TASE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tel Aviv Stock Exchange\n",
    "* Scrape historical data"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:39:50.021987Z",
     "start_time": "2024-09-14T12:39:49.230546Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import os\n",
    "from pathlib import Path\n",
    "\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.support.wait import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "from selenium.webdriver.common.by import By\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:39:50.661839Z",
     "start_time": "2024-09-14T12:39:50.656835Z"
    }
   },
   "source": [
    "#params\n",
    "data_dir = Path.cwd() / 'data'\n",
    "data_dir.mkdir(exist_ok=True)\n",
    "file_history = data_dir / 'securityHistoryInDayData.csv'"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:39:51.587261Z",
     "start_time": "2024-09-14T12:39:51.581259Z"
    }
   },
   "source": [
    "def clean_history():\n",
    "    '''\n",
    "    removes tmp downloaded file\n",
    "    '''\n",
    "    \n",
    "    if os.path.exists(file_history):\n",
    "        os.remove(file_history)    "
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Static\n",
    "* Defined dict of symbols"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:39:53.474447Z",
     "start_time": "2024-09-14T12:39:53.453627Z"
    }
   },
   "source": [
    "#symbol to tick\n",
    "symbols = {\n",
    "    #TA-35\n",
    "    'HRL.TA-35' : 1, #Harel Sal (4A) TA 35\n",
    "    'KSM.TA-35' : 10, #KSM ETF (4A) TA-35\n",
    "    'MTF.TA-35' : 1, #MTF SAL (4A) TA-35\n",
    "    'PSG.TA-35' : 1, #Psagot ETF (4A) TA-35\n",
    "    'TC.TA35' : 1, #Tachlit SAL (40) TA 35\n",
    "    \n",
    "    #TA-125\n",
    "    'TC.TA125' : 1, #Tachlit SAL (40) TA 125\n",
    "    'KSM.TA-125' : 10, #KSM ETF (4A) TA-125\n",
    "    'PSG.TA-125' : 1, #Psagot ETF (4A) TA-125\n",
    "    'HRL.TA-125' : 1, #Harel Sal (4A) TA 125M\n",
    "    'MTF.TA-125' : 1, #MTF SAL (4A) TA-125\n",
    "    \n",
    "    #stocks\n",
    "    'TEVA' : 1,\n",
    "    'NICE' : 10,\n",
    "    'ICL' : 1,\n",
    "    'ESLT' : 10,\n",
    "    'PRGO' : 10\n",
    "}\n",
    "\n",
    "#symbol to url\n",
    "urls = {\n",
    "    #TA-35    \n",
    "    'HRL.TA-35' : 'https://www.tase.co.il/en/market_data/etf/1148907/historical_data/inday',\n",
    "    'KSM.TA-35' : 'https://www.tase.co.il/en/market_data/etf/1146570/historical_data/inday',\n",
    "    'MTF.TA-35' : 'https://www.tase.co.il/en/market_data/etf/1150184/historical_data/inday',\n",
    "    'PSG.TA-35' : 'https://www.tase.co.il/en/market_data/etf/1148790/historical_data/inday',\n",
    "    'TC.TA35' : 'https://www.tase.co.il/en/market_data/etf/1143700/historical_data/inday',\n",
    "    \n",
    "    #TA-125\n",
    "    'TC.TA125' : 'https://www.tase.co.il/en/market_data/etf/1143718/historical_data/inday',\n",
    "    'KSM.TA-125' : 'https://www.tase.co.il/en/market_data/etf/1146356/historical_data/inday',\n",
    "    'PSG.TA-125' : 'https://www.tase.co.il/en/market_data/etf/1148808/historical_data/inday',\n",
    "    'HRL.TA-125' : 'https://www.tase.co.il/en/market_data/etf/1148899/historical_data/inday',\n",
    "    'MTF.TA-125' : 'https://www.tase.co.il/en/market_data/etf/1150283/historical_data/inday',\n",
    "    \n",
    "    #stocks\n",
    "    'TEVA' : 'https://www.tase.co.il/en/market_data/security/00629014/historical_data/inday',\n",
    "    'NICE' : 'https://www.tase.co.il/en/market_data/security/00273011/historical_data/inday',\n",
    "    'ICL' : 'https://www.tase.co.il/en/market_data/security/00281014/historical_data/inday',\n",
    "    'ESLT' : 'https://www.tase.co.il/en/market_data/security/01081124/historical_data/inday',\n",
    "    'PRGO' : 'https://www.tase.co.il/en/market_data/security/01130699/historical_data/inday'\n",
    "}"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:39:58.362651Z",
     "start_time": "2024-09-14T12:39:54.879891Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#init\n",
    "options = webdriver.ChromeOptions()\n",
    "prefs = {\"download.default_directory\": str(data_dir)}\n",
    "options.add_experimental_option(\"prefs\", prefs)\n",
    "driver = webdriver.Chrome(options=options)\n",
    "driver.maximize_window()\n",
    "wait = WebDriverWait(driver, 10)\n",
    "\n",
    "url = 'https://www.tase.co.il/en'\n",
    "driver.get(url)\n",
    "print('Please log in...')"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please log in...\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:42:57.875120Z",
     "start_time": "2024-09-14T12:40:10.235859Z"
    }
   },
   "source": [
    "#run flow\n",
    "for symbol, tick in symbols.items():\n",
    "    if not (data_dir / f'{symbol}.csv').exists():\n",
    "        retry = 3\n",
    "        done = False\n",
    "        \n",
    "        while(not done and retry > 0):\n",
    "            try:\n",
    "                retry -= 1\n",
    "                url = urls[symbol]\n",
    "                print(symbol, tick, url)\n",
    "    \n",
    "                #clean\n",
    "                clean_history()\n",
    "    \n",
    "                #request\n",
    "                driver.get(url)\n",
    "                time.sleep(5)\n",
    "    \n",
    "                #Download data\n",
    "                wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'icon-download'))).click()\n",
    "    \n",
    "                #CSV\n",
    "                wait.until(EC.presence_of_element_located((By.LINK_TEXT, 'CSV'))).click()\n",
    "                time.sleep(5)\n",
    "    \n",
    "                #clone\n",
    "                file_history.rename(data_dir / f'{symbol}.csv')            \n",
    "                \n",
    "                #done\n",
    "                done = True\n",
    "            except Exception as e:\n",
    "                print(symbol, 'retry', retry, e)\n",
    "        \n",
    "#clean\n",
    "clean_history()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HRL.TA-35 1 https://www.tase.co.il/en/market_data/etf/1148907/historical_data/inday\n",
      "KSM.TA-35 10 https://www.tase.co.il/en/market_data/etf/1146570/historical_data/inday\n",
      "MTF.TA-35 1 https://www.tase.co.il/en/market_data/etf/1150184/historical_data/inday\n",
      "PSG.TA-35 1 https://www.tase.co.il/en/market_data/etf/1148790/historical_data/inday\n",
      "TC.TA35 1 https://www.tase.co.il/en/market_data/etf/1143700/historical_data/inday\n",
      "TC.TA125 1 https://www.tase.co.il/en/market_data/etf/1143718/historical_data/inday\n",
      "KSM.TA-125 10 https://www.tase.co.il/en/market_data/etf/1146356/historical_data/inday\n",
      "PSG.TA-125 1 https://www.tase.co.il/en/market_data/etf/1148808/historical_data/inday\n",
      "HRL.TA-125 1 https://www.tase.co.il/en/market_data/etf/1148899/historical_data/inday\n",
      "MTF.TA-125 1 https://www.tase.co.il/en/market_data/etf/1150283/historical_data/inday\n",
      "TEVA 1 https://www.tase.co.il/en/market_data/security/00629014/historical_data/inday\n",
      "NICE 10 https://www.tase.co.il/en/market_data/security/00273011/historical_data/inday\n",
      "ICL 1 https://www.tase.co.il/en/market_data/security/00281014/historical_data/inday\n",
      "ESLT 10 https://www.tase.co.il/en/market_data/security/01081124/historical_data/inday\n",
      "PRGO 10 https://www.tase.co.il/en/market_data/security/01130699/historical_data/inday\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dynamic\n",
    "* Top-k by turnover"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stocks"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:43:31.425427Z",
     "start_time": "2024-09-14T12:43:19.758404Z"
    }
   },
   "source": [
    "k = 5\n",
    "\n",
    "#load\n",
    "url = 'https://info.tase.co.il/eng/marketdata/stocks/marketdata/Pages/MarketData.aspx'\n",
    "driver.get(url)\n",
    "time.sleep(5)\n",
    "\n",
    "#sort\n",
    "wait.until(EC.presence_of_element_located((By.ID, 'OverallTurnOverValue DESC'))).click()\n",
    "time.sleep(5)\n",
    "\n",
    "#top-k\n",
    "elements = wait.until(EC.presence_of_all_elements_located((By.XPATH, '//td[@class=\"DataGridItemReg\"]//a[@class=\"linkInGrid\"]')))[:k]"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:43:31.534290Z",
     "start_time": "2024-09-14T12:43:31.432390Z"
    }
   },
   "source": [
    "#collect raw data\n",
    "data = []\n",
    "for e in elements:\n",
    "    symbol = e.text\n",
    "    href = e.get_attribute('href')\n",
    "    data_href = 'https://www.tase.co.il/en/market_data/security/' + href.split('/')[-1] + '/historical_data/inday'\n",
    "    print(symbol, data_href)\n",
    "    data.append([symbol, href, data_href])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEUMI https://www.tase.co.il/en/market_data/security/00604611/historical_data/inday\n",
      "POALIM https://www.tase.co.il/en/market_data/security/00662577/historical_data/inday\n",
      "MIZRAHI TEFAHOT https://www.tase.co.il/en/market_data/security/00695437/historical_data/inday\n",
      "DISCOUNT https://www.tase.co.il/en/market_data/security/00691212/historical_data/inday\n",
      "TEVA https://www.tase.co.il/en/market_data/security/00629014/historical_data/inday\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ETF"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:43:43.418201Z",
     "start_time": "2024-09-14T12:43:31.645937Z"
    }
   },
   "source": [
    "#load\n",
    "url = 'https://www.tase.co.il/en/market_data/etfs'\n",
    "driver.get(url)\n",
    "time.sleep(5)\n",
    "\n",
    "#sort\n",
    "wait.until(EC.presence_of_element_located((By.XPATH, '//button[@aria-label=\"Sort descending by Turnover (NIS thousands) \"]'))).click()\n",
    "time.sleep(5)\n",
    "\n",
    "#top-k\n",
    "elements = wait.until(EC.presence_of_all_elements_located((By.CLASS_NAME, 'item-name')))[:k]"
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:43:43.527294Z",
     "start_time": "2024-09-14T12:43:43.436091Z"
    }
   },
   "source": [
    "#collect raw data\n",
    "for e in elements:\n",
    "    symbol = e.text\n",
    "    href = e.get_attribute('href')\n",
    "    data_href = 'https://www.tase.co.il/en/market_data/security/' + href.split('/')[-1] + '/historical_data/inday'\n",
    "    print(symbol, data_href)\n",
    "    data.append([symbol, href, data_href])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MTF SAL (4D) S&P 500 https://www.tase.co.il/en/market_data/security/1150333/historical_data/inday\n",
      "Tachlit SAL (4D) NIKKEI 225 https://www.tase.co.il/en/market_data/security/1144419/historical_data/inday\n",
      "Tachlit SAL (4D) Index World Value U.S.D Hedged https://www.tase.co.il/en/market_data/security/1189596/historical_data/inday\n",
      "KSM ETF (6D) Leveraged NASDAQ 100 x3 Monthly https://www.tase.co.il/en/market_data/security/1146976/historical_data/inday\n",
      "KSM ETF (4A) TA-Insurance IL https://www.tase.co.il/en/market_data/security/1146125/historical_data/inday\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Flow"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:45:27.538103Z",
     "start_time": "2024-09-14T12:43:43.544296Z"
    }
   },
   "source": [
    "#run flow\n",
    "for d in data:\n",
    "    symbol = d[0]\n",
    "    if not (data_dir / f'{symbol}.csv').exists():\n",
    "        retry = 3\n",
    "        done = False\n",
    "        \n",
    "        while(not done and retry > 0):\n",
    "            try:    \n",
    "                retry -= 1\n",
    "                \n",
    "                href = d[1]\n",
    "                data_href = d[2]\n",
    "                print(symbol, data_href)\n",
    "    \n",
    "                #clean\n",
    "                clean_history()\n",
    "    \n",
    "                #go to data\n",
    "                driver.get(data_href)\n",
    "                time.sleep(5)\n",
    "    \n",
    "                #Download data\n",
    "                wait.until(EC.presence_of_element_located((By.CLASS_NAME, 'icon-download'))).click()\n",
    "                \n",
    "                #CSV\n",
    "                wait.until(EC.presence_of_element_located((By.LINK_TEXT, 'CSV'))).click()\n",
    "                time.sleep(5)\n",
    "    \n",
    "                #clone\n",
    "                # shutil.copyfile(file_history, data_dir + symbol + '.csv')\n",
    "                file_history.rename(data_dir / f'{symbol}.csv')\n",
    "                #done\n",
    "                done = True            \n",
    "            except Exception as e:\n",
    "                print(symbol, 'retry', retry, e)            \n",
    "            \n",
    "#clean\n",
    "clean_history()\n",
    "\n",
    "#terminate\n",
    "driver.quit()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEUMI https://www.tase.co.il/en/market_data/security/00604611/historical_data/inday\n",
      "POALIM https://www.tase.co.il/en/market_data/security/00662577/historical_data/inday\n",
      "MIZRAHI TEFAHOT https://www.tase.co.il/en/market_data/security/00695437/historical_data/inday\n",
      "DISCOUNT https://www.tase.co.il/en/market_data/security/00691212/historical_data/inday\n",
      "MTF SAL (4D) S&P 500 https://www.tase.co.il/en/market_data/security/1150333/historical_data/inday\n",
      "Tachlit SAL (4D) NIKKEI 225 https://www.tase.co.il/en/market_data/security/1144419/historical_data/inday\n",
      "Tachlit SAL (4D) Index World Value U.S.D Hedged https://www.tase.co.il/en/market_data/security/1189596/historical_data/inday\n",
      "KSM ETF (6D) Leveraged NASDAQ 100 x3 Monthly https://www.tase.co.il/en/market_data/security/1146976/historical_data/inday\n",
      "KSM ETF (4A) TA-Insurance IL https://www.tase.co.il/en/market_data/security/1146125/historical_data/inday\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Zoom In"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:45:27.569058Z",
     "start_time": "2024-09-14T12:45:27.556063Z"
    }
   },
   "source": [
    "def load_data(symbol):\n",
    "    df = pd.read_csv(data_dir / f'{symbol}.csv', skiprows=2).dropna()\n",
    "    df['Time'] = pd.to_datetime(df['Time'])\n",
    "    df = df.sort_values(by='Time').reset_index().drop(['index'], axis=1)\n",
    "    return df"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:45:27.632060Z",
     "start_time": "2024-09-14T12:45:27.587058Z"
    }
   },
   "source": [
    "df = load_data('TEVA')\n",
    "df.head()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marcb\\AppData\\Local\\Temp\\ipykernel_23252\\1905010339.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  df['Time'] = pd.to_datetime(df['Time'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                 Time  Last Rate  Change (%)   Volume  Turnover(NIS)  \\\n",
       "0 2024-09-14 09:59:00     6720.0        2.14  73851.0     4962787.20   \n",
       "1 2024-09-14 09:59:00     6733.0        2.34     15.0        1009.95   \n",
       "2 2024-09-14 09:59:00     6731.0        2.31    502.0       33789.62   \n",
       "3 2024-09-14 09:59:00     6734.0        2.36     69.0        4646.46   \n",
       "4 2024-09-14 10:00:00     6732.0        2.33    309.0       20801.88   \n",
       "\n",
       "   Cumulative Turnover (NIS)  \n",
       "0                  4962787.0  \n",
       "1                  5002233.0  \n",
       "2                  4996577.0  \n",
       "3                  5001223.0  \n",
       "4                  5023035.0  "
      ],
      "text/html": [
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       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Last Rate</th>\n",
       "      <th>Change (%)</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Turnover(NIS)</th>\n",
       "      <th>Cumulative Turnover (NIS)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-09-14 09:59:00</td>\n",
       "      <td>6720.0</td>\n",
       "      <td>2.14</td>\n",
       "      <td>73851.0</td>\n",
       "      <td>4962787.20</td>\n",
       "      <td>4962787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-09-14 09:59:00</td>\n",
       "      <td>6733.0</td>\n",
       "      <td>2.34</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1009.95</td>\n",
       "      <td>5002233.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-09-14 09:59:00</td>\n",
       "      <td>6731.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>502.0</td>\n",
       "      <td>33789.62</td>\n",
       "      <td>4996577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-09-14 09:59:00</td>\n",
       "      <td>6734.0</td>\n",
       "      <td>2.36</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4646.46</td>\n",
       "      <td>5001223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-09-14 10:00:00</td>\n",
       "      <td>6732.0</td>\n",
       "      <td>2.33</td>\n",
       "      <td>309.0</td>\n",
       "      <td>20801.88</td>\n",
       "      <td>5023035.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-14T12:45:27.897838Z",
     "start_time": "2024-09-14T12:45:27.694768Z"
    }
   },
   "source": [
    "df.groupby('Time').mean()['Last Rate'].plot(title='TEVA')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'TEVA'}, xlabel='Time'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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