tuprstahmin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "from isyatirimhisse import fetch_data, fetch_financials, visualize_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "tuprs=yf.download(\"TUPRS.IS\",start=\"2018-01-01\",end=\"2023-10-01\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol='TUPRS'\n",
    "start_year='2016'\n",
    "end_year='2023'\n",
    "save_to_excel=True\n",
    "language='tr'\n",
    "\n",
    "veri = fetch_financials(\n",
    "    symbol=symbol,\n",
    "    start_year=start_year,\n",
    "    end_year=end_year,\n",
    "    save_to_excel=save_to_excel,\n",
    "    language=language\n",
    ")\n",
    "bilanco=pd.read_excel(\"finansallar_TUPRS_20231001.xlsx\")\n",
    "bilanco=bilanco.T\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "bilanco.columns=bilanco.iloc[1]\n",
    "bilanco=bilanco.drop([bilanco.index[0],bilanco.index[1]])\n",
    "bilanco[\"Çeyreklik Kar\"]=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016/3               0\n",
       "2016/6       335011000\n",
       "2016/9       585738000\n",
       "2016/12      808927000\n",
       "2017/3       875759000\n",
       "2017/6      1463521000\n",
       "2017/9       999818000\n",
       "2017/12      501458000\n",
       "2018/3       386787000\n",
       "2018/6      1035290000\n",
       "2018/9       550687000\n",
       "2018/12     1788681000\n",
       "2019/3      -362295000\n",
       "2019/6       885904000\n",
       "2019/9      -140172000\n",
       "2019/12      201893000\n",
       "2020/3     -2249085000\n",
       "2020/6      -162291000\n",
       "2020/9      -394966000\n",
       "2020/12      383417000\n",
       "2021/3      -652256000\n",
       "2021/6      1722416000\n",
       "2021/9      1066251000\n",
       "2021/12     1476813000\n",
       "2022/3      1145064000\n",
       "2022/6     10853901000\n",
       "2022/9     11618668000\n",
       "2022/12    17642934000\n",
       "2023/3      6792657000\n",
       "2023/6      7331480000\n",
       "Name: Çeyreklik Kar, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(1,30):\n",
    "   if i%4==0:\n",
    "      bilanco[\"Çeyreklik Kar\"].iloc[i]=bilanco[\"DÖNEM KARI (ZARARI)\"].iloc[i]\n",
    "   else:\n",
    "      bilanco[\"Çeyreklik Kar\"].iloc[i]=bilanco[\"DÖNEM KARI (ZARARI)\"].iloc[i]-bilanco[\"DÖNEM KARI (ZARARI)\"].iloc[i-1]\n",
    "bilanco[\"Çeyreklik Kar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018/3     3.351584e+09\n",
       "2018/6     2.923353e+09\n",
       "2018/9     2.474222e+09\n",
       "2018/12    3.761445e+09\n",
       "2019/3     3.012363e+09\n",
       "2019/6     2.862977e+09\n",
       "2019/9     2.172118e+09\n",
       "2019/12    5.853300e+08\n",
       "2020/3    -1.301460e+09\n",
       "2020/6    -2.349655e+09\n",
       "2020/9    -2.604449e+09\n",
       "2020/12   -2.422925e+09\n",
       "2021/3    -8.260960e+08\n",
       "2021/6     1.058611e+09\n",
       "2021/9     2.519828e+09\n",
       "2021/12    3.613224e+09\n",
       "2022/3     5.410544e+09\n",
       "2022/6     1.454203e+10\n",
       "2022/9     2.509445e+10\n",
       "2022/12    4.126057e+10\n",
       "2023/3     4.690816e+10\n",
       "2023/6     4.338574e+10\n",
       "Name: Yıllıklandırılmış Kar, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bilanco[\"Yıllıklandırılmış Kar\"]=bilanco[\"Çeyreklik Kar\"].rolling(4).sum()\n",
    "bilanco=bilanco.dropna()\n",
    "bilanco[\"Yıllıklandırılmış Kar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Yıllık Kar'}>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDkAAAHfCAYAAABaqwMsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABo1ElEQVR4nO3deXjU1d3+8Xv27AkQCAQIOyIoOwooS0FFFi2CS5WKv1ofW6Faxdq69bFaK2pV2grSal2futSqVCgIWhURQZBdRfYtEAghIQlZZp/fH1kkzCRkksnMZPJ+XRcX8XyXnMQcMt97zvkcQ2FhoU8AAAAAAADNnDHSHQAAAAAAAAgFQg4AAAAAABATCDkAAAAAAEBMIOQAAAAAAAAxgZADAAAAAADEBEIOAAAAAAAQEwg5AAAAAABATCDkAAAAAAAAMYGQAwAAAAAAxARCDgAAoNtuu01paWk6//zzAx4///zzlZaWpttuuy3oe8+dO1dpaWlKS0trZC8BAADqRsgBAECU8vl8mjhxotLS0tSmTRtt3LixXtdt3LhRbdq0UVpamiZOnCifz9fEPY28zz//vDpImTt3bp3nFhYWaty4cdXn33jjjXI6nWHqKQAAaEqEHAAARCmDwaD58+crPj5eHo9Hv/jFL876MO50OnX77bfL4/EoPj5e8+fPl8FgCFOPo19+fr6uuOIKbdq0SZJ09dVX6+WXX5bVao1wzwAAQCgQcgAAEMV69Oih+++/X5L03Xff6amnnqrz/Kefflrbt2+XJN1///3q0aNHk/exucjNzdWUKVP09ddfS5JmzJih559/XmazOcI9AwAAoULIAQBAlJs1a5aGDBkiSZo3b56++eabgOd99913mjdvniRpyJAhmjVrVtj6GO1ycnI0efJkfffdd5Kkm2++WfPnz5fRyEshAABiCb/ZAQCIciaTSQsWLJDVapXL5apejnI6r9er22+/XU6nU1arVQsWLJDJZIpQj6PLoUOHNGnSJO3Zs0dSRZHVZ555hmU8AADEIEIOAACagT59+uiee+6RJG3evFnz58+vcfy5557Thg0bJEn33HOP+vTpU+P466+/Xl1o8+DBgyHt28GDB6vv/frrrzfoHrt27dJ5552ntLQ0ZWVlac2aNSHp2/79+zVp0iQdOHBAkjRnzpyzFib96quv9Oijj2ry5Mnq3bu32rZtq86dO+vCCy/UnDlztGPHjjqvP3OnmtzcXD3yyCMaMWKEsrKylJaWpv/85z8h+foAAEBNLEIFAKCZuOuuu7R48WJ9/fXXmjt3riZPnqyePXtq//79euyxxyRVbPV61113Rbinwdm0aZOuvvpqFRQUKCMjQ++8806tW9kGY/fu3bryyit19OhRSdJ9992n3/zmN3Ve8/rrr2v27Nl+7S6XSzt37tTOnTv16quv6oknntAtt9xy1j5s2LBBP/rRj3TixImGfREAACAohBwAADQTZrNZ8+fP1/jx42W323X77bdr6dKl+uUvf6mysrLq482pkObKlSv14x//WCUlJeratasWLVqkbt26Nfq+27dv19SpU3X8+HFJ0iOPPKI77rjjrNd5PB6lpaVp0qRJGjlypHr06KGEhAQdO3ZMW7du1d/+9jfl5+frnnvuUa9evTRmzJha71VaWqqZM2eqvLxcc+bM0Q9+8AMlJSVp586dysrKavTXCAAA/DWfV0EAAEADBgzQnXfeqaeeekpr167VtGnTtGrVKknSnXfeqQEDBkS4h/X3/vvv63/+53/kdDrVt29fvffee2rfvn2j7/vNN9/o73//u/Lz82UwGPTEE0/o1ltvrde1l1xyia6++molJCTUaB8wYIAmTJign/3sZ5o0aZK+/fZbPf7443WGHAUFBUpISNCyZcs0cODA6vZBgwY16OsCAABnR00OAACamV//+tfVNTdWrlwpqaJmx69//esI9io4r7zyin7yk5/I6XRq+PDhWrZsWUgCDklaunSp8vPzJUmPP/54vQMOScrMzPQLOE6XmppavaXv2rVrVVBQUOf97rjjjhoBBwAAaFqEHAAANDNWq7XG9qdGo1Hz58+X1WqNcM/q5+mnn9add94pr9eryy67TIsWLVJaWlrI7n/6rinLly+Xw+Fo8L1KS0t18OBBfffdd9q+fbu2b98ui8VSffzrr7+u8/rrrruuwZ8bAAAELyIhxz//+U/deeedGjt2rNq1a9eoauxn2rZtmx555BFNmzZNPXr0UFpamiZPnnzW695++22NGzdOmZmZ6tKli6677jpt2bIlJH0CACDUhg4dqo4dO0qSOnbsqKFDh0a4R/Vz//336/e//70k6dprr9Ubb7yh+Pj4kH6OW265Reeee64k6dNPP9XMmTPlcrnqfX1+fr4eeeQRDR06VJ06ddKAAQM0YsQIjRw5UiNHjtS1115bfW5dMzmSkpJCUl8EAADUX0RCjkcffVSvvPKKsrOzlZGREdJ7L126VM8884xWr15d73s/9dRTuvXWW5WXl6ef/OQnmjp1qtasWaMJEyboyy+/DGn/AABoyZ577jlJUu/evbVw4cImKZLaunVr/fvf/1bPnj0lSStWrNDNN98st9t91mu3bNmiYcOG6ZlnntGePXvk8/nqPL+8vLzWY6mpqcF1HAAANFpEQo5nn31W27Zt0969e3XzzTeH9N5Tp07VypUrdeTIES1atOis5+/du1ePP/64evbsqdWrV+sPf/iD/vznP2vp0qWSpF/+8pfyer0h7SMAAC3VD3/4Q0nSrl279Ktf/eqsIUJDZWRkaPHixerataskacmSJfr5z39e5+90p9Op//f//p8KCgpksVg0e/ZsLV26VDt37lRubq4KCwtVWFhYY6ZnXf2vWk4EAADCJyK/fceOHRvU1ml5eXm67777NGjQILVr107du3fXjTfeqO3bt/ude+6552rgwIE11svW5fXXX5fb7dbdd99d4x2X/v37a/r06dq5c6fWrl1b774CAIDavfjii7riiiskSS+//LLuvvvuJgs6MjMztXjxYnXq1EmS9M4772j27Nm1fr5Vq1bpwIEDkirqhvzhD3/QRRddpIyMDNlsturzTp482ST9BQAAjRf1bzHs379fY8eO1cKFC9WtWzfdeuutuvTSS/Xxxx/r0ksv1YYNGxp1/9WrV0uSxo0b53ds/PjxkqQvvviiUZ8DAABUMJvNevnll6uDjpdeeqlJZ3RkZWVpyZIl6tChgyTpzTff1F133RXw83333XfVH1911VW13nPz5s2h7ygAAAiJqA85fv7zn+vYsWN699139d577+nRRx/V3/72N61atUpGo1F33HFHo+6/d+9eJSUlBazf0aNHj+pzAABAaJjNZr300kuaMmWKpIrZHffcc0+Tfb5u3brp/fffV9u2bSVVbF/7m9/8xu88j8dT/XFZWVnAe3m9Xr366qtN01EAANBoUR1ybN26VevWrdP1119fPauiSs+ePTVz5szq7dwaqri4WCkpKQGPJScnV58DAABCx2Kx6OWXX67eAe3vf/97kwYdvXv31vvvv6/WrVtLkp5//nn97//+b41zunfvXv3xG2+8EfA+Dz/8sLZu3dpk/QQAAI0T+pLmIVS1FCUvL09z5871O7579+7qv/v27RvWvgEAgMaxWCx65ZVXdNNNN2nZsmV64YUXZDAY9OSTTzbJ5+vbt68WLVqkK6+8UkVFRfrLX/4iq9WqBx98UFLFMtW2bdsqLy9Pjz76qA4dOqQpU6aoTZs22rdvn1599VV99tlnGj58OLuvAQAQpaI65Kgq7LVixQqtWLGi1vNKS0sb/DlSUlJqnalx6tSp6nMAAEDoWSwWvfrqq5o5c6Y++OADPf/88zIajXr88ceb5PMNGDBA7777rq666iqdOnVKTz31lOLj43X33XcrMTFRf/3rXzVjxgzZ7Xa9/PLLevnll2tcf/HFF+uPf/yjRowY0ST9AwAAjRPVy1Wqlos8+eST1du2Bfpzww03NPhz9OjRQyUlJcrNzfU7VlWLo6o2BwAACL2qoGPixImSpL/+9a+67777muzzDR06VG+//bYSExMlSb///e/17LPPSqqYzfHpp5/q2muvVYcOHWSxWJSenq6LLrpIf/7zn7V48WIlJCQ0Wd8AAEDjGAoLC5umnHk9zZs3Tw8//LAWLFigGTNm1Di2ceNGjR8/Xtdcc41eeOGFoO+dm5urc845RxdddJGWLl0a8JyHH35Y8+bN08KFC3X99dfXODZr1iy98cYbWrp0qS666KKgPz8AAAAAAAifqJ7JMWTIEA0dOlTvvPOO3nvvPb/jXq+3egvYhpoxY4bMZrOefvppFRUVVbdv27ZN7777rs455xympAIAAAAA0AxEZCbHa6+9prVr10qStm/frq1bt2r48OHq1q2bJGnEiBGaOXOmJOnAgQO64oorlJ2drWHDhmnAgAGKi4vT4cOH9dVXX+nEiRM1lprs2rVL8+bNkyTZ7XYtWrRI7dq1q7E7y8KFC2v056mnntKjjz6qzp0768orr1RJSYnee+89OZ1Ovf/++xo+fHiTfj8AAAAAAEDjRSTkuO222/Tmm2/Wevz666+vEUQUFhZq/vz5WrZsmfbv3y+TyaSMjAwNHjxYV155pa644orqcz///PMa/x1IYWGhX9vbb7+thQsXaseOHbJYLBo+fLjuv/9+DRw4MOivDwAAAAAAhF/Ea3IAAAAAAACEQlTX5AAAAAAAAKgvQg4AAAAAABATCDkAAAAAAEBMIOQAAAAAAAAxgZADAAAAAADEBEKOerLb7dq3b5/sdnukuwK0WIxDIPIYh0DkMQ6ByGMcRi9CjiB4PJ5IdwFo8RiHQOQxDoHIYxwCkcc4jE6EHAAAAAAAICYQcgAAAAAAgJhAyAEAAAAAAGICIQcAAAAAAIgJhBwAAAAAACAmEHIAAAAAAICYQMgBAAAAAABiAiEHAAAAAACICYQcAAAAAAAgJhByAAAAAACAmEDIAQAAAAAAYoI50h0AAAAAAKC5KHN7tbPQLaMn0j1BIIQcAAAAAADUw9KD5bpzTaHy7F7ZjPG6qaBU9w+xKs3GIolowf8JAAAAAADO4rVdpbrx0wLl2b2SJIfXoOd32jX0vVy9urNUHq8vwj2ERMgBAAAAAECdnv36lO74olCBcowTdq9+uaZQY5fk6YtjjvB3DjUQcgAAAAAAEIDP59MjG4v02w3FZz336wKXJn9wQj/5tEDZJe4w9A6BEHIAAAAAAHAGj9enOWsL9cy2kqCuW3SgXMPey9XczcUqc3ubqHeoDSEHAAAAAACncXp8+p9VJ/XyzrIGXW/3SE9sOaUL3juu9/aVyeejXke4EHIAAAAAAFCpzO3VDR/n67395bWe84chCfpTX7t6JNf9SH241KObPzupSR+c0NZ8Z6i7igAIOQAAAAAAkFTo8OqqFfn675HABURNBun50a30097xuqi1V59OTNOjw1KUYjHUed+1uU6NXZynX35xUnnlnqboOioRcgAAAAAAWrzcMo8mf5CndccDz7iIM0mvj2+ta3skVLdZTQb94rxkbZyeoZm9E1RX1OGT9OquMg15L1cLvi2R08MSlqZAyAEAAAAAaNEOnHLr8mV5+vZk4F1RUiwGvXtZui7vHB/weNt4k/5yUSt9ekVbjciw1vm5ip0+PbC+SBe9f1z/PWxvdN9REyEHAAAAAKDF+u6kSxOX5Wn/qcDLSNLjjFp8ebouam87670Gplu1bGK6XhzTSh0TTHWeu7vIras/ytd1H53QniJXg/oOf4QcAAAAAIAWaWOeU5M+yNPRssBbvXZKNOmDSekamF737IzTGQwGTe+eoK+mt9OvByYrru6sQysOOzTi38f14PoiFTnZcraxCDkAAAAAAC3OZzl2Xbn8hE46AtfG6JVq1vJJ6eqVamnQ/RPMRt0/KEXrp2Xoqq6Bl7lUcXml+d+WaOi7uXptV6m8bDnbYIQcAAAAAIAWZcnBcl3zUb5K3YHDhAFtLPpgUro6JZkb/bmyksx6+Qet9Z+J6Tqvdd2BSZ7dqzu+KNS4JXn6MjfwDi+oGyEHAAAAAKDF+MfuUt30aYFqWxlyUXurllyervSzrTMJ0sXtbfrsiraaNyJNrW11P4pvyXfp8mUndMtnBTpSypazwSDkAAAAAAC0CPO/OaVfrC6Ut5bVIJd3jtM7l6Yrxdo0j8omo0E/6ZOoTdMzdFvfRJnr2nNW0jv7yjXsvVw9uaVY5bXMOkFNhBwAAAAAgJjm8/n06MZiPfhVca3nXNsjXv83rrXiz5Y8hECazai5F6bpi6ntNC6z7l1bytw+Pbb5lC5YlKv3D5TLR72OOhFyAAAAAABiltfn06++LNJT207Ves6t5ybqr6NayWJs+oDjdOekWfTuZW301iWt1T257uUx2SUe3fRpga5YfkJfF7DlbG0IOQAAAAAAMcnl9enWVSf14o7SWs+5d2CynrgwVUZDeAOOKgaDQZd3jtfaqzL0yNAUJVvq7sfqY06NWXxcc9YUKt9OvY4zEXIAAAAAAGJOmdurGR/n65195bWe8/iFqbp3UIoMEQo4TmczGXTH+cnaMC1DM3ol1Hmu1ye9tLNUg9/N1cJvS+SqrchIC0TIAQAAAACIKYUOr6Z/mK8PDwfehtVkkP46qpV+3jcpzD07u4wEkxZc3EqfTGmrC9pa6zy3yOnTfeuLdPG/j+uTI/Yw9TC6EXIAAAAAAGLG8XKPrlh+QmtznQGP20zS/41rrR/1rHu2RKQNbmvVisnpen50K3VIqPvRfWeRW9M+zNf1/83XvmJ3mHoYnQg5AAAAAAAx4VCJW5cvzau1MGeyxaB3Lk3XpKz4MPesYQwGg67tkaCvpmXoV/2TZau7Nqk+yLbrwkW5euirIp1yecPTyShDyAEAAAAAaPZ2FLp0+dI87TsVuBhnG5tRSy5P16gOdW/ZGo2SLEY9OCRF667K0JVd4uo81+WV/vxNiYa+m6vXd5fK28K2nCXkAAAAAAA0a5vynJq07IRyygLPXuiYYNIHk9I1ML3uGhfRrmuyWa+Na6P3J6Srb5q5znNzy72avbpQl/wnT18dD7x0JxYRcgAAAAAAmq1VRx26cvkJFTgCBxw9U8xaPjldvdMsYe5Z0xmTadOqH7bTU8NT1cpW984wm064dOnSPN26qkA5pbG/5SwhBwAAAACgWVp6sFzXfHRCJe7ASzL6t7bog0np6pxU96yH5shsNOiWc5O0aXp73Xpuokxn2QX37b3lGvX+cZW5Y7tWByEHAAAAAKDZeWN3qWZ+WiBHLZMTRmRYtWRiutrGn6VaZzPXymbUk8PTtPqH7TQ2s+56I7ecm6gEc2zHALH91QEAAAAAYs7Cb0s0a3WhPLXU1JzQyab3LktXqrXlPPKe28qiRZe10evjWqtrsn+w0ynRpF+enxSBnoVXy/k/DgAAAABo1nw+n/6wqVj3rS+q9ZxrusfrH+PbKN58lvUbMchgMGhyl3h9OTVDDw1JUeJp34NHhqbE/CwOSYq9hUkAAAAAgJjj9fn0m3VFeuG70lrP+Z8+iXpieKqMhpYXcJwuzmzQXf2T9aOeCXp4Q5EOlXh0Vbf4SHcrLAg5AAAAAABRzeX1afbnJ/X2vvJaz7lnQLLuH5QsQwsPOE7XIcGkv45uLYfH12K+L4QcAAAAAICoVe726f99mq8Vhx21nvPYBama1S/26000lO1sW6/EEEIOAAAAAEBUKnJ6df1/87Um1xnwuMkgPXtRmm7olRjmniFaEXIAAAAAAKJOXrlH0z/M17YCV8DjVqP00tjWmtKlZdSaQP0QcgAAAAAAokp2iVtXrcjXnmJ3wONJZoNeH99GYzJtYe4Zoh0hBwAAAAAgauwqdOmqFfk6UuYJeLy1zah3Lm2jwW2tYe4ZmgNCDgAAAABAVNhywqnpH+Yr3+ENeDwzwaj3JqSrT5olzD1Dc0HIAQAAAACIuM+POnTDx/k65fIFPN492aR/X56urCQeY1E7fjoAAAAAABG17FC5frKyQI7AK1R0fmuL3r2sjdrFm8LbMTQ7hBwAAAAAgIh5a0+ZZq8+KU/gCRwakWHVm+PbKM1mDG/H0CzxUwIAAAAAiIi/bi/Rzz+vPeC4tKNN715GwIH64ycFAAAAABB28785pXvXFdV6fHq3eL0+vo0SzDy2ov74aQEAAAAAhNXOQpd+t6G41uM/7ZOo50e3ktVkCGOvEAuoyQEAAAAACKtXdpbKXcsSlV/1T9YDg5NlMBBwIHiEHAAAAACAsHF6fHp7b3nAY48OS9EvzksOc48QS1iuAgAAAAAImw8P25Xv8Pq1/6hHPAEHGo2QAwAAAAAQNm/sKQvY/uPeiWHuCWJRSEKOP/3pT0pLS1NaWpq++uqrUNwSAAAAABBj8so9+jDb7tfeJcmkkRnWCPQIsabRIcf27ds1d+5cJSaSugEAAAAAavf2vvKABUev75kgI4VGEQKNCjlcLpduu+02nX/++Zo8eXKo+gQAAAAAiDE+n0+v7y4NeOz6nglh7g1iVaNCjqeeeko7duzQ/PnzZTKZQtUnAAAAAECM2Vbg0vaTbr/2Ue2t6pLMxp8IjQaHHFu2bNHTTz+t3/zmN+rTp08o+wQAAAAAiDFv7A5ccPSGXpQ+QOg0KC5zOBzVy1R++ctfBn293e5faCbaOZ3OGn8DCD/GIRB5jEMg8hiHaI6cHp/+tdc/5Eg0S5e1NzS7Z0TGYfjExcUFdX6DQo7HHntMe/fu1cqVKxu0TCUnJ0cej6chnzricnNzI90FoMVjHAKRxzgEIo9xiObkkxMmFThtfu3j27hVcPSwCiLQp1BgHDYtk8mk7t27B3VN0CHH+vXr9eyzz+ree+9V3759g71ckpSZmdmg6yLJ6XQqNzdXGRkZslrZ2giIBMYhEHmMQyDyGIdojj7eXyzJ5dd+83mt1bmdJfwdaiTGYfQKKuRwu9267bbb1K9fP911110N/qTBTjeJJlartVn3H4gFjEMg8hiHQOQxDtFcHC/36OMc/4Cja7JJYzonydCMt45lHEafoEKOkpIS7d27V5LUtm3bgOdceumlkqR//OMfmjJlSiO7BwAAAABozv65t0wen3/7DT0TmnXAgegUVMhhs9l04403Bjy2Zs0a7d27VxMnTlR6erqysrJC0kEAAAAAQPPk8/n0ZoBdVQySru+ZEP4OIeYFFXLEx8fr2WefDXjstttu0969ezVnzhwNGzYsJJ0DAAAAADRfW/Nd2l7o9msf3cGmzkkN2gcDqJMx0h0AAAAAAMSm1/f4z+KQpBt6MYsDTYOQAwAAAAAQcg6PT+/s8w85ki0GXdGFYp1oGiELORYuXKjCwkKWqgAAAAAAtDzbrpMO/4qjV3WLV4KZ99vRNPjJAgAAAACE3Bu7SwO230DBUTQhQg4AAAAAQEgdK/Pov0ccfu3dk026sJ01Aj1CS0HIAQAAAAAIqbf3lsnjv1JFN/RKlMFgCH+H0GIQcgAAAAAAQsbn8+mNALuqGCT9qEd8+DuEFoWQAwAAAAAQMptPuLSj0O3XPjbTpk5J5gj0CC0JIQcAAAAAIGQCzeKQKDiK8CDkAAAAAACEhN3t07/2+YccKRaDJneJi0CP0NIQcgAAAAAAQuKD7HIVOf0rjk7rFq8EM4+faHr8lAEAAAAAQuKN3bUsVenFUhWEByEHAAAAAKDRjpZ59HGOw6+9Z4pZw9paI9AjtESEHAAAAACARvvnnjJ5/Veq6IZeCTIYDOHvEFokQg4AAAAAQKP4fL6Au6oYDdKPerBUBeFDyAEAAAAAaJSNJ1zaVeT2a/9Bpk2ZiaYI9AgtFSEHAAAAAKBRai042pNZHAgvQg4AAAAAQIOVu316Z79/yJFiNWhSVnwEeoSWjJADAAAAANBgyw6Vq9jpX3H06m4JijdTcBThRcgBAAAAAGiwQAVHpYpdVYBwI+QAAAAAADRITqlHn+Y4/Np7p5o1JN0SgR6hpSPkAAAAAAA0yFt7y+T1X6miG3omyGBgqQrCj5ADAAAAABA0n88XcFcVo0G6jl1VECGEHAAAAACAoH2V59SeYrdf+/hMmzokmCLQI4CQAwAAAADQAIFmcUgUHEVkEXIAAAAAAIJS5vbqvf3lfu2pVoMmdo6PQI+ACoQcAAAAAICgLD1oV7HLv+Lo1d0TFGem4Cgih5ADAAAAABCUN/YEXqoyg4KjiDBCDgAAAABAvR0ucWtljsOvvU+aWYPSLRHoEfA9Qg4AAAAAQL29tbdc/gtVpBt6JshgYKkKIouQAwAAAABQLz6fT2/sLvVrNxmka3uwVAWRR8gBAAAAAKiXdced2nfK49d+SUeb2ieYItAjoCZCDgAAAABAvdRWcPSGXolh7gkQGCEHAAAAAOCsSl1eLdpf7teeZjXo8s5xEegR4I+QAwAAAABwVv85ZNcpl3/J0Wu6J8hmouAoogMhBwAAAADgrN7YHXipyoxeFBxF9CDkAAAAAADU6VCJW6uOOvza+6aZNaCNJQI9AgIj5AAAAAAA1OmtPWXyX6giXd8rQQYDS1UQPQg5AAAAAAC18vl8AXdVMRmka7uzVAXRhZADAAAAAFCrtblOHTjl8Wu/tFOcMhJMEegRUDtCDgAAAABArQLN4pCkG3oyiwPRh5ADAAAAABBQqcurf+8v92tvbTPq8s5xEegRUDdCDgAAAABAQIsP2lXi9i85enX3eFlNFBxF9CHkAAAAAAAE9Mbu0oDtM3qxVAXRiZADAAAAAODn4Cm3Pj/m9Gvv18qs/q0tEegRcHaEHAAAAAAAP2/WVnC0V6IMBpaqIDoRcgAAAAAAavD6fAFDDrNBurZ7fAR6BNQPIQcAAAAAoIY1uU4dLPH4tV/WOU5t400R6BFQP4QcAAAAAIAa3thdy1KVnhQcRXQj5AAAAAAAVCtxefX+gXK/9jY2oy7rFBeBHgH1R8gBAAAAAKj2/oFylbp9fu3X9IiX1UTBUUQ3Qg4AAAAAQLU3atlVZUavxDD3BAgeIQcAAAAAQJJ04JRbXxxz+rWf39qi81tbItAjIDiEHAAAAAAASbXP4qDgKJoLQg4AAAAAgLw+n94MEHKYDRX1OIDmgJADAAAAAKDVx5zKLvH4tV/eOU7pcaYI9AgIHiEHAAAAAEBv7C4N2H5DL5aqoPkg5AAAAACAFu6Uy6vFB+1+7elxRl3aKS4CPQIahpADAAAAAFq4f+8vV5nb59d+bY94WYyGCPQIaBhCDgAAAABo4WrfVSUxzD0BGoeQAwAAAABasH3Fbq3Ndfq1D2hj0XmtLRHoEdBwhBwAAAAA0ILVPouDgqNofszBXmC32/XII49o8+bN2r9/v06ePKnU1FR169ZNN954o6677jpZLKR9AAAAABDtvD6f3goQcliM0tXd4yPQI6Bxgp7JUVpaqpdeekkGg0GXXXaZZs+erSlTpignJ0e/+MUvdN1118nr9TZFXwEAAAAAIfT5UYcOl3r82id2jlObOFMEegQ0TtAzOVq1aqVDhw7JarXWaHe73Zo6dao++eQTffTRR5owYULIOgkAAAAACL3Xa1uq0oulKmiegp7JYTQa/QIOSTKbzZoyZYokad++fY3vGQAAAACgyRQ5vVpywO7X3i7eqPEd4yLQI6DxQlZ41Ov16uOPP5Yk9e3bN1S3BQAAAAA0gfcPlKvc4/Nrv7Z7gixGQwR6BDRe0MtVqjidTj399NPy+Xw6efKkPvvsM+3atUszZszQmDFj6rzWbvdPC6Od0+ms8TeA8GMcApHHOAQij3GIUPnHzpKA7dOzTM3ymS2cGIfhExcX3KwiQ2FhoX90Vw8lJSXq1KnT9zcyGPSLX/xCDz30kMzmurOTffv2yePxL24DAAAAAGh6h8oNmr7Rf/eUc5M8em2gIwI9AvyZTCZ17949qGsaPJMjKSlJhYWF8nq9Onr0qJYvX65HHnlEX331ld5++22lpKTUem1mZmZDP23EOJ1O5ebmKiMjI2BNEgBNj3EIRB7jEIg8xiFC4R9byySV+7XfeE6KOnemHsfZMA6jV4NDjipGo1EdO3bUT3/6U7Vp00b/7//9Pz399NN6+OGHa70m2Okm0cRqtTbr/gOxgHEIRB7jEIg8xiEayuP16Z0DhX7tVqN0/TkpirOFrHRjzGMcRp+Q/vT+4Ac/kCStXr06lLcFAAAAAITIqqMOHSnzLx8wKSterQg40MyF9Cf42LFjkiSLxRLK2wIAAAAAQuSNPWUB22/omRDmngChF3TIsWPHDpWV+Q+KsrIyPfDAA5KkSy+9tPE9AwAAAACEVKHDqyUH/WtxZMQbNa6jLQI9AkIr6JocixYt0nPPPafhw4crKytLycnJysnJ0X//+18VFBRoxIgRmjVrVlP0FQAAAADQCP8+UC57gI0ur+uRILPREP4OASEWdMhx+eWX69ixY1q/fr3Wr1+v0tJSpaSkqF+/fpo+fbp+/OMfn3ULWQAAAABA+L2xu5alKr1YqoLYEHQaMWjQIA0aNKgp+gIAAAAAaCK7i1xan+f0ax+SblGfNOoqIjZQOhcAAAAAWgBmcaAlIOQAAAAAgBjn8fr01l7/kMNqlKZ3I+RA7CDkAAAAAIAYt/KoQ0fLvH7tk7PilWbjsRCxg59mAAAAAIhxtS1VmcFSFcQYQg4AAAAAiGGFDq/+c6jcr71DglE/yLRFoEdA0yHkAAAAAIAY9t7+cjk8/u3X9UiQyWgIf4eAJkTIAQAAAAAx7I09pQHbb+jJUhXEHkIOAAAAAIhROwtd2pDn8msf1tai3mmWCPQIaFqEHAAAAAAQo2orOHpDz8Qw9wQID0IOAAAAAIhBbq9P/9zrH3LYTNJV3eIj0COg6RFyAAAAAEAM+jTHoWPlXr/2KVnxSrPxKIjYxE82AAAAAMSg2paqzOhFwVHELkIOAAAAAIgxJx1eLT1U7teemWDUmA62CPQICA9CDgAAAACIMe/uK5PTf6WKftQzQSajIfwdAsKEkAMAAAAAYswbe2rbVYWlKohthBwAAAAAEEO+O+nSphMuv/YL21nVM9USgR4B4UPIAQAAAAAxhFkcaMkIOQAAAAAgRri9Pv1zr3/IEWeSpnaLj0CPgPAi5AAAAACAGPHxEYeOl/tXHL2iS7xSrTz+IfbxUw4AAAAAMeKNPaUB22f0YqkKWgZCDgAAAACIAQV2jz44ZPdr75Ro0qj2tgj0CAg/Qg4AAAAAiAHv7CuX03+lin7UI0EmoyH8HQIigJADAAAAAGJAbbuqXM+uKmhBCDkAAAAAoJn7tsClLfkuv/YRGVb1SDVHoEdAZBByAAAAAEAzxywOoAIhBwAAAAA0Yy6vT2/v9Q854k0GTe0aH4EeAZFDyAEAAAAAzdgHh+zKs/tXHL2ia5xSrDzyoWXhJx4AAAAAmimnx6ffbyoOeGxGz8Qw9waIPEIOAAAAAGimXthRqt1Fbr/2zkkmjepgjUCPgMgi5AAAAACAZuiE3aMntgSexXF3/2QZDYYw9wiIPEIOAAAAAGiG/rCpWMVOn1/7ea0turEXu6qgZSLkAAAAAIBm5usCl17dFXjb2LkXpMpkZBYHWiZCDgAAAABoRnw+n+5bVyiv/yQOXdklTqM62MLfKSBKEHIAAAAAQDOy5KBdq485/dptJumRYakR6BEQPQg5AAAAAKCZsLt9+u1XRQGP/aJfkromm8PcIyC6EHIAAAAAQDPx3PYSHSzx+LW3jzfqrv7JEegREF0IOQAAAACgGTha5tHTW08FPPbQ0FQlWXi8AxgFAAAAANAMPLKxWKVu/2qjQ9Ituq5HfAR6BEQfQg4AAAAAiHKb8px6c08tW8ZemCqjgS1jAYmQAwAAAACims/n073rAhcbvbZ7vC5ox5axQBVCDgAAAACIYu/sK9f6PP8tYxPMBj00lC1jgdMRcgAAAABAlCp1efXQhsCzOO48P0kdE01h7hEQ3Qg5AAAAACBK/fmbEuWUef3aOyWadPt5bBkLnImQAwAAAACiUHaJW3/5OvCWsb8flqJ4M8VGgTMRcgAAAABAFHpoQ7HsHv/2ERlWTe3KlrFAIIQcAAAAABBl1hxz6L395X7tBklzL0iVgS1jgYAIOQAAAAAginh9Pt23PnCx0R/3StDAdGuYewQ0H4QcAAAAABBFXt9dpq35Lr/2ZItBvx2SEoEeAc0HIQcAAAAARIlip1ePbCwOeOyeAclqF8+WsUBdCDkAAAAAIEo8vfWU8uz+W8Z2TzbpZ32TItAjoHkh5AAAAACAKLCv2K3ntpcEPPboBamymSg2CpwNIQcAAAAARIEHvyqSy38Sh36QadPEznHh7xDQDBFyAAAAAECErcyxa9khu1+7ySA9xpaxQL0RcgAAAABABLm9Pt23LvCWsTf3SdS5rSxh7hHQfBFyAAAAAEAEvbKzVN8Vuv3a06wG3TcwOQI9ApovQg4AAAAAiJCTDq/+sDnwlrH3DUpR6zi2jAWCQcgBAAAAABHy+OZinXT4/Nr7pJl1c5/ECPQIaN4IOQAAAAAgAnYUuvT3HaUBjz12QaosRoqNAsEi5AAAAACAMPP5fHpgfZE8/pM4dHnnOI3ryJaxQEMEFXLk5OToueee01VXXaXzzjtPbdu2Ve/evXXjjTdqw4YNTdVHAAAAAIgpHx526OMjDr92i1H6w7DUCPQIiA3mYE5+/vnn9ac//UndunXTD37wA6Wnp2vv3r1aunSpli5dqr///e+aNm1aU/UVAAAAAJo9p8en+9cXBjz2875J6pEa1GMagNMENXoGDx6s//znP7r44otrtK9Zs0Y//OEPNWfOHE2ePFk2my2knQQAAACAWPH8dyXaW+zxa0+PM+pXA9gyFmiMoJarXHnllX4BhySNHDlSo0aNUmFhobZv3x6yzgEAAABALMkr9+jJLacCHvvfISlKtVI2EWiMkI0gi8UiSTKZ2McZAAAAAAL5w6ZiFbv8q42e39qiGT0TItAjILaEJOTIzs7WypUr1b59e/Xr1y8UtwQAAACAmLIt36lXd5UFPPb4hakysWUs0GiNrmjjcrn0s5/9TA6HQ7/73e/qNZPDbrc39tOGndPprPE3gPBjHAKRxzgEIo9x2Dz5fD795stiBdgxVld0tmpImq9ZPie1VIzD8ImLC247ZUNhYWGgcVYvXq9XP/vZz/Svf/1LN910k/785z/X67p9+/bJ4/EvtAMAAAAAsejjEybdu8N/gwarwad/DbErM67Bj2VAzDKZTOrevXtQ1zR4JofX69Xs2bP1r3/9S9dee63mzZtX72szMzMb+mkjxul0Kjc3VxkZGbJarZHuDtAiMQ6ByGMcApHHOGx+7B6fFmwulOT1Oza7b4Iu7JUe9j6hcRiH0atBIYfX69WsWbP01ltv6eqrr9bChQtlNNa/vEew002iidVqbdb9B2IB4xCIPMYhEHmMw+ZjwdZTyi71Dzg6JBh196A0xVnYUaW5YhxGn6BH0+kBx7Rp0/S3v/2NHVUAAAAAIICjZR49sy3wlrG/G5qqJAIOIKSCGlFVS1TeeustTZ06Vc8//zwBBwAAAADU4uENRSp1+9fbGNrWomu6x0egR0BsC2q5yhNPPKE333xTSUlJ6tmzp/74xz/6nTN58mT1798/ZB0EAAAAgOZoQ55Tb+0tD3js8QvTZDSwZSwQakGFHIcOHZIklZSU6Kmnngp4TlZWFiEHAAAAgBbN6/Pp3nWFAY9d1yNeQ9tSrBJoCkGFHAsXLtTChQubqi8AAAAAEBP+ta9cG/Jcfu2JZoMeGpIagR4BLQNVbgAAAAAghEpdXj28oSjgsbv6JyszkbqGQFMh5AAAAACAEPrT1yXKKfPfMrZzkkmz+yVFoEdAy0HIAQAAAAAhcqjErWe/Cbxl7KPDUhVvptgo0JQIOQAAAAAgRB76qlh2j3/7yAyrruwSF/4OAS0MIQcAAAAAhMAXxxxadMB/y1iDpMcvTJWBLWOBJkfIAQAAAACN5PH6dN+6wMVGZ/ZOUP82bBkLhAMhBwAAAAA00ut7yrStwH/L2BSLQQ8OTolAj4CWiZADAAAAABqhyOnV7zcWBzx2z8BktY1ny1ggXAg5AAAAAKARntp6Snl2/y1je6SY9LNz2TIWCCdCDgAAAABooL1Fbv11e0nAY3+4IFVWE8VGgXAi5AAAAACABnrwqyK5/CdxaHxHmyZ0YstYINwIOQAAAACgAT49YtcH2Xa/dpOhYhYHW8YC4UfIAQAAAABBcnt9um994C1jf9onUX3SLGHuEQCJkAMAAAAAgvbSjlLtKHT7tbeyGXTfILaMBSKFkAMAAAAAglBg9+ixzYG3jL1/UIpa2XjMAiKF0QcAAAAAQZi75ZQKnT6/9nPTzPrJOYkR6BGAKoQcAAAAAFBP35106aUdpQGPzb0wVWYjxUaBSCLkAAAAAIB68Pl8un99kTz+kzg0KStOYzPZMhaINEIOAAAAAKiH5dl2fZrj8Gu3GKVHh6VGoEcAzkTIAQAAAABn4fT49EAtW8bO6puk7inmMPcIQCCEHAAAAABwFn/bXqJ9pzx+7e3ijbp7QHIEegQgEEIOAAAAAKjD8XKP/rj1VMBjvx2cohQrj1VAtGA0AgAAAEAdHt1UrGKXf7XRAW0smtErIQI9AlAbQg4AAAAAqMXWfKf+b1dZwGNzL0iV0cCWsUA0IeQAAAAAgAB8Pp/uXVekADvGalq3eI1sbwt7nwDUjZADAAAAAAJ4/4Bda3Odfu1xJunhoSkR6BGAsyHkAAAAAIAzlLt9+u2GwFvG3nF+sjonsWUsEI0IOQAAAADgDPO/OaXsEv8tYzMTjPrleUkR6BGA+iDkAAAAAIDT5JR6NO/rkoDHHh6aqkQLj1FAtGJ0AgAAAMBpfrexSGVu/3KjF7S16uru8RHoEYD6IuQAAAAAgEpfHXfq7b3lAY89fmGqDGwZC0Q1Qg4AAAAAkOTw+HTHFycDHru+Z4IGt7WGuUcAgkXIAQAAAACSntxSrO8K3X7tSWaDHhrClrFAc0DIAQAAAKDF23zCqT/VUmz07gHJap9gCnOPADQEIQcAAACAFs3h8WnW5yfl8a81qvNbW/QLtowFmg1CDgAAAAAt2h+3nAq4TMVskJ4b1UoWI8VGgeaCkAMAAABAi7XlhFPzvj4V8Ng9A5N1fmtLmHsEoDEIOQAAAAC0SGdbpjKnf3L4OwWgUQg5AAAAALRIf9x6SttZpgLEFEIOAAAAAC3OlhNOzdsWeJnKrwawTAVorgg5AAAAALQozjqWqZzHMhWgWSPkAAAAANCi1LlM5eI0WU0sUwGaK0IOAAAAAC3GlhNOPVPLMpW7BySrfxtrmHsEIJQIOQAAAAC0CE6PT7NW175M5W6WqQDNHiEHAAAAgBbhj1tPaftJlqkAsYyQAwAAAEDMq2uZyhyWqQAxg5ADAAAAQExzenyaXcsylX6tzPoVy1SAmEHIAQAAACCmPbXtlL4NsEzFZJCeG9WKZSpADCHkAAAAABCztuY79czWWpap9E/WAJapADGFkAMAAABATKpYplIod4BlKn1bmXXPAJapALGGkAMAAABATHp62yl9U+DyazcZpOcuZpkKEIsIOQAAAADEnG35Tj1dxzKVgeksUwFiESEHAAAAgJji9Pg0i2UqQItEyAEAAAAgpjzDMhWgxSLkAAAAABAzvi5w6alalqncxTIVIOYRcgAAAACICS6vT7M+Pxl4mUoay1SAloCQAwAAAEBMeGbbKX1d2zKVUa1kY5kKEPMIOQAAAAA0e18XuPTHLbUsUzmfZSpAS0HIAQAAAKBZO+sylYEsUwFaCkIOAAAAAM3avDqWqSxgmQrQogQdcvzzn//UnXfeqbFjx6pdu3ZKS0vT66+/3hR9AwAAAIA6fVPg0h9r2U3lzvOTNIhlKkCLYg72gkcffVTZ2dlq06aNMjIylJ2d3RT9AgAAAIA6VS1TcXn9j52bZtavB6aEv1MAIiromRzPPvustm3bpr179+rmm29uij4BAAAAwFnN23ZK29hNBcBpgp7JMXbs2CboBgAAAADUX13LVH7JMhWgxaLwKAAAAIBmxeX1afbqwMtU+qSZ9RuWqQAtVtAzOULBbrdH4tM2itPprPE3gPBjHAKRxzgEIo9xKM37pkxb8/2XqRgN0rwLEuVzOWT3PwyEDOMwfOLi4oI6PyIhR05OjjweTyQ+daPl5uZGugtAi8c4BCKPcQhEXksdh3tKDXr6mzhJ/vU2buzoUnrZUWWXhb9faJla6jgMF5PJpO7duwd1TURCjszMzEh82kZxOp3Kzc1VRkaGrFbW9wGRwDgEIo9xCEReSx6HLq9PP/2wSG6f/xumvVNMemRka4qNIixa8jiMdhEJOYKdbhJNrFZrs+4/EAsYh0DkMQ6ByGuJ43D+1lPadtI/4DAapIWjWys1kYdNhFdLHIfRjsKjAAAAAKLe9pMuPbGlOOCxO85L0pC2BBwACDkAAAAARDmX16dZnwfeTeWcVLPuZTcVAJUIOQAAAABEtb98XaItteym8tyoVoozU4cDQIWga3K89tprWrt2rSRp+/btkqT/+7//0+rVqyVJI0aM0MyZM0PYRQAAAAAt1faTLj3OMhUA9RR0yLF27Vq9+eabNdq+/PJLffnll9X/TcgBAAAAoLHcXp9mrw68TKU3y1QABBB0yLFw4UItXLiwKfoCAAAAANX+8k2JNp9gmQqA+qMmBwAAAICos/2kS49vDrxM5fZ+SRrKMhUAARByAAAAAIgqVctUnLUsU7lvEMtUAARGyAEAAAAgqjxbxzKVBRezTAVA7Qg5AAAAAESN7066NLeWZSq/6JekYe1YpgKgdoQcAAAAAKJCXctUerFMBUA9EHIAAAAAiArPflOiTbXtpnJxK8WzTAXAWRByAAAAAIi4HYW1L1OZzTIVAPVEyAEAAAAgotxen2Z/XvsylftZpgKgngg5AAAAAETU/G9KtDHAMhWDpAUXp7FMBUC9EXIAAAAAiJgdhS49VscylQva2cLcIwDNGSEHAAAAgIioa5lKzxSzHhjMMhUAwSHkAAAAABARC75lmQqA0CLkAAAAABB2O+tYpjKrX5IuzGCZCoDgEXIAAAAACCuP16fZq0/K4fE/1jPFrAdZpgKggQg5AAAAAITVgm9LtCEv8DKV+SxTAdAIhBwAAAAAwmZXoUt/qGWZym39EjWcZSoAGoGQAwAAAEBY1LVMpUeKiWUqABqNkAMAAABAWDz3bYm+qmWZyoKLWynBzOMJgMbhXxEAAAAATW5XoUuPskwFQBMj5AAAAADQpOpaptI9mWUqAEKHkAMAAABAk3puex3LVEaxTAVA6PCvCQAAAIAms7vIpT9sCrxM5ed9EzWCZSoAQoiQAwAAAECTyC3zaPbnhbLXskzlt0NYpgIgtMyR7gAAAACA2OD1+bQ136UV2XatOGzX5hP+S1SkimUq89lNBUATIOQAAAAA0GAlLq9W5ji0ItuuDw/blVvuPes1P+ubqJHtWaYCIPQIOQAAAAAE5cApd8VsjWy7Vh9zyHn2XKNat2STfstuKgCaCCEHAAAAgDq5vT6tO+6sDjZ2FrkbdB+zQVpwcSslWlimAqBpEHIAAAAA8FNg9+i/RyqWofz3iF1FTl+j7te/tUX/OySFZSoAmhQhBwAAAAD5fD59V/j9MpT1eU55G5FrxJmk0R1smtA5Tpd1ilPnJB49ADQ9/qUBAAAAWii726fPj1XM1liebdfh0gB7vQYhM8GoCZ3jNKFznEZ3sLF7CoCwI+QAAAAAWpCcUo8+PFwRaqw66lCZu+HTNQyShra1aELneE3oHKfzWpllMBhC11kACBIhBwAAABDDvD6fNp1waXnlMpSvC1yNul+KxaBxHStma1zayab0OFOIegoAjUfIAQAAAMSYYqdXn+Y4tDzbrv8etivPHsQerwH0TDFXL0MZkWGVxchsDQDRiZADAAAAiAF7i9xafrhitsaaYw41YhWKLEZpZEZF0dAJneLUI5XHBgDNA/9aAY2Qb/doS75LJx1etY0zqUuySZkJJllNvLsBAACaltPj07och1YcLteH2Q7tKXY36n5t44y6tFPFbI0fZNqUYqVoKIDmh5ADqKdyt0/b8p3aeMKlTSec2pjn1P5T/hXIDZIyE0zqnGRSVpJJWUnm6o87J5nUKdGsODMhCAAACI7X59OhEo8+y7ZryV6r1q07qVOuRkzXkDSgjUWXdYrT5Z3jNCjdIiNFQwE0c4QcQAAer0+7itzaeMKpTXkubTzh1LcFrnpN+/RJOlLm0ZEyj748HvicjHhjZehhrg4/qsKQzokmJVp45wQAgJas2OnV9pMufXvSpW8L3Pr2pEvbT7pOCzXMqnjVEZwEs0FjOth0eec4XdopTpmJFA0FEFsIOeqp1O3TFwVG9Yp3KSPZpFY2o1KtRpkpuhQTjpR6tDHPWT1DY0u+q9HvjNQlt9yr3HKvvsoLXN28jc2orOTK2R+J/kEI00cBAIgNHq9P+0+59e1Jt74pqAg1vilw6VCJ/2zRhuqcZNLllctQLm5vY0YpgJhGyFFPh0o8unN7nLS9WFJxdXuK1aBWVqNa2c74YzUqzWYI2N7KZqRmQwQVOb3acqJi2UlVsHG0rHEVx0Mt3+FVvsOrzScChyBpVkPAWSBVy2PSrAb2qAcAIMoU2D369qS7cnZGRaDx3Um3yj2hfWPFaJAubGfVhMpgo0+amdcFAFoMQo56OukM/Mun2OlTsdOjg0Gm7Ulmg9JqBCA1w5K0AMFIK5tR8STvQXF6fPr2ZEWYsfGES5vynNpV5G7A5M7oUuj0qbDAVes+98kWgzonmtQ5uSIIyUo0KSvZrM6JJmUlm9TGZuTFDgAATcTl9Wl3kbs6yKj6O6cJ31RJsxp0SaeKnVAu6RSnVjZmfQJomQg56qnQEdpfSiVun0rcHh0uDS4ciTPptJkiRrU+c6ZIVUhirQxOKs9JMMf+O/s+n0/7ij3aWLnkZNMJp7YVuOQI3WzPakaDdG6aWUPaWjUk3aoeqWbllnmUXeLRoRKPskvcFX+XelTWmP3bGuiUy6fthW5tLwxcZT3BbFDHRJOSLAZZjQZZjJLVaJDVZJDVqMq/Kz62mAyyGQ2ymiSLsbLdVHn+6R+fdq3FaJDttI9r3POMaylwBgBozo6Xe/RtgUvfVC4z+fakW7sKXXKGYZJo7xSTJmbFa0LnOF3QzsoyagAQIUe9FdYykyPc7B7paJk36OUVNpNqBCKtz/jTKu6MtriKoCSaf1nmlVcFGt/vdtJU/586JZo0tK1VQ9ItGtzWqgFtLEqqR3FQn8+nfIe3Ovw4dMqtQ6XfByHZJZ4mrf1RmzJ3xTtM0cBsqApDKsIPm6n20MUkr+LcVvUqKFPXVI86Jpqq/6SyRAcA0ITsbp92FlXNyvh+yUmePTxLXjsnmXReK4v6tbaod5JPGY48Xdirk+Li4sLy+QGguSDkqKfalqs0F44GhiMpVoN/IFIZgpzZVtWe1ASzRkpdXm3Nd1XvdrLhhFPZISzIdbpUq0GD062VszQsGpxuVUZCwyqPGwwGpceZlB5n0qB0/+M+n09FTp8OVgYeNWaBlHh0qMQdNQFbU3H7JHf1bJf6fK1mKa9cUnmN1qTK2Sln/umc9P3HCWam7gIA6ubz+XSk1L92xu4it0JcOiOgJLNBfVtZ1K+1Wf0qQ42+rSxKPa3ouN1uV3Z2bL8+AICGIuSopx9mWZXuKpAlJV0lXpNOOr066aj4U+jw6qTDV6MtHL8Ew6Gq5siBU/UPFKxGVc8WaXXazJCAM0lOa7dUzhpxe33aUeiunp2xIc+p7wrd8jbB99RqlM5vbakINCpDje4p5rAtoTAYDEqzGZRms2pAm8DnFDu91YFHdRBS+n0QciJM7yBFuxK3TzuL3NpZxwyVVjaDOiaa1THRpE5nhCGdEk3KTDBRFBgAWpByt686yPjmtECjKAxvMBgkdU8xVQcZ/VpZdF5ri7KSTCzlBIBGIOSop06JJo1s5VXnzrazTgv0+Xw65fJ9H4JUhx/ft50ZkhRUfhyO9ZtNzen9fovUYKRYKmqI5Nm9TVbHoleqWYPTLZVLT6zq19oiW5Q/1KZYjerX2qh+rS0Bj5e6vDpcuQTmUIAZIcH+f4hlFWOwYs10IAZJ7eKNfrNBvg9EzGofb5QpipdxAQBqV+jwat1xp9bmOrTmmFOb851yheHXZKrVoPNOCzL6tbKoT5pZifVY+goACA4hRxMwGAxKsRqUYjWqS3L9r/P5fCpzVwYhTt8ZM0X8w5GKYxUzSCJR3DLUil0+FbtCtwSlXbxRQ9KtFYFGW4sGtrEqLQYrjSdajDonzahz0gKHIHa3T4dLTw8/KsKQY+VeOT0+Ob0+Ob2Sy+OTw+uTyyM5vTU/jpWZSWfj0/cB3aZatu81GaQOCf4zQU4PQ9Lj2L0GAKJBbplHa3OdWpPr0Jpcp74tcDXpDmsmQ8UbKmfOzshM4PcCAIQLIUcUMRgMSrQYlGgxqlOQ19rdvtNmjFT8KThtlsjpf07av/84FmaOSFKi2aCB6RYNOa2WRsdEEy8oJMWZDeqZalHP1MAhSH14KoMQp9dXGYxUbI/n9PjkqAxInF6fHJ7Kdq9PzsqAxFkZllSEJhXXnv5xjaDF65PDEzh0cXp8KnV7dazMI4c3cv9fPT7pcGndOyPZTFJmwvfBR48Usy7uYNOQdCvLYQCgifh8Ph0sqQw1jjm0JtehvcVNU79LktLjjNWzMvq1Mqtfa4vOSbUozsy/8wAQSYQcMSLObFB7s0ntgyiQ6fP5VOr2VQQe9prhSG2hSIHDG5Z1qnUxGaS+rSwakv59LY1zUs0sIWhCJqNB8UYpXpH9Htvtdh06lK3EjI464TbrSKmn+s/h0/4+WupRJCc3OTzS/lMe7T+9ls3mU0o0GzQyw6rRHWwanWnT+a0trLsGgAby+nzaWeiunqmx9phTR8pCH2pYjdI5ad8HGVU7nLSLb1hRcgBA0yLkaMEMBoOSLAYlWYzKSqr/dW5vxayRqmCkRiBSGZj4tTm8cjTwdUeXJJOGtLVW19Lo38bCLhktmMEgtbEZ1TG19mKtHq9Px+3eMwIQd8XHJRVtueXeJp2yHEip26ePjjj00RGHpIptnUd1sGpMhziN6WBT9xRmHwFAbdxen74ucOmLYxVLT77MdarAEdopqZkJxhpLTfq1tqhXqrm6ODoAIPoRciBoZuP326LWV1W9karQo0Y4Yq8ZiJiNFcW5KpaeWIL6PIBUMfOkQ4JJHRJMGto28DlOj09Hyzw1ZoNUBSJVs0JC/eL5TAUOr94/YNf7B+ySKgocj+pg05gONo3JtKlDA7cuBoBYYHf7tPFExdKTtblOrT/uVEkIp+mlWg0anmHTyAyrBqdb1a+VWa15zQEAzR4hB8Li9HojnYOYNQI0FavJoC7JZnVJrv2fwTK3VzlnLIc5MxA55QrdC+7DpR69uadMb+4pkyT1TjVrTOXSllHtbTFZOBcAqhQ7vVp/vHLpSW7FNvKhrB2WEW/UyAybRmRYNbK9TX1bhW/LeABA+BByAEAtEsxG9Uw11lm0tcjpDVgb5EjlEpnsEk+DX6TvKnJrV5FbL+wolUHSgDaW6lkewzOsLNsC0KydsHu05tj3ocbXBS55Q7iOsGuySSMqZ2qMzGBJIAC0FIQcANAIqVajUq1G9W0VOAhxeHxaf9ypz446tCrHoY0nnA3aktcnaUu+S1vyXfrzNyWyGqVh7SqKmI7pYNOQtlbWjAOIatklbq3JdWptZU2NXUXukN7/3DSzRravCDVGZNiUmcjSEwBoiQg5AKAJ2UwGjepg06gONmlwxXTstblOfXbUrs9yHPr2ZMNe5Du90hfHnPrimFNzN59Sktmgke0rQ4/MOPVjGjaACPL5fNpdVBFqrMl1aM0xZ51bbwfLZKiY3Va1/GREhpV6GgAASYQcABBWKVajJnSO04TOcZKkvHKPPj/qqJjpcdRRc9vZIJS4ffrwsEMfHnZIKlYbm7FGEdNuyUzTBtB0fL6qnU++X35ywh66gho2kzS0bcWyk5EZVg1rZ1WShSV7AAB/hBwAEEFt402a1j1B07onSJIOlbj1WU5F4PHZUYeOlzfsISHf4dW/D5Tr3wfKJVXs3DImsyL0GN3Bpvbs3AKgkVxen7445tDSQ3Z9cMge0pkayRaDhrezakTl8pNB6VbZTAS1AICzI+QAgCiSlWTWjb3NurF3onw+n3YUuqtneaw+5lCxs2FV+Q6XevT67jK9vrti55ZzUs0aXRl6XMzOLQDqqdjp1cdH7Fp2yK4Vh+0N/jfpTOlxxopdTzJsGtneqvNaWWSizhAAoAEIOQAgShkMBp3byqJzW1n0875Jcnt92prv0mdHHfosx6F1xx2yN/CN051Fbu0scuuF70plNEgDK3duGd3BpuEZNsWbebgAUOFomUcfHLJr2aFyrTrqCMm2rp2TTBqRYdVFlTU1eqWaWVIHAAgJQg4AaCbMRoOGtLVqSFur5vRPlt3t0/o8p1blOPTZUbs2nXA1aOcWr0/adMKlTSdcmvd1xc4tF7SzakLnOE3Oilf3FH5VAC2Jz+fTziK3lh2ya+nBcm084Wr0PXunmit2PWlfEWpkJfHvCgCgaTToN8ymTZs0d+5crVu3Tm63W3379tXs2bN11VVXhbp/AIBaxJkNGl05++JBpajY6dUXx76v57G9ETu3rD7m1OpjTv32q2Kdm2bWpKyKwGNguoVdW4AY5PFWhKZVwca+BhZBliSjQTq/taV6K9cRGVa1jacOEAAgPIIOOVatWqXp06crLi5O06ZNU1JSkhYvXqyf/OQnOnz4sG6//fam6CcA4CxSrEZNzIrXxKx4SdLx03Zu+SzHoYMlDXto+a7Qre8KS/T0thJ1SDBqUla8JmXFaVR7m6wUAgSarXK3T5/mVNTXWJ5tb9RuKK1tFTtHTcqK05gONqVYqfMDAIgMQ2FhYb0nN7vdbg0bNkw5OTn66KOP1L9/f0lSUVGRxo8fr0OHDmnDhg3Kyspqsg5Hit1uV3Z2tjp37qy4uLhIdwdokRiHjXPglFurKouYrmrEzi1VUiwGXdKp4qHm0k5xSuWhpkVgHDZv+XaPlmdXBBufHHGovCFr3Cp1TTZVz/K6sJ1VZgqFhg3jEIg8xmH0Cmomx6pVq7R//37NmDGjOuCQpNTUVM2ZM0ezZs3Sm2++qd/85jch7ygAoHG6JpvVNdmsmZU7t3xXWLFd7WdHHVpzzKFiV3APO8Uun97bX6739pfLYpQubm/T5Kw4TcyKV8dEpqYD0WJ/sVtLD5Vr2SG7vjzulLcRG6IMSrdoUuc4Te4Sr3PTKBYKAIg+QYUcq1evliSNGzfO79j48eMlSV988UUIugUAaEoGg0F9W1nUt5VFt/Wr2LllS75Ln+U49EmOXWtzg3sQcnmlT3Mc+jTHoV99WcSDEBBBPl/FeF56yK5lB8u1vbBh9XkkyWyQRnewaRIBJgCgmQgq5Ni7d68kqUePHn7HMjIylJSUpH379p31Pna7PZhPGxWcTmeNvwGEH+OwaZ2XLJ13jkWzz7GowOHVf3NcWn7YqU+POlUeZDmPzSdc2nzCpT9sPqUuSUZd3tGqyztZdUG6WSamtDdrjMPo5PT4tOa4S8uPOLXisEtHG7EcLdli0LgOFk3sZNW4DpbT6mu4ZLc3fqcVNB7jEIg8xmH4BLscKKiaHFdddZU+/fRTbdq0Sd27d/c7fu6556q0tFSHDh2q8z779u2Tx9Pwqt0AgPCxe6T1hSatKqj4c9LV8JAizezTxa09GtvGowvTPIrjTWGgwUrc0pqTJn2Wb9IXJ00q9TR8bLazejWqcmwOSfXKQokdAEAUMJlMAbOHukRkk/LMzMxIfNpGcTqdys3NVUZGhqxWa6S7A7RIjMPI6SVphiq2mdyY79YHh51aftip/SXBvVtc6DboP8fN+s9xs+JN0pj2Fk3oZNWlmValx/FU1RwwDiMrp8yjFUdcWnHYqS+Ou+RqRP3gPqkmXd7Jqss7WjWgtYllZc0I4xCIPMZh9Aoq5EhJSZEkFRcXBzx+6tQppaWlnfU+zbn6rNVqbdb9B2IB4zCyRidIoztLj/l82lnk1rJDdi09WK6NJ4Kbxl7ukZYfcWn5EZeMhlJd2M6qyZU7NXRLiUgGjyAwDsOjqkjwskN2LT1Urs1BjrPTGQ3S8HbW6h1RGGfNH+MQiDzGYfQJ6rdbVS2OvXv3auDAgTWO5ebmqqSkRIMHDw5Z5wAA0ctgMKhPmkV90iya0z9ZR8s8+uCQXcsOleuzo46g3mH2+qS1uU6tzXXqwa+K1TfNrElZ8ZqUFadB6RbeYUaL4vL6tP64szrYOHCq4Ut8400GjetYUTj08s5xasMaMQBAjAsq5Ljooov0zDPP6JNPPtH06dNrHPv444+rzwEAtDwdEky6uU+ibu6TqGKnVx8fsWvZIbtWHLar2BncnpXbC93aXnhKT207pcwEoyZmxWtyVpwubm+T1UTggdji9fm0/aRbnx11aFWOXV8cc6rE3fB9XtvYjJqYFadJWXEam2lTgpmlYACAliOokGPMmDHq2rWr3nnnHf3sZz9T//79JUlFRUV65plnZLVa9aMf/ahJOgoAaD5SrEZd1S1BV3VLqNj1IdehpQcrQo8jZcG9K51T5tWLO0r14o5SpVgMurRTxcPbJZ3ilGrl4Q3N04FTbq066tBnOQ59dtShE/ZGFNeQ1D3ZpMldKmY/XdDWyi5GAIAWK6iQw2w26y9/+YumT5+uyZMna9q0aUpKStLixYuVnZ2t3//+9+rSpUtT9RUA0AxZTQaNzYzT2Mw4PTncp635Li2tnIa//aQ7qHsVu3x6d3+53t1fLotRGtXepsld4jSxc7wyE5mGj+h1wu7RqspAY2WOQwdLGr/L3JB0iyZlxWtylzidk2pmWRcAAGrA7iqjR4/W8uXLNXfuXC1atEgul0t9+/bVww8/rGnTpjVFHwEAMcJgMGhgulUD0616YHCKDpxya2llHY+1uU55g5ih7/JKn+Q49EmOQ3evLdKgdIsubGdV31YWnZtmUZ9WZiWzDyYipMTl1ZpjzspQw65vgwz0ArEapdEdbJqUFa+JWXHqkECwBwDAmRpUVnvIkCF65513Qt0XAEAL0zXZrNn9kjS7X5Ly7R6tyLZr6SG7PjniULknuJoEm0+4/HaeyEoy6dxWFvVNM+vcVhad28qi3qlm2ajrgRBzenzakFcRanyW49CGPKcaUVajWorVoAmVS7TGd4xTCku0AACoE3uHAQCiQps4k27olagbeiWqzO3VyhyHlh2y64NDduU7Glav4FCJR4dKPFqR/X2bySD1SDFXzPhoZda5aRb1bWVWt2QzdQxQb16fT98UuKpDjbW5TpWGINUwGaTB6RaN6RCn0Zk2DW9npdguAABBIOQAAESdBLOxcgvZeHm8Pq3Pq9xO82C59jViO01J8vikXUVu7Spy698Hvm+PM0m9UyuCj36Vsz7OTTOrY6KJWgeQz+fTgVOe6poanx91NDh8O9O5aWaN7mDT2EybRra3UVAXAIBGIOQAAEQ1k9GgERk2jciw6ZGhKdpZ5K7cqaVcG89YntIYdo+0rcClbQUuSeXV7SkWQ3XgUbXkpV8rs9rEUQ8h1h0v92hVZajx2VGHskNQLFSSOiWaNCbTpjEdbBrdwab21NYAACBkCDkAAM2GwWBQnzSL+qRZdPeAZOWUerQ8267l2eXamu9Sbnlo3lk/XbHLp3XHnVp33FmjvV28UeemVcz8oNhpbCh2erUmtyLUWJXj0PbCxhcLlaRWNoNGd7BpTIc4jc20qVsys4MAAGgqhBwAgGYrM9Gkm/sk6uY+iZKkfLtH3xW69d1Jl7476dZ3hS59e9KlYmcIKkCe4Xi5V8fLK97hPx3FTpsPh8enr/Kc+iynoq7GxhNOBVnvNqAEs0EjMqzVMzX6t7HISKgBAEBYEHIAAGJGmziTLm5v0sXtbdVtPp9POWXeyuDDpe2VIciOQpfsoVl9UENdxU5Pn/VBsdPw8/p82pbvql6CsjbXGfQuPoGYDNLQttbquhpD21oJtQAAiBBCDgBATDMYDOqYaFLHRJMu6RRX3e7xVhSS3F7oqjHzY3eROyTv5p/u9GKn7x+wV7dbjVKazagEs0EJZoMSzQbFm42nffx9e0LlfyeajUqwGKqvqfnHWH2uxaiYXBLh9fnk8FTMwnB4fHJ4K/62eyq2cbVXtXsqzrN7fCp0erXmmEOfH3PopCM0/3P7tjJrTAebxmTaNDLDxtauAABECUIOAECLZDIa1CPVrB6pZl3RJb663eHxaXdR5ZKXQpe2n6z4+GCIik6ezumtWPbSFEwGVQce9Q1I6hOuGNxeFbokU5lHBqe7RqhgrwofqoMHn5yVQUONAML7fQBRM5g47Xp35fVe1fgczqb5dp1VVpKpOtQY3cGmdvEUCwUAIBoRcgAAcBqbyaDzWlt0XmtLjfYSl1c7Ct3aXrnspar2R1MUOw0Fj6+iaGqxK/T1SKQESYVNcN/o0cZmrCgWmlmxBKVrMi+ZAABoDviNDQBAPSRZjBra1qqhba012vPtnurZHt8VVix72V7YNMVO0XQSzQaNzLBqdKZNYzPj1K+VmWKhAAA0Q4QcAAA0Qps4k0Z1MGlUh5rFTo+Ufr/Ty/bKmR87m6jYKYJnNkjD2lkrt3atKBZqpVgoAADNHiEHAAAhZjAY1CnJrE5JZl0aoNjpt5XBx+FSj8rcPpW6fSpzeVXm9qm86r9P+8OckNA4r7VFYyp3QBmRYVWShWKhAADEGkIOAADC5PRip1d2jT/7BaqYFWL3SGVur0orQ5CqYKT64zoCkoo/3hr/ffq1Ze7IRChWoxRnMshqMlT8XfnfNrNBNqNBNpNBcSZVH7dV/9Fpxw2nHVf1ORX3q7jeVnlOZoJJaTZCDQAAYh0hBwAAUcxgMCjeLMWbTWrTBPf3+ioCj3KPT6UuX4CwpGZAUmx3qexUkdq1SlOizaI4c2X4YKwIGmoEF6bvg4aawYSodwEAAJoEIQcAAC2Y0WBQosWgRIuUHnf28+12u7Kz89W5c7zi4upxAQAAQBgxbxMAAAAAAMQEQg4AAAAAABATCDkAAAAAAEBMIOQAAAAAAAAxgZADAAAAAADEBEIOAAAAAAAQEwg5AAAAAABATCDkAAAAAAAAMYGQAwAAAAAAxARCDgAAAAAAEBMIOQAAAAAAQEwg5AiCyWSKdBeAFo9xCEQe4xCIPMYhEHmMw+hkKCws9EW6EwAAAAAAAI3FTA4AAAAAABATCDkAAAAAAEBMIOQAAAAAAAAxgZADAAAAAADEBEIOAAAAAAAQEwg5AAAAAABATCDkAAAAAAAAMaHZhBw5OTl67rnndNVVV+m8885T27Zt1bt3b914443asGFDwGuKi4t1//3367zzzlO7du10/vnn67e//a1KSkr8zs3Ly9MzzzyjmTNnqn///kpLS1NaWlqdffL5fFq8eLGmTJmic845Rx06dNDQoUN155136sCBA3VeO2LECI0dO1aSZLfbdf/992vixInq06ePMjIy1Lt3b02YMEH/+Mc/5HK56vMtAppcNI5Dr9er559/XqNHj1aHDh3UuXNnTZw4UcuWLTvr13P6ODzdkiVLNHXqVHXr1k0ZGRnq37+/fvrTn+rw4cNnvSfQ1Jp6HK5du1YPPPCAxowZUz0Ghg0bpoceekiFhYW19mvTpk265pprlJWVpczMTF1yySVatGhRnV+L2+1Wly5dNHPmzOo2u92uJ598UhdeeKEyMjLUpUsXXX311fryyy/r9w0CQigax9s///lP3XnnnRo7dqzatWuntLQ0vf766/X6es78vRfMvVwul95//339/Oc/1wUXXKCOHTuqU6dOGj9+vF588UV5PJ569QEIlWgbn419puP5MHQMhYWFvkh3oj5+97vf6U9/+pO6deumiy++WOnp6dq7d6+WLl0qn8+nv//975o2bVr1+aWlpbr88sv19ddfa9y4cerfv7+2bdumTz75RIMHD9ayZcsUFxdXff7nn3+uK664QgaDQT169FBOTo7KysrqfEH3wAMPaMGCBWrfvr0mTZqk5ORkffPNN/rkk0+UlJSkFStWqG/fvn7X7d+/X4MGDdIDDzyge+65R/n5+erXr58GDx6snj17Kj09XYWFhfroo4+UnZ2tcePG6Z133pHR2GwyKcSoaBuHPp9PN910kxYvXqxu3brpkksukdPp1LJly5SXl6cnn3xSt956a8BrzxyHVfe766679Morr6hbt24aP368kpKSdPToUX3xxRd64YUXNGLEiNB9Q4EGaOpx2Lt3b+Xn52v48OHq37+/DAaDVq9erW3btqlr16768MMP1a5duxp9WrVqlaZPn664uDhNmzZNSUlJWrx4sbKzs/X73/9et99+e8Cv5bPPPtMPf/hD/e1vf9N1110nu92uH/7wh1q3bp369eun0aNHq6ioSEuWLFFZWZlee+01TZ48uWm+sUAA0Tjezj//fGVnZ6tNmzZKSEhQdna2FixYoBkzZtT5tQT6vRfMvXbt2qULLrhASUlJGj16tHr16qXi4mItX75cR48e1YQJE/TWW2/JYDA05lsO1Fu0jc/GPNPxfBhazSbkWLx4sVq3bq2LL764RvuaNWv0wx/+UImJidq5c6dsNpsk6bHHHtOTTz6pO++8U7/73e+qz68aDP/7v/+rOXPmVLcfP35cu3fvVv/+/ZWcnKxhw4Zp9+7dtT5c5ebm6txzz1XHjh21evVqpaamVh9bsGCBHnjgAc2YMUMLFizwu3b+/Pl68MEH9cUXX6hfv37yer1yu92yWq01znO73Zo6dapWr16tf/7zn5owYUKw3zYgpKJtHL7//vu66aabNHz4cC1atEjx8fGSKn7JjB07VsePH9f69evVpUsXv2vPHIeStHDhQt1333265ZZb9MQTT8hkMtW4xu12y2w2B/19A0Kpqcfhn/70J1133XXq0KFDdZvP59OvfvUrvfjii7rlllv01FNPVR9zu90aNmyYcnJy9NFHH6l///6SpKKiIo0fP16HDh3Shg0blJWV5fe1/PrXv9ZLL72kPXv2KC0tTc8++6x++9vfaurUqXrxxRerx+D+/fs1ZswYWa1Wbd68WcnJyY3/RgL1EG3jTZJWrlyp7t27KysrS/PmzdPDDz9cr5Aj0O+9YO6Vk5OjZcuW6frrr1diYmJ1e2lpqaZMmaLNmzfrlVde0dSpU+v+pgIhEm3jszHPdDwfhlaziX6uvPJKvx9gSRo5cqRGjRqlwsJCbd++XVLFD9///d//KSkpqTqprnLPPfcoKSlJr732Wo32du3a6aKLLqr3C6dDhw7J6/Vq+PDhNQIOSbr88sslSSdOnAh47bJly9SlS5fqXzBGo9HvB1iSzGazpkyZIknat29fvfoFNKVoG4dLly6VJM2ZM6c64JCkNm3aaNasWXI4HLVOuz1zHJaXl+uJJ55Q165d9fjjj/sFHJIIOBAVmnoc3nnnnTVe0EmSwWCovv6LL76ocWzVqlXav3+/rr766uqAQ5JSU1M1Z84cOZ1OvfnmmwG/lg8++EAjRoyoXpZWtczs3nvvrTEGu3XrphkzZujEiRN6//33a/3eAKEWbeNNksaOHRswNDybM3/vBXuvzMxM3XLLLTUCDklKTEzU7Nmza+0v0FSibXw25pmO58PQajYhR10sFoskVb8g2rt3r44ePaoLL7ww4D/EF154oQ4cONCo9fU9evSQ1WrVl19+qeLi4hrHli9fLkkaM2aM33X5+flat26dJk2adNbP4fV69fHHH0tSwGUvQDSJxDg8fvy4JAWcqVHV9vnnn/sdCzQOP/nkExUWFmry5MnyeDxavHix5s2bp5deeolfImg2mnIcnnnvKqtXr5YkjRs3zu+a8ePHSwr84LN161ZlZ2fXGIe5ubmS6h7Tq1atOmtfgXCIxHhrqGBefzZEqPsLNFY0jc+zPdPxfBh6zf5tyezsbK1cuVLt27evTr727t0rSerevXvAa7p3766PP/5Ye/fuVadOnRr0eVu3bq2HHnpIDz74oC644IIaNTlWrVqlW265JWAtgOXLl8vj8QRcU+x0OvX000/L5/Pp5MmT+uyzz7Rr1y7NmDEjYGACRItIjcM2bdpIkg4ePKhzzjmnxrGDBw9Kkvbs2eN3XaBxuGXLFkkVv7AuuuiiGtcZjUbNmjVLjz76aIP6CYRDU4/Df/zjH5L8w4yqz9GjRw+/azIyMpSUlBQwKKyatXH6i7o2bdpo3759OnjwoPr06VPj/KoxXfX5gEiK1HhrqLpef4ZCqPsLNEakx2ewz3Q8H4Zesw45XC6Xfvazn8nhcOh3v/tddZpWNbPizGUkVVJSUmqc11CzZ89WZmam7rjjDr300kvV7SNGjNDVV18dcGr7smXL1Lp164DFC51Op5544onq/zYYDLr99tv10EMPNaqfQFOK5Di85JJL9O6772revHkaPXp0dbGogoICLVy4UFJFXYAzBRqHVcvLFixYoAEDBuiTTz5R7969tW3bNt15552aP3++unXrpp/+9KcN7i/QVJp6HG7btk1PPPGE2rZtq1/+8pc1jlVdW3WvMyUnJwe8/7Jly3TeeefVmLVxySWX6KuvvtKTTz6pF154ofrrOHDgQPXSs0BjGginSI63hqrr9WdjvfLKK/roo480evRoXXbZZSG/PxCMaBifwT7T8XwYes12uYrX69WsWbO0Zs0a3XTTTfrRj34U9j488cQTuvXWWzVnzhx9++23Onz4sD744APZ7XZNmTLFbwvL8vJyffrpp7rssssCTm9KSkpSYWGhCgoK9O233+qpp57Sa6+9pilTpjQ6kAGaQqTH4TXXXKNRo0Zp7dq1GjlypO655x7dddddGj58eHVdjzOrTtc2Dr1eryTJarXq9ddf1+DBg5WUlKSRI0fqlVdekdFo1Pz588P3xQH11NTj8MCBA7ruuuvk8Xj04osvVs+gaozs7Gx9/fXXflNzZ82apT59+ui9997TmDFjdP/992v27NkaPXq0OnfuLMl/TAPh1BzH29lefzbG8uXLdc8996hz5856/vnnQ3pvIFjRMj6Deabj+bBpNMtXCl6vV7Nnz9a//vUvXXvttZo3b16N41VJXG3v9pztXaf6WLlypebOnav/+Z//0V133aWOHTsqKSlJI0aM0FtvvSWLxaIHH3ywxjWffvqpysrKzrreymg0qmPHjvrpT3+qP//5z/ryyy/19NNPN7ivQFOIhnFoNpv1zjvv6N5775XRaNSrr76qJUuWaNKkSdXFo9LT02tcU9s4rOrHwIED/YpM9e3bV127dtX+/fvr3FYaCLemHocHDhzQlClTlJ+fr1dffVWjR4/2O+ds736dOnXK7/5VRYPPHIfJyclavny5Zs2apeLiYr3wwgv69NNP9ZOf/ER//OMfJfmPaSBcomG8NUR9X38G68MPP9RNN92kdu3aacmSJWrfvn1I7w8EIxrHZ32e6Xg+bBrNbrlKVUL31ltv6eqrr9bChQv93tWpWhdcW7HAqvZA64fr66OPPpIkjRo1yu9YRkaGevXqpW3btqmkpERJSUmSVL33clUhtvr4wQ9+IOn7wm5ANIiWcShJNptN9957r+69994a7VUFRwcNGlSjvbZx2KtXL0m1T2Osarfb7Y3qLxAqTT0Oq17Q5ebm6pVXXqneOexMVdfu3btXAwcOrHEsNzdXJSUlGjx4cI32ZcuWqVOnTn7nS1JaWpoee+wxPfbYYzXaq5arnDmmgXCIlvHWEA15/Xk2K1as0MyZM9WmTRstWbJEXbt2Ddm9gWA1h/FZ2zMdz4dNo1nN5Dj9B3jatGn629/+FnBaT48ePdShQwetW7dOpaWlNY6VlpZq3bp16tKlS4OLHUoV66Ok2reJzc/Pl9ForK6+6/V6tWLFCo0ZM8avom9djh07Jun7Kr5ApEXTOKzLv/71L0nS9OnTa/S9tnFYFVju2rXL714ul0v79u1TYmIi7yIjKjT1ODz9Bd1LL71UZ7HCiy66SFLFDkVnqqoAX3WOJBUWFmrNmjWaOHFi/b9gBR7TQDhE03hrSN8b8vqzLlUBR6tWrbRkyZJaCzkC4dBcxmegZzqeD5tOswk5qqYgvfXWW5o6daqef/75WtcVGgwG3XjjjSopKame3lrlj3/8o0pKSnTTTTc1qj/Dhw+XJD333HN+055eeuklHTlyRBdccIFsNpskaf369crLyws4MHbs2KGysjK/9rKyMj3wwAOSpEsvvbRR/QVCIdrGoRR4ivz777+vf/zjHxo8eLCuuOKK6va6xmG3bt00btw47du3z2+f9Hnz5qmoqEiTJ08OWFAYCKemHodVL+iOHTumF198scYYCmTMmDHq2rWr3nnnHW3btq26vaioSM8884ysVmuNddEffvih3G53rS8UA43pBQsWaOXKlZoyZYrfrBCgKUXbeAtWXb/3GuKjjz7SzJkzlZaWpiVLljR6NibQGNE2PoN9puP5sOkYCgsLfZHuRH3MnTtXTzzxhJKSkvTzn/884A/w5MmT1b9/f0kVidyECRP0zTffaNy4cRowYIC2bt2qTz75RIMHD9bSpUsVHx9f4/rbbrut+uOlS5equLhY119/fXXbXXfdpd69e0uSPB6PrrjiCq1Zs0Zt27bVxIkTlZqaqq1bt2rVqlWKj4/Xf/7zHw0ZMkSS9Nvf/lYLFizQjh071K5dO7+v7bnnntPw4cOVlZWl5ORk5eTk6L///a8KCgo0YsQIvffee379BcIt2sahJF1wwQXq2LGjevfurbi4OG3cuFGrV69W165dtXjxYmVlZVWfW9c4lKT9+/frsssuU15eniZMmFC97GzVqlXq3Lmz/vvf/yojI6Ph30AgBJp6HJ5//vnKzs7WsGHDat0e77777qvx36tWrdL06dMVFxenadOmKSkpSYsXL1Z2drZ+//vf6/bbb68+96abbtLKlSu1Z8+egO9CderUSRdffLF69Oghg8Gg1atXa8uWLRo0aJAWLVqktLS0hnzbgAaJxvH22muvae3atZKk7du3a+vWrRo+fLi6desmqWKXv5kzZ0o6+++9YO61a9cujRo1Sg6HQ9OnT1fPnj397peVlaUZM2bU8R0FQifaxmewz3Q8HzadZhNy3HbbbXrzzTfrPGfBggU1/mEtKirS448/riVLlig3N1cZGRmaOnWqfvOb31TvvHC6s71wWrJkSY0aHA6HQ88995wWLVqkPXv2yOl0ql27drr44ot1991365xzzqk+d8iQIUpPT9eKFSv87rt582a98sorWr9+vXJyclRaWqqUlBT169dP06dP149//GPePUZUiMZxOHfuXC1ZskTZ2dlyuVzq0qWLrrjiCt1xxx1+xaPqGodVDh8+rMcee0wff/yxCgoKlJGRoYkTJ+rXv/612rZtW2ffgHBo6nFYnxAhUAHejRs3au7cuVq/fr1cLpf69u2r2bNna9q0adXnOBwO9ejRQxMnTtQLL7wQ8N5z5szR559/riNHjshgMKhHjx665pprdOutt1bPjgTCJRrH29n6dP3111dvo36233vB3Ovzzz8/6zvZF110UXVhYaCpRdv4DPaZjufDptNsQo7mbOfOnbrwwgv18MMPh2y/cwDBYRwCkffRRx/pmmuu0csvv6yrrroq0t0BYhq/94DoxfhsWs2mJkdztmzZMkn+W+UBCB/GIRB5y5Ytk9VqDekuDwAC4/ceEL0Yn02LmRwAAAAAACAmMJMDAAAAAADEBEIOAAAAAAAQEwg5AAAAAABATCDkAAAAAAAAMYGQAwAAAAAAxARCDgAAAAAAEBMIOQAAAAAAQEwg5AAAAAAAADGBkAMAAAAAAMQEQg4AAAAAABATCDkAAAAAAEBM+P/dTLdSRGY4BwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bilanco[\"Yıllıklandırılmış Kar\"].plot(kind=\"line\",figsize=(12,5),title=\"Yıllık Kar\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiyat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>15.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>15.485714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>19.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>16.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>17.814285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>16.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>20.514284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>18.085714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>10.835714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>12.857142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>11.385714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>15.528571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>12.857142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>13.535714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>16.371428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>22.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>30.628571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>37.785713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>41.371429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>75.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>75.728569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>79.300003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>159.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Fiyat\n",
       "Date                  \n",
       "2018-03-31   15.542857\n",
       "2018-06-30   15.485714\n",
       "2018-09-30   19.085714\n",
       "2018-12-31   16.642857\n",
       "2019-03-31   17.814285\n",
       "2019-06-30   16.400000\n",
       "2019-09-30   20.514284\n",
       "2019-12-31   18.085714\n",
       "2020-03-31   10.835714\n",
       "2020-06-30   12.857142\n",
       "2020-09-30   11.385714\n",
       "2020-12-31   15.528571\n",
       "2021-03-31   12.857142\n",
       "2021-06-30   13.535714\n",
       "2021-09-30   16.371428\n",
       "2021-12-31   22.057142\n",
       "2022-03-31   30.628571\n",
       "2022-06-30   37.785713\n",
       "2022-09-30   41.371429\n",
       "2022-12-31   75.699997\n",
       "2023-03-31   75.728569\n",
       "2023-06-30   79.300003\n",
       "2023-09-30  159.000000"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fiyat=pd.DataFrame()\n",
    "dönem=pd.date_range(start=\"2018-03-30\",end=\"2023-10-01\",freq=\"Q\")\n",
    "fiyat[\"Fiyat\"]=tuprs[\"Close\"].resample(\"Q\").last()\n",
    "\n",
    "\n",
    "\n",
    "# Güncellenmiş DataFrame'i gösterelim\n",
    "fiyat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>TUPRS</th>\n",
       "      <th>Dönen Varlıklar</th>\n",
       "      <th>Nakit ve Nakit Benzerleri</th>\n",
       "      <th>Finansal Yatırımlar</th>\n",
       "      <th>Ticari Alacaklar</th>\n",
       "      <th>Finans Sektörü Faaliyetlerinden Alacaklar</th>\n",
       "      <th>Diğer Alacaklar</th>\n",
       "      <th>Stoklar</th>\n",
       "      <th>Canlı Varlıklar</th>\n",
       "      <th>Diğer Dönen Varlıklar</th>\n",
       "      <th>(Ara Toplam)</th>\n",
       "      <th>...</th>\n",
       "      <th>Müşteri Sözleşmelerinden Doğan Varlıklar</th>\n",
       "      <th>Müşteri Sözleşmelerinden Doğan Varlıklar</th>\n",
       "      <th>Kullanım Hakkı Varlıkları</th>\n",
       "      <th>Müşteri Söz. Doğan Yük.</th>\n",
       "      <th>Ertelenmiş Gelirler (Müşteri Söz. Doğan Yük. Dış.Kal.)</th>\n",
       "      <th>Müşteri Söz.Doğan Yük.</th>\n",
       "      <th>Ertelenmiş Gelirler (Müşteri Söz.Doğan Yük. Dış.Kal.)</th>\n",
       "      <th>Finansman Gideri Öncesi Faaliyet Karı/Zararı</th>\n",
       "      <th>Çeyreklik Kar</th>\n",
       "      <th>Yıllıklandırılmış Kar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>20311172000</td>\n",
       "      <td>6716597000</td>\n",
       "      <td>0</td>\n",
       "      <td>5495351000</td>\n",
       "      <td>0</td>\n",
       "      <td>95708000</td>\n",
       "      <td>6716365000</td>\n",
       "      <td>0</td>\n",
       "      <td>1287151000</td>\n",
       "      <td>20311172000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>742391000</td>\n",
       "      <td>386787000</td>\n",
       "      <td>3.351584e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>21951521000</td>\n",
       "      <td>5886217000</td>\n",
       "      <td>0</td>\n",
       "      <td>6768881000</td>\n",
       "      <td>0</td>\n",
       "      <td>169479000</td>\n",
       "      <td>7702377000</td>\n",
       "      <td>0</td>\n",
       "      <td>1424567000</td>\n",
       "      <td>21951521000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1894425000</td>\n",
       "      <td>1035290000</td>\n",
       "      <td>2.923353e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>30250105000</td>\n",
       "      <td>9957795000</td>\n",
       "      <td>0</td>\n",
       "      <td>9319393000</td>\n",
       "      <td>0</td>\n",
       "      <td>102835000</td>\n",
       "      <td>8495315000</td>\n",
       "      <td>0</td>\n",
       "      <td>2374767000</td>\n",
       "      <td>30250105000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2743321000</td>\n",
       "      <td>550687000</td>\n",
       "      <td>2.474222e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>20962205000</td>\n",
       "      <td>5982828000</td>\n",
       "      <td>0</td>\n",
       "      <td>5428738000</td>\n",
       "      <td>0</td>\n",
       "      <td>29048000</td>\n",
       "      <td>6764745000</td>\n",
       "      <td>0</td>\n",
       "      <td>2756846000</td>\n",
       "      <td>20962205000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4878000</td>\n",
       "      <td>0</td>\n",
       "      <td>3638000</td>\n",
       "      <td>5992802000</td>\n",
       "      <td>1788681000</td>\n",
       "      <td>3.761445e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>28061876000</td>\n",
       "      <td>11328854000</td>\n",
       "      <td>0</td>\n",
       "      <td>6429066000</td>\n",
       "      <td>0</td>\n",
       "      <td>90855000</td>\n",
       "      <td>8410339000</td>\n",
       "      <td>0</td>\n",
       "      <td>1802762000</td>\n",
       "      <td>28061876000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>197898000</td>\n",
       "      <td>0</td>\n",
       "      <td>6856000</td>\n",
       "      <td>0</td>\n",
       "      <td>6102000</td>\n",
       "      <td>291064000</td>\n",
       "      <td>-362295000</td>\n",
       "      <td>3.012363e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>29730712000</td>\n",
       "      <td>9889401000</td>\n",
       "      <td>0</td>\n",
       "      <td>8699923000</td>\n",
       "      <td>0</td>\n",
       "      <td>58621000</td>\n",
       "      <td>8384370000</td>\n",
       "      <td>0</td>\n",
       "      <td>2698397000</td>\n",
       "      <td>29730712000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>194004000</td>\n",
       "      <td>0</td>\n",
       "      <td>14145000</td>\n",
       "      <td>0</td>\n",
       "      <td>7854000</td>\n",
       "      <td>1670500000</td>\n",
       "      <td>885904000</td>\n",
       "      <td>2.862977e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>28945418000</td>\n",
       "      <td>10106005000</td>\n",
       "      <td>0</td>\n",
       "      <td>6063377000</td>\n",
       "      <td>0</td>\n",
       "      <td>64775000</td>\n",
       "      <td>9488710000</td>\n",
       "      <td>0</td>\n",
       "      <td>3222551000</td>\n",
       "      <td>28945418000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>210639000</td>\n",
       "      <td>0</td>\n",
       "      <td>10043000</td>\n",
       "      <td>0</td>\n",
       "      <td>7593000</td>\n",
       "      <td>2341383000</td>\n",
       "      <td>-140172000</td>\n",
       "      <td>2.172118e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>27918955000</td>\n",
       "      <td>10652009000</td>\n",
       "      <td>0</td>\n",
       "      <td>4787906000</td>\n",
       "      <td>0</td>\n",
       "      <td>45617000</td>\n",
       "      <td>9468581000</td>\n",
       "      <td>0</td>\n",
       "      <td>2964842000</td>\n",
       "      <td>27918955000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>200701000</td>\n",
       "      <td>0</td>\n",
       "      <td>20813000</td>\n",
       "      <td>0</td>\n",
       "      <td>7820000</td>\n",
       "      <td>2427378000</td>\n",
       "      <td>201893000</td>\n",
       "      <td>5.853300e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>22938512000</td>\n",
       "      <td>10995278000</td>\n",
       "      <td>0</td>\n",
       "      <td>4422517000</td>\n",
       "      <td>0</td>\n",
       "      <td>64655000</td>\n",
       "      <td>5300465000</td>\n",
       "      <td>0</td>\n",
       "      <td>2155597000</td>\n",
       "      <td>22938512000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>195416000</td>\n",
       "      <td>0</td>\n",
       "      <td>4118000</td>\n",
       "      <td>0</td>\n",
       "      <td>8347000</td>\n",
       "      <td>-1884615000</td>\n",
       "      <td>-2249085000</td>\n",
       "      <td>-1.301460e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>27389948000</td>\n",
       "      <td>18177886000</td>\n",
       "      <td>0</td>\n",
       "      <td>1721218000</td>\n",
       "      <td>0</td>\n",
       "      <td>46082000</td>\n",
       "      <td>5503471000</td>\n",
       "      <td>0</td>\n",
       "      <td>1941291000</td>\n",
       "      <td>27389948000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>182686000</td>\n",
       "      <td>0</td>\n",
       "      <td>44803000</td>\n",
       "      <td>0</td>\n",
       "      <td>10545000</td>\n",
       "      <td>-1663474000</td>\n",
       "      <td>-162291000</td>\n",
       "      <td>-2.349655e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>25053851000</td>\n",
       "      <td>10891062000</td>\n",
       "      <td>0</td>\n",
       "      <td>4675650000</td>\n",
       "      <td>0</td>\n",
       "      <td>46256000</td>\n",
       "      <td>7233811000</td>\n",
       "      <td>0</td>\n",
       "      <td>2207072000</td>\n",
       "      <td>25053851000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202462000</td>\n",
       "      <td>0</td>\n",
       "      <td>41065000</td>\n",
       "      <td>0</td>\n",
       "      <td>10471000</td>\n",
       "      <td>-1386302000</td>\n",
       "      <td>-394966000</td>\n",
       "      <td>-2.604449e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>31281757000</td>\n",
       "      <td>20319133000</td>\n",
       "      <td>0</td>\n",
       "      <td>1840427000</td>\n",
       "      <td>0</td>\n",
       "      <td>19464000</td>\n",
       "      <td>7700630000</td>\n",
       "      <td>0</td>\n",
       "      <td>1402103000</td>\n",
       "      <td>31281757000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202147000</td>\n",
       "      <td>0</td>\n",
       "      <td>129254000</td>\n",
       "      <td>0</td>\n",
       "      <td>9504000</td>\n",
       "      <td>-646837000</td>\n",
       "      <td>383417000</td>\n",
       "      <td>-2.422925e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>39091232000</td>\n",
       "      <td>21144505000</td>\n",
       "      <td>0</td>\n",
       "      <td>3757556000</td>\n",
       "      <td>0</td>\n",
       "      <td>22590000</td>\n",
       "      <td>11385118000</td>\n",
       "      <td>0</td>\n",
       "      <td>2781463000</td>\n",
       "      <td>39091232000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>199664000</td>\n",
       "      <td>0</td>\n",
       "      <td>48353000</td>\n",
       "      <td>0</td>\n",
       "      <td>10913000</td>\n",
       "      <td>-460834000</td>\n",
       "      <td>-652256000</td>\n",
       "      <td>-8.260960e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>43652956000</td>\n",
       "      <td>19365360000</td>\n",
       "      <td>0</td>\n",
       "      <td>6004582000</td>\n",
       "      <td>0</td>\n",
       "      <td>21106000</td>\n",
       "      <td>15064903000</td>\n",
       "      <td>0</td>\n",
       "      <td>3197005000</td>\n",
       "      <td>43652956000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>198336000</td>\n",
       "      <td>0</td>\n",
       "      <td>7120000</td>\n",
       "      <td>0</td>\n",
       "      <td>12324000</td>\n",
       "      <td>1537753000</td>\n",
       "      <td>1722416000</td>\n",
       "      <td>1.058611e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>49497111000</td>\n",
       "      <td>20797151000</td>\n",
       "      <td>0</td>\n",
       "      <td>7940663000</td>\n",
       "      <td>0</td>\n",
       "      <td>24345000</td>\n",
       "      <td>16259918000</td>\n",
       "      <td>0</td>\n",
       "      <td>4475034000</td>\n",
       "      <td>49497111000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>189720000</td>\n",
       "      <td>0</td>\n",
       "      <td>31013000</td>\n",
       "      <td>0</td>\n",
       "      <td>11158000</td>\n",
       "      <td>4075702000</td>\n",
       "      <td>1066251000</td>\n",
       "      <td>2.519828e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>66515384000</td>\n",
       "      <td>21176186000</td>\n",
       "      <td>0</td>\n",
       "      <td>13796682000</td>\n",
       "      <td>0</td>\n",
       "      <td>20566000</td>\n",
       "      <td>23757526000</td>\n",
       "      <td>0</td>\n",
       "      <td>7764424000</td>\n",
       "      <td>66515384000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>185775000</td>\n",
       "      <td>0</td>\n",
       "      <td>32315000</td>\n",
       "      <td>0</td>\n",
       "      <td>9987000</td>\n",
       "      <td>1477304000</td>\n",
       "      <td>1476813000</td>\n",
       "      <td>3.613224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>78450795000</td>\n",
       "      <td>19315983000</td>\n",
       "      <td>262819000</td>\n",
       "      <td>19190824000</td>\n",
       "      <td>0</td>\n",
       "      <td>24383000</td>\n",
       "      <td>34126130000</td>\n",
       "      <td>0</td>\n",
       "      <td>5530656000</td>\n",
       "      <td>78450795000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>181142000</td>\n",
       "      <td>0</td>\n",
       "      <td>36760000</td>\n",
       "      <td>0</td>\n",
       "      <td>8422000</td>\n",
       "      <td>3302824000</td>\n",
       "      <td>1145064000</td>\n",
       "      <td>5.410544e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>120342056000</td>\n",
       "      <td>37229784000</td>\n",
       "      <td>273628000</td>\n",
       "      <td>26999095000</td>\n",
       "      <td>0</td>\n",
       "      <td>31477000</td>\n",
       "      <td>46900891000</td>\n",
       "      <td>0</td>\n",
       "      <td>8907181000</td>\n",
       "      <td>120342056000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>136663000</td>\n",
       "      <td>0</td>\n",
       "      <td>685642000</td>\n",
       "      <td>0</td>\n",
       "      <td>10453000</td>\n",
       "      <td>15958296000</td>\n",
       "      <td>10853901000</td>\n",
       "      <td>1.454203e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>121808910000</td>\n",
       "      <td>40093559000</td>\n",
       "      <td>2226497000</td>\n",
       "      <td>29299925000</td>\n",
       "      <td>0</td>\n",
       "      <td>83193000</td>\n",
       "      <td>35168735000</td>\n",
       "      <td>0</td>\n",
       "      <td>14937001000</td>\n",
       "      <td>121808910000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148942000</td>\n",
       "      <td>0</td>\n",
       "      <td>312976000</td>\n",
       "      <td>0</td>\n",
       "      <td>11621000</td>\n",
       "      <td>30001429000</td>\n",
       "      <td>11618668000</td>\n",
       "      <td>2.509445e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>119710705000</td>\n",
       "      <td>47635158000</td>\n",
       "      <td>2135177000</td>\n",
       "      <td>21417520000</td>\n",
       "      <td>0</td>\n",
       "      <td>40622000</td>\n",
       "      <td>36046008000</td>\n",
       "      <td>0</td>\n",
       "      <td>12436220000</td>\n",
       "      <td>119710705000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>219531000</td>\n",
       "      <td>0</td>\n",
       "      <td>116249000</td>\n",
       "      <td>0</td>\n",
       "      <td>3199000</td>\n",
       "      <td>45971985000</td>\n",
       "      <td>17642934000</td>\n",
       "      <td>4.126057e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>100817820000</td>\n",
       "      <td>38905040000</td>\n",
       "      <td>241616000</td>\n",
       "      <td>17067008000</td>\n",
       "      <td>0</td>\n",
       "      <td>63249000</td>\n",
       "      <td>35411730000</td>\n",
       "      <td>0</td>\n",
       "      <td>9129177000</td>\n",
       "      <td>100817820000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240747000</td>\n",
       "      <td>0</td>\n",
       "      <td>201905000</td>\n",
       "      <td>0</td>\n",
       "      <td>11410000</td>\n",
       "      <td>11437621000</td>\n",
       "      <td>6792657000</td>\n",
       "      <td>4.690816e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>128564158000</td>\n",
       "      <td>46342824000</td>\n",
       "      <td>66140000</td>\n",
       "      <td>30630746000</td>\n",
       "      <td>0</td>\n",
       "      <td>81321000</td>\n",
       "      <td>39449976000</td>\n",
       "      <td>0</td>\n",
       "      <td>11993151000</td>\n",
       "      <td>128564158000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>241909000</td>\n",
       "      <td>0</td>\n",
       "      <td>130303000</td>\n",
       "      <td>0</td>\n",
       "      <td>11519000</td>\n",
       "      <td>20131398000</td>\n",
       "      <td>7331480000</td>\n",
       "      <td>4.338574e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "TUPRS      Dönen Varlıklar Nakit ve Nakit Benzerleri Finansal Yatırımlar  \\\n",
       "2018-03-31     20311172000                6716597000                   0   \n",
       "2018-06-30     21951521000                5886217000                   0   \n",
       "2018-09-30     30250105000                9957795000                   0   \n",
       "2018-12-31     20962205000                5982828000                   0   \n",
       "2019-03-31     28061876000               11328854000                   0   \n",
       "2019-06-30     29730712000                9889401000                   0   \n",
       "2019-09-30     28945418000               10106005000                   0   \n",
       "2019-12-31     27918955000               10652009000                   0   \n",
       "2020-03-31     22938512000               10995278000                   0   \n",
       "2020-06-30     27389948000               18177886000                   0   \n",
       "2020-09-30     25053851000               10891062000                   0   \n",
       "2020-12-31     31281757000               20319133000                   0   \n",
       "2021-03-31     39091232000               21144505000                   0   \n",
       "2021-06-30     43652956000               19365360000                   0   \n",
       "2021-09-30     49497111000               20797151000                   0   \n",
       "2021-12-31     66515384000               21176186000                   0   \n",
       "2022-03-31     78450795000               19315983000           262819000   \n",
       "2022-06-30    120342056000               37229784000           273628000   \n",
       "2022-09-30    121808910000               40093559000          2226497000   \n",
       "2022-12-31    119710705000               47635158000          2135177000   \n",
       "2023-03-31    100817820000               38905040000           241616000   \n",
       "2023-06-30    128564158000               46342824000            66140000   \n",
       "\n",
       "TUPRS      Ticari Alacaklar Finans Sektörü Faaliyetlerinden Alacaklar  \\\n",
       "2018-03-31       5495351000                                         0   \n",
       "2018-06-30       6768881000                                         0   \n",
       "2018-09-30       9319393000                                         0   \n",
       "2018-12-31       5428738000                                         0   \n",
       "2019-03-31       6429066000                                         0   \n",
       "2019-06-30       8699923000                                         0   \n",
       "2019-09-30       6063377000                                         0   \n",
       "2019-12-31       4787906000                                         0   \n",
       "2020-03-31       4422517000                                         0   \n",
       "2020-06-30       1721218000                                         0   \n",
       "2020-09-30       4675650000                                         0   \n",
       "2020-12-31       1840427000                                         0   \n",
       "2021-03-31       3757556000                                         0   \n",
       "2021-06-30       6004582000                                         0   \n",
       "2021-09-30       7940663000                                         0   \n",
       "2021-12-31      13796682000                                         0   \n",
       "2022-03-31      19190824000                                         0   \n",
       "2022-06-30      26999095000                                         0   \n",
       "2022-09-30      29299925000                                         0   \n",
       "2022-12-31      21417520000                                         0   \n",
       "2023-03-31      17067008000                                         0   \n",
       "2023-06-30      30630746000                                         0   \n",
       "\n",
       "TUPRS      Diğer Alacaklar      Stoklar Canlı Varlıklar Diğer Dönen Varlıklar  \\\n",
       "2018-03-31        95708000   6716365000               0            1287151000   \n",
       "2018-06-30       169479000   7702377000               0            1424567000   \n",
       "2018-09-30       102835000   8495315000               0            2374767000   \n",
       "2018-12-31        29048000   6764745000               0            2756846000   \n",
       "2019-03-31        90855000   8410339000               0            1802762000   \n",
       "2019-06-30        58621000   8384370000               0            2698397000   \n",
       "2019-09-30        64775000   9488710000               0            3222551000   \n",
       "2019-12-31        45617000   9468581000               0            2964842000   \n",
       "2020-03-31        64655000   5300465000               0            2155597000   \n",
       "2020-06-30        46082000   5503471000               0            1941291000   \n",
       "2020-09-30        46256000   7233811000               0            2207072000   \n",
       "2020-12-31        19464000   7700630000               0            1402103000   \n",
       "2021-03-31        22590000  11385118000               0            2781463000   \n",
       "2021-06-30        21106000  15064903000               0            3197005000   \n",
       "2021-09-30        24345000  16259918000               0            4475034000   \n",
       "2021-12-31        20566000  23757526000               0            7764424000   \n",
       "2022-03-31        24383000  34126130000               0            5530656000   \n",
       "2022-06-30        31477000  46900891000               0            8907181000   \n",
       "2022-09-30        83193000  35168735000               0           14937001000   \n",
       "2022-12-31        40622000  36046008000               0           12436220000   \n",
       "2023-03-31        63249000  35411730000               0            9129177000   \n",
       "2023-06-30        81321000  39449976000               0           11993151000   \n",
       "\n",
       "TUPRS       (Ara Toplam)  ... Müşteri Sözleşmelerinden Doğan Varlıklar  \\\n",
       "2018-03-31   20311172000  ...                                        0   \n",
       "2018-06-30   21951521000  ...                                        0   \n",
       "2018-09-30   30250105000  ...                                        0   \n",
       "2018-12-31   20962205000  ...                                        0   \n",
       "2019-03-31   28061876000  ...                                        0   \n",
       "2019-06-30   29730712000  ...                                        0   \n",
       "2019-09-30   28945418000  ...                                        0   \n",
       "2019-12-31   27918955000  ...                                        0   \n",
       "2020-03-31   22938512000  ...                                        0   \n",
       "2020-06-30   27389948000  ...                                        0   \n",
       "2020-09-30   25053851000  ...                                        0   \n",
       "2020-12-31   31281757000  ...                                        0   \n",
       "2021-03-31   39091232000  ...                                        0   \n",
       "2021-06-30   43652956000  ...                                        0   \n",
       "2021-09-30   49497111000  ...                                        0   \n",
       "2021-12-31   66515384000  ...                                        0   \n",
       "2022-03-31   78450795000  ...                                        0   \n",
       "2022-06-30  120342056000  ...                                        0   \n",
       "2022-09-30  121808910000  ...                                        0   \n",
       "2022-12-31  119710705000  ...                                        0   \n",
       "2023-03-31  100817820000  ...                                        0   \n",
       "2023-06-30  128564158000  ...                                        0   \n",
       "\n",
       "TUPRS      Müşteri Sözleşmelerinden Doğan Varlıklar Kullanım Hakkı Varlıkları  \\\n",
       "2018-03-31                                        0                         0   \n",
       "2018-06-30                                        0                         0   \n",
       "2018-09-30                                        0                         0   \n",
       "2018-12-31                                        0                         0   \n",
       "2019-03-31                                        0                 197898000   \n",
       "2019-06-30                                        0                 194004000   \n",
       "2019-09-30                                        0                 210639000   \n",
       "2019-12-31                                        0                 200701000   \n",
       "2020-03-31                                        0                 195416000   \n",
       "2020-06-30                                        0                 182686000   \n",
       "2020-09-30                                        0                 202462000   \n",
       "2020-12-31                                        0                 202147000   \n",
       "2021-03-31                                        0                 199664000   \n",
       "2021-06-30                                        0                 198336000   \n",
       "2021-09-30                                        0                 189720000   \n",
       "2021-12-31                                        0                 185775000   \n",
       "2022-03-31                                        0                 181142000   \n",
       "2022-06-30                                        0                 136663000   \n",
       "2022-09-30                                        0                 148942000   \n",
       "2022-12-31                                        0                 219531000   \n",
       "2023-03-31                                        0                 240747000   \n",
       "2023-06-30                                        0                 241909000   \n",
       "\n",
       "TUPRS      Müşteri Söz. Doğan Yük.  \\\n",
       "2018-03-31                       0   \n",
       "2018-06-30                       0   \n",
       "2018-09-30                       0   \n",
       "2018-12-31                       0   \n",
       "2019-03-31                       0   \n",
       "2019-06-30                       0   \n",
       "2019-09-30                       0   \n",
       "2019-12-31                       0   \n",
       "2020-03-31                       0   \n",
       "2020-06-30                       0   \n",
       "2020-09-30                       0   \n",
       "2020-12-31                       0   \n",
       "2021-03-31                       0   \n",
       "2021-06-30                       0   \n",
       "2021-09-30                       0   \n",
       "2021-12-31                       0   \n",
       "2022-03-31                       0   \n",
       "2022-06-30                       0   \n",
       "2022-09-30                       0   \n",
       "2022-12-31                       0   \n",
       "2023-03-31                       0   \n",
       "2023-06-30                       0   \n",
       "\n",
       "TUPRS      Ertelenmiş Gelirler (Müşteri Söz. Doğan Yük. Dış.Kal.)  \\\n",
       "2018-03-31                                                  0       \n",
       "2018-06-30                                                  0       \n",
       "2018-09-30                                                  0       \n",
       "2018-12-31                                            4878000       \n",
       "2019-03-31                                            6856000       \n",
       "2019-06-30                                           14145000       \n",
       "2019-09-30                                           10043000       \n",
       "2019-12-31                                           20813000       \n",
       "2020-03-31                                            4118000       \n",
       "2020-06-30                                           44803000       \n",
       "2020-09-30                                           41065000       \n",
       "2020-12-31                                          129254000       \n",
       "2021-03-31                                           48353000       \n",
       "2021-06-30                                            7120000       \n",
       "2021-09-30                                           31013000       \n",
       "2021-12-31                                           32315000       \n",
       "2022-03-31                                           36760000       \n",
       "2022-06-30                                          685642000       \n",
       "2022-09-30                                          312976000       \n",
       "2022-12-31                                          116249000       \n",
       "2023-03-31                                          201905000       \n",
       "2023-06-30                                          130303000       \n",
       "\n",
       "TUPRS      Müşteri Söz.Doğan Yük.  \\\n",
       "2018-03-31                      0   \n",
       "2018-06-30                      0   \n",
       "2018-09-30                      0   \n",
       "2018-12-31                      0   \n",
       "2019-03-31                      0   \n",
       "2019-06-30                      0   \n",
       "2019-09-30                      0   \n",
       "2019-12-31                      0   \n",
       "2020-03-31                      0   \n",
       "2020-06-30                      0   \n",
       "2020-09-30                      0   \n",
       "2020-12-31                      0   \n",
       "2021-03-31                      0   \n",
       "2021-06-30                      0   \n",
       "2021-09-30                      0   \n",
       "2021-12-31                      0   \n",
       "2022-03-31                      0   \n",
       "2022-06-30                      0   \n",
       "2022-09-30                      0   \n",
       "2022-12-31                      0   \n",
       "2023-03-31                      0   \n",
       "2023-06-30                      0   \n",
       "\n",
       "TUPRS      Ertelenmiş Gelirler (Müşteri Söz.Doğan Yük. Dış.Kal.)  \\\n",
       "2018-03-31                                                  0      \n",
       "2018-06-30                                                  0      \n",
       "2018-09-30                                                  0      \n",
       "2018-12-31                                            3638000      \n",
       "2019-03-31                                            6102000      \n",
       "2019-06-30                                            7854000      \n",
       "2019-09-30                                            7593000      \n",
       "2019-12-31                                            7820000      \n",
       "2020-03-31                                            8347000      \n",
       "2020-06-30                                           10545000      \n",
       "2020-09-30                                           10471000      \n",
       "2020-12-31                                            9504000      \n",
       "2021-03-31                                           10913000      \n",
       "2021-06-30                                           12324000      \n",
       "2021-09-30                                           11158000      \n",
       "2021-12-31                                            9987000      \n",
       "2022-03-31                                            8422000      \n",
       "2022-06-30                                           10453000      \n",
       "2022-09-30                                           11621000      \n",
       "2022-12-31                                            3199000      \n",
       "2023-03-31                                           11410000      \n",
       "2023-06-30                                           11519000      \n",
       "\n",
       "TUPRS      Finansman Gideri Öncesi Faaliyet Karı/Zararı Çeyreklik Kar  \\\n",
       "2018-03-31                                    742391000     386787000   \n",
       "2018-06-30                                   1894425000    1035290000   \n",
       "2018-09-30                                   2743321000     550687000   \n",
       "2018-12-31                                   5992802000    1788681000   \n",
       "2019-03-31                                    291064000    -362295000   \n",
       "2019-06-30                                   1670500000     885904000   \n",
       "2019-09-30                                   2341383000    -140172000   \n",
       "2019-12-31                                   2427378000     201893000   \n",
       "2020-03-31                                  -1884615000   -2249085000   \n",
       "2020-06-30                                  -1663474000    -162291000   \n",
       "2020-09-30                                  -1386302000    -394966000   \n",
       "2020-12-31                                   -646837000     383417000   \n",
       "2021-03-31                                   -460834000    -652256000   \n",
       "2021-06-30                                   1537753000    1722416000   \n",
       "2021-09-30                                   4075702000    1066251000   \n",
       "2021-12-31                                   1477304000    1476813000   \n",
       "2022-03-31                                   3302824000    1145064000   \n",
       "2022-06-30                                  15958296000   10853901000   \n",
       "2022-09-30                                  30001429000   11618668000   \n",
       "2022-12-31                                  45971985000   17642934000   \n",
       "2023-03-31                                  11437621000    6792657000   \n",
       "2023-06-30                                  20131398000    7331480000   \n",
       "\n",
       "TUPRS      Yıllıklandırılmış Kar  \n",
       "2018-03-31          3.351584e+09  \n",
       "2018-06-30          2.923353e+09  \n",
       "2018-09-30          2.474222e+09  \n",
       "2018-12-31          3.761445e+09  \n",
       "2019-03-31          3.012363e+09  \n",
       "2019-06-30          2.862977e+09  \n",
       "2019-09-30          2.172118e+09  \n",
       "2019-12-31          5.853300e+08  \n",
       "2020-03-31         -1.301460e+09  \n",
       "2020-06-30         -2.349655e+09  \n",
       "2020-09-30         -2.604449e+09  \n",
       "2020-12-31         -2.422925e+09  \n",
       "2021-03-31         -8.260960e+08  \n",
       "2021-06-30          1.058611e+09  \n",
       "2021-09-30          2.519828e+09  \n",
       "2021-12-31          3.613224e+09  \n",
       "2022-03-31          5.410544e+09  \n",
       "2022-06-30          1.454203e+10  \n",
       "2022-09-30          2.509445e+10  \n",
       "2022-12-31          4.126057e+10  \n",
       "2023-03-31          4.690816e+10  \n",
       "2023-06-30          4.338574e+10  \n",
       "\n",
       "[22 rows x 149 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dönem=pd.date_range(start=\"2018-03-30\",end=\"2023-07-01\",freq=\"Q\")\n",
    "bilanco=bilanco.set_index(dönem)\n",
    "bilanco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiyat</th>\n",
       "      <th>Ödenmiş Sermaye</th>\n",
       "      <th>Özkaynak</th>\n",
       "      <th>Duran Varlıklar</th>\n",
       "      <th>Dönen Varlıklar</th>\n",
       "      <th>Kısa Vadeli Yükümlülükler</th>\n",
       "      <th>Uzun Vadeli Yükümlülükler</th>\n",
       "      <th>Toplam Yükümlülük</th>\n",
       "      <th>Çeyreklik Kar</th>\n",
       "      <th>Yıllıklandırılmış Kar</th>\n",
       "      <th>HBK</th>\n",
       "      <th>Toplam Varlıklar</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>15.542857</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>7.341450e+09</td>\n",
       "      <td>1.787333e+10</td>\n",
       "      <td>2.031117e+10</td>\n",
       "      <td>2.106889e+10</td>\n",
       "      <td>9.774157e+09</td>\n",
       "      <td>3.084305e+10</td>\n",
       "      <td>3.867870e+08</td>\n",
       "      <td>3.351584e+09</td>\n",
       "      <td>13.383905</td>\n",
       "      <td>3.818450e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>15.485714</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>8.052826e+09</td>\n",
       "      <td>1.825045e+10</td>\n",
       "      <td>2.195152e+10</td>\n",
       "      <td>2.072457e+10</td>\n",
       "      <td>1.142458e+10</td>\n",
       "      <td>3.214914e+10</td>\n",
       "      <td>1.035290e+09</td>\n",
       "      <td>2.923353e+09</td>\n",
       "      <td>11.673847</td>\n",
       "      <td>4.020197e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>19.085714</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>7.517618e+09</td>\n",
       "      <td>1.881608e+10</td>\n",
       "      <td>3.025010e+10</td>\n",
       "      <td>2.709297e+10</td>\n",
       "      <td>1.445560e+10</td>\n",
       "      <td>4.154857e+10</td>\n",
       "      <td>5.506870e+08</td>\n",
       "      <td>2.474222e+09</td>\n",
       "      <td>9.880329</td>\n",
       "      <td>4.906618e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>16.642857</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>9.945829e+09</td>\n",
       "      <td>1.907352e+10</td>\n",
       "      <td>2.096220e+10</td>\n",
       "      <td>1.594954e+10</td>\n",
       "      <td>1.414036e+10</td>\n",
       "      <td>3.008990e+10</td>\n",
       "      <td>1.788681e+09</td>\n",
       "      <td>3.761445e+09</td>\n",
       "      <td>15.020605</td>\n",
       "      <td>4.003573e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>17.814285</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.181468e+10</td>\n",
       "      <td>2.554416e+10</td>\n",
       "      <td>2.806188e+10</td>\n",
       "      <td>2.514518e+10</td>\n",
       "      <td>1.664619e+10</td>\n",
       "      <td>4.179136e+10</td>\n",
       "      <td>-3.622950e+08</td>\n",
       "      <td>3.012363e+09</td>\n",
       "      <td>12.029291</td>\n",
       "      <td>5.360604e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>16.400000</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.302556e+10</td>\n",
       "      <td>2.655035e+10</td>\n",
       "      <td>2.973071e+10</td>\n",
       "      <td>2.699196e+10</td>\n",
       "      <td>1.626355e+10</td>\n",
       "      <td>4.325550e+10</td>\n",
       "      <td>8.859040e+08</td>\n",
       "      <td>2.862977e+09</td>\n",
       "      <td>11.432747</td>\n",
       "      <td>5.628106e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>20.514284</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.299984e+10</td>\n",
       "      <td>2.673530e+10</td>\n",
       "      <td>2.894542e+10</td>\n",
       "      <td>2.735773e+10</td>\n",
       "      <td>1.532315e+10</td>\n",
       "      <td>4.268088e+10</td>\n",
       "      <td>-1.401720e+08</td>\n",
       "      <td>2.172118e+09</td>\n",
       "      <td>8.673934</td>\n",
       "      <td>5.568072e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>18.085714</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.313654e+10</td>\n",
       "      <td>2.759260e+10</td>\n",
       "      <td>2.791896e+10</td>\n",
       "      <td>2.812027e+10</td>\n",
       "      <td>1.425475e+10</td>\n",
       "      <td>4.237502e+10</td>\n",
       "      <td>2.018930e+08</td>\n",
       "      <td>5.853300e+08</td>\n",
       "      <td>2.337403</td>\n",
       "      <td>5.551156e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>10.835714</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.079117e+10</td>\n",
       "      <td>2.801768e+10</td>\n",
       "      <td>2.293851e+10</td>\n",
       "      <td>2.470684e+10</td>\n",
       "      <td>1.545818e+10</td>\n",
       "      <td>4.016502e+10</td>\n",
       "      <td>-2.249085e+09</td>\n",
       "      <td>-1.301460e+09</td>\n",
       "      <td>-5.197130</td>\n",
       "      <td>5.095619e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>12.857142</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.032834e+10</td>\n",
       "      <td>2.816125e+10</td>\n",
       "      <td>2.738995e+10</td>\n",
       "      <td>2.644354e+10</td>\n",
       "      <td>1.877932e+10</td>\n",
       "      <td>4.522286e+10</td>\n",
       "      <td>-1.622910e+08</td>\n",
       "      <td>-2.349655e+09</td>\n",
       "      <td>-9.382894</td>\n",
       "      <td>5.555120e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>11.385714</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>9.870505e+09</td>\n",
       "      <td>2.856028e+10</td>\n",
       "      <td>2.505385e+10</td>\n",
       "      <td>2.188961e+10</td>\n",
       "      <td>2.185402e+10</td>\n",
       "      <td>4.374363e+10</td>\n",
       "      <td>-3.949660e+08</td>\n",
       "      <td>-2.604449e+09</td>\n",
       "      <td>-10.400365</td>\n",
       "      <td>5.361414e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>15.528571</td>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>1.292543e+10</td>\n",
       "      <td>3.233247e+10</td>\n",
       "      <td>3.128176e+10</td>\n",
       "      <td>2.876643e+10</td>\n",
       "      <td>2.192236e+10</td>\n",
       "      <td>5.068880e+10</td>\n",
       "      <td>3.834170e+08</td>\n",
       "      <td>-2.422925e+09</td>\n",
       "      <td>-8.802410</td>\n",
       "      <td>6.361423e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>12.857142</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.072768e+10</td>\n",
       "      <td>3.069589e+10</td>\n",
       "      <td>3.909123e+10</td>\n",
       "      <td>3.886958e+10</td>\n",
       "      <td>2.018986e+10</td>\n",
       "      <td>5.905944e+10</td>\n",
       "      <td>-6.522560e+08</td>\n",
       "      <td>-8.260960e+08</td>\n",
       "      <td>-3.298855</td>\n",
       "      <td>6.978712e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>13.535714</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.252027e+10</td>\n",
       "      <td>3.138632e+10</td>\n",
       "      <td>4.365296e+10</td>\n",
       "      <td>4.379602e+10</td>\n",
       "      <td>1.872299e+10</td>\n",
       "      <td>6.251901e+10</td>\n",
       "      <td>1.722416e+09</td>\n",
       "      <td>1.058611e+09</td>\n",
       "      <td>4.227359</td>\n",
       "      <td>7.503928e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>16.371428</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.380030e+10</td>\n",
       "      <td>3.157183e+10</td>\n",
       "      <td>4.949711e+10</td>\n",
       "      <td>4.922227e+10</td>\n",
       "      <td>1.804636e+10</td>\n",
       "      <td>6.726864e+10</td>\n",
       "      <td>1.066251e+09</td>\n",
       "      <td>2.519828e+09</td>\n",
       "      <td>10.062447</td>\n",
       "      <td>8.106894e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>22.057142</td>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>1.908100e+10</td>\n",
       "      <td>3.974173e+10</td>\n",
       "      <td>6.651538e+10</td>\n",
       "      <td>6.482870e+10</td>\n",
       "      <td>2.234741e+10</td>\n",
       "      <td>8.717611e+10</td>\n",
       "      <td>1.476813e+09</td>\n",
       "      <td>3.613224e+09</td>\n",
       "      <td>13.126729</td>\n",
       "      <td>1.062571e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>30.628571</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.596500e+10</td>\n",
       "      <td>3.820097e+10</td>\n",
       "      <td>7.845080e+10</td>\n",
       "      <td>8.072647e+10</td>\n",
       "      <td>1.996030e+10</td>\n",
       "      <td>1.006868e+11</td>\n",
       "      <td>1.145064e+09</td>\n",
       "      <td>5.410544e+09</td>\n",
       "      <td>21.605964</td>\n",
       "      <td>1.166518e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>37.785713</td>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>2.815029e+10</td>\n",
       "      <td>3.969311e+10</td>\n",
       "      <td>1.203421e+11</td>\n",
       "      <td>1.120432e+11</td>\n",
       "      <td>1.984162e+10</td>\n",
       "      <td>1.318849e+11</td>\n",
       "      <td>1.085390e+10</td>\n",
       "      <td>1.454203e+10</td>\n",
       "      <td>58.070789</td>\n",
       "      <td>1.600352e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>41.371429</td>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>4.590032e+10</td>\n",
       "      <td>4.267824e+10</td>\n",
       "      <td>1.218089e+11</td>\n",
       "      <td>9.469894e+10</td>\n",
       "      <td>2.388789e+10</td>\n",
       "      <td>1.185868e+11</td>\n",
       "      <td>1.161867e+10</td>\n",
       "      <td>2.509445e+10</td>\n",
       "      <td>91.167331</td>\n",
       "      <td>1.644872e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>75.699997</td>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>6.718577e+10</td>\n",
       "      <td>4.918163e+10</td>\n",
       "      <td>1.197107e+11</td>\n",
       "      <td>7.889541e+10</td>\n",
       "      <td>2.281115e+10</td>\n",
       "      <td>1.017066e+11</td>\n",
       "      <td>1.764293e+10</td>\n",
       "      <td>4.126057e+10</td>\n",
       "      <td>149.898339</td>\n",
       "      <td>1.688923e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>75.728569</td>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>6.149322e+10</td>\n",
       "      <td>5.027799e+10</td>\n",
       "      <td>1.008178e+11</td>\n",
       "      <td>6.822506e+10</td>\n",
       "      <td>2.137752e+10</td>\n",
       "      <td>8.960258e+10</td>\n",
       "      <td>6.792657e+09</td>\n",
       "      <td>4.690816e+10</td>\n",
       "      <td>170.415866</td>\n",
       "      <td>1.510958e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>79.300003</td>\n",
       "      <td>1.926796e+09</td>\n",
       "      <td>6.835625e+10</td>\n",
       "      <td>5.338801e+10</td>\n",
       "      <td>1.285642e+11</td>\n",
       "      <td>8.548752e+10</td>\n",
       "      <td>2.810840e+10</td>\n",
       "      <td>1.135959e+11</td>\n",
       "      <td>7.331480e+09</td>\n",
       "      <td>4.338574e+10</td>\n",
       "      <td>22.517038</td>\n",
       "      <td>1.819522e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Fiyat  Ödenmiş Sermaye      Özkaynak  Duran Varlıklar  \\\n",
       "Date                                                                    \n",
       "2018-03-31  15.542857     2.504190e+08  7.341450e+09     1.787333e+10   \n",
       "2018-06-30  15.485714     2.504190e+08  8.052826e+09     1.825045e+10   \n",
       "2018-09-30  19.085714     2.504190e+08  7.517618e+09     1.881608e+10   \n",
       "2018-12-31  16.642857     2.504190e+08  9.945829e+09     1.907352e+10   \n",
       "2019-03-31  17.814285     2.504190e+08  1.181468e+10     2.554416e+10   \n",
       "2019-06-30  16.400000     2.504190e+08  1.302556e+10     2.655035e+10   \n",
       "2019-09-30  20.514284     2.504190e+08  1.299984e+10     2.673530e+10   \n",
       "2019-12-31  18.085714     2.504190e+08  1.313654e+10     2.759260e+10   \n",
       "2020-03-31  10.835714     2.504190e+08  1.079117e+10     2.801768e+10   \n",
       "2020-06-30  12.857142     2.504190e+08  1.032834e+10     2.816125e+10   \n",
       "2020-09-30  11.385714     2.504190e+08  9.870505e+09     2.856028e+10   \n",
       "2020-12-31  15.528571     2.752570e+08  1.292543e+10     3.233247e+10   \n",
       "2021-03-31  12.857142     2.504190e+08  1.072768e+10     3.069589e+10   \n",
       "2021-06-30  13.535714     2.504190e+08  1.252027e+10     3.138632e+10   \n",
       "2021-09-30  16.371428     2.504190e+08  1.380030e+10     3.157183e+10   \n",
       "2021-12-31  22.057142     2.752570e+08  1.908100e+10     3.974173e+10   \n",
       "2022-03-31  30.628571     2.504190e+08  1.596500e+10     3.820097e+10   \n",
       "2022-06-30  37.785713     2.504190e+08  2.815029e+10     3.969311e+10   \n",
       "2022-09-30  41.371429     2.752570e+08  4.590032e+10     4.267824e+10   \n",
       "2022-12-31  75.699997     2.752570e+08  6.718577e+10     4.918163e+10   \n",
       "2023-03-31  75.728569     2.752570e+08  6.149322e+10     5.027799e+10   \n",
       "2023-06-30  79.300003     1.926796e+09  6.835625e+10     5.338801e+10   \n",
       "\n",
       "            Dönen Varlıklar  Kısa Vadeli Yükümlülükler  \\\n",
       "Date                                                     \n",
       "2018-03-31     2.031117e+10               2.106889e+10   \n",
       "2018-06-30     2.195152e+10               2.072457e+10   \n",
       "2018-09-30     3.025010e+10               2.709297e+10   \n",
       "2018-12-31     2.096220e+10               1.594954e+10   \n",
       "2019-03-31     2.806188e+10               2.514518e+10   \n",
       "2019-06-30     2.973071e+10               2.699196e+10   \n",
       "2019-09-30     2.894542e+10               2.735773e+10   \n",
       "2019-12-31     2.791896e+10               2.812027e+10   \n",
       "2020-03-31     2.293851e+10               2.470684e+10   \n",
       "2020-06-30     2.738995e+10               2.644354e+10   \n",
       "2020-09-30     2.505385e+10               2.188961e+10   \n",
       "2020-12-31     3.128176e+10               2.876643e+10   \n",
       "2021-03-31     3.909123e+10               3.886958e+10   \n",
       "2021-06-30     4.365296e+10               4.379602e+10   \n",
       "2021-09-30     4.949711e+10               4.922227e+10   \n",
       "2021-12-31     6.651538e+10               6.482870e+10   \n",
       "2022-03-31     7.845080e+10               8.072647e+10   \n",
       "2022-06-30     1.203421e+11               1.120432e+11   \n",
       "2022-09-30     1.218089e+11               9.469894e+10   \n",
       "2022-12-31     1.197107e+11               7.889541e+10   \n",
       "2023-03-31     1.008178e+11               6.822506e+10   \n",
       "2023-06-30     1.285642e+11               8.548752e+10   \n",
       "\n",
       "            Uzun Vadeli Yükümlülükler  Toplam Yükümlülük  Çeyreklik Kar  \\\n",
       "Date                                                                      \n",
       "2018-03-31               9.774157e+09       3.084305e+10   3.867870e+08   \n",
       "2018-06-30               1.142458e+10       3.214914e+10   1.035290e+09   \n",
       "2018-09-30               1.445560e+10       4.154857e+10   5.506870e+08   \n",
       "2018-12-31               1.414036e+10       3.008990e+10   1.788681e+09   \n",
       "2019-03-31               1.664619e+10       4.179136e+10  -3.622950e+08   \n",
       "2019-06-30               1.626355e+10       4.325550e+10   8.859040e+08   \n",
       "2019-09-30               1.532315e+10       4.268088e+10  -1.401720e+08   \n",
       "2019-12-31               1.425475e+10       4.237502e+10   2.018930e+08   \n",
       "2020-03-31               1.545818e+10       4.016502e+10  -2.249085e+09   \n",
       "2020-06-30               1.877932e+10       4.522286e+10  -1.622910e+08   \n",
       "2020-09-30               2.185402e+10       4.374363e+10  -3.949660e+08   \n",
       "2020-12-31               2.192236e+10       5.068880e+10   3.834170e+08   \n",
       "2021-03-31               2.018986e+10       5.905944e+10  -6.522560e+08   \n",
       "2021-06-30               1.872299e+10       6.251901e+10   1.722416e+09   \n",
       "2021-09-30               1.804636e+10       6.726864e+10   1.066251e+09   \n",
       "2021-12-31               2.234741e+10       8.717611e+10   1.476813e+09   \n",
       "2022-03-31               1.996030e+10       1.006868e+11   1.145064e+09   \n",
       "2022-06-30               1.984162e+10       1.318849e+11   1.085390e+10   \n",
       "2022-09-30               2.388789e+10       1.185868e+11   1.161867e+10   \n",
       "2022-12-31               2.281115e+10       1.017066e+11   1.764293e+10   \n",
       "2023-03-31               2.137752e+10       8.960258e+10   6.792657e+09   \n",
       "2023-06-30               2.810840e+10       1.135959e+11   7.331480e+09   \n",
       "\n",
       "            Yıllıklandırılmış Kar         HBK  Toplam Varlıklar  \n",
       "Date                                                             \n",
       "2018-03-31           3.351584e+09   13.383905      3.818450e+10  \n",
       "2018-06-30           2.923353e+09   11.673847      4.020197e+10  \n",
       "2018-09-30           2.474222e+09    9.880329      4.906618e+10  \n",
       "2018-12-31           3.761445e+09   15.020605      4.003573e+10  \n",
       "2019-03-31           3.012363e+09   12.029291      5.360604e+10  \n",
       "2019-06-30           2.862977e+09   11.432747      5.628106e+10  \n",
       "2019-09-30           2.172118e+09    8.673934      5.568072e+10  \n",
       "2019-12-31           5.853300e+08    2.337403      5.551156e+10  \n",
       "2020-03-31          -1.301460e+09   -5.197130      5.095619e+10  \n",
       "2020-06-30          -2.349655e+09   -9.382894      5.555120e+10  \n",
       "2020-09-30          -2.604449e+09  -10.400365      5.361414e+10  \n",
       "2020-12-31          -2.422925e+09   -8.802410      6.361423e+10  \n",
       "2021-03-31          -8.260960e+08   -3.298855      6.978712e+10  \n",
       "2021-06-30           1.058611e+09    4.227359      7.503928e+10  \n",
       "2021-09-30           2.519828e+09   10.062447      8.106894e+10  \n",
       "2021-12-31           3.613224e+09   13.126729      1.062571e+11  \n",
       "2022-03-31           5.410544e+09   21.605964      1.166518e+11  \n",
       "2022-06-30           1.454203e+10   58.070789      1.600352e+11  \n",
       "2022-09-30           2.509445e+10   91.167331      1.644872e+11  \n",
       "2022-12-31           4.126057e+10  149.898339      1.688923e+11  \n",
       "2023-03-31           4.690816e+10  170.415866      1.510958e+11  \n",
       "2023-06-30           4.338574e+10   22.517038      1.819522e+11  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(fiyat.head(22))\n",
    "df1[\"Ödenmiş Sermaye\"]=bilanco[\"Ödenmiş Sermaye\"].astype(float)\n",
    "df1[\"Özkaynak\"]=bilanco[\"Özkaynaklar\"].astype(float)\n",
    "df1[\"Duran Varlıklar\"]=bilanco[\"Duran Varlıklar\"].astype(float)\n",
    "df1[\"Dönen Varlıklar\"]=bilanco[\"Dönen Varlıklar\"].astype(float)\n",
    "df1[\"Kısa Vadeli Yükümlülükler\"]=bilanco[\"Kısa Vadeli Yükümlülükler\"].astype(float)\n",
    "df1[\"Uzun Vadeli Yükümlülükler\"]=bilanco[\"Uzun Vadeli Yükümlülükler\"].astype(float)\n",
    "df1[\"Toplam Yükümlülük\"]=df1[\"Kısa Vadeli Yükümlülükler\"]+df1[\"Uzun Vadeli Yükümlülükler\"]\n",
    "df1[\"Çeyreklik Kar\"]=bilanco[\"Çeyreklik Kar\"].astype(float)\n",
    "df1[\"Yıllıklandırılmış Kar\"]=bilanco[\"Yıllıklandırılmış Kar\"].astype(float)\n",
    "df1[\"HBK\"]=df1[\"Yıllıklandırılmış Kar\"]/df1[\"Ödenmiş Sermaye\"]\n",
    "df1[\"Toplam Varlıklar\"]=df1[\"Duran Varlıklar\"]+df1[\"Dönen Varlıklar\"]\n",
    "df1\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df1[[\"Ödenmiş Sermaye\",\"Özkaynak\",\"Toplam Varlıklar\",\"Toplam Yükümlülük\",\"Çeyreklik Kar\",\"HBK\",\"Dönen Varlıklar\",\"Duran Varlıklar\",\"Kısa Vadeli Yükümlülükler\",\"Uzun Vadeli Yükümlülükler\",\"Yıllıklandırılmış Kar\"]]\n",
    "y=df1[\"Fiyat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.76402008, 0.10936523, 0.073839  ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Veri ölçekleme\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(X)\n",
    "\n",
    "# PCA uygulama\n",
    "pca = PCA(n_components=3)  # 10 ana bileşen seç, bu sayıyı değiştirebilirsin\n",
    "principal_components = pca.fit_transform(scaled_data)\n",
    "\n",
    "# Varyansı kontrol et\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=principal_components\n",
    "y=df1[\"Fiyat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9677968386893779"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "model = LinearRegression()\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "\n",
    "# Modeli eğitin\n",
    "\n",
    "\n",
    "\n",
    "results = model.fit(X_train, y_train)\n",
    "\n",
    "# Modelin özetini görüntüleyin\n",
    "results.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tahmin</th>\n",
       "      <th>Gerçek</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-03-31</th>\n",
       "      <td>17.403332</td>\n",
       "      <td>15.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-30</th>\n",
       "      <td>17.607139</td>\n",
       "      <td>15.485714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>19.295152</td>\n",
       "      <td>16.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>19.517742</td>\n",
       "      <td>16.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>14.473873</td>\n",
       "      <td>10.835714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>16.045798</td>\n",
       "      <td>15.528571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>18.318188</td>\n",
       "      <td>13.535714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>22.367809</td>\n",
       "      <td>22.057142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>69.803862</td>\n",
       "      <td>75.728569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Tahmin     Gerçek\n",
       "Date                            \n",
       "2018-03-31  17.403332  15.542857\n",
       "2018-06-30  17.607139  15.485714\n",
       "2018-12-31  19.295152  16.642857\n",
       "2019-06-30  19.517742  16.400000\n",
       "2020-03-31  14.473873  10.835714\n",
       "2020-12-31  16.045798  15.528571\n",
       "2021-06-30  18.318188  13.535714\n",
       "2021-12-31  22.367809  22.057142\n",
       "2023-03-31  69.803862  75.728569"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin1=pd.DataFrame(results.predict(X_test),columns=[\"Tahmin\"],index=y_test.index)\n",
    "tahmin1[\"Gerçek\"]=y_test\n",
    "tahmin1=tahmin1.sort_index()\n",
    "tahmin1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15b225f1fd0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(tahmin1[\"Tahmin\"],label=\"Tahmin\")\n",
    "plt.plot(tahmin1[\"Gerçek\"],label=\"Gerçek\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df1[[\"Ödenmiş Sermaye\",\"Özkaynak\",\"Toplam Yükümlülük\",\"Çeyreklik Kar\",\"HBK\",\"Dönen Varlıklar\",\"Duran Varlıklar\",\"Kısa Vadeli Yükümlülükler\",\"Uzun Vadeli Yükümlülükler\",\"Yıllıklandırılmış Kar\"]]\n",
    "y=df1[\"Fiyat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                   Ödenmiş Sermaye   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -360.838\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            727.677\n",
      "Time:                                   18:15:51   BIC                            730.176\n",
      "Sample:                               03-31-2018   HQIC                           727.925\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.6904      1.251      0.552      0.581      -1.761       3.142\n",
      "ma.S.L4        0.0573      7.310      0.008      0.994     -14.270      14.385\n",
      "sigma2      1.294e+17        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               129.67\n",
      "Prob(Q):                              0.95   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):              60.19   Skew:                             3.62\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        14.43\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 9.46e+46. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                          Özkaynak   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -404.407\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            814.813\n",
      "Time:                                   18:15:51   BIC                            817.313\n",
      "Sample:                               03-31-2018   HQIC                           815.062\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.5913      0.211      2.801      0.005       0.178       1.005\n",
      "ma.S.L4        0.3355      0.548      0.612      0.541      -0.739       1.410\n",
      "sigma2      3.107e+19        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.65   Jarque-Bera (JB):                 0.71\n",
      "Prob(Q):                              0.42   Prob(JB):                         0.70\n",
      "Heteroskedasticity (H):              11.22   Skew:                             0.45\n",
      "Prob(H) (two-sided):                  0.01   Kurtosis:                         2.56\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 5.73e+55. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                 Toplam Yükümlülük   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -421.099\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            848.199\n",
      "Time:                                   18:15:51   BIC                            850.698\n",
      "Sample:                               03-31-2018   HQIC                           848.447\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.1253      0.283      0.442      0.658      -0.430       0.681\n",
      "ma.S.L4       -0.5145      0.382     -1.346      0.178      -1.263       0.234\n",
      "sigma2       2.23e+20   3.54e-22   6.29e+41      0.000    2.23e+20    2.23e+20\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):                 0.23\n",
      "Prob(Q):                              0.86   Prob(JB):                         0.89\n",
      "Heteroskedasticity (H):               4.65   Skew:                            -0.12\n",
      "Prob(H) (two-sided):                  0.08   Kurtosis:                         2.48\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 3.63e+58. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                     Çeyreklik Kar   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -399.280\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            804.561\n",
      "Time:                                   18:15:51   BIC                            807.060\n",
      "Sample:                               03-31-2018   HQIC                           804.809\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.0647      0.319      0.203      0.839      -0.561       0.690\n",
      "ma.S.L4       -0.5629      0.569     -0.989      0.323      -1.679       0.553\n",
      "sigma2      2.115e+19        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 6.57\n",
      "Prob(Q):                              0.96   Prob(JB):                         0.04\n",
      "Heteroskedasticity (H):              35.96   Skew:                            -0.75\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.66\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 8.4e+54. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                               HBK   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                 -87.538\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            181.076\n",
      "Time:                                   18:15:51   BIC                            183.576\n",
      "Sample:                               03-31-2018   HQIC                           181.325\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.9993    162.791      0.006      0.995    -318.065     320.063\n",
      "ma.S.L4       -0.4315      1.907     -0.226      0.821      -4.169       3.306\n",
      "sigma2      1340.0633   2.19e+05      0.006      0.995   -4.27e+05     4.3e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.49   Jarque-Bera (JB):                62.92\n",
      "Prob(Q):                              0.48   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):             198.60   Skew:                            -2.68\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                        10.75\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                   Dönen Varlıklar   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -420.719\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            847.438\n",
      "Time:                                   18:15:51   BIC                            849.937\n",
      "Sample:                               03-31-2018   HQIC                           847.686\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.1446      0.482      0.300      0.764      -0.800       1.090\n",
      "ma.S.L4       -0.4023      0.621     -0.648      0.517      -1.619       0.815\n",
      "sigma2      2.197e+20        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.02   Jarque-Bera (JB):                 2.12\n",
      "Prob(Q):                              0.89   Prob(JB):                         0.35\n",
      "Heteroskedasticity (H):               6.33   Skew:                             0.47\n",
      "Prob(H) (two-sided):                  0.04   Kurtosis:                         4.45\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 3.46e+56. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:                   Duran Varlıklar   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -386.061\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            778.122\n",
      "Time:                                   18:15:51   BIC                            780.621\n",
      "Sample:                               03-31-2018   HQIC                           778.370\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1         -0.0657      0.264     -0.249      0.804      -0.583       0.452\n",
      "ma.S.L4        0.3367      0.218      1.548      0.122      -0.090       0.763\n",
      "sigma2      4.156e+18        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   2.23   Jarque-Bera (JB):                 0.38\n",
      "Prob(Q):                              0.14   Prob(JB):                         0.83\n",
      "Heteroskedasticity (H):              11.00   Skew:                             0.11\n",
      "Prob(H) (two-sided):                  0.01   Kurtosis:                         2.30\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:         Kısa Vadeli Yükümlülükler   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -420.317\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            846.633\n",
      "Time:                                   18:15:51   BIC                            849.133\n",
      "Sample:                               03-31-2018   HQIC                           846.882\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.1754      0.259      0.678      0.498      -0.332       0.683\n",
      "ma.S.L4       -0.5652      0.507     -1.114      0.265      -1.559       0.429\n",
      "sigma2      2.145e+20        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.06   Jarque-Bera (JB):                 0.19\n",
      "Prob(Q):                              0.81   Prob(JB):                         0.91\n",
      "Heteroskedasticity (H):               5.45   Skew:                            -0.19\n",
      "Prob(H) (two-sided):                  0.06   Kurtosis:                         2.65\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number  4e+58. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:         Uzun Vadeli Yükümlülükler   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -396.576\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            799.152\n",
      "Time:                                   18:15:51   BIC                            801.652\n",
      "Sample:                               03-31-2018   HQIC                           799.401\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1         -0.0908      0.469     -0.194      0.846      -1.010       0.828\n",
      "ma.S.L4       -0.5691      0.531     -1.071      0.284      -1.610       0.472\n",
      "sigma2      1.504e+19        nan        nan        nan         nan         nan\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):                 1.75\n",
      "Prob(Q):                              0.87   Prob(JB):                         0.42\n",
      "Heteroskedasticity (H):               1.96   Skew:                             0.71\n",
      "Prob(H) (two-sided):                  0.43   Kurtosis:                         2.34\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 3.75e+54. Standard errors may be unstable.\n",
      "                                     SARIMAX Results                                     \n",
      "=========================================================================================\n",
      "Dep. Variable:             Yıllıklandırılmış Kar   No. Observations:                   22\n",
      "Model:             SARIMAX(0, 1, 1)x(0, 1, 1, 4)   Log Likelihood                -402.081\n",
      "Date:                           Sun, 01 Oct 2023   AIC                            810.163\n",
      "Time:                                   18:15:51   BIC                            812.662\n",
      "Sample:                               03-31-2018   HQIC                           810.411\n",
      "                                    - 06-30-2023                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ma.L1          0.7283      0.359      2.030      0.042       0.025       1.432\n",
      "ma.S.L4       -0.5068      0.778     -0.651      0.515      -2.033       1.019\n",
      "sigma2      2.959e+19   4.09e-21   7.23e+39      0.000    2.96e+19    2.96e+19\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.33   Jarque-Bera (JB):                 5.63\n",
      "Prob(Q):                              0.57   Prob(JB):                         0.06\n",
      "Heteroskedasticity (H):              19.56   Skew:                             1.15\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         4.62\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 2.74e+57. Standard errors may be unstable.\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Tahminleri saklamak için bir liste\n",
    "predictions = []\n",
    "\n",
    "# Her bir sütun için döngü\n",
    "for i in range(0,10):  \n",
    "    # SARIMAX modelini oluşturma\n",
    "    sarimax_model = SARIMAX(X.iloc[:, i], \n",
    "                            order=(0, 1, 1), \n",
    "                            seasonal_order=(0, 1, 1, 4))  # Çeyreklik veri için mevsimsel periyot 4\n",
    "\n",
    "    # Modeli eğitme\n",
    "    sarimax_fit = sarimax_model.fit(disp=False)\n",
    "\n",
    "    # Modelin özeti (isteğe bağlı)\n",
    "    print(sarimax_fit.summary())\n",
    "\n",
    "    # Gelecekteki 6 değeri tahmin etme\n",
    "    future_predictions = sarimax_fit.get_forecast(steps=6).predicted_mean\n",
    "\n",
    "    # Tahminleri listeye ekleme\n",
    "    predictions.append(future_predictions)\n",
    "\n",
    "# Tahminleri bir DataFrame'e dönüştürme\n",
    "predictions_df = pd.DataFrame(predictions).T  # Transpoze alarak sütunlarla satırları ters çeviriyoruz\n",
    "predictions_df.columns = X.columns  # Sütun adlarını X DataFrame'inden alıyoruz\n",
    "\n",
    "\n",
    "extended_X = pd.concat([X, predictions_df], ignore_index=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ödenmiş Sermaye</th>\n",
       "      <th>Özkaynak</th>\n",
       "      <th>Toplam Yükümlülük</th>\n",
       "      <th>Çeyreklik Kar</th>\n",
       "      <th>HBK</th>\n",
       "      <th>Dönen Varlıklar</th>\n",
       "      <th>Duran Varlıklar</th>\n",
       "      <th>Kısa Vadeli Yükümlülükler</th>\n",
       "      <th>Uzun Vadeli Yükümlülükler</th>\n",
       "      <th>Yıllıklandırılmış Kar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>9.945829e+09</td>\n",
       "      <td>3.008990e+10</td>\n",
       "      <td>1.788681e+09</td>\n",
       "      <td>15.020605</td>\n",
       "      <td>2.096220e+10</td>\n",
       "      <td>1.907352e+10</td>\n",
       "      <td>1.594954e+10</td>\n",
       "      <td>1.414036e+10</td>\n",
       "      <td>3.761445e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.181468e+10</td>\n",
       "      <td>4.179136e+10</td>\n",
       "      <td>-3.622950e+08</td>\n",
       "      <td>12.029291</td>\n",
       "      <td>2.806188e+10</td>\n",
       "      <td>2.554416e+10</td>\n",
       "      <td>2.514518e+10</td>\n",
       "      <td>1.664619e+10</td>\n",
       "      <td>3.012363e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.302556e+10</td>\n",
       "      <td>4.325550e+10</td>\n",
       "      <td>8.859040e+08</td>\n",
       "      <td>11.432747</td>\n",
       "      <td>2.973071e+10</td>\n",
       "      <td>2.655035e+10</td>\n",
       "      <td>2.699196e+10</td>\n",
       "      <td>1.626355e+10</td>\n",
       "      <td>2.862977e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.299984e+10</td>\n",
       "      <td>4.268088e+10</td>\n",
       "      <td>-1.401720e+08</td>\n",
       "      <td>8.673934</td>\n",
       "      <td>2.894542e+10</td>\n",
       "      <td>2.673530e+10</td>\n",
       "      <td>2.735773e+10</td>\n",
       "      <td>1.532315e+10</td>\n",
       "      <td>2.172118e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.313654e+10</td>\n",
       "      <td>4.237502e+10</td>\n",
       "      <td>2.018930e+08</td>\n",
       "      <td>2.337403</td>\n",
       "      <td>2.791896e+10</td>\n",
       "      <td>2.759260e+10</td>\n",
       "      <td>2.812027e+10</td>\n",
       "      <td>1.425475e+10</td>\n",
       "      <td>5.853300e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.079117e+10</td>\n",
       "      <td>4.016502e+10</td>\n",
       "      <td>-2.249085e+09</td>\n",
       "      <td>-5.197130</td>\n",
       "      <td>2.293851e+10</td>\n",
       "      <td>2.801768e+10</td>\n",
       "      <td>2.470684e+10</td>\n",
       "      <td>1.545818e+10</td>\n",
       "      <td>-1.301460e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.032834e+10</td>\n",
       "      <td>4.522286e+10</td>\n",
       "      <td>-1.622910e+08</td>\n",
       "      <td>-9.382894</td>\n",
       "      <td>2.738995e+10</td>\n",
       "      <td>2.816125e+10</td>\n",
       "      <td>2.644354e+10</td>\n",
       "      <td>1.877932e+10</td>\n",
       "      <td>-2.349655e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>9.870505e+09</td>\n",
       "      <td>4.374363e+10</td>\n",
       "      <td>-3.949660e+08</td>\n",
       "      <td>-10.400365</td>\n",
       "      <td>2.505385e+10</td>\n",
       "      <td>2.856028e+10</td>\n",
       "      <td>2.188961e+10</td>\n",
       "      <td>2.185402e+10</td>\n",
       "      <td>-2.604449e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>1.292543e+10</td>\n",
       "      <td>5.068880e+10</td>\n",
       "      <td>3.834170e+08</td>\n",
       "      <td>-8.802410</td>\n",
       "      <td>3.128176e+10</td>\n",
       "      <td>3.233247e+10</td>\n",
       "      <td>2.876643e+10</td>\n",
       "      <td>2.192236e+10</td>\n",
       "      <td>-2.422925e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.072768e+10</td>\n",
       "      <td>5.905944e+10</td>\n",
       "      <td>-6.522560e+08</td>\n",
       "      <td>-3.298855</td>\n",
       "      <td>3.909123e+10</td>\n",
       "      <td>3.069589e+10</td>\n",
       "      <td>3.886958e+10</td>\n",
       "      <td>2.018986e+10</td>\n",
       "      <td>-8.260960e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.252027e+10</td>\n",
       "      <td>6.251901e+10</td>\n",
       "      <td>1.722416e+09</td>\n",
       "      <td>4.227359</td>\n",
       "      <td>4.365296e+10</td>\n",
       "      <td>3.138632e+10</td>\n",
       "      <td>4.379602e+10</td>\n",
       "      <td>1.872299e+10</td>\n",
       "      <td>1.058611e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.380030e+10</td>\n",
       "      <td>6.726864e+10</td>\n",
       "      <td>1.066251e+09</td>\n",
       "      <td>10.062447</td>\n",
       "      <td>4.949711e+10</td>\n",
       "      <td>3.157183e+10</td>\n",
       "      <td>4.922227e+10</td>\n",
       "      <td>1.804636e+10</td>\n",
       "      <td>2.519828e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>1.908100e+10</td>\n",
       "      <td>8.717611e+10</td>\n",
       "      <td>1.476813e+09</td>\n",
       "      <td>13.126729</td>\n",
       "      <td>6.651538e+10</td>\n",
       "      <td>3.974173e+10</td>\n",
       "      <td>6.482870e+10</td>\n",
       "      <td>2.234741e+10</td>\n",
       "      <td>3.613224e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>1.596500e+10</td>\n",
       "      <td>1.006868e+11</td>\n",
       "      <td>1.145064e+09</td>\n",
       "      <td>21.605964</td>\n",
       "      <td>7.845080e+10</td>\n",
       "      <td>3.820097e+10</td>\n",
       "      <td>8.072647e+10</td>\n",
       "      <td>1.996030e+10</td>\n",
       "      <td>5.410544e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>2.504190e+08</td>\n",
       "      <td>2.815029e+10</td>\n",
       "      <td>1.318849e+11</td>\n",
       "      <td>1.085390e+10</td>\n",
       "      <td>58.070789</td>\n",
       "      <td>1.203421e+11</td>\n",
       "      <td>3.969311e+10</td>\n",
       "      <td>1.120432e+11</td>\n",
       "      <td>1.984162e+10</td>\n",
       "      <td>1.454203e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>4.590032e+10</td>\n",
       "      <td>1.185868e+11</td>\n",
       "      <td>1.161867e+10</td>\n",
       "      <td>91.167331</td>\n",
       "      <td>1.218089e+11</td>\n",
       "      <td>4.267824e+10</td>\n",
       "      <td>9.469894e+10</td>\n",
       "      <td>2.388789e+10</td>\n",
       "      <td>2.509445e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>6.718577e+10</td>\n",
       "      <td>1.017066e+11</td>\n",
       "      <td>1.764293e+10</td>\n",
       "      <td>149.898339</td>\n",
       "      <td>1.197107e+11</td>\n",
       "      <td>4.918163e+10</td>\n",
       "      <td>7.889541e+10</td>\n",
       "      <td>2.281115e+10</td>\n",
       "      <td>4.126057e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>2.752570e+08</td>\n",
       "      <td>6.149322e+10</td>\n",
       "      <td>8.960258e+10</td>\n",
       "      <td>6.792657e+09</td>\n",
       "      <td>170.415866</td>\n",
       "      <td>1.008178e+11</td>\n",
       "      <td>5.027799e+10</td>\n",
       "      <td>6.822506e+10</td>\n",
       "      <td>2.137752e+10</td>\n",
       "      <td>4.690816e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>1.926796e+09</td>\n",
       "      <td>6.835625e+10</td>\n",
       "      <td>1.135959e+11</td>\n",
       "      <td>7.331480e+09</td>\n",
       "      <td>22.517038</td>\n",
       "      <td>1.285642e+11</td>\n",
       "      <td>5.338801e+10</td>\n",
       "      <td>8.548752e+10</td>\n",
       "      <td>2.810840e+10</td>\n",
       "      <td>4.338574e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>3.068731e+09</td>\n",
       "      <td>8.922653e+10</td>\n",
       "      <td>1.096555e+11</td>\n",
       "      <td>7.098405e+09</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>1.314340e+11</td>\n",
       "      <td>5.725046e+10</td>\n",
       "      <td>7.989469e+10</td>\n",
       "      <td>2.961965e+10</td>\n",
       "      <td>3.886548e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>3.067313e+09</td>\n",
       "      <td>1.157734e+11</td>\n",
       "      <td>1.068448e+11</td>\n",
       "      <td>1.004490e+10</td>\n",
       "      <td>10.193759</td>\n",
       "      <td>1.346747e+11</td>\n",
       "      <td>6.280044e+10</td>\n",
       "      <td>7.727185e+10</td>\n",
       "      <td>3.012962e+10</td>\n",
       "      <td>3.126745e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>3.068731e+09</td>\n",
       "      <td>1.093465e+11</td>\n",
       "      <td>1.063003e+11</td>\n",
       "      <td>4.788541e+09</td>\n",
       "      <td>9.535972</td>\n",
       "      <td>1.270630e+11</td>\n",
       "      <td>6.470100e+10</td>\n",
       "      <td>7.873765e+10</td>\n",
       "      <td>2.919652e+10</td>\n",
       "      <td>2.926333e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>4.814883e+09</td>\n",
       "      <td>1.133618e+11</td>\n",
       "      <td>1.266129e+11</td>\n",
       "      <td>7.991423e+09</td>\n",
       "      <td>6.214746</td>\n",
       "      <td>1.543725e+11</td>\n",
       "      <td>6.829905e+10</td>\n",
       "      <td>9.486824e+10</td>\n",
       "      <td>3.216545e+10</td>\n",
       "      <td>2.992327e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>6.020732e+09</td>\n",
       "      <td>1.334991e+11</td>\n",
       "      <td>1.220548e+11</td>\n",
       "      <td>7.906442e+09</td>\n",
       "      <td>5.104248</td>\n",
       "      <td>1.569583e+11</td>\n",
       "      <td>7.212623e+10</td>\n",
       "      <td>8.876199e+10</td>\n",
       "      <td>3.401322e+10</td>\n",
       "      <td>3.073131e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>6.019314e+09</td>\n",
       "      <td>1.600460e+11</td>\n",
       "      <td>1.192441e+11</td>\n",
       "      <td>1.085294e+10</td>\n",
       "      <td>5.239691</td>\n",
       "      <td>1.601989e+11</td>\n",
       "      <td>7.767621e+10</td>\n",
       "      <td>8.613915e+10</td>\n",
       "      <td>3.452318e+10</td>\n",
       "      <td>3.153935e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Ödenmiş Sermaye      Özkaynak  Toplam Yükümlülük  Çeyreklik Kar  \\\n",
       "2018-12-31     2.504190e+08  9.945829e+09       3.008990e+10   1.788681e+09   \n",
       "2019-03-31     2.504190e+08  1.181468e+10       4.179136e+10  -3.622950e+08   \n",
       "2019-06-30     2.504190e+08  1.302556e+10       4.325550e+10   8.859040e+08   \n",
       "2019-09-30     2.504190e+08  1.299984e+10       4.268088e+10  -1.401720e+08   \n",
       "2019-12-31     2.504190e+08  1.313654e+10       4.237502e+10   2.018930e+08   \n",
       "2020-03-31     2.504190e+08  1.079117e+10       4.016502e+10  -2.249085e+09   \n",
       "2020-06-30     2.504190e+08  1.032834e+10       4.522286e+10  -1.622910e+08   \n",
       "2020-09-30     2.504190e+08  9.870505e+09       4.374363e+10  -3.949660e+08   \n",
       "2020-12-31     2.752570e+08  1.292543e+10       5.068880e+10   3.834170e+08   \n",
       "2021-03-31     2.504190e+08  1.072768e+10       5.905944e+10  -6.522560e+08   \n",
       "2021-06-30     2.504190e+08  1.252027e+10       6.251901e+10   1.722416e+09   \n",
       "2021-09-30     2.504190e+08  1.380030e+10       6.726864e+10   1.066251e+09   \n",
       "2021-12-31     2.752570e+08  1.908100e+10       8.717611e+10   1.476813e+09   \n",
       "2022-03-31     2.504190e+08  1.596500e+10       1.006868e+11   1.145064e+09   \n",
       "2022-06-30     2.504190e+08  2.815029e+10       1.318849e+11   1.085390e+10   \n",
       "2022-09-30     2.752570e+08  4.590032e+10       1.185868e+11   1.161867e+10   \n",
       "2022-12-31     2.752570e+08  6.718577e+10       1.017066e+11   1.764293e+10   \n",
       "2023-03-31     2.752570e+08  6.149322e+10       8.960258e+10   6.792657e+09   \n",
       "2023-06-30     1.926796e+09  6.835625e+10       1.135959e+11   7.331480e+09   \n",
       "2023-09-30     3.068731e+09  8.922653e+10       1.096555e+11   7.098405e+09   \n",
       "2023-12-31     3.067313e+09  1.157734e+11       1.068448e+11   1.004490e+10   \n",
       "2024-03-31     3.068731e+09  1.093465e+11       1.063003e+11   4.788541e+09   \n",
       "2024-06-30     4.814883e+09  1.133618e+11       1.266129e+11   7.991423e+09   \n",
       "2024-09-30     6.020732e+09  1.334991e+11       1.220548e+11   7.906442e+09   \n",
       "2024-12-31     6.019314e+09  1.600460e+11       1.192441e+11   1.085294e+10   \n",
       "\n",
       "                   HBK  Dönen Varlıklar  Duran Varlıklar  \\\n",
       "2018-12-31   15.020605     2.096220e+10     1.907352e+10   \n",
       "2019-03-31   12.029291     2.806188e+10     2.554416e+10   \n",
       "2019-06-30   11.432747     2.973071e+10     2.655035e+10   \n",
       "2019-09-30    8.673934     2.894542e+10     2.673530e+10   \n",
       "2019-12-31    2.337403     2.791896e+10     2.759260e+10   \n",
       "2020-03-31   -5.197130     2.293851e+10     2.801768e+10   \n",
       "2020-06-30   -9.382894     2.738995e+10     2.816125e+10   \n",
       "2020-09-30  -10.400365     2.505385e+10     2.856028e+10   \n",
       "2020-12-31   -8.802410     3.128176e+10     3.233247e+10   \n",
       "2021-03-31   -3.298855     3.909123e+10     3.069589e+10   \n",
       "2021-06-30    4.227359     4.365296e+10     3.138632e+10   \n",
       "2021-09-30   10.062447     4.949711e+10     3.157183e+10   \n",
       "2021-12-31   13.126729     6.651538e+10     3.974173e+10   \n",
       "2022-03-31   21.605964     7.845080e+10     3.820097e+10   \n",
       "2022-06-30   58.070789     1.203421e+11     3.969311e+10   \n",
       "2022-09-30   91.167331     1.218089e+11     4.267824e+10   \n",
       "2022-12-31  149.898339     1.197107e+11     4.918163e+10   \n",
       "2023-03-31  170.415866     1.008178e+11     5.027799e+10   \n",
       "2023-06-30   22.517038     1.285642e+11     5.338801e+10   \n",
       "2023-09-30   12.665000     1.314340e+11     5.725046e+10   \n",
       "2023-12-31   10.193759     1.346747e+11     6.280044e+10   \n",
       "2024-03-31    9.535972     1.270630e+11     6.470100e+10   \n",
       "2024-06-30    6.214746     1.543725e+11     6.829905e+10   \n",
       "2024-09-30    5.104248     1.569583e+11     7.212623e+10   \n",
       "2024-12-31    5.239691     1.601989e+11     7.767621e+10   \n",
       "\n",
       "            Kısa Vadeli Yükümlülükler  Uzun Vadeli Yükümlülükler  \\\n",
       "2018-12-31               1.594954e+10               1.414036e+10   \n",
       "2019-03-31               2.514518e+10               1.664619e+10   \n",
       "2019-06-30               2.699196e+10               1.626355e+10   \n",
       "2019-09-30               2.735773e+10               1.532315e+10   \n",
       "2019-12-31               2.812027e+10               1.425475e+10   \n",
       "2020-03-31               2.470684e+10               1.545818e+10   \n",
       "2020-06-30               2.644354e+10               1.877932e+10   \n",
       "2020-09-30               2.188961e+10               2.185402e+10   \n",
       "2020-12-31               2.876643e+10               2.192236e+10   \n",
       "2021-03-31               3.886958e+10               2.018986e+10   \n",
       "2021-06-30               4.379602e+10               1.872299e+10   \n",
       "2021-09-30               4.922227e+10               1.804636e+10   \n",
       "2021-12-31               6.482870e+10               2.234741e+10   \n",
       "2022-03-31               8.072647e+10               1.996030e+10   \n",
       "2022-06-30               1.120432e+11               1.984162e+10   \n",
       "2022-09-30               9.469894e+10               2.388789e+10   \n",
       "2022-12-31               7.889541e+10               2.281115e+10   \n",
       "2023-03-31               6.822506e+10               2.137752e+10   \n",
       "2023-06-30               8.548752e+10               2.810840e+10   \n",
       "2023-09-30               7.989469e+10               2.961965e+10   \n",
       "2023-12-31               7.727185e+10               3.012962e+10   \n",
       "2024-03-31               7.873765e+10               2.919652e+10   \n",
       "2024-06-30               9.486824e+10               3.216545e+10   \n",
       "2024-09-30               8.876199e+10               3.401322e+10   \n",
       "2024-12-31               8.613915e+10               3.452318e+10   \n",
       "\n",
       "            Yıllıklandırılmış Kar  \n",
       "2018-12-31           3.761445e+09  \n",
       "2019-03-31           3.012363e+09  \n",
       "2019-06-30           2.862977e+09  \n",
       "2019-09-30           2.172118e+09  \n",
       "2019-12-31           5.853300e+08  \n",
       "2020-03-31          -1.301460e+09  \n",
       "2020-06-30          -2.349655e+09  \n",
       "2020-09-30          -2.604449e+09  \n",
       "2020-12-31          -2.422925e+09  \n",
       "2021-03-31          -8.260960e+08  \n",
       "2021-06-30           1.058611e+09  \n",
       "2021-09-30           2.519828e+09  \n",
       "2021-12-31           3.613224e+09  \n",
       "2022-03-31           5.410544e+09  \n",
       "2022-06-30           1.454203e+10  \n",
       "2022-09-30           2.509445e+10  \n",
       "2022-12-31           4.126057e+10  \n",
       "2023-03-31           4.690816e+10  \n",
       "2023-06-30           4.338574e+10  \n",
       "2023-09-30           3.886548e+10  \n",
       "2023-12-31           3.126745e+10  \n",
       "2024-03-31           2.926333e+10  \n",
       "2024-06-30           2.992327e+10  \n",
       "2024-09-30           3.073131e+10  \n",
       "2024-12-31           3.153935e+10  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extended_X=extended_X.set_index(pd.date_range(start=\"2018-03-31\",end=\"2025-01-01\",freq=\"Q\"))\n",
    "extended_X[\"Yıllıklandırılmış Kar\"]=extended_X[\"Çeyreklik Kar\"].rolling(4).sum()\n",
    "extended_X[\"HBK\"]=extended_X[\"Yıllıklandırılmış Kar\"]/extended_X[\"Ödenmiş Sermaye\"]\n",
    "extended_X=extended_X.dropna()\n",
    "extended_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=extended_X[[\"Ödenmiş Sermaye\",\"Özkaynak\",\"Toplam Yükümlülük\",\"Çeyreklik Kar\",\"HBK\",\"Dönen Varlıklar\",\"Duran Varlıklar\",\"Kısa Vadeli Yükümlülükler\",\"Uzun Vadeli Yükümlülükler\",\"Yıllıklandırılmış Kar\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7521414 , 0.16530152, 0.04773518, 0.01610426, 0.00876795])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Veri ölçekleme\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(X)\n",
    "\n",
    "# PCA uygulama\n",
    "pca = PCA(n_components=5)  \n",
    "principal_components1 = pca.fit_transform(scaled_data)\n",
    "\n",
    "# Varyansı kontrol et\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-12-31     16.642857\n",
       "2019-03-31     17.814285\n",
       "2019-06-30     16.400000\n",
       "2019-09-30     20.514284\n",
       "2019-12-31     18.085714\n",
       "2020-03-31     10.835714\n",
       "2020-06-30     12.857142\n",
       "2020-09-30     11.385714\n",
       "2020-12-31     15.528571\n",
       "2021-03-31     12.857142\n",
       "2021-06-30     13.535714\n",
       "2021-09-30     16.371428\n",
       "2021-12-31     22.057142\n",
       "2022-03-31     30.628571\n",
       "2022-06-30     37.785713\n",
       "2022-09-30     41.371429\n",
       "2022-12-31     75.699997\n",
       "2023-03-31     75.728569\n",
       "2023-06-30     79.300003\n",
       "2023-09-30    159.000000\n",
       "Freq: Q-DEC, Name: Fiyat, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df1[\"Fiyat\"]\n",
    "y.loc[pd.Timestamp('2023-09-30')] = 159\n",
    "y=y.tail(20)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814895563235547"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LinearRegression()\n",
    "model.fit(principal_components1[:20],y)\n",
    "model.score(principal_components1[:20],y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tahmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>9.364467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-31</th>\n",
       "      <td>13.843729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-30</th>\n",
       "      <td>17.719812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-30</th>\n",
       "      <td>20.552071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>25.282494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>20.808549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>12.109826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>4.557943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>13.988244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>11.018657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-30</th>\n",
       "      <td>17.097033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>19.598292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>25.289551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>23.241865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-30</th>\n",
       "      <td>34.650404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-30</th>\n",
       "      <td>45.036495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31</th>\n",
       "      <td>76.735853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31</th>\n",
       "      <td>73.370556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>86.574471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>153.559677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>198.776676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-31</th>\n",
       "      <td>199.243431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-30</th>\n",
       "      <td>259.113956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-09-30</th>\n",
       "      <td>321.505091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-12-31</th>\n",
       "      <td>355.299354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Tahmin\n",
       "2018-12-31    9.364467\n",
       "2019-03-31   13.843729\n",
       "2019-06-30   17.719812\n",
       "2019-09-30   20.552071\n",
       "2019-12-31   25.282494\n",
       "2020-03-31   20.808549\n",
       "2020-06-30   12.109826\n",
       "2020-09-30    4.557943\n",
       "2020-12-31   13.988244\n",
       "2021-03-31   11.018657\n",
       "2021-06-30   17.097033\n",
       "2021-09-30   19.598292\n",
       "2021-12-31   25.289551\n",
       "2022-03-31   23.241865\n",
       "2022-06-30   34.650404\n",
       "2022-09-30   45.036495\n",
       "2022-12-31   76.735853\n",
       "2023-03-31   73.370556\n",
       "2023-06-30   86.574471\n",
       "2023-09-30  153.559677\n",
       "2023-12-31  198.776676\n",
       "2024-03-31  199.243431\n",
       "2024-06-30  259.113956\n",
       "2024-09-30  321.505091\n",
       "2024-12-31  355.299354"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tahmin3=pd.DataFrame(model.predict(principal_components1),index=pd.date_range(start=\"2018-12-31\",end=\"2025-01-01\",freq=\"Q\"),columns=[\"Tahmin\"])\n",
    "tahmin3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2018-12-31     16.642857\n",
       "2019-03-31     17.814285\n",
       "2019-06-30     16.400000\n",
       "2019-09-30     20.514284\n",
       "2019-12-31     18.085714\n",
       "2020-03-31     10.835714\n",
       "2020-06-30     12.857142\n",
       "2020-09-30     11.385714\n",
       "2020-12-31     15.528571\n",
       "2021-03-31     12.857142\n",
       "2021-06-30     13.535714\n",
       "2021-09-30     16.371428\n",
       "2021-12-31     22.057142\n",
       "2022-03-31     30.628571\n",
       "2022-06-30     37.785713\n",
       "2022-09-30     41.371429\n",
       "2022-12-31     75.699997\n",
       "2023-03-31     75.728569\n",
       "2023-06-30     79.300003\n",
       "2023-09-30    159.000000\n",
       "Freq: Q-DEC, Name: Fiyat, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "tahmin3.index = pd.to_datetime(tahmin3.index)\n",
    "tahmin3.index = tahmin3.index.date\n",
    "y.index = pd.to_datetime(y.index)\n",
    "y.index = y.index.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "y.tail(12).plot(figsize=(12,7),label=\"Fiyat\",color=\"black\",linestyle=\"-\",lw=3)\n",
    "tahmin3[\"Tahmin\"].tail(17).plot(label=\"Tahmin\",color=\"red\",linestyle=\"--\",lw=3)\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator(bymonth=[1,4,7,10]))  # Çeyrekler için ay ayırıcıları\n",
    "plt.xticks(rotation=45)\n",
    "plt.scatter(tahmin3.index[-5::],tahmin3[\"Tahmin\"][-5::],color=\"black\",marker=\"o\",s=70)\n",
    "plt.title(\"TUPRS Fiyat Tahmini\")\n",
    "plt.xlabel(\"Tarih\")\n",
    "plt.ylabel(\"Fiyat\")\n",
    "\n",
    "for i in range(-5,-4):\n",
    "    plt.annotate(str(round(tahmin3[\"Tahmin\"][i],2)), (tahmin3.index[i], tahmin3[\"Tahmin\"][i]), textcoords=\"offset points\", xytext=(-5,5), ha='center',weight=\"bold\",fontsize=10)\n",
    "for i in range(-4,0):\n",
    "    plt.annotate(str(round(tahmin3[\"Tahmin\"][i],2)), (tahmin3.index[i], tahmin3[\"Tahmin\"][i]), textcoords=\"offset points\", xytext=(0,5), ha='center',fontsize=10,weight=\"bold\")\n",
    "\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}