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2022-03-09 00:26:31 +03:00 · 2021-06-24 23:47:09 +09:00
parent 249492e8bb
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--- a/simple_technical_analysis_stock_screener_demo.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "simple-technical-analysis-stock-screener-demo.ipynb",
+      "provenance": [],
+      "authorship_tag": "ABX9TyOv5Rg0eovcgPOOu//hyU/J",
+      "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/simple_technical_analysis_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": "vnhzHjCEeKmK"
+      },
+      "source": [
+        "A demo Colab Notebook for my article: \n",
+        "https://levelup.gitconnected.com/automate-your-stock-screening-using-python-9107dda724c3\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t_3iZkcseOYp"
+      },
+      "source": [
+        "# install required libraries (on colab)\n",
+        "!pip install bs4\n",
+        "!pip install requests\n",
+        "# import required libraries \n",
+        "from bs4 import BeautifulSoup\n",
+        "import ast\n",
+        "import pandas as pd\n",
+        "import re\n",
+        "import requests\n",
+        "from datetime import datetime"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FUUL-MhDd_xd"
+      },
+      "source": [
+        "def get_stock_price(ticker):\n",
+        "  # pass a ticker name to i3investor website url\n",
+        "  url = \"https://klse.i3investor.com/servlets/stk/chart/{}.jsp\". format(ticker)\n",
+        "  # get response from the site and extract the price data\n",
+        "  response = requests.get(url, headers={'User-Agent':'test'})\n",
+        "  soup = BeautifulSoup(response.content, \"html.parser\")\n",
+        "  script = soup.find_all('script')\n",
+        "  data_tag = script[19].contents[0] #changed to 19 from 20\n",
+        "  chart_data = ast.literal_eval(re.findall('\\[(.*)\\]', data_tag.split(';')[0])[0])\n",
+        "  # tabulate the price data into a dataframe\n",
+        "  chart_df = pd.DataFrame(chart_data, columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume'])\n",
+        "  # convert timestamp into readable date\n",
+        "  chart_df['Date'] = chart_df['Date'].apply(lambda x: \\\n",
+        "      datetime.utcfromtimestamp(int(x)/1000).strftime('%Y-%m-%d'))\n",
+        "  return chart_df\n",
+        "\n",
+        "def add_EMA(price, day):\n",
+        "  return price.ewm(span=day).mean()\n",
+        "\n",
+        "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 in a list\n",
+        "  return [stock.getText() for stock in table.find_all('a')]\n",
+        "\n",
+        "# function to check for EMA crossing\n",
+        "def check_EMA_crossing(df):\n",
+        "  # condition 1: EMA18 is higher than EMA50 at the last trading day\n",
+        "  cond_1 = df.iloc[-1]['EMA18'] > df.iloc[-1]['EMA50']\n",
+        "  # condition 2: EMA18 is lower than EMA50 the previous day\n",
+        "  cond_2 = df.iloc[-2]['EMA18'] < df.iloc[-2]['EMA50']\n",
+        "  # condition 3: to filter out stocks with less than 50 candles\n",
+        "  cond_3 = len(df.index) > 50 \n",
+        "  # will return True if all 3 conditions are met\n",
+        "  return (cond_1 and cond_2 and cond_3)\n",
+        "\n",
+        "# main program\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 each_stock in stock_list:\n",
+        "  print(each_stock)\n",
+        "  # Step 1: get stock price for each stock\n",
+        "  price_chart_df = get_stock_price(each_stock)\n",
+        "  # Step 2: add technical indicators (in this case EMA)\n",
+        "  price_chart_df['EMA18']=add_EMA(price_chart_df['Close'],18)\n",
+        "  price_chart_df['EMA50']=add_EMA(price_chart_df['Close'],50)\n",
+        "  price_chart_df['EMA100']=add_EMA(price_chart_df['Close'],100)\n",
+        "  # if all 3 conditions are met, add stock into screened list\n",
+        "  if check_EMA_crossing(price_chart_df):\n",
+        "      screened_list.append(each_stock)\n",
+        "print(screened_list)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}