</th>\n",
       "      <th>ethereum_close</th>\n",
       "      <th>tether_close</th>\n",
       "      <th>bnb_close</th>\n",
       "      <th>xrp_close</th>\n",
       "      <th>gold_close</th>\n",
       "      <th>silver_close</th>\n",
       "      <th>copper_close</th>\n",
       "      <th>sp_close</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>hash</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>112.531998</td>\n",
       "      <td>123.404999</td>\n",
       "      <td>112.219002</td>\n",
       "      <td>120.779999</td>\n",
       "      <td>120.779999</td>\n",
       "      <td>86864600</td>\n",
       "      <td>7143.580078</td>\n",
       "      <td>320.884003</td>\n",
       "      <td>1.008180</td>\n",
       "      <td>1.990770</td>\n",
       "      <td>0.217488</td>\n",
       "      <td>1285.599976</td>\n",
       "      <td>16.943001</td>\n",
       "      <td>3.0800</td>\n",
       "      <td>2584.620117</td>\n",
       "      <td>2.806786e+10</td>\n",
       "      <td>2.338989e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-11-10</td>\n",
       "      <td>121.344002</td>\n",
       "      <td>121.665001</td>\n",
       "      <td>101.757004</td>\n",
       "      <td>105.585999</td>\n",
       "      <td>105.585999</td>\n",
       "      <td>84614000</td>\n",
       "      <td>6618.140137</td>\n",
       "      <td>299.252991</td>\n",
       "      <td>1.006010</td>\n",
       "      <td>1.796840</td>\n",
       "      <td>0.206483</td>\n",
       "      <td>1272.400024</td>\n",
       "      <td>16.844000</td>\n",
       "      <td>3.0695</td>\n",
       "      <td>2582.300049</td>\n",
       "      <td>2.805339e+10</td>\n",
       "      <td>2.337782e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>105.750000</td>\n",
       "      <td>127.106003</td>\n",
       "      <td>103.877998</td>\n",
       "      <td>119.615997</td>\n",
       "      <td>119.615997</td>\n",
       "      <td>107708000</td>\n",
       "      <td>6357.600098</td>\n",
       "      <td>314.681000</td>\n",
       "      <td>1.008990</td>\n",
       "      <td>1.670470</td>\n",
       "      <td>0.210430</td>\n",
       "      <td>1277.300049</td>\n",
       "      <td>17.023001</td>\n",
       "      <td>3.1115</td>\n",
       "      <td>2584.840088</td>\n",
       "      <td>2.805339e+10</td>\n",
       "      <td>2.337782e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-12</td>\n",
       "      <td>119.597000</td>\n",
       "      <td>133.675003</td>\n",
       "      <td>110.617996</td>\n",
       "      <td>123.856003</td>\n",
       "      <td>123.856003</td>\n",
       "      <td>144948000</td>\n",
       "      <td>5950.069824</td>\n",
       "      <td>307.907990</td>\n",
       "      <td>1.012470</td>\n",
       "      <td>1.519690</td>\n",
       "      <td>0.197339</td>\n",
       "      <td>1281.500000</td>\n",
       "      <td>17.052000</td>\n",
       "      <td>3.0600</td>\n",
       "      <td>2578.870117</td>\n",
       "      <td>2.805339e+10</td>\n",
       "      <td>2.337782e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-11-13</td>\n",
       "      <td>128.960007</td>\n",
       "      <td>136.528000</td>\n",
       "      <td>120.921997</td>\n",
       "      <td>123.402000</td>\n",
       "      <td>123.402000</td>\n",
       "      <td>116200000</td>\n",
       "      <td>6559.490234</td>\n",
       "      <td>316.716003</td>\n",
       "      <td>1.009350</td>\n",
       "      <td>1.686620</td>\n",
       "      <td>0.203442</td>\n",
       "      <td>1276.500000</td>\n",
       "      <td>16.950001</td>\n",
       "      <td>3.0490</td>\n",
       "      <td>2564.620117</td>\n",
       "      <td>2.805339e+10</td>\n",
       "      <td>2.337782e+08</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2155</th>\n",
       "      <td>2023-10-04</td>\n",
       "      <td>147.168442</td>\n",
       "      <td>150.702347</td>\n",
       "      <td>145.940781</td>\n",
       "      <td>150.469055</td>\n",
       "      <td>150.469055</td>\n",
       "      <td>59400400</td>\n",
       "      <td>27799.394531</td>\n",
       "      <td>1647.838135</td>\n",
       "      <td>1.000437</td>\n",
       "      <td>213.413086</td>\n",
       "      <td>0.532931</td>\n",
       "      <td>1955.300049</td>\n",
       "      <td>22.944000</td>\n",
       "      <td>3.5820</td>\n",
       "      <td>4314.600098</td>\n",
       "      <td>3.423015e+11</td>\n",
       "      <td>2.852512e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2156</th>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>150.474197</td>\n",
       "      <td>151.328369</td>\n",
       "      <td>148.565491</td>\n",
       "      <td>149.623718</td>\n",
       "      <td>149.623718</td>\n",
       "      <td>55704972</td>\n",
       "      <td>27415.912109</td>\n",
       "      <td>1611.476440</td>\n",
       "      <td>1.000089</td>\n",
       "      <td>210.679672</td>\n",
       "      <td>0.523366</td>\n",
       "      <td>1955.300049</td>\n",
       "      <td>22.944000</td>\n",
       "      <td>3.5820</td>\n",
       "      <td>4314.600098</td>\n",
       "      <td>3.338542e+11</td>\n",
       "      <td>2.782118e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157</th>\n",
       "      <td>2023-10-06</td>\n",
       "      <td>149.623337</td>\n",
       "      <td>152.669296</td>\n",
       "      <td>148.641647</td>\n",
       "      <td>151.992264</td>\n",
       "      <td>151.992264</td>\n",
       "      <td>49535004</td>\n",
       "      <td>27946.597656</td>\n",
       "      <td>1645.831543</td>\n",
       "      <td>1.000436</td>\n",
       "      <td>213.555527</td>\n",
       "      <td>0.526193</td>\n",
       "      <td>1955.300049</td>\n",
       "      <td>22.944000</td>\n",
       "      <td>3.5820</td>\n",
       "      <td>4314.600098</td>\n",
       "      <td>3.580862e+11</td>\n",
       "      <td>2.984052e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2158</th>\n",
       "      <td>2023-10-07</td>\n",
       "      <td>151.988235</td>\n",
       "      <td>155.247528</td>\n",
       "      <td>151.100983</td>\n",
       "      <td>155.212143</td>\n",
       "      <td>155.212143</td>\n",
       "      <td>61159796</td>\n",
       "      <td>27968.839844</td>\n",
       "      <td>1634.511230</td>\n",
       "      <td>1.000550</td>\n",
       "      <td>212.364471</td>\n",
       "      <td>0.521906</td>\n",
       "      <td>1955.300049</td>\n",
       "      <td>22.944000</td>\n",
       "      <td>3.5820</td>\n",
       "      <td>4314.600098</td>\n",
       "      <td>3.445453e+11</td>\n",
       "      <td>2.871211e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2159</th>\n",
       "      <td>2023-10-08</td>\n",
       "      <td>155.219528</td>\n",
       "      <td>156.482040</td>\n",
       "      <td>153.763336</td>\n",
       "      <td>156.191223</td>\n",
       "      <td>156.191223</td>\n",
       "      <td>59858985</td>\n",
       "      <td>27935.089844</td>\n",
       "      <td>1633.548584</td>\n",
       "      <td>1.000460</td>\n",
       "      <td>211.519989</td>\n",
       "      <td>0.517961</td>\n",
       "      <td>1955.300049</td>\n",
       "      <td>22.944000</td>\n",
       "      <td>3.5820</td>\n",
       "      <td>4314.600098</td>\n",
       "      <td>3.412980e+11</td>\n",
       "      <td>2.831266e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2160 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date        Open        High         Low       Close   Adj Close  \\\n",
       "0    2017-11-09  112.531998  123.404999  112.219002  120.779999  120.779999   \n",
       "1    2017-11-10  121.344002  121.665001  101.757004  105.585999  105.585999   \n",
       "2    2017-11-11  105.750000  127.106003  103.877998  119.615997  119.615997   \n",
       "3    2017-11-12  119.597000  133.675003  110.617996  123.856003  123.856003   \n",
       "4    2017-11-13  128.960007  136.528000  120.921997  123.402000  123.402000   \n",
       "...         ...         ...         ...         ...         ...         ...   \n",
       "2155 2023-10-04  147.168442  150.702347  145.940781  150.469055  150.469055   \n",
       "2156 2023-10-05  150.474197  151.328369  148.565491  149.623718  149.623718   \n",
       "2157 2023-10-06  149.623337  152.669296  148.641647  151.992264  151.992264   \n",
       "2158 2023-10-07  151.988235  155.247528  151.100983  155.212143  155.212143   \n",
       "2159 2023-10-08  155.219528  156.482040  153.763336  156.191223  156.191223   \n",
       "\n",
       "         Volume  bitcoin_close  ethereum_close  tether_close   bnb_close  \\\n",
       "0      86864600    7143.580078      320.884003      1.008180    1.990770   \n",
       "1      84614000    6618.140137      299.252991      1.006010    1.796840   \n",
       "2     107708000    6357.600098      314.681000      1.008990    1.670470   \n",
       "3     144948000    5950.069824      307.907990      1.012470    1.519690   \n",
       "4     116200000    6559.490234      316.716003      1.009350    1.686620   \n",
       "...         ...            ...             ...           ...         ...   \n",
       "2155   59400400   27799.394531     1647.838135      1.000437  213.413086   \n",
       "2156   55704972   27415.912109     1611.476440      1.000089  210.679672   \n",
       "2157   49535004   27946.597656     1645.831543      1.000436  213.555527   \n",
       "2158   61159796   27968.839844     1634.511230      1.000550  212.364471   \n",
       "2159   59858985   27935.089844     1633.548584      1.000460  211.519989   \n",
       "\n",
       "      xrp_close   gold_close  silver_close  copper_close     sp_close  \\\n",
       "0      0.217488  1285.599976     16.943001        3.0800  2584.620117   \n",
       "1      0.206483  1272.400024     16.844000        3.0695  2582.300049   \n",
       "2      0.210430  1277.300049     17.023001        3.1115  2584.840088   \n",
       "3      0.197339  1281.500000     17.052000        3.0600  2578.870117   \n",
       "4      0.203442  1276.500000     16.950001        3.0490  2564.620117   \n",
       "...         ...          ...           ...           ...          ...   \n",
       "2155   0.532931  1955.300049     22.944000        3.5820  4314.600098   \n",
       "2156   0.523366  1955.300049     22.944000        3.5820  4314.600098   \n",
       "2157   0.526193  1955.300049     22.944000        3.5820  4314.600098   \n",
       "2158   0.521906  1955.300049     22.944000        3.5820  4314.600098   \n",
       "2159   0.517961  1955.300049     22.944000        3.5820  4314.600098   \n",
       "\n",
       "        difficulty          hash  \n",
       "0     2.806786e+10  2.338989e+08  \n",
       "1     2.805339e+10  2.337782e+08  \n",
       "2     2.805339e+10  2.337782e+08  \n",
       "3     2.805339e+10  2.337782e+08  \n",
       "4     2.805339e+10  2.337782e+08  \n",
       "...            ...           ...  \n",
       "2155  3.423015e+11  2.852512e+09  \n",
       "2156  3.338542e+11  2.782118e+09  \n",
       "2157  3.580862e+11  2.984052e+09  \n",
       "2158  3.445453e+11  2.871211e+09  \n",
       "2159  3.412980e+11  2.831266e+09  \n",
       "\n",
       "[2160 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Merge difficulty and hash into XMR-Monro (our main df)\n",
    "for file in difficulty_hash_final_files:\n",
    "    df = pd.merge(df, file, on='Date')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Add Target vector as a separate column to dataframe for later reference\n",
    "### This is Target Vector\n",
    "df['Price_Direction'] = np.where(df['Close'].shift(-1) > df['Close'], 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Calculate High-Low and Open-Close differences and add them as new features to our df for improving our model.\n",
    "df['HighLow'] = df['High'] - df['Low']\n",
    "df['OpenClose'] = df['Open'] - df['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Close\n",
       "False    2146\n",
       "True       13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Calculate RSI Indicator\n",
    "\n",
    "### First Calculate the difference of each day's price with prior day\n",
    "change = df[\"Close\"].diff()\n",
    "change = change.dropna()\n",
    "\n",
    "### Create two copies of the Closing price Series\n",
    "change_up = change.copy()\n",
    "change_down = change.copy()\n",
    "\n",
    "# Separate gains(change_up) and losses(change_down)\n",
    "change_up[change_up<0] = 0\n",
    "change_down[change_down>0] = 0\n",
    "\n",
    "### Verify no mistakes were made\n",
    "change.equals(change_up+change_down)\n",
    "\n",
    "### Calculate the rolling average of average up and average down\n",
    "window_size = 14\n",
    "avg_up = change_up.rolling(window_size).mean()\n",
    "avg_down = change_down.rolling(window_size).mean().abs()\n",
    "\n",
    "### Calculate RSI using its formula\n",
    "rsi = 100 * avg_up / (avg_up + avg_down)\n",
    "\n",
    "### Add RSI index to dataframe\n",
    "df['RSI'] = rsi\n",
    "rsi.isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Calculate SMA Indicator\n",
    "\n",
    "### Get the window of series of observations of specified window size \n",
    "window_size = 14\n",
    "windows = df['Close'].rolling(window_size) \n",
    "  \n",
    "### Create a series of simple moving averages of each window \n",
    "sma = windows.mean() \n",
    "\n",
    "### Add SMA index to dataframe\n",
    "df['SMA'] = sma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Calculate EMA Indicator\n",
    "\n",
    "ema = df['Close'].ewm(span=14, adjust=False).mean()\n",
    "### Add EMA indicator to dataframe\n",
    "df['EMA'] = ema\n",
    "\n",
    "### Comparison plot b/w stock values & EMA\n",
    "plt.plot(df['Close'], label=\"Stock Values\")\n",
    "plt.plot(ema, label=\"EMA Values\")\n",
    "plt.xlabel(\"Days\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend()\n",
    "plt.show() \n",
    "### We can see that EMA index predicts out stock value (closing price) pretty good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Calculate Bollinger Band Indicator\n",
    "\n",
    "sma = df['Close'].rolling(window=14).mean()\n",
    "rstd = df['Close'].rolling(window=14).std()\n",
    "\n",
    "upper_band = sma + 2 * rstd\n",
    "lower_band = sma - 2 * rstd\n",
    "\n",
    "### Option 1\n",
    "### Calculate Band Width (Bollinger Band as single indicator)\n",
    "# df['Band_Width'] = (upper_band - lower_band) / sma\n",
    "\n",
    "### Option 2\n",
    "### Calculate Bollinger band as separate indicators\n",
    "df['Upper_Band'] = upper_band\n",
    "df['Lower_Band'] = lower_band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Calculate MACD Indicator\n",
    "\n",
    "### Calculate Short-term (12 day) Exponential Moving Average (EMA)\n",
    "short_term_ema = df['Close'].ewm(span=12, adjust=False).mean()\n",
    "\n",
    "### Calculate Long-term (26 day) Exponential Moving Average (EMA)\n",
    "long_term_ema = df['Close'].ewm(span=26, adjust=False).mean()\n",
    "\n",
    "### Calculate MACD Line\n",
    "macd = short_term_ema - long_term_ema\n",
    "\n",
    "### Calculate Signal Line (9-day EMA of MACD)\n",
    "signal_line = macd.ewm(span=9, adjust=False).mean()\n",
    "\n",
    "### Add MACD and Signal Line to your DataFrame\n",
    "df['MACD'] = macd\n",
    "df['Signal_Line'] = signal_line\n",
    "\n",
    "### Create a binary column for buy (1) and sell (0) signals\n",
    "df['MACD_Signal'] = (df['MACD'] > df['Signal_Line']).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "line": {
          "color": "rgba(173,204,255,0.2)"
         },
         "name": "Lower Band",
         "type": "scatter",
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   "source": [
    "### Plot Bollinger band Indicator and its upper/lower limits along with SMA and EMA indicators for Analysis purposes\n",
    " \n",
    "df_chart = df.set_index('Date')\n",
    "df_chart = df_chart[['Close']]\n",
    "\n",
    "sma = df_chart.rolling(window=20).mean().dropna()\n",
    "rstd = df_chart.rolling(window=20).std().dropna()\n",
    "ema = df_chart[['Close']].ewm(span=20, adjust=False).mean().dropna()\n",
    "\n",
    "upper_band = sma + 2 * rstd\n",
    "lower_band = sma - 2 * rstd\n",
    "\n",
    "upper_band = upper_band.rename(columns={'Close': 'upper'})\n",
    "lower_band = lower_band.rename(columns={'Close': 'lower'})\n",
    "bb = df_chart.join(upper_band).join(lower_band)\n",
    "bb = bb.dropna()\n",
    "\n",
    "buyers = bb[bb['Close'] <= bb['lower']]\n",
    "sellers = bb[bb['Close'] >= bb['upper']]\n",
    "\n",
    "\n",
    "### Plot begins here\n",
    "\n",
    "pio.templates.default = \"plotly_dark\"\n",
    "\n",
    "### Create figure\n",
    "fig = go.Figure()\n",
    "\n",
    "### Add lower_band to figure\n",
    "fig.add_trace(go.Scatter(x=lower_band.index, \n",
    "                         y=lower_band['lower'], \n",
    "                         name='Lower Band', \n",
    "                         line_color='rgba(173,204,255,0.2)'\n",
    "                        ))\n",
    "\n",
    "### Add upper_band to figure\n",
    "fig.add_trace(go.Scatter(x=upper_band.index, \n",
    "                         y=upper_band['upper'], \n",
    "                         name='Upper Band', \n",
    "                         fill='tonexty', \n",
    "                         fillcolor='rgba(173,204,255,0.2)', \n",
    "                         line_color='rgba(173,204,255,0.2)'\n",
    "                        ))\n",
    "\n",
    "### Add actual close price to figure\n",
    "fig.add_trace(go.Scatter(x=df_chart.index, \n",
    "                         y=df_chart['Close'], \n",
    "                         name='Close', \n",
    "                         line_color='#636EFA'\n",
    "                        ))\n",
    "\n",
    "### Add SMA Indicator to figure\n",
    "fig.add_trace(go.Scatter(x=sma.index, \n",
    "                         y=sma['Close'], \n",
    "                         name='SMA', \n",
    "                         line_color='#FECB52'\n",
    "                        ))\n",
    "\n",
    "### Add EMA Indicator to figure\n",
    "fig.add_trace(go.Scatter(x=ema.index, \n",
    "                         y=ema['Close'], \n",
    "                         name='EMA', \n",
    "                         line_color='#FFFFFF'\n",
    "                        ))\n",
    "\n",
    "### Add buy oppurtunities to figure\n",
    "fig.add_trace(go.Scatter(x=buyers.index, \n",
    "                         y=buyers['Close'], \n",
    "                         name='Buyers', \n",
    "                         mode='markers',\n",
    "                         marker=dict(\n",
    "                             color='#00CC96',\n",
    "                             size=10,\n",
    "                             )\n",
    "                         ))\n",
    "\n",
    "### Add sell oppurtunities to figure\n",
    "fig.add_trace(go.Scatter(x=sellers.index, \n",
    "                         y=sellers['Close'], \n",
    "                         name='Sellers', \n",
    "                         mode='markers', \n",
    "                         marker=dict(\n",
    "                             color='#EF553B',\n",
    "                             size=10,\n",
    "                             )\n",
    "                         ))\n",
    "\n",
    "# Limit X-axis to match our dataframe's date range (its index)\n",
    "fig.update_xaxes(range=[df_chart.index.min(), df_chart.index.max()])\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Drop NaN values because of the nature of RSI, EMA, SMA and MACD calculations .\n",
    "df.dropna(inplace=True)\n",
    "### Reset Index\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "### Assign features and target \n",
    "y = df['Price_Direction']\n",
    "X = df.drop(columns=['Price_Direction', 'Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Feature scaling\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Split data into train and validation sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 11)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;n_neighbors&#x27;: range(1, 11)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': range(1, 11)})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Use Grid Search for optimal K\n",
    "\n",
    "param_grid = {\n",
    "    'n_neighbors': range(1, 11)  \n",
    "}\n",
    "knn = KNeighborsClassifier()\n",
    "grid_search = GridSearchCV(knn, param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best n_neighbors(or k) by grid search is : 1\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create and Train KNeighborsClassifier model \n",
    "best_n_neighbors = grid_search.best_params_['n_neighbors']\n",
    "print(f\"Best n_neighbors(or k) by grid search is : {best_n_neighbors}\\n\")\n",
    "knn = KNeighborsClassifier(n_neighbors=best_n_neighbors)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score : 54.21412300683372\n",
      "Precision score : 56.666666666666664\n",
      "Recall score : 51.96506550218341\n",
      "AUC score : 53.1182111430515\n"
     ]
    }
   ],
   "source": [
    "### Performance Metrics for applied KNN algorithm on input data. \n",
    "f1 = f1_score(y_test, y_predict)\n",
    "print(f\"F1 score : {f1 * 100}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_predict) * 100}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_predict) * 100}\")\n",
    "print(f\"AUC score : {roc_auc_score(y_test, y_predict) * 100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score : 68.2615629984051\n",
      "Precision score : 53.768844221105525\n",
      "Recall score : 93.44978165938865\n",
      "AUC score : 50.49373505079985\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(kernel='linear', probability=True) \n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Use GridSearch to find the best C value\n",
    "param_grid = {'C': [0.1, 1, 10, 100]}\n",
    "grid_search = GridSearchCV(svm, param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Use the best estimator from GridSearch\n",
    "best_svm = grid_search.best_estimator_\n",
    "\n",
    "# Get the decision scores for the test set\n",
    "decision_scores = best_svm.decision_function(X_test)\n",
    "\n",
    "# Define a custom threshold (e.g., 0.2, you can adjust this value)\n",
    "custom_threshold = 0.1\n",
    "\n",
    "# Apply the threshold to get binary predictions\n",
    "y_predict = (decision_scores > custom_threshold).astype(int)\n",
    "\n",
    "f1 = f1_score(y_test, y_predict)\n",
    "print(f\"F1 score : {f1 * 100}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_predict) * 100}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_predict) * 100}\")\n",
    "print(f\"AUC score : {roc_auc_score(y_test, y_predict) * 100}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_preds = cross_val_predict(best_svm, X_train, y_train)\n",
    "cm = confusion_matrix(y_train, y_preds)\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.title('Confusion matrix')\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add some extra features (mostly from previous days)\n",
    "df_expanded = df.copy()\n",
    "n_days = 5\n",
    "columns_to_drop = ['Date', 'Adj Close', 'Price_Direction']\n",
    "\n",
    "for col in df_expanded.drop(columns_to_drop, axis=1).columns:\n",
    "    for i in range(1, n_days + 1):\n",
    "        df_expanded[col + f' Diff -{i}'] = (df_expanded[col].shift(i - 1) - df_expanded[col].shift(i))\n",
    "\n",
    "# for col in df_expanded.drop(columns_to_drop, axis=1).columns:\n",
    "#     if 'Diff' in col:\n",
    "#         for i in range(1, n_days + 1):\n",
    "#             df_expanded[col + f' Diff -{i}'] = (df_expanded[col].shift(i - 1) - df_expanded[col].shift(i))\n",
    "            \n",
    "df_expanded['CloseOpenDiff'] = df_expanded['Close'] - df_expanded['Open']\n",
    "df_expanded['CloseLowDiff'] = df_expanded['Close'] - df_expanded['Low']\n",
    "df_expanded['CloseHighDiff'] = df_expanded['Close'] - df_expanded['High']\n",
    "df_expanded['VolumeCloseRatioDiff'] = df_expanded['Volume'] / df_expanded['Close']\n",
    "\n",
    "for col in df_expanded.columns:\n",
    "    if 'Diff' in col:\n",
    "        df_expanded[col] = np.where(df_expanded[col] > 0, 1, 0)\n",
    "            \n",
    "df_expanded.dropna(inplace=True)\n",
    "df_expanded.reset_index(drop=True, inplace=True)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test split\n",
    "first_date_totest = '2023-09-08'\n",
    "last_date_totest = '2023-10-07'\n",
    "test = df_expanded[(df_expanded['Date'] >= first_date_totest) & (df_expanded['Date'] <= last_date_totest)]\n",
    "train = df_expanded[df_expanded['Date'] < first_date_totest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
      "Best params : {'class_weight': 'balanced', 'max_depth': 3, 'max_features': 'sqrt', 'n_estimators': 150}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Train and Find best Random Forest Clissifier model\n",
    "\n",
    "y_train = train['Price_Direction'].values\n",
    "columns_to_drop = list(filter(lambda col: not 'Diff' in col, train.columns.values))\n",
    "features = train.drop(columns_to_drop, axis=1)\n",
    "X_train = scaler.fit_transform(features)\n",
    "\n",
    "\n",
    "params = {\n",
    "    'n_estimators': [150, 250, 300, 350, 400],\n",
    "    'max_depth': [2, 3],\n",
    "    'class_weight': [None, 'balanced'],\n",
    "    'max_features': ['auto', 'sqrt']\n",
    "}\n",
    "\n",
    "cv = StratifiedKFold(n_splits=5)\n",
    "rfc = RandomForestClassifier(random_state=42)\n",
    "grid_search = GridSearchCV(rfc, params, refit=True, cv=cv, scoring='f1_macro', verbose=1)\n",
    "grid_search.fit(X_train, y_train)\n",
    "best_params = grid_search.best_params_\n",
    "print(f\"Best params : {best_params}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.56      0.55      1004\n",
      "           1       0.58      0.55      0.56      1102\n",
      "\n",
      "    accuracy                           0.56      2106\n",
      "   macro avg       0.56      0.56      0.56      2106\n",
      "weighted avg       0.56      0.56      0.56      2106\n",
      "\n",
      "ROC AUC score : 0.5558311219730876\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate random forest model\n",
    "rfc_best = RandomForestClassifier(random_state=42, **best_params)\n",
    "y_preds = cross_val_predict(rfc_best, X_train, y_train, cv=cv)\n",
    "print(classification_report(y_train, y_preds))\n",
    "print(f\"ROC AUC score : {roc_auc_score(y_train, y_preds)}\")\n",
    "cm = confusion_matrix(y_train, y_preds)\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.title('Confusion matrix')\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.47      0.58      0.52        12\n",
      "           1       0.67      0.56      0.61        18\n",
      "\n",
      "    accuracy                           0.57        30\n",
      "   macro avg       0.57      0.57      0.56        30\n",
      "weighted avg       0.59      0.57      0.57        30\n",
      "\n",
      "ROC AUC score : 0.5694444444444444\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test on test set\n",
    "y_preds = []\n",
    "for _, row in test.iterrows():\n",
    "    model = RandomForestClassifier(random_state=42, **best_params)\n",
    "    model.fit(X_train, y_train)\n",
    "    y_test = np.array([row['Price_Direction']])\n",
    "    x_test = row[features.columns].values.reshape(1, -1)\n",
    "    x_test = scaler.transform(x_test)\n",
    "    y_pred = model.predict(x_test)\n",
    "    y_preds.append(y_pred[0])\n",
    "    X_train = np.concatenate((X_train, x_test), axis=0)\n",
    "    y_train = np.concatenate((y_train, y_test), axis=0)\n",
    "    \n",
    "y_true = test['Price_Direction'].values\n",
    "print(classification_report(y_true, y_preds))\n",
    "print(f\"ROC AUC score : {roc_auc_score(y_true, y_preds)}\")\n",
    "cm = confusion_matrix(y_true, y_preds)\n",
    "plt.figure(figsize=(7, 5))\n",
    "plt.title('Confusion matrix')\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: -3.2309440302805776\n",
      "p-value: 0.018261949206582806\n",
      "Data is stationary\n"
     ]
    }
   ],
   "source": [
    "### Time Series Model \n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "# ADF Test\n",
    "result = adfuller(train['Close'])\n",
    "print('ADF Statistic:', result[0])\n",
    "print('p-value:', result[1])\n",
    "if result[1] <= 0.05:\n",
    "    print('Data is stationary')\n",
    "else:\n",
    "    print('Data is not stationary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RpIiICNnt9nrnGTRokFatWqU333xTL774ompra3X99dfr4MGDkuScrzl1ZmVlKSQkxDlER0e3dtUAAIAX87q3qBISEjRlyhSNGDFCN954o15//XWFh4frL3/5S4vrzMzMVEVFhXM4cOBAG7YYAAB4G7cGnLCwMPn7+6usrMxlfFlZmWw2W5Pq6Ny5s66++mrt3btXkpzzNadOq9Wq4OBglwEAAJiXWwNOQECA4uLilJub6xxXW1ur3NxcJSQkNKmOmpoaff7554qMjJQk9evXTzabzaVOh8OhrVu3NrlOAABgbp3cvYCMjAzdfffdGjlypEaNGqVly5apqqpK06ZNkyRNmTJFvXr1UlZWliRp0aJFuu666zRw4ECVl5frqaee0ldffaV77rlH0vk3rGbPnq1f//rXuuKKK9SvXz899thjioqK0vjx4929OgAAwAe4PeBMmjRJR44c0fz582W32zVixAjl5OQ4HxIuKSmRn9+3F5K++eYbzZgxQ3a7Xd26dVNcXJw++ugjxcbGOss88sgjqqqqUlpamsrLyzV69Gjl5OTU6RAQAAB0TBbDMAxPN6K9ORwOhYSEqKKiok2fxzl55pxi578nSfrXomQFBTQtP7Z0PgAAOpLmnL+97i0qAACA1iLgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA0yHgAAAA02mXgLNixQrFxMQoMDBQ8fHx2rZtW4Nln3nmGX3/+99Xt27d1K1bNyUlJdUpP3XqVFksFpchJSXF3asBAAB8hNsDzrp165SRkaEFCxbo008/1fDhw5WcnKzDhw/XWz4vL0933nmnPvjgA+Xn5ys6OlpjxozR119/7VIuJSVFpaWlzuHll19296oAAAAf4faAs3TpUs2YMUPTpk1TbGysVq5cqaCgIK1atare8tnZ2brvvvs0YsQIDR48WM8++6xqa2uVm5vrUs5qtcpmszmHbt26uXtVAACAj3BrwDlz5owKCgqUlJT07QL9/JSUlKT8/Pwm1XHy5EmdPXtW3bt3dxmfl5ennj17atCgQZo5c6aOHTvWYB3V1dVyOBwuAwAAMC+3BpyjR4+qpqZGERERLuMjIiJkt9ubVMfcuXMVFRXlEpJSUlL0wgsvKDc3V0uWLNHmzZs1duxY1dTU1FtHVlaWQkJCnEN0dHTLVwoAAHi9Tp5uQGOeeOIJrV27Vnl5eQoMDHSOT01Ndf596NChGjZsmAYMGKC8vDwlJibWqSczM1MZGRnOzw6Hg5ADAICJufUKTlhYmPz9/VVWVuYyvqysTDabrdF5f/vb3+qJJ57Q+++/r2HDhjVatn///goLC9PevXvrnW61WhUcHOwyAAAA83JrwAkICFBcXJzLA8IXHhhOSEhocL4nn3xSixcvVk5OjkaOHHnJ5Rw8eFDHjh1TZGRkm7QbAAD4Nre/RZWRkaFnnnlGa9as0a5duzRz5kxVVVVp2rRpkqQpU6YoMzPTWX7JkiV67LHHtGrVKsXExMhut8tut6uyslKSVFlZqYcfflgff/yx9u/fr9zcXI0bN04DBw5UcnKyu1cHAAD4ALc/gzNp0iQdOXJE8+fPl91u14gRI5STk+N88LikpER+ft/mrD//+c86c+aMfvzjH7vUs2DBAv3qV7+Sv7+/duzYoTVr1qi8vFxRUVEaM2aMFi9eLKvV6u7VAQAAPqBdHjJOT09Xenp6vdPy8vJcPu/fv7/Rurp06aL33nuvjVoGAADMiN+iAgAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApkPAAQAApuPVP7aJSys+WqVXth/QwW9OqXe3LvrJyGj1C+vq6WYBAOBRBBwf9sr2A5r32g5ZLBYZhiGLxaK/bN6nJROGaeJIfi0dANBxcYvKRxUfrdK813ao1pBqag2XP+e+tkP7j1Z5uokAAHgMAcdHvbL9gCwWS73TLBaL1m0/0M4tAgDAexBwfNTBb07JMIx6pxmGoYPfnGrnFgEA4D0IOD6qd7cujV7B6d2tSzu3CAAA70HA8VE/GRnd6BWcSTxkDADowAg4PqpfWFctmTBMfhddxPG3WORnkZZMGKYYXhUHAHRgvCbuwyaOjNaQXsEa+/sPJUnTRsforvi+hBsAQIdHwPFxfXt8G2Yy/vNKBQWY95+UTg0BwPN85Vhs3rMhTIVODQHA83zpWMwzOPB6dGoIAJ7na8diAg68Hp0aAoDn+dqxmIADr0enhgDgeb52LCbgwOvRqSEAeJ6vHYsJOPB6dGoIAJ7na8di3qKC17vQqeHc/3u4TTrfqaEhg04NAS/mideJfeUVZl/ka8diAg58Ap0aAr7FE68T+9IrzL7Kl47F3KKCz/hup4be+B8KgGdeJ/a1V5h9ma8ciwk4AIA25YnXiX3tFWZPKz5apSU5u3X/y59pSc5uFZswAHKLCkCb4NkHXOCJ14l97RVmT+oot/IIOIAbdZSTfkc5YKJpnK8T1xM43PU6sSeW6YsuvpXn3Fb/9+fc13bo2pjuXnvLqbm4RQW4ySvbDyjxd3l6esuX+uuOQ3p6y5dK/F2e1pvsUjnPPuC7PPE6sa+9wuwpHelWHgEHcIOOdNLvSAdMNM2F14n9Ltot/C0W+VnktteJPbFMX9SRbuURcAA36Egn/Y50wETTTRwZrb8+MNr5edroGG2ac5Nbb1l6Ypm+xtd6I24NnsFpZ/n7jtUZd/psjfPvW788rsDO/k2urzXz+hpfWtfCA+WqbeCkX2sYKjxQXu++0BZKK04pr+iIjlRWK/wyq24aFK7IEPcdtOo/VLpOd9e6wrtd/H/2ewPCVFpxWqUVp023TF9yRc/LGj02Xdnz8kv+f23qsThhQI+WN7QNEHAANwi/zCqLpPoOI5b/m+4OeUWH9fT/fulctkXS2zsO6b9v6K8br+zplmXeNChcb+84VO80Q9LNg9yzXJhbewd1T2rNujZ33siQLvrvG/rrL1u+dD5j7Gc5/3/1v2/oL1tIYBuskXcg4ABu4ImTfmnFKT39v+cPWheC1YU//7LlSw2KCHbLwasjHTDRPjwR1D2lNeva0nlvvLKnYnp01bzXP5ckpQyx6T+vspnu/yrP4ABucOGkf/Gtbj+LZLG476SfV3SkwdtFFkkfFB1u82VecOOVPZX1w6HOzylDbFo6cYTpTkZwv4uDeq0hlz//suVL2U10u6k169ra7RQR/O0xaGJctOnCjdROAWfFihWKiYlRYGCg4uPjtW3btkbLr1+/XoMHD1ZgYKCGDh2qd99912W6YRiaP3++IiMj1aVLFyUlJWnPnj3uXAVTKq04pZe3lWj5pj16eVuJSit4GLQttfdJ/0hldb23xKTz3+6OVFa7ZbkXeOKA6Yv7sK+1ub3b68mg3t5as64daTu1lNtvUa1bt04ZGRlauXKl4uPjtWzZMiUnJ6uoqEg9e9Y90H/00Ue68847lZWVpdtvv10vvfSSxo8fr08//VRDhgyRJD355JNavny51qxZo379+umxxx5TcnKy/vWvfykwsOkH1ZNnzqnTmXNttq4nL6rrZAP1Xvxw1gXVF42rrmd6Y1o67//uOaLnP9pf59Lmf13fT6OvCGtWG9pLa7aTp4R26ez8+w+GRcna2b/efaAtdAvq3OhzP92COrtt2VL7//v44j7sa21ubXtbsk+UOU43GtTLHKcb3Y996TjRmnX15HZq6rwNnQdbozl1WoyG3u9sI/Hx8br22mv1xz/+UZJUW1ur6Oho3X///Zo3b16d8pMmTVJVVZXeeecd57jrrrtOI0aM0MqVK2UYhqKiojRnzhw99NBDkqSKigpFRERo9erVSk1NrVNndXW1qqu//fbqcDgUHR2t6NmvyM8a1NarDAAA3KC2+qQOLPuJKioqFBwc3GhZt96iOnPmjAoKCpSUlPTtAv38lJSUpPz8/Hrnyc/PdykvScnJyc7yxcXFstvtLmVCQkIUHx/fYJ1ZWVkKCQlxDtHR9IkAAICZufUW1dGjR1VTU6OIiAiX8REREdq9e3e989jt9nrL2+125/QL4xoq812ZmZnKyMhwfr5wBWfbLxIvmQDb2tYvj7fr8uqzcvM+bdt/vL6fbJHFIo2K6a57bxxQ77x2x2k9uuHzBufN+uFQl2cx6lN9tkb3Zn96vi2Tr5G1Hfqzac0y27O9bbF9yxyntWXPER2rPKMelwXohivCLzlPWyy3NZq7jT21D7dm3ta0+YKW7ostma8t2ttSH+45qlUfFbvcGjMkt9/K88RxojXr6qnt1FTx/bu3eZ0Oh0ORy5pWtkO8Jm61WmW11u13JCigk4IC2ncTeEPndBHBgY0+qxERHNhgO/P3HWt03o/2HdOdo/o0uS3Wzv7tvk1as0x3t7cttm/fHl31sx7N65a+rf9dW6Mp29hT+3Br5m1Nmy+wO759M+atHYeUdFVEk/pLacl8bdHelkqKjdCQXiH6oOiws3+Xmwf1bNc3fdrrONGadfWG7dQYd5xfzzWjTree3cPCwuTv76+ysjKX8WVlZbLZbPXOY7PZGi1/4c+ysjJFRka6lBkxYkQbtt68WtNHi6ff1DE7T21fX/t39dQ+3Jp5W9s30oU+Ty7I2WnX33baL9nnSUvn83QHjraQwHYL1RdcHATXFxxocoBsrdasqye2k69w6zM4AQEBiouLU25urnNcbW2tcnNzlZCQUO88CQkJLuUlaePGjc7y/fr1k81mcynjcDi0devWBuuEq4v7aLnQN0tT+2i50ENvfZraQ+93DyLe/ppse2qL7etLy20pT+3DrZm3NW2+uM+TC5rS50lL52tte31RXtFhPbrhc+fnnJ12zVn/T23+N69b+yq335/JyMjQ3XffrZEjR2rUqFFatmyZqqqqNG3aNEnSlClT1KtXL2VlZUmSfv7zn+vGG2/U7373O912221au3attm/frqefflrS+R8Dmz17tn7961/riiuucL4mHhUVpfHjx7t7dUzjxit7alBEcLMvbXrqW2hreeqbWXN56luzp7+tt4Qn9uHWbqeWtvlCnycN3S76oOhwvd/iWzpfa9vraxoKgpJ7ewGHe7k94EyaNElHjhzR/PnzZbfbNWLECOXk5DgfEi4pKZGf37cXkq6//nq99NJL+uUvf6lHH31UV1xxhd544w1nHziS9Mgjj6iqqkppaWkqLy/X6NGjlZOT06w+cNCyS5sXd8v/3QfbWvotVHLvQcRToaolWrN9fXG5rdXe+3BbbKeWtLmlt8ba4tZjR7gF0togKPnOl6iOpF2esE1PT1d6enq90/Ly8uqMmzhxoiZOnNhgfRaLRYsWLdKiRYvaqolohvb+FtoavvjNzFPfmj35bb29Tw6tWVdPbKeW/nirp3701de0Ngj60peojqRDvEXlTTz98/Ft6YfX9GpW+Ze2lTQ63VDbb58lObvlZ7Gopp53Xf0sFv378IlLrsfFPWfG9+/ebm/eNXf7+upyX9l+QL+46NmH93aWKWenXUsmDNPEke7ts6o169qe28kWEqh3Grg1JkkZ/3mlYsLqvjnX0vk6mi17jmhb8fEGjxMjokMbPDYVH63SMw18iXp6y5dKvbYP29hD+LFNtJve3brIcvGvT17EYrGod7e2/8Z+8JtTaqizbsMwdPAbHnD2pOKjVZr32g7nCUGSagxDtYY097Ud2n+0ynON8yL9wrpqyYRh8rNI/n4Wlz+XTBjW4Am0pfN1ND8ZGd3ocWJSI0H7le0HGj2urdt+oE3aiObjCg7azU9GRusvm/fVO+1SB5GWcoaqeg5e7gpVaDrnyaGBf5912w9obspgD7TM+0wcGa1rY7pr3fYDOvjNKfXu1kWTRkZfMqS0dL6O5EIQnPvaDlksFhmG4fzzUkGQL1Hei4CDdtOag0hLeSJUoek4OTRPTFjXFgW+ls7XkbQ0CPIlynsRcNCu2vvbpCdCFZqOkwO8SUuCIF+ivBcBB+2uvb9Nconee3FygK/jS5T3IuCgQ+ASvXfi5AAz4EuUdyLgAPAoTg4wA75EeR8CDnAJ+499+6ry0o3/1uT4vurHybdNcXIA0NboBwdoxCvbD+j25R86Pz//4X4l/i5P6+nbAgC8GgEHaACd0AGA7yLgAA2gh1IA8F0EHKABdEIHAL6LgAM0wBO/nQUAaBsEHKABrfkBPgCAZxFwgAbwS8wA4LvoBwdoBJ3QAYBvIuAAl0AndADge7hFBQAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATIeAAwAATMetAef48eOaPHmygoODFRoaqunTp6uysrLR8vfff78GDRqkLl26qE+fPnrggQdUUVHhUs5isdQZ1q5d685VAQAAPqSTOyufPHmySktLtXHjRp09e1bTpk1TWlqaXnrppXrLHzp0SIcOHdJvf/tbxcbG6quvvtK9996rQ4cO6dVXX3Up+/zzzyslJcX5OTQ01J2rAgAAfIjFMAzDHRXv2rVLsbGx+uSTTzRy5EhJUk5Ojm699VYdPHhQUVFRTapn/fr1uuuuu1RVVaVOnc7nMYvFog0bNmj8+PEtapvD4VBISIgqKioUHBzcojoAAED7as752223qPLz8xUaGuoMN5KUlJQkPz8/bd26tcn1XFiJC+HmglmzZiksLEyjRo3SqlWr1FhOq66ulsPhcBkAAIB5ue0Wld1uV8+ePV0X1qmTunfvLrvd3qQ6jh49qsWLFystLc1l/KJFi3TLLbcoKChI77//vu677z5VVlbqgQceqLeerKwsLVy4sGUrAgAAfE6zr+DMmzev3od8Lx52797d6oY5HA7ddtttio2N1a9+9SuXaY899pi+973v6eqrr9bcuXP1yCOP6KmnnmqwrszMTFVUVDiHAwcOtLp9AADAezX7Cs6cOXM0derURsv0799fNptNhw8fdhl/7tw5HT9+XDabrdH5T5w4oZSUFF1++eXasGGDOnfu3Gj5+Ph4LV68WNXV1bJarXWmW63WescDAABzanbACQ8PV3h4+CXLJSQkqLy8XAUFBYqLi5Mkbdq0SbW1tYqPj29wPofDoeTkZFmtVr311lsKDAy85LIKCwvVrVs3QgwAAJDkxmdwrrrqKqWkpGjGjBlauXKlzp49q/T0dKWmpjrfoPr666+VmJioF154QaNGjZLD4dCYMWN08uRJvfjiiy4PBIeHh8vf319vv/22ysrKdN111ykwMFAbN27Ub37zGz300EPuWhUAAOBj3NoPTnZ2ttLT05WYmCg/Pz9NmDBBy5cvd04/e/asioqKdPLkSUnSp59+6nzDauDAgS51FRcXKyYmRp07d9aKFSv04IMPyjAMDRw4UEuXLtWMGTPcuSoAAMCHuK0fHG9GPzgAAPger+gHBwAAwFMIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHQIOAAAwHTcGnCOHz+uyZMnKzg4WKGhoZo+fboqKysbneemm26SxWJxGe69916XMiUlJbrtttsUFBSknj176uGHH9a5c+fcuSoAAMCHdHJn5ZMnT1Zpaak2btyos2fPatq0aUpLS9NLL73U6HwzZszQokWLnJ+DgoKcf6+pqdFtt90mm82mjz76SKWlpZoyZYo6d+6s3/zmN25bFwAA4DsshmEY7qh4165dio2N1SeffKKRI0dKknJycnTrrbfq4MGDioqKqne+m266SSNGjNCyZcvqnf63v/1Nt99+uw4dOqSIiAhJ0sqVKzV37lwdOXJEAQEBl2ybw+FQSEiIKioqFBwc3LIVBAAA7ao552+33aLKz89XaGioM9xIUlJSkvz8/LR169ZG583OzlZYWJiGDBmizMxMnTx50qXeoUOHOsONJCUnJ8vhcOiLL76ot77q6mo5HA6XAQAAmJfbblHZ7Xb17NnTdWGdOql79+6y2+0NzvfTn/5Uffv2VVRUlHbs2KG5c+eqqKhIr7/+urPei8ONJOfnhurNysrSwoULW7M6AADAhzQ74MybN09LlixptMyuXbta3KC0tDTn34cOHarIyEglJiZq3759GjBgQIvqzMzMVEZGhvOzw+FQdHR0i9sIAAC8W7MDzpw5czR16tRGy/Tv3182m02HDx92GX/u3DkdP35cNputycuLj4+XJO3du1cDBgyQzWbTtm3bXMqUlZVJUoP1Wq1WWa3WJi8TAAD4tmYHnPDwcIWHh1+yXEJCgsrLy1VQUKC4uDhJ0qZNm1RbW+sMLU1RWFgoSYqMjHTW+/jjj+vw4cPOW2AbN25UcHCwYmNjm7k2AADAjNz2kPFVV12llJQUzZgxQ9u2bdM//vEPpaenKzU11fkG1ddff63Bgwc7r8js27dPixcvVkFBgfbv36+33npLU6ZM0Q033KBhw4ZJksaMGaPY2Fj97Gc/0z//+U+99957+uUvf6lZs2ZxlQYAAEhyc0d/2dnZGjx4sBITE3Xrrbdq9OjRevrpp53Tz549q6KiIudbUgEBAfr73/+uMWPGaPDgwZozZ44mTJigt99+2zmPv7+/3nnnHfn7+yshIUF33XWXpkyZ4tJvDgAA6Njc1g+ON6MfHAAAfI9X9IMDAADgKQQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOgQcAABgOm4NOMePH9fkyZMVHBys0NBQTZ8+XZWVlQ2W379/vywWS73D+vXrneXqm7527Vp3rgoAAPAhndxZ+eTJk1VaWqqNGzfq7NmzmjZtmtLS0vTSSy/VWz46OlqlpaUu455++mk99dRTGjt2rMv4559/XikpKc7PoaGhbd5+AADgm9wWcHbt2qWcnBx98sknGjlypCTpD3/4g2699Vb99re/VVRUVJ15/P39ZbPZXMZt2LBBP/nJT3TZZZe5jA8NDa1TFgAAQHLjLar8/HyFhoY6w40kJSUlyc/PT1u3bm1SHQUFBSosLNT06dPrTJs1a5bCwsI0atQorVq1SoZhNFhPdXW1HA6HywAAAMzLbVdw7Ha7evbs6bqwTp3UvXt32e32JtXx3HPP6aqrrtL111/vMn7RokW65ZZbFBQUpPfff1/33XefKisr9cADD9RbT1ZWlhYuXNiyFQEAAD6n2Vdw5s2b1+CDwBeG3bt3t7php06d0ksvvVTv1ZvHHntM3/ve93T11Vdr7ty5euSRR/TUU081WFdmZqYqKiqcw4EDB1rdPgAA4L2afQVnzpw5mjp1aqNl+vfvL5vNpsOHD7uMP3funI4fP96kZ2deffVVnTx5UlOmTLlk2fj4eC1evFjV1dWyWq11plut1nrHAwAAc2p2wAkPD1d4ePglyyUkJKi8vFwFBQWKi4uTJG3atEm1tbWKj4+/5PzPPfecfvCDHzRpWYWFherWrRshBgAASHLjMzhXXXWVUlJSNGPGDK1cuVJnz55Venq6UlNTnW9Qff3110pMTNQLL7ygUaNGOefdu3evtmzZonfffbdOvW+//bbKysp03XXXKTAwUBs3btRvfvMbPfTQQ+5aFQAA4GPc2g9Odna20tPTlZiYKD8/P02YMEHLly93Tj979qyKiop08uRJl/lWrVql3r17a8yYMXXq7Ny5s1asWKEHH3xQhmFo4MCBWrp0qWbMmOHOVQEAAD7EYjT2frVJORwOhYSEqKKiQsHBwZ5uDgAAaILmnL/5LSoAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6BBwAAGA6bgs4jz/+uK6//noFBQUpNDS0SfMYhqH58+crMjJSXbp0UVJSkvbs2eNS5vjx45o8ebKCg4MVGhqq6dOnq7Ky0g1rAAAAfJXbAs6ZM2c0ceJEzZw5s8nzPPnkk1q+fLlWrlyprVu3qmvXrkpOTtbp06edZSZPnqwvvvhCGzdu1DvvvKMtW7YoLS3NHasAAAB8lMUwDMOdC1i9erVmz56t8vLyRssZhqGoqCjNmTNHDz30kCSpoqJCERERWr16tVJTU7Vr1y7Fxsbqk08+0ciRIyVJOTk5uvXWW3Xw4EFFRUU1qU0Oh0MhISGqqKhQcHBwq9YPAAC0j+acvzu1U5suqbi4WHa7XUlJSc5xISEhio+PV35+vlJTU5Wfn6/Q0FBnuJGkpKQk+fn5aevWrfrhD39Yb93V1dWqrq52fq6oqJB0fkMBAADfcOG83ZRrM14TcOx2uyQpIiLCZXxERIRzmt1uV8+ePV2md+rUSd27d3eWqU9WVpYWLlxYZ3x0dHRrmw0AANrZiRMnFBIS0miZZgWcefPmacmSJY2W2bVrlwYPHtycat0uMzNTGRkZzs+1tbU6fvy4evToIYvF0qbLcjgcio6O1oEDB7j91Qi206WxjZqG7dQ0bKemYTs1jae2k2EYOnHiRJMeSWlWwJkzZ46mTp3aaJn+/fs3p0onm80mSSorK1NkZKRzfFlZmUaMGOEsc/jwYZf5zp07p+PHjzvnr4/VapXVanUZ19Q3u1oqODiY/xxNwHa6NLZR07Cdmobt1DRsp6bxxHa61JWbC5oVcMLDwxUeHt6iBl1Kv379ZLPZlJub6ww0DodDW7dudb6JlZCQoPLychUUFCguLk6StGnTJtXW1io+Pt4t7QIAAL7Hba+Jl5SUqLCwUCUlJaqpqVFhYaEKCwtd+qwZPHiwNmzYIEmyWCyaPXu2fv3rX+utt97S559/rilTpigqKkrjx4+XJF111VVKSUnRjBkztG3bNv3jH/9Qenq6UlNTm/wGFQAAMD+3PWQ8f/58rVmzxvn56quvliR98MEHuummmyRJRUVFzjeaJOmRRx5RVVWV0tLSVF5ertGjRysnJ0eBgYHOMtnZ2UpPT1diYqL8/Pw0YcIELV++3F2r0WxWq1ULFiyoc0sMrthOl8Y2ahq2U9OwnZqG7dQ0vrCd3N4PDgAAQHvjt6gAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHAAAIDpEHDa0IoVKxQTE6PAwEDFx8dr27Ztnm6SV/nVr34li8XiMnjbz3p4wpYtW3THHXcoKipKFotFb7zxhst0wzA0f/58RUZGqkuXLkpKStKePXs801gPutR2mjp1ap39KyUlxTON9aCsrCxde+21uvzyy9WzZ0+NHz9eRUVFLmVOnz6tWbNmqUePHrrssss0YcIElZWVeajF7a8p2+imm26qsz/de++9HmqxZ/z5z3/WsGHDnL0VJyQk6G9/+5tzurfvRwScNrJu3TplZGRowYIF+vTTTzV8+HAlJyfX+WmJju4//uM/VFpa6hw+/PBDTzfJ46qqqjR8+HCtWLGi3ulPPvmkli9frpUrV2rr1q3q2rWrkpOTdfr06XZuqWddajtJUkpKisv+9fLLL7djC73D5s2bNWvWLH388cfauHGjzp49qzFjxqiqqspZ5sEHH9Tbb7+t9evXa/PmzTp06JB+9KMfebDV7asp20iSZsyY4bI/Pfnkkx5qsWf07t1bTzzxhAoKCrR9+3bdcsstGjdunL744gtJPrAfGWgTo0aNMmbNmuX8XFNTY0RFRRlZWVkebJV3WbBggTF8+HBPN8OrSTI2bNjg/FxbW2vYbDbjqaeeco4rLy83rFar8fLLL3ughd7hu9vJMAzj7rvvNsaNG+eR9nizw4cPG5KMzZs3G4Zxfv/p3LmzsX79emeZXbt2GZKM/Px8TzXTo767jQzDMG688Ubj5z//ueca5aW6detmPPvssz6xH3EFpw2cOXNGBQUFSkpKco7z8/NTUlKS8vPzPdgy77Nnzx5FRUWpf//+mjx5skpKSjzdJK9WXFwsu93usm+FhIQoPj6efaseeXl56tmzpwYNGqSZM2fq2LFjnm6Sx13oLb579+6SpIKCAp09e9Zlnxo8eLD69OnTYfep726jC7KzsxUWFqYhQ4YoMzNTJ0+e9ETzvEJNTY3Wrl2rqqoqJSQk+MR+5LafauhIjh49qpqaGkVERLiMj4iI0O7duz3UKu8THx+v1atXa9CgQSotLdXChQv1/e9/Xzt37tTll1/u6eZ5JbvdLkn17lsXpuG8lJQU/ehHP1K/fv20b98+Pfrooxo7dqzy8/Pl7+/v6eZ5RG1trWbPnq3vfe97GjJkiKTz+1RAQIBCQ0NdynbUfaq+bSRJP/3pT9W3b19FRUVpx44dmjt3roqKivT66697sLXt7/PPP1dCQoJOnz6tyy67TBs2bFBsbKwKCwu9fj8i4KDdjB071vn3YcOGKT4+Xn379tUrr7yi6dOne7BlMIPU1FTn34cOHaphw4ZpwIABysvLU2Jiogdb5jmzZs3Szp07edatEQ1to7S0NOffhw4dqsjISCUmJmrfvn0aMGBAezfTYwYNGqTCwkJVVFTo1Vdf1d13363Nmzd7ullNwi2qNhAWFiZ/f/86T4+XlZXJZrN5qFXeLzQ0VFdeeaX27t3r6aZ4rQv7D/tW8/Xv319hYWEddv9KT0/XO++8ow8++EC9e/d2jrfZbDpz5ozKy8tdynfEfaqhbVSf+Ph4Sepw+1NAQIAGDhyouLg4ZWVlafjw4fr973/vE/sRAacNBAQEKC4uTrm5uc5xtbW1ys3NVUJCggdb5t0qKyu1b98+RUZGeropXqtfv36y2Wwu+5bD4dDWrVvZty7h4MGDOnbsWIfbvwzDUHp6ujZs2KBNmzapX79+LtPj4uLUuXNnl32qqKhIJSUlHWafutQ2qk9hYaEkdbj96btqa2tVXV3tG/uRp59yNou1a9caVqvVWL16tfGvf/3LSEtLM0JDQw273e7ppnmNOXPmGHl5eUZxcbHxj3/8w0hKSjLCwsKMw4cPe7ppHnXixAnjs88+Mz777DNDkrF06VLjs88+M7766ivDMAzjiSeeMEJDQ40333zT2LFjhzFu3DijX79+xqlTpzzc8vbV2HY6ceKE8dBDDxn5+flGcXGx8fe//9245pprjCuuuMI4ffq0p5vermbOnGmEhIQYeXl5RmlpqXM4efKks8y9995r9OnTx9i0aZOxfft2IyEhwUhISPBgq9vXpbbR3r17jUWLFhnbt283iouLjTfffNPo37+/ccMNN3i45e1r3rx5xubNm43i4mJjx44dxrx58wyLxWK8//77hmF4/35EwGlDf/jDH4w+ffoYAQEBxqhRo4yPP/7Y003yKpMmTTIiIyONgIAAo1evXsakSZOMvXv3erpZHvfBBx8YkuoMd999t2EY518Vf+yxx4yIiAjDarUaiYmJRlFRkWcb7QGNbaeTJ08aY8aMMcLDw43OnTsbffv2NWbMmNEhv2DUt40kGc8//7yzzKlTp4z77rvP6NatmxEUFGT88Ic/NEpLSz3X6HZ2qW1UUlJi3HDDDUb37t0Nq9VqDBw40Hj44YeNiooKzza8nf3Xf/2X0bdvXyMgIMAIDw83EhMTneHGMLx/P7IYhmG03/UiAAAA9+MZHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDoEHAAAYDr/H9qBg0zlGDpmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACF and PACF\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_acf(train['Close'], lags=30, title='ACF')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plot_pacf(train['Close'], lags=30, title='PACF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb Cell 36\u001b[0m line \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X50sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m first_date_totest \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m2023-09-08\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X50sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m last_date_totest \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m2023-10-07\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X50sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m test \u001b[39m=\u001b[39m df[(df[\u001b[39m'\u001b[39m\u001b[39mDate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m first_date_totest) \u001b[39m&\u001b[39m (df[\u001b[39m'\u001b[39m\u001b[39mDate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m last_date_totest)]\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X50sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m train \u001b[39m=\u001b[39m df[df[\u001b[39m'\u001b[39m\u001b[39mDate\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m<\u001b[39m first_date_totest]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "# Train Test split\n",
    "first_date_totest = '2023-09-08'\n",
    "last_date_totest = '2023-10-07'\n",
    "test = df[(df['Date'] >= first_date_totest) & (df['Date'] <= last_date_totest)]\n",
    "train = df[df['Date'] < first_date_totest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['Price_Direction'].values\n",
    "columns_to_drop = df.drop(columns=['Signal_Line', 'Price_Direction', 'Adj Close'])\n",
    "train.set_index('Date', inplace=True)\n",
    "# train = train[['Price_Direction']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb Cell 38\u001b[0m line \u001b[0;36m5\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X52sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmetrics\u001b[39;00m \u001b[39mimport\u001b[39;00m accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X52sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m p, d, q \u001b[39m=\u001b[39m \u001b[39m2\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m1\u001b[39m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X52sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m model \u001b[39m=\u001b[39m ARIMA(train[\u001b[39m'\u001b[39m\u001b[39mClose\u001b[39m\u001b[39m'\u001b[39m], order\u001b[39m=\u001b[39m(p, d, q))\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X52sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m results \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39mfit()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/revolver/Coding/Quera/proj2/crypto-market-analysis/Q3.ipynb#X52sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m results\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train' is not defined"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc\n",
    "\n",
    "p, d, q = 2, 1, 1\n",
    "model = ARIMA(train['Close'], order=(p, d, q))\n",
    "results = model.fit()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2106    143.122784\n",
       "dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Threshold the forecasted probability to predict 1 or 0\n",
    "forecast_steps = 1\n",
    "forecast = results.forecast(steps=forecast_steps)\n",
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(428,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Threshold the forecasted probability to predict 1 or 0\n",
    "threshold = 0.5  # You can adjust this threshold as needed\n",
    "predicted_class = 1 if forecast[0] > threshold else 0\n",
    "print(\"Predicted class for tomorrow:\", predicted_class)"
   ]
  }
 ],
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