chart.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Welcome To Colaboratory",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satishchaudhary382/awesome-machine-learning/blob/master/chart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avu12vKv3YTo"
      },
      "source": [
        "!pip install selenium\n",
        "!apt-get update \n",
        "!apt install chromium-chromedriver\n",
        "\n",
        "!pip install MarketProfile\n",
        "!pip install fake-useragent\n",
        "!pip install mpl_finance\n",
        "!pip install finta\n",
        "# !pip install -U git+https://github.com/twopirllc/pandas-ta\n",
        "# !pip install numpy --upgrade\n",
        "!pip install mplfinance\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iesbpLO2Z7U"
      },
      "source": [
        "#@title Importing Libraries\n",
        "import time\n",
        "import datetime\n",
        "import pandas as pd\n",
        "import numpy as np \n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from fake_useragent import UserAgent\n",
        "import requests\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "from finta import TA\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from selenium import webdriver\n",
        "from market_profile import MarketProfile\n",
        "import random\n",
        "import pandas_ta as ta\n",
        "useragent = UserAgent()\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.pyplot as plt\n",
        "from bokeh.plotting import figure, output_file, show\n",
        "import bokeh\n",
        "from sklearn.preprocessing import StandardScaler,MinMaxScaler\n",
        "import mplfinance as mpl\n",
        "import altair as alt\n",
        "from math import pi\n",
        "from bokeh.io import output_notebook\n",
        "from bokeh.models import Label\n",
        "from datetime import datetime as dt\n",
        "# Call once to configure Bokeh to display plots inline in the notebook.\n",
        "output_notebook()\n",
        "%matplotlib inline\n",
        "from math import pi\n",
        "from bokeh.models import Span\n",
        "import matplotlib.dates as mpl_dates\n",
        "import pandas_datareader as pdr\n",
        "\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "from selenium.webdriver.support.ui import WebDriverWait\n",
        "from selenium.webdriver.support import expected_conditions as EC\n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.common.exceptions import TimeoutException\n",
        "from selenium.webdriver.support.ui import Select\n",
        "\n",
        "import urllib.request"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "MhakgPzM60Tv",
        "outputId": "3847a042-f13d-4a14-ea2c-caf3f6fcc90f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title Finding IP address\n",
        "from selenium import webdriver\n",
        "from selenium.webdriver.support.ui import WebDriverWait\n",
        "from selenium.webdriver.common.by import By\n",
        "from selenium.webdriver.support import expected_conditions as EC\n",
        "from selenium.common.exceptions import TimeoutException\n",
        "\n",
        "chrome_options = webdriver.ChromeOptions()\n",
        "chrome_options.add_argument('--headless')\n",
        "chrome_options.add_argument('--no-sandbox')\n",
        "chrome_options.add_argument('--disable-dev-shm-usage')\n",
        "chrome_options.add_argument(\"start-maximized\")\n",
        "chrome_options.add_experimental_option(\"excludeSwitches\", [\"enable-automation\"])\n",
        "chrome_options.add_experimental_option('useAutomationExtension', False)\n",
        "wd = webdriver.Chrome('chromedriver',chrome_options=chrome_options)\n",
        "driver =webdriver.Chrome('chromedriver',chrome_options=chrome_options)\n",
        "\n",
        "\n",
        "driver.get(\"https://sslproxies.org/\")\n",
        "driver.execute_script(\"return arguments[0].scrollIntoView(true);\", WebDriverWait(driver, 20).until(EC.visibility_of_element_located((By.XPATH, \"//table[@class='table table-striped table-bordered dataTable']//th[contains(., 'IP Address')]\"))))\n",
        "ips = [my_elem.get_attribute(\"innerHTML\") for my_elem in WebDriverWait(driver, 5).until(EC.visibility_of_all_elements_located((By.XPATH, \"//table[@class='table table-striped table-bordered dataTable']//tbody//tr[@role='row']/td[position() = 1]\")))]\n",
        "ports = [my_elem.get_attribute(\"innerHTML\") for my_elem in WebDriverWait(driver, 5).until(EC.visibility_of_all_elements_located((By.XPATH, \"//table[@class='table table-striped table-bordered dataTable']//tbody//tr[@role='row']/td[position() = 2]\")))]\n",
        "driver.quit()\n",
        "proxies = []\n",
        "for i in range(0, len(ips)):\n",
        "    proxies.append(ips[i]+':'+ports[i])\n",
        "print(proxies)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['90.182.166.59:80', '144.217.101.245:3129', '123.253.36.99:8080', '103.81.114.140:3128', '198.50.163.192:3129', '119.81.189.194:80', '2.187.208.44:8080', '8.210.30.49:8080', '128.199.214.87:3128', '213.230.97.169:3128', '169.57.1.84:8123', '14.192.31.4:50838', '51.222.21.81:32768', '103.115.164.99:8080', '51.222.197.197:32769', '82.102.8.60:3128', '51.222.197.139:32769', '80.73.9.238:808', '51.222.215.86:32769', '79.143.87.140:9090']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "no2Yhli-KxNp",
        "outputId": "72ca195a-037c-44d9-c20d-77e7a36a41c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#@title Checking available proxy\n",
        "p = []\n",
        "for proxy in proxies:\n",
        "  try:\n",
        "      requests.request('get',\n",
        "          \"http://httpbin.org/ip\",\n",
        "          proxies={'http':f'{proxy}'},\n",
        "          timeout = 5\n",
        "      )\n",
        "      break\n",
        "  except IOError:\n",
        "      print( \"Connection error! (Check proxy)\")\n",
        "      p.append(proxy)\n",
        "      print(\"Proxy Ready and available: \", proxy)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Connection error! (Check proxy)\n",
            "Proxy Ready and available:  90.182.166.59:80\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "vWd6umDi3RyO"
      },
      "source": [
        "#@title Stockinfo Nepal \n",
        "class nepse():\n",
        "  def __init__(self,ticker):\n",
        "    self.ticker = ticker\n",
        "\n",
        "  def getdata(self):\n",
        "    try: \n",
        "      useragent = UserAgent()\n",
        "      headers = {'User-Agent': useragent.random}\n",
        "      req= urllib.request.Request(f'https://nepalstockinfo.com/companyhistory/{self.ticker}', headers={'User-Agent': f'{useragent.random}'})\n",
        "      authinfo = urllib.request.HTTPBasicAuthHandler()\n",
        "\n",
        "      proxy_support = urllib.request.ProxyHandler({\"http\" : p[0]})\n",
        "\n",
        "      # build a new opener that adds authentication and caching FTP handlers\n",
        "      opener = urllib.request.build_opener(proxy_support, authinfo,\n",
        "                                          urllib.request.CacheFTPHandler)\n",
        "\n",
        "      # install it\n",
        "      urllib.request.install_opener(opener)\n",
        "      source = urllib.request.urlopen(req).read()\n",
        "      # source = urllib.request.urlopen(f'https://nepalstockinfo.com/companyhistory/{self.ticker}').read()\n",
        "      soup = BeautifulSoup(source,'lxml')\n",
        "      table = soup.find('table', attrs={'class':'table table-bordered stripe row-border order-column example_datatable_fixedcolumn'})\n",
        "      table_rows = table.find_all('tr')\n",
        "      data = []\n",
        "      for tr in table_rows:\n",
        "        td = tr.find_all('td')\n",
        "        row = [tr.text for tr in td]\n",
        "        data.append(row)\n",
        "      Date = pd.to_datetime([data[i][1] for i in range(1,len(data))])\n",
        "      Close = pd.to_numeric([data[i][2] for i in range(1,len(data))])\n",
        "      Max = pd.to_numeric([data[i][4] for i in range(1,len(data))])\n",
        "      Min = pd.to_numeric([data[i][5] for i in range(1,len(data))])\n",
        "      Volume = pd.to_numeric([data[i][8] for i in range(1,len(data))])\n",
        "      df = pd.DataFrame({\"Date\":Date,\n",
        "                \"High\":Max,\n",
        "                \"Low\":Min,\n",
        "                \"Close\":Close,\n",
        "                \"Volume\": Volume})\n",
        "      return df.sort_values(by = 'Date',ascending = True)\n",
        "    except:\n",
        "      print(f\"{self.ticker} may not be present in the listing of NEPSE/ Check your connection/ Check your code\")\n",
        "\n",
        "  \n",
        "\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvw1B10c4q9h"
      },
      "source": [
        "\n",
        "t = nepse(\"NABIL\")"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhadPjzw9aQo"
      },
      "source": [
        "nabil = t.getdata()"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ui1TvJv3sqn"
      },
      "source": [
        "def extract(index):\n",
        "  value = []\n",
        "  for i in range(1,len(data)):\n",
        "    try:\n",
        "      value.append(data[i][index])\n",
        "    except:\n",
        "      pass\n",
        "  return value"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEOP5dFtsVnz"
      },
      "source": [
        "stock = 'ICFC'\n",
        "data = []\n",
        "chrome_options = webdriver.ChromeOptions()\n",
        "chrome_options.add_argument('--headless')\n",
        "chrome_options.add_argument('--no-sandbox')\n",
        "chrome_options.add_argument('--disable-dev-shm-usage')\n",
        "chrome_options.add_argument(f\"user-agent = {useragent.random}\")\n",
        "wd = webdriver.Chrome('chromedriver',chrome_options=chrome_options)\n",
        "driver =webdriver.Chrome('chromedriver',chrome_options=chrome_options)\n",
        "\n",
        "delay = 10\n",
        "\n",
        "url = f\"https://merolagani.com/CompanyDetail.aspx?symbol={stock}\"\n",
        "wait = WebDriverWait(driver, 10)\n",
        "driver.get(url)\n",
        "\n",
        "price = wait.until(EC.element_to_be_clickable((By.XPATH,\"//a[@title='Price History']\")))\n",
        "price.click()\n",
        "time.sleep(5)\n",
        "\n",
        "\n",
        "# table = soup.find('table', attrs={'class':'table table-bordered table-striped table-hover'})\n",
        "# table_rows = table.find_all('tr')\n",
        "# for tr in table_rows:\n",
        "#   td = tr.find_all('td')\n",
        "#   row = [tr.text for tr in td]\n",
        "#   print(row)\n",
        "a=1\n",
        "# while a<3:\n",
        "tables = driver.find_element_by_xpath(\"//table[@class='table table-bordered table-striped table-hover']\")\n",
        "driver.execute_script(\"window.open(arguments[0], '_blank');\", tables)\n",
        "soup = BeautifulSoup(driver.page_source,'lxml')\n",
        "table = soup.find('table', attrs={'class':'table table-bordered table-striped table-hover'})\n",
        "table_rows = table.find_all('tr')\n",
        "for tr in table_rows:\n",
        "  td = tr.find_all('td')\n",
        "  row = [tr.text for tr in td]\n",
        "  data.append(row)\n",
        "# next = driver.find_element_by_xpath('//*[@title=\"Next Page\"]')\n",
        "# driver.execute_script(\"window.open(arguments[0], '_blank');\", next).click()\n",
        "# driver.find_element_by_xpath('//*[@title=\"Next Page\"]').click()\n",
        "time.sleep(5)\n",
        "  # a+=1\n",
        "driver.quit()\n",
        "\n",
        "Date = pd.to_datetime([i.replace(\",\",\"\") for i in extract(1)])\n",
        "Close = pd.to_numeric([i.replace(\",\",\"\") for i in extract(2)])\n",
        "Open = pd.to_numeric([i.replace(\",\",\"\") for i in extract(6)])\n",
        "Low = pd.to_numeric([i.replace(\",\",\"\") for i in extract(5)])\n",
        "High = pd.to_numeric([i.replace(\",\",\"\") for i in extract(4)])\n",
        "Volume = pd.to_numeric([i.replace(\",\",\"\") for i in extract(7)])\n",
        "df = pd.DataFrame({\"Date\":Date,\n",
        "                  \"Open\":Open,\n",
        "                  \"High\":High,\n",
        "                  \"Low\":Low,\n",
        "                  \"Close\":Close,\n",
        "                  \"Volume\":Volume})\n",
        "df = df.sort_values(by = \"Date\",ascending = True)\n",
        "df = df.set_index(\"Date\")\n",
        "df.to_csv(f\"{stock}.csv\")\n"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lugvqVkx5KEY"
      },
      "source": [
        "stock = \"DIS\""
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "_UFvIscc2-nZ"
      },
      "source": [
        "#@title TRIMA band\n",
        "def trima(df):\n",
        "  df['trima'] = ta.trima(df['Close'],21)\n",
        "  df['range'] = ta.atr(df.High, df.Low, df.Close, 14) * 4\n",
        "  df['H'] = ta.wma((df['trima'] + df['range']),21)\n",
        "  df['L'] = ta.wma((df['trima'] - df['range']),21)\n",
        "  df['SD_UP'] = df['H'].rolling(20).std()\n",
        "  # df['SD_DOWN'] = ((df['trima'] - df['range'])).rolling(20).std()\n",
        "  df['HH'] = ta.wma((df['trima'] + df['range']),21)+(ta.atr(df.High, df.Low, df.Close,21))*1\n",
        "  df['LL'] = ta.wma((df['trima'] - df['range']),21)-(ta.atr(df.High, df.Low, df.Close,21))*1\n",
        "  return df[['HH','H','LL','L']]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "vD4JRwgP4yg_",
        "outputId": "7f4273cf-3f36-414d-f4e4-8e46beb2f330",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "#@title Market Profile\n",
        "from market_profile import MarketProfile\n",
        "\n",
        "df = pdr.get_data_yahoo(f'{stock}',\"12-1-2020\")\n",
        "# df = pd.read_csv(f\"/content/{stock}.csv\")\n",
        "\n",
        "\n",
        "class Analysis:\n",
        "  \n",
        "  def __init__(self, dataframe):\n",
        "    self.dataframe = dataframe\n",
        "    self.profile = []\n",
        "    self.levels = []\n",
        "    self.level = []\n",
        "\n",
        "  # Diagram for candle stick \n",
        "  def candlestick(self):\n",
        "    return self.dataframe\n",
        "\n",
        "  # Calculation of Market Profile\n",
        "  def market_profile(self):\n",
        "    for i in range(1,len(self.dataframe),20):\n",
        "      try:\n",
        "        mp = MarketProfile(self.dataframe)\n",
        "        mp_slice = mp[self.dataframe.index[0+i]: self.dataframe.index[20+i]]\n",
        "        self.profile.append(mp_slice.poc_price)\n",
        "      except:\n",
        "        pass\n",
        "    return self.profile\n",
        "\n",
        "\n",
        "a = Analysis(df)\n",
        "\n",
        "\n",
        "b = a.market_profile()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-292fd5d85ceb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmarket_profile\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMarketProfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpdr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data_yahoo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'{stock}'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"12-1-2020\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;31m# df = pd.read_csv(f\"/content/{stock}.csv\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'stock' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "vMSj4mq5qgpo"
      },
      "source": [
        "#@title Smoothed HA\n",
        "def SHA(df, length):\n",
        "  import warnings\n",
        "  warnings.filterwarnings(\"ignore\")\n",
        "  \n",
        "  import matplotlib.pyplot as plt\n",
        "  from bokeh.plotting import figure, output_file, show\n",
        "  import bokeh\n",
        "  from math import pi\n",
        "  from bokeh.io import output_notebook\n",
        "  from bokeh.models import Label\n",
        "  from datetime import datetime as dt\n",
        "  # Call once to configure Bokeh to display plots inline in the notebook.\n",
        "  output_notebook()\n",
        "  %matplotlib inline\n",
        "  from math import pi\n",
        "  from bokeh.models import Span\n",
        "  w_l = length\n",
        "  df['O'],df['H'],df['L'],df['C']= ta.ema(df['Open'], w_l),ta.ema(df['High'], w_l),ta.ema(df['Low'], w_l),ta.ema(df['Close'], w_l)\n",
        "  df = df.dropna()\n",
        "  df['C_1'] = (df['O']+df['H']+df['L']+df['O'])/4\n",
        "  O,C = [df['O'][0]],[df['C'][0]]\n",
        "  o,c = df['O'].shift(-1),df['C'].shift(-1)\n",
        "  t= (o+c)/2\n",
        "  r = t.shift(1)\n",
        "  r[np.isnan(r)]=df['O'][0]\n",
        "  df['O_1'] = t\n",
        "  df['H_1'] = df[['H','O_1']].max(axis=1)\n",
        "  df['L_1'] = df[['L','O_1']].min(axis=1)\n",
        "  df['O_2'] = ta.ema(df['O_1'],w_l)\n",
        "  df['L_2'] = ta.ema(df['L_1'],w_l)\n",
        "  df['H_2'] = ta.ema(df['H_1'],w_l)\n",
        "  df['C_2'] = ta.ema(df['C_1'],w_l)\n",
        "  df['C_2'][-1] = df.C_1[-1]\n",
        "  # df = df.dropna()\n",
        "# df = df[['O_2','H_2','L_2','C_2','Volume']]\n",
        "  df = df.reset_index()\n",
        "  return df[['Date','Open','High','Low','Close','O_2','H_2','L_2','C_2']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_sIv_TJHzja"
      },
      "source": [
        "# df = pd.read_csv(\"/content/ICFC.csv\")\n",
        "# df = df.set_index(\"Date\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkzXYoGw9kUy"
      },
      "source": [
        "class Analysis():\n",
        "  def __init__(self, dataframe,filter1, filter2):\n",
        "    self.dataframe = dataframe\n",
        "    self.filter1 = filter1\n",
        "    self.filter2 = filter2\n",
        "\n",
        "  # Calculation of heikin ashi\n",
        "  def SHA(self):\n",
        "    ha1 = SHA(self.dataframe, self.filter1)\n",
        "    ha2 = SHA(self.dataframe, self.filter2)\n",
        "    return ha1,ha2\n",
        "\n",
        "\n",
        "  #Calculation of Squeeze\n",
        "  def squeeze(self):\n",
        "    s = self.dataframe\n",
        "    s['low'] = ta.squeeze(self.dataframe['High'], self.dataframe['Low'], self.dataframe['Close'], 20,2,20,1)['SQZ_ON']\n",
        "    s['med'] = ta.squeeze(self.dataframe['High'], self.dataframe['Low'], self.dataframe['Close'], 20,1.6,20,1)['SQZ_ON']\n",
        "    s['high'] = ta.squeeze(self.dataframe['High'], self.dataframe['Low'], self.dataframe['Close'], 20,1,20,1)['SQZ_ON']\n",
        "    low_h = s.apply(lambda x: x['High']+6 if x['low']!=0 else np.nan, axis = 1)\n",
        "    low_l = s.apply(lambda x: x['Low']-6 if x['low']!=0 else np.nan, axis =1 )\n",
        "    med_h = s.apply(lambda x: x['High']+10 if x['med']!=0 else np.nan, axis = 1)\n",
        "    med_l = s.apply(lambda x: x['Low']-10 if x['med']!=0 else np.nan, axis =1 )\n",
        "    high_h = s.apply(lambda x: x['High']+14 if x['high']!=0 else np.nan, axis = 1)\n",
        "    high_l = s.apply(lambda x: x['Low']-14 if x['high']!=0 else np.nan, axis =1 )\n",
        "    return s,low_h,low_l, med_h, med_l, high_h, high_l\n",
        "    # return s\n",
        "\n",
        "  # Even better sine wave \n",
        "  def ebsw(self):\n",
        "    ebsw = ta.ssf(ta.ebsw(self.dataframe['Close']).fillna(method = 'bfill'))\n",
        "    scaler = MinMaxScaler(feature_range=(self.SHA(self.filter1,self.filter2)[0]['C_2'].min(), self.SHA(self.filter1,self.filter2)[0]['C_2'].max()))\n",
        "    final_ebsw = scaler.fit_transform(ebsw.values.reshape(-1,1))\n",
        "    return final_ebsw\n",
        "\n",
        "  # Triangular moving average\n",
        "  def trima(self):\n",
        "    dfs = trima(self.dataframe)\n",
        "    return dfs\n",
        "\n",
        "  # Wave Trend Oscillator\n",
        "  def wto(self):\n",
        "    wto = self.dataframe\n",
        "    wto['wt1'] = TA.WTO(wto)['WT1.'].fillna(method = 'bfill')\n",
        "    wto['wt2'] = TA.WTO(wto)['WT2.'].fillna(method = 'bfill')\n",
        "    return pd.DataFrame({'wt1':wto['wt1'],\n",
        "                         \"wt2\":wto['wt2']}, index = self.dataframe.index)\n",
        "\n",
        "  # Bear and Bull power\n",
        "  def bbp(self):\n",
        "    efi = TA.EFI(self.dataframe)\n",
        "    efi_1 = TA.EFI(self.dataframe, 14)\n",
        "    efi_2 = TA.EFI(self.dataframe, 21)\n",
        "    return (efi+efi_1+efi_2)/3\n",
        "  \n",
        "  # plot the data\n",
        "  def plot(self):\n",
        "    fig = make_subplots(rows=4, cols=1, shared_xaxes=True, \n",
        "               vertical_spacing=0.02, \n",
        "               row_width=[0.4,0.4,0.4,0.7]) # subplot_titles=(f'{stock}') if want to add title\n",
        "\n",
        "    # Candle stick chart\n",
        "    fig.add_trace(go.Candlestick(x=self.dataframe.index,open=self.dataframe['Open'],high=self.dataframe['High'],low=self.dataframe['Low'],close=self.dataframe['Close'], \n",
        "                                name = 'Candlestick',increasing_line_color= '#000000', decreasing_line_color= '#000000',\n",
        "                                decreasing_fillcolor = \"#FFFFFF\", increasing_fillcolor = \"#000000\", \n",
        "                                increasing_line_width=1,decreasing_line_width=1), row=1, col=1)\n",
        "\n",
        "    # Heikin ashi chart 1\n",
        "    fig.add_trace(go.Candlestick(x=self.SHA()[0]['Date'],open=self.SHA()[0]['O_2'],high=self.SHA()[0]['H_2'],low=self.SHA()[0]['L_2'],close=self.SHA()[0]['C_2'], \n",
        "                                 name = 'Trend Filter1',\n",
        "                                 decreasing_fillcolor = \"#d90910\", increasing_fillcolor = \"#00d438\", \n",
        "                                 increasing_line_width=1,decreasing_line_width=1), row=2,col=1)\n",
        "    # # Heikin ashi chart 2\n",
        "    fig.add_trace(go.Candlestick(x=self.SHA()[1]['Date'],open=self.SHA()[1]['O_2'],high=self.SHA()[1]['H_2'],low=self.SHA()[1]['L_2'],close=self.SHA()[1]['C_2'], \n",
        "                                name = 'Trend Filter1',\n",
        "                                 decreasing_fillcolor = \"#d90910\", increasing_fillcolor = \"#00d438\", \n",
        "                                 increasing_line_width=1,decreasing_line_width=1), row=2,col=1)\n",
        "\n",
        "    # SR related with volume\n",
        "    for line in b:\n",
        "      fig.add_trace(go.Scatter(x = self.dataframe.index, y = pd.Series(np.array([line for _ in range(len(df))])), mode = 'lines', line = dict(color = \"red\", width = 1), name = \"VP S/R\"))\n",
        "    \n",
        "    #TRIMA\n",
        "    # Upper and Lower Resistance\n",
        "    fig.add_trace(go.Scatter(x=self.trima().index, y=self.trima().HH,fill=None, mode='lines',line_color='green',line=dict(color='green',width=1),opacity=0.3, name = \"Upper Resistance 1\"),row=1,col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.trima().index, y=self.trima().H,fill='tonexty',mode='lines', line_color='green',line=dict(color='green',width=1),opacity=0.3,name = \"Upper Resistance 2\"),row=1,col=1)   \n",
        "    fig.add_trace(go.Scatter(x=self.trima().index, y=self.trima().LL,fill=None, mode='lines',line_color='red',line=dict(color='red',width=1), name = \"Lower Support 1\"),row=1,col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.trima().index, y=self.trima().L,fill='tonexty',mode='lines', line_color='red',line=dict(color='red',width=1), name = \"Lower Support 2\"),row=1,col=1)\n",
        "\n",
        "    # Squeeze \n",
        "    fig.add_trace(go.Scatter(x=self.squeeze()[0].index, y=self.squeeze()[1],mode='markers', marker_size = 3,marker_color = '#42f5e6'), row=1, col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.squeeze()[0].index, y=self.squeeze()[2],mode='markers', marker_size = 3,marker_color = '#42f5e6'), row=1, col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.squeeze()[0].index, y=self.squeeze()[3],mode='markers', marker_size = 4,marker_color = '#f542e3'), row=1, col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.squeeze()[0].index, y=self.squeeze()[4],mode='markers', marker_size = 4,marker_color = '#f542e3'), row=1, col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.squeeze()[0].index, y=self.squeeze()[5],mode='markers', marker_size = 5,marker_color = '#f70a0a'), row=1, col=1)\n",
        "    fig.add_trace(go.Scatter(x=self.squeeze()[0].index, y=self.squeeze()[6],mode='markers', marker_size = 5,marker_color = '#f70a0a'), row=1, col=1)\n",
        "    \n",
        "    # Wave Trend Oscillator\n",
        "    fig.add_trace(go.Scatter(x = self.wto().index, y = self.wto()['wt1'],modes = 'lines'), row=3, col=1)\n",
        "    # fig.add_trace(go.Scatter(x = self.wto()[1].index, y = self.wto()[1],mode = 'lines',line_color='red',line=dict(color='red',width=1),name='WT2'), row = 3, col = 1)\n",
        "    # fig.add_trace(go.Scatter(x = self.wto()[0].index, y = pd.Series(np.array([0 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "    # fig.add_trace(go.Scatter(x = self.wto()[0].index, y = pd.Series(np.array([53 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "    # fig.add_trace(go.Scatter(x = self.wto()[0].index, y = pd.Series(np.array([60 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "    # fig.add_trace(go.Scatter(x = self.wto()[0].index, y = pd.Series(np.array([-53 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "    # fig.add_trace(go.Scatter(x = self.wto()[0].index, y = pd.Series(np.array([-60 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "\n",
        "\n",
        "\n",
        "    fig.update_layout(xaxis_rangeslider_visible=False,showlegend = False,barmode='stack',height=1000) #xaxis_title=\"Date\"\n",
        "    fig.update_xaxes(rangeslider= {'visible':False}, row=1, col=1)\n",
        "    fig.update_xaxes(rangeslider= {'visible':False}, row=2, col=1)\n",
        "    fig.update_xaxes(showgrid=True,gridwidth=1, gridcolor='gray')\n",
        "    # fig.update_yaxes(showgrid=False, gridwidth=1, gridcolor='gray')\n",
        "    fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)','paper_bgcolor': 'rgba(0, 0, 0, 0)'})\n",
        "    fig.show()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCjHLs8G5-Ln"
      },
      "source": [
        "df = pdr.get_data_yahoo(\"DIS\",\"12-1-2020\")"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nEWY68k-BgU"
      },
      "source": [
        "d = Analysis(df,5,9)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-F9yYZ09-CjZ",
        "outputId": "837cdf5f-cb62-45fe-a6be-a55285407cad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "d.wto()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wt1</th>\n",
              "      <th>wt2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-12-01</th>\n",
              "      <td>121.212121</td>\n",
              "      <td>105.761047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-02</th>\n",
              "      <td>121.212121</td>\n",
              "      <td>105.761047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-03</th>\n",
              "      <td>113.030807</td>\n",
              "      <td>105.761047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-04</th>\n",
              "      <td>103.708900</td>\n",
              "      <td>105.761047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-07</th>\n",
              "      <td>85.092359</td>\n",
              "      <td>105.761047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-17</th>\n",
              "      <td>-67.209992</td>\n",
              "      <td>-55.662676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-18</th>\n",
              "      <td>-70.221511</td>\n",
              "      <td>-62.184346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-19</th>\n",
              "      <td>-72.210506</td>\n",
              "      <td>-67.612448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-20</th>\n",
              "      <td>-70.537325</td>\n",
              "      <td>-70.044833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-21</th>\n",
              "      <td>-66.749317</td>\n",
              "      <td>-69.929665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   wt1         wt2\n",
              "Date                              \n",
              "2020-12-01  121.212121  105.761047\n",
              "2020-12-02  121.212121  105.761047\n",
              "2020-12-03  113.030807  105.761047\n",
              "2020-12-04  103.708900  105.761047\n",
              "2020-12-07   85.092359  105.761047\n",
              "...                ...         ...\n",
              "2021-05-17  -67.209992  -55.662676\n",
              "2021-05-18  -70.221511  -62.184346\n",
              "2021-05-19  -72.210506  -67.612448\n",
              "2021-05-20  -70.537325  -70.044833\n",
              "2021-05-21  -66.749317  -69.929665\n",
              "\n",
              "[119 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdJ-U-iD40s0"
      },
      "source": [
        "#@title\n",
        "# Load Data \n",
        "# stock= \"amzn\"\n",
        "# df = pdr.get_data_yahoo(f'{stock}',\"12-1-2020\")\n",
        "df = pd.read_csv(f\"/content/{stock}.csv\")\n",
        "df = df.set_index(\"Date\")\n",
        "\n",
        "# Heikin ashi 1\n",
        "h1 = df.copy(deep = True)\n",
        "ha1 = SHA(h1,5)\n",
        "# Heikin ashi 2\n",
        "h2 = df.copy(deep = True)\n",
        "ha2 = SHA(h2,7)\n",
        "\n",
        "# Squeeze\n",
        "s = df.copy(deep = True)\n",
        "s['low'] = ta.squeeze(df['High'],df['Low'], df['Close'],20,2,20,1)['SQZ_ON']\n",
        "s['med'] = ta.squeeze(df['High'],df['Low'], df['Close'],20,1.6,20,1)['SQZ_ON']\n",
        "s['high'] = ta.squeeze(df['High'],df['Low'], df['Close'],20,1,20,1)['SQZ_ON']\n",
        "\n",
        "s['low_h'] = s.apply(lambda x: x['High']+6 if x['low']!=0 else np.nan, axis = 1)\n",
        "s['low_l'] = s.apply(lambda x: x['Low']-6 if x['low']!=0 else np.nan, axis =1 )\n",
        "s['med_h'] = s.apply(lambda x: x['High']+10 if x['med']!=0 else np.nan, axis = 1)\n",
        "s['med_l'] = s.apply(lambda x: x['Low']-10 if x['med']!=0 else np.nan, axis =1 )\n",
        "s['high_h'] = s.apply(lambda x: x['High']+14 if x['high']!=0 else np.nan, axis = 1)\n",
        "s['high_l'] = s.apply(lambda x: x['Low']-14 if x['high']!=0 else np.nan, axis =1 )\n",
        "\n",
        "# Even better sine wave\n",
        "ebsw = df.copy(deep = True)\n",
        "ebsw['ebsw'] = ta.ssf(ta.ebsw(df['Close']).fillna(method = 'bfill'))\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(ha1['C_2'].min(), ha1['C_2'].max()))\n",
        "ebsw['final'] =  scaler.fit_transform(ebsw['ebsw'].values.reshape(-1, 1))\n",
        "\n",
        "# TRIMA\n",
        "tma = df.copy(deep = True)\n",
        "dfs = trima(tma)\n",
        "\n",
        "# WTO\n",
        "o = df.copy(deep = True)\n",
        "o['WT1'] = TA.WTO(df)['WT1.'].fillna(method = 'bfill')\n",
        "o['WT2'] = TA.WTO(df)['WT2.'].fillna(method = 'bfill')\n",
        "\n",
        "# Bear and Bull\n",
        "bb = df.copy(deep = True)\n",
        "bb['EFI'] = TA.EFI(bb)\n",
        "bb['EFI1'] = TA.EFI(bb,14)\n",
        "bb['EFI2'] = TA.EFI(bb,20)\n",
        "bb['m'] = (bb['EFI']+bb['EFI1']+bb['EFI2'])/3\n",
        "bb['colorm'] = np.where(bb['m']<0, 'red','green')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-NcTfyD46qN",
        "outputId": "3f92e869-bb6f-4dc5-f6f1-2fce6039ec25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "fig = make_subplots(rows=4, cols=1, shared_xaxes=True, \n",
        "               vertical_spacing=0.02, \n",
        "               row_width=[0.4,0.4,0.4,0.7]) # subplot_titles=(f'{stock}') if want to add title\n",
        "\n",
        "# Candle stick chart\n",
        "fig.add_trace(go.Candlestick(x=df.index,open=df['Open'],high=df['High'],low=df['Low'],close=df['Close'], \n",
        "                             name = 'Candlestick',increasing_line_color= '#000000', decreasing_line_color= '#000000',\n",
        "                             decreasing_fillcolor = \"#FFFFFF\", increasing_fillcolor = \"#000000\", \n",
        "                             increasing_line_width=1,decreasing_line_width=1), row=1, col=1)\n",
        "\n",
        "#Heikin ashi chart 1\n",
        "# fig.add_trace(go.Candlestick(x=ha1['Date'],open=ha1['O_2'],high=ha1['C_2'],low=ha1['O_2'],close=ha1['C_2'], \n",
        "#                              name = 'Trend Filter1',increasing_line_color= '#FF0000', decreasing_line_color= '#0D600D',\n",
        "#                              decreasing_fillcolor = \"#0D600D\", increasing_fillcolor = \"#FF0000\", \n",
        "#                              increasing_line_width=2,decreasing_line_width=2), row=2,col=1)\n",
        "\n",
        "#Heikin ashi chart 2\n",
        "fig.add_trace(go.Candlestick(x=ha2['Date'],open=ha2['O_2'],high=ha2['C_2'],low=ha2['O_2'],close=ha2['C_2'], \n",
        "                             name = 'Trend Filter2',increasing_line_color= '#FF0000', decreasing_line_color= '#0D600D',\n",
        "                             decreasing_fillcolor = \"#0D600D\", increasing_fillcolor = \"#FF0000\", \n",
        "                             increasing_line_width=2,decreasing_line_width=2), row=2,col=1)\n",
        "fig.add_trace(go.Scatter(x = ebsw.index, y = ebsw['final'] ,mode = 'lines', line_color='black',line=dict(color='black',width=1), \n",
        "                         opacity = 0.4, name = 'Cycle'), row=2, col=1)\n",
        "\n",
        "# Squeeze \n",
        "fig.add_trace(go.Scatter(x=s.index, y=s.low_h,mode='markers', marker_size = 3,marker_color = '#42f5e6'), row=1, col=1)\n",
        "fig.add_trace(go.Scatter(x=s.index, y=s.low_l,mode='markers', marker_size = 3,marker_color = '#42f5e6'), row=1, col=1)\n",
        "fig.add_trace(go.Scatter(x=s.index, y=s.med_h,mode='markers', marker_size = 4,marker_color = '#f542e3'), row=1, col=1)\n",
        "fig.add_trace(go.Scatter(x=s.index, y=s.med_l,mode='markers', marker_size = 4,marker_color = '#f542e3'), row=1, col=1)\n",
        "fig.add_trace(go.Scatter(x=s.index, y=s.high_h,mode='markers', marker_size = 5,marker_color = '#f70a0a'), row=1, col=1)\n",
        "fig.add_trace(go.Scatter(x=s.index, y=s.high_l,mode='markers', marker_size = 5,marker_color = '#f70a0a'), row=1, col=1)\n",
        "\n",
        "#Volume Profile \n",
        "# SR Volume Profile\n",
        "for line in b:\n",
        "  fig.add_trace(go.Scatter(x = df.index, y = pd.Series(np.array([line for _ in range(len(df))])), mode = 'lines', line = dict(color = \"red\", width = 1), name = \"VP S/R\"))\n",
        "\n",
        "#TRIMA\n",
        "# Upper and Lower Resistance\n",
        "fig.add_trace(go.Scatter(x=dfs.index, y=dfs.HH,fill=None, mode='lines',line_color='green',line=dict(color='green',width=1),opacity=0.3, name = \"Upper Resistance 1\"),row=1,col=1)\n",
        "fig.add_trace(go.Scatter(x=dfs.index, y=dfs.H,fill='tonexty',mode='lines', line_color='green',line=dict(color='green',width=1),opacity=0.3,name = \"Upper Resistance 2\"),row=1,col=1)   \n",
        "fig.add_trace(go.Scatter(x=dfs.index, y=dfs.LL,fill=None, mode='lines',line_color='red',line=dict(color='red',width=1), name = \"Lower Support 1\"),row=1,col=1)\n",
        "fig.add_trace(go.Scatter(x=dfs.index, y=dfs.L,fill='tonexty',mode='lines', line_color='red',line=dict(color='red',width=1), name = \"Lower Support 2\"),row=1,col=1)\n",
        "\n",
        "#OverBought and OverSold\n",
        "#Over Bought and Over Sold along with Mass Index\n",
        "fig.add_trace(go.Scatter(x = o.index, y = ta.ssf(o['WT1']),fill =None, mode = 'lines',line_color='green',line=dict(color='green',width=1),name='WT1'), row = 3, col = 1)\n",
        "fig.add_trace(go.Scatter(x = o.index, y = ta.ssf(o['WT2']), mode = 'lines',line_color='red',line=dict(color='red',width=1),name='WT2'), row = 3, col = 1)\n",
        "fig.add_trace(go.Scatter(x = o.index, y = pd.Series(np.array([0 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "fig.add_trace(go.Scatter(x = o.index, y = pd.Series(np.array([53 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "fig.add_trace(go.Scatter(x = o.index, y = pd.Series(np.array([60 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "fig.add_trace(go.Scatter(x = o.index, y = pd.Series(np.array([-53 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "fig.add_trace(go.Scatter(x = o.index, y = pd.Series(np.array([-60 for _ in range(len(o))])), mode = 'lines', line = dict(color = 'black', width =1, dash = 'dot'), name = 'Zero Line'), row = 3,col = 1)\n",
        "\n",
        "#Bear and Bull volume\n",
        "fig.add_trace(go.Bar(name='Bull and Bear',x=bb.index,y=bb.m,marker_color=bb['colorm']), row= 4, col = 1)\n",
        "\n",
        "fig.update_layout(xaxis_rangeslider_visible=False,showlegend = False,barmode='stack',height=1000) #xaxis_title=\"Date\"\n",
        "fig.update_xaxes(rangeslider= {'visible':False}, row=1, col=1)\n",
        "fig.update_xaxes(rangeslider= {'visible':False}, row=2, col=1)\n",
        "fig.update_xaxes(showgrid=True,gridwidth=1, gridcolor='gray')\n",
        "# fig.update_yaxes(showgrid=False, gridwidth=1, gridcolor='gray')\n",
        "fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)','paper_bgcolor': 'rgba(0, 0, 0, 0)'})\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "JWLksRlN5sm-",
        "outputId": "aefbda05-cb96-4266-ca1f-4558f3162c8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "SHA(h2,5)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
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              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>O_2</th>\n",
              "      <th>H_2</th>\n",
              "      <th>L_2</th>\n",
              "      <th>C_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>285.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>290.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-01-03</td>\n",
              "      <td>267.0</td>\n",
              "      <td>272.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-01-04</td>\n",
              "      <td>269.0</td>\n",
              "      <td>270.0</td>\n",
              "      <td>265.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-01-05</td>\n",
              "      <td>267.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-01-06</td>\n",
              "      <td>262.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>271.016955</td>\n",
              "      <td>277.952593</td>\n",
              "      <td>271.003951</td>\n",
              "      <td>275.200864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>2021-05-17</td>\n",
              "      <td>338.0</td>\n",
              "      <td>339.0</td>\n",
              "      <td>331.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>325.826196</td>\n",
              "      <td>328.536970</td>\n",
              "      <td>318.229403</td>\n",
              "      <td>322.216102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>2021-05-18</td>\n",
              "      <td>332.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>331.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>330.952506</td>\n",
              "      <td>332.759689</td>\n",
              "      <td>321.036038</td>\n",
              "      <td>325.353335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2021-05-19</td>\n",
              "      <td>367.0</td>\n",
              "      <td>367.0</td>\n",
              "      <td>346.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>336.958366</td>\n",
              "      <td>338.398841</td>\n",
              "      <td>325.057205</td>\n",
              "      <td>330.791734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2021-05-20</td>\n",
              "      <td>351.0</td>\n",
              "      <td>383.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>378.0</td>\n",
              "      <td>346.243374</td>\n",
              "      <td>347.203691</td>\n",
              "      <td>329.393589</td>\n",
              "      <td>336.259719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2021-05-23</td>\n",
              "      <td>378.0</td>\n",
              "      <td>415.0</td>\n",
              "      <td>358.0</td>\n",
              "      <td>415.0</td>\n",
              "      <td>346.243374</td>\n",
              "      <td>357.754630</td>\n",
              "      <td>334.499361</td>\n",
              "      <td>358.880459</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date   Open   High  ...         H_2         L_2         C_2\n",
              "0   2020-12-31  285.0  290.0  ...         NaN         NaN         NaN\n",
              "1   2021-01-03  267.0  272.0  ...         NaN         NaN         NaN\n",
              "2   2021-01-04  269.0  270.0  ...         NaN         NaN         NaN\n",
              "3   2021-01-05  267.0  267.0  ...         NaN         NaN         NaN\n",
              "4   2021-01-06  262.0  266.0  ...  277.952593  271.003951  275.200864\n",
              "..         ...    ...    ...  ...         ...         ...         ...\n",
              "91  2021-05-17  338.0  339.0  ...  328.536970  318.229403  322.216102\n",
              "92  2021-05-18  332.0  352.0  ...  332.759689  321.036038  325.353335\n",
              "93  2021-05-19  367.0  367.0  ...  338.398841  325.057205  330.791734\n",
              "94  2021-05-20  351.0  383.0  ...  347.203691  329.393589  336.259719\n",
              "95  2021-05-23  378.0  415.0  ...  357.754630  334.499361  358.880459\n",
              "\n",
              "[96 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dm5Z9iDC5s12"
      },
      "source": [
        "df = pdr.get_data_yahoo(\"V\",\"12-1-2020\")"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmMaCFVKnzuI"
      },
      "source": [
        "def SHA(df, length):\n",
        "  import warnings\n",
        "  warnings.filterwarnings(\"ignore\")\n",
        "  \n",
        "  import matplotlib.pyplot as plt\n",
        "  from bokeh.plotting import figure, output_file, show\n",
        "  import bokeh\n",
        "  from math import pi\n",
        "  from bokeh.io import output_notebook\n",
        "  from bokeh.models import Label\n",
        "  from datetime import datetime as dt\n",
        "  # Call once to configure Bokeh to display plots inline in the notebook.\n",
        "  output_notebook()\n",
        "  %matplotlib inline\n",
        "  from math import pi\n",
        "  from bokeh.models import Span\n",
        "  w_l = length\n",
        "  df['O'],df['H'],df['L'],df['C']= ta.ema(df['Open'], w_l),ta.ema(df['High'], w_l),ta.ema(df['Low'], w_l),ta.ema(df['Close'], w_l)\n",
        "  df = df.dropna()\n",
        "  df['C_1'] = (df['O']+df['H']+df['L']+df['O'])/4\n",
        "  O,C = [df['O'][0]],[df['C'][0]]\n",
        "  o,c = df['O'].shift(-1),df['C'].shift(-1)\n",
        "  t= (o+c)/2\n",
        "  r = t.shift(1)\n",
        "  r[np.isnan(r)]=df['O'][0]\n",
        "  df['O_1'] = t\n",
        "  df['H_1'] = df[['H','O_1']].max(axis=1)\n",
        "  df['L_1'] = df[['L','O_1']].min(axis=1)\n",
        "  df['O_2'] = ta.ema(df['O_1'],w_l)\n",
        "  df['L_2'] = ta.ema(df['L_1'],w_l)\n",
        "  df['H_2'] = ta.ema(df['H_1'],w_l)\n",
        "  df['C_2'] = ta.ema(df['C_1'],w_l)\n",
        "  df['C_2'][-1] = df.C_1[-1]\n",
        "  # df = df.dropna()\n",
        "# df = df[['O_2','H_2','L_2','C_2','Volume']]\n",
        "  df = df.reset_index()\n",
        "  return df[['Date','Open','High','Low','Close','O_2','H_2','L_2','C_2']]"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGCeLakcolQd"
      },
      "source": [
        "HA = SHA(df,4)[['Date','O_2',\"H_2\",\"L_2\",\"C_2\"]]"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWj1QHAcomYQ"
      },
      "source": [
        "HA.columns = ['Date','Open','High','Low','Close']"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M9pXylkMo3VZ"
      },
      "source": [
        "Ha = HA.set_index(\"Date\")"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgPnoh-3o3xS",
        "outputId": "3e2c6a55-78a8-41e7-b240-e6da9ba7e6c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "source": [
        "mpl.plot(Ha, type='candle', style = \"yahoo\",figratio = (20,10))"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1150x575 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKSbpnxJo_RE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}