Analysis v2 26.05.2021.ipynb

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  "metadata": {
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      "name": "Welcome To Colaboratory",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "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%20v2%2026.05.2021.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 = \"KMCDB\""
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "uuPQanuOAZTi",
        "outputId": "d8365bbb-6b49-445f-85ff-a9dfead664a2"
      },
      "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",
        "  # 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": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Done Scrapping SINDU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "id": "roQnqLJ5AZVn",
        "outputId": "b5981450-325b-4d01-e954-ef81f2d8b6c2"
      },
      "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": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-3d1827ab154f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mLow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mVolume\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mDate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
          ]
        }
      ]
    },
    {
      "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": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "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": 51,
      "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": 52,
      "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": 53,
      "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": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6vZ7iPzpF8sz",
        "outputId": "2c5da928-6ef9-492d-f433-187d20fa5b3c"
      },
      "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": 55,
      "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\": \"KMCDB\"}, \"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",
              "                            \n",
              "var gd = document.getElementById('0e3a808d-a582-4ad0-a45d-b7a659011503');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        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": {
        "id": "rtvz9iBkF8xo"
      },
      "source": [
        "stock = \"SDLBSL\"\n",
        "# df = pd.read_csv(f\"/content/{stock}.csv\")\n",
        "df = pdr.get_data_yahoo(\"V\",\"1-1-2020\")\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')"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "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=1000,\n",
        "    height=500\n",
        ")\n"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "Di27MkGWF81r",
        "outputId": "5c9f953e-4445-42bb-bd46-b503f0a19b0a"
      },
      "source": [
        "line+line1+line2+line3+line4+line5+line6+line7"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "alt.LayerChart(...)"
            ],
            "text/html": [
              "\n",
              "<div id=\"altair-viz-51584a2c26a142bb8cd8af9a8ce5cf6f\"></div>\n",
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              "    let outputDiv = document.currentScript.previousElementSibling;\n",
              "    if (outputDiv.id !== \"altair-viz-51584a2c26a142bb8cd8af9a8ce5cf6f\") {\n",
              "      outputDiv = document.getElementById(\"altair-viz-51584a2c26a142bb8cd8af9a8ce5cf6f\");\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",
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              "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": {\"type\": \"line\", \"interpolate\": \"basis\"}, \"encoding\": {\"color\": {\"value\": \"#11bf2e\"}, \"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mab\"}}}, {\"mark\": {\"type\": \"line\", \"interpolate\": \"basis\"}, \"encoding\": {\"color\": {\"value\": \"#d91009\"}, \"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mbb\"}}}, {\"mark\": {\"type\": \"line\", \"interpolate\": \"basis\"}, \"encoding\": {\"color\": {\"value\": \"#eded09\"}, \"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"mid\"}}}, {\"mark\": {\"type\": \"line\", \"interpolate\": \"basis\"}, \"encoding\": {\"x\": {\"type\": \"temporal\", \"field\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"up\"}}}, {\"mark\": {\"type\": 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              "</script>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        },
        "id": "4mzU-vy9F83Y",
        "outputId": "65865f03-4f3e-4eaf-cd81-f01f10540c38"
      },
      "source": [
        "df.set_index(\"Date\")['Close'].plot(figsize = (20,10))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7b81ac0250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "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": {
        "id": "eGgTfIExF85d"
      },
      "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": []
    }
  ]
}