SS+Analysis.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/algorithmic-trading-with-python/blob/master/SS%2BAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FAFCDEHHppS"
      },
      "source": [
        "!pip install selenium\n",
        "!apt-get update \n",
        "!apt install chromium-chromedriver\n",
        "!pip install pandas_ta\n",
        "!pip install MarketProfile\n",
        "!pip install fake-useragent\n",
        "!pip install mpl_finance"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUbzGGyAHprH"
      },
      "source": [
        "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",
        "\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",
        "\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",
        "from mpl_finance import candlestick_ohlc\n",
        "import matplotlib.dates as mpl_dates\n",
        "import pandas_datareader as pdr\n",
        "import time\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"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "LbmS7nq8Hvvl"
      },
      "source": [
        "#@title\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 = \"https://merolagani.com/CompanyDetail.aspx?symbol=nhpc\"\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",
        "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",
        "\n",
        "data = []\n",
        "data1 = []\n",
        "data2 = []\n",
        "a = 1\n",
        "delay = 5\n",
        "\n",
        "l = [range(2,102),range(2,99)]\n",
        "while a<3:\n",
        "  for i in l:\n",
        "    # frame = driver.find_element_by_xpath('//*[@id=\"ctl00_ContentPlaceHolder1_CompanyDetail1_divDataPrice\"]/div[2]')\n",
        "    # driver.switch_to.frame(frame)\n",
        "    try:\n",
        "      for j in i:\n",
        "        WebDriverWait(driver, 10).until(EC.visibility_of_all_elements_located((By.ID, 'ctl00_ContentPlaceHolder1_CompanyDetail1_divDataPrice')))\n",
        "        # c = len(driver.find_element_by_xpath(f'//*[@id=\"ctl00_ContentPlaceHolder1_CompanyDetail1_divDataPrice\"]/div[2]/table/tbody/tr[{j}]'))\n",
        "        d = driver.find_element_by_xpath(f'//*[@id=\"ctl00_ContentPlaceHolder1_CompanyDetail1_divDataPrice\"]/div[2]/table/tbody/tr[{j}]')\n",
        "        data2.append(d.text)\n",
        "      driver.find_elements_by_xpath('//*[@title=\"Next Page\"]')[0].click()\n",
        "      # next.click()\n",
        "      time.sleep(5)\n",
        "      a+=1\n",
        "    except:\n",
        "      pass\n",
        "  \n",
        "  # driver.find_elements_by_xpath('//*[@title=\"Next Page\"]')[0].click()\n",
        "  #   # next.click()\n",
        "  # time.sleep(5)\n",
        "  # a+=1\n",
        "\n",
        "\n",
        "driver.quit()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BhJlTCJIBVE",
        "outputId": "08696c06-1690-4f6b-8479-43f09a02debf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "lists = ['sindu']\n",
        "# lists = ['gilb','nmfbs','smfbs','albsl','vlbs','uslb','gmfbs','ddbl','fowad','jslbb','nadep','snlb','msmbs','mlbbl','glbsl','sabsl']\n",
        "for name in lists:\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(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",
        "\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",
        "\n",
        "  url = f\"https://www.sharesansar.com/company/{name}\"\n",
        "  wait = WebDriverWait(driver, 10)\n",
        "  driver.get(url)\n",
        "  wait.until(EC.element_to_be_clickable((By.XPATH,'//*[@id=\"btn_cpricehistory\"]'))).click()\n",
        "\n",
        "  # driver.find_element_by_xpath('//*[@id=\"btn_cpricehistory\"]').click()\n",
        "  time.sleep(2)\n",
        "\n",
        "  # driver.find_element_by_div(\"table table-bordered table-striped table-hover\")\n",
        "  data = []\n",
        "  a = 1\n",
        "  try:\n",
        "    while a<7:\n",
        "      \n",
        "      for i in range(1,21):\n",
        "        for j in range(1,9):\n",
        "          d = driver.find_element_by_xpath(f'//*[@id=\"myTableCPriceHistory\"]/tbody/tr[{i}]/td[{j}]')\n",
        "          data.append(d.text)\n",
        "\n",
        "      a +=1\n",
        "      driver.find_element_by_xpath('//*[@id=\"myTableCPriceHistory_next\"]').click()#f'//*[@id=\"myTableCPriceHistory_paginate\"]/span/a[{a}]').click()\n",
        "      print(f\"Scraped page {a} of {name}\")\n",
        "      time.sleep(1)\n",
        "  except:\n",
        "    print(\"Error scraping\")\n",
        "\n",
        "        # driver.find_element_by_xpath('//*[@id=\"myTableCPriceHistory\"]/tbody/tr[1]').text\n",
        "    # //*[@id=\"myTableCPriceHistory\"]/tbody/tr[1]/td[1]\n",
        "  driver.quit()\n",
        "\n",
        "  Date = [data[i] for i in range(1, len(data),8)]\n",
        "  Open = [float(data[i].replace(',', '')) for i in range(2, len(data),8)]\n",
        "  High = [float(data[i].replace(',', '')) for i in range(3, len(data),8)]\n",
        "  Low = [float(data[i].replace(',', '')) for i in range(4, len(data),8)]\n",
        "  Close = [float(data[i].replace(',', '')) for i in range(5, len(data),8)]\n",
        "  Volume = [float(data[i].replace(',', '')) for i in range(7, len(data),8)]\n",
        "  Turnover = [float(data[i].replace(',', '')) for i in range(8, len(data),8)]\n",
        "  final_df = {\"Date\": Date,\n",
        "              \"Open\" : Open,\n",
        "              \"High\" : High,\n",
        "              \"Low\":Low,\n",
        "              \"Close\":Close,\n",
        "              \"Volume\": Volume, \n",
        "              }\n",
        "\n",
        "  # import pandas as pd\n",
        "  raw = pd.DataFrame(final_df)\n",
        "  raw['Date'] = raw['Date'].apply(pd.to_datetime)\n",
        "  raw['Open'] = raw['Open'].apply(pd.to_numeric)\n",
        "  raw['High'] = raw['High'].apply(pd.to_numeric)\n",
        "  raw['Low'] = raw['Low'].apply(pd.to_numeric)\n",
        "  raw['Close'] = raw['Close'].apply(pd.to_numeric)\n",
        "\n",
        "  raw.sort_values(by = \"Date\", ascending = True, inplace = True)\n",
        "  raw.set_index(\"Date\", inplace = True)\n",
        "  raw.to_csv(f\"/content/{name}_1.csv\")\n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scraped page 2 of sindu\n",
            "Scraped page 3 of sindu\n",
            "Scraped page 4 of sindu\n",
            "Scraped page 5 of sindu\n",
            "Scraped page 6 of sindu\n",
            "Scraped page 7 of sindu\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLYjft_bIlGj"
      },
      "source": [
        "stock = 'sindu_1'\n",
        "# !pip install finta"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEB6ZksJI8WE"
      },
      "source": [
        "#@title SHA\n",
        "\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": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1SAOVCCHI1DB"
      },
      "source": [
        "#@title MArket Profile\n",
        "from market_profile import MarketProfile\n",
        "\n",
        "df = pd.read_csv(f\"/content/{stock}.csv\")\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": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pXc_upZI1Ex"
      },
      "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": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dFodtfzd3iC",
        "outputId": "60bf9c45-f00c-4341-ccd0-30f6e02315b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "def apz(df):\n",
        "  # df['trima'] = ta.trima(df['Close'],21)\n",
        "  df['U'] = TA.APZ(df)['UPPER']\n",
        "  df['L'] = TA.APZ(df)['LOWER']\n",
        "  # df['range'] = ta.atr(df.High, df.Low, df.Close, 14) * 4\n",
        "  # df['H'] = ta.wma((df['U'] + df['range']),21)\n",
        "  # df['L'] = ta.wma((df['L'] - df['range']),21)\n",
        "  # df['SD_UP'] = df['H'].rolling(20).std()\n",
        "  # df['SD_DOWN'] =df['L'].rolling(20).std()\n",
        "  # df['HH'] = ta.wma((df['H'] + df['range']),21)+(ta.atr(df.High, df.Low, df.Close,21))*1\n",
        "  # df['LL'] = ta.wma((df['L'] - df['range']),21)-(ta.atr(df.High, df.Low, df.Close,21))*1\n",
        "  # return df[['HH','H','LL','L']]\n",
        "  return df[['U',\"L\"]]\n",
        "df = pd.read_csv(\"/content/sindu_1.csv\")\n",
        "df.set_index(\"Date\", inplace = True)\n",
        "apz(df)"
      ],
      "execution_count": 88,
      "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>U</th>\n",
              "      <th>L</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>2020-11-19</th>\n",
              "      <td>177.000000</td>\n",
              "      <td>157.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-22</th>\n",
              "      <td>178.970522</td>\n",
              "      <td>153.482993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-23</th>\n",
              "      <td>179.397659</td>\n",
              "      <td>150.279250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-24</th>\n",
              "      <td>181.072833</td>\n",
              "      <td>150.428485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-11-25</th>\n",
              "      <td>182.902394</td>\n",
              "      <td>151.258229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-09</th>\n",
              "      <td>284.911305</td>\n",
              "      <td>232.745643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-10</th>\n",
              "      <td>282.760221</td>\n",
              "      <td>231.015882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-11</th>\n",
              "      <td>281.108421</td>\n",
              "      <td>229.875462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-12</th>\n",
              "      <td>280.015135</td>\n",
              "      <td>229.132346</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-13</th>\n",
              "      <td>281.235458</td>\n",
              "      <td>230.732007</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     U           L\n",
              "Date                              \n",
              "2020-11-19  177.000000  157.000000\n",
              "2020-11-22  178.970522  153.482993\n",
              "2020-11-23  179.397659  150.279250\n",
              "2020-11-24  181.072833  150.428485\n",
              "2020-11-25  182.902394  151.258229\n",
              "...                ...         ...\n",
              "2021-05-09  284.911305  232.745643\n",
              "2021-05-10  282.760221  231.015882\n",
              "2021-05-11  281.108421  229.875462\n",
              "2021-05-12  280.015135  229.132346\n",
              "2021-05-13  281.235458  230.732007\n",
              "\n",
              "[120 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aurpG3ZSIe33",
        "outputId": "56f272bf-97c7-4e7a-9000-6abbb66f91f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import pandas as pd\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "from finta import TA\n",
        "import numpy as np\n",
        "#Load data\n",
        "df = pd.read_csv(f\"/content/{stock}.csv\")\n",
        "\n",
        "\n",
        "df = df[df['Date']>\"1-1-2021\"]\n",
        "df.set_index(\"Date\", inplace = True)\n",
        "\n",
        "v = df.copy() # for volatility\n",
        "o = df.copy() # for OB/OS\n",
        "efi = df.copy() # for EFI\n",
        "basp = df.copy()\n",
        "t = SHA(df,3)\n",
        "dfs = trima(df)\n",
        "m = SHA(df,5)\n",
        "\n",
        "# Volitility \n",
        "\n",
        "v['macd'] = TA.MACD(v,9,19,6)['MACD']\n",
        "v['signal'] = TA.MACD(v,9,19,6)['SIGNAL']\n",
        "# v['squeeze'] = ta.squeeze(v.High,v.Low, v.Close, 20,1.6,20,1, lazybear = True)['SQZ_ON']\n",
        "v['s'] = TA.SQZMI(v)\n",
        "v['squeeze'] = v.apply(lambda x: 1 if x['s'] == True else 0, axis = 1)\n",
        "v['stc'] = (TA.STC(v,23,50,3,3))*(v['Close'].max()/100)\n",
        "mid = (v['Close'].max()*.5)\n",
        "upp = (v['Close'].max()*.75)\n",
        "low = (v['Close'].max()*.25)\n",
        "\n",
        "# OB/OS\n",
        "o['WT1'] = TA.WTO(df)['WT1.'] \n",
        "o['WT2'] = TA.WTO(df)['WT2.']\n",
        "\n",
        "# Mass Index  : Used to indentify trend exhaustion and plotted on same graph of OB/OS\n",
        "# mass['MI'] = TA.MI(df)\n",
        "# EFI\n",
        "efi['EFI'] = TA.EFI(efi)\n",
        "efi['color'] = np.where(efi['EFI']<0, 'red', 'green')\n",
        "\n",
        "# BAPS\n",
        "basp['basp'] = TA.BASPN(basp)['Buy.'] - TA.BASPN(basp)['Sell.']\n",
        "basp['color']  =np.where(basp['basp']>0, 'green', 'red')\n",
        "\n",
        "# Create subplots and mention plot grid size\n",
        "fig = make_subplots(rows=4, cols=1, shared_xaxes=True, \n",
        "               vertical_spacing=0.05, \n",
        "               row_width=[0.2, 0.2,0.2,0.7]) # 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=t['Date'],open=t['O_2'],high=t['C_2'],low=t['O_2'],close=t['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=1,decreasing_line_width=1), row=1,col=1)\n",
        "\n",
        "#Heikin ashi chart 2\n",
        "fig.add_trace(go.Candlestick(x=m['Date'],open=m['O_2'],high=m['C_2'],low=m['O_2'],close=m['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=1,decreasing_line_width=1), row=1,col=1)\n",
        "\n",
        "# SR Volume Profile\n",
        "for line in b:\n",
        "  fig.add_trace(go.Scatter(x = dfs.index, y = pd.Series(np.array([line for _ in range(len(dfs))])), mode = 'lines', line = dict(color = \"red\", width = 1), name = \"VP S/R\"))\n",
        "\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",
        "# Squeeze and STC\n",
        "fig.add_trace(go.Scatter(x=v.index, y=v.Close,mode='markers',marker_color=v['squeeze'], marker_size = 6, name = 'Volatility & Momentum'), row=2, col=1)\n",
        "fig.add_trace(go.Scatter(x=v.index, y=v['stc'] ,fill='tozeroy', line_color='black',line=dict(color='black',width=1), opacity = 0.4, name = 'Momentum'), row=2, col=1)\n",
        "fig.add_trace(go.Scatter(x = v.index, y = pd.Series(np.array([mid for _ in range(len(v))])),mode = 'lines',line = dict(color= 'black', width = 1), name = 'Midline'), row = 2, col=1)\n",
        "fig.add_trace(go.Scatter(x = v.index, y = pd.Series(np.array([upp for _ in range(len(v))])),mode = 'lines',line = dict(color= 'red', width = 1,dash='dot'), name = 'Upper-line'), row = 2, col=1)\n",
        "fig.add_trace(go.Scatter(x = v.index, y = pd.Series(np.array([low for _ in range(len(v))])),mode = 'lines',line = dict(color= 'green', width = 1,dash='dot'), name = 'Lower-line'), row = 2, col=1)\n",
        "\n",
        "#Over Bought and Over Sold along with Mass Index\n",
        "fig.add_trace(go.Scatter(x = o.index, y = 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 = 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",
        "\n",
        "fig.add_trace(go.Bar(name='Bull and Bear',x=basp.index,y=basp.basp,marker_color=basp['color']), row= 4, col = 1)\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout(title = \"Candlestick\", yaxis_title ='Price',xaxis_rangeslider_visible=False,showlegend = False,barmode='stack', height=1000) #xaxis_title=\"Date\"\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': '#FFFFFF','paper_bgcolor': '#FFFFFF'})\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "-U0blDnqIfSK"
      },
      "source": [
        "#@title SHA\n",
        "\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": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Utzg-A4zaLZi",
        "outputId": "765ba4c4-7f45-4b24-bb5f-dd0d760a83ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "df = pd.read_csv(\"/content/sindu_1.csv\")\n",
        "df.set_index(\"Date\", inplace = True)\n",
        "plt.plot(TA.APZ(df)['UPPER'])\n",
        "plt.plot(TA.APZ(df)['LOWER'])\n",
        "plt.plot(df[\"Close\"])"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7044fd8c10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4IyInD10dejX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}