Analysis 27.05.2021.ipynb

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      "name": "Welcome To Colaboratory",
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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "accelerator": "GPU"
  },
  "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/Analysis%2027.05.2021.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGGLVwo6AaTz"
      },
      "source": [
        "**Scraping**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4l00HslsAZRl"
      },
      "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",
        "# !pip install plotly\n",
        "# !pip install altair\n",
        "# !pip install pandas_datareader"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "EEMcWGIgAxQf"
      },
      "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": {
        "id": "MN6SsPDqFqN1"
      },
      "source": [
        "stock = \"CHL\""
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuPQanuOAZTi",
        "outputId": "b6001f78-9559-49cd-9051-b47eff95f7b9"
      },
      "source": [
        "#@title Scarping\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(10)\n",
        "\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",
        "# driver.find_element_by_xpath('//*[@title=\"Next Page\"]').click()\n",
        "# wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR,'a.Next Page'))).click()\n",
        "# driver.find_element_by_xpath(\"//a[@title='Page 2']\").click()\n",
        "# driver.find_element_by_xpath(\"//div[@id='ctl00_ContentPlaceHolder1_CompanyDetail1_divDataPrice']/div[1]/div[2]/a[6]\").click()\n",
        "time.sleep(5)\n",
        "a+=1\n",
        "driver.quit()\n",
        "print(f\"Done Scrapping {stock}\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done Scrapping CHL\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roQnqLJ5AZVn"
      },
      "source": [
        "#@title Preparing Data OHLCV\n",
        "Date = []\n",
        "Close = []\n",
        "High = []\n",
        "Open = []\n",
        "Low = []\n",
        "Volume = []\n",
        "for i in range(len(data)):\n",
        "  try:\n",
        "    Date.append(data[i][1])\n",
        "    Close.append(data[i][2])\n",
        "    High.append(data[i][4])\n",
        "    Low.append(data[i][5])\n",
        "    Open.append(data[i][6])\n",
        "    Volume.append(data[i][7])\n",
        "  except:\n",
        "    pass\n",
        "df = pd.DataFrame({\"Date\":Date,\n",
        "                   \"Close\":Close,\n",
        "                   \"Open\":Open,\n",
        "                   \"High\":High,\n",
        "                   \"Low\":Low,\n",
        "                   \"Close\":Close,\n",
        "                   \"Volume\":Volume}) \n",
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df['Open'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Open']])\n",
        "df['High'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['High']])\n",
        "df['Low'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Low']])\n",
        "df['Close'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Close']])\n",
        "df['Volume'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Volume']])\n",
        "# df.sort_values(by = \"Date\", ascending = True, axis = 1)\n",
        "df = df.set_index(\"Date\")\n",
        "df.to_csv(f\"{stock}.csv\")\n"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "HMlASYNqFBUU"
      },
      "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": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQINuJCaF8k1"
      },
      "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",
        "    def __init__(self, dataframe):\n",
        "        self.dataframe = dataframe\n",
        "        self.profile = []\n",
        "        self.levels = []\n",
        "        self.level = []\n",
        "\n",
        "  # Calculation of Market Profile\n",
        "    def market_profile(self):\n",
        "        for i in range(1,len(self.dataframe),21):\n",
        "            try:\n",
        "                mp = MarketProfile(self.dataframe)\n",
        "                mp_slice = mp[self.dataframe.index[0+i]: self.dataframe.index[21+i]]\n",
        "                self.profile.append(mp_slice.poc_price)\n",
        "            except:\n",
        "                pass\n",
        "        return self.profile\n",
        "\n",
        "\n",
        "a = Analysis(df)\n",
        "b = a.market_profile()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "37hjlZ5zF8m1"
      },
      "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_2[-2]\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": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "EZ11KeHqF8o8"
      },
      "source": [
        "#@title Fractals\n",
        "def fractals(df,number):\n",
        "  data = df.copy(deep = True)\n",
        "  def wilFractal(df):\n",
        "      periods = (-2, -1, 1, 2)\n",
        "\n",
        "      bear_fractal = pd.Series(np.logical_and.reduce([\n",
        "        df['High'] > df['High'].shift(period) for period in periods\n",
        "      ]), index=df.index)\n",
        "\n",
        "      bull_fractal = pd.Series(np.logical_and.reduce([\n",
        "        df['Low'] < df['Low'].shift(period) for period in periods\n",
        "      ]), index=df.index)\n",
        "\n",
        "      return bear_fractal, bull_fractal\n",
        "  data = df.reset_index()\n",
        "  data['bull'] = wilFractal(data)[0]\n",
        "  final = []\n",
        "  final1 = []\n",
        "  l = data[data['bull']==True][-3:].index.values\n",
        "  value = data[data.index==l[number]]\n",
        "  for i in range(int(value.index.values)):\n",
        "      final.append(np.nan)\n",
        "  for i in range(int(value.index.values), len(data)):\n",
        "      final1.append(float(value['High'].values))\n",
        "  return final+final1"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftefP4IlF8q3"
      },
      "source": [
        "#@title Indicator Calculation\n",
        "\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",
        "df = df.sort_index(ascending = True)\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(h2,8)\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']+3 if x['low']!=0 else np.nan, axis = 1)\n",
        "s['low_l'] = s.apply(lambda x: x['Low']-3 if x['low']!=0 else np.nan, axis =1 )\n",
        "s['med_h'] = s.apply(lambda x: x['High']+5 if x['med']!=0 else np.nan, axis = 1)\n",
        "s['med_l'] = s.apply(lambda x: x['Low']-5 if x['med']!=0 else np.nan, axis =1 )\n",
        "s['high_h'] = s.apply(lambda x: x['High']+8 if x['high']!=0 else np.nan, axis = 1)\n",
        "s['high_l'] = s.apply(lambda x: x['Low']-8 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,7)\n",
        "bb['EFI1'] = TA.EFI(bb,9)\n",
        "bb['EFI2'] = TA.EFI(bb,13)\n",
        "bb['m'] = (bb['EFI']+bb['EFI1']+bb['EFI2'])/3\n",
        "bb['colorm'] = np.where(bb['m']<0, 'red','green')\n",
        "sign = bb[\"m\"] >= 0\n",
        "\n",
        "bb[\"cumsum\"] = bb[\"m\"].groupby((sign != sign.shift()).cumsum()).cumsum()\n",
        "\n",
        "# Fractals\n",
        "fract = df.copy(deep = True)\n",
        "fract['a'] = fractals(fract,0)\n",
        "fract['b'] = fractals(fract,1)\n",
        "fract['c'] = fractals(fract,2)\n"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6vZ7iPzpF8sz",
        "outputId": "af5838a9-ecc3-4d4b-e2de-cfb442aefbc2"
      },
      "source": [
        "#@title Plotting\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['L_2'],close=ha1['C_2'], \n",
        "                             name = 'Trend Filter1', \n",
        "                             increasing_line_width=1,decreasing_line_width=1), 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['L_2'],close=ha2['C_2'], \n",
        "                             name = 'Trend Filter2', \n",
        "                             increasing_line_width=1,decreasing_line_width=1), 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",
        "# Bull fractals\n",
        "\n",
        "fig.add_trace(go.Scatter(x = fract.index, y = fract.a, mode = 'lines', line = dict(color = \"blue\",width = 1)),row=1, col = 1)\n",
        "fig.add_trace(go.Scatter(x = fract.index, y = fract.b, mode = 'lines', line = dict(color = \"blue\",width = 1)),row=1, col = 1)\n",
        "fig.add_trace(go.Scatter(x = fract.index, y = fract.c, mode = 'lines', line = dict(color = \"blue\",width = 1)),row=1, col = 1)\n",
        "\n",
        "\n",
        "\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",
        "fig.add_trace(go.Bar(name='Bull and Bear',x=bb.index,y=bb['cumsum'],marker_color=bb['colorm']), row= 4, col = 1)\n",
        "\n",
        "\n",
        "fig.update_layout(title = stock, 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",
        "\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(title_text = \"Price\",row=1, col=1)\n",
        "fig.update_yaxes(title_text = \"Price\",row=2, col=1)\n",
        "fig.update_yaxes(title_text = \"Value\",row=3, col=1)\n",
        "fig.update_yaxes(title_text = \"Volume\",row=4, col=1)\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": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"CHL\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"gridcolor\": \"gray\", \"gridwidth\": 1, \"matches\": \"x4\", \"rangeslider\": {\"visible\": false}, \"showgrid\": true, \"showticklabels\": false}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"gridcolor\": \"gray\", \"gridwidth\": 1, \"matches\": \"x4\", \"rangeslider\": {\"visible\": false}, \"showgrid\": true, \"showticklabels\": false}, \"xaxis3\": {\"anchor\": \"y3\", \"domain\": [0.0, 1.0], \"gridcolor\": \"gray\", \"gridwidth\": 1, \"matches\": \"x4\", \"showgrid\": true, \"showticklabels\": false}, \"xaxis4\": {\"anchor\": \"y4\", \"domain\": [0.0, 1.0], \"gridcolor\": \"gray\", \"gridwidth\": 1, \"showgrid\": true}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.6536842105263156, 0.9999999999999998], \"title\": {\"text\": \"Price\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.43578947368421045, 0.6336842105263156], \"title\": {\"text\": \"Price\"}}, \"yaxis3\": {\"anchor\": \"x3\", \"domain\": [0.21789473684210522, 0.4157894736842105], \"title\": {\"text\": \"Value\"}}, \"yaxis4\": {\"anchor\": \"x4\", \"domain\": [0.0, 0.19789473684210523], \"title\": {\"text\": \"Volume\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "rtvz9iBkF8xo",
        "outputId": "911d4835-b056-4a79-935a-bdcce7d27755"
      },
      "source": [
        "# stock = \"SDLBSL\"\n",
        "# df = pd.read_csv(f\"/content/{stock}.csv\")\n",
        "import pandas_ta as ta\n",
        "import pandas_datareader as pdr\n",
        "import altair as alt\n",
        "import pandas as pd\n",
        "df = pdr.get_data_yahoo(\"DIS\",\"1-1-2020\")\n",
        "\n",
        "\n",
        "df = df.reset_index()\n",
        "# df = df.set_index(\"Date\")\n",
        "df = df.sort_values(by ='Date',ascending = True)\n",
        "length = 13\n",
        "df['rsi'] = ta.rsi(df['Close'],length)\n",
        "df['ma'] = ta.sma(df['rsi'],34)\n",
        "df['offs'] = 1.6185*df['rsi'].rolling(34).std()\n",
        "\n",
        "df['up'] = df['ma'] + df['offs']\n",
        "df['dn'] = df['ma'] - df['offs']\n",
        "df['mid'] = (df['up']+df['dn'])/2\n",
        "df['mab'] = ta.sma(df['rsi'],2)\n",
        "df['mbb'] = ta.sma(df['rsi'],7)\n",
        "# df[['up','dn','mid','mab','mbb']].plot(figsize = (20,10))\n",
        "# plt.plot(df['up'])\n",
        "# plt.hlines([40 for i in range(len(df))],df.index.min(), df.index.max(), colors = 'red')\n",
        "# plt.hlines(60,df.index.min(), df.index.max(), colors = 'red')\n",
        "df"
      ],
      "execution_count": 16,
      "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>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>rsi</th>\n",
              "      <th>ma</th>\n",
              "      <th>offs</th>\n",
              "      <th>up</th>\n",
              "      <th>dn</th>\n",
              "      <th>mid</th>\n",
              "      <th>mab</th>\n",
              "      <th>mbb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>148.199997</td>\n",
              "      <td>145.100006</td>\n",
              "      <td>145.289993</td>\n",
              "      <td>148.199997</td>\n",
              "      <td>9502100</td>\n",
              "      <td>148.199997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>2020-01-03</td>\n",
              "      <td>147.899994</td>\n",
              "      <td>146.050003</td>\n",
              "      <td>146.399994</td>\n",
              "      <td>146.500000</td>\n",
              "      <td>7320200</td>\n",
              "      <td>146.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-06</td>\n",
              "      <td>146.029999</td>\n",
              "      <td>144.309998</td>\n",
              "      <td>145.539993</td>\n",
              "      <td>145.649994</td>\n",
              "      <td>8262500</td>\n",
              "      <td>145.649994</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>2020-01-07</td>\n",
              "      <td>146.869995</td>\n",
              "      <td>145.419998</td>\n",
              "      <td>145.990005</td>\n",
              "      <td>145.699997</td>\n",
              "      <td>6906500</td>\n",
              "      <td>145.699997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>2020-01-08</td>\n",
              "      <td>146.130005</td>\n",
              "      <td>144.820007</td>\n",
              "      <td>145.490005</td>\n",
              "      <td>145.399994</td>\n",
              "      <td>6984200</td>\n",
              "      <td>145.399994</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>2021-05-20</td>\n",
              "      <td>171.899994</td>\n",
              "      <td>168.979996</td>\n",
              "      <td>169.210007</td>\n",
              "      <td>171.360001</td>\n",
              "      <td>7400700</td>\n",
              "      <td>171.360001</td>\n",
              "      <td>30.677670</td>\n",
              "      <td>41.879895</td>\n",
              "      <td>12.726564</td>\n",
              "      <td>54.606459</td>\n",
              "      <td>29.153331</td>\n",
              "      <td>41.879895</td>\n",
              "      <td>27.194546</td>\n",
              "      <td>28.757796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>2021-05-21</td>\n",
              "      <td>173.119995</td>\n",
              "      <td>171.520004</td>\n",
              "      <td>172.220001</td>\n",
              "      <td>172.399994</td>\n",
              "      <td>8097500</td>\n",
              "      <td>172.399994</td>\n",
              "      <td>33.929973</td>\n",
              "      <td>41.431161</td>\n",
              "      <td>12.735799</td>\n",
              "      <td>54.166961</td>\n",
              "      <td>28.695362</td>\n",
              "      <td>41.431161</td>\n",
              "      <td>32.303822</td>\n",
              "      <td>28.746262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>350</th>\n",
              "      <td>2021-05-24</td>\n",
              "      <td>175.110001</td>\n",
              "      <td>171.750000</td>\n",
              "      <td>172.449997</td>\n",
              "      <td>174.309998</td>\n",
              "      <td>7346700</td>\n",
              "      <td>174.309998</td>\n",
              "      <td>39.570655</td>\n",
              "      <td>41.085182</td>\n",
              "      <td>12.424485</td>\n",
              "      <td>53.509667</td>\n",
              "      <td>28.660696</td>\n",
              "      <td>41.085182</td>\n",
              "      <td>36.750314</td>\n",
              "      <td>29.317229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>2021-05-25</td>\n",
              "      <td>177.449997</td>\n",
              "      <td>175.399994</td>\n",
              "      <td>175.600006</td>\n",
              "      <td>176.169998</td>\n",
              "      <td>8089700</td>\n",
              "      <td>176.169998</td>\n",
              "      <td>44.563666</td>\n",
              "      <td>40.998874</td>\n",
              "      <td>12.330598</td>\n",
              "      <td>53.329473</td>\n",
              "      <td>28.668276</td>\n",
              "      <td>40.998874</td>\n",
              "      <td>42.067160</td>\n",
              "      <td>31.595017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>352</th>\n",
              "      <td>2021-05-26</td>\n",
              "      <td>177.899994</td>\n",
              "      <td>176.199997</td>\n",
              "      <td>176.619995</td>\n",
              "      <td>176.470001</td>\n",
              "      <td>8083100</td>\n",
              "      <td>176.470001</td>\n",
              "      <td>45.352632</td>\n",
              "      <td>40.948168</td>\n",
              "      <td>12.272291</td>\n",
              "      <td>53.220459</td>\n",
              "      <td>28.675877</td>\n",
              "      <td>40.948168</td>\n",
              "      <td>44.958149</td>\n",
              "      <td>34.565272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>353 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date        High         Low  ...        mid        mab        mbb\n",
              "0   2020-01-02  148.199997  145.100006  ...        NaN        NaN        NaN\n",
              "1   2020-01-03  147.899994  146.050003  ...        NaN        NaN        NaN\n",
              "2   2020-01-06  146.029999  144.309998  ...        NaN        NaN        NaN\n",
              "3   2020-01-07  146.869995  145.419998  ...        NaN        NaN        NaN\n",
              "4   2020-01-08  146.130005  144.820007  ...        NaN        NaN        NaN\n",
              "..         ...         ...         ...  ...        ...        ...        ...\n",
              "348 2021-05-20  171.899994  168.979996  ...  41.879895  27.194546  28.757796\n",
              "349 2021-05-21  173.119995  171.520004  ...  41.431161  32.303822  28.746262\n",
              "350 2021-05-24  175.110001  171.750000  ...  41.085182  36.750314  29.317229\n",
              "351 2021-05-25  177.449997  175.399994  ...  40.998874  42.067160  31.595017\n",
              "352 2021-05-26  177.899994  176.199997  ...  40.948168  44.958149  34.565272\n",
              "\n",
              "[353 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qpvh6SPF80A"
      },
      "source": [
        "line = alt.Chart(df).mark_line(interpolate='basis').encode(\n",
        "    x='Date:T',\n",
        "    y='mab:Q',color=alt.value(\"#11bf2e\"))\n",
        "line1 = alt.Chart(df).mark_line(interpolate='basis').encode(\n",
        "    x='Date:T',\n",
        "    y='mbb:Q',color=alt.value(\"#d91009\"))\n",
        "line2 = alt.Chart(df).mark_line(interpolate='basis').encode(\n",
        "    x='Date:T',\n",
        "    y='mid:Q',color=alt.value(\"#eded09\"))\n",
        "line3 = alt.Chart(df).mark_line(interpolate='basis').encode(\n",
        "    x='Date:T',\n",
        "    y='up:Q')\n",
        "line4 = alt.Chart(df).mark_line(interpolate='basis').encode(\n",
        "    x='Date:T',\n",
        "    y='dn:Q')\n",
        "line5 = alt.Chart(pd.DataFrame({'y': [50]})).mark_rule().encode(y='y')\n",
        "line6 = alt.Chart(pd.DataFrame({'y': [60]})).mark_rule().encode(y='y')\n",
        "line7 = alt.Chart(pd.DataFrame({'y': [40]})).mark_rule().encode(y='y').properties(\n",
        "    width=800,\n",
        "    height=500\n",
        ")\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "Di27MkGWF81r",
        "outputId": "c42bf26e-27f0-4912-a642-d51018170b92"
      },
      "source": [
        "line+line1+line2+line3+line4+line5+line6+line7"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.LayerChart(...)"
            ],
            "text/html": [
              "\n",
              "<div id=\"altair-viz-ddebf32659e047f3b3639e185a7c4d03\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "  (function(spec, embedOpt){\n",
              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-ddebf32659e047f3b3639e185a7c4d03\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-ddebf32659e047f3b3639e185a7c4d03\");\n",
              "    }\n",
              "    const paths = {\n",
              "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
              "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
              "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
              "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
              "    };\n",
              "\n",
              "    function loadScript(lib) {\n",
              "      return new Promise(function(resolve, reject) {\n",
              "        var s = document.createElement('script');\n",
              "        s.src = paths[lib];\n",
              "        s.async = true;\n",
              "        s.onload = () => resolve(paths[lib]);\n",
              "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
              "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "      });\n",
              "    }\n",
              "\n",
              "    function showError(err) {\n",
              "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
              "      throw err;\n",
              "    }\n",
              "\n",
              "    function displayChart(vegaEmbed) {\n",
              "      vegaEmbed(outputDiv, spec, embedOpt)\n",
              "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
              "    }\n",
              "\n",
              "    if(typeof define === \"function\" && define.amd) {\n",
              "      requirejs.config({paths});\n",
              "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
              "    } else if (typeof vegaEmbed === \"function\") {\n",
              "      displayChart(vegaEmbed);\n",
              "    } else {\n",
              "      loadScript(\"vega\")\n",
              "        .then(() => loadScript(\"vega-lite\"))\n",
              "        .then(() => loadScript(\"vega-embed\"))\n",
              "        .catch(showError)\n",
              "        .then(() => displayChart(vegaEmbed));\n",
              "    }\n",
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              "</script>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "4mzU-vy9F83Y",
        "outputId": "72c04455-23fd-4d81-f391-4cad88c8c60f"
      },
      "source": [
        "df.set_index(\"Date\")['Close'].plot(figsize = (20,10))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f32abafbb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "eGgTfIExF85d",
        "outputId": "4d681b41-dd1e-44f6-de93-c6f5b6d8dcf5"
      },
      "source": [
        "df = pdr.get_data_yahoo(\"DIS\",\"1-1-2020\")\n",
        "\n",
        "df[df['Close']==85.76000213623047]"
      ],
      "execution_count": 21,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-23</th>\n",
              "      <td>87.279999</td>\n",
              "      <td>81.089996</td>\n",
              "      <td>84.489998</td>\n",
              "      <td>85.760002</td>\n",
              "      <td>32246600</td>\n",
              "      <td>85.760002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low       Open      Close    Volume  Adj Close\n",
              "Date                                                                       \n",
              "2020-03-23  87.279999  81.089996  84.489998  85.760002  32246600  85.760002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcWRYYE4S-9J",
        "outputId": "eb714105-0f14-4e95-ae6f-bf511e363a26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# df['Close'].tail()\n",
        "176.169998/85.76000213623047"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.054221007599236"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GAJqebo0S_GG",
        "outputId": "c7eb68e0-416d-4b06-e73d-bd89cf851c37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "df[df['Close']==df['Close'].max()]"
      ],
      "execution_count": 25,
      "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>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-03-08</th>\n",
              "      <td>203.020004</td>\n",
              "      <td>193.789993</td>\n",
              "      <td>197.309998</td>\n",
              "      <td>201.910004</td>\n",
              "      <td>25093200</td>\n",
              "      <td>201.910004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  High         Low  ...    Volume   Adj Close\n",
              "Date                                ...                      \n",
              "2021-03-08  203.020004  193.789993  ...  25093200  201.910004\n",
              "\n",
              "[1 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vR8_1gGgS_L9",
        "outputId": "4c71c29e-eef4-43bd-905d-60fae71f40c6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "176.169/500.91"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.35169790980415644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCek5jV0S_Qk"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkYn29InS_VF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BsUnnK9AZXK"
      },
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df['Open'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Open']])\n",
        "df['High'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['High']])\n",
        "df['Low'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Low']])\n",
        "df['Close'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Close']])\n",
        "df['Volume'] = pd.to_numeric([float(f\"{i}\".replace(\",\",\"\")) for i in df['Volume']])\n",
        "df.sort_values(by = \"Date\", ascending = True)\n",
        "df = df.set_index(\"Date\")\n",
        "df.to_csv(\"ALICL.csv\")"
      ],
      "execution_count": 73,
      "outputs": []
    }
  ]
}