178 lines
8.2 KiB
Plaintext
178 lines
8.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['Dassault Systemes SE']\n",
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"['Schneider Electric SE']\n",
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"['China Electronics Huada Technology Co. Ltd.']\n",
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"['Kingsoft Cloud Holdings Ltd. ADR']\n",
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"['Baidu Inc.']\n",
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"['Kingdee International Software Group Co. Ltd.']\n",
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"['Tencent Holdings Ltd.']\n",
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"['Xiaomi Corp.']\n",
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"['AsiaInfo Technologies Ltd.']\n",
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"['Fanuc Corp.']\n",
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"['Omron Corp.']\n",
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"['Mitsubishi Electric Corp.']\n",
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"['Hexagon AB Series B']\n",
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"['Hollysys Automation Technologies Ltd.']\n",
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"['JD.com Inc. ADR']\n",
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"['Autodesk Inc.']\n",
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"['Altair Engineering Inc. Cl A']\n",
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"['Ansys Inc.']\n",
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"['Honeywell International Inc.']\n",
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"['PTC Inc.']\n",
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"['Texas Instruments Inc.']\n",
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"['Cadence Design Systems Inc.']\n",
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"['Cisco Systems Inc.']\n",
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"['Microsoft Corp.']\n",
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"['Analog Devices Inc.']\n",
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"['Amazon.com Inc.']\n",
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"['Intel Corp.']\n",
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"['ABB Ltd. ADR']\n",
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"['International Business Machines Corp.']\n",
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"['Oracle Corp.']\n",
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"['Salesforce Inc.']\n",
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"['SAP SE ADR']\n",
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"['Emerson Electric Co.']\n",
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"['Dell Technologies Inc. Cl C']\n",
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"['Hewlett Packard Enterprise Co.']\n",
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"['Rockwell Automation Inc.']\n",
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"['General Electric Co.']\n",
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"['Synopsys Inc.']\n",
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"['STMicroelectronics N.V.']\n",
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"['Alibaba Group Holding Ltd. ADR']\n",
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"['Siemens AG']\n",
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"['Infineon Technologies AG']\n"
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]
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}
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],
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"source": [
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"from urllib.request import urlopen\n",
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"from bs4 import BeautifulSoup\n",
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"import time\n",
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"import json\n",
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"import pandas as pd\n",
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"\n",
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"select_year = 2021\n",
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"Firm = pd.read_csv(\"Firm.csv\", index_col=0)\n",
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"select_firm = Firm.loc[Firm[\"Source\"]==\"marketwatch\",:\"Type_Region\"]\n",
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"select_firm[\"Exchange\"] = None\n",
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"select_firm[\"Crawled_Name\"] = None\n",
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"select_firm[\"Stock_Currency\"] = None\n",
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"\n",
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"for code, row in select_firm.iterrows():\n",
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" try:\n",
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" url = f\"https://www.marketwatch.com/investing/stock/{row['Stock_Code']}/financials/balance-sheet\"\n",
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" if row['Stock_Region'] != \"US\":\n",
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" url += f\"?countrycode={row['Stock_Region']}\"\n",
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" html = urlopen(url)\n",
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" time.sleep(0.1)\n",
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" bsObj = BeautifulSoup(html.read(), 'html.parser')\n",
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" except Exception as e:\n",
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" print(e)\n",
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" print(\"Can't open url.\")\n",
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" else:\n",
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" basic_info = bsObj.find(name=\"script\", attrs={'type':\"application/ld+json\"}).contents[0]\n",
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" basic_info = json.loads(basic_info.replace('\\n','').replace('\\t',''))\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Exchange\"] = basic_info[\"exchange\"]\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Crawled_Name\"] = basic_info[\"name\"]\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Stock_Currency\"] = basic_info[\"priceCurrency\"]\n",
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"\n",
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" bs_table = bsObj.find(name=\"table\", attrs={\"aria-label\":\"Financials - Assets data table\"})\n",
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" if bs_table is not None:\n",
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" bs = bs_table.find(name=\"tr\", attrs={\"class\":\"table__row\"}).find_all(lambda tag: tag.name == 'th' and \n",
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" tag.get('class') == ['overflow__heading'])\n",
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" years_list = [i.div.contents[0] for i in bs][0:-1]\n",
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" years_list = list(map(int, years_list))\n",
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" bs = bs_table.find_all(name=\"tr\", attrs={\"class\":\"table__row is-highlighted\"})\n",
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" for i in bs:\n",
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" if i.td.div.contents[0] == 'Total Assets':\n",
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" string = i.find(\"div\", attrs={\"class\":\"chart--financials js-financial-chart\"}).attrs['data-chart-data']\n",
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" total_assets_list = string.split(',')\n",
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" total_assets_list = list(map(lambda x: float(x) if x!='' else 0, total_assets_list))\n",
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" total_assets = pd.DataFrame({\"year\":years_list,'total_asset':total_assets_list})\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Assets\"] = total_assets.loc[total_assets['year']==select_year,'total_asset'].to_list()[0]\n",
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" try:\n",
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" url = f\"https://www.marketwatch.com/investing/stock/{row['Stock_Code']}/financials\"\n",
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" if row['Stock_Region'] != \"US\":\n",
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" url += f\"?countrycode={row['Stock_Region']}\"\n",
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" html = urlopen(url)\n",
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" time.sleep(0.1)\n",
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" bsObj = BeautifulSoup(html.read(), 'html.parser')\n",
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" except Exception as e:\n",
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" print(e)\n",
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" print(\"Can't open url.\")\n",
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" else:\n",
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" basic_info = bsObj.find(name=\"script\", attrs={'type':\"application/ld+json\"}).contents[0]\n",
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" basic_info = json.loads(basic_info.replace('\\n','').replace('\\t',''))\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Exchange\"] = basic_info[\"exchange\"]\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Crawled_Name\"] = basic_info[\"name\"]\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Stock_Currency\"] = basic_info[\"priceCurrency\"]\n",
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"\n",
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" bs_table = bsObj.find(name=\"table\", attrs={\"aria-label\":\"Financials - data table\"})\n",
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" if bs_table is not None:\n",
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" bs = bs_table.find(name=\"tr\", attrs={\"class\":\"table__row\"}).find_all(lambda tag: tag.name == 'th' and \n",
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" tag.get('class') == ['overflow__heading'])\n",
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" years_list = [i.div.contents[0] for i in bs][0:-1]\n",
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" years_list = list(map(int, years_list))\n",
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" bs = bs_table.find_all(name=\"tr\", attrs={\"class\":\"table__row is-highlighted\"})\n",
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" for i in bs:\n",
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" if i.td.div.contents[0] == 'Sales/Revenue':\n",
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" string = i.find(\"div\", attrs={\"class\":\"chart--financials js-financial-chart\"}).attrs['data-chart-data']\n",
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" total_revenue_list = string.split(',')\n",
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" total_revenue_list = list(map(lambda x: float(x) if x!='' else 0, total_revenue_list))\n",
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" total_revenue = pd.DataFrame({\"year\":years_list,'total_revenue':total_revenue_list})\n",
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" select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Revenue\"] = total_revenue.loc[total_revenue['year']==select_year,'total_revenue'].to_list()[0]\n",
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" print(select_firm.loc[select_firm[\"Stock_Code\"]==row['Stock_Code'], \"Crawled_Name\"].to_list())\n",
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"\n",
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"select_firm['Exchange_Rate'] = select_firm['Stock_Currency'].map({'EUR': 7.2505, 'HKD': 0.88, 'JPY': 0.051, 'SEK': 0.65, 'USD': 6.88})\n",
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"select_firm['Assets_zh'] = select_firm['Assets'] * select_firm['Exchange_Rate'] * 0.00000001\n",
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"select_firm['Revenue_zh'] = select_firm['Revenue'] * select_firm['Exchange_Rate'] * 0.00000001\n",
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"select_firm.to_csv('MarketWatch.csv', index=False, encoding='utf-8-sig')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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