stock-screener-trading/stock_breakout_demo.ipynb

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  "metadata": {
    "colab": {
      "name": "stock-breakout-demo.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPb6yM7WZVZklXLxS2XINbG",
      "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/stock_breakout_demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSsUMWfRbVIo"
      },
      "source": [
        "!pip install yfinance\n",
        "!pip install mplfinance\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import math\n",
        "from mplfinance.original_flavor import candlestick_ohlc\n",
        "import matplotlib.dates as mpl_dates\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "id": "y3LBv6t6k3YT",
        "outputId": "e026ca61-0599-44ce-9ba7-3a4a99dc70d8"
      },
      "source": [
        "# method 1: fractal candlestick pattern\n",
        "\n",
        "def get_stock_price(symbol):\n",
        "  df = yf.download(symbol, start='2021-02-01', threads= False)\n",
        "  df['Date'] = pd.to_datetime(df.index)\n",
        "  df['Date'] = df['Date'].apply(mpl_dates.date2num)\n",
        "  df = df.loc[:,['Date', 'Open', 'High', 'Low', 'Close']]\n",
        "  return df\n",
        "\n",
        "def is_support(df,i):\n",
        "  cond1 = df['Low'][i] < df['Low'][i-1] \n",
        "  cond2 = df['Low'][i] < df['Low'][i+1] \n",
        "  cond3 = df['Low'][i+1] < df['Low'][i+2] \n",
        "  cond4 = df['Low'][i-1] < df['Low'][i-2]\n",
        "  return (cond1 and cond2 and cond3 and cond4)\n",
        "\n",
        "def is_resistance(df,i):\n",
        "  cond1 = df['High'][i] > df['High'][i-1] \n",
        "  cond2 = df['High'][i] > df['High'][i+1] \n",
        "  cond3 = df['High'][i+1] > df['High'][i+2] \n",
        "  cond4 = df['High'][i-1] > df['High'][i-2]\n",
        "  return (cond1 and cond2 and cond3 and cond4)\n",
        "\n",
        "def is_far_from_level(value, levels, df):\n",
        "    ave =  np.mean(df['High'] - df['Low'])\n",
        "    return np.sum([abs(value - level) < ave for _, level in levels]) == 0\n",
        "\n",
        "def plot_all(levels, df):\n",
        "    fig, ax = plt.subplots(figsize=(16, 9), dpi=300)\n",
        "    candlestick_ohlc(ax,df.values,width=0.6, colorup='green', colordown='red', alpha=0.8)\n",
        "    date_format = mpl_dates.DateFormatter('%d %b %Y')\n",
        "    ax.xaxis.set_major_formatter(date_format)\n",
        "    for level in levels:\n",
        "        plt.hlines(level[1], xmin=df['Date'][level[0]], xmax=max(df['Date']), colors='blue', linestyle='--')\n",
        "    fig.show()\n",
        "\n",
        "stock_code = 'COO'\n",
        "df = get_stock_price(stock_code)\n",
        "levels = []\n",
        "for i in range(2,len(df)-2):\n",
        "  if is_support(df,i):\n",
        "    l = df['Low'][i]\n",
        "    if is_far_from_level(l, levels, df):\n",
        "      levels.append((i,l))\n",
        "  elif is_resistance(df,i):\n",
        "    l = df['High'][i]\n",
        "    if is_far_from_level(l, levels, df):\n",
        "      levels.append((i,l))\n",
        "\n",
        "plot_all(levels, df)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 4800x2700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 768
        },
        "id": "_e72AMBMmFCk",
        "outputId": "c485ecdd-a695-4848-827b-70b0c0c38ff9"
      },
      "source": [
        "#method 2: window shifting method\n",
        "symbol = 'COO'\n",
        "df = get_stock_price(symbol)\n",
        "\n",
        "pivots = []\n",
        "max_list = []\n",
        "min_list = []\n",
        "for i in range(5, len(df)-5):\n",
        "    high_range = df['High'][i-5:i+4]\n",
        "    current_max = high_range.max()\n",
        "\n",
        "    if current_max not in max_list:\n",
        "        max_list = []\n",
        "    max_list.append(current_max)\n",
        "    if len(max_list) == 5 and is_far_from_level(current_max, pivots, df):\n",
        "        pivots.append((high_range.idxmax(), current_max))\n",
        "    \n",
        "    low_range = df['Low'][i-5:i+5]\n",
        "    current_min = low_range.min()\n",
        "    if current_min not in min_list:\n",
        "        min_list = []\n",
        "    min_list.append(current_min)\n",
        "    if len(min_list) == 5 and is_far_from_level(current_min, pivots, df):\n",
        "        pivots.append((low_range.idxmin(), current_min))\n",
        "\n",
        "plot_all(pivots, df)     "
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 4800x2700 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfwDlfy1bk0V"
      },
      "source": [
        "# screening\n",
        "def has_breakout(levels, previous, last):\n",
        "  for _, level in levels:\n",
        "    cond1 = (previous['Open'] < level) # to make sure previous candle is below the level\n",
        "    cond2 = (last['Open'] > level) and (last['Low'] > level)\n",
        "  return (cond1 and cond2)\n",
        "\n",
        "def detect_level_method_1(df):\n",
        "  levels = []\n",
        "  for i in range(2,df.shape[0]-2):\n",
        "    if is_support(df,i):\n",
        "      l = df['Low'][i]\n",
        "      if is_far_from_level(l, levels, df):\n",
        "        levels.append((i,l))\n",
        "    elif is_resistance(df,i):\n",
        "      l = df['High'][i]\n",
        "      if is_far_from_level(l, levels, df):\n",
        "        levels.append((i,l))\n",
        "  return levels\n",
        "\n",
        "def detect_level_method_2(df):\n",
        "  levels = []\n",
        "  max_list = []\n",
        "  min_list = []\n",
        "  for i in range(5, len(df)-5):\n",
        "      high_range = df['High'][i-5:i+4]\n",
        "      current_max = high_range.max()\n",
        "\n",
        "      if current_max not in max_list:\n",
        "          max_list = []\n",
        "      max_list.append(current_max)\n",
        "      if len(max_list) == 5 and is_far_from_level(current_max, levels, df):\n",
        "          levels.append((high_range.idxmax(), current_max))\n",
        "      \n",
        "      low_range = df['Low'][i-5:i+5]\n",
        "      current_min = low_range.min()\n",
        "      if current_min not in min_list:\n",
        "          min_list = []\n",
        "      min_list.append(current_min)\n",
        "      if len(min_list) == 5 and is_far_from_level(current_min, levels, df):\n",
        "          levels.append((low_range.idxmin(), current_min))\n",
        "  return levels\n",
        "\n",
        "# lists to store the screened results\n",
        "screened_list_1 = [] \n",
        "screened_list_2 = []\n",
        "\n",
        "# get the full stock list of S&P 500 \n",
        "payload=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')\n",
        "stock_list = payload[0]['Symbol'].values.tolist()\n",
        "\n",
        "for symbol in stock_list:\n",
        "  try: \n",
        "    df = get_stock_price(symbol)\n",
        "    \n",
        "    levels_1 = detect_level_method_1(df)\n",
        "    if (has_breakout(levels_1[-5:], df.iloc[-2], df.iloc[-1])):\n",
        "      screened_list_1.append(symbol)\n",
        "\n",
        "    levels_2 = detect_level_method_2(df)\n",
        "    if (has_breakout(levels_2[-5:], df.iloc[-2], df.iloc[-1])):\n",
        "      screened_list_2.append(symbol)\n",
        "\n",
        "  except Exception as e:\n",
        "    print(e)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-Wr1hlWAbF9",
        "outputId": "14870ee7-cf5b-49ef-f204-bb045e52c2d8"
      },
      "source": [
        "screened_list_1"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ADBE', 'COO', 'COST', 'FE']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1yMuWocAcTw",
        "outputId": "96030b77-bbaf-4e99-dc45-8a03d147b0d7"
      },
      "source": [
        "screened_list_2"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ACN', 'BDX', 'COO', 'DRI', 'IT', 'INTU', 'MDT', 'CRM']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    }
  ]
}