SS+Analysis v2.ipynb

{
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  "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%20v2.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BhJlTCJIBVE",
        "outputId": "08696c06-1690-4f6b-8479-43f09a02debf"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "4dFodtfzd3iC",
        "outputId": "60bf9c45-f00c-4341-ccd0-30f6e02315b5"
      },
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aurpG3ZSIe33",
        "outputId": "56f272bf-97c7-4e7a-9000-6abbb66f91f0"
      },
      "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": "bOe05VSKmByy"
      },
      "source": [
        "df = pd.read_csv(\"/content/sindu_1.csv\")"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "GqUpr1ZJl4CJ"
      },
      "source": [
        "#@title Short SR\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from mpl_finance import candlestick2_ohlc\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.signal import argrelmax\n",
        "from scipy.signal import argrelmin\n",
        "\n",
        "#Read CSV file with Pandas\n",
        "# df = pd.read_csv('EURCHFClean.csv')\n",
        "\n",
        "#Set Pandas columns\n",
        "# df.columns = [ 'High', 'Low', 'Open','Close', 'Volume']\n",
        "\n",
        "# #Set Date column as index\n",
        "# df = df.set_index(df.Date)\n",
        "# df['Date'] = pd.to_datetime(df['Date']).tolist()\n",
        "\n",
        "#Use only 100 data rows \n",
        "# df = df.iloc[-90:]\n",
        "\n",
        "#Make copies of Pandas Dataframe columns data\n",
        "df_open = df.Open.copy()\n",
        "df_high = df.High.copy()\n",
        "df_low = df.Low.copy()\n",
        "df_close = df.Close.copy()\n",
        "df_volume = df.Volume.copy()\n",
        "\n",
        "#Find Low Extremes in data set from Low price values\n",
        "df_support = argrelmin(df_low.values, order=5)\n",
        "support_prices = df_low.iloc[df_support[0]]\n",
        "support_prices_lower = df_open.iloc[df_support[0]]\n",
        "\n",
        "#Find High Extremes in data set from High price values\n",
        "resistance = argrelmax(df_high.values, order=5)\n",
        "resistance_prices = df_high.iloc[resistance[0]]\n",
        "resistance_prices_higher = df_open.iloc[resistance[0]]\n",
        "\n",
        "print('Support prices', support_prices)\n",
        "print('Support:', df_support)\n",
        "print('Resistance:', resistance)\n",
        "\n",
        "#Plot Market OHLC Data Candles chart\n",
        "fig, ax = plt.subplots(figsize=[15, 9])\n",
        "candlestick2_ohlc(ax, df['Open'], df['High'], df['Low'], df['Close'], colorup='green', colordown='red', width=0.5)\n",
        "\n",
        "#Plot Suppport and Resistance zones\n",
        "# plt.gca().add_patch(plt.Rectangle((df_low[df_support[0]], df_high[5]), height=0.001, width=5))\n",
        "plt.scatter(df_support, support_prices)\n",
        "plt.scatter(resistance, resistance_prices)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5Yp4nFEoUcA"
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}