mod1.ipynb

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    "language_info": {
      "name": "python",
      "codemirror_mode": {
        "name": "ipython",
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    "version": 3,
    "colab": {
      "name": "mod1.ipynb",
      "version": "0.3.2",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vnCCCio0mLX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiBgAJX_0mLc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df = pd.read_csv(\"HEROMOTOCO.csv\")\n",
        "df = df[df[\"Series\"] == \"EQ\"]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzydD3in0mLf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df.Date = pd.to_datetime(df['Date'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqqD0Cwu0mLi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df['Day_Perc_Change'] = df['Close Price'].pct_change()*100\n",
        "df = df.drop(df.index[0])\n",
        "# VWAP = sum(price*volume)/sum(volume)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1uWESF70mLk",
        "colab_type": "code",
        "colab": {},
        "outputId": "5e7b4672-01b6-46ec-d4a2-7c773b5e2c96"
      },
      "source": [
        "\n",
        "def cases(num):\n",
        "    print(num)\n",
        "    if num >= -0.5 and num <= 0.5 : return 'Slight or No change'\n",
        "    elif num >= 0.5 and num <= 1 : return 'Slight positive'\n",
        "    elif num >= -1 and num <= -0.5 : return 'Slight negative'\n",
        "    elif num >= 1 and num <= 3 : return 'Positive'\n",
        "    elif num >= -3  and num <= -1 : return 'Negative'\n",
        "    elif num >= 3 and num <= 7 : return 'Among top gainers'\n",
        "    elif num >= -7  and num <= -3 : return 'Among top losers'\n",
        "    elif num > 7 : return 'Bull run'\n",
        "    elif num < -7 : return 'Bear drop'\n",
        "\n",
        "df[\"Trend\"] = df.Day_Perc_Change.apply(cases)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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            "-0.49680489978572995\n",
            "0.6669842782277335\n",
            "1.4614292475153778\n",
            "1.2183517734201565\n",
            "-0.8645161290322556\n",
            "-0.3904724716907504\n",
            "-1.8834817345205535\n",
            "1.8073893687454712\n",
            "4.486844072357599\n",
            "-1.0659417309033747\n",
            "-1.2925502106043374\n",
            "0.37544412292589424\n",
            "2.7149816629262746\n",
            "-0.2238209432454119\n",
            "-0.1709127810713973\n",
            "-1.852941700996924\n",
            "-0.5760075589635427\n",
            "-2.4252060602737657\n",
            "-2.0771680089904376\n",
            "-0.18744859509191025\n",
            "-0.22421094992621793\n",
            "-1.0601928321745668\n",
            "-0.04853049656403652\n",
            "-1.2138515022626195\n",
            "-0.6861434413337419\n",
            "1.0848262892210192\n",
            "0.20954507177408654\n",
            "0.4475278483486278\n",
            "0.4085682600828866\n",
            "2.199228816679266\n",
            "-0.7128772940997896\n",
            "-1.2049343110296484\n",
            "1.4225795852098155\n",
            "-1.1148590703790484\n",
            "0.25824853530680425\n",
            "1.491676598362246\n",
            "2.3031175423311456\n",
            "1.4422186840448825\n",
            "0.06935192451591465\n",
            "-1.8985610329922964\n",
            "-1.912994980479632\n",
            "-0.991262485548039\n",
            "0.013400206746050358\n",
            "-0.3158196956646542\n",
            "-3.538786482334877\n",
            "1.5864800843999438\n",
            "0.5368969706470272\n",
            "-1.3292274109300672\n",
            "-0.15604630032000077\n",
            "-1.0940312976042144\n",
            "1.5221826619194356\n",
            "-1.1998817850457977\n",
            "0.13759821321739363\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoHRCTFM0mLp",
        "colab_type": "code",
        "colab": {},
        "outputId": "1f862500-6c34-4fce-a44e-47f4961674ef"
      },
      "source": [
        "\n",
        "print(df[['Trend', 'Total Traded Quantity']].groupby(['Trend'], as_index=False).mean().sort_values(by='Total Traded Quantity', ascending=False))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Trend  Total Traded Quantity\n",
            "2             Bull run           2.742733e+06\n",
            "1     Among top losers           9.763052e+05\n",
            "0    Among top gainers           6.578466e+05\n",
            "4             Positive           4.888002e+05\n",
            "3             Negative           4.579074e+05\n",
            "7      Slight positive           4.221623e+05\n",
            "6  Slight or No change           3.759908e+05\n",
            "5      Slight negative           3.724389e+05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMqBvKGN0mLt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "export_csv = df.to_csv(r'week2.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVcBeux-0mLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}