machine_learning/Combined_Above_i{DMA}_RandomForest.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Combined Above_i{DMA}_RandomForest.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOPIbR7eYhI9E+OyOZs/t6o",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anirbanghoshsbi/.github.io/blob/master/machine_learning/Combined_Above_i%7BDMA%7D_RandomForest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzJUKx8m84df",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baeb54bf-bedb-4b15-c3a1-ed2b2721c4ea"
      },
      "source": [
        "!pip install yfinance "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.72-py2.py3-none-any.whl (27 kB)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.9.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 7.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.6.15)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.28.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.9.1 requests-2.28.1 yfinance-0.1.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hr4xsGCC6Xcy"
      },
      "source": [
        "##### SET THIS IN THE BEGINNING ###########\n",
        "\n",
        "#data='lix15'\n",
        "data = 'nifty100'\n",
        "ema_interval= 50 #30#40#50 (Number of stocks above  EMA)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Emn8S9AUoT9"
      },
      "source": [
        "### Set Start of Train Date Below ####\n",
        "START = \"2013-01-01\"  #yyyy-mm-dd\n",
        "END = '2020-06-01'\n",
        "####Set Start of Test Date Below ####\n",
        "\n",
        "START_testdate= '2021-02-01'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xS0cKMCfIr5O"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "# Plotting graphs\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "# Import libraries\n",
        "import os\n",
        "import sys\n",
        "import requests\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import plot_roc_curve\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from pandas_datareader import data as pdr\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgWMxhWr2lt0"
      },
      "source": [
        "lix15= ['bhartiartl.ns','hindalco.ns','ioc.ns','indusindbk.ns','ongc.ns','powergrid.ns','sbin.ns','sunpharma.ns','tatamotors.ns','tatasteel.ns','ULTRACEMCO.ns']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPsl1pv1p9Sq"
      },
      "source": [
        "nifty100='ongc.ns,indigo.ns,tcs.ns,\\\n",
        "tatamotors.ns,hdfcbank.ns,ofss.ns,\\\n",
        "wipro.ns,infy.ns,hindzinc.ns,\\\n",
        "nestleind.ns,adaniports.ns,\\\n",
        "marico.ns,pghh.ns,\\\n",
        "techm.ns,BAJAJ-AUTO.NS,\\\n",
        "HINDUNILVR.NS,HCLTECH.NS,\\\n",
        "HEROMOTOCO.NS,\\\n",
        "RELIANCE.NS,\\\n",
        "BPCL.NS,BRITANNIA.NS,\\\n",
        "POWERGRID.NS,\\\n",
        "TORNTPHARM.NS,\\\n",
        "COLPAL.NS,\\\n",
        "BHARTIARTL.NS,ICICIGI.NS,ITC.NS,IOC.NS,\\\n",
        "AXISBANK.NS,DRREDDY.NS,HDFCLIFE.NS,\\\n",
        "TATASTEEL.NS,UPL.NS,ABBOTINDIA.NS,\\\n",
        "HDFC.NS,SBILIFE.NS,DMART.NS,\\\n",
        "GODREJCP.NS,\\\n",
        "UBL.NS,\\\n",
        "ASIANPAINT.NS,\\\n",
        "DABUR.NS,\\\n",
        "PIDILITIND.NS,\\\n",
        "TITAN.NS,\\\n",
        "COALINDIA.NS,\\\n",
        "GAIL.NS,\\\n",
        "PETRONET.NS,HDFCAMC.NS,HAVELLS.NS,\\\n",
        "ICICIBANK.NS,MARUTI.NS,BAJAJHLDNG.NS,NAUKRI.NS,\\\n",
        "HINDPETRO.NS,LT.NS,GRASIM.NS,\\\n",
        "CONCOR.NS,JSWSTEEL.NS,M&M.NS,\\\n",
        "ULTRACEMCO.NS,BIOCON.NS,IGL.NS,PFC.NS,\\\n",
        "HINDALCO.NS,CADILAHC.NS,KOTAKBANK.NS,\\\n",
        "BANDHANBNK.NS,NHPC.NS,\\\n",
        "DIVISLAB.NS,\\\n",
        "CIPLA.NS,\\\n",
        "BERGEPAINT.NS,\\\n",
        "ZEEL.NS,\\\n",
        "SHREECEM.NS,\\\n",
        "BAJFINANCE.NS,\\\n",
        "LUPIN.NS,\\\n",
        "SIEMENS.NS,BANKBARODA.NS,\\\n",
        "NTPC.NS,GICRE.NS,MUTHOOTFIN.NS,ACC.NS,INDUSINDBK.NS,\\\n",
        "AMBUJACEM.NS,BAJAJFINSV.NS,PNB.NS,EICHERMOT.NS,PAGEIND.NS,\\\n",
        "SBIN.NS,MOTHERSUMI.NS,ICICIPRULI.NS,SUNPHARMA.NS,MCDOWELL-N.NS,\\\n",
        "AUROPHARMA.NS,\\\n",
        "ADANITRANS.NS,BOSCHLTD.NS,\\\n",
        "DLF.NS,SRTRANSFIN.NS,PEL.NS,NMDC.NS'\n",
        "nifty100 = nifty100.split(',')\n",
        "# ,infratel.ns"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48dvyr69wbhO"
      },
      "source": [
        "if data=='lix15':\n",
        "  ticker = lix15 \n",
        "else:\n",
        "  ticker= nifty100"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrWQlOcDIwvn"
      },
      "source": [
        "lst= []"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPRVFzHMIw2I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62fe038f-7921-477a-fa53-a129e098ad75"
      },
      "source": [
        "for i in range(len(ticker)):\n",
        "  dfi = yf.download(ticker[i], start=START , end = END)\n",
        "  \n",
        "  dfi = dfi.dropna()\n",
        "  dfi = dfi.iloc[:,:4]\n",
        "  #df.head(3)\n",
        "  #print('Done : '+ nifty10[i])\n",
        "  #print(df.head(2))\n",
        "  lst.append(dfi)\n",
        "\n",
        "#df1.head(3)\n",
        "print('\\n All Completed \\n')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\n",
            "1 Failed download:\n",
            "- CADILAHC.NS: No data found, symbol may be delisted\n",
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            "\n",
            " All Completed \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4J9Tyt9Iwx5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32ae7d71-f07c-4f21-da95-79ccb8b839ac"
      },
      "source": [
        "tker = '^NSEI'\n",
        "nifty = yf.download(tker, start=START, end=END)\n",
        "nifty=nifty.dropna()\n",
        "nifty=nifty.iloc[:,:4]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AyiVHPaUyLtQ"
      },
      "source": [
        "nlst=[]\n",
        "for i in range(len(ticker)):\n",
        "  nlst.append(lst[i].Close)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zN5y5i3-AOqw"
      },
      "source": [
        "def get_sum(nlst,ema_interval):\n",
        "  df = pd.DataFrame(nlst).T\n",
        "  namelist =[]\n",
        "  for i in range(1,len(ticker)+1):\n",
        "    namelist.append('Close'+str(i))\n",
        "  df.columns = namelist\n",
        "  for name in namelist:\n",
        "    name=str(name)\n",
        "    var = name+f'ema{ema_interval}'\n",
        "    df[var] = df[name].ewm(span=ema_interval, adjust=False).mean()  \n",
        "  lst_= []\n",
        "  for name in namelist:\n",
        "    name=str(name)\n",
        "    varname = name+f'ema{ema_interval}'\n",
        "    lst_.append((df[varname].diff() > 0 ).astype(int))\n",
        "  df1 = pd.DataFrame(lst_).T\n",
        "  fin_var='sum'+str(ema_interval)\n",
        "  df1[fin_var]=df1.sum(axis=1)\n",
        "  final_df=df1[[fin_var]]\n",
        "  return final_df"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cJmS3EwBLBL"
      },
      "source": [
        "df0=get_sum(nlst,5)\n",
        "df=get_sum(nlst,10)\n",
        "df5=get_sum(nlst,15)\n",
        "df1=get_sum(nlst,20)\n",
        "df25=get_sum(nlst,25)\n",
        "df2=get_sum(nlst,30)\n",
        "df35=get_sum(nlst,35)\n",
        "df3=get_sum(nlst,19)\n",
        "df45=get_sum(nlst,21)\n",
        "df4=get_sum(nlst,22)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QurXepz-C39Y"
      },
      "source": [
        "dataframe_f = pd.concat([df0,df,df1,df25,df35,df45,df5,df2,df3,df4],axis=1)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dv-ZWUwSTkOn"
      },
      "source": [
        "dataframe_f['std']=np.std(dataframe_f.values,axis=1)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDwwl2LpDDR7"
      },
      "source": [
        "dataframe_f.tail(2)\n",
        "dataframe_f['composite']=dataframe_f.sum(axis=1)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5U80w2IP9IM"
      },
      "source": [
        "dataframe_f['total'] = dataframe_f['composite'].rolling(14).mean().diff(1)/dataframe_f['composite'].rolling(14).mean()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKzMMmLyTIKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d8f5c84-7e3b-43c4-9801-cc125a2a6baf"
      },
      "source": [
        "dataframe_f['log_rtn'] = np.log(dataframe_f['composite']).diff()\n",
        "\n",
        "# Var Swap (returns are not demeaned)\n",
        "dataframe_f['real_var'] = np.square(dataframe_f['log_rtn']).rolling(14).sum() * 0.11\n",
        "dataframe_f['real_vol'] = np.sqrt(dataframe_f['real_var'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmGpRDZ-TcGA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "9228f063-1bc8-431e-fd0a-7857ea7b3d80"
      },
      "source": [
        "dataframe_f.tail(2)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sum5  sum10  sum20  sum25  sum35  sum21  sum15  sum30  sum19  \\\n",
              "Date                                                                       \n",
              "2020-05-28    83     78     74     70     59     72     75     64     73   \n",
              "2020-05-29    86     82     80     77     69     80     81     73     79   \n",
              "\n",
              "            sum22       std   composite     total   log_rtn  real_var  \\\n",
              "Date                                                                    \n",
              "2020-05-28     71  6.394529  725.394529  0.051077  0.185502  0.121799   \n",
              "2020-05-29     79  4.498889  790.498889  0.050770  0.085949  0.121025   \n",
              "\n",
              "            real_vol  \n",
              "Date                  \n",
              "2020-05-28  0.348997  \n",
              "2020-05-29  0.347886  "
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              "      <th>real_vol</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2020-05-28</th>\n",
              "      <td>83</td>\n",
              "      <td>78</td>\n",
              "      <td>74</td>\n",
              "      <td>70</td>\n",
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              "      <td>0.348997</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-29</th>\n",
              "      <td>86</td>\n",
              "      <td>82</td>\n",
              "      <td>80</td>\n",
              "      <td>77</td>\n",
              "      <td>69</td>\n",
              "      <td>80</td>\n",
              "      <td>81</td>\n",
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              "      <td>0.050770</td>\n",
              "      <td>0.085949</td>\n",
              "      <td>0.121025</td>\n",
              "      <td>0.347886</td>\n",
              "    </tr>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Et9pqjEJhKN"
      },
      "source": [
        "# I Just need the Close\n",
        "price_data = dataframe_f.copy()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBGSlIJ9JhR1"
      },
      "source": [
        "price_data=pd.concat([price_data,nifty['Close']],axis=1)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EhPGv9ON6Kx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "d679b295-7306-4048-c7b3-329e028fe2ef"
      },
      "source": [
        "dataframe_f.tail(2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sum5  sum10  sum20  sum25  sum35  sum21  sum15  sum30  sum19  \\\n",
              "Date                                                                       \n",
              "2020-05-28    83     78     74     70     59     72     75     64     73   \n",
              "2020-05-29    86     82     80     77     69     80     81     73     79   \n",
              "\n",
              "            sum22       std   composite     total   log_rtn  real_var  \\\n",
              "Date                                                                    \n",
              "2020-05-28     71  6.394529  725.394529  0.051077  0.185502  0.121799   \n",
              "2020-05-29     79  4.498889  790.498889  0.050770  0.085949  0.121025   \n",
              "\n",
              "            real_vol  \n",
              "Date                  \n",
              "2020-05-28  0.348997  \n",
              "2020-05-29  0.347886  "
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              "      <th>2020-05-29</th>\n",
              "      <td>86</td>\n",
              "      <td>82</td>\n",
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              "      <td>77</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "\n",
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              "          const element = document.querySelector('#df-9d0e151e-e910-4cba-accd-4fa21a7848d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34jscDSYJhdD"
      },
      "source": [
        "features = dataframe_f.columns.tolist()"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-Ll-YzAwLUC"
      },
      "source": [
        "# Days Out"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NU0rhsvqJhg6"
      },
      "source": [
        "# define the number of days out you want to predict\n",
        "days_out =30\n",
        "\n",
        "# create a new column that will house the flag, and for each group calculate the diff compared to 30 days ago. Then use Numpy to define the sign.\n",
        "price_data['Signal_Flag'] = price_data['Close'].transform(lambda x : np.sign(x.diff(days_out)))\n",
        "#price_data['Signal_Flag_INR'] = price_data['INRClose'].transform(lambda x : np.sign(x.diff(days_out)))\n",
        "# print the first 50 rows"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueCzWqogJhaD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "08055dbb-51fe-4ffe-a79a-d096fe9a04ce"
      },
      "source": [
        "# Create a column we wish to predict\n",
        "'''\n",
        "    In this case, let's create an output column that will be 1 if the closing price at time 't' is greater than 't-1' and 0 otherwise.\n",
        "    In other words, if the today's closing price is greater than yesterday's closing price it would be 1.\n",
        "'''\n",
        "\n",
        "# Group by the `Symbol` column, then grab the `Close` column.\n",
        "close_groups = price_data['Close']\n",
        "\n",
        "# Apply the lambda function which will return -1.0 for down, 1.0 for up and 0.0 for no change.\n",
        "close_groups = close_groups.transform(lambda x : np.sign(x.diff(days_out)))\n",
        "\n",
        "# add the data to the main dataframe.\n",
        "price_data['Prediction'] = close_groups\n",
        "\n",
        "# for simplicity in later sections I'm going to make a change to our prediction column. To keep this as a binary classifier I'll change flat days and consider them up days.\n",
        "price_data.loc[price_data['Prediction'] == 0.0] = 1.0\n",
        "\n",
        "# print the head\n",
        "price_data.tail(3)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sum5  sum10  sum20  sum25  sum35  sum21  sum15  sum30  sum19  \\\n",
              "Date                                                                       \n",
              "2020-05-27    73     65     61     57     49     60     62     50     61   \n",
              "2020-05-28    83     78     74     70     59     72     75     64     73   \n",
              "2020-05-29    86     82     80     77     69     80     81     73     79   \n",
              "\n",
              "            sum22       std   composite     total   log_rtn  real_var  \\\n",
              "Date                                                                    \n",
              "2020-05-27     58  6.575713  602.575713  0.025199  0.158913  0.120241   \n",
              "2020-05-28     71  6.394529  725.394529  0.051077  0.185502  0.121799   \n",
              "2020-05-29     79  4.498889  790.498889  0.050770  0.085949  0.121025   \n",
              "\n",
              "            real_vol        Close  Signal_Flag  Prediction  \n",
              "Date                                                        \n",
              "2020-05-27  0.346757  9314.950195          1.0         1.0  \n",
              "2020-05-28  0.348997  9490.099609          1.0         1.0  \n",
              "2020-05-29  0.347886  9580.299805          1.0         1.0  "
            ],
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              "      <th></th>\n",
              "      <th>sum5</th>\n",
              "      <th>sum10</th>\n",
              "      <th>sum20</th>\n",
              "      <th>sum25</th>\n",
              "      <th>sum35</th>\n",
              "      <th>sum21</th>\n",
              "      <th>sum15</th>\n",
              "      <th>sum30</th>\n",
              "      <th>sum19</th>\n",
              "      <th>sum22</th>\n",
              "      <th>std</th>\n",
              "      <th>composite</th>\n",
              "      <th>total</th>\n",
              "      <th>log_rtn</th>\n",
              "      <th>real_var</th>\n",
              "      <th>real_vol</th>\n",
              "      <th>Close</th>\n",
              "      <th>Signal_Flag</th>\n",
              "      <th>Prediction</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",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2020-05-27</th>\n",
              "      <td>73</td>\n",
              "      <td>65</td>\n",
              "      <td>61</td>\n",
              "      <td>57</td>\n",
              "      <td>49</td>\n",
              "      <td>60</td>\n",
              "      <td>62</td>\n",
              "      <td>50</td>\n",
              "      <td>61</td>\n",
              "      <td>58</td>\n",
              "      <td>6.575713</td>\n",
              "      <td>602.575713</td>\n",
              "      <td>0.025199</td>\n",
              "      <td>0.158913</td>\n",
              "      <td>0.120241</td>\n",
              "      <td>0.346757</td>\n",
              "      <td>9314.950195</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-28</th>\n",
              "      <td>83</td>\n",
              "      <td>78</td>\n",
              "      <td>74</td>\n",
              "      <td>70</td>\n",
              "      <td>59</td>\n",
              "      <td>72</td>\n",
              "      <td>75</td>\n",
              "      <td>64</td>\n",
              "      <td>73</td>\n",
              "      <td>71</td>\n",
              "      <td>6.394529</td>\n",
              "      <td>725.394529</td>\n",
              "      <td>0.051077</td>\n",
              "      <td>0.185502</td>\n",
              "      <td>0.121799</td>\n",
              "      <td>0.348997</td>\n",
              "      <td>9490.099609</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-29</th>\n",
              "      <td>86</td>\n",
              "      <td>82</td>\n",
              "      <td>80</td>\n",
              "      <td>77</td>\n",
              "      <td>69</td>\n",
              "      <td>80</td>\n",
              "      <td>81</td>\n",
              "      <td>73</td>\n",
              "      <td>79</td>\n",
              "      <td>79</td>\n",
              "      <td>4.498889</td>\n",
              "      <td>790.498889</td>\n",
              "      <td>0.050770</td>\n",
              "      <td>0.085949</td>\n",
              "      <td>0.121025</td>\n",
              "      <td>0.347886</td>\n",
              "      <td>9580.299805</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f81k4Q__Ohzh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a537f92-8160-4578-ce97-74922bd03dd4"
      },
      "source": [
        "# We need to remove all rows that have an NaN value.\n",
        "print('Before NaN Drop we have {} rows and {}  columns in original dataset'.format(price_data.shape[0], price_data.shape[1]))\n",
        "\n",
        "# Any row that has a `NaN` value will be dropped.\n",
        "price_data = price_data.dropna()\n",
        "\n",
        "# Display how much we have left now.\n",
        "print('After NaN Drop we have {} rows and {} columns in original dataset'.format(price_data.shape[0], price_data.shape[1]))\n",
        "\n",
        "# Print the head.\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before NaN Drop we have 1821 rows and 19  columns in original dataset\n",
            "After NaN Drop we have 1768 rows and 19 columns in original dataset\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ekqv1ZhSOnj3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be982a8a-7c57-4b18-9af6-03fff2f906d0"
      },
      "source": [
        "# Number of trees is not a parameter that should be tuned, but just set large enough usually. There is no risk of overfitting in random forest with growing number of # trees, as they are trained independently from each other. \n",
        "n_estimators = list(range(200, 2000, 200))\n",
        "\n",
        "# Number of features to consider at every split\n",
        "max_features = ['auto', 'sqrt', None, 'log2']\n",
        "\n",
        "# Maximum number of levels in tree\n",
        "# Max depth is a parameter that most of the times should be set as high as possible, but possibly better performance can be achieved by setting it lower.\n",
        "max_depth = list(range(10, 110, 10))\n",
        "max_depth.append(None)\n",
        "\n",
        "# Minimum number of samples required to split a node\n",
        "# Higher values prevent a model from learning relations which might be highly specific to the particular sample selected for a tree. Too high values can also lead to # under-fitting hence depending on the level of underfitting or overfitting, you can tune the values for min_samples_split.\n",
        "min_samples_split = [2, 5, 10, 20, 30, 40]\n",
        "\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [1, 2, 7, 12, 14, 16 ,20]\n",
        "\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True, False]\n",
        "\n",
        "# Create the random grid\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf,\n",
        "               'bootstrap': bootstrap}\n",
        "\n",
        "print(random_grid)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800], 'max_features': ['auto', 'sqrt', None, 'log2'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None], 'min_samples_split': [2, 5, 10, 20, 30, 40], 'min_samples_leaf': [1, 2, 7, 12, 14, 16, 20], 'bootstrap': [True, False]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHDZ-3j3Ongc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "a7c5fc4d-2099-4886-efdc-9c6dd49612da"
      },
      "source": [
        "X_Cols = price_data[features]\n",
        "Y_Cols = price_data['Prediction']\n",
        "#,'log5vol','log5','RSI','Price_Rate_Of_Change','log5volM','log5M','MMI_roc'\n",
        "# Split X and y into X_train and X_test\n",
        "samp_train= int(len(price_data)*.75) -50\n",
        "samp_test= samp_train+50\n",
        "X_train = X_Cols[:samp_train]\n",
        "X_test = X_Cols[samp_test:]\n",
        "y_train = Y_Cols[:samp_train]\n",
        "y_test = Y_Cols[samp_test:]\n",
        "\n",
        "\n",
        "#X_train, X_test, y_train, y_test = train_test_split(X_Cols, Y_Cols, random_state = 0)\n",
        "\n",
        "\n",
        "# New Random Forest Classifier to house optimal parameters\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "# Specfiy the details of our Randomized Search\n",
        "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
        "'''\n",
        "# Fit the random search model############################~~~~~~~~~~~~~~~~~~################\n",
        "rf_random.fit(X_train, y_train)\n",
        "print(f\"Best score: {rf_random.best_score_}\")\n",
        "print(\"Best parameters set:\")\n",
        "best_parameters = rf_random.best_estimator_.get_params()\n",
        "for param_name in sorted(random_grid.keys()):\n",
        "  print(f\"\\t{param_name}: {best_parameters[param_name]}\")\n",
        "'''\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n# Fit the random search model############################~~~~~~~~~~~~~~~~~~################\\nrf_random.fit(X_train, y_train)\\nprint(f\"Best score: {rf_random.best_score_}\")\\nprint(\"Best parameters set:\")\\nbest_parameters = rf_random.best_estimator_.get_params()\\nfor param_name in sorted(random_grid.keys()):\\n  print(f\"\\t{param_name}: {best_parameters[param_name]}\")\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0M4IaajOh4t"
      },
      "source": [
        "# Create a Random Forest Classifier\n",
        "rand_frst_clf = RandomForestClassifier(n_estimators = 600,max_depth=40,min_samples_leaf=20,max_features = \"sqrt\",min_samples_split=10, criterion = \"gini\", bootstrap=True,random_state = 42)\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CICRlPPIe2W",
        "outputId": "c5c7168a-2eea-432b-fa45-30f4dabc8698"
      },
      "source": [
        "samp_train,samp_test"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1276, 1326)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ycz6zEiJO9zl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "26b7638e-7ab1-415a-b1ca-c5617b05a660"
      },
      "source": [
        "# Fit the data to the model\n",
        "rand_frst_clf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = rand_frst_clf.predict(X_test)\n",
        "\n",
        "# Define the traget names\n",
        "target_names = ['Down Day', 'Up Day']\n",
        "\n",
        "# Build a classifcation report\n",
        "report = classification_report(y_true = y_test, y_pred = y_pred, target_names = target_names, output_dict = True)\n",
        "\n",
        "# Add it to a data frame, transpose it for readability.\n",
        "report_df = pd.DataFrame(report).transpose()\n",
        "report_df"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              precision    recall  f1-score     support\n",
              "Down Day       0.723502  0.897143  0.801020  175.000000\n",
              "Up Day         0.920000  0.775281  0.841463  267.000000\n",
              "accuracy       0.823529  0.823529  0.823529    0.823529\n",
              "macro avg      0.821751  0.836212  0.821242  442.000000\n",
              "weighted avg   0.842201  0.823529  0.825451  442.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e2d25fd-71d7-4688-b6d8-37783bee77f0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Down Day</th>\n",
              "      <td>0.723502</td>\n",
              "      <td>0.897143</td>\n",
              "      <td>0.801020</td>\n",
              "      <td>175.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Up Day</th>\n",
              "      <td>0.920000</td>\n",
              "      <td>0.775281</td>\n",
              "      <td>0.841463</td>\n",
              "      <td>267.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.823529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.821751</td>\n",
              "      <td>0.836212</td>\n",
              "      <td>0.821242</td>\n",
              "      <td>442.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.842201</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.825451</td>\n",
              "      <td>442.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e2d25fd-71d7-4688-b6d8-37783bee77f0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e2d25fd-71d7-4688-b6d8-37783bee77f0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e2d25fd-71d7-4688-b6d8-37783bee77f0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7U2mw_TO9wW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a5a7c194-9e26-493e-da4d-4b0e0dd267f3"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
        "\n",
        "rf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "true_negatives = rf_matrix[0][0]\n",
        "false_negatives = rf_matrix[1][0]\n",
        "true_positives = rf_matrix[1][1]\n",
        "false_positives = rf_matrix[0][1]\n",
        "\n",
        "accuracy = (true_negatives + true_positives) / (true_negatives + true_positives + false_negatives + false_positives)\n",
        "percision = true_positives / (true_positives + false_positives)\n",
        "recall = true_positives / (true_positives + false_negatives)\n",
        "specificity = true_negatives / (true_negatives + false_positives)\n",
        "\n",
        "print('Accuracy: {}'.format(float(accuracy)))\n",
        "print('Percision: {}'.format(float(percision)))\n",
        "print('Recall: {}'.format(float(recall)))\n",
        "print('Specificity: {}'.format(float(specificity)))\n",
        "\n",
        "disp = plot_confusion_matrix(rand_frst_clf, X_test, y_test, display_labels = ['Down Day', 'Up Day'], normalize = 'true', cmap=plt.cm.Blues)\n",
        "disp.ax_.set_title('Confusion Matrix - Normalized')\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8235294117647058\n",
            "Percision: 0.92\n",
            "Recall: 0.7752808988764045\n",
            "Specificity: 0.8971428571428571\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9NIrqUSPDkT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b663a3af-d6b3-465b-ed16-9362d7d11b74"
      },
      "source": [
        "feature_imp = pd.Series(rand_frst_clf.feature_importances_, index=X_Cols.columns).sort_values(ascending=False)\n",
        "feature_imp"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sum35        0.220271\n",
              "sum30        0.170963\n",
              "real_var     0.162920\n",
              "real_vol     0.159724\n",
              "sum25        0.079663\n",
              "sum22        0.064454\n",
              "sum21        0.036118\n",
              "sum20        0.024537\n",
              "total        0.022217\n",
              "composite    0.020785\n",
              "sum19        0.009017\n",
              "sum15        0.007634\n",
              "sum5         0.006896\n",
              "sum10        0.005956\n",
              "log_rtn      0.004991\n",
              "std          0.003855\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cw0GDvmrOhv6"
      },
      "source": [
        ""
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Opt2DxoKSpE-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e4aeb43-cf6d-40c9-e4ad-ccad2fe4cb77"
      },
      "source": [
        "Ticker = '^NSEI'\n",
        "nifty_n = yf.download(Ticker, start=START_testdate)\n",
        "nifty_n=nifty_n.dropna()\n",
        "nifty_n=nifty_n.iloc[:,:4]"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sH0oZvCTMoIG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "0a0cee44-b485-4e0e-ba69-e67767a260bd"
      },
      "source": [
        "nifty_n.tail(2)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Open     High           Low         Close\n",
              "Date                                                         \n",
              "2022-07-07  16113.750000  16150.5  16045.950195  16132.900391\n",
              "2022-07-08  16273.650391  16275.5  16157.900391  16220.599609"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ebf9248-db82-4081-9df4-7da4c048e2a1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-07-07</th>\n",
              "      <td>16113.750000</td>\n",
              "      <td>16150.5</td>\n",
              "      <td>16045.950195</td>\n",
              "      <td>16132.900391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-08</th>\n",
              "      <td>16273.650391</td>\n",
              "      <td>16275.5</td>\n",
              "      <td>16157.900391</td>\n",
              "      <td>16220.599609</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUlacFVHJhYT",
        "outputId": "852e99a6-7bcc-4b19-f104-5d0cb3655a24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "lst1=[]\n",
        "for i in range(len(ticker)):\n",
        "  dfn = yf.download(ticker[i], start=START_testdate)\n",
        "  dfn = dfn.dropna()\n",
        "  dfn = dfn.iloc[:,:4]\n",
        "  #df.head(3)\n",
        "  #print('Done : '+ nifty10[i])\n",
        "  #print(df.head(2))\n",
        "  lst1.append(dfn)\n",
        "\n",
        "#df1.head(3)\n",
        "print('\\n All Completed \\n')\n",
        "#df1.head(3)\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "\n",
            "1 Failed download:\n",
            "- CADILAHC.NS: No data found, symbol may be delisted\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "\n",
            " All Completed \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldFHI4kzHbdH"
      },
      "source": [
        "nlst=[]\n",
        "for i in range(len(ticker)):\n",
        "  nlst.append(lst1[i].Close)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05kYT_pVzdTF"
      },
      "source": [
        "dfn=get_sum(nlst,10)\n",
        "df1n=get_sum(nlst,20)\n",
        "df2n=get_sum(nlst,30)\n",
        "df3n=get_sum(nlst,19)\n",
        "df4n=get_sum(nlst,21)\n",
        "df5n=get_sum(nlst,5)\n",
        "df15n=get_sum(nlst,15)\n",
        "df25n=get_sum(nlst,25)\n",
        "df35n=get_sum(nlst,35)\n",
        "df45n=get_sum(nlst,22)\n",
        "\n",
        "\n",
        "df_new=pd.concat([dfn,df1n,df2n,df3n,df4n,df5n,df15n,df25n,df35n,df45n],axis=1)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqYxPft3VUyl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99a30edc-ea47-45c7-a00a-0032a1a87e12"
      },
      "source": [
        "df_new['std']=np.std(df_new.values,axis=1)\n",
        "df_new['composite']=df_new.sum(axis=1)\n",
        "df_new['total'] = df_new['composite'].rolling(14).mean().diff(1)/df_new['composite'].rolling(14).mean()\n",
        "df_new['log_rtn'] = np.log(df_new['composite']).diff()\n",
        "\n",
        "# Var Swap (returns are not demeaned)\n",
        "df_new['real_var'] = np.square(df_new['log_rtn']).rolling(14).sum() * 0.11\n",
        "df_new['real_vol'] = np.sqrt(df_new['real_var'])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/arraylike.py:364: RuntimeWarning: divide by zero encountered in log\n",
            "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMscQlNtJhHF"
      },
      "source": [
        "price_data_new = df_new.copy()\n",
        "price_data_new=pd.concat([price_data_new,nifty_n['Close']],axis=1)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GV7-qkZOFxhc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "cc500616-09cb-43bd-e5b8-00ef88a85cb4"
      },
      "source": [
        "price_data_new.tail(2)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sum10  sum20  sum30  sum19  sum21  sum5  sum15  sum25  sum35  \\\n",
              "Date                                                                       \n",
              "2022-07-07     90     82     68     82     80    91     86     78     62   \n",
              "2022-07-08     87     79     70     80     79    84     84     77     66   \n",
              "\n",
              "            sum22       std   composite     total   log_rtn  real_var  \\\n",
              "Date                                                                    \n",
              "2022-07-07     80  8.584288  807.584288  0.135096  0.126768  0.631240   \n",
              "2022-07-08     79  6.053924  791.053924  0.113421 -0.020681  0.535534   \n",
              "\n",
              "            real_vol         Close  \n",
              "Date                                \n",
              "2022-07-07  0.794506  16132.900391  \n",
              "2022-07-08  0.731802  16220.599609  "
            ],
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-990fa3a1-7952-4a7b-8c98-c3a54cfe6177')\"\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-990fa3a1-7952-4a7b-8c98-c3a54cfe6177 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-990fa3a1-7952-4a7b-8c98-c3a54cfe6177');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5MY6JmaOMEP"
      },
      "source": [
        "# Group by the `Symbol` column, then grab the `Close` column.\n",
        "close_groups = price_data_new['Close']\n",
        "\n",
        "# Apply the lambda function which will return -1.0 for down, 1.0 for up and 0.0 for no change.\n",
        "close_groups = close_groups.transform(lambda x : np.sign(x.diff(days_out)))\n",
        "\n",
        "# add the data to the main dataframe.\n",
        "price_data_new['Prediction'] = close_groups\n",
        "price_data_new = price_data_new.dropna()"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY8t427vW7-n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "b1132356-5fbc-4f7b-ee6f-4edba41c4b6a"
      },
      "source": [
        "ppp =price_data_new.reset_index()\n",
        "ppp.head(2)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  sum10  sum20  sum30  sum19  sum21  sum5  sum15  sum25  sum35  \\\n",
              "0 2021-03-16     35     44     51     44     45    35     41     50     54   \n",
              "1 2021-03-17     21     27     31     26     26    17     23     29     32   \n",
              "\n",
              "   sum22       std   composite     total   log_rtn  real_var  real_vol  \\\n",
              "0     45  5.969925  449.969925  0.005488  0.104541  0.102837  0.320682   \n",
              "1     28  4.358899  264.358899 -0.034532 -0.531873  0.126810  0.356104   \n",
              "\n",
              "          Close  Prediction  \n",
              "0  14910.450195         1.0  \n",
              "1  14721.299805         1.0  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>sum10</th>\n",
              "      <th>sum20</th>\n",
              "      <th>sum30</th>\n",
              "      <th>sum19</th>\n",
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              "      <th>sum22</th>\n",
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              "      <th>composite</th>\n",
              "      <th>total</th>\n",
              "      <th>log_rtn</th>\n",
              "      <th>real_var</th>\n",
              "      <th>real_vol</th>\n",
              "      <th>Close</th>\n",
              "      <th>Prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-03-16</td>\n",
              "      <td>35</td>\n",
              "      <td>44</td>\n",
              "      <td>51</td>\n",
              "      <td>44</td>\n",
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              "      <td>35</td>\n",
              "      <td>41</td>\n",
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              "      <td>54</td>\n",
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              "      <td>5.969925</td>\n",
              "      <td>449.969925</td>\n",
              "      <td>0.005488</td>\n",
              "      <td>0.104541</td>\n",
              "      <td>0.102837</td>\n",
              "      <td>0.320682</td>\n",
              "      <td>14910.450195</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>2021-03-17</td>\n",
              "      <td>21</td>\n",
              "      <td>27</td>\n",
              "      <td>31</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>32</td>\n",
              "      <td>28</td>\n",
              "      <td>4.358899</td>\n",
              "      <td>264.358899</td>\n",
              "      <td>-0.034532</td>\n",
              "      <td>-0.531873</td>\n",
              "      <td>0.126810</td>\n",
              "      <td>0.356104</td>\n",
              "      <td>14721.299805</td>\n",
              "      <td>1.0</td>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7acd0f21-aff8-45a1-ac56-48060242958c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7acd0f21-aff8-45a1-ac56-48060242958c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZzPIxZWOMJS"
      },
      "source": [
        "pred = pd.DataFrame(rand_frst_clf.predict(price_data_new[features]))\n",
        "pred = pd.concat([pred , ppp.Date,ppp.Close],axis=1)\n",
        "pred.columns=['pred','Date','Close']\n",
        "pred= pred.set_index('Date')"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHDIgAP4182i"
      },
      "source": [
        "l=len(pred)\n"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCvOl8bNrbhk"
      },
      "source": [
        "# PREDICTIONS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4oxYJkz2cfN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "3c288841-8e94-48ad-e1a8-3bbaea2a935f"
      },
      "source": [
        "fig, ax = plt.subplots(2,1,figsize=(20,7))\n",
        "ax[0].plot(nifty_n.index.values[-l:],nifty_n['Close'].values[-l:],'*-',linewidth=1 )\n",
        "ax[1].plot(nifty_n.index.values[-l:],pred.pred.values[-l:],'*-',linewidth=1 )\n",
        "\n",
        "\n",
        "if data=='lix15':\n",
        "  plt.suptitle('Random Forest Advance Decline Line **LIX15**')\n",
        "  plt.savefig('LIX.png')\n",
        "else:\n",
        "  plt.suptitle('Random Forest Advance Decline Line **NIFTY100**')\n",
        "  plt.savefig('n100.png') \n",
        "plt.show()   "
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkFHMjy6OMN9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "054fb0d8-46e3-4c7c-f9a9-e16f4e0acab4"
      },
      "source": [
        "pred.tail(3)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            pred         Close\n",
              "Date                          \n",
              "2022-07-06  -1.0  15989.799805\n",
              "2022-07-07   1.0  16132.900391\n",
              "2022-07-08   1.0  16220.599609"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fafd65d4-650b-4215-88a8-a459e0474e58\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pred</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-07-06</th>\n",
              "      <td>-1.0</td>\n",
              "      <td>15989.799805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-07</th>\n",
              "      <td>1.0</td>\n",
              "      <td>16132.900391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-07-08</th>\n",
              "      <td>1.0</td>\n",
              "      <td>16220.599609</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fafd65d4-650b-4215-88a8-a459e0474e58')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fafd65d4-650b-4215-88a8-a459e0474e58 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fafd65d4-650b-4215-88a8-a459e0474e58');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hK0j3dAFVGzW"
      },
      "source": [
        "pred=pred.reset_index()\n",
        "# saving the dataframe\n",
        "pred[['Date','pred']].to_csv('dma.csv')"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuO-sQjfOMS0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c828180a-9f8f-41b0-a5d7-e39245624e41"
      },
      "source": [
        "days_out"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hmhm4AEIYyaL"
      },
      "source": [
        "#pred=pred.reset_index()"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXZoforrYxlr"
      },
      "source": [
        "#\n",
        "#fig, ax = plt.subplots(1,1,figsize=(20,7))\n",
        "#Identifying the buy/sell zone\n",
        "pred['Buy_ind'] = np.where((pred['pred']==1), 1, 0)\n",
        "pred['Sell_ind'] = np.where((pred['pred']==-1), 0, 1)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFUpHtw2Y7Ut"
      },
      "source": [
        "nifty_n['50_EMA'] = nifty_n[\"Close\"].rolling(50).mean()"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhkKOdrXOMMl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "outputId": "f45fb403-4ae8-4b4c-ab6e-a8fe52b531eb"
      },
      "source": [
        "plt.figure(figsize=(20,7))\n",
        "## plotting the buy and sell signals on graph\n",
        "plt.plot(nifty_n.index.values[-l:],nifty_n['Close'].values[-l:],'-',linewidth=1)\n",
        "plt.scatter(pred.loc[pred['Buy_ind'] ==1 , 'Date'].values,pred.loc[pred['Buy_ind'] ==1, 'Close'].values, label='skitscat', color='green', s=25, marker=\"^\")\n",
        "plt.scatter(pred.loc[pred['Sell_ind'] ==0 , 'Date'].values,pred.loc[pred['Sell_ind'] ==0, 'Close'].values, label='skitscat', color='red', s=25, marker=\"v\")\n",
        "plt.plot(nifty_n.index.values[-l:],nifty_n['50_EMA'].values[-l:])\n",
        "## Adding labels\n",
        "plt.xlabel('Date')  \n",
        "plt.ylabel('Close Price')  \n",
        "plt.title('AD Nifty price with buy and sell signal ' ) \n",
        "\n",
        "plt.savefig('a.png')\n",
        "plt.show();\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXxgcsSb9uOD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b874025-f618-4617-dab3-c87295f57040"
      },
      "source": [
        "pred.pred.tail(5)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "322   -1.0\n",
              "323   -1.0\n",
              "324   -1.0\n",
              "325    1.0\n",
              "326    1.0\n",
              "Name: pred, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3Rq1cXbaIwP"
      },
      "source": [
        "pred['actual']=np.where(pred.Close>pred.Close.shift(1),1,-1)\n",
        "pred['dummyS']=pred['pred'].shift(1)\n",
        "pred=pred.dropna()"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gH4ero_eaItJ",
        "outputId": "20872201-1d50-4280-9595-0860fa2a45fb"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
        "\n",
        "rf_matrix = confusion_matrix(pred.actual, pred.dummyS)\n",
        "\n",
        "true_negatives = rf_matrix[0][0]\n",
        "false_negatives = rf_matrix[1][0]\n",
        "true_positives = rf_matrix[1][1]\n",
        "false_positives = rf_matrix[0][1]\n",
        "from sklearn.metrics import average_precision_score\n",
        "average_precision = average_precision_score(pred.actual, pred.dummyS)\n",
        "\n",
        "accuracy = (true_negatives + true_positives) / (true_negatives + true_positives + false_negatives + false_positives)\n",
        "percision = true_positives / (true_positives + false_positives)\n",
        "recall = true_positives / (true_positives + false_negatives)\n",
        "specificity = true_negatives / (true_negatives + false_positives)\n",
        "f1score= 2*recall*percision/(percision+recall)\n",
        "print('Accuracy: {}'.format(float(accuracy)))\n",
        "print('Percision: {}'.format(float(percision)))\n",
        "print('Recall: {}'.format(float(recall)))\n",
        "print('Specificity: {}'.format(float(specificity)))\n",
        "print('F1-Score: {}'.format(float(f1score)))\n",
        "print('Average Precision : {}'.format(float(average_precision)))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.5214723926380368\n",
            "Percision: 0.5337423312883436\n",
            "Recall: 0.5209580838323353\n",
            "Specificity: 0.5220125786163522\n",
            "F1-Score: 0.5272727272727272\n",
            "Average Precision : 0.523456155174314\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd7OrjEg64gb"
      },
      "source": [
        ""
      ],
      "execution_count": 56,
      "outputs": []
    }
  ]
}