stock_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>    table {align:left;display:block}    th {font-size: 15px}    td {font-size: 15px}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "table_css = \"\\\n",
    "    table {align:left;display:block}\\\n",
    "    th {font-size: 15px}\\\n",
    "    td {font-size: 15px}\\\n",
    "\"\n",
    "HTML('<style>{}</style>'.format(table_css))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Cài đặt 1 số thư viện cần thiết để chạy code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cài đặt trình duyệt Chrome và Chrome Diver để thu thập dữ liệu\n",
    "**Cách 1: Tải chromedriver thủ công**\n",
    "<br> Link tải: https://chromedriver.storage.googleapis.com/index.html?path=100.0.4896.60/\n",
    "<br>\n",
    "- Chromedriver cần được tải đúng phiên bản tương ứng với hệ điều hành sử dụng\n",
    "- Phần source code này được chạy trên hệ điều hành Windows, vì vậy chromedriver_win32.rar sẽ được tải và sử dụng.\n",
    "- Sau khi tải thành công, ta giải nén file ta nhận được file chromedriver.exe\n",
    "- Đặt file chromedriver.exe chung với thư mục chứa file code này\n",
    "\n",
    "<u>**Lưu ý:** Nếu sử dụng phương pháp 1 gặp lỗi\n",
    "*\"SessionNotCreatedException: Message: session not created: This version of ChromeDriver only supports Chrome version 100\"* khi chạy dòng lệnh *\"webdriver.Chrome(executable_path='chromedriver.exe')\"*, ta có thể sử dụng phương pháp 2 </u>\n",
    "<br>\n",
    "\n",
    "**Cách 2: Sử dụng thư viện WebDriverManager**\n",
    "<br> Cài đặt WebDriverManager bằng lệnh\n",
    "<br> *pip install webdriver-manager*\n",
    "<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### **Phiên bản một số thư viện sử dụng và cách cài đặt**\n",
    "    \n",
    "| Library | Version |\n",
    "| --- | --- | \n",
    "| numpy | 1.19.5 |\n",
    "| pandas | 1.1.5 |\n",
    "| matplotlib | 3.3.4 |\n",
    "| selenium | 3.141.0 |\n",
    "| sklearn | 0.19.0 |\n",
    "| tslearn | 0.5.2 |\n",
    "| keras | 2.4.3 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "from datetime import datetime\n",
    "import time\n",
    "import os\n",
    "\n",
    "#Thư viện cào dữ liệu (Task 1)\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import Select\n",
    "\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.common.exceptions import NoSuchElementException, StaleElementReferenceException\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "\n",
    "#Thư viện vẽ hình\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FormatStrFormatter, FuncFormatter, MultipleLocator\n",
    "\n",
    "#Thư viện chuẩn hóa (Task 2)\n",
    "from tslearn.preprocessing import TimeSeriesScalerMeanVariance\n",
    "\n",
    "#Thư viện huấn luyện mô hình (Task 4)\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **Task 1. Data Collection**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Task 1.a - Choosing 100 stock symbols***\n",
    "**Choosing 100 stock symbols from Ho Chi Minh Stock Exchange (HOSE). Refer at\n",
    "https://quotes.vcbs.com.vn/a/exchange.html**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#start_index = 1 có nghĩa là cào từ dòng thứ nhất\n",
    "def crawl_symbols(wd, start_index=1, n_symbol=100):\n",
    "    web_link = 'https://quotes.vcbs.com.vn/a/exchange.html?symbol=HSX'\n",
    "    wd.get(web_link)\n",
    "    time.sleep(3)\n",
    "\n",
    "    symbol_list = []\n",
    "    #Lấy table body của table chứa các stock symbol\n",
    "    tbody = wd.find_element(by=By.XPATH, value='//table[@id=\"tblScroll1\"]/tbody')\n",
    "    for i in range(start_index, n_symbol+1):\n",
    "        try:\n",
    "            symbol = tbody.find_element(by=By.XPATH, value='./tr[{}]/td[contains(@class, \"StockSymbol\")]'.format(i))\n",
    "        except NoSuchElementException:\n",
    "            break\n",
    "        symbol_list.append(symbol.text)\n",
    "    return symbol_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crawl data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[WDM] - ====== WebDriver manager ======\n",
      "[WDM] - Current google-chrome version is 102.0.5005\n",
      "[WDM] - Get LATEST chromedriver version for 102.0.5005 google-chrome\n",
      "[WDM] - Driver [C:\\Users\\tatta\\.wdm\\drivers\\chromedriver\\win32\\102.0.5005.61\\chromedriver.exe] found in cache\n"
     ]
    }
   ],
   "source": [
    "#Tạo webdriver mở bằng trình duyệt chrome\n",
    "#Cách 1\n",
    "#wd = webdriver.Chrome(executable_path='chromedriver.exe')\n",
    "\n",
    "#Cách 2\n",
    "# from webdriver_manager.chrome import ChromeDriverManager\n",
    "wd = webdriver.Chrome(ChromeDriverManager().install())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of crawled stock symbols: 200\n",
      "The first 5 stock symbols: ['AAA', 'AAM', 'AAT', 'ABR', 'ABS']\n"
     ]
    }
   ],
   "source": [
    "#Cào 100 mã cổ phiếu \n",
    "#Cào thêm 100 mã, vì có thể sẽ có mã không đủ dữ liệu 3 năm => lấy mã khác\n",
    "#=> Cào 200 mã \n",
    "stocksymbols = crawl_symbols(wd, n_symbol=200)\n",
    "\n",
    "print('Number of crawled stock symbols:', len(stocksymbols))\n",
    "print('The first 5 stock symbols:', stocksymbols[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 100 stock symbols:\n",
      " ['AAA', 'AAM', 'AAT', 'ABR', 'ABS', 'ABT', 'ACB*', 'ACC', 'ACL', 'ADG', 'ADS', 'AGG', 'AGM', 'AGR', 'AMD', 'ANV', 'APC', 'APG', 'APH', 'ASG', 'ASM', 'ASP', 'AST', 'BAF', 'BBC', 'BCE', 'BCG', 'BCM', 'BFC', 'BHN', 'BIC', 'BID', 'BKG', 'BMC', 'BMI', 'BMP', 'BRC', 'BSI', 'BTP', 'BTT', 'BVH', 'BWE', 'C32', 'C47', 'CAV', 'CCI', 'CCL', 'CDC', 'CEE', 'CHP', 'CIG', 'CII', 'CKG', 'CLC', 'CLL', 'CLW*', 'CMG', 'CMV', 'CMX', 'CNG', 'COM', 'CRC', 'CRE', 'CSM', 'CSV', 'CTD', 'CTF', 'CTG', 'CTI', 'CTR', 'CTS', 'CVT', 'D2D', 'DAG', 'DAH', 'DAT', 'DBC', 'DBD', 'DBT', 'DC4', 'DCL', 'DCM', 'DGC', 'DGW', 'DHA', 'DHC', 'DHG', 'DHM', 'DIG', 'DLG', 'DMC', 'DPG', 'DPM', 'DPR', 'DQC', 'DRC', 'DRH', 'DRL', 'DSN', 'DTA']\n"
     ]
    }
   ],
   "source": [
    "print('First 100 stock symbols:\\n', stocksymbols[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra 100 stock symbols:\n",
      " ['DTL', 'DTT', 'DVP', 'DXG', 'DXS', 'DXV', 'EIB', 'ELC', 'EMC', 'EVE', 'EVF', 'EVG', 'FCM', 'FCN', 'FDC', 'FIR', 'FIT', 'FLC', 'FMC', 'FPT', 'FRT', 'FTS', 'GAB', 'GAS', 'GDT', 'GEG', 'GEX', 'GIL', 'GMC', 'GMD', 'GMH', 'GSP', 'GTA', 'GVR', 'HAG', 'HAH', 'HAI', 'HAP', 'HAR', 'HAS', 'HAX', 'HBC', 'HCD', 'HCM', 'HDB', 'HDC', 'HDG', 'HHP', 'HHS', 'HHV', 'HID', 'HII', 'HMC', 'HNG', 'HOT', 'HPG', 'HPX', 'HQC', 'HRC', 'HSG', 'HSL', 'HT1', 'HTI', 'HTL', 'HTN', 'HTV', 'HU1', 'HU3', 'HUB', 'HVH', 'HVN', 'HVX', 'IBC', 'ICT', 'IDI', 'IJC', 'ILB', 'IMP', 'ITA', 'ITC', 'ITD', 'JVC', 'KBC', 'KDC', 'KDH', 'KHG', 'KHP', 'KMR', 'KOS', 'KPF', 'KSB', 'L10', 'LAF', 'LBM', 'LCG', 'LCM', 'LDG', 'LEC', 'LGC', 'LGL']\n"
     ]
    }
   ],
   "source": [
    "print('Extra 100 stock symbols:\\n', stocksymbols[100:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Đóng webdriver hiện tại\n",
    "#wd.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ***Task 1.b - Collecting the daily stock prices***\n",
    "**Collecting the daily stock prices (closing prices) of those stock symbols in 2020, 2021, 2022**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hàm tắt quảng cáo nếu có\n",
    "def close_Ad_if_exists(wd):\n",
    "    try:\n",
    "        close_btn = wd.find_element(by=By.XPATH, value='//div[@class=\"mfp-content\"]/*/button[@class=\"mfp-close\"]')\n",
    "        wd.execute_script(\"arguments[0].click();\", close_btn)\n",
    "    except NoSuchElementException:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hàm chuyển trang kế tiếp\n",
    "#Nếu còn trang kế => Chuyển trang và trả về True\n",
    "#Ngược lại => Trả về False\n",
    "def click_next_page(wd):\n",
    "    try:\n",
    "        #Page navigation để chuyển trang\n",
    "        page_navigation = wd.find_element(by=By.XPATH, value='//*[@id=\"ctl00_webPartManager_wp1770166562_wp1427611561_callbackData\"]/div[2]')\n",
    "        #Trang kế tiếp\n",
    "        next_page = page_navigation.find_element(by=By.XPATH, value='./span/following-sibling::a')\n",
    "        wd.execute_script(\"arguments[0].click();\", next_page)\n",
    "    except StaleElementReferenceException:\n",
    "        #Trang chưa được load hoàn toàn => sleep thêm đợi trang load\n",
    "        print('StaleElementReferenceException. Trying again after 0.3 seconds...')\n",
    "        time.sleep(0.3)\n",
    "    except NoSuchElementException:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hàm cào dữ liệu của 1 stocksymbol nhập vào\n",
    "#Mặc định là 1 năm, nếu muốn thay đổi khoảng thời gian thì nhập thêm start_date và end_date\n",
    "def crawl_daily_prices(wd, stocksymbol, list_dates=set(), start_date=\"\", end_date=\"\", sleep_time=0.5):\n",
    "    web_link = 'https://www.stockbiz.vn/Stocks/' + stocksymbol + '/HistoricalQuotes.aspx'\n",
    "    wd.get(web_link)\n",
    "    time.sleep(sleep_time)\n",
    "    close_Ad_if_exists(wd)\n",
    "    \n",
    "    #Chọn ngày tháng bắt đầu, và ngày tháng kết thúc nếu có\n",
    "    #End date nếu bỏ trống thì mặc định là ngày hiện tại\n",
    "    if (start_date!=\"\"):\n",
    "        input_start_date = wd.find_element(by=By.XPATH, value='//input[@id=\"ctl00_webPartManager_wp1770166562_wp1427611561_dtStartDate_picker_picker\"]')\n",
    "        input_start_date.send_keys(start_date);\n",
    "    if (end_date!=\"\"):\n",
    "        input_end_date = wd.find_element(by=By.XPATH, value='//input[@id=\"ctl00_webPartManager_wp1770166562_wp1427611561_dtEndDate_picker_picker\"]')\n",
    "        input_end_date.send_keys(end_date);\n",
    "    view_btn = wd.find_element(by=By.XPATH, value='//*[@id=\"ctl00_webPartManager_wp1770166562_wp1427611561_btnView\"]')\n",
    "    view_btn.click()\n",
    "    time.sleep(sleep_time)\n",
    "\n",
    "    closing_price_dict = {}\n",
    "    while (True):\n",
    "        try:\n",
    "            #Table body chứa nội dung cần cào dữ liệu\n",
    "            tbody = wd.find_element(by=By.XPATH, value='//div[@id=\"ctl00_webPartManager_wp1770166562_wp1427611561_callbackData\"]/table/tbody')\n",
    "\n",
    "            #Cào từng dòng dữ liệu trong table body\n",
    "            table_rows = tbody.find_elements(by=By.XPATH, value='./tr')\n",
    "            for i in range(1, len(table_rows)):\n",
    "                list_td = table_rows[i].find_elements(by=By.XPATH, value='./td')\n",
    "\n",
    "                date = list_td[0].text\n",
    "                closing_price = float(list_td[5].text.replace(',', '.'))*1000\n",
    "                \n",
    "                list_dates.add(date)\n",
    "                closing_price_dict[date] = closing_price\n",
    "        except StaleElementReferenceException:\n",
    "            #Trang chưa được load hoàn toàn => sleep thêm đợi trang load\n",
    "            print('StaleElementReferenceException. Trying again after %.1f seconds...' %sleep_time)\n",
    "            time.sleep(sleep_time)\n",
    "            continue\n",
    "\n",
    "        #Chuyển trang \n",
    "        has_next_page = click_next_page(wd) # Sang trang mới\n",
    "        if not has_next_page:               # Nếu hết trang thì dừng\n",
    "            break\n",
    "        time.sleep(sleep_time)              # Chờ trang load\n",
    "    \n",
    "    return closing_price_dict, list_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Crawl daily stock prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tạo webdriver mở bằng trình duyệt chrome\n",
    "#wd = webdriver.Chrome(executable_path='chromedriver.exe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AAA', 'AAM', 'AAT', 'ABR', 'ABS']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In ra 5 mã cổ phiếu đầu tiên\n",
    "stocksymbols[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<< Crawling stock symbol 1: AAA >>\n",
      "<< Crawling stock symbol 2: AAM >>\n",
      "<< Crawling stock symbol 3: AAT >>\n",
      "<< Crawling stock symbol 4: ABR >>\n",
      "<< Crawling stock symbol 5: ABS >>\n",
      "<< Crawling stock symbol 6: ABT >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 7: ACB >>\n",
      "<< Crawling stock symbol 8: ACC >>\n",
      "<< Crawling stock symbol 9: ACL >>\n",
      "<< Crawling stock symbol 10: ADG >>\n",
      "<< Crawling stock symbol 11: ADS >>\n",
      "<< Crawling stock symbol 12: AGG >>\n",
      "<< Crawling stock symbol 13: AGM >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 14: AGR >>\n",
      "<< Crawling stock symbol 15: AMD >>\n",
      "<< Crawling stock symbol 16: ANV >>\n",
      "<< Crawling stock symbol 17: APC >>\n",
      "<< Crawling stock symbol 18: APG >>\n",
      "<< Crawling stock symbol 19: APH >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 20: ASG >>\n",
      "<< Crawling stock symbol 21: ASM >>\n",
      "<< Crawling stock symbol 22: ASP >>\n",
      "<< Crawling stock symbol 23: AST >>\n",
      "<< Crawling stock symbol 24: BAF >>\n",
      "<< Crawling stock symbol 25: BBC >>\n",
      "<< Crawling stock symbol 26: BCE >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 27: BCG >>\n",
      "<< Crawling stock symbol 28: BCM >>\n",
      "<< Crawling stock symbol 29: BFC >>\n",
      "<< Crawling stock symbol 30: BHN >>\n",
      "<< Crawling stock symbol 31: BIC >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 32: BID >>\n",
      "<< Crawling stock symbol 33: BKG >>\n",
      "<< Crawling stock symbol 34: BMC >>\n",
      "<< Crawling stock symbol 35: BMI >>\n",
      "<< Crawling stock symbol 36: BMP >>\n",
      "<< Crawling stock symbol 37: BRC >>\n",
      "<< Crawling stock symbol 38: BSI >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 39: BTP >>\n",
      "<< Crawling stock symbol 40: BTT >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 41: BVH >>\n",
      "<< Crawling stock symbol 42: BWE >>\n",
      "<< Crawling stock symbol 43: C32 >>\n",
      "<< Crawling stock symbol 44: C47 >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 45: CAV >>\n",
      "<< Crawling stock symbol 46: CCI >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 47: CCL >>\n",
      "<< Crawling stock symbol 48: CDC >>\n",
      "<< Crawling stock symbol 49: CEE >>\n",
      "<< Crawling stock symbol 50: CHP >>\n",
      "<< Crawling stock symbol 51: CIG >>\n",
      "<< Crawling stock symbol 52: CII >>\n",
      "<< Crawling stock symbol 53: CKG >>\n",
      "<< Crawling stock symbol 54: CLC >>\n",
      "<< Crawling stock symbol 55: CLL >>\n",
      "<< Crawling stock symbol 56: CLW >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 57: CMG >>\n",
      "<< Crawling stock symbol 58: CMV >>\n",
      "<< Crawling stock symbol 59: CMX >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 60: CNG >>\n",
      "<< Crawling stock symbol 61: COM >>\n",
      "<< Crawling stock symbol 62: CRC >>\n",
      "<< Crawling stock symbol 63: CRE >>\n",
      "<< Crawling stock symbol 64: CSM >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 65: CSV >>\n",
      "<< Crawling stock symbol 66: CTD >>\n",
      "<< Crawling stock symbol 67: CTF >>\n",
      "<< Crawling stock symbol 68: CTG >>\n",
      "<< Crawling stock symbol 69: CTI >>\n",
      "<< Crawling stock symbol 70: CTR >>\n",
      "<< Crawling stock symbol 71: CTS >>\n",
      "<< Crawling stock symbol 72: CVT >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 73: D2D >>\n",
      "<< Crawling stock symbol 74: DAG >>\n",
      "<< Crawling stock symbol 75: DAH >>\n",
      "<< Crawling stock symbol 76: DAT >>\n",
      "<< Crawling stock symbol 77: DBC >>\n",
      "<< Crawling stock symbol 78: DBD >>\n",
      "<< Crawling stock symbol 79: DBT >>\n",
      "<< Crawling stock symbol 80: DC4 >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 81: DCL >>\n",
      "<< Crawling stock symbol 82: DCM >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 83: DGC >>\n",
      "<< Crawling stock symbol 84: DGW >>\n",
      "<< Crawling stock symbol 85: DHA >>\n",
      "<< Crawling stock symbol 86: DHC >>\n",
      "<< Crawling stock symbol 87: DHG >>\n",
      "<< Crawling stock symbol 88: DHM >>\n",
      "<< Crawling stock symbol 89: DIG >>\n",
      "<< Crawling stock symbol 90: DLG >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 91: DMC >>\n",
      "<< Crawling stock symbol 92: DPG >>\n",
      "<< Crawling stock symbol 93: DPM >>\n",
      "<< Crawling stock symbol 94: DPR >>\n",
      "<< Crawling stock symbol 95: DQC >>\n",
      "<< Crawling stock symbol 96: DRC >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 97: DRH >>\n",
      "<< Crawling stock symbol 98: DRL >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 99: DSN >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "<< Crawling stock symbol 100: DTA >>\n"
     ]
    }
   ],
   "source": [
    "all_dates = set()\n",
    "N_symbol = len(stocksymbols)\n",
    "\n",
    "#Cào dữ liệu hằng ngày cho 100 mã cổ phiếu (stock symbol) đầu tiên\n",
    "daily_stock_prices = {}\n",
    "for i in range(100):\n",
    "    print('<< Crawling stock symbol %d: %s >>' %(i+1, stocksymbols[i]))\n",
    "    \n",
    "    #- start date: 01/01/2020\n",
    "    #- end date: ngày hiện tại (ngày chạy code cào dữ liệu)\n",
    "    daily_price, all_dates = crawl_daily_prices(wd, \n",
    "                                                stocksymbol=stocksymbols[i], \n",
    "                                                list_dates=all_dates, \n",
    "                                                start_date=\"01/01/2020\", \n",
    "                                                sleep_time=0.3)\n",
    "    daily_stock_prices[stocksymbols[i]] = daily_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------ Reviewing crawled datas ------\n",
      "Stock symbol AAA: 600 dates\n",
      "Stock symbol AAM: 600 dates\n",
      "Stock symbol AAT: 296 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 101: DTL >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ABR: 593 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 102: DTT >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ABS: 551 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 103: DVP >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ABT: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 104: DXG >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ACB: 595 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 105: DXS >>\n",
      "Crawled 215 dates << Crawling stock symbol 106: DXV >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ACC: 600 dates\n",
      "Stock symbol ACL: 600 dates\n",
      "Stock symbol ADG: 590 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 107: EIB >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ADS: 600 dates\n",
      "Stock symbol AGG: 595 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 108: ELC >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol AGM: 600 dates\n",
      "Stock symbol AGR: 600 dates\n",
      "Stock symbol AMD: 600 dates\n",
      "Stock symbol ANV: 600 dates\n",
      "Stock symbol APC: 600 dates\n",
      "Stock symbol APG: 600 dates\n",
      "Stock symbol APH: 460 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 109: EMC >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ASG: 413 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 110: EVE >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol ASM: 600 dates\n",
      "Stock symbol ASP: 600 dates\n",
      "Stock symbol AST: 600 dates\n",
      "Stock symbol BAF: 119 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 111: EVF >>\n",
      "Crawled 592 dates << Crawling stock symbol 112: EVG >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol BBC: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 113: FCM >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol BCE: 600 dates\n",
      "Stock symbol BCG: 600 dates\n",
      "Stock symbol BCM: 593 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 114: FCN >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol BFC: 600 dates\n",
      "Stock symbol BHN: 600 dates\n",
      "Stock symbol BIC: 600 dates\n",
      "Stock symbol BID: 600 dates\n",
      "Stock symbol BKG: 349 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 115: FDC >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol BMC: 600 dates\n",
      "Stock symbol BMI: 600 dates\n",
      "Stock symbol BMP: 600 dates\n",
      "Stock symbol BRC: 600 dates\n",
      "Stock symbol BSI: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 116: FIR >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol BTP: 600 dates\n",
      "Stock symbol BTT: 600 dates\n",
      "Stock symbol BVH: 600 dates\n",
      "Stock symbol BWE: 600 dates\n",
      "Stock symbol C32: 600 dates\n",
      "Stock symbol C47: 600 dates\n",
      "Stock symbol CAV: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 117: FIT >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol CCI: 600 dates\n",
      "Stock symbol CCL: 600 dates\n",
      "Stock symbol CDC: 600 dates\n",
      "Stock symbol CEE: 600 dates\n",
      "Stock symbol CHP: 600 dates\n",
      "Stock symbol CIG: 600 dates\n",
      "Stock symbol CII: 600 dates\n",
      "Stock symbol CKG: 546 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 118: FLC >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol CLC: 600 dates\n",
      "Stock symbol CLL: 600 dates\n",
      "Stock symbol CLW: 600 dates\n",
      "Stock symbol CMG: 600 dates\n",
      "Stock symbol CMV: 600 dates\n",
      "Stock symbol CMX: 600 dates\n",
      "Stock symbol CNG: 600 dates\n",
      "Stock symbol COM: 600 dates\n",
      "Stock symbol CRC: 600 dates\n",
      "Stock symbol CRE: 600 dates\n",
      "Stock symbol CSM: 600 dates\n",
      "Stock symbol CSV: 600 dates\n",
      "Stock symbol CTD: 600 dates\n",
      "Stock symbol CTF: 600 dates\n",
      "Stock symbol CTG: 600 dates\n",
      "Stock symbol CTI: 600 dates\n",
      "Stock symbol CTR: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 119: FMC >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol CTS: 600 dates\n",
      "Stock symbol CVT: 600 dates\n",
      "Stock symbol D2D: 600 dates\n",
      "Stock symbol DAG: 600 dates\n",
      "Stock symbol DAH: 600 dates\n",
      "Stock symbol DAT: 600 dates\n",
      "Stock symbol DBC: 600 dates\n",
      "Stock symbol DBD: 600 dates\n",
      "Stock symbol DBT: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 120: FPT >>\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol DC4: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 121: FRT >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol DCL: 600 dates\n",
      "Stock symbol DCM: 600 dates\n",
      "Stock symbol DGC: 594 dates\n",
      "...Crawl another stock symbol...\n",
      "<< Crawling stock symbol 122: FTS >>\n",
      "StaleElementReferenceException. Trying again after 0.3 seconds...\n",
      "Crawled 600 dates -> OK\n",
      "Stock symbol DGW: 600 dates\n",
      "Stock symbol DHA: 600 dates\n",
      "Stock symbol DHC: 600 dates\n",
      "Stock symbol DHG: 600 dates\n",
      "Stock symbol DHM: 600 dates\n",
      "Stock symbol DIG: 600 dates\n",
      "Stock symbol DLG: 600 dates\n",
      "Stock symbol DMC: 600 dates\n",
      "Stock symbol DPG: 600 dates\n",
      "Stock symbol DPM: 600 dates\n",
      "Stock symbol DPR: 600 dates\n",
      "Stock symbol DQC: 600 dates\n",
      "Stock symbol DRC: 600 dates\n",
      "Stock symbol DRH: 600 dates\n",
      "Stock symbol DRL: 600 dates\n",
      "Stock symbol DSN: 600 dates\n",
      "Stock symbol DTA: 600 dates\n"
     ]
    }
   ],
   "source": [
    "N_dates = len(all_dates)   #Danh sách tất cả các ngày cập nhật giá cổ phiếu\n",
    "symbol_use = []            #Ghi lại các mã cổ phiếu đủ dữ liệu\n",
    "\n",
    "k = 100                    #Tìm đến mã tiếp theo nếu mã đã cào không đủ dữ liệu\n",
    "crawled_data = list(daily_stock_prices.items())\n",
    "print('------ Reviewing crawled datas ------')\n",
    "for i in range(len(crawled_data)):\n",
    "    stock = crawled_data[i][0]\n",
    "    n = len(crawled_data[i][1])\n",
    "    print('Stock symbol %s: %d dates' %(stock, n))\n",
    "    if n<N_dates:\n",
    "        #Cào dữ liệu từ mã khác nếu mã hiện tại không đủ dữ liệu\n",
    "        print('...Crawl another stock symbol...')\n",
    "        while n<N_dates:\n",
    "            new_stock = stocksymbols[k]\n",
    "            print('<< Crawling stock symbol %d: %s >>' %(k+1, new_stock))\n",
    "\n",
    "            daily_price, dates = crawl_daily_prices(wd, \n",
    "                                                    stocksymbol=new_stock,\n",
    "                                                    list_dates=set(), \n",
    "                                                    start_date=\"01/01/2020\", \n",
    "                                                    sleep_time=0.3)\n",
    "            daily_stock_prices[new_stock] = daily_price\n",
    "            n = len(daily_price)\n",
    "            print('Crawled %d dates' %n, end=' ')\n",
    "            if n>=N_dates:\n",
    "                print('-> OK')\n",
    "            k += 1\n",
    "        symbol_use.append(new_stock)\n",
    "    else:\n",
    "        symbol_use.append(stock)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total crawled:  122\n",
      "Symbol used:  100\n"
     ]
    }
   ],
   "source": [
    "print('Total crawled: ', len(daily_stock_prices))\n",
    "print('Symbol used: ', len(symbol_use))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write all crawled to csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['02/01/2020', '03/01/2020', '06/01/2020', '07/01/2020', '08/01/2020', '09/01/2020', '10/01/2020', '13/01/2020']\n"
     ]
    }
   ],
   "source": [
    "#Sort lại ngày tăng dần để làm index cho dataframe\n",
    "temp = pd.DataFrame({'Date': list(all_dates)})\n",
    "\n",
    "temp['Date'] = pd.to_datetime(temp['Date'], dayfirst=True)\n",
    "\n",
    "temp = temp.sort_values('Date', ascending=True)\n",
    "temp['Date'] = temp['Date'].dt.strftime('%d/%m/%Y')\n",
    "sortedDate = list(temp.Date)\n",
    "print(sortedDate[:8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>02/01/2020</th>\n",
       "      <th>03/01/2020</th>\n",
       "      <th>06/01/2020</th>\n",
       "      <th>07/01/2020</th>\n",
       "      <th>08/01/2020</th>\n",
       "      <th>09/01/2020</th>\n",
       "      <th>10/01/2020</th>\n",
       "      <th>13/01/2020</th>\n",
       "      <th>14/01/2020</th>\n",
       "      <th>15/01/2020</th>\n",
       "      <th>...</th>\n",
       "      <th>18/05/2022</th>\n",
       "      <th>19/05/2022</th>\n",
       "      <th>20/05/2022</th>\n",
       "      <th>23/05/2022</th>\n",
       "      <th>24/05/2022</th>\n",
       "      <th>25/05/2022</th>\n",
       "      <th>26/05/2022</th>\n",
       "      <th>27/05/2022</th>\n",
       "      <th>30/05/2022</th>\n",
       "      <th>31/05/2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAA</th>\n",
       "      <td>12750.0</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAM</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12450.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTL</th>\n",
       "      <td>23100.0</td>\n",
       "      <td>23100.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>26100.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>23900.0</td>\n",
       "      <td>22700.0</td>\n",
       "      <td>21750.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>19750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40650.0</td>\n",
       "      <td>40300.0</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTT</th>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>11550.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DVP</th>\n",
       "      <td>36400.0</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>36350.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>55600.0</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56800.0</td>\n",
       "      <td>57600.0</td>\n",
       "      <td>58200.0</td>\n",
       "      <td>57500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC</th>\n",
       "      <td>24100.0</td>\n",
       "      <td>23600.0</td>\n",
       "      <td>24250.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>24300.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28650.0</td>\n",
       "      <td>28200.0</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>30700.0</td>\n",
       "      <td>30150.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>30400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRH</th>\n",
       "      <td>3710.0</td>\n",
       "      <td>3730.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3320.0</td>\n",
       "      <td>3210.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>2940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14200.0</td>\n",
       "      <td>14100.0</td>\n",
       "      <td>14300.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>14750.0</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRL</th>\n",
       "      <td>53000.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>52400.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>52200.0</td>\n",
       "      <td>52200.0</td>\n",
       "      <td>52800.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66900.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>65100.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>67000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSN</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>58100.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>57900.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>57900.0</td>\n",
       "      <td>57800.0</td>\n",
       "      <td>57800.0</td>\n",
       "      <td>58500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46100.0</td>\n",
       "      <td>45900.0</td>\n",
       "      <td>45750.0</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>46800.0</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>47800.0</td>\n",
       "      <td>47000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTA</th>\n",
       "      <td>5900.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>5880.0</td>\n",
       "      <td>6050.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     02/01/2020  03/01/2020  06/01/2020  07/01/2020  08/01/2020  09/01/2020  \\\n",
       "AAA     12750.0     12700.0     12300.0     12400.0     12000.0     12050.0   \n",
       "AAM     11950.0     12500.0     12500.0     12500.0     12500.0     12500.0   \n",
       "DTL     23100.0     23100.0     24700.0     26100.0     25000.0     23900.0   \n",
       "DTT     13500.0     13500.0     13500.0     13500.0     13500.0     13500.0   \n",
       "DVP     36400.0     36500.0     36500.0     36350.0     36000.0     36100.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "DRC     24100.0     23600.0     24250.0     24950.0     24300.0     25200.0   \n",
       "DRH      3710.0      3730.0      3500.0      3500.0      3320.0      3210.0   \n",
       "DRL     53000.0     53100.0     52400.0     52500.0     52500.0     52200.0   \n",
       "DSN     58000.0     58100.0     58000.0     58000.0     57900.0     58000.0   \n",
       "DTA      5900.0      6000.0      5990.0      5900.0      5890.0      5880.0   \n",
       "\n",
       "     10/01/2020  13/01/2020  14/01/2020  15/01/2020  ...  18/05/2022  \\\n",
       "AAA     12100.0     12000.0     11900.0     11900.0  ...     11800.0   \n",
       "AAM     13000.0     13000.0     12900.0     12900.0  ...     12350.0   \n",
       "DTL     22700.0     21750.0     21000.0     19750.0  ...     40650.0   \n",
       "DTT     13500.0     13500.0     13500.0     13500.0  ...     12500.0   \n",
       "DVP     36000.0     36000.0     36100.0     36050.0  ...     56100.0   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "DRC     25000.0     24700.0     24900.0     24800.0  ...     28650.0   \n",
       "DRH      3220.0      3140.0      3160.0      2940.0  ...     14200.0   \n",
       "DRL     52200.0     52800.0     53100.0     53000.0  ...     66900.0   \n",
       "DSN     57900.0     57800.0     57800.0     58500.0  ...     46000.0   \n",
       "DTA      6050.0      6000.0      5890.0      5530.0  ...     12600.0   \n",
       "\n",
       "     19/05/2022  20/05/2022  23/05/2022  24/05/2022  25/05/2022  26/05/2022  \\\n",
       "AAA     11800.0     12100.0     11900.0     12000.0     12600.0     12750.0   \n",
       "AAM     12400.0     12100.0     12050.0     12050.0     12450.0     12200.0   \n",
       "DTL     40300.0     39000.0     38500.0     37500.0     37500.0     38000.0   \n",
       "DTT     12500.0     12500.0     12150.0     12150.0     12500.0     12050.0   \n",
       "DVP     56100.0     55600.0     56100.0     56000.0     56300.0     56800.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "DRC     28200.0     28700.0     28900.0     28900.0     29400.0     30700.0   \n",
       "DRH     14100.0     14300.0     14000.0     14500.0     14800.0     14550.0   \n",
       "DRL     65000.0     65100.0     65000.0     66000.0     67000.0     67000.0   \n",
       "DSN     46000.0     46100.0     45900.0     45750.0     47000.0     46800.0   \n",
       "DTA     12550.0     12550.0     12550.0     12200.0     12350.0     12350.0   \n",
       "\n",
       "     27/05/2022  30/05/2022  31/05/2022  \n",
       "AAA     12650.0     12750.0     12600.0  \n",
       "AAM     12400.0     12350.0     12350.0  \n",
       "DTL     38000.0     39000.0     37500.0  \n",
       "DTT     11550.0     12350.0     12850.0  \n",
       "DVP     57600.0     58200.0     57500.0  \n",
       "..          ...         ...         ...  \n",
       "DRC     30150.0     30450.0     30400.0  \n",
       "DRH     14600.0     14750.0     14000.0  \n",
       "DRL     68000.0     68000.0     67000.0  \n",
       "DSN     47600.0     47800.0     47000.0  \n",
       "DTA     12400.0     12100.0     12100.0  \n",
       "\n",
       "[100 rows x 600 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tổng hợp các dữ liệu đầy đủ đã cào được vào 1 dataframe\n",
    "all_stock_price = {}\n",
    "for symbol in symbol_use:\n",
    "    keys = list(daily_stock_prices[symbol].keys())\n",
    "    values = list(daily_stock_prices[symbol].values())\n",
    "    all_stock_price[symbol] = pd.Series(values, index=keys)\n",
    "\n",
    "df = pd.DataFrame(data=all_stock_price, index=list(sortedDate))\n",
    "#dateIndex = pd.Series(list(sortedDate), name='Date')\n",
    "#df = pd.DataFrame(data=all_stock_price, index=dateIndex)\n",
    "#df.reset_index()\n",
    "df = df.T\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ghi file tổng hợp vào file csv\n",
    "df.to_csv('allDailyStockPrice.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ghi các dữ liệu của từng mã cào được vào file csv, đặt trong folder dailyStockCSV\n",
    "#Vd: dailyStockCSV/dailyStockPrice_AAA.csv\n",
    "\n",
    "#Tạo folder nếu chưa có\n",
    "if not os.path.exists('dailyStockPriceCSV'):\n",
    "    os.makedirs('dailyStockPriceCSV')\n",
    "if not os.path.exists('dailyStockPriceCSV/stockHasLessData'):\n",
    "    os.makedirs('dailyStockPriceCSV/stockHasLessData')\n",
    "    \n",
    "#Ghi dữ liệu\n",
    "N = len(daily_stock_prices)\n",
    "daily_stock_prices_list = list(daily_stock_prices.items())\n",
    "for i in range(N):\n",
    "    symbol = daily_stock_prices_list[i][0]\n",
    "    daily = daily_stock_prices_list[i][1]\n",
    "    \n",
    "    if symbol not in symbol_use:\n",
    "        filename = 'dailyStockPriceCSV/stockHasLessData/dailyStockPrice_' + symbol + '.csv'\n",
    "    else:\n",
    "        filename = 'dailyStockPriceCSV/dailyStockPrice_' + symbol + '.csv'\n",
    "    with open(filename, 'w', newline='', encoding='utf-8') as f:\n",
    "        writer = csv.writer(f)\n",
    "        \n",
    "        #Ghi header\n",
    "        header_row = ['Date', 'Closing price']\n",
    "        writer.writerow(header_row)\n",
    "\n",
    "        #Ghi các dòng dữ liệu\n",
    "        data = list(daily.items())\n",
    "        for d in data:\n",
    "            writer.writerow(list(d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **Task 2: Pre-processing**\n",
    "**Normalize data by TimeSeriesScalerMeanVariance.<br> \n",
    "Show an image that includes the daily prices and an image that includes the daily prices after normalizing of 5 stock symbols.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(row):\n",
    "    old_price = row.copy()\n",
    "    normalize = TimeSeriesScalerMeanVariance(mu=0., std=1.)\n",
    "    new_price = normalize.fit_transform([old_price])\n",
    "    return old_price, new_price[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>02/01/2020</th>\n",
       "      <th>03/01/2020</th>\n",
       "      <th>06/01/2020</th>\n",
       "      <th>07/01/2020</th>\n",
       "      <th>08/01/2020</th>\n",
       "      <th>09/01/2020</th>\n",
       "      <th>10/01/2020</th>\n",
       "      <th>13/01/2020</th>\n",
       "      <th>14/01/2020</th>\n",
       "      <th>15/01/2020</th>\n",
       "      <th>...</th>\n",
       "      <th>18/05/2022</th>\n",
       "      <th>19/05/2022</th>\n",
       "      <th>20/05/2022</th>\n",
       "      <th>23/05/2022</th>\n",
       "      <th>24/05/2022</th>\n",
       "      <th>25/05/2022</th>\n",
       "      <th>26/05/2022</th>\n",
       "      <th>27/05/2022</th>\n",
       "      <th>30/05/2022</th>\n",
       "      <th>31/05/2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAA</th>\n",
       "      <td>12750.0</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAM</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12450.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTL</th>\n",
       "      <td>23100.0</td>\n",
       "      <td>23100.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>26100.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>23900.0</td>\n",
       "      <td>22700.0</td>\n",
       "      <td>21750.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>19750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40650.0</td>\n",
       "      <td>40300.0</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTT</th>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>11550.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DVP</th>\n",
       "      <td>36400.0</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>36350.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>55600.0</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56800.0</td>\n",
       "      <td>57600.0</td>\n",
       "      <td>58200.0</td>\n",
       "      <td>57500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC</th>\n",
       "      <td>24100.0</td>\n",
       "      <td>23600.0</td>\n",
       "      <td>24250.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>24300.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28650.0</td>\n",
       "      <td>28200.0</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>30700.0</td>\n",
       "      <td>30150.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>30400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRH</th>\n",
       "      <td>3710.0</td>\n",
       "      <td>3730.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3320.0</td>\n",
       "      <td>3210.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>2940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14200.0</td>\n",
       "      <td>14100.0</td>\n",
       "      <td>14300.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>14750.0</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRL</th>\n",
       "      <td>53000.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>52400.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>52200.0</td>\n",
       "      <td>52200.0</td>\n",
       "      <td>52800.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66900.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>65100.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>67000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSN</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>58100.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>57900.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>57900.0</td>\n",
       "      <td>57800.0</td>\n",
       "      <td>57800.0</td>\n",
       "      <td>58500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46100.0</td>\n",
       "      <td>45900.0</td>\n",
       "      <td>45750.0</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>46800.0</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>47800.0</td>\n",
       "      <td>47000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTA</th>\n",
       "      <td>5900.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>5880.0</td>\n",
       "      <td>6050.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     02/01/2020  03/01/2020  06/01/2020  07/01/2020  08/01/2020  09/01/2020  \\\n",
       "AAA     12750.0     12700.0     12300.0     12400.0     12000.0     12050.0   \n",
       "AAM     11950.0     12500.0     12500.0     12500.0     12500.0     12500.0   \n",
       "DTL     23100.0     23100.0     24700.0     26100.0     25000.0     23900.0   \n",
       "DTT     13500.0     13500.0     13500.0     13500.0     13500.0     13500.0   \n",
       "DVP     36400.0     36500.0     36500.0     36350.0     36000.0     36100.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "DRC     24100.0     23600.0     24250.0     24950.0     24300.0     25200.0   \n",
       "DRH      3710.0      3730.0      3500.0      3500.0      3320.0      3210.0   \n",
       "DRL     53000.0     53100.0     52400.0     52500.0     52500.0     52200.0   \n",
       "DSN     58000.0     58100.0     58000.0     58000.0     57900.0     58000.0   \n",
       "DTA      5900.0      6000.0      5990.0      5900.0      5890.0      5880.0   \n",
       "\n",
       "     10/01/2020  13/01/2020  14/01/2020  15/01/2020  ...  18/05/2022  \\\n",
       "AAA     12100.0     12000.0     11900.0     11900.0  ...     11800.0   \n",
       "AAM     13000.0     13000.0     12900.0     12900.0  ...     12350.0   \n",
       "DTL     22700.0     21750.0     21000.0     19750.0  ...     40650.0   \n",
       "DTT     13500.0     13500.0     13500.0     13500.0  ...     12500.0   \n",
       "DVP     36000.0     36000.0     36100.0     36050.0  ...     56100.0   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "DRC     25000.0     24700.0     24900.0     24800.0  ...     28650.0   \n",
       "DRH      3220.0      3140.0      3160.0      2940.0  ...     14200.0   \n",
       "DRL     52200.0     52800.0     53100.0     53000.0  ...     66900.0   \n",
       "DSN     57900.0     57800.0     57800.0     58500.0  ...     46000.0   \n",
       "DTA      6050.0      6000.0      5890.0      5530.0  ...     12600.0   \n",
       "\n",
       "     19/05/2022  20/05/2022  23/05/2022  24/05/2022  25/05/2022  26/05/2022  \\\n",
       "AAA     11800.0     12100.0     11900.0     12000.0     12600.0     12750.0   \n",
       "AAM     12400.0     12100.0     12050.0     12050.0     12450.0     12200.0   \n",
       "DTL     40300.0     39000.0     38500.0     37500.0     37500.0     38000.0   \n",
       "DTT     12500.0     12500.0     12150.0     12150.0     12500.0     12050.0   \n",
       "DVP     56100.0     55600.0     56100.0     56000.0     56300.0     56800.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "DRC     28200.0     28700.0     28900.0     28900.0     29400.0     30700.0   \n",
       "DRH     14100.0     14300.0     14000.0     14500.0     14800.0     14550.0   \n",
       "DRL     65000.0     65100.0     65000.0     66000.0     67000.0     67000.0   \n",
       "DSN     46000.0     46100.0     45900.0     45750.0     47000.0     46800.0   \n",
       "DTA     12550.0     12550.0     12550.0     12200.0     12350.0     12350.0   \n",
       "\n",
       "     27/05/2022  30/05/2022  31/05/2022  \n",
       "AAA     12650.0     12750.0     12600.0  \n",
       "AAM     12400.0     12350.0     12350.0  \n",
       "DTL     38000.0     39000.0     37500.0  \n",
       "DTT     11550.0     12350.0     12850.0  \n",
       "DVP     57600.0     58200.0     57500.0  \n",
       "..          ...         ...         ...  \n",
       "DRC     30150.0     30450.0     30400.0  \n",
       "DRH     14600.0     14750.0     14000.0  \n",
       "DRL     68000.0     68000.0     67000.0  \n",
       "DSN     47600.0     47800.0     47000.0  \n",
       "DTA     12400.0     12100.0     12100.0  \n",
       "\n",
       "[100 rows x 600 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Đọc dữ liệu đã cào được\n",
    "df = pd.read_csv(\"allDailyStockPrice.csv\", index_col = 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12750. 12700. 12300. ... 12650. 12750. 12600.]\n",
      " [11950. 12500. 12500. ... 12400. 12350. 12350.]\n",
      " [23100. 23100. 24700. ... 38000. 39000. 37500.]\n",
      " [13500. 13500. 13500. ... 11550. 12350. 12850.]\n",
      " [36400. 36500. 36500. ... 57600. 58200. 57500.]]\n",
      "\n",
      " Index(['AAA', 'AAM', 'DTL', 'DTT', 'DVP'], dtype='object')\n",
      "\n",
      " Index(['02/01/2020', '03/01/2020', '06/01/2020', '07/01/2020', '08/01/2020'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "data = df.values[:5]\n",
    "print(data)\n",
    "\n",
    "stock = df.index\n",
    "print('\\n', stock[:5])\n",
    "\n",
    "date = df.columns\n",
    "print('\\n', date[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original data:\n",
      "[12750. 12700. 12300. 12400. 12000.]\n",
      "\n",
      "Normalized data:\n",
      "[-0.71092678 -0.7295311  -0.87836564 -0.841157   -0.98999154]\n"
     ]
    }
   ],
   "source": [
    "orginal_price, normalized_price = normalize(data[0])\n",
    "print('Original data:')\n",
    "print(orginal_price[:5])\n",
    "print()\n",
    "print('Normalized data:')\n",
    "print(normalized_price.flatten()[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotStockPrice(price, symbol):\n",
    "    fig = plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    #Xác định số ngày hiển thị trên trục x\n",
    "    n_date = 20\n",
    "    x_start = 0\n",
    "    x_end = len(date)+1\n",
    "    x_step = x_end//n_date\n",
    "\n",
    "    #Vẽ hình\n",
    "    plt.plot(date, price)\n",
    "    \n",
    "    #Đặt tiêu đề và chú thích\n",
    "    plt.title('STOCK SYMBOL: '+ symbol, fontsize=20)\n",
    "    plt.xlabel(\"Date\", fontsize=12)\n",
    "    plt.ylabel(\"Price\", fontsize=12)\n",
    "    plt.xticks(np.arange(x_start, x_end, x_step), rotation=45, fontsize=10)\n",
    "    plt.yticks(fontsize=10)\n",
    "    \n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vẽ hình\n",
    "for i in range(5):\n",
    "    plotStockPrice(price=data[i], symbol=stock[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Task 3. Train & Predict time series data**\n",
    "**Choosing a stock symbol to train & test <br>**\n",
    "- Train: Using stock prices in 2020 and 2021 to train any time series prediction method. <br>\n",
    "- Test: Predict the stock prices the first 4 months in 2022. <br>\n",
    "**Show the visualization of results like the following example.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>02/01/2020</th>\n",
       "      <th>03/01/2020</th>\n",
       "      <th>06/01/2020</th>\n",
       "      <th>07/01/2020</th>\n",
       "      <th>08/01/2020</th>\n",
       "      <th>09/01/2020</th>\n",
       "      <th>10/01/2020</th>\n",
       "      <th>13/01/2020</th>\n",
       "      <th>14/01/2020</th>\n",
       "      <th>15/01/2020</th>\n",
       "      <th>...</th>\n",
       "      <th>18/05/2022</th>\n",
       "      <th>19/05/2022</th>\n",
       "      <th>20/05/2022</th>\n",
       "      <th>23/05/2022</th>\n",
       "      <th>24/05/2022</th>\n",
       "      <th>25/05/2022</th>\n",
       "      <th>26/05/2022</th>\n",
       "      <th>27/05/2022</th>\n",
       "      <th>30/05/2022</th>\n",
       "      <th>31/05/2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAA</th>\n",
       "      <td>12750.0</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAM</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12450.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTL</th>\n",
       "      <td>23100.0</td>\n",
       "      <td>23100.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>26100.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>23900.0</td>\n",
       "      <td>22700.0</td>\n",
       "      <td>21750.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>19750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40650.0</td>\n",
       "      <td>40300.0</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>37500.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>38000.0</td>\n",
       "      <td>39000.0</td>\n",
       "      <td>37500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTT</th>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>13500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>11550.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DVP</th>\n",
       "      <td>36400.0</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>36500.0</td>\n",
       "      <td>36350.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>36100.0</td>\n",
       "      <td>36050.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>55600.0</td>\n",
       "      <td>56100.0</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>56300.0</td>\n",
       "      <td>56800.0</td>\n",
       "      <td>57600.0</td>\n",
       "      <td>58200.0</td>\n",
       "      <td>57500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRC</th>\n",
       "      <td>24100.0</td>\n",
       "      <td>23600.0</td>\n",
       "      <td>24250.0</td>\n",
       "      <td>24950.0</td>\n",
       "      <td>24300.0</td>\n",
       "      <td>25200.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>24700.0</td>\n",
       "      <td>24900.0</td>\n",
       "      <td>24800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28650.0</td>\n",
       "      <td>28200.0</td>\n",
       "      <td>28700.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>28900.0</td>\n",
       "      <td>29400.0</td>\n",
       "      <td>30700.0</td>\n",
       "      <td>30150.0</td>\n",
       "      <td>30450.0</td>\n",
       "      <td>30400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRH</th>\n",
       "      <td>3710.0</td>\n",
       "      <td>3730.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>3320.0</td>\n",
       "      <td>3210.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>2940.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14200.0</td>\n",
       "      <td>14100.0</td>\n",
       "      <td>14300.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>14800.0</td>\n",
       "      <td>14550.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>14750.0</td>\n",
       "      <td>14000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRL</th>\n",
       "      <td>53000.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>52400.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>52500.0</td>\n",
       "      <td>52200.0</td>\n",
       "      <td>52200.0</td>\n",
       "      <td>52800.0</td>\n",
       "      <td>53100.0</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66900.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>65100.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>67000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSN</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>58100.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>57900.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>57900.0</td>\n",
       "      <td>57800.0</td>\n",
       "      <td>57800.0</td>\n",
       "      <td>58500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46000.0</td>\n",
       "      <td>46100.0</td>\n",
       "      <td>45900.0</td>\n",
       "      <td>45750.0</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>46800.0</td>\n",
       "      <td>47600.0</td>\n",
       "      <td>47800.0</td>\n",
       "      <td>47000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTA</th>\n",
       "      <td>5900.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>5880.0</td>\n",
       "      <td>6050.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>5890.0</td>\n",
       "      <td>5530.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12550.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     02/01/2020  03/01/2020  06/01/2020  07/01/2020  08/01/2020  09/01/2020  \\\n",
       "AAA     12750.0     12700.0     12300.0     12400.0     12000.0     12050.0   \n",
       "AAM     11950.0     12500.0     12500.0     12500.0     12500.0     12500.0   \n",
       "DTL     23100.0     23100.0     24700.0     26100.0     25000.0     23900.0   \n",
       "DTT     13500.0     13500.0     13500.0     13500.0     13500.0     13500.0   \n",
       "DVP     36400.0     36500.0     36500.0     36350.0     36000.0     36100.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "DRC     24100.0     23600.0     24250.0     24950.0     24300.0     25200.0   \n",
       "DRH      3710.0      3730.0      3500.0      3500.0      3320.0      3210.0   \n",
       "DRL     53000.0     53100.0     52400.0     52500.0     52500.0     52200.0   \n",
       "DSN     58000.0     58100.0     58000.0     58000.0     57900.0     58000.0   \n",
       "DTA      5900.0      6000.0      5990.0      5900.0      5890.0      5880.0   \n",
       "\n",
       "     10/01/2020  13/01/2020  14/01/2020  15/01/2020  ...  18/05/2022  \\\n",
       "AAA     12100.0     12000.0     11900.0     11900.0  ...     11800.0   \n",
       "AAM     13000.0     13000.0     12900.0     12900.0  ...     12350.0   \n",
       "DTL     22700.0     21750.0     21000.0     19750.0  ...     40650.0   \n",
       "DTT     13500.0     13500.0     13500.0     13500.0  ...     12500.0   \n",
       "DVP     36000.0     36000.0     36100.0     36050.0  ...     56100.0   \n",
       "..          ...         ...         ...         ...  ...         ...   \n",
       "DRC     25000.0     24700.0     24900.0     24800.0  ...     28650.0   \n",
       "DRH      3220.0      3140.0      3160.0      2940.0  ...     14200.0   \n",
       "DRL     52200.0     52800.0     53100.0     53000.0  ...     66900.0   \n",
       "DSN     57900.0     57800.0     57800.0     58500.0  ...     46000.0   \n",
       "DTA      6050.0      6000.0      5890.0      5530.0  ...     12600.0   \n",
       "\n",
       "     19/05/2022  20/05/2022  23/05/2022  24/05/2022  25/05/2022  26/05/2022  \\\n",
       "AAA     11800.0     12100.0     11900.0     12000.0     12600.0     12750.0   \n",
       "AAM     12400.0     12100.0     12050.0     12050.0     12450.0     12200.0   \n",
       "DTL     40300.0     39000.0     38500.0     37500.0     37500.0     38000.0   \n",
       "DTT     12500.0     12500.0     12150.0     12150.0     12500.0     12050.0   \n",
       "DVP     56100.0     55600.0     56100.0     56000.0     56300.0     56800.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "DRC     28200.0     28700.0     28900.0     28900.0     29400.0     30700.0   \n",
       "DRH     14100.0     14300.0     14000.0     14500.0     14800.0     14550.0   \n",
       "DRL     65000.0     65100.0     65000.0     66000.0     67000.0     67000.0   \n",
       "DSN     46000.0     46100.0     45900.0     45750.0     47000.0     46800.0   \n",
       "DTA     12550.0     12550.0     12550.0     12200.0     12350.0     12350.0   \n",
       "\n",
       "     27/05/2022  30/05/2022  31/05/2022  \n",
       "AAA     12650.0     12750.0     12600.0  \n",
       "AAM     12400.0     12350.0     12350.0  \n",
       "DTL     38000.0     39000.0     37500.0  \n",
       "DTT     11550.0     12350.0     12850.0  \n",
       "DVP     57600.0     58200.0     57500.0  \n",
       "..          ...         ...         ...  \n",
       "DRC     30150.0     30450.0     30400.0  \n",
       "DRH     14600.0     14750.0     14000.0  \n",
       "DRL     68000.0     68000.0     67000.0  \n",
       "DSN     47600.0     47800.0     47000.0  \n",
       "DTA     12400.0     12100.0     12100.0  \n",
       "\n",
       "[100 rows x 600 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Hàm normalize đã được định nghĩa ở task 2\n",
    "def normalize(row):\n",
    "    old_price = row.copy()\n",
    "    normalize = TimeSeriesScalerMeanVariance(mu=0., std=1.)\n",
    "    new_price = normalize.fit_transform([old_price])\n",
    "    return old_price, new_price[0]\n",
    "\n",
    "#Đọc dữ liệu đã cào được\n",
    "df = pd.read_csv(\"allDailyStockPrice.csv\", index_col = 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lấy daily stock price của stock symbol nhập vào\n",
    "def get_daily_stock_price(stock_sym):\n",
    "    stock_data = df.loc[stock_sym].values\n",
    "    dates = df.columns\n",
    "    #Chuẩn hóa\n",
    "    original, normalized = normalize(stock_data)\n",
    "    #Đổi shape cho phù hợp để đưa vào mô hình huấn luyện\n",
    "    normalized = normalized.reshape(len(normalized), 1)\n",
    "    dates = np.array(dates).reshape(len(dates), 1)\n",
    "    return original, normalized, dates\n",
    "\n",
    "#Chia tập data thành tập train và test\n",
    "#- Train: năm 2020 và 2021\n",
    "#- Test: từ năm 2022 đến hiện tại\n",
    "def train_test_split(input_dates, input_prices):\n",
    "    for i in range(input_dates.shape[0]):\n",
    "        year = input_dates[i,0].split('/')[2]\n",
    "        if year=='2022':\n",
    "            break\n",
    "    train_price = input_prices[:i, :]\n",
    "    test_price = input_prices[i:, :]\n",
    "\n",
    "    train_date = input_dates[:i, :]\n",
    "    test_date = input_dates[i:, :]\n",
    "    \n",
    "    return train_price, test_price, train_date, test_date\n",
    "\n",
    "#Chia đầu vào X, đầu ra y\n",
    "#Giá trị look_back bằng x nghĩa là chúng ta lấy x bước thời gian trước, để dự đoán cho thời gian tiếp theo.\n",
    "def X_y_split(dataset, look_back):\n",
    "    X, y = [], []\n",
    "    for i in range(look_back, len(dataset)):\n",
    "        X.append(dataset[(i-look_back):i, :])\n",
    "        y.append(dataset[i:(i+1), 0])\n",
    "    return np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_and_train_model(X_train, y_train, X_test, y_test, epochs=50, batch_size=20, verbose=1):\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(LSTM(50, activation='relu',input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))\n",
    "    model.add(Dropout(rate=0.1))\n",
    "    \n",
    "    model.add(LSTM(50, activation='relu', return_sequences=True))\n",
    "    model.add(Dropout(rate=0.1))\n",
    "    \n",
    "    model.add(LSTM(50, activation='relu', return_sequences=False))\n",
    "    model.add(Dropout(rate=0.1))\n",
    "    \n",
    "    model.add(Dense(1))\n",
    "    model.summary()\n",
    "    \n",
    "    model.compile(loss = 'mean_squared_error', optimizer = 'adam')\n",
    "    history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs = epochs, batch_size = batch_size, verbose=verbose)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictPrice(model, train_data, look_back, end_date=datetime(2022, 4, 30)):\n",
    "    #Nhập train_data để lấy dữ liệu x ngày cuối của năm 2021, dự đoán cho ngày đầu của năm 2022\n",
    "    start_date=datetime(2022, 1, 1)\n",
    "    dates = pd.date_range(start=start_date, end=end_date)\n",
    "    dates_str = [d.strftime('%d/%m/%Y') for d in dates]\n",
    "    \n",
    "    num_days = len(dates_str)\n",
    "    #look_back là x, có nghĩa là Lấy dữ liệu x ngày cuối năm 2021 để dự đoán ngày đầu năm 2022\n",
    "    X_predict = np.zeros((1, num_days+look_back, 1))\n",
    "    X_predict[0, 0:look_back] = train_data[-look_back:]\n",
    "\n",
    "    #Nếu không nhập end_date, mặc định dự đoán 4 tháng đầu năm 2022\n",
    "    y_pred=np.zeros((num_days, 1))\n",
    "    for d in range(num_days):\n",
    "        new_price = model.predict(X_predict[:, d:d+look_back, :])[0]\n",
    "        X_predict[0][d+look_back] = new_price\n",
    "        y_pred[d,:] = new_price\n",
    "    \n",
    "    return y_pred, np.array(dates_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverse_normalize(org_data, normalized_pred):\n",
    "    #org_data là dữ liệu huấn luyện gốc trước khi chuẩn hóa\n",
    "    normalized_pred = normalized_pred.ravel()\n",
    "    mean = np.sum(org_data)/org_data.shape[0]\n",
    "    sum_val = 0.0\n",
    "    for x in org_data:\n",
    "        sum_val += (x - mean)** 2\n",
    "    std = math.sqrt(sum_val/org_data.shape[0])\n",
    "    \n",
    "    x = [round((y*std) + mean) for y in normalized_pred]\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualizePrediction(known_dates, known_price, predict_dates, predicted_price, symbol, title='STOCK PRICE PREDICTION'):\n",
    "    #Chuẩn bị dữ liệu plot x-axis\n",
    "    known_dates = known_dates.ravel()\n",
    "    predict_dates = predict_dates.ravel()\n",
    "    \n",
    "    start_date = datetime.strptime(known_dates[0], '%d/%m/%Y')\n",
    "    end_date = datetime.strptime(known_dates[-1], '%d/%m/%Y')\n",
    "    d = datetime.strptime(predict_dates[-1], '%d/%m/%Y')\n",
    "    if end_date<d:\n",
    "        end_date = d\n",
    "    all_dates = [d.strftime('%d/%m/%Y') for d in pd.date_range(start=start_date, end=end_date)]\n",
    "    \n",
    "    #Đánh số các ngày\n",
    "    all_dates_index = [i for i in range(len(all_dates))]\n",
    "    known_dates_index = [all_dates.index(d) for d in known_dates]\n",
    "    predict_dates_index = [all_dates.index(d) for d in predict_dates]\n",
    "    \n",
    "    #Vẽ hình\n",
    "    fig = plt.figure(figsize=(15, 8))\n",
    "    plt.plot(known_dates_index, known_price, color='blue', linewidth=3, label='Actual_Data')\n",
    "    plt.plot(predict_dates_index, predicted_price, color='orange', linewidth=3, label='Predicted_Price')\n",
    "    #Vẽ x-axis (ngày tháng năm)\n",
    "    ax = plt.gca()\n",
    "    ax.xaxis.set_ticks([all_dates_index[i] for i in range(1, len(all_dates_index), len(all_dates_index)//10)])\n",
    "    ax.xaxis.set_major_formatter(FuncFormatter(lambda x, y: format(all_dates[x])))\n",
    "    #Gán nhãn x-axis, y-axis\n",
    "    plt.title('%s - SYMBOL: %s' %(title, symbol), fontsize='18')\n",
    "    plt.xlabel('Dates', fontsize=13)\n",
    "    plt.ylabel('Stock Price', fontsize=13)\n",
    "    \n",
    "    plt.grid()\n",
    "    plt.legend(loc='upper left', prop={'size': 12})\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>02/01/2020</th>\n",
       "      <th>03/01/2020</th>\n",
       "      <th>06/01/2020</th>\n",
       "      <th>07/01/2020</th>\n",
       "      <th>08/01/2020</th>\n",
       "      <th>09/01/2020</th>\n",
       "      <th>10/01/2020</th>\n",
       "      <th>13/01/2020</th>\n",
       "      <th>14/01/2020</th>\n",
       "      <th>15/01/2020</th>\n",
       "      <th>...</th>\n",
       "      <th>18/05/2022</th>\n",
       "      <th>19/05/2022</th>\n",
       "      <th>20/05/2022</th>\n",
       "      <th>23/05/2022</th>\n",
       "      <th>24/05/2022</th>\n",
       "      <th>25/05/2022</th>\n",
       "      <th>26/05/2022</th>\n",
       "      <th>27/05/2022</th>\n",
       "      <th>30/05/2022</th>\n",
       "      <th>31/05/2022</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAA</th>\n",
       "      <td>12750.0</td>\n",
       "      <td>12700.0</td>\n",
       "      <td>12300.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>11800.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>12600.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12650.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>12600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAM</th>\n",
       "      <td>11950.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>12900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12050.0</td>\n",
       "      <td>12450.0</td>\n",
       "      <td>12200.0</td>\n",
       "      <td>12400.0</td>\n",
       "      <td>12350.0</td>\n",
       "      <td>12350.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 600 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     02/01/2020  03/01/2020  06/01/2020  07/01/2020  08/01/2020  09/01/2020  \\\n",
       "AAA     12750.0     12700.0     12300.0     12400.0     12000.0     12050.0   \n",
       "AAM     11950.0     12500.0     12500.0     12500.0     12500.0     12500.0   \n",
       "\n",
       "     10/01/2020  13/01/2020  14/01/2020  15/01/2020  ...  18/05/2022  \\\n",
       "AAA     12100.0     12000.0     11900.0     11900.0  ...     11800.0   \n",
       "AAM     13000.0     13000.0     12900.0     12900.0  ...     12350.0   \n",
       "\n",
       "     19/05/2022  20/05/2022  23/05/2022  24/05/2022  25/05/2022  26/05/2022  \\\n",
       "AAA     11800.0     12100.0     11900.0     12000.0     12600.0     12750.0   \n",
       "AAM     12400.0     12100.0     12050.0     12050.0     12450.0     12200.0   \n",
       "\n",
       "     27/05/2022  30/05/2022  31/05/2022  \n",
       "AAA     12650.0     12750.0     12600.0  \n",
       "AAM     12400.0     12350.0     12350.0  \n",
       "\n",
       "[2 rows x 600 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Step by step example: Stock symbol AAA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(600, 1)\n",
      "[[-0.71092678]\n",
      " [-0.7295311 ]]\n",
      "\n",
      "(600, 1)\n",
      "[['02/01/2020']\n",
      " ['03/01/2020']]\n"
     ]
    }
   ],
   "source": [
    "#Lấy dữ liệu giá cổ phiếu của mã cổ phiếu AAA\n",
    "original_price, normalized_price, date_data = get_daily_stock_price('AAA')\n",
    "print(normalized_price.shape)\n",
    "print(normalized_price[:2])\n",
    "print()\n",
    "print(date_data.shape)\n",
    "print(date_data[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train's shape: (488, 14, 1)\n",
      "y_train's shape: (488, 1)\n",
      "X_test's shape: (84, 14, 1)\n",
      "y_test's shape: (84, 1)\n",
      "\n",
      "End of train_date: ['31/12/2021']\n",
      "Start of test_date: ['04/01/2022']\n",
      "\n",
      "train[14]= [-0.76673973]\n",
      "y_train[0]= [-0.76673973]\n"
     ]
    }
   ],
   "source": [
    "#Chia train data và test data\n",
    "train_data, test_data, train_dates, test_dates = train_test_split(date_data, normalized_price)\n",
    "\n",
    "look_back = 14\n",
    "X_train, y_train = X_y_split(train_data, look_back)\n",
    "X_test, y_test = X_y_split(test_data, look_back)\n",
    "#X_date_test, Y_date_test = X_y_split(test_dates, look_back)\n",
    "\n",
    "print(\"X_train's shape:\", X_train.shape)\n",
    "print(\"y_train's shape:\", y_train.shape)\n",
    "print(\"X_test's shape:\", X_test.shape)\n",
    "print(\"y_test's shape:\", y_test.shape)\n",
    "\n",
    "print(\"\\nEnd of train_date:\", train_dates[-1])\n",
    "print(\"Start of test_date:\", test_dates[0])\n",
    "\n",
    "print('\\ntrain[%s]=' %look_back, train_data[look_back])\n",
    "print('y_train[0]=', y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 14, 50)            10400     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 14, 50)            20200     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "25/25 [==============================] - 6s 62ms/step - loss: 0.7504 - val_loss: 0.4369\n",
      "Epoch 2/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.2664 - val_loss: 0.4509\n",
      "Epoch 3/50\n",
      "25/25 [==============================] - 1s 27ms/step - loss: 0.2075 - val_loss: 0.3622\n",
      "Epoch 4/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.1795 - val_loss: 0.3036\n",
      "Epoch 5/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.1523 - val_loss: 0.3218\n",
      "Epoch 6/50\n",
      "25/25 [==============================] - 1s 28ms/step - loss: 0.1296 - val_loss: 0.1901\n",
      "Epoch 7/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.1340 - val_loss: 0.1546\n",
      "Epoch 8/50\n",
      "25/25 [==============================] - 1s 30ms/step - loss: 0.1005 - val_loss: 0.1451\n",
      "Epoch 9/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0808 - val_loss: 0.1148\n",
      "Epoch 10/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0712 - val_loss: 0.0965\n",
      "Epoch 11/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0739 - val_loss: 0.1049\n",
      "Epoch 12/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0742 - val_loss: 0.1117\n",
      "Epoch 13/50\n",
      "25/25 [==============================] - 1s 29ms/step - loss: 0.1058 - val_loss: 0.0981\n",
      "Epoch 14/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0798 - val_loss: 0.0868\n",
      "Epoch 15/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0709 - val_loss: 0.0744\n",
      "Epoch 16/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0724 - val_loss: 0.0779\n",
      "Epoch 17/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0636 - val_loss: 0.0714\n",
      "Epoch 18/50\n",
      "25/25 [==============================] - 1s 31ms/step - loss: 0.0671 - val_loss: 0.0789\n",
      "Epoch 19/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0629 - val_loss: 0.0756\n",
      "Epoch 20/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0584 - val_loss: 0.1413\n",
      "Epoch 21/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0797 - val_loss: 0.0712\n",
      "Epoch 22/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0618 - val_loss: 0.0688\n",
      "Epoch 23/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0564 - val_loss: 0.0615\n",
      "Epoch 24/50\n",
      "25/25 [==============================] - 1s 28ms/step - loss: 0.0578 - val_loss: 0.0669\n",
      "Epoch 25/50\n",
      "25/25 [==============================] - 1s 28ms/step - loss: 0.0569 - val_loss: 0.0656\n",
      "Epoch 26/50\n",
      "25/25 [==============================] - 1s 33ms/step - loss: 0.0565 - val_loss: 0.0668\n",
      "Epoch 27/50\n",
      "25/25 [==============================] - 1s 29ms/step - loss: 0.0600 - val_loss: 0.0588\n",
      "Epoch 28/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0461 - val_loss: 0.0652\n",
      "Epoch 29/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0553 - val_loss: 0.0744\n",
      "Epoch 30/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0520 - val_loss: 0.0790\n",
      "Epoch 31/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0678 - val_loss: 0.0834\n",
      "Epoch 32/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0525 - val_loss: 0.0600\n",
      "Epoch 33/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0557 - val_loss: 0.0847\n",
      "Epoch 34/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0572 - val_loss: 0.0571\n",
      "Epoch 35/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0546 - val_loss: 0.0680\n",
      "Epoch 36/50\n",
      "25/25 [==============================] - 1s 31ms/step - loss: 0.0620 - val_loss: 0.0705\n",
      "Epoch 37/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0527 - val_loss: 0.0572\n",
      "Epoch 38/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0509 - val_loss: 0.0721\n",
      "Epoch 39/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0473 - val_loss: 0.0610\n",
      "Epoch 40/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0562 - val_loss: 0.0519\n",
      "Epoch 41/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0510 - val_loss: 0.0641\n",
      "Epoch 42/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0542 - val_loss: 0.0634\n",
      "Epoch 43/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0499 - val_loss: 0.0751\n",
      "Epoch 44/50\n",
      "25/25 [==============================] - 1s 30ms/step - loss: 0.0500 - val_loss: 0.0619\n",
      "Epoch 45/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0500 - val_loss: 0.0680\n",
      "Epoch 46/50\n",
      "25/25 [==============================] - 1s 25ms/step - loss: 0.0518 - val_loss: 0.0846\n",
      "Epoch 47/50\n",
      "25/25 [==============================] - 1s 24ms/step - loss: 0.0534 - val_loss: 0.0534\n",
      "Epoch 48/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0478 - val_loss: 0.0696\n",
      "Epoch 49/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0503 - val_loss: 0.0562\n",
      "Epoch 50/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0480 - val_loss: 0.0519\n"
     ]
    }
   ],
   "source": [
    "#Tạo mô hình và huấn luyện\n",
    "AAA_model = create_and_train_model(X_train, y_train, X_test, y_test, \n",
    "                                   epochs=50, batch_size=20, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phương pháp 1: Dựa trên những giá trị vừa dự đoán được, để dự đoán giá trị các ngày tiếp theo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ed = datetime.strptime(date_data.ravel()[-1], '%d/%m/%Y')\n",
    "#Dự đoán giá \n",
    "y_pred, date_pred = predictPrice(model=AAA_model, train_data=train_data, \n",
    "                                     look_back=look_back, end_date=ed)\n",
    "#Chuyển về lại vnđ\n",
    "y_pred_price = inverse_normalize(original_price, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted price 151 days, start from 01/01/2022 to 31/05/2022\n",
      "Result of first 5 days of 2022:\n",
      "01/01/2022 : 1.741615 (Normalized price) -> 19341 (vnd)\n",
      "02/01/2022 : 1.432207 (Normalized price) -> 18510 (vnd)\n",
      "03/01/2022 : 1.163595 (Normalized price) -> 17788 (vnd)\n",
      "04/01/2022 : 0.916647 (Normalized price) -> 17124 (vnd)\n",
      "05/01/2022 : 0.713578 (Normalized price) -> 16578 (vnd)\n"
     ]
    }
   ],
   "source": [
    "print('Predicted price %d days, start from %s to %s' %(len(date_pred), date_pred[0], date_pred[-1]))\n",
    "print('Result of first 5 days of 2022:')\n",
    "for i in range(5):\n",
    "    print('%s : %.6f (Normalized price) -> %d (vnd)' %(date_pred[i], y_pred[i], y_pred_price[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vẽ biểu đồ kết quả dự đoán giá\n",
    "visualizePrediction(known_dates=date_data, known_price=original_price,\n",
    "                     predict_dates=date_pred, predicted_price=y_pred_price, \n",
    "                     symbol='AAA', title='PRICE PREDICTION METHOD 1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Phương pháp 2: Dự đoán từng ngày dựa trên những giá trị đã có trong năm 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trích dữ liệu đầu vào X, y thuộc tập test_data\n",
    "X_test, y_test = X_y_split(test_data, look_back)\n",
    "X_date_test, y_date_test = X_y_split(test_dates, look_back)\n",
    "\n",
    "#Dự đoán trên những dữ liệu đã có\n",
    "y_pred2 = AAA_model.predict(X_test)\n",
    "y_pred2_price = inverse_normalize(original_price, y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['24/01/2022' '25/01/2022' '26/01/2022' '27/01/2022' '28/01/2022']\n",
      "[16024, 15898, 15968, 16100, 16266]\n"
     ]
    }
   ],
   "source": [
    "print(y_date_test[:5].ravel())\n",
    "print(y_pred2_price[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted price 84 days, start from ['24/01/2022'] to ['31/05/2022']\n",
      "Result of first 5 days of 2022:\n",
      "['24/01/2022'] : 0.507223 (Normalized price) -> 16024 (vnd)\n",
      "['25/01/2022'] : 0.460333 (Normalized price) -> 15898 (vnd)\n",
      "['26/01/2022'] : 0.486451 (Normalized price) -> 15968 (vnd)\n",
      "['27/01/2022'] : 0.535444 (Normalized price) -> 16100 (vnd)\n",
      "['28/01/2022'] : 0.597248 (Normalized price) -> 16266 (vnd)\n"
     ]
    }
   ],
   "source": [
    "print('Predicted price %d days, start from %s to %s' %(len(y_date_test), y_date_test[0], y_date_test[-1]))\n",
    "print('Result of first 5 days of 2022:')\n",
    "for i in range(5):\n",
    "    print('%s : %.6f (Normalized price) -> %d (vnd)' %(y_date_test[i], y_pred2[i], y_pred2_price[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vẽ biểu đồ kết quả dự đoán giá\n",
    "visualizePrediction(known_dates=date_data, known_price=original_price, \n",
    "                    predict_dates=y_date_test, predicted_price=y_pred2_price, \n",
    "                    symbol='AAA', title='PRICE PREDICTION METHOD 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Test on other stock symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AAA', 'AAM', 'DTL', 'DTT', 'DVP', 'DXG', 'DXV', 'ACC', 'ACL', 'EIB',\n",
      "       'ADS', 'ELC', 'AGM', 'AGR', 'AMD', 'ANV', 'APC', 'APG', 'EMC', 'EVE',\n",
      "       'ASM', 'ASP', 'AST', 'EVG', 'FCM', 'BCE', 'BCG', 'FCN', 'BFC', 'BHN',\n",
      "       'BIC', 'BID', 'FDC', 'BMC', 'BMI', 'BMP', 'BRC', 'FIR', 'BTP', 'BTT',\n",
      "       'BVH', 'BWE', 'C32', 'C47', 'FIT', 'CCI', 'CCL', 'CDC', 'CEE', 'CHP',\n",
      "       'CIG', 'CII', 'FLC', 'CLC', 'CLL', 'CLW', 'CMG', 'CMV', 'CMX', 'CNG',\n",
      "       'COM', 'CRC', 'CRE', 'CSM', 'CSV', 'CTD', 'CTF', 'CTG', 'CTI', 'FMC',\n",
      "       'CTS', 'CVT', 'D2D', 'DAG', 'DAH', 'DAT', 'DBC', 'DBD', 'FPT', 'FRT',\n",
      "       'DCL', 'DCM', 'FTS', 'DGW', 'DHA', 'DHC', 'DHG', 'DHM', 'DIG', 'DLG',\n",
      "       'DMC', 'DPG', 'DPM', 'DPR', 'DQC', 'DRC', 'DRH', 'DRL', 'DSN', 'DTA'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "sb = df.index\n",
    "print(sb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHOOSE SYMBOL: ACL\n"
     ]
    }
   ],
   "source": [
    "symbol = sb[8]\n",
    "print('CHOOSE SYMBOL:', symbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_9 (LSTM)                (None, 14, 50)            10400     \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 14, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_10 (LSTM)               (None, 14, 50)            20200     \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 14, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_11 (LSTM)               (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 50,851\n",
      "Trainable params: 50,851\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "25/25 [==============================] - 5s 40ms/step - loss: 0.6194 - val_loss: 0.5773\n",
      "Epoch 2/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.1540 - val_loss: 0.6462\n",
      "Epoch 3/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.1452 - val_loss: 0.8993\n",
      "Epoch 4/50\n",
      "25/25 [==============================] - 1s 20ms/step - loss: 0.1595 - val_loss: 0.1750\n",
      "Epoch 5/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0831 - val_loss: 0.3478\n",
      "Epoch 6/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0809 - val_loss: 0.3379\n",
      "Epoch 7/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.1189 - val_loss: 0.1938\n",
      "Epoch 8/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0796 - val_loss: 0.2108\n",
      "Epoch 9/50\n",
      "25/25 [==============================] - 0s 20ms/step - loss: 0.0944 - val_loss: 0.1873\n",
      "Epoch 10/50\n",
      "25/25 [==============================] - 3s 114ms/step - loss: 0.0695 - val_loss: 0.1397\n",
      "Epoch 11/50\n",
      "25/25 [==============================] - 1s 23ms/step - loss: 0.0696 - val_loss: 0.1351\n",
      "Epoch 12/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0529 - val_loss: 0.1263\n",
      "Epoch 13/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0732 - val_loss: 0.1185\n",
      "Epoch 14/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0879 - val_loss: 0.4348\n",
      "Epoch 15/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0890 - val_loss: 0.1127\n",
      "Epoch 16/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0581 - val_loss: 0.1889\n",
      "Epoch 17/50\n",
      "25/25 [==============================] - 0s 20ms/step - loss: 0.0543 - val_loss: 0.1724\n",
      "Epoch 18/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0690 - val_loss: 0.0927\n",
      "Epoch 19/50\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.0539 - val_loss: 0.1955\n",
      "Epoch 20/50\n",
      "25/25 [==============================] - 1s 20ms/step - loss: 0.0536 - val_loss: 0.1923\n",
      "Epoch 21/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.1117 - val_loss: 0.1022\n",
      "Epoch 22/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0386 - val_loss: 0.1343\n",
      "Epoch 23/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0700 - val_loss: 0.1033\n",
      "Epoch 24/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0721 - val_loss: 0.0837\n",
      "Epoch 25/50\n",
      "25/25 [==============================] - 1s 20ms/step - loss: 0.0615 - val_loss: 0.0720\n",
      "Epoch 26/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0443 - val_loss: 0.3095\n",
      "Epoch 27/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0618 - val_loss: 0.2012\n",
      "Epoch 28/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0514 - val_loss: 0.1095\n",
      "Epoch 29/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0441 - val_loss: 0.1062\n",
      "Epoch 30/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0710 - val_loss: 0.0895\n",
      "Epoch 31/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0674 - val_loss: 0.0744\n",
      "Epoch 32/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0475 - val_loss: 0.1187\n",
      "Epoch 33/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0395 - val_loss: 0.1764\n",
      "Epoch 34/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0502 - val_loss: 0.1176\n",
      "Epoch 35/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0802 - val_loss: 0.0903\n",
      "Epoch 36/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0884 - val_loss: 0.0850\n",
      "Epoch 37/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0442 - val_loss: 0.0718\n",
      "Epoch 38/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0494 - val_loss: 0.0946\n",
      "Epoch 39/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0324 - val_loss: 0.1271\n",
      "Epoch 40/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0467 - val_loss: 0.1813\n",
      "Epoch 41/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0440 - val_loss: 0.1507\n",
      "Epoch 42/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0588 - val_loss: 0.0779\n",
      "Epoch 43/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0484 - val_loss: 0.0893\n",
      "Epoch 44/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0440 - val_loss: 0.0819\n",
      "Epoch 45/50\n",
      "25/25 [==============================] - 1s 20ms/step - loss: 0.0461 - val_loss: 0.1120\n",
      "Epoch 46/50\n",
      "25/25 [==============================] - 1s 21ms/step - loss: 0.0721 - val_loss: 0.0669\n",
      "Epoch 47/50\n",
      "25/25 [==============================] - 1s 20ms/step - loss: 0.0380 - val_loss: 0.1050\n",
      "Epoch 48/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0436 - val_loss: 0.1346\n",
      "Epoch 49/50\n",
      "25/25 [==============================] - 1s 22ms/step - loss: 0.0417 - val_loss: 0.0911\n",
      "Epoch 50/50\n",
      "25/25 [==============================] - 1s 26ms/step - loss: 0.0674 - val_loss: 0.0962\n"
     ]
    }
   ],
   "source": [
    "#Lấy dữ liệu của mã cổ phiếu\n",
    "original_price, normalized_price, date_data = get_daily_stock_price(symbol)\n",
    "#Chia train data và test data\n",
    "train_data, test_data, train_dates, test_dates = train_test_split(date_data, normalized_price)\n",
    "#Chia X, y\n",
    "look_back = 14\n",
    "X_train, y_train = X_y_split(train_data, look_back)\n",
    "X_test, y_test = X_y_split(test_data, look_back)\n",
    "\n",
    "#Tạo và huấn luyện mô hình\n",
    "stock_model = create_and_train_model(X_train, y_train, X_test, y_test, \n",
    "                                     epochs=50, batch_size=20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dự đoán giá cổ phiếu\n",
    "#Phương pháp: Lấy giá trị dự đoán được, để dự đoán giá trị tiếp theo\n",
    "end_date = datetime.strptime(date_data.ravel()[-1], '%d/%m/%Y')\n",
    "y_pred, date_pred = predictPrice(model=stock_model, train_data=train_data, \n",
    "                                 look_back=look_back, end_date=end_date)\n",
    "y_pred_price = inverse_normalize(original_price, y_pred)\n",
    "\n",
    "#Vẽ biểu đồ dự đoán giá\n",
    "visualizePrediction(known_dates=date_data, known_price=original_price,\n",
    "                    predict_dates=date_pred, predicted_price=y_pred_price, \n",
    "                    symbol=symbol, title='PRICE PREDICTION METHOD 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cx4k3sU4TY8wY7N+RwY7nOzQ4ntJxnJaWkkhWGTDOGDMJO2HOtMjjb42d2bQE+I3jOI2peLAU/I10Bw7CLhHzNZ6lNVLwO/ydMSbsg6H/RV47dsWuhfgA/hlXw/wMGwRfDbvRcZwNxphXsBXEfo7jLHAc515jzADsB0HTjTGPAN9i/4ZGYiuRvYhdnuP7kWpp0HLHcTLRTV9EUFAUkex3LfaNyR8ILG4d5DjOv40x7wHnYSeu+Cn2Dckc4H/YkBFcZzHalS1Mq7u4tcL12Dd+12AnxYgqxnZNDfMwtnuaV7QqWYcNdjOx3eHui0xjnxDHcT4xxowGfosdR3kNdkKRpcBHwJGO4zyf6PUSfMz/GmO+xE4Ochh2FsjN2DeWvwP+kcBSKV5XAicTOxFKuv0jzvH3gYwFRQDHcS4yxryM/T2ehQ18a4BPgSsdx3mmmbtHf47NkftMBW4F7m3uQ5uAbbDBDOC2OOekOiiuxla1DsIG276RNizDdme/3XGct1P8mMny/o1sxoa/P2Er/r6/8Tb+Di+Jc7ye2NeOuCKvBbsBT7fQI+G/2A/lTsXOHovjOFcbY16KtP8o7AdMjdguwI9j/12vC1znkMhX0FQyM55bRACTwAezIiIiIiIi0olojKKIiIiIiIj4KCiKiIiIiIiIj4KiiIiIiIiI+CgoioiIiIiIiI+CooiIiIiIiPh06uUxqqurncGDB7d3M0KtX7+e8vLy9m5GztDzlRw9X8nR85UcPV/J0fOVHD1fydHzlRw9X8nR89UxfPbZZ8sdx+kZPN6pg+LgwYP59NNP27sZocaNG8fYsWPbuxk5Q89XcvR8JUfPV3L0fCVHz1dy9HwlR89XcvR8JUfPV8dgjJkTdlxdT0VERERERMRHQVFERERERER8FBRFRERERETER0FRREREREREfDr1ZDbx1NXVMX/+fDZu3NhubejatSuTJ09ut8fPNel+vkpKSujfvz+FhYVpewwRERERkWyhoBhi/vz5VFZWMnjwYIwx7dKGdevWUVlZ2S6PnYvS+Xw5jsOKFSuYP38+Q4YMSctjiIiIiIhkE3U9DbFx40Z69OjRbiFRsosxhh49erRrhVlEREREJJMUFONQSBQv/T2IiIiISGeioCgiIiIiIiI+CooiIiIiIiLio6AoSbvqqqv48Y9/3N7NEBERERGRNFFQzFFjx46lW7dubNq0qcVz77//fvbaa68MtMo1e/ZsjDFUVFRQUVFB7969Ofzww3njjTcSvkZ7tFtERERERBQUc9Ls2bMZP348xhief/759m5Os1avXk1NTQ1ffvklBx54IEcffTT3339/ezdLRERERESaoaCYgx588EF23313TjvtNB544IGm4/PmzeOYY46hZ8+e9OjRg7PPPpvJkyfzy1/+kg8//JCKigqqqqoAW5G85557mu4brN6de+65DBgwgC5durDzzjszfvz4NrW5T58+nHvuuVx11VVcfPHFNDY2AnDTTTcxdOhQKisrGTVqFM888wxA3Ha/9NJL7LjjjnTp0oUBAwZw1VVXtaldIiIiIiISS0ExAcZk7isRDz74IKeccgqnnHIKr732GkuWLKGhoYHDDz+cQYMGMXv2bBYsWMCJJ57IyJEjueuuu9hjjz2oqalh9erVCT3GLrvswsSJE1m5ciUnn3wyxx9/fErWETzmmGNYunQpU6dOBWDo0KGMHz+eNWvWcOWVV/LjH/+YRYsWxW13eXk5Dz74IKtXr+all17iH//4B88++2yb2yUiIiIiIi4FxRzz3nvvMWfOHH70ox+x8847M3ToUB555BE++eQTFi5cyM0330x5eTklJSVtGt/34x//mB49elBQUMAFF1zApk2bmsJdW2yxxRYArFy5EoDjjz+eLbbYgry8PE444QSGDx/OJ598Evf+Y8eOZdtttyUvL4/tttuOk046iXfeeafN7RIREREREZeCYo554IEHOOigg6iurgbg5JNP5oEHHmDevHkMGjSIgoKClDzOLbfcwsiRI+natStVVVWsWbOG5cuXt/m6CxYsAKB79+6ArY7usMMOVFVVUVVVxaRJk5p9nI8//pj99tuPnj170rVrV+66666UtEtEREREOo5Nm+A3v4Gf/ASWLWvv1uSm1KSKDs5x2rsFVm1tLU888QQNDQ306dMHgE2bNrF69Wp69+7N3Llzqa+vjwmLJqRPa3l5ORs2bGjaX7x4cdP2+PHj+dOf/sRbb73F6NGjycvLo1u3bjgpeCKeeeYZevXqxYgRI5gzZw5nnnkmb731FnvssQf5+fnssMMOTY8T1u6TTz6Zs88+m1deeYWSkhLOO+88BUURERER8bntNrjzTrttDDz4YPu2JxepophDnn32WfLz8/n222+ZOHEiEydOZPLkyey99948++yz9O3bl9///vesX7+ejRs38v777wPQu3dv5s+fz+bNm5uutcMOO/D000+zYcMGpk+fzr///e+m29atW0dBQQE9e/akvr6ea665hrVr17ap7UuWLOHvf/87V199NTfeeCN5eXmsX78eYww9e/YE4L777mPSpElN9wlr97p16+jevTslJSV88sknPPLII21ql4iIiIh0PP/4h7v90EPt145cpqCYQx544AFOP/10Bg4cSJ8+fZq+zj77bB599FFeeOEFpk+fzsCBA+nfvz+PP/44APvvvz+jR4+mT58+TV1Wzz//fIqKiujduzennnoqp5xyStPjHHzwwRxyyCFstdVWDBo0iJKSEgYMGNCqNldVVVFeXs62227Lyy+/zJNPPskZZ5wBwKhRo7jgggvYY4896N27N19//TV77rln033D2n3nnXdyxRVXUFlZyTXXXMOPfvSjVrVLRERERETiM6noTpirxowZ43z66acxxydPnszIkSPboUWudevWUVlZ2a5tyCWZeL6y4e8iVcaNG8fYsWPbuxk5Q89XcvR8JUfPV3L0fCVHz1dy9HwlJ5ufr0GDYO5cd78TR54WGWM+cxxnTPC4KooiIiIiIiLio6Aorfbwww9TUVER8zV69Oj2bpqIiIiIdGKqILadZj2VVjvllFN8YxtFRERERLKBgmLbqaIoIiIiIiIiPgqKIiIiIiLSoaii2HYKiiIiIiIiIuKjoCgiIiIiIh2KKoptp6AoIiIiIiIdioJi2ykoCgCnnXYal19+OQDjx49nxIgRGXlcYwzTp09P6TUPPfRQHnjggZReU0RERESkM1FQzDGDBw+mtLSUiooKevfuzWmnnUZNTU1KH2Pvvfdm6tSpLZ53//33s9dee6X0scOcdtppFBUVUVFRQffu3TnwwAOZMmVK3PNfeeUVTj311LS3S0RERESykyqKbaegmINeeOEFampq+Pzzz/n000+57rrrfLfX19e3U8vS56KLLqKmpob58+fTq1cvTjvttJhzHMehsbEx840TEREREelgFBRzWL9+/Tj00EOZNGkSxhjuuOMOhg8fzvDhwwF48cUX2WGHHaiqquJ73/seX331VdN9v/jiC3baaScqKys54YQT2LhxY9Nt48aNo3///k378+bN45hjjqFnz5706NGDs88+m8mTJ/PLX/6SDz/8kIqKCqqqqgDYtGkTF154IQMHDqR379788pe/pLa2tulaN998M3379mWLLbbg3nvvTfpnLisr4+STT2bSpEkAjB07lssuu4wDDzyQsrIyZs6cydixY7nnnnua7vOvf/2LkSNHUllZyahRo/j8888BWLhwIcceeyw9e/ZkyJAh/O1vf0u6PSIiIiKSfVRRbLuC9m5ATnjEZO6xTk78r3revHm8/PLLHHPMMbz00ks8++yzfPzxx5SWlvLFF19wxhln8MILLzBmzBj+85//cMQRRzB16lSMMRx11FGcd955nH322Tz33HOcdNJJXHzxxTGP0dDQwOGHH87+++/PQw89RH5+Pp9++ikjR47krrvu4p577uG9995rOv/3v/89M2bMYOLEiRQWFnLyySdzzTXXcOONN/Lqq69yyy238NZbbzFkyBDOPPPMpJ+empoaHn74YXbcccemYw899BBPPfUUO+20E07gVeHJJ5/kqquu4tlnn2XMmDHMmDGDwsJCGhsb+eEPf8iRRx7Jo48+yvz58znggAMYMWIEBx98cNLtEhEREZHskWxQvOEGeOMN+32PPdLTplyjimIOOuqoo6iqqmKvvfZi33335dJLLwXgkksuoXv37pSWlnL33Xfzi1/8gt122438/HxOPfVUiouL+eijj/joo4+oq6vjvPPOo7CwkOOOO45ddtkl9LE++eQTFi5cyM0330x5eTklJSVxxyU6jsPdd9/NbbfdRvfu3amsrOTSSy/lscceA+CJJ57g9NNPZ5tttqG8vJyrrroq4Z/5lltuoaqqimHDhlFTU8P999/fdNtpp53GyJEjKSgooLCw0He/e+65h4suuohddtkFYwzDhg1j0KBBTJgwgWXLlnHFFVdQVFTElltuyZlnntnUVhERERHpHCZPhssug3Hj4A9/aO/WZA9VFHPQs88+ywEHHBBzfMCAAU3bc+bM4YEHHuD2229vOrZ582YWLlyIMYZ+/fphjFspHTRoUOhjzZs3j0GDBlFQ0PKfyrJly9iwYQM777xz0zHHcWhoaABsV0/vbfEeM8yFF14YMxYzyvtzB82bN4+hQ4fGHJ8zZw4LFy5s6jILtnq69957J9wmEREREclOyVQUv/jC3V68OPVtyVUKiolIojtoe/IGvwEDBnDZZZdx2WWXxZz3zjvvsGDBAhzHabrP3LlzQwPVgAEDmDt3LvX19TFh0ft4ANXV1ZSWlvLNN9/Qr1+/mGv17duXefPmNe3PnTs3uR8wjmA7vAYMGMCMGTNCjw8ZMoRp06alpA2dzbJlUFMDQ4a0d0tERERE2ubbb93turr2a0e2UdfTDurMM8/krrvu4uOPP8ZxHNavX89LL73EunXr2GOPPSgoKOBvf/sbdXV1PP3003zyySeh19l1113p27cvv//971m/fj0bN27k/fffB6B3797Mnz+fzZs3A5CXl8eZZ57J+eefz9KlSwFYsGABr732GgA/+tGPuP/++/n222/ZsGEDV199ddqfh5///OfccsstfPbZZziOw/Tp05kzZw677rorlZWV/PGPf6S2tpaGhgYmTZrEhAkT0t6mXDdjBvTvD1tuCa+80t6tEREREYmVTEVx8mR3O/K2VlBQ7LDGjBnDv/71L84++2y6devGsGHDmsb1FRUV8fTTT3P//ffTvXt3Hn/8cY455pjQ6+Tn5/PCCy8wffp0Bg4cSP/+/Xn88ccB2H///Rk9ejR9+vShuroagD/+8Y8MGzaM3XffnS5dunDAAQc0rcl46KGHct5557H//vszbNgw9t9//7Q/D8cffzyXXXYZJ598MpWVlRx11FGsXLmS/Px8XnzxRSZOnMiQIUOorq7m5z//OWvWrEl7m3Ldeee5L6KHHdauTREREREJFVwxrbkV1LxBURVFlwnOEtmZjBkzxvn0009jjk+ePJmRI0e2Q4tc69ato7Kysl3bkEsy8Xxlw99FqowbN46xY8e26r6jR/u7aHSGl5C2PF+dkZ6v5Oj5So6er+To+UqOnq/kZPPz1aULrFvn7m/cCMXFsefV1UFZGUSXIe/ZEyId4zoNY8xnjuOMCR5XRVFEkpKnVw0RERHJcsEupPEqhdOnuyGxufM6I73lk6wwevRoKioqYr4efvjh9m6aBDQzd5CIiIhIu3OcxIOit9spaIyil2Y9lazwzTfftHcTJEGqKIqIiEg2a2iIHRoTLwB6h9OAKopeessXR2ceuymx9PfgUkVRREREsllYKEy0olhX1znmX0iEgmKI/Px86vRxgnjU1dXFrCPZWQVfPBsa2qcdIiIiImHaEhTBP2axM1NQDFFVVcWSJUtobG4eXek0GhsbWbJkCV27dm3vpmSFmhr/vlYUERERkWwSFhTDjjU2wpQpiZ3bGalEEqK6upr58+c3rf/XHjZu3EhJSUm7PX6uSffzVV5e3rRWZGe3erV/f9Uq6N69XZoiIiIiEiPRiuKcOVBbm9i5nZGCYoi8vDwGDhzYrm0YN24cO+64Y7u2IZfo+coMx4mtIK5a1T5tEREREQmTaEUxrNtpvHM7I3U9FZGEbdgQ229fQVFERESySaIVxeCMp82d2xkpKIpIwsLGIwa7ooqIiIi0p0SDoiqKzVNQFJGEhYVCVRRFREQkm7S166kqipaCoogkLKyiqKAoIiIi2SSRiqLj+LueVlU1f//OSEFRRBK2dm3sMQVFERERySZhFcFg+Fu82P0AvLISBg1q/v6dkWY9FZGEbdoUe0xBUURERLJJIhVFb7fTUaPsmorN3b8zUkVRRBIW9sKpoCgiIiLZJJGg6O12OnIkFBXFP7ezUlAUkYSpoigiIiLZLpHJbIIVxcLC5u/fGSkoikjCVFEUERGRbJds11NVFMMpKIpIwhQURUREJNu1puupKoqxFBRFJGEKiiIiIpLtwt6veIfPrFwJS5bY7ZISGDxYFcUwCooikrCwMYpr1vhnChMRERFpT2FB0bvEl7fb6YgRkJ+vimIYBUURSVjYC2djI6xbl/m2iIiIiIRpqQdUcCIb8FcUV6xIT7tyjYKiiCQs3ids6n4qIiIi2SLs/crKle727Nnu9vDh9ru3onj22fDZZ2lpWk5RUBSRhCkoioiISLZrqaK4caO7XVFhv3srigAnn5z6duUaBUURSZiCooiIiGS7loKi9/ZoQPRWFAG++y717co1CooikrCwyWxAQVFERESyR0tdT723Fxfb78GKoigoikgSVFEUERGRbJeKiqIoKIpIErwvrFVV7raCooiIiGSL1gRFVRRjKSiKSMK8L6y9e7vbCooiIiKSLcKC4oYN7hAa71AaVRTjU1AUkYQpKIqIiEi2a2moTKJdTx0nte3KNQqKIpIw7ydwffq426tXZ7wpIiIiIqGSCYrNTWZTW5vaduWajARFY0yJMeYTY8yXxphvjDFXR44PMcZ8bIyZbox53BhTFDleHNmfHrl9sOdal0SOTzXGHOw5fkjk2HRjzO8z8XOJdDaqKIqIiEi2ixcUozOfhlUU6+vjn99ZZaqiuAnY33Gc7YEdgEOMMbsDfwRucxxnGLAK+Fnk/J8BqyLHb4uchzFmFHAiMBo4BLjTGJNvjMkH7gAOBUYBJ0XOFZEUUlAUERGRbNearqc1NfHP76wyEhQdK/r0F0a+HGB/4KnI8QeAoyLbR0b2idz+fWOMiRx/zHGcTY7jzAKmA7tGvqY7jjPTcZzNwGORc0UkhRQURUREJNt536906+ZuR9+vhE1mExYUVVHMkEjlbyKwFHgDmAGsdhwnWuidD/SLbPcD5gFEbl8D9PAeD9wn3nERSSHvC2uvXu62gqKIiIhki5Y+2E60otjZg2JBph7IcZwGYAdjTBXwDLB1ph7byxhzFnAWQO/evRk3blx7NKNFNTU1Wdu2bKTnKzmtfb5WrhwDVAAwf/5nwM6R4w5vv/0OxqSujdlEf1/J0fOVHD1fydHzlRw9X8nR85WcbH2+li3bAagCoKRkFWDLip9/Pptx42azevUuQDkAEyd+wpo1G1i7dktgoO86H344hW7dFmeq2VknY0ExynGc1caYt4E9gCpjTEGkatgfWBA5bQEwAJhvjCkAugIrPMejvPeJdzz4+HcDdwOMGTPGGTt2bCp+rJQbN24c2dq2bKTnKzmtfb68M4Ltt9/OlJbaGcEaGw1jxoylsjJ1bcwm+vtKjp6v5Oj5So6er+To+UqOnq/kZOvzVVrqbo8Y0Y2JE+12166DGTt2MAWeBLT33rsybBiMHg0vveSvLPbqtTVjx7ZLbSsrZGrW056RSiLGmFLgQGAy8DZwXOS0U4HnItvPR/aJ3P4/x3GcyPETI7OiDgGGA58AE4DhkVlUi7AT3jyf9h9MpJMJdtUI6/cvIiIi0p5a0/W0Z0+YMwdOPtm9rbN3Pc3UGMW+wNvGmK+woe4Nx3FeBC4GfmeMmY4dg/jvyPn/BnpEjv8O+D2A4zjfAE8A3wKvAr9xHKchUpE8G3gNG0CfiJwrIinUXFDUWooiIiKSDbzvV7zrPkeDX9hkNgDdu8Oee8ae31llpOup4zhfATuGHJ+JnbE0eHwjcHyca10PXB9y/GXg5TY3VkTiCr6wVlS4++vXZ749IiIiIkGtqShGqbeUK2OznopI7gu+sJaXu/ths4WJiIiIZFr0/cqg6tkcWb4Pz1/wQypL1yYUFLt3d7c7e0VRQVFEEuZ9YS0u9gdFVRRFREQkG0Tfr/x8v3vo0TCeH+70Inf/7Kym4Bd8P+OloOhSUBSRhAU/gVPXUxEREck20fcrJ+3xaNOxE/d4nJq1m2lshPp699yCwEA8dT11KSiKSEIaGuwXgDGQn6+upyIiIpJ9okHxkxn+qVB+sP0zrFnj7hcVEbMGtCqKLgVFEUlIsJpojLqeioiISPaJvmcpLKjzHf/V9//B4sXufnB8IkDXrm54XLPGX33sbBQURSQhYQO/1fVUREREsonjQF0kH5YW1vpuGzvqHdbO+7ZpPywo5udDVZW735mX/1JQFJGEhA38VtdTERERySYNDTYsApQV18bc3m35XU3bYUERNE4xSkFRRBISVlFU11MRERHJJt73K6VFG2NuH9j4EMY0ArEznkZpnKKloCgiCdm0yd1uS9fTDRvg2WfxjREQERERSQVvUAyrKJbkraa82L5piVdRVFC0FBRFJCGpqiiefTYcfTTstJM7hkBEREQkFfwVxdigCFBSaCuN6nraPAVFEUmIt6LYljGK991nvy9aBBMmpKZtIiIiIpBYUIweV0WxeQqKIpKQsKCYbNfT6DqMUV26tL1dIiIiIlG+oOiZ9XTNBvdNh4JiYhQURSQhYWMUk+16unSpf7+goO3tEhEREYnyfbBd6E5ms2q9259UQTExCooikpBULI8RnMAmOn21iIiISCrUenqbFhe4O76gGKk0xpv1VGMULQVFEUlIKrqeLlrk31dQFBERkVTasMF+z8+rpzC/HgCHPNbWul1PS4qan8xGFUVLQVFEEtLSZDatCYoiIiIiqRR9P+KdyMbJK6V2c2nTfrSiqKDYPAVFEUlIKmY9VUVRRERE0ilaUfTNeJofCIpJjFFU11MRkRaETWZTXAx5kVeRujr/OMYwCooiIiKSTtGgWOKZyMYUlFJbl3hQ9I5RVEVRRKQFYZPZGAP9+rnHP/yw+WtoMhsRERFJp7Cup6aghM0NsUGxd+/wawSDYmd9v6KgKCIJCet6CnD44e720083f43g8hid9YVXRERE0iNe11NvUIxWG3/4w/BrlJbaL7A9phKZh6EjUlAUkYTEC4rHHONuv/RS89dYvty/r6AoIiIiqdQUFAv9QbGusaRpt7Solp49Ya+94l/HO07xzjtT3MgcoaAoIgmJFxR3393dnj+/+fAXDIoiIiIiqRTW9dQGRX/X0223hfz8+NfxVhtvvrlzfritoCgiCfGOUfQO/i4vd/c3bXI/yQtqaIgdEN4ZX3RFREQkfcIms4kJioW1vg+9w9xyi7u9fHnn7H6qoCgiCYlXUTQGevRw91esCL//qlXQ2Og/pqAoIiIiqRQ+RrGEOsdfUWwpKJaXQ9++7v6aNSlsZI5QUBSRhMQLipBYUAzrdqqgKCIiIqkUr+tpQyAoxlsaw6uqyt1evTolzcspCooikhAFRREREcl2oRXFglIacCezKSnc2GJFEaBrV3e7M1YUC9q7ASKSGxQURUREJNvFWx6jgUDX0/qWr9XZK4oKiiKSkHiT2UDrg6KIiIhIKjV1PfUtj1FGgwl0PU0gKHb2iqK6nopIQlRRFBERkWwXrSiWFXumYS8oo5HkZj0FVRRVURSRhDQXFKur3W0FRREREWkvTUGxyBMU80tx8gJdTxN4D9LZK4oKiiKSEFUURUREJNtFu576Kor5ZTR6gmJJ4UYSKCiqotjeDRCR3KCgKCIiItkutKJYUAZ57qynpUW1FJmWr6WKoohIApqbzKZnT3d7wYLw+ysoioiISLqFjlHML8PJD3Q9TWCmls5eUdRkNiKSkOYqisOHu9vffQcNDbH3X7YsPe0SERERAWhshNrIZKcxFcVgUExyHUUFRRGROFrqetqrl93euBHmzIm9vyqKIiIikk7r1rnblaX+oGgKAstjBHpHhfFWFDtj11MFRRFJSHNBEWDUKHd78mT/bZs3w9q1sfdRUBQREZFU8fZeqizzr6NIQfLLY6iiKCKSAO8YxbAX15Ej3e1vv/XfFm+CGwVFERERSRVfUAxUFPMLi2hstDPYFBbUU1JU3+L1ysvd7Q0b4p/XUSkoikhCvBXFsO4a3ori1Kn+28K6nYKCooiIiKSO9/1GeYl/MpuiIkPtZs8SGUUbW7ye94Nx7/ugzkJBUUQS0lLX0wED3O0lS+z3+siHdfGCooiIiEiqeCuKpd7JbPJLKSqC2jo3KJYVebqmxuF9v+PtWdVZKCiKSEJaCoretRSXL4e//Q26dIEzz1RFUURERNLPGxRLCvxdT4uKYGOdu5ZiSTAoOg58+Qd470RYb2fl8/ag6owVRa2jKCIJaSkoVle72ytWwLnn2u177vH38fdSUBQREZFU8X4wXZTv73paXIyv62lpYSAoLngevrnObjsNsPeT6nra3g0QkezX2Oh2IwUoLIw9J1hR9Pr44/DrKiiKiIhIqkQrinmmgQLjSXb5JbbrqScoFhcEguL0f7nb854C/O936uvt+6HOREFRRFrk7ZdfVATGxJ7TrZt7fNUq/21TpoRfV0FRREREUiUaFEuLAktjGBMSFAOT2TTETmtqTOcep6igKCItaqnbKUBBgQ2LYeKtPaSgKCIiIqkSDYplxf7xiUDLFcX68PUvOvM4RQVFEWlRIkER/N1PRURERDIpOvSlrMg/PhGImfW0KD8YFNf79x3bz7Qzj1NUUBSRFiUaFL0T2sRTUeFuq6IoIiIiqdJcRTE4mU1RXiAo1i70729a2XS/KHU9FREJ8L4wtrWi2LOnu62gKCIiIqmwcSPU1NjtLqVxKoqeoFjorSjW1cDmlf4LblradL+mQ6ooioj4eV8YvS+YQYlUFBUURUREJNW8M6737eWtKNpwGBMUjScoLng+9oIbl0FdDcXF7psVBUURkYBUjVEsKoIuXdx9BUURERFJhWi3U4A+1eEVxY11JU2HS78+C2qXwNpp8MkvYy/4yVnwZBfuPvlwwL5h6WxdTwvauwEikv1SNUZx82b/0hoKiiIiIpIK3qDYu0eNu1NQDtg1Eb0VRQAm/AJqZkP9utgLrvsOgL2HvsyQnrOYtWxLVRRFRIISHaOYSNfTsDUYRUREREI11kPNzBZP83Y97d3DE/wKbVcmY0KC4vznYPWXdjuvCLY4LPTa1ZX24gqKIiIB6VoeQxVFERERiauxDl7/Hjw/FCb+vtlTvRXF6q6eoFhQCdj3HN7lMWLs/BcY/qvQm7qVrwIUFEVEYrRlMpuqKsjPt9uXXqqupyIiItK8+fPhnHPgjYdeh5UT7MFv/9jsfbxBsUeXte5OYWXTZkxFMWrgCTDsl7aiuNeTsMeD0O+HTTdHg2IyYxTvuQeOOgqOPNJ+v+OOxO+bLTRGUURa1JaK4s47w2WXwbRp8OMfw7HHurcpKIqIiEjQVVfBv/8NW5wwngOP8NxQVwOFFaH38XY9rarwdj21QbGkJE5QrBwOu90d+STbwMDj7PGl493rla8GEq8ofvklnHmm/9hzz8GAAXDEEeH3yUaqKIpIi9oymU1xMey3H5x1FpSVqaIoIiIizZsyxX7//ui3/DesnxX3Pt6KYpdSb9dTO0Zxhx2gV99AUMwrgr3/2zSO0aeoqmkz2a6nkyeHH7/uutx676OgKCItSnQym+7dY48Fz1dQFBERkeasXQs9Kpaz85DP/Dd8fCbUrw+9jzcoVhTHVhSNgT9cGRg/c/waqNo2vBFF3Zo2k+16utbT83XsWKjuupb8vHomTIA33kjsGtlAQVFEWpRoRbGgwI5J9GouKIqIiIgErV0L39/mLfLyAp8or/gYpv4t9D7erqdlheFjFIuM5zhAfglxhQTFRCuK3qC4007wyR8Pov6hQtbd24XKuk8Tu0gWUFAUkRYlOpkNxHY/bS5YqqIoIiIiQWvXwkHbvh5+48z7Qg97K4rF+bHLYwDQkMRsNCkKil26QP9eKwFb6dxjr/AxltlIQVFEWpRoRRFiJ7RR11MRERFJlOPA2rVO/KBYUBlzqKEBVqxw9wtN7PIYAAw+ya0ibn1B8w3xBsWy1gfFykoobFwVet1sp1lPRaRFyQTFliqKCooiIiISz8aNMKzXVAb0mB9+glMXc2jVKvc9RVUV5DXEjlEEoLgHHPwprP4K+h/ZfEPaMEZxnefhu3RxYHNuBkVVFEWkRYlOZgOqKIqIiEjrrV3jcMspFzbtPz3haHa89HP3hI3LYu7j7XbasydQ5ynpBSuQVaNtZbGgrPmGpKjraffKdeA0RNpSDvktjOHJIgqKItKitlQUSwLjxBUURUREJMzmzfDYDfdy+I4vNR17/euDmLxwpHvSpuUxbyC8QbG6GqiLM0YxGSkKij0qVnquGTI9fBZTUBSRFqVyMhvNeioiIiJh/vlP6Lp5vO/YMxOOZlNdCetqI5PAOPVQt9p3jnfG0149G6G+xj1Q0MrJYwqrwNhRelXlaxje57tWLY9RVaagKCIdWCons/FSRVFERESifvc76F7uBqtf33cHS9f2BmDZup7uiYHup96KYr8+nnUW88sgL791jcnLhy0Obdr9zYF3tG4ym5LcHJ8ICooiOaWhARYsyPzjJjNGUZPZiIiISGvU10N3T1fNSfO3adpettYTFDc1ExR7etdQbGW306itzm7aPGKn5xMOit7JbCqLPBXFYlUURSQN6uthzz2hf3+4/PLMPraWxxAREZF0ir4n8AbFlTVusEq0oti3Os6Mp63Ra18cx75xGVg9l4a6xPqeeiuKZQWqKIpImr3zDnz8sd2+/npbXcwULY8hIiIi6RQNe96up76g2ExF0buGYq/ua9ydtlYU84vZYPrbzbxGuubPbvEudh1Id78kT2MURSTN3nnHv//ll5l77HRNZqOgKCIiIgCTJwM4voriqvVuBc5XUdzkSYbASk8W69HFGxSr2tyuDXlDm7a7F81o8fxJk9z3N2VlkF/vDYqqKIpIGrz1ln//3Xcz99jJVBS7Bz4s06ynIiIi0pIFC6CseANFBXUA1G4uYdS2pXTtam9fs6Gre3LdGt99V3l6d1aVeW4r6kpbbSxwg2J1SfNB0XFg773d/Q0bgM3erqeqKIpIiq1d63Y7jXrvvcw9fjKT2RQW0vSi3tL5qiiKiIgI2LAX7HY6aBC88ordby4oeiuKXUu9FcW2B8VNRW5Q7FXWfFCsqYE1nocfNgzYrIqiiKTRu+/GjkmcNi1zj59MRRH8E9qUlPhvU9dTERERCVq9OjCRzfruXHcd7LGHrdKtrfWMN6xb67uvNyhWFKc2KNYVu0GxT3nzQXHjRv/+oYcC6+e5B0p6t7k9maSgKJID3nwz9ticOZl7/GSDYq9e7nZ5uf82BUUREREJWrXKHxSHjuzOqFF2u0sXWFPrCX2b3TDoOP6up2UFq92dFARFp6R/03a30sXNnhtcPuPPtzqwdop7oMuINrcnkxQURXJAWFBcs8bfvSGdkpnMBuAXv4CCAthpJ9hlF/9tCooiIiIStHq1v+tpWZU7nq9Ll/hdT9etc3tdlZdDfmNqxyiaUncSnaqSZc2c6a8obrklFNQvhvrIch2FXVRRFJHmOY6dEWv9+sTOX7wYvvnGbhcV2XUUozJVVUy2onjaaXaa6wkTID/ff5uCooiIiAQFK4reiV8qK+N3PfV2O+3eHf/4xRRUFPO8QbGs+aDofb9UUgKsneoe6LJ1zs3op6AokmGXXALbbgtjxkBtbcvne2c73WMPGDnS3c9EUKyvt2E1qjLBtWurqiBPrzAiIiKSgGBF0TvxS3MVRW9Q7NaNlAfFwrKubK4vBKC8aD3Ux3/z5q0oFhfj73ZamVvdTkFBUSTjnn7afp8yBd54o+XzvUHxgANg0CB3PxNB8csv7SxeYKuZffq07XqqKIqIiEjQqlXQtSw85DUXFL3jE7t3xzd+kaKqNreruMSwbK13Dcf4VcXmK4oKiiLSAu+4wpdeavn8GZ4JtnbbLfNB0bte4z77tL3XhIKiiIiIBK1eHQiKRf6gGNP1NPImIt1dT4uLYdk6T1DcuDTuubEVxUDX0xyjoCiSYWs9Mzq//HLLYSlazQPbndM7RnFx85NvpUQwKLaVgqKIiIgErVoVfw3ELl2grqGI2s2RNbecBmjYgOPAVVe5d7EVxdWh12itoiLaUFHM3RlPQUFRJKPq6vyfNs2fbye2aY530puKCujpea1a1vyY6jZrbITx4919BUURERFJtcZG2+MqXtfT6PwIvu6nm9fwwQfw7bfuoZgxiimY9TS2ohj/zZf3PV5F6UZYPzuyZ6ByWJvbkmkKiiIZtG5d7LGw7qcXXgjbbWeXxfBWFCsqoLra3V++PPVt9Jo8GVassNvV1bB1CnpN5NiEXyIiIpJmayM9SZvregqx3U8nT/Zf56gjnZR3PS0qgqVr3AWinWaCoreiOKjHdCDyiXjFEMgvaXNbMi0jQdEYM8AY87Yx5ltjzDfGmHMjx68yxiwwxkyMfB3muc8lxpjpxpipxpiDPccPiRybboz5vef4EGPMx5HjjxtjEljtTSSzvN1Oo154wR8GJ0yAW2+Fr7+Giy6KDYqZrCimenxikCqKIiIiEp2QprmupxA7oY33A/ijjoLvjfrKdksFu25hfgJrerXAGFi5wX3z1bAhsYrioG65PeMpZK6iWA9c4DjOKGB34DfGmFGR225zHGeHyNfLAJHbTgRGA4cAdxpj8o0x+cAdwKHAKOAkz3X+GLnWMGAV8LMM/WwiCQsLih98YLtU/O9/dn/cOPe2r77yT35TXt6+QTEV1PVUREREvJqCYjOznkKwougPiqNHA7Mfdg9s8YOUtW/FenfKd6dmbtzzvEFxQNfcnsgGMhQUHcdZ5DjO55HtdcBkoF8zdzkSeMxxnE2O48wCpgO7Rr6mO44z03GczcBjwJHGGAPsDzwVuf8DwFFp+WFE2iAsKEbdfbf97g1nDQ3udmGh7f5QUWG/g12H0TuGMZUcB955x91XUBQREZF0WLLEfm8pKK7a4K6tyKblvqBYWdkIsx9xDww+JWXtm73SUxFcNznued6up1tU5PbSGNAOYxSNMYOBHYGPI4fONsZ8ZYy51xgT/e33A+Z57jY/cize8R7Aasdx6gPHRbJK2BjFqMcft8HQO3mMV0WF/W6Mv6qYrnGKM2bAokV2u2tXO2YyFRQURURExMvO4u7QpdTziXpR7GQ2s5cNdm9fN8P3vmpEt3egdoHdKa6GvgelrH1zVo1s2i5YPwUaG0LP81YU+5Tl9oynAAWZfDBjTAXwX+A8x3HWGmP+AVyLHel5LXArcEaa23AWcBZA7969Geft55dFampqsrZt2ShXnq8PP+yJ7VENAwZsYN68sqbbqqs3cd99X7FmzS6h9y0s3Mi4cR8BUFq6M2BfNV999TNGjGgmgYZI5Pl65ZU+gO0qMXLkCsaP/zqpx4hn8eIRQF8ApkyZyrhxi1Jy3XTKlb+vbKHnKzl6vpKj5ys5er6So+crOal6vj78cCAVJT3Jz2sEoMEUM/7d95tutz2sxjJt8fCmY4unj2fatCVAbwD6bb6j6bYF+XsxzXP/tlpRsxuLV/emT9USTONGPvrf42ws2CLmvClTBgFDAIfqIjcofjBpJZsnj0tZezIlY0HRGFOIDYkPO47zNIDjOEs8t/8LeDGyuwAY4Ll7/8gx4hxfAVQZYwoiVUXv+T6O49wN3A0wZswYZ+zYsW37wdJk3LhxZGvbslGuPF/TprnbBx5Yxr77wqmn2v0VK4rjhkSAHj1Kmn7GwYNh+nR7fODAnUn2R0/k+fJWNvffv0fKnt///MfdHjFiBGPHZv+nbLny95Ut9HwlR89XcvR8JUfPV3L0fCUnVc/Xc89Bl1L3rXt+cbeY61ZUwHeLt2ra71O2lrIyGxKLCzeyQ/c3m27rt+dF9Ou5R5vbFdW1K0xeOJI+VTa67D6yC/QbG3Pe669H2la1mLLCyIf4hV343v5H5+S075ma9dQA/wYmO47zZ8/xvp7TjgaiK8o9D5xojCk2xgwBhgOfABOA4ZEZTouwE9487ziOA7wNHBe5/6nAc+n8mURawztGsUsX+OlPoV+kk7Tj2GUx4ol2PYXMdD31jn2MdvlINXU9FRERkSVLgktjVMWc06ULvooi66Y1dT39wQ4vUUjk/hVbQvXuKW1fcTF8u2CUe2BN+DjF6BjFEX0DE9nkYEiEzFUU9wR+AnxtjJkYOXYpdtbSHbBdT2cDvwBwHOcbY8wTwLfYGVN/4zh2rltjzNnAa0A+cK/jON9Erncx8Jgx5jrgC2wwFckq3r700YHZgwbBgpD6d1ERbN7s7peXu9uZDoplZfHPS5bGKIqIiIjX4sXxl8aI6tIFpi7agvUbyygv2QCbV5JXtwLowQ93esE9cfApKQ9mRUUweYE7TpG134aeFx2j2KfrYvdg2cCUtiWTMhIUHcd5Dwj7jb3czH2uB64POf5y2P0cx5mJnRVVJGsFK4rgnyErqls32Gsvu8ZilLei6N1O16yn3ut6Q2pbKSiKiIiI15IlMCDOjKdRlZXgOHlMXzKM7Qd9BUCP4mlAD3Ye8pl7Yp/UTWITlWxFsap8tefO3VPenkzJ+KynIp1ZWFA87LDY8/beG8aM8R/zhsOSEnfbO8NWKikoioiISCbEdj0NrygCTFvidj/tUzaNksJaRm4RDW4Gum2f8vYVF9sxik3WfBv6Jib6nqxb+Sr3YGFVytuTKQqKIhkUFhRPPx0GBnolbLttZOFYD29QLC11t9MVFDdscLcVFEVERCQd6upgxQqoKlvtHgwJV9H3Td8tcie06d/lO7YZMImC/MhyFZXDoDD1EysUFcHi1X1YtT7Srvp1ULsw5rymiqL3ZynqFnNerlBQFMmgmhp3Oxr8hgyBOXP8C9ofdxyMGuW/b3tWFNM1RlFEREQ6t6VL7Xd/uKqKOa+pouiZ0GZQj2nsOOgL96RuO6W+gdiKIphA99PYcYqhFcWQnyVXKCiKZJA31HmrggC33w5HHAG33AI77ADDhsW/jjco1tamtIlN0tX11EsVRRERkc5tSWSxPN+4vgSD4vA+09iq73fuSVXbpr6BRINicEKb2HGK0fd5HaWimLF1FEXEHxS9YQ9gu+3sOkJRhYX+2+fNC7+vxiiKiIhIrmoKii10PY2+F/GupbhVn++YuXRLz0mDU94+cINiSxXFaNdTjVEUkaQ1FxRb4q1AZmKMooKiiIiIpNviyEoSLXU9jYa1ZWt7srHBlhcrS2vYdegn7knlg9LSxqIi+903oU0zFUV/19PcrSgqKIpkULJB8ZFH7Hdj4NJLw++ryWxEREQkV4V2PQ2pwkWDIhiWb/KMU6ye656UpqCYbEWxpdCbKxQURTIo2aB44okwYQJ89x2M9HyIlekxiumazEZBUUREpHML7XoaEq68732W1G4VczsmH0r7prRtUdGgOG/FAOoaI2+KNi2Hjct856miKCKtlmxQNMaupxic2CbdFUXH8VcUNeupiIiIpEP4GMXYdRTdiiIsrhkecztl/SEvPdOvRLueOk4eK+s9b8o2zPWdZyuKTosT8+QKBUWRDGrLGEWvdI9RrK11q30lJZCfn/rHAFUURUREOrvoGMWuZWvcg82MUQRYsC6kopimbqfBx95Y38XdqV/vO2/DBqgsXUd+XqM9UFAOeYHZCXOIgqJIBqUqKKa7opiu8YmgrqciIiLiSrTrqTeszVsdVlHMUFBs8CxsXecukO04sGpVy7O35hIFRZEMcRx/qPO+6CQr3WMU0zU+ERQURURExLVkCRQVbKKsOPKGxhRAfuybD+97nzkrQ4JixeD0NJBgRdHzCXqD+4Zp/Xqor+844xNBQVEkY+rq3GBUWNi27pzp7nqarqUxQEFRRESkU3AccBqbPaWuDlasCOl2GjKhgTesrazpxvJ11f4T0rSGIrhjFAFq68IriitX2u+dMigaY7Y0xlxqjLkjsj/CGDM6fU0T6VhS1e00eH8FRREREckqq7+GV7aHx0vgzX1h0nXQ2BBz2tKlAA4n7fGoezBOd01vUFy0CL5bFKgqpjEodvEMS1xX63lj5Bmj2GmDojHmQOBLYHfgJ5HD1cAtaWqXSIejoGhp1lMREZGOZ9Ik+OEP4fm/PgCv7WbDYmMdLH0XvvoDzLgn5j7LFtXwyG9O5q8/Pc89WDk09Pre9z5Tp8K0xYGgmMaup716udsr13oqivVuRXFVJB92uqAI3AQc7zjOEUD044DPgZ3S0iqRDihV4xOD99+4MX5l7vbbYdtt4f77k7t+Oiez8VJFUUREpGM47jjYvuA6juh5GjSETKAw7c6Y//ir5lzGSd97zD3QdRvY5c7Q63vf+6xfD3OWByavKe3fypa3rHdvd3v56vCgGF5RrEpbmzIh0aA41HGcVyPbDoDjOLVA7s73KpJhqawo5uX5XzDtuj1+M2bAOefYT/jOPBPmzEn8+prMRkRERJIxdSqcud+/3ANdRsJeT7n7q7+ClZ/57tN9w7NN22/P/Tkc/AlUbBl6/eCH7BvrAm+m8otIF29FcelK9xP0TRvU9RRgnjFmG+8BY8z2wOyUt0ikg0plUAxeI6z76T/+4W7X18PNNyd+bY1RFBERkWT1qFjh7hzwLgw8Fgb/xD224PmmzTdeWUuXfLtg/eb6Ql5ZficUeGbrCwi+d3r8oxNobIy8qeh/VFub3ixvUFyywq0oPnhvDVdeabc7c9fTvwFPG2N+DOQbY44F/gPclraWiXQwmQyKtbVw773+Y2++mfi1FRRFREQkGQX5dVSU2DcQjU4eFPewN/Q/0j1pwYtNmw/d/k3T9tRFI+jZu/mOisGK4sylQ/nxP/7DjLxfwU63tq3xLSgqgqoqu7221g2K5cXrueYau91pK4qO4/wLuBm4GMgHrgb+6jjOQ2lsm0iHks6gGFxL8fHH3U+2opYvT/zaCooiIiKSjK6l7hIXG+qr3P/w+x4IeZEQuOoLWG/HwhTVukFx0rxtfOMAw4TN7/DoByezatidcburplK0fes3uW+MKortGMXa2k4cFMGGRcdxtnUcp8JxnG0cx4mdukhE4kp1UGxuLcU7Q8aBr1oFjc0vZ9QknZPZaNZTERGRjqeqfHXT9oa6KveGwi7Q+/vu/sz7WbwYtuk/qenQpPnb0LVr89ePNxHg4MFJN7VVot1Paza6FcWKEhsUFy/uxF1PjTHHhIxR3NYYc1RaWiXSAWWq6+mECfYL/C+qjY2wenVi107nZDZeqiiKiIh0DN6AtN4bFAGGnuFuz7iXLz5vYGS/yU2Hpi0dxe67N3/9sPdO5eXQo0crGtsKYRXF8mL7hmnRIrfnVreyThYUgT8CKwPHVgJ/Sm1zRDquTAXFhx92t084Abb09MZY4Rlj3hx1PRUREZFkVJWtbtqu2Vzlv7HfEVBcbbc3zGXtd2+y9RZTmm6+9KaRrep6Onhw5noqNVdRXLQIvvrKHut0FUWgt+M4C70HHMdZAPRNfZNEOqZMBUXvMhiHH+7/pE1BUURERFKtvt4fFNfWVvlPyC+GIT9t2t226K8MqrYznjY4Beyw19AWH6OgwC4P5pWpbqfgVhTDguI770THKDp0q+h8QXGhMWa090Bkf3HqmyTSMaVzjOKGDXDssfZF7Nln3ePV1a0Lipkao6igKCIikvs2bvSPUVy9vir2pKE/a9ocVfVK03YNW7qT3bQg+P6pPYJiWNfTp5+2+13L1lCYX2938kshPwVv+NpRokHxQeBxY8whxpihxphDgEeBB9LXtM6nvt6OLfvnP9u7JZIOqQ6K3gD3v//ZF6mlS/3nBINiojOfpnOMooKiiIhIx1Jb668orlhXFXtS11FQ/b3Y+xZunfDjBLufZjIohnU9ra5cwQm7P8aCBXZ/y14z3TuUZ7BxaVKQ4Hl/AroCTwLlwHrgH8BNaWpXp9PQAFtsAcuW2f0nnyxq3wZJyqU6KG61lbv9xhvh5/TokX1dT0VERKQDqauh7MvfcPPJDzYdWr62KvzcoT+H5R/4DjWU50ZQjFYUN9b538Q99tuT2FhXwnOfHcXQXjPcGypa7k6b7RJdR7HecZyLHcepxI5XrHQc5yLHcerT3L5OIz8fRns69371VQtzBEvOSXVQ3G47d3vSpPBzsjEoqqIoIiLSgUy7g/KlD/oOLVlVBcBHHwU+zB50InTbqWl3XW0F9f1OSvihsqGiCIYZS/zrNt5wwqUADOvjqShmYG3HdEt4HcUox3GWpaMhAvvs425/9VVVu7VD0iPVQXH77d3tsMBVXm5fUBUURUREJG0m3xxzaPGKKl55BfbcEw46CP7+98gNBaVw8CccftvbHH3b0ww5bxYVA3ZI+KGyYYwiwFn/vpsPvtujaX9Uv8mM7Pctu4z0VBQrO3BF0RjzhWd7mjHmu7CvzDSzc/AHRVUUO5pUB8Vhw5q/TjQgtmaMoiazERERkUSsrY2d2XNlTRUnnGDXcAb47W/d9xb1jfm89OlYnv30aFaur6aqKvHH8lYUM7mGIkBFhfu+63/ffJ89r/6AJz8+run2M/f7F6MHerue5n5Fsbkxit6PB65Ld0MEdt/dTv1bXw+zZlUweTKMHNnerZJUSXVQLCiAbbaBTz8Nvz36YjpokHts8uTwc4M0mY2IiIi05Ouvu1C2uhu7BopnS9f2Yt06/7F//QvOPTe6jITVrZsdfpUob1DM5BqKYB+rd2//MmSPf3QCx+/2FADnH/oX/x068hhFx3EeATDGFAC1wGOO4zwQ/MpUQzuD8nI44AB3/7zzkn8jvX49nHwyHHMMzJzZ8vmSOakOiuAfpxgUDXvbbee+kE6Z4q8WtnRfSG9FUURERHLXN990xXFi/2OfOGeHmGN/+hNs2uQfBpNsRdD7/imT3U6j3HGK1jMTjub1rw+MPbGwK1QOy0yj0qjFMYqRCWvucRxnUwba0+nddJO7mOjrr8MLLyR3/0cegUcfhWeegf3283/qIe0rHUHRO04xqMauAUt5OYwYYbcbG+HLL5u/ZkOD21Zj/Os1ppoqiiIiIrlr/vxSelT4J0DYsKmU+x4o5uGH4cUX3bF9CxfC22/7h8EkGxSDFcVM845TBGh08jnx9sdiJrdhwNEJrw2ZzRKdzOZTY0wztQtJle23h7POcvd/9zv76Uui5s71b++/P01ru0j7ynRFMRoUAXZyJxjje9+D1avjv3h5K45lZamvAKrrqYiISMcwf34Z1ZX+CRA+XnIiJ59se7j94Af2K2rKFP+cHLkWFIMVxfx8WLW+O0fc+jw1m9z1FRn4o8w2LE0SDYpvAy8YYy4zxvzYGHNy9Cudjeusrr0WKivrAJgxA26/PfH7BvuDz5xpw+LixSlsoLRKpoPiL3/pbu+6q/+2N98MfCTmEQyKqaagKCIi0jEsWVRAVfkad39NL56deYPvnH793O0//9l//4EDk3s8b1DbOvHlF1MmWFH87W9hjz1gDaOZv+Xz0H0XGHEe9D0k841Lg0SD4hlAI/Bz4Frg+siXJrlJg+pqOP302U37//534vcNBkWA776zYx8TnfFS0iMdQbF7d+jf33+sTx/7+77ySvfYGWf4z1m8OH4D0jk+ERQURUREOoL168HZ5HZf2ri5mH5nL2Bzfh/fed73KfPm+a9x3nnJPeb558Nuu8FPfgKHHppkg1MgGBSrq+GDD+zPtfW++8Ehn8DOt3WYCRmam/W0ieM4Q9LdEPE77LBF3HPPcDZssGX6996Dzz5zw16fPvYfSZcu/vt5g+IJJ8BTT9kxZ998AwceCP/7n51hSjIvHUERbHfl+fPd/Y8/jv2ErrLSjl09KbKm7dKlgRVrPRQURUREpCXTp+Prdjpr2RAaGgti5jbwVhSjSkvt0Khk35Nuuy189FErGpsiwa6n0fdJHSQXxmg2KBpjCoHLgB2BT4A/Ria3kTQrLm7ksMNs0APYe+/Yc778Eu6+23/MGxR/8hM44gj48Y/tG/KJE+Hgg+HNN2MDpqROTQ3ceSdMmwZHHw2HHWaPpysobrcdvPSSux9vPSJveGwuKHq7KafjQ4WO+mIqIiLSmUyb5g+Ky9dVA7HvcYI9nwBOOSU3CxfBimI6PlDPJi11Pf0T8CtgM3AucE3aWyRNjjyy+duffjq2IuMNipWVdiCxt+vqhAk2SAT7iEvq/P3vcPHFcM898MMfut0s0llRjDLGLggbxh8UbQMaG+2Y2LPPdtc1muFZK3ZompcAUkVRREQkN333HQzo4fYljQbFRCqKv/pVOluWPvEqih1VS0HxaOBAx3GOBw4Gjk1/kyRq331jj1VUuJ/ArFgRu4D62rXudrRqePrpcNdd7nHHgQsuaN/SfUf29dfudmMjTJ1qt9NZUYzq2tVdXiWob193UdtVq4qorYVXXoErroA77rDfwXYliRqWhiWA1PVUREQk902bBkfu/FzT/sS5OwCx73F69PDPVrr77v7Z2HOJKop+3R3H+QrAcZwvgOr0N0miBgyAIYHRodtsA2PHuvvvvuu/PVhRjPrFL+Cvf/WfO2lSSpopAbW14fvpCoojRtgXXYDjjot/Xn6+/1O9+fPt4rdRd9xhv6e7oqigKCIikvvmz17HD3Zwx7488ZFdEiJYUTTGvqeN+vWvM9G69Oje3b9fkNBsL7kr0VlPozS6KMO8a80AjBoFe+3l7n/2mf/2eEER4Jxz7Ji5KC2ZkR6ZDop5efDOO+FjVoO83U/nzQsfz+gNiqooioiISJhhJS9QWmTf3Hw1d1umLBwJhL/H+cUv7Pddd4Uf5fASg9GeWVHeJcU6opaCYrkx5rvoF9DFux85Jml04on+/aFDbRfCqOByGM0FRfBXIxUU08MbCL376QqKAEVFtgtqSxPFeIPi3Ln2fl7Ll6uiKCIiIs1bswYOHvlE0/4TH7vpL2yuhAsvhGXL4MMP/d1Qc106PlDPJi0VTM9o4XZJs4MPtlXEb7+1+7vt5h+H6K1ebd5sv8B+4hEWRvp4lrZRUEyPsIqi4/iDYnu9SHq7fsydC0uW+G9//nn307Hu3WO7WKSCZj0VERHJbTOnrOHQ7V9p2n/8wxOatsMKFWDXHOwIXnnFrud4yCGw447t3Zr0ajYoOo7zQKYaIuGMgf/+185KOXIk7L8/vPaae7s3fASriWFvyBUU0y8YFDduhLo6t3pWUNB+fdqDFcVFi/y3X321u/2976W/PaooioiI5J6NM56juNBWJz6ftSPTlwxvui3e7OsdxSGH2K/OoIMPwewYtt7arn0Y5R0k7A0lLXU7BQXFTAirKKaz22kygkEx+Dcwd667HRwfmyrqeioiIpKb1q2z8yFsvfhxu8o68PhHJ/jOifceVHJPspPZSBZoS1D0TuuroJgeYRXFbAyKkydDTU38cxUURURExOvmm+14w71GvNd07KlP/FOud/SKYmeioJiD2hIUu3Rxg8r69c0HBWmdXKkozp/vbg8eDM88Az172v1hw9K3xpGCooiISG76/HMozN9M1zI7YUZDYx6zlvnXclNQ7DjaFBSNMYWpaogkri1B0Rh1P023bK4odu0a/gLepw8cdRRMmQJPPAHjx0Nhmv51KyiKiIjkpnXroFv5qqb9Veu70aOHP06o62nHkVBQNMZcGXIsH3g85S2SFsULit7ZUJv7R6qgmD7B2U0huyqKxvirilHRZTC6d4fjj/f/jaSjDSIiIpJ71q2D7hUrm/bX11cxxF9QpLw8w42StEm0oniiMeYngWP3A91S2xxJhDdoeAPISvffbbPLGsQLivPmwapVsedL4urqoLHRfyybKooQHhRHjcp8O0AVRRERkVwSDIpVvYppaPCfk6eBbR1Gor/KHwA3GmP2AzDG3AmMAH6YroZJfPEqiitWuNs9esS/f1hQfPZZO05t6FD47rtUtLJzCnY7jR7L9qA4cmTmHl9dT0VERHLTunXQvdwNivV5ldTVtWODJK0SCoqO48wEjgceMcbcC+wFHOw4jqZCaQfBimL0zXZbguITT9hK2KpVcNVVKWtqpxMWFLOtojhgQOwxBUURERFpSbCiWJfXRUGxA4sbFI0xW3i/gDnATcAhwGlAaeS4ZFheHhQXu/vRENKWoLh0qXvs8cdVVWytXKwoFhS4YxQzQUFRREQk9zQ0wIYNsRXF+vp2bJSkVUEzt80Hgm/jom/xJkS2HSA/De2SFpSWwqZNdru21u63JSguX+4ea2yEm26Ce+9NXXs7i1yoKAaD4rbbpm+G0zAKiiIiIrknuqSaKoqdR3NBcUgzt0k7Ky2F1avtdjSctCUoLlvmP+ehh+CKK+y4RUlcLlYUjzoqs4+vWU9FRERyT3QZNm9QrDeqKHZkcYOi4zhzMtkQSU7YhDatCYqLFtmqTjAo1tfDkCHw6qtw8MFtb29nEVwaA7IvKPbr598/5pj2aQeooigiIpIrmoJiubeiqMlsOrJE11F83Rizf+DY940xr6SnWdKSsCUyEg2KvXu720uW2MpkvH/khxwCgwbBxx+7xxwHNm9OusmdQryup97j7R0Ui4th//2XAHDYYTB6dGYfX11PRUREck9oRVFdTzu0RJfH2Al4N3BsPLBLapsjiQpWFBsa/GsgNreOYkkJVFXZ7YYGmDrVvW3QIDjhBP/5c+fCQQfBk0/agLjHHlBRAY880uYfo8OJ1/V07Vp3v7Iyc+2J5/LLJ/Pdd/D885nvCqqgKCIiknviVRTV9bTjSjQoNgLB6S7ycSe3kQwLBsXVq92F3rt2bXlyEm/306+/drd79oTHHoNLL/Wfv3Yt/OhH0LevrS7W1cEpp8CkSW36MTqceBVFb1Ds2jVz7YnHGBg+HPLbYSoqBUUREZHcEw2KVeWrm47V51Vw6qnuOSedlNk2SXolGhQ/A34bOHY28HlqmyOJCgZFb3WvuW6nUfGCYnW1/X7llfDTn/qX4QBYudK/v+228NFHibW5M4hXUVyzxt3v0iVz7clGCooiIiK5JxoUu5S6n343mAquuQZ++EM7XOkvf2mftkl6NDfrqdfFwDhjzLHAd8BwYAQwNk3tkhZ4g+Ihh/hvG5LAfLXeoOitCvbsab8XFcEDD9ivNWvgvPPg/vvDr/Xoo7D77om0uuMLC4qbNvmDYjZUFNuTZj0VERHJPdGg2LXUfVNTb8ro1s0OZZGOJ6GKouM4XwGjgP8CayPfRzmO82Ua2ybN8AZFr622gr/9reX7e4PiV1+529Gg6NW1K9x3X2wgjfpcdeUmYUERYOlSd7uzB0UvVRRFRERyw7p1UFSwiZKiyELepoBGU9z8nSSnJVpRxHGcxcCf0tgWSUJYUDznHLjxRigra/n+3qDonS21uWrkvvva5TKCJk604yPzEu3I3IGFLY8BdnbZKHU9dbcVFEVERHLDunXQtczTRaqoq7oJdXAJv7U3xhxrjHnFGDMp8v3YdDZMmrdhg3//7bfhr39NLCSCPyh67bNP/Pvsuqt/P1p9rKmB6dMTe9yOLl5Fcf58d7uzVxQVFEVERHLPhg3+8YkUdPJPvjuBRNdRPAu4G/gCuC3y/Z/GmF+ksW3SjM8+8++PHZvc/cOCYlUVbLNN/Pvssw/suKPd/vWvYeed3du++CK5x++o4gXFaL9+UEVRQVFERCT3bNjgH59IUSf/5LsTSLSieB5wmOM4lzqO82/HcS4FDoscl3Zw0EHu9l57JX//sKC4557Ndx8tKIAPP7ST3/z9725oBI1TjPIGxVGjws9RRbG9WyAiIiLJqq0NdD0t7ORvaDqBRIPiFsCEwLHPgDgdGCXdLrgAhg61a+E99ljy9+/dO/ZY//4t36+4GEaPtm/2d9rJPa6gaHmD4kknhYeizl5R9FJFUUREJDfEBkW9oenoEg2KU4BTAsdOwi6VIe1g6FD47jv71a9f8vevqoo9lmyA8QbFL77Qm37wB8XBg22V1qu8vH0Wuc8m6noqIiKSe1RR7HwSDYoXY8ckjjfGPGiMeRf4J3BR+pomLWnLLKMlJXatRK9kg+KQIW43yhUrYN681reno/AGxdJSODYw5VNn73YKCooiIiK5qLY2MJmNgmKHl+g6iu9g11F8GagBXgFGR45LjgoGw2SDojH+cYqa0Ma/PEZpKRx9tP92dTtVUBQREclFmsym80l01tN9HMeZ7TjOjY7j/DryfbYxZu90N1DSJ1jdqqxM/hqa0MYvWFEcNAjGjHGPqaKooCgiIpKLNEax80m08+KLcY4/l6qGSOa1taIIseMUO7tgUAR/91MFRc16KiIikos0RrHzSTQoxry1M8ZUAo2pbY5kUjC0tDUoqqIYHhR/+lPo0cNuH3lk5tuUzVRRFBERyQ21tYGup6oodngFzd1ojJkGOECpMSY4w2kv4I10NUzSLxVBccQIG4hqa2HBApg928722VmFBcUttoApU2DxYthmm/ZpVzZR11MREZHcs2ED9KhY4R4o6tF+jZGMaDYoAtdhq4n/AK73HG8EFgNvp6ldkgHBYNiaMYr5+TB2LLzyit1/+mn43e/a3LScFRYUAaqr7ZcoKIqIiOSi2lqorlzuHijpCayNe77kvmaDouM4DwAYY6Y4jvNRZpokmZKKiiLYMXjRoPjkk507KHpnPS0pab92ZDMFRRERkdziOCFBsbgaBcWOrdkxisaYAmNMoTckGmNOM8b8xRhzTPqbJ+mUislswI67iy4i/9FH8OGHbWtXLotXURSXgqKIiEhuqauDxkaHnl2WuQeL1VWqo2tpMpvHgdOjO8aYy4G7gb2Ah40xP09j2yTNgl1NKypad53qajjlFHf/1ltb36ZcFv20LUpBMZxmPRUREckttbVQWbqOooI6eyC/DArK2rdRknYtBcUx+JfG+C3wc8dxxgA/Bn6VroZJ+uXlNb+fjPPPd7c7a0Vx82a3QlZY6FZZJT5VFEVERLLfhg1QXRHsdiodXUvRoJvjOAsBjDEjga7AE5HbngUGp61lknaprOxssw0UFdnthQthzRr/7XV1NkyefjqsWBF7/45A1cTEqOupiIhIbgkfnygdXUtBcb0xJtohcQwwyXGc6HQdhpZnTbUnGjPAGPO2MeZbY8w3xphzI8e7G2PeMMZMi3zvFjlujDF/M8ZMN8Z8ZYzZyXOtUyPnTzPGnOo5vrMx5uvIff5mjDq4tSSVz1BBAWy1lbs/ebL/9mefhb/8Be6/H/7859Q9bip8+ikceigcfjgsX97y+fEoKCZGQVFERCRHNNYD9j2Ob3xiSc92apBkUktBcTxwrTFma+AXwKue20YAixJ8nHrgAsdxRgG7A78xxowCfg+85TjOcOCtyD7AocDwyNdZ2OU5MMZ0B64EdgN2Ba6MhsvIOWd67ndIgm3rtIYMSe31Ro1yt4NBceZMd/uzz1L7uK1VVwdXXw277w6vvgovvQT/+U/rr6egmBgFRRERkezV0GCH0zD9bniyC7xzJBvX16qi2Am1FBQvxgaub4EugLcWdArwXiIP4jjOIsdxPo9srwMmA/2AI4EHIqc9ABwV2T4SeNCxPgKqjDF9gYOBNxzHWek4zirgDeCQyG1dHMf5yHEcB3jQcy2J4+ijYd99bah5+OG2X2/kSHf722/9t3m7ok6Z0vbHaivHsRXEq66yL4hRCxa0/prepTEUFONTUBQREclO8+fDoEGwxRaw+fNroKEWFjzPoAWn0LfKUx9SUOwUWlpHcRYw0hjT3XGclYGb/wRsTvYBjTGDgR2Bj4HejuNE/+oWA70j2/2AeZ67zY8ca+74/JDjYY9/FrZKSe/evRk3blyyP0JG1NTUZKRtV10FmzfnUVTUSFsfzpiewGgA/vOfWg4++BMKCmwS+Pbb4UR/JXPnOrz22niKixvb9oAeyT5fixcX8/rre8QcnzJlIePGfdeqNkydWoHtoQ319esYNy5LSqchMvX3Feabb6qBbQBYtmwZ48Z90y7tSEZ7Pl+5SM9XcvR8JUfPV3L0fCWnsz9f1147kgULetOtfCVF9e6n59W1z3DTic807c9cWMPcdeM6/fPV0SU0xjAkJOI4zupkHywy3vG/wHmO46z1DiN0HMcxxqS9vuA4zt3YJT4YM2aMM3bs2HQ/ZKuMGzeObG1bPGPGwO2328lqFi8uZdmyfTnpJHvbv//tnuc4hr5992G77VL32Mk+X/Fe0yort2Ds2C1a1YYCz7+mnj0rs/r3155/X97JjKqre2b18xSVi/8e25Oer+To+UqOnq/k6PlKTmd/vvbbz37fZsCkZs/bcvcz2LLn9zr989XRtWFBhOQYYwqxIfFhx3GejhxeEuk2SuT70sjxBcAAz937R441d7x/yHHJoIoK+JVnwZT333e3g7OgTp2amTbFM2eOu13mWQZo7drWX1NjFBOjrqciIiLZx/t/8rYDvo573ry1o6E6tleWdDwZCYqRGUj/DUx2HMc7zvF5IDpz6anAc57jP43Mfro7sCbSRfU14CBjTLfIJDYHAa9FbltrjNk98lg/9VxLMshbJZzv6QwcDGDZFBS33dbdVlBMPwVFERGR7DN7trvtDYorBv6Jdzbfz6Y6uw7a87OuTO3U+ZK1MlVR3BP4CbC/MWZi5Osw4CbgQGPMNOCAyD7Ay8BMYDrwL+DX0NQF9lpgQuTrGk+32F8D90TuMwN4JRM/mPgN8NR753lGk7ZXRfG11+DEE+F///MfV1BsPwqKIiIi2WfCBHd7RF/3jdqkeaN57qtTGXjOXIb9bhoLC45vh9ZJe0hojGJbOY7zHnbdxTDfDznfAX4T51r3AveGHP+U6AwZ0m76ezoAe4Nie1QU6+vhkMgiKU89ZZfDiIYUb1DcxvNX05aguHixu12tycDiUlAUERHJPt6gOLh6dtP24pot+eADWLq2N0vX9mb33TPfNmkfGRujKJ1D376Qn2+3ly1zl4wIVhSnTEl/SPAGt4YGf8UvXkVx3brWP97cue72wIGtv05Hp94qIiIi2eeTT+z3PNNA/+7u+KHpCwfy+efueQqKnYeCoqRUfr4Ni1ELFtjAFgyK69b5g1w6BNdEXLXK3faOn0xVRVFBMXmqKIqISC5rbITly1s+L9s1NMBnkVW9+nZbRGFBPQBL1/Tk5dfLqKuztw0fDj17tlMjJeMUFCXlvOMUv/99Gxzr62PPS3f3U28YBFgZGc26ebNb6czPty940aUtNm+GTZta93jerrbe50D81PVUREQ6gsZG2G036N0b7rijvVvTNlOmwPr1dnvnEW63qznLB/HBB+55e2iy005FQVFSzhuSvF08g9orKHq7l3bpYoNLly7usa++at3jqaKYGAVFERHpCF54AT791AbGs89u79a0jXd84j47e4LiikG+8xQUOxcFRUk574Q2zUl3UIzX9dTbvTQaEL1Bcdddk+9GsmkTLFpkt/PyYIstkrt/Z6KgKCIiHcHChe3dgtTxBsUdAxVFLwXFzkVBUVIu0W6X7VVR9AbFykr/96jHHkvusbyhdIstoLAwuft3JgqKIiLSETQ2tncLUscbFIf1dcfSzF3udpGqqPDP6yAdn4KipFxzQdG7bMSUKeltR7CiGBYUo5XE4LkTJyb3WBqfmDjNeioiIh1BQ0N7tyA16urgyy/d/d5VS5u2l6zp3bS9227uzPbSOSgoSso11/X01792g8Ls2a2fOCYRyXQ9jYbIqPfeS+6xND6xdVRRFBGRXBWcqC9X/09bvtxO5gf2A/1iZ1nTbcvWuVOcqttp56OgKCkXVlHbZht46y244goYFOnu3tgIs2alrx3BpS7iTWYDsPXW/nOnToUVKxJ/LAXFxKnrqYiIdATRGdSjoktI5BrvvAw9ewIb3aC4dG2vpm0Fxc5HQVFSrndvd7mJqFGjYP/9bZeF3m4vBt/ahqm2YYN/v7mup3/+MxQX+8//9tvEH0tBMXEKiiIi0hEEP5COLi+Ra7xBsboa2OSpKK51K4q7757BRklWUFCUlMvPj5310zurqHe7LQvcN8dxYoNiWNfT6CQ2hx5qZy094QT3tm+/hWXLYifFCeMNihqj2DwFRRER6Qi8PZQgd4OitwdVr+oG2OQeqG3sQX4+/N//Qffu7dA4aVcKipIWwbDkDYfeGUbTFRQ3bYoNIc1VFAG6dYPttnP3b7kF+vSBwYNh3LjmH887mY0qis1TUBQRkY6gI1YUB/ZdCUT+cy7qxpKlhaxZA3/6U7s0TdqZgqKkRXBCm3gVxeCncalSWxt7LLrOYdgYxaiRI93t6dPtOMqGBnj88fiP5Tgwx11ySEGxBZr1VEREOoIOGRR7ud1OKe5JSQmUl2e+TZIdFBQlLYIVRW8VMRNdT4PdTsEujLtxY/yKIviDopc3CAatWQM1NXa7rExdM5KhiqKIiOSq4HuYsPceucAbFPt29wTFkp6xJ0unoqAoadFc19P2Copgl+RoLigOG+bOyuqtfDUXFIPjE1Uxa566noqISEfQUcYoeoNi767+iqJ0bgqKkhaJdj298kp4993UP368oDhzZvhkNlEFBfD663DrrfDBB+7xOXPihxqNT0yOgqKIiHQEHbHraXXFUndHQbHTK2j5FJHkJTqZDcBhh7ldN1Ml0aAYrCgCbLUV/O53NsSUl9sX/vXr7WQ4PXrEnq+lMZKjoCgiIh1BRwmK3llP+xZ4Pr2vGJL5xkhWUUVR0iJYUYw3RhHS88LaXFBcsiR+W7yMcbuhQvzupwqKyVHXXBER6QhaExQbGuD446FvX3jxxfS0K1nRimJRwSa6bXjZvaHf4e3TIMkaCoqSFr17+/cLPLXrsHC2aVNqHz9eUPzgA5g1y24XF8Pw4c1fJ9mgqDUUk6OKooiI5KL6+tgZ1hOZzOaRR+Cpp2DxYvj979PTtmRFg+J+o94mrzEy8LJiS+i6Tfs1SrKCgqKkRV7gL8tbYQwLiqtXp/bxvS/W3rD38cfu9m67QUlJ89cZ4ul18c034eeoopgcdT0VEZFc5/2/PyqRiuL997vb8d5XZNLGje7wn2N2eda9of9R6gIkCoqSPq++asPYLbdAv37u8bCguGpVah/bGxS3ifOB2D77tHyd3Xd3t8ePDz9Hk9kkR0FRRERyXdh7gpaCYn09fPqp/1jYus+ZFB2faEwjR455zr2h/1Ht0h7JLgqKkjYHHwwffQQXXOA/Hq+iuGqVnW30nXfa/tjeoDhgQOwEOgC77trydbxh8v337Yt80FLPBGF9+ybexs5KQVFERHJd2Izt69fbLqU33RQbCAHmz48d1xhWmcykaLfTXbacQO8ui+1OcTVUf6/9GiVZQ0FRMi4stK1aBb/8JVx4IRx0UNtfOL1BsawMttwy9pxEQt2gQW6VcP16ePNN/+0NDf5PA8vKkm9rZ6OgKCIiuS6sorhmjf2Q/JJL4Pvfjw2F3sn0oubMgcmT4be/hddeS09bmxOtKB415ln3YL8jIC8/842RrKOgKBkXVlH87jt44gm7vXmzXcuwLRIJitXViV3rsMPc7fPPt+2L9zjBsZkSS0MeREQkl9XVwYwZsccfegi++spur10LX37pv93bAylq+nT7PuPvf4dDD4XHH099e5sTrSgetfOz7kF1O5UIva2VjCsujj32j3/497/9tm2PEQxwQ0KWAuqZ4Dqyf/iDWwWdMgVuu829zTseobw8+XZ2dqooiohIrlmwABobWz4v+F4mrKL4f/8Hs2fbbceJHa6TbsuXQ/eKFYzsN8UeyC+BPgdkthGStRQUpV2ccYZ/f+pU//5tt9mZUp96qnXXb6miWFqaeLDbYgu4+mp3/5pr3K6x3qBYUdG6tnY26noqIiJtFTZnQKZ4l8vaaaf4w04mT/bvh1UUg0tqLFgQ22U1nZYvh2G9p7sHKreCgtLMNUCymoKitIt77oEzz2z+nAUL4KyzWveCGRw3GAyKiXY7jfrtb2Hbbe32hg22CyqootgaCooiItIWjz5qe/rst59d3iHTvEFx2DAYOTL8vGBQDKsohpk/v3Xtao3ly2Fob08/2oqhmXtwyXoKitIujIExY1o+b9Uq228/WS1VFBPtdhpVUAB33unuP/20Xf4juvYQKCgmSkFRRETa4o9/tAFx3Dg7W3qmeYPioEFw9NHu/vDh7va339oeSJMm2f/vvBXFBx6AmTPt1+zZ/lnWFyxIW9NjLF8OQ3t5gmLlsMw9uGQ9BUVpN1VVscdOOcX/Igt2munFixO/bmMjPPOMu19WZl/Ivbp2Tfx6UXvtBaee6u4feqh/0h11PU2MgqKIiLSFd5KY66/P/OMHg+KvfgX77w97720/RC6N9NycP9/evu22Ngh+8YV7vz597PwJQ4bYcwYMcG/LZEVxxYpA11NVFMVDQVHaTbdu/v3u3eHee+0MqJs3u1051q2DSy9N/Lr//Ke/K0pZGZSU+M8JjglI1J/+5A+43rGLqigmRrOeiohIW3j/T6+thQkTMvv4waDYvTu89ZZdW3HLLeEnP4m9z3vv+edj6N3bf3v//u52piqKjgPffBPoelqpoCguBUVpN9tsA4WF7v6pp0JRkd0uLIS//MW97b77Ev+P4N57/fujRsWe4+0ymoxeveCGG8JvU1BMniqKIiKSrOh7hag77sjs4weDYtDvfx8+w7tXr17+/X793O1MVRRnzYIFCxy26vOde1AVRfFQUJR207cvvPIK/PrXtuvIddf5bz/oIDjiCHf/nHPsAvfNWbwYPv3U3X/iCX93jqjgC3QyfvlL2HXX2OMKiolR11MREWktx4n9sPexx9yF49OtsdGd+RzCg+KQIfDaa/Cb38All8Ann8Dpp7trLQ8Zkh0Vxf/9D/pWLaJX12X2QEEFlIf8QNJpKShKu/r+9+0ngZdeGj699J//7H5y+NFHcN55zV/v1Vfd7X33heOPd/efftrdvvnmVjcZY+DKK2OPa4xiYhQURUSktTZsiF3DcNMm2/MoE5YutY8HdihKly7h5+27r52M74YbYJddbG+nKVPgrrvsJDx5gXfg3qA4fTppt3IlXHUV7DjYM3Cy2w5gFA3Epb8GyWpDh9ouHFF33AELF9rtceNgxx3huOOgvt6mj5decs/9wQ/81zrqKPup3qRJsPPObWuX9wU9ShXFxCgoiohIa61bF378ySeTv5bjxIbOlrTU7bQ5w4fDL34BAwfG3jZypNtddcqU9IfFf/3LVi53Gvy5e7Dbjul9UMk5CoqS9a680nZTBfuiPnMmvPMOHHYYTJwI//0vvPtuNXV1/llIDzvMfx1j7Kd6o0e3vU3esQRRCoqJUVAUEZHW8gZF7zjA2bOTu86CBbDVVvZr5szE79eWoNicigo4+GB3/7//Td21w0ycaL/7K4oKiuKnoChZLy8PdtvN3X/6aVstrK11j02d2oX334e1a+3+wIHhk9ikSvfusTOpqutpYjTrqYiItJY3KA4f7nbh9HYJTcTpp9uq3YwZ8Ic/JH6/dAVFgGOOcbfffTe11w6KzsC64yBPUOyuoCh+CoqSE6IVRYDbboP16/23T5tWwcsvu/s/+EF6A4kxsVVFVRSTp4qiiIgkwxsUq6r87w8WLUr8Om+84W6/8kri90tnUNxuO3c72QppMhzHLkXWrXwlQ3pFHiivCLqmoMuVdCgKipITvP8RRHXt6m5PnVrJ44+7+8Fup+kQHKeooJgYdT0VEZHW8gbFykr/h7Z//WvLs6OHCZsdPZ50BkXv9ebMSd//kQsW2A/cdxg00T3YdRvIK4x7H+mcFBQlJ/TpE3vsoYegWze7vWFDQdN01cXFsP/+6W/T8OH+fQXFxCgoiohIawWDovdD27/8BR5+uOVrbN7s3w+boC6edAbFbt3cYSzr19uZSdMh2u3UN5FN953S82CS0xQUJScEK4ojR8Lhh4dXDo8+OnypjVT74Q/9+wqKiVFQFBGR1orORQB2aYrgMJAbb2z5Gtde699P5v+idAZFY2KrijHm/he+uAjePhQ2tG7BxaagOEQznkrzCtq7ASKJCAbFgw+2L6i33w677goTJsxh0KBB9OwJP/1pZtp04IH+fe/saxKfgqKIiLRWsKIY/JB21arm7z97Nlx3nf9YTU1ij716tRtUS0uhZ8/E7peMQYPgm2/s9pw5sFOw0Df1r7BsvN1e9SWUhUzD3oJoUNxlywnuwe5jkm+sdHiqKEpOCAbFvfe237t1g3POgZ/9bBbXXQfnnut2R0230lK46CK7PWAAjNFrbELiTTKk0CgiIi1prusptDyR3ddfN3/NMNdfb9dtvv1299jAgemZNC+sonjjjbD99nbIDVXbuiesCflh4vjvf23ovPxyu05jt/KVDO8TWawxrxC6bd/2xkuHo6AoOaFXL//+Xnu1TzuCbrgBPv0UJk2CoqL2bk3uiYbDjz+GwYNh7NjkpjcXEZHOJRgUTzjB/x5h8eLYmdG95s+PPdZcRfHdd224mjgRrrjCPZ7qbqdh150zx7b3ssvgq69sj6kvZnuC4urEguKbb8Jxx8H8GUsZtORM/nnYYL680RMMq7aHfHWLklgKipITCgrgkkugsBDOPz82OLaX/HzYeWc7TkISE9b19NZbYe5ceOcdeO219mmXiIhkr5UrYdq02KBYVWW7k1ZVucdnzIh/nbCgGK+i6Dhw9dXht2UqKH71lb/Hzb3/9QbFrxK65h132O8vXPBDztzvHgb3nMOAHp4noseubWixdGQKipIzbrjBfkr45z+3d0ukLcKC4nffucdWrMhse0REJDvV18ODD8Ihh9gPiLfaCu65x709Gg5LS2H33d3jzQXFBSHzv8SrKN5xB/zvf+G3jRzZbNNbLRgUJ0/23/7WZ9u4O2unQGNdi9ecPRu6V6xgt2GfxN5o8mHL01vXWOnwNJmN5JRCLfGT84JB0XFg5kz32IYNmW+TiIhkn9/+Fu66K/7tW23lbg8e7G6HVQ2bu239emhshDxP+eTLL+HCC/3nbbmlPW+nneBnP2u26a3WUlCcMqMrTtlAzIa5NiSumQzdtmv2mvPnw46DvmjaX7GuO9+7+gN+cOBq/vzPoVBSncofQToQBUURyahgUFy50t/tp7mxJSIi0nm8+Wb824yBESPc/e7d3e3ozKeOA089ZQNXQ4OtQHo/mPRav952ZY1un3iiO2Z+hx3go48yM7t5nz52zoPNm20PmwkT/Lc7DqwvHkPFhsji0cvGNxsUN26E5cthx93coPjUJ8fx3aIRHF4NlKThh5AOQ0FRRDIqOEtc8D9tVRRFRAT8XUKrquzyFFGDBvnXTPbOeB4979134Uc/in/9wkKoq3MfKxoUb7jBzgwK9jEeeyxzS2Dl5dmZ1KPdZ78KGYa4oG4sI3ja7iwZB1v9Ju71Fi6033ca7K6Z+MUcu2aiN2iLhNEYRRFpN8Fup6CgKCIilre3SXC281Gj/PveyWyiFcW7745/7ZISG8iCj7VuXYFvGYy//jXzgaqliXImLRvr7ix9B5zGuOdGu9ruONitKH4xW0FREqOgKCIZFex6GgyK6noqIiKNjf7/D6LrJ0cFJ5MJqyh6u6MG/eAHbgUR3OrluHE9m0LjyJFwxhlJNTslWgqKE2eNhuLIuMJNy2Dpu3HPXbAAyorXs1UfO2tcQ2MeX821XVUVFKUlCooiklEtBUVVFEVExPt/QVkZnHIKdO1q94uK4Cc/8Z/vDYrRimLYBHhz59qxj4884g+K0XA4Y0ZF07Gf/tQ/wU2mtBQUZ87Kg4EnuAdm3Bv33FmzYJv+k8jLs9OMT100go11pXTtCr17p6K10pEpKIpIRikoiohIS7zdTisqoF8/243y/fdh0SLYfnv/+d6up9GK4po1/nPOOcd2N/3+923YrHAzYVNFce5cd+Dj6NFt/jFaJSwoDh/ubs+eDQz1lDrnPw31tTH3eflluPZa2GHQxKZjE+fsAMA++8TOGSASpKAoIhkVDIqzZvlvV9dTERHxTmQTDXQVFfC974V3KQ2rKK5d6z/nssv8+2EVxTlz3KAYHAeZKWFBcd993e1Zs4BuO0KXSN/R+vWw6FXf+Y8+CkceaWc99U5kEw2KxxyT4kZLh6RZT0Uko7xBsa7OfjLspYqiiIh4K4reQBdPSxXFV16BXr3i32fZMhswV66005sWF/vXZsyksKC4555w77127OaiRbBxk6Gk/1Hw7R/tCeOPgYIKKOvH/JX9qPuwH9cc248FK/vxoz2earrOpzPHAPDDH2bgB5Gcp6AoIu0mbOFjBUUREQmrKDana1f7QaTj2EpiQ4M/KEbHN3oNHepuT5vmX4pixAjIz0++3anQv7/7s0QNHWq7zc6ZY/fnzoWt+h/jBkWA+hpYO5X+BVP5aWDyH4DavEGUD9mXJ66CHj3S+iNIB6GupyKSUS2NiVDXUxER8QbFRCqKeXn+MLhmjb/raZcusffxjvubNg3+/nd3f8yYxNuaakVF/pAIdgZWb4Vz1iygelfY9mqoGIqT1/JCj6Xb/YYXXszn+ONT2lzpwFRRFJGMaikoqqIoIiLByWwSUVXldjtdtarliqI3KL73nv+DynPOSbSl6dGnDyxebLd32QWqq21QfOcde2z27MiJ215B/cgrOOssh+eeXMkW3RbSr9sCDvjeAn778wUUNyyADQuh60jY+rzM/yCS0xQURSSjFBRFRKQlyXY9BTuhTTRArV7dckXR2/XU+3jbbRc7q2qmnX02XH65rab+5z/22JAh7u3Rn3PjRjj5ZHjmGQP0YGVND7baZVt++xc7zlKkLdT1VEQySl1PRUSkJcl2PQX/zKdLlrj/nxgTHjbLyuy4v6Dq6sTbmS6XXgpvvw0TJ8JWW9lj3q6nN91kxxmWlsIzz7jHzzgDHn9cIVFSQ0FRRLLKhg2xYzNERKRzaU3X0/793e1Jk9ztLl3sGMYw3u6nUWHdVDPNGBg7Frbc0j3mrSgCrFzp37/gArjnHihQf0FJEQVFEcmoliqKDQ122YxENTbaT1w3b25Ts0REJIu0pqLoDVVffOFuh3U7jQoLit5lM7JJc8t1HHUU3Hxzy//HiiRDQVFEMirsP7GSEv8bgW++gRdfTCz8nXQS7LgjHHywKpEiIh1FayqK8YJicxXCbK0ohunXL/5te++tkCipp6AoIhkV9h/ZkCH+NwI77WQXAz7vvOav1dgITzxht8eNg6lTU9VKERFpT62ZzMYbFL/7zt3uKBXF5tZ13HHHzLVDOg8FRRHJqLCg2K+fnVQg6B//aL5KGByf8d57bWubiIhkh9Z0PfWO4fP+39FchTA6UYxXtlYUm6OgKOmgoCgiGRUWFLt0CQ+KANOnx7/W0qX+/XffbX27RESk/TU0wHXXwdNPu8cSrSj26WOHMgQ1F/y23DJ2optsrSgCXHtt7LE998zuNkvu0rxIItLuKir805p7ffhheNcggGXL/PtvvWW7o8ab3U5ERLLbww/DH/7gP5ZoRTEvz1YVJ0/2H2+u62lREQwaBLNmuceyuaJ44YV2SY9Bg2xvnDfesBPZiKSD3k6JSEaFVRQrK6FXr/DzP/ww/rWCFcWFC+Gdd1rfNhERaV9XXRV7LNGKIvjHKUa1FPyCH0Zmc3WupAROPdUunTF8OPz617DFFu3dKumoFBRFJKPCgmJFBfTuHX7+Bx/Ev1awogjw0EPJtWfjRtu9dePG5O4nIiKp19gYeyyZoBhcaxCaryhC7DjFbK4oimSSgqKIZFSyQXHSJP806V7BiiLAk0/Chg2JteWbb2wXnuHDoW9f/wLNIiKSeWETmCXa9RQ6fkVRJJMUFEUko5LtetrYCJ98En5bWFCsqYHnnmu5HbW1cMIJsHy53V+9Gi65pOX7SeY4jv3dz5nT3i0RkUxpa0UxFUFRFUURS0FRRDIq2YoiwK9+BZ99Fnvc2/V0m23c7US6n15wga0oer34oqqK2eS++2C33WDYMJg9u71bIyKZEAyKeXlQWpr4/cOCYktdT4cN8+8rKIpYCooi0u7CKooHHuhuT5tmp//+9lv/Od6K4vnnu9uvvw5LlsR/vGeesWs0hnnllcTaLOn3s5/Z7/X1cP317dsWEcmMYFCsqAj/gDGesDGKLQW/IUOge3e7PWjQegq0JoAIoKAoIhmWaEUxON33pk3w+9/7j3krirvsAnvvbbcbGuxYxTCOA+ec4+4feyz861/u/rvv2olt/vQnOO88WLGiuZ9GMkW/B5HOIThGMZlup9Hze/b0H2upolhQYD8k/N3v4PLLv23+ZJFORJ+ZiEhGJRoU99wz9ryXXrJjCaMTDXgrir162TGH48fb/bffhrPPttu1tbbiuG6dfSMwf7493rWrDYnRcYpgu59uvz18953d37ABTj452Z9SUq28vL1bICKZEFZRTNaWW/o/SEykK+muu9qvcePWJ/+AIh2UKooiklHxJrMJBoEePWDUKP+xxkZ3YpP6eli50r1mjx52Xamod991P5m+/nr45z/hkUfguOPcc3bZBbp1s+NT+vRxj0dDIsBTT0F9fRL9niQl1qzx74fNhCgiHc/6QE7La8U71eA4RY05FGkdBUURyah4FUVj4Le/tfuHHQb9+9vun8E3CdEq4ooVbnjo3t12HRo50gZGsFXCvDz4+c/949u8k6LstJPbpp//PLy9q1bBF19UJfMjSgoEZzr1Vn1FpGPavDl2eaPWfEgUDIotdT0VkXAKiiKSUfEqigB/+5sNCC++aPd/8ANYsAAOP9w9NzpJTbDbKdhguM8+/mv/+9/x2xINigBXXglnnQX5+bYyefzx7m3PP79Fsz+TpJ6CokjnE+xJAOHLZbTEO6FNcbH9EpHkKSiKSLvzjkEZONAfJvv08X86HA2K3vEn3okLLr8cBg9O7HF32cXdLiiw3VNrauz4xiuucG97772evPVWYteU1Jg2zb/v/X2LSMcUFhTbWlFUNVGk9RQURSSjwiqKZWXN38c70c0dd9jJacIqimCrhLNmwQ03+K9RXOxWGwcNsreHrbdVUmK/b7ONf+bVI4+E999vvp3SNitWwMMP2zUzb7zRf9vcue4kRCLSsaxZAw88AHffHXtba4LiqFFQWGi3hw5tW9tEOjPNeioiGRUMihUVLU9W4A2Cs2bB7rvDMceE3x4V7IJ6++1w5pnJtfW22+CTT2DhQjvBwuGHw8SJNmhK6v3gB/Dxx/Fv33FHGxbVjUykYzn5ZHj55fDbWtP1tHdvO6P1c8/BxRe3rW0inVlGKorGmHuNMUuNMZM8x64yxiwwxkyMfB3mue0SY8x0Y8xUY8zBnuOHRI5NN8b83nN8iDHm48jxx40xRZn4uUQkeWFBsSXBpTO++gquusrdD66ZBTBmjDuT6eDB8NOfJtNK935vvQXdum0G7NIcp5xiZ1yV1Fm2DE48MTYkBv9Wli+33YJvucVOUBSc9EJEcs/8+fFDIrR+xuNTT4Wnn4bddmvd/UUkc11P7wcOCTl+m+M4O0S+XgYwxowCTgRGR+5zpzEm3xiTD9wBHAqMAk6KnAvwx8i1hgGrgJ+l9acRkVYLvvmPTmTTnLCKYUu3FxfDa6/B1VfDG2+0vgq19dZw7bWTyM+3+++/D9de27prSbj/+z94/PHY45dd5nYFjjriCHv+5ZfDvfdmpn0ikj7PPONub7WVO/t1VPCDQhHJnIwERcdx3gVWJnj6kcBjjuNschxnFjAd2DXyNd1xnJmO42wGHgOONMYYYH/gqcj9HwCOSmX7RSR1UlFRDIoXJLfbzk5KM2xYYm2LZ/TotVxzjbt/zTWw556waFHbriuwcaMdmxQ0dqydifbcc/3H6+rc7W+/TWvTRCQDXnnF3T7/fDv79QsvuMfCxi2KSGa092Q2Zxtjvop0Te0WOdYPmOc5Z37kWLzjPYDVjuPUB46LSA5oTUXxuuvcwNmzJxx4YOrbFXTxxbDffu7+Bx/AXXel/3E7ujfeiD12+eXw+ut2JtobboD77gu/77p16W2biKSfd5KqXXe13w8/HL78EqZPtx/4iUj7aM/JbP4BXAs4ke+3Amek+0GNMWcBZwH07t2bcePGpfshW6WmpiZr25aN9Hwlpz2fr02b8oB9PPsrGDfu6xbvd/jhW/Hii1tw9NHz2XPP6Tz0UAnLlxczYsQ6vviiFbMdJKGmpobx48fxq18V8fbb32s6/vHHixk3bkpaHzsXJfr3VV9v+N3vdgLcTwu22WYNY8dO5P333YFJgwfD978/krfe6k2fPrUsXlwKwMyZyxk3bhK5Tq9fydHzlZxsf74WLtwDsGMDpk//kLVrNzXdtnIlzJsX545pku3PV7bR89WxtVtQdBxnSXTbGPMvILLENguAAZ5T+0eOEef4CqDKGFMQqSp6zw973LuBuwHGjBnjjB07tm0/SJqMGzeObG1bNtLzlZz2fL42bvTvDxrUI6G2jB0La9dCly79sf/MM8f7fD36KJx0kj1eVtaHsWP7ZLQtuSDRv6+bbrIVA7BjET/8ELbfvivG7Btz7tixdgr9zz4r5fvft8eKiqo7xL97vX4lR89XcrL5+XIc+7oedcQRe8SMS860bH6+spGer46t3bqeGmP6enaPBqIfCz8PnGiMKTbGDAGGA58AE4DhkRlOi7AT3jzvOI4DvA0cF7n/qcBzmfgZRCR5rZnMJiobFk7u0cPdDlscWhIzdap/5tprroEddghfZzOqa1f/34v3DaaI5J41a9xxxxUVsZNXiUj7ykhF0RjzKDAWqDbGzAeuBMYaY3bAdj2dDfwCwHGcb4wxTwDfAvXAbxzHaYhc52zgNSAfuNdxnG8iD3Ex8Jgx5jrgC+Dfmfi5RCR5rZnMJpt4w6qCSus0Nto1LTdFepjtvLOdxCIRev5FOo7ly93t6ur2a4eIhMtIUHQc56SQw3HDnOM41wPXhxx/GYhZbcdxnJnYWVFFJMu1paKYDbp2dbc//RT++1849tj2a08uuvtuGD/ebhcUwL//bb8nwhsUNZmNSG5btszdDlsPV0TaV3vPeioinVwuVxQBjjtOyzQkw3H8XU4vugi23z7x+6uiKNJxeCuKCooi2UdBUUQyKte7nnorilF//Wvm25GrampgSWQqs5IS+MMfkrt/WRnkRf7nqq31r6soIrnFW1FU11OR7KOgKCIZletdT8vKID+/vVuRu7zdRauqkp+8whj/34y6n4rkLnU9FcluCooiklG5XlE0Jrb7qYJj4rzBrrUfEmicokjHoMlsRLKbgqKIZFSuVxTDbNhgv69cCbNmtW9bsl2qg2Kuj1NctgyWLClu72aItIsVK9xtBUWR7KOgKCIZlesVRXCDYdTSpTYgDhwIW24JTz7ZPu3KBakIih1lLcWvv7Z/MyefvLvGuUqn5P33Gzb+W0Tal4KiiLSrXAyK0fX/opYsgRtvhPXr7f4ZZ2S+TbmiI1cUx4+HZ5+FhobEzv/732HjRmhsNJx3Hrz5ZjpbJ5J9vP9+g136RaT9KSiKSLsqL2/vFrTdkiXw7rvufk0NXHEFvP56+7UpW3nfGHakoPjpp7DPPnD00XDSSS2HxXfesetJel1xhV0+RKSzUFAUyW4KiiLSrnKxohi0dCnMn+8/du21cOih8NFH7dOmbJXqiuKaNW1rT6q88467/eSTcNdd8c9dsQLGjo09/uGHMG5cqlsmkr0UFEWym4KiiLSrXAyKwbE0dXVut1OvxkY49VT7XaxUBMX+/d3tKVPa1p5U8U7KAXDTTXZyozATJsS/znXXpa5NItlOQVEkuykoikjGHXmk/X7IIXZdwlzz2GOJn/vdd/DQQ+lrS65JRVDcaSd3+4svbBBvaLBf7dV1MxgU58+HbbaBV1+1bfK2a/Jkd7tXL3jwwY+bllj53//ggw/S316RbJCK1wMRSR8FRRHJuP/+Fz75BJ5/vr1b0jqHHGID4C9+YffLyuDEE+GBB8LP//3vO996f3/+M3TrBv/3f/7jqXhjuOOO7va4cfb5LyiwX8OHw6RJrbtuWwSDIsCiRbb7cV4eDBkCn39uj3uD4iWXwIABtfz4x+4xVRWlM2hsVFAUyXYKiiKScfn5sMsuUFjY3i1pveHD7Ti02bPtGMVHH4Wf/hTGjIk9d/FiuP76jDex3SxaVMIFF8Dq1XDrrfZ7VCreGA4YAD16uPveWWhnzLCziabSzJm2yvfBBzB1anjV0hsUzznHVgq95syBq6+2299+6x4fOdJ+v/RSd+mYV16Bzz5LXftFstH69e6/pfJymqrqIpI9FBRFRNpg0CD/zK133WXXUtx/f/+slrffbhdX7wwee2xA07bjwMKF7m2pCIrGwM47x799+vTWXTfMX/8KQ4fCnnvar623hquuij3PGxRPO81WNQ87zH/OG2/YyY2+/NI9Fg2KW20FJ5zgHr/pplT9BCLZSeMTRbKfgqKISArtvLOtar31Fvz857D99vb4hg1wxx3t27ZMcBx4//1q37GlS93tVHU1O/RQ//4BB7jbc+a0/rpBYeNL//pX2LzZf8wbFHv0gJ494Zln4Fe/co/X1sIee9jlUwC6d7fV0aiLL3a333qr7W0XyWapWCpHRNJLQVFEJE2MgQsucPc7w1IZM2bAihXFvmNLlrjbqQqKRx/t3z/lFHd77tzUzTTrDYDFkR9rzRo7JvXCC+Hee+3spsGgCFBUBHfeCccdF3vdigpbcY52NwXYbjt7H4BVqzrfuFbpXLx/36ooimQnBUURkTQaPdrdXrSo/dqRKe++G3ssWlFcuBDef9893pY3h4MGuWsRVlbCEUfYCh3Yap83nLaFd4kL74Qzzzxjx1/+7Gc2GEbHSRYXx87ke9RR/v1994Wvv4Zjj/Ufz8uDgQPd/VRWRkWyieP4u1crKIpkJwVFEZE02mILd3vBgtjb16yxs6eed15sd8Zc9N57sceioe2ii/zH29rd7OGH4cYb4c03bUgcNMi9LRUhq67O7R5nDBxzTMv3qaryVwkBTj4Z7rvP/o4fftgugTF4cPj9U/0zSOcye7b9e7vxxvZuSfOefNJ+2BKloCiSnQrauwEiIh1Zz552Nr+GBts9cdMmtwsjwD//6U56069f7HISucY7UUvU9dfDQQf5Q2RJiV0yoi222MIuPRI1aJBdVxFsyNp997Zd3ztba7du/hAXT1gl0xg7wU0ivI9x+OF2Yp6hQxO7r8hZZ9lJkx591E6otdtu7d2iWI2N7tJCUaWl7dMWEWmeKooiImmUnw99+rj7we6n3glMghW3XNPQ4F8j0OuII/wVsgULbFfLVPKGrHnz2n69Vavc7W7doH//2HOmTYNTT3X39923bY/p7XoKcNtttho5eDBcfnnbrt2RvPGGfU6OP75jVOJTobbWPi9R3u1E1NT4J5hJl+ee838IA1oORiRbKSiKiKSZt/upd6kI8IdISN0kLO1h1iz7ZjXMmjXu9ogR7njCVPKuregNea3lHZ/YvbvtHhesfPTrB3/5C2y7rZ2I5te/bttjBsPoHXfYmVPnzLGV2bCuvc2pr0/deM1scvPN9jl56ik7oZDAO+/4973ri7bk73+3/366dYPLLmvd469d65/UKczixXDNNbHHg+N4RSQ7KCiKiKRZc0GxosK//8036W9Pukya5G4HK2Ne0bUDU61rV3c7FZWRYFA0xp2VNKq01I5L/PJLe/6PftS2xxw1KvaY9w3/DTe4242N8MknsdWZqOnT7XPdt69d37Mj8Y73vekmO560s3v1Vf9+MmNcb73VVmYbG+3fWDLPZ0ODDe69etkPvsImtAK7rEzfvjBxonusVy/7wVGwK6qIZAcFRRGRNIsXFIOL0QM88khm2pQO3qB40EHxxx2FhaFU8E6I4a1gtpY3KHbr1vy5xkB5edsfc/fd7dIb8bzxhg2OjY121tTddrNrM65f755TUwO33w7Dh9uw6Djwr3+1vW3ZxFu5mjPHThLU2b3yin8/maAYrDrPn5/Y/ebMgf32s93mN22yFexbbw0/N/g3eM459nEnT4Ytt0y8rSKSOQqKIiJpFi8orl4NGzb4z73nnuS6jGUTb1DcbTf7xvDQQ22lY//93dv23DM9j5/qiqK3+2o6usqGMcZORBIvmNbX2+f5ttvg2WftsSlT3AmR3nnHdoM95xz//T7/PG1NTqvVq+Ef//C333FiuzjecIOtbHVWM2fCd9/5jyUaFDdujO0ynsh9Gxrs2OPx4/3Hn38eXnop9vxgb4noxF3BWYJFJHto1lMRkTSLFxTDlstYvtyOu/IuIJ8rvG8Et9nGVseiP8duu9kugn362PCYDumsKEaDYs+eqbl2SwYNij/O8l//ih2X96c/2Tf3f/ubDVJhgjPu5oLzz4f777dLqcyaZcfRbdiQT329/7xp0+ySC81VYzuyYDURbFUw+DyFCfs7SyQofv01fPWV3c7Pt//+otc6/HD7gUZ0Hdl16/z3ffXV8MmhRCS7qKIoIpJm/fq5295wGK971x13pLc96bB5s61sRQW7l1ZV2aB43nnpqyB4K4rp6nr6j3+4x9LZTTi4FMfPfuZu//OfsWPIFi+2Y8CiIbGiwj8bK8R2c84F999vv69b53YvXbu2MPTc66/P7cmg2iIsKDY0wNixMGtW832ivX/nUc0FxWXL4IwzbJfTqGOPhSuv9J/n7Q7sfd0bOhQOPrjZJolIllBQFBFJs0QqigcfDIWR978ffuiuB5grpk1zqxe9e29slwW00zmZTTQofv/78Nprdor/E05o+2PE4w2Khxxil4EIqqyM7WIKcOCBNrTffz/suqt7PKyCnUui3U29QXHwYHdCqEmTbDXeq64Ofv5zW8WeOzcz7cy0jRvhf/9z97fayt1+/3246KLtmu3OnmxF8Ze/tEu2eCdR2ndfOPtsf0X30Ufd4O79UMz7wZmIZDcFRRGRNIsXFL1v0EaN8oeBO++MvU5Dg30jtu228OmniT12XR0ceaTtCvrll8m1Oxne8YmDB6+Pf2Iapbrrqfca0aBojJ2o54gjUr8OpNdxx9nufEVF8Ic/wPe+536QEPWXv8CNN/pDZY8e8PTT7ptxb/e+RCcoyVbRYLJmjTtqZuhQ/5IkJ5xgf0e9etluz3/8I/z737ar4yWXZLa9mfLuu+4Yw622gt/9zn/78uXF/POf8e+fTEXxm2/s31fQPvvYv9d773UndZo92wbIRx7x9zZQl1OR3KGgKCKSZt27u8sqrF1rZ6UEfxDp2tX/hve552Kv88wz8PjjNpQdfnhij/3CC3ZyiW++gaOPbv7chQvtOLDW8I5PHDKkfYJiqiuK3mtkukK69972dzFrlg2JlZVw4YXu7cOH266lZWXw+uvurJG33upfcsVbvYkGxeXL449jzCbByWmiFcVly9yBlj162Oeld2//ucuW2YDyhz+4xx55JHcnimqOt9vpoYfaNQmD3bsvuCD8NQXCg+Lnn4eviepdniVq4EC3q3lpqf917L33bGD/7W/dY6ooiuQOBUURkTQzxl9VXLTIfg8GkT32cKtGy5b5lzwA+OADd9s7nf2mTbZaGDZxxZtvutuzZoW/KQQ7vqtfPxs4Tj655Z8JYOpU92fwVhTbKygWF7vP3+bNtkteWwSDfKYNGOD/u7noIhsae/a0s+Pm59vjW21llxhYvTp2XKK3enPHHfZvsWdP250128fzBcP+vHl28pRbbtm66Vh1tf153ngjsWsmel4uqKuDzz6zHyBFHXqoDc2XXuo/t77e9lgIjmVsbLQz5QatWgVPPOE/Nm0aPPZY7LnXXuuvrt94I5x5Zvx2q6IokjsUFEVEMiCs+2kwiOTl2XAQFez+VVXl39+wwZ4zahTssAP85jexjxsMS7vuat9EPvGE+0Z84UL7Zi/q0Uft5Cheq1fDiy/a+ziOfaytt4Zhw2yFKhu6nhqT2glt2rOiGKaqyo45W7rUdvXzKioKD7PDh7vbs2e726+/Dh995O7X1dlj0Q8xskEwKE6ZErswe48e9vu229og05LXX09N27LBaafBmDHu60Rpqe3qCXDddbYiu2ABbLGFLQ3W1cFhh9lJjz77zH7wMGSIO2FQ9BpRwe7vN93kfriQl+dWtYMfLOXn2+Vapk+3H0BVV/tv33prRCRHKCiKiGSANyhGJxUJq1h5x5sFg2LwjfNbb9mZB2fOtPt3322XE/jvf92uhdOn++8zY4b9xP+EE+AHP7DHbr01tkve5MnudmOjnT3xhz+0Y/POPdd9E7lsmQ2W0ccxBgYNCiwOmUGp7H7a3hXFVDjsMPtmPji+EezfSdRPf2onVNp11/hV50wLBv2lS/3hFtyqKtjxil5HHhk72c+776aufe3p009jZ93dbz8oKXH38/Ls686f/zyRwYPd4+edZwPmzTfHTvBz4YVuN/lPPrEfCI0fb1+LHnzQPe+dd+y6jfffDwVxFlobOtR+KPXZZ/bvcNQo+9pxwAGt/KFFJOMUFEVEMiCRrqfQfFBctsy/f8QRsWMK//IXOxFKdPKKGTPit+m992wl8K67Ym+7+GK46ir4+GMbAqMT4bzzDtx+u//cc85xg+mwYVBc3H59GlM1oY3j+O+fDRXF1igutm/mFy+23VWjk/KAnZTEcWwgiHYpnD8f+vZ118drD45j23zZZS2fu/fe7rYxcNZZ7v7ll9vq2dq1bqD86qv461O2t6++sm3++uuWz/3zn2OPHXJI+Lm9e2/i5psTa8NWW/ln873zTvsh0amnul3b990X9torseuBHcP40kt2HPNf/pLeSaBEJLX0z1VEJAP69nW3o0Ex2YpiMCg259JL7ZiuaDfXggK7Fl1wxsJtt7VdWIMmTICrr4bdd4cRIxJ/3N13T/zcdEhVRXHjRveNcVGRv1KTi7p3t2sxLl7sPkezZ9sPBIJr2m3eDDvtZCdAiU68lEkvvQSnn267Onuddpr9Ov74eVx0ka2ge9fyA1stv+IKG3zHjLHHKith553ttuPY5U2yzebNdnzh9dfDdtvZLrbz5sU//733Yo8demj888Nu69UrdhxiYaF/MhqIHcd4+eXxH0dEOhYFRRGRDOjTx91ubUVx+fLY6+bnw9/+Fts1ctUqOOkkd3/QIDsb5tFHh49lBPjJT5r/GbyOOMKdBt/r/PMTv0Y6pKqi2BG6nYYpKrJdiKNuvtm/Hl5UQ4OtWo0aZRdXP+gg/1i2dLr++thjJ5xg1+677z749a9n8Mc/2glTgrN7du9uP+AIrnHprbb98Y/ZN+vr22/7l865++74FdVVq2JD5F572Wp+POXldj3JqHPPtV1Cjz8e/u//7LHSUrtO6G67xb/WDjvYc0Skc1BQFBHJgHRUFAsK4KGH7NTzEybYCox3wfH333e3t9vO3d5pp9j2jRwZWynYYYfYCXTALs3xxBM2QHj9+Mew446x52dSqiazybaJbFLp2GNjjw0ebGfP/Oc/Yc893ePz5tlw9sYb9vftXQ8vHRob/eNjo9oa1n/zG3eilokT7Ycr2SRsbcKHHgo/19stuF8/+2/++edbfozbb7ePM3267QIanX30uuvsa8ekSXbiGWNsl90wJ54YG85FpONSUBQRyQBvUIzOKNqWoPjee3Z5imjVcPhwOznNMceETxYRnQ0RwoPibbfZa0QrLyecYMcnfv65/7zHH7dvSouL7X0+/dS266OP7GLb7c0b6trS9bSjVhTBdkP0BvozzrBjUI86yo7xGz/eTlwSnK3ScezMl+k0YUJ4wG9rWO/Vy1/tvugi+OKLtl0zVRzHrncaJmwJE29QPOAAG/y9Y0/jKSmxPQqCk/4UFdnXjuhanGAnnwkb33zMMS0/joh0HP/f3p2HS1Gd+x7/voyioMggIiCCEiIaRaM4PHqDQhTMSTSaY5Kjxjgk11lPzpMcczzRG02MRhITvSfJgzNqHGKORuMQFSXmBlHECcUjIKIiRlSiiAMYXPePd5VV1bu7dzf0tPf+fZ6nn11dVat61burq3v1mlRQFBFpgMIaxTVr0pFGu3dPaztGjEh/sV++3PsugU9+ncyr2LOnz6eX/WKXMPPaguxokJCfTmHnnX1EyB49fMCalSu9n5oZ3HWX5+/GG/0L5KhRPnVG9+4+cfbhh6f5697d+34NGuTN1YqNrNloqlFsX+/eXiB7+mm/xq64In+OZt4M+fnn286Hd911bQdQqqW77iq+vhaF9XPOSfsqrl3rtWPN6INZ6LnnSk9Lkm2OmkgGloJ8S4FaGz0aLrvMrxfwHxGy062ISOengqKISAMMHJgOI//22z7Uf2KzzdLCV69eaaEyBB+FEvL9E5PmYaXssIMP6JEYMCD/hdIMbrvNB7E555x8bYRZvj8leJPUDz7wQkKrq1VBsTPXKIIX8j/zmfwPGIUGDPC+cmvXpoPGrFvnffzq5c47i6/feOMNP3avXj6VS9K3duHC/PukWe6/v/S2wultIF+juPPOtc9P1vHH+w9J77yTjqQsIl2HCooiIg3QrRsMGZI+z/b1KqyxyjY/PfVUmDUrv392qo1SfvADH4xkzz29xqiwhhGqqwFshdrCSrTX9HTVqspqxTpzjWK1evb06ylx1VX+A8aqVd5EtdwULJVavdoH1pk3z58Xzs1XzYi/5YwZk5/epVSTz3pbtMjnQVy9Ol9Q/NWv8hPYF8Z23TrvS5ioZ41iYuON9R4Q6apKTJMqIiK1NnQovPqqLx9wQLq+sMZq5Eh4+GFfvusufwwblm7fa6/2X8vM+2Q1exTSRitXo7h8uffVXLzYB/J4/vniNVVr1/q2YsfsqiZO9ObOs2d7fKZNgyVLvKA1eLA3nxw4cP2Pf8wx+YGY9tnH5/SbPt2fFxuAZ3195SvpQEwvv+z9ABs5t9+qVbD77n59fvOb/kNQYvLktA8z5K9D8Gv3gw98eehQj72ISL2oRlFEpEGS/lGFCgsi2UJhIilgQr6/oeSVqlF86y0f1j9pyrdsmRd6wGtp5s3zGq2pU73JZXbQFhUU/YeH7Ki406entXFvvOH9WDdEtpAIPpjKhRf6FBGXX57OiVgL/fqlza3XroXXX6/dsSvx4IPpjxhXX+3zm4JPTL/ddvlawj/8IT+VR6P6J4qIgGoURUQa5qKL/Avq9dfn50GbODG/X7GCYta++9Y8a51GqRrFadPaTu3wpz95AeWmm4rPJZjYc8+aZrHDmjLFR8x9/PG0Vitx1VU+f2E2/jfe6P3aPvzQn++0k4+UW1iLW2xOwy98wadm+dGPanoKnxg50ucjBB9duFxfzVpbt674+kmTvEB+0EFemH33Xe9HOXcuTJjg+zSyf6KIiGoURUQapF8/+MlPYOlSn+Pwl7+E3/++7cTahxziA2+A96e6/fa0f+MBB7QdbEZS2YLKww/Df/2XL8+Z03bfadO8IFOskDhqFBx3HNx9t88bKW1rFbNWrcr3/Xv3XW/eOWuWx37OHK+FLDYgSmET4b328nk966ncNDT1lh2YKiuZ1qZPH28em8jOp6gaRRFpJNUoiog0WLdu3t9r772Lbx81ymsS3ngjbXK3aJHX5OyxR+Py2REVDrpxyinwz/8MCxaUTzd4sBfC99/fR/gcNap+eezIDj7YR9V99tm2284/3+fZGzfO+4MW1jqC/0BS2G+2cKCa+++v/6TuzSwolhqYZ9KkdPmoo7yWFrxm9mc/8x+PVKMoIo2kGkURkRY0cmS+X1a/fj4Qy0YbNS9PHUGx/oR33JFOR7LRRsXnnzz/fB8N9dhjVUgsp1s3OPLI4ts++MDnJvzww7RZJ6RzhIL/2FEoW8O2++61mQqjPa1WozhuXH5U5M99zudUTfa/5x6v+X75ZV/XqxeMHVv3rIpIF6eCooiIdBr9+rVdl23u+OlPp3MCZg0fXr88dTa77pp/PmVK+gPG/PnedDTbH3SPPdKm1C++mC9EQr6GrVGjeG67bbp8553w0UeNeV0oXqM4dWr+ebducMQR6fNrr83XJo4b13GmrBGRjksFRRER6TSKzRc5d266vP32xUeNVUGxcrvskn8+aZLP2Zl48kmf7iKxxRaw447p88cey6fPFpwGDapZNsuaPDl9rZdeghtuaMzrQv58u3WDz38ezjyz7X5HHZUu33EH3Hxz+nz8+LplT0TkEyooiohIlzF2bPGCYnsjzUqqWK3fCSf4IEzF9O+f71v761/nt2ebYjaqRnGTTeCMM9Lnv/1tY14X8uc7Zw7ce2/xAvK4cemUOmvWpAMzgY8KKyJSbyooiohIl7HNNt4/rbCJav/+zchNx3X88f63e3cfodMMZsxIm5hmbb45nHhi+vzWW+GFF9LnzWh6Cvkau5kz2zaJrZdqalC/85226/r0adtUVUSkHlRQFBGRTuVb3yq9beRIL9Rk+6hB/UfZ7GwuvNAfd9/thW/wwndhs1LwQvhnPuN9GRMzZ/rflSt9RM9EIwuKW2/tg+cA/OMfPmBMvYVQXcH48MO9X23WYYd5jaiISL2poCgiIp3KxRf74B8//nHbbclol9mpCKR6AwbA977n/euydtjBaxCzkufZfR96yP8WTpXRyIIi+JQoiexgMfXyzjs+KixA797tF/h69IDbbktHRB0/3udfFRFpBBUURUSkU9lkE5/CoVjNYjJozZln+iArAJdd1ri8dXbduuWndYG0We+++6brfvtbnwZjxox03SabFB+Rtp7GjUuX25trM7FundcMro+HH86/diU12WPHwjPPwO23w+zZXkgXEWkEFRRFRKRTGjy4bZ+5ZEqBQYO8n9zixWl/O6mN7bfPP09qFHfZBfr29eUQfN7FRL9+3iQz2d4o2YLic8+1v/+SJT7P5rBhacHy44897euvF0+zZo1PGzJ/vhf2Ep/7XOX5HDQIvvjF/JyUIiL1poKiiIh0WtmJ1Qv17du2r6JsuFIFxR49vDlwYWFnq63gr39tTiFo7Ni0Vu+FF9JmoQALF3q/xRUr0nVXXgmvvAKvvQZf/rL3bdx7by9wbrml11S/9FK6//LlMGIE7LSTP37zm3RbsdF3RURaiQqKIiLSaZ11Vrr87W83Lx9dSbaWDvIjyp52mvfTe++99PHKKz7YTTP06eM1hOA1gwsXekHvtNO8EDl1KowZA0uX+j533pmmXbjQC4aPPJKuu/BCH9znppv8+YwZ+cFrEj17wj771OOMRERqp0ezMyAiIlIvRx3lE8AvWVJ8UnOpvcIaxcKpR3r2TJsAt4Idd/TrA+CPf/Raz/ffT7evWgXTp8P55+eby0J+xNasI47wZs/JoD2QTsuy0UZw0kmNH7hHRKRaKiiKiEin1a2bj4IqjTN4sNcqLljgzS5bffCViRPTvoMXXJAvJCauvx7OPtv7tFZi3TqfX/Ljj9N1DzwAo0dvcHZFRBpGBUURERGpqVtu8ZFNDz0Uundvdm7Ky06V8u67xfd5+WWvVVy3Ll239da+PvGDH8Dq1XDzzfDqq/lC4rBhaRNXEZGOQn0URUREpKa23x7OO89HOm11O+6YTpWSNXo0nHBC+vz009PlqVO9hnDYMH8+cSKcey78/Oc+BUZhofCIIyqbCkNEpJWoRlFERES6rG7dvFbxhhvy67/7XZ/gPjtSaeKww3zE3AUL4M9/zo9gOmIEzJ3rA9+sWeOjoR54YF1PQUSkLlRQFBERkS6tWEFxyy1hjz181NNFi9L1X/oSHHusL2+6qc9vWGjgQPjGN+qXXxGRRlDTUxEREenSJk9uu27oUG8ueuSR+fUnn6xmpCLSNaigKCIiIl3ayJGw3Xb5dUOH+t8jj0wLhgMGwH77NTZvIiLNooKiiIiIdHmFtYpDhvjf0aPh0kthr73gqqtaaw5IEZF6UkFRREREurzsNBkDBkDv3unzk0+G2bO9f6KISFehgqKIiIh0eZMnewERYO+9m5sXEZFWoFFPRUREpMvr3x9mzoRZs+Bf/qXZuRERaT4VFEVERETweRPHj292LkREWoOanoqIiIiIiEiOCooiIiIiIiKSo4KiiIiIiIiI5KigKCIiIiIiIjkqKIqIiIiIiEiOCooiIiIiIiKSo4KiiIiIiIiI5KigKCIiIiIiIjkqKIqIiIiIiEiOCooiIiIiIiKSo4KiiIiIiIiI5DSkoGhmV5rZCjN7JrNugJndZ2aL4t/N43ozs0vMbLGZPW1mu2bSHB33X2RmR2fWf9bM5sc0l5iZNeK8REREREREOqNG1SheDUwpWHcmMDOEMAaYGZ8DTAXGxMe3gV+DFyyBc4A9gAnAOUnhMu7zrUy6wtcSERERERGRCjWkoBhCeAhYWbD6YOCauHwNcEhm/Yzg5gD9zWwocCBwXwhhZQjh78B9wJS4bdMQwpwQQgBmZI4lIiIiIiIiVWpmH8UhIYTX4vLfgCFxeRjwSma/ZXFdufXLiqwXERERERGR9dCj2RkACCEEMwuNeC0z+zbepJUhQ4Ywa9asRrxs1VavXt2yeWtFild1FK/qKF7VUbyqo3hVR/GqjuJVHcWrOopX59bMguLrZjY0hPBabD66Iq5/FRiR2W94XPcqMLFg/ay4fniR/YsKIUwHpgPstttuYeLEiaV2bapZs2bRqnlrRYpXdRSv6ihe1VG8qqN4VUfxqo7iVR3FqzqKV+fWzILi7cDRwAXx7x8y608xsxvxgWveiYXJPwHnZwawOQD4fghhpZmtMrM9gUeAbwCXVpKBefPmvWlmL9XulGpqEPBmszPRgShe1VG8qqN4VUfxqo7iVR3FqzqKV3UUr+ooXp3DyGIrG1JQNLMb8NrAQWa2DB+99ALgZjM7DngJODzufhdwELAYeB84BiAWCM8D5sb9zg0hJAPknISPrNoHuDs+2hVCGLxBJ1ZHZvZYCGG3Zuejo1C8qqN4VUfxqo7iVR3FqzqKV3UUr+ooXtVRvDq3hhQUQwhfL7FpUpF9A3ByieNcCVxZZP1jwI4bkkcRERERERFxzRz1VERERERERFqQCoqta3qzM9DBKF7VUbyqo3hVR/GqjuJVHcWrOopXdRSv6ihenZh5S08RERERERERpxpFERERERERyVFBsQpmNsXMnjezxWZ2Zlx3hZk9ZWZPm9ktZtY3s/9QM7s3Lh9tZovi4+jMPj82s1fMbHWR1xtqZvea2Xgze9jMno2v89XMPqPM7JGYp5vMrFdc/x0zWxD3n2lmIzNpiualxrHayMwejbF51sx+GNefEvMazGxQQZqeZvZ4XG4T67i+3Xh30HiNMLMHYx6eNbPT4/qbzOzJ+FhqZk8WpJtnZr3N7LNmNj+e1yVmZpl9TjWz/4nH/WmR9JuZ2Z2ZfS7IbO8d87A4xm2buP7zMe38+Hf/TJqSeWlAvM6L/8Mn47WwVSZNJdfX/mb2uJk9Y2bXmFmPwvSlXjvuM8DM7ovXyn0Wp/MxsyNivuab2Wwz2zmTpmheGhSvovktEq8rzWyFmT1TcNyL4nXztJndamb9C7Z3yOur1Dmb31vmxOvrMTObkNnWqveve8zsbTP7Yz3ilHmdotdI3PZvVnDPrzBeZv4ZudDMnjOz0wrT1+H9WPI8aqnE9VXp+7GV7l+Nilex719/sfTzcbmZ3VYiXq10/yr5na+WSsTrajN7MROz8Zn9W+7+Ve5Y0kAhBD0qeADdgReA0UAv4ClgHLBpZp+fA2dmnh8D/BswAFgS/24elzeP++wJDAVWF3nNJP2ngDFx3VbAa0D/+Pxm4Gtx+TfAiXF5P2DjuHwicFNcLpmXGsfLgL5xuSc+x+WewC7ANsBSYFBBmv3wOTCLxjruU0m8O2K8hgK7xuV+wMLknDP7/Aw4O/N8FHB7XH40xtfw6WGmZs7rfqB3fL5FYXpgY2C/uK4X8JdM+pOA38Tlr2XisguwVVzeEXg1c9yieWlEvAquj9OSvFdyfeE/nL0CfCrufy5wXJH0Jf9XwE+TaxI4E7gwLu9N+p6fCjxS7r7SwHgVzW/2fOPy/wJ2BZ4pOO4BQI+4fGFB+g57fZU6Z+DeTN4PAmZVen3FfRp6/4rPJwFfBP5YjziVi1dcPwL4Ez4N1qDM+kridQwwA+gWn29RJH3N3o/lzqNB11e778dS8aIJ969GxavcNZLZ5/fANwrPt51rsxn3r5Lf+eodL3waua+USNNy969yx9KjcQ/VKFZuArA4hLAkhLAWuBE4OISwCvyXT3wex5BJMwX/InMgcF8IYWUI4e/AfXEbIYQ5IYTXSrzmFODuEMLCEMKiuP9yYAUwOL7m/sAtcf9rgEPifg+GEN6P6+cAw+NyybzUUnDJL2Y94yOEEJ4IISwtkSyJV9FYx+O2G+8OGq/XQgiPx+V3geeAYcn2mPfDgRsyyaYA95jZUPwGPieEEPAvVofEfU4ELgghrInHXlGYPoTwfgjhwbh9LfA46fkfjMcJPG6TzMzi/3F5XP8s0Cf+ulouLzVTKl7J9RFtQvH3Y6nrayCwNoSwMO5/H3BYYfp2/lfZeGWvr9nx+oH89VXyWq+lMnkumt8oiRchhIeAlRQIIdwbQvhHfJo9ryR9h7y+Yl6LnXMANo3LmwHLM9ta8f5FCGEm8O56hKAqpa4R4GLge+TPFSqIF37/OjeE8HF8jcL7V63fj+XOo6ZKvE4l78dWun81Kl5l75Nmtin+3rgtk6bl7l/xGOW+89XK+nyutNz9q9Sxqg+HbAgVFCs3DP+1LrEsrsPMrgL+Bnwa/0UGM+sOjA0hLCiXtpSC9Nn1E/BfeV7APxjeztzoSh33OOINc33ysr7MrLt5U8kVeGHrkXaS7AfMai+PFcQ7m4cOE69EbL6yC14Lm9gXeD25aUZTgHtifpaVyOOngH1jc48/m9nuRdJnX7s/XvswM6765Pxj3N7B45h1GPB4LIyWy0tdFMYradoDHAGcndm1vevrTaCHmSUTB38Frw0pTF/ytYEhmS8BfwOGFMlyK11f5fLb5nzbcSzpeUEnub4KnAFcFK+vacD3M9ta8f7VVGZ2MF6b8lSRzZXEa1vgq+bNfO82szFF0mdfbxs27P3YbJW8H1vp/tUo7d0nDwFmFvxQ2Ir3r0YpF68fx2acFyeF16il718Fx5IGUkGxBkIIx+DV4s8BSRvqPch/0a9Wm/Tx1/RrgWOSX1jbY2ZHArsBF21AXtZLCGFdCGE8/uvQBDPbsdS+ZjYMWJn5VanccSuKd0eLV3z9vngTmjMKPvS+TqY2MbbtHx5CWNLOIXvgzWb3BL4L3GyuTXrz/iw3AJdUcNwkzQ54k53/Xcn+tVYsXiGEs0III4DrgVPifu1eX7GG6mvAxWb2KF4Ls65U+jL/q+zxcrUoZrYf/kH47+t90hugXJ6z+a3m/Rj3Pwv4Bx7zotdnR7y+ijgR+Nd4ff0rcAXo/lUiLxsD/0H+x5pkW6Xx6g18GELYDbgMuLJU+o74fiyn2vdjV7h/lVH4+aj7V3Hfxwt4u+PfC/4dWv/+tT7HktpRQbFyr5L/dW54XAd4oQivok+aekwl/TWqbNoSsumTphV3AmeFEObE1W8B/S3tsJ47rplNBs4CvpT5NWt98rJBQghvAw9SvsnmFLwfC1SQx3bi3SHjZWY98Q/u60MI/51Z3wM4FLgps/u+wP/L5DHbZCabx2XAfwf3KPAxMKggfWI6sCiE8IvMuk/OP+ZjMzyOmNlw4Fa8X8gLmf1L5aWmSsUr43rS66Oi6yuE8HAIYd8QwgTgIbz/TmH6cq/9evxQSz7cVmTS7ARcjjdZf6u9vNRaiTyXym/ufNs57jeBfwKOiF8uoRNcXyUcDSSx+x3eTAta9/7VTNvi/byeMrOleH4fN7MtqTxey0jjfSuwU1yu1/ux2Sp5P7bS/atRSp6z+QBJE/D3S6JV71+NUjRewZsdh3h/uIoOcP8qcSxppNACHSU7wgOvmVmCf/AlHXx3ALaL2w1vijQtPp8N9IvLA4AX8cFQNo/LAwqOv7rgeTZ9L7ypwxlF8vU78p2DT4rLu+BV9GMK9m83LzWK12DSDsx98A7g/5TZvpT8wAa/Az7dTqytwnh3xHgZ3t/qF0W2TQH+XLDuImBK5nnhAB8HxfUn4H18wJuhvhL3KUz/I/yLQ7eC1zmZfGf9m+Ny//h/ObRIfovmpRHxyv7/gFOBWyq9vuK2LeLf3vEa2r9I+nL/q4vIDwbx07i8NbAY2Ltg/5J5aVC8SuX3k/PN7LsNbQeDmAIsAAa3c312qOur1Dnjv6JPjMuTgHmVXl804f6VSTeROg9mU+oayWxbSrznVxKvuO0C4NjMOcwtkr5m78dKzqPO11e778d24tXQ+1ej4tXOOZ8AXFPkfdKS969M2noOZlPqHjQ0cw38Ah+/oKLri+Z8/yp5LD0a92h6BjrSAx/lbmG8oM/Ca2T/CswHnsFrMDbFC0kPFKQ9Nt5oF+PV58n6n+K/mn4c//6fwvTAkcBHwJOZx/i4bTT+xWlxfBMmo1veD7ye2f/29vJS41jtBDwBPB1jc3Zcf1o8z3/gA0Fcjo+y9US5WMd1FcW7g8ZrH7yZz9OZPCSFvauBEwr2nwv0yTzfLcbkBeD/AhbX9wKui9seJ/3i8El6/Je9gH8JTl77+LhtoxinxTFuo+P6/wTeK4jxFuXy0oh44R/mz8T1d+B9ICq6vuL6i2Icnid+OBWmb+d/NRD/YFsUr6kBcf3lwN8z+z/WXl4aFK82+S0RrxvwEec+wt+/x8X1i/EfH5JjJl+aOvT1VeqcYxzn4V+eHgE+W+n1RfPuX38B3gA+iOdxYKPiVbB9Kd6aoZr3Y3+8NmE+8DCwc2F6av9+LHsedb6+Kn0/ttL9q1HxKnXOs8gX6lr9/tXmO1+j4gU8QHr/uQ7oW+n1RRPuX+WOpUfjHsmXSamh2M56eAjhgmak72jMbB/gyBDCCeuZvqvFazhwWQhhajPSdzQ1uL42KH1HU4N46fqqLn1Xu3/p/VgFxas6un9VR/cvaY8KiiIiIiIiIpKjwWxEREREREQkRwVFERERERERyVFBUURERERERHJUUBQREREREZEcFRRFREREREQkRwVFERGRKpnZLDNbY2bvmtk7ZrbEzK41s89WcYylcXh5ERGRlqOCooiIyPo5L4TQL4SwGbAf8BIwx8y+3OR8iYiIbDAVFEVERDZQCOGlEMJ/AjOAS82dbmb/E2sdXzazn5hZdwAzuwPYGrjczFab2b1xfQ8z+w8zW2hmb5vZX81st+R1zGyymT1hZqvM7E0zu78Z5ysiIp2fCooiIiK1cyMwDBgLLAOmApsCBwPHAscDhBC+CLwMHB9C6BtCOCCm/2HcdwowELgSuMfMNo/bZwCXAJvF1/lRA85JRES6IBUURUREamdZ/DswhPD7EMKLwT0BXAtMKpXQzAw4DfhuCGFJCGFdCOEK4DXgC3G3tcC2wJAQwpoQwqy6nYmIiHRpKiiKiIjUzvD49y0z+7qZzTWzt8zsHeBkYHCZtIOAvsAdsdnp22b2NjA6c9yDgTHAfDNbYGZn1OUsRESky+vR7AyIiIh0Il8FXgXeA64DDgXuDiGsNbNpwG6ZfT8uSPtmTDc5hDC32MFDCE8BX421j/sA95rZ0yGEB2p8HiIi0sWpRlFERGQDmdkIM/sh8E3gdLxmsBvwBvCRme0JHFWQ7G947SAAIYQA/BKYZmZj4nH7mtmBZraVmfUys6PNbFDc9+94YXNdnU9PRES6IPPPGhEREamUmc0C9sL7DAbgLWA28MsQwqNxn7OBU4FewIPAUmB8CGFi3H4QcCkwAJgTQphqZj3wforH481N3wPmxOOsAG4DJgAbxee/CiFMq/f5iohI16OCooiIiIiIiOSo6amIiIiIiIjkqKAoIiIiIiIiOSooioiIiIiISI4KiiIiIiIiIpKjgqKIiIiIiIjkqKAoIiIiIiIiOSooioiIiIiISI4KiiIiIiIiIpKjgqKIiIiIiIjk/H+25oI7VbQR8AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dự đoán giá cổ phiếu\n",
    "#Phương pháp: Dự đoán từng ngày dựa trên những giá trị đã có trong năm 2022\n",
    "X_test, y_test = X_y_split(test_data, look_back)\n",
    "X_date_test, y_date_test = X_y_split(test_dates, look_back)\n",
    "\n",
    "y_pred2 = stock_model.predict(X_test)\n",
    "y_pred2_price = inverse_normalize(original_price, y_pred2)\n",
    "#Vẽ biểu đồ kết quả test mô hình\n",
    "visualizePrediction(known_dates=date_data, known_price=original_price, \n",
    "                    predict_dates=y_date_test, predicted_price=y_pred2_price, \n",
    "                    symbol=symbol, title='PRICE PREDICTION METHOD 2')"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}