stock-screener-trading/automate_stock_screener_demo.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "automate-stock-screener-demo.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPclecTAnBBunNXKyPKq9Lh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yongghongg/stock-screener/blob/main/automate_stock_screener_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YGeoERrc3Lb"
      },
      "source": [
        "A demo Colab Notebook for my article: \n",
        "https://levelup.gitconnected.com/automate-your-stock-screening-using-python-9107dda724c3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EmMPYxT_iXX"
      },
      "source": [
        "# install required libraries (on colab)\n",
        "!pip install yfinance\n",
        "!pip install bs4\n",
        "!pip install requests\n",
        "# import required libraries \n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import yfinance as yf\n",
        "import email\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qG87rhOC-xih"
      },
      "source": [
        "def get_stock_list():\n",
        "  # this is the website we're going to scrape from\n",
        "  url = \"https://www.malaysiastock.biz/Stock-Screener.aspx\"\n",
        "  response = requests.get(url, headers={'User-Agent':'test'})\n",
        "  soup = BeautifulSoup(response.content, \"html.parser\")\n",
        "  table = soup.find(id = \"MainContent2_tbAllStock\")\n",
        "  # return the result (only ticker code) in a list\n",
        "  stock_list = table.find_all('a')\n",
        "  return [stock_code.get('href')[-4:] for stock_code in stock_list]\n",
        "\n",
        "def get_stock_price(code):\n",
        "  # you can change the start date\n",
        "  data = yf.download(code, start=\"2021-01-01\", threads= False)\n",
        "  return data\n",
        "\n",
        "def add_EMA(price, day):\n",
        "  return price.ewm(span=day).mean()\n",
        "\n",
        "def add_STOCH(close, low, high, period, k, d=0): \n",
        "    STOCH_K = ((close - low.rolling(window=period).min()) / (high.rolling(window=period).max() - low.rolling(window=period).min())) * 100\n",
        "    STOCH_K = STOCH_K.rolling(window=k).mean()\n",
        "    if d == 0:\n",
        "      return STOCH_K\n",
        "    else:\n",
        "      STOCH_D = STOCH_K.rolling(window=d).mean()\n",
        "      return STOCH_D\n",
        "\n",
        "def check_bounce_EMA(df):\n",
        "  candle1 = df.iloc[-1]\n",
        "  candle2 = df.iloc[-2]\n",
        "  cond1 = candle1['EMA18'] > candle1['EMA50'] > candle1['EMA100']\n",
        "  cond2 = candle1['STOCH_%K(5,3,3)'] <= 30 or candle1['STOCH_%D(5,3,3)'] <= 30\n",
        "  cond3 = candle2['Low'] < candle2['EMA50'] and \\\n",
        "          candle2['Close'] > candle2['EMA50'] and \\\n",
        "          candle1['Low'] > candle1 ['EMA50']\n",
        "  return cond1 and cond2 and cond3\n",
        "\n",
        "# a list to store the screened results\n",
        "screened_list = [] \n",
        "# get the full stock list\n",
        "stock_list = get_stock_list()\n",
        "for stock_code in stock_list:\n",
        "\n",
        "  print(stock_code) # remove this if you dont want the ticker to be printed\n",
        "  try: \n",
        "    # Step 1: get stock price for each stock\n",
        "    price_chart_df = get_stock_price(stock_code + \".KL\") # edit/remove \".KL\" for other exchange market\n",
        "\n",
        "    # Step 2: add technical indicators (in this case EMA)\n",
        "    close = price_chart_df['Close']\n",
        "    low = price_chart_df['Low']\n",
        "    open = price_chart_df['Open']\n",
        "    high = price_chart_df['High']\n",
        "    price_chart_df['EMA18'] = add_EMA(close,18)\n",
        "    price_chart_df['EMA50'] = add_EMA(close,50)\n",
        "    price_chart_df['EMA100'] = add_EMA(close,100)\n",
        "    price_chart_df['STOCH_%K(5,3,3)'] = add_STOCH(close, low, high, 5, 3)\n",
        "    price_chart_df['STOCH_%D(5,3,3)'] = add_STOCH(close, low, high, 5, 3, 3)\n",
        "\n",
        "    # if all 3 conditions are met, add stock into screened list\n",
        "    if check_bounce_EMA(price_chart_df):\n",
        "      screened_list.append(stock_code)\n",
        "      print(screened_list)\n",
        "  except Exception as e:\n",
        "    print(e)\n",
        "\n",
        "## this cell may take a few minutes to finish"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TD3iwcVT_o19"
      },
      "source": [
        "# configure email and message\n",
        "msg = email.message_from_string(\", \".join(screened_list))\n",
        "msg['From'] = 'YOUR_EMAIL@gmail.com' # change to your email \n",
        "msg['To'] = 'YOUR_EMAIL@gmail.com'\n",
        "msg['Subject'] = \"EMA Bounce Result for Today!\"\n",
        "\n",
        "s = smtplib.SMTP(\"smtp.gmail.com\",587)\n",
        "## for yahoo mail user: s = smtplib.SMTP(\"smtp.mail.yahoo.com\",587) \n",
        "## for hotmail user: s = smtplib.SMTP(\"smtp.live.com\",587)\n",
        "s.ehlo() \n",
        "s.starttls()\n",
        "s.ehlo()\n",
        "s.login(email_from,\"YOUR_PASSWORD\") # change to your password\n",
        "s.sendmail(email_from, [email_to] + [email_cc], msg.as_string())\n",
        "s.quit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcIveOlyI3h_"
      },
      "source": [
        "# for US stock list\n",
        "# credit to https://github.com/shilewenuw/get_all_tickers/issues/12\n",
        "def get_US_stock_list(exchange):\n",
        "  headers = {\n",
        "      'authority': 'api.nasdaq.com',\n",
        "      'accept': 'application/json, text/plain, */*',\n",
        "      'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Safari/537.36',\n",
        "      'origin': 'https://www.nasdaq.com',\n",
        "      'sec-fetch-site': 'same-site',\n",
        "      'sec-fetch-mode': 'cors',\n",
        "      'sec-fetch-dest': 'empty',\n",
        "      'referer': 'https://www.nasdaq.com/',\n",
        "      'accept-language': 'en-US,en;q=0.9',\n",
        "  }\n",
        "\n",
        "  params = (\n",
        "      ('tableonly', 'true'),\n",
        "      ('limit', '25'),\n",
        "      ('offset', '0'),\n",
        "      ('download', 'true'),\n",
        "      ('exchange', exchange)\n",
        "  )\n",
        "\n",
        "  r = requests.get('https://api.nasdaq.com/api/screener/stocks', headers=headers, params=params)\n",
        "  data = r.json()['data']\n",
        "  df = pd.DataFrame(data['rows'], columns=data['headers'])\n",
        "  df_filtered = df[~df['symbol'].str.contains(\"\\.|\\^|\\s\")]\n",
        "  return df_filtered['symbol'].tolist()\n",
        "\n",
        "stock_list = get_US_stock_list('nyse') # or 'nasdaq' or 'amex'"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}