internship_module_3.ipynb

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      "name": "internship_module_3.ipynb",
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      "metadata": {
        "id": "ipZ5z6de_zC9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "sns.set(style=\"darkgrid\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R31TP-bm_zDG",
        "colab_type": "code",
        "colab": {},
        "outputId": "57902b40-0279-4206-c546-b097356ab0fa"
      },
      "source": [
        "\n",
        "df = pd.read_csv(\"GOLD.csv\")\n",
        "df.Date = pd.to_datetime(df['Date'])\n",
        "df = df.set_index('Date')\n",
        "print(df.index.dtype == \"datetime64[ns]\")\n",
        "# print(df.columns)\n",
        "print(df.info())\n",
        "\n",
        "# for furthure exploration\n",
        "df_droped = df.dropna()\n",
        "df_to_fill = df.iloc[411 :, :]\n",
        "\n",
        "# checking for empty column\n",
        "empties = np.where(pd.isnull(df))\n",
        "print('(starting_row, col) = ', empties[0][0], empties[1][0])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 512 entries, 2017-05-04 to 2019-05-03\n",
            "Data columns (total 8 columns):\n",
            "Price       512 non-null int64\n",
            "Open        512 non-null int64\n",
            "High        512 non-null int64\n",
            "Low         512 non-null int64\n",
            "Vol.        512 non-null object\n",
            "Change %    512 non-null object\n",
            "Pred        411 non-null float64\n",
            "new         512 non-null float64\n",
            "dtypes: float64(2), int64(4), object(2)\n",
            "memory usage: 36.0+ KB\n",
            "None\n",
            "(starting_row, col) =  411 6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NUwtFHG_zDP",
        "colab_type": "code",
        "colab": {},
        "outputId": "d3443a44-be4f-4c2f-ca10-9d9e6be0c23d"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "X = df_droped[['Open', 'High', 'Low']].values\n",
        "pred = df_droped.Pred.values\n",
        "new =  df_droped.new.values\n",
        "\n",
        "#_______________________________linear Regression______________________\n",
        "reg = LinearRegression().fit(X, pred)\n",
        "print('pred\\'s score =', reg.score(X, pred))\n",
        "print('pred\\'s coeff. = ', reg.coef_, 'bias  = ', reg.intercept_)\n",
        "\n",
        "# pridicting the emty column and filling the rows of Pred\n",
        "pred_to_fill = reg.predict(df_to_fill[['Open', 'High', 'Low']])\n",
        "new_pridicted_pred = np.hstack((df_droped['Pred'].values, pred_to_fill))\n",
        "df.Pred = new_pridicted_pred\n",
        "print('')\n",
        "\n",
        "reg2 = LinearRegression().fit(X, new)\n",
        "print('new\\'s score =', reg2.score(X, new))\n",
        "print('new\\'s coeff. = ', reg2.coef_, 'bias = ', reg2.intercept_)\n",
        "\n",
        "\n",
        "ax = sns.kdeplot(pred_to_fill, label='Pridicted Entries', shade=True, color=\"r\")\n",
        "ax = sns.kdeplot(df_droped['Pred'], label='Pred Entries', shade=True, color=\"b\")"
      ],
      "execution_count": 0,
      "outputs": [
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          "text": [
            "pred's score = 0.796205928915778\n",
            "pred's coeff. =  [ 1.87728492  0.55565405 -2.43980328] bias  =  203.5283857599398\n",
            "\n",
            "new's score = 0.9052828458304777\n",
            "new's coeff. =  [-1.56905117  1.7921037  -0.22656291] bias =  102.77188726035811\n"
          ],
          "name": "stdout"
        },
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xlink:href=\"#ArialMT-80\"/>\r\n      <use x=\"66.699219\" xlink:href=\"#ArialMT-114\"/>\r\n      <use x=\"100\" xlink:href=\"#ArialMT-101\"/>\r\n      <use x=\"155.615234\" xlink:href=\"#ArialMT-100\"/>\r\n      <use x=\"211.230469\" xlink:href=\"#ArialMT-32\"/>\r\n      <use x=\"239.013672\" xlink:href=\"#ArialMT-69\"/>\r\n      <use x=\"305.712891\" xlink:href=\"#ArialMT-110\"/>\r\n      <use x=\"361.328125\" xlink:href=\"#ArialMT-116\"/>\r\n      <use x=\"389.111328\" xlink:href=\"#ArialMT-114\"/>\r\n      <use x=\"422.412109\" xlink:href=\"#ArialMT-105\"/>\r\n      <use x=\"444.628906\" xlink:href=\"#ArialMT-101\"/>\r\n      <use x=\"500.244141\" xlink:href=\"#ArialMT-115\"/>\r\n     </g>\r\n    </g>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"p3122d12862\">\r\n   <rect height=\"217.44\" width=\"334.8\" x=\"50.341094\" y=\"13.2\"/>\r\n  </clipPath>\r\n </defs>\r\n</svg>\r\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3z1dEnk4_zDZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "4aa57b2b-901e-443e-f689-9fab6415d12b"
      },
      "source": [
        "#_______________________________Polynomial Regression__________________________\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "model = Pipeline([('poly', PolynomialFeatures(degree=2)),\n",
        "                ('linear', LinearRegression())])\n",
        "\n",
        "model = model.fit(X, pred)\n",
        "print(model.score(X, pred))\n",
        "print(model.named_steps['linear'].coef_)\n",
        "print('')\n",
        "\n",
        "model = model.fit(X, new)\n",
        "print('new\\'s score =', model.score(X, new))\n",
        "print('new\\'s coeff. = ', model.named_steps['linear'].coef_)\n",
        "print('')\n",
        "\n",
        "print(f\"\"\"Since the new column is giving the Score for Simple Linear Regression\\\n",
        "higher than the pred's column Score hence i think  new is linear function of\\\n",
        "OHL columns. Also the pred is polynomial function of OHL columns because it gives\\\n",
        "more +ve change in Score than the linear one while comparing to the new column\"\"\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8159060375045508\n",
            "[ 0.00000000e+00  8.31012462e-01  4.41739689e+00 -4.79572276e+00\n",
            "  7.22421621e-04 -2.88457330e-03  1.50145869e-03  1.87985683e-03\n",
            " -1.03173471e-03 -1.94673740e-04]\n",
            "\n",
            "new's score = 0.9147522370858088\n",
            "new's coeff. =  [ 0.00000000e+00 -2.09388978e+00  3.74002532e+00 -1.41689019e+00\n",
            "  3.62468554e-04 -1.47862677e-03  7.85041523e-04  9.75820068e-04\n",
            " -5.52469295e-04 -9.59030628e-05]\n",
            "\n",
            "Since the new column is giving the Score for Simple Linear Regressionhigher than the pred's column Score hence i think  new is linear function ofOHL columns. Also the pred is polynomial function of OHL columns because it givesmore +ve change in Score than the linear one while comparing to the new column\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QeANZQMB_zDe",
        "colab_type": "code",
        "colab": {},
        "outputId": "c9fcc0c8-baac-4246-d845-afb0531f6059"
      },
      "source": [
        "\n",
        "# calling nifty as index\n",
        "ndf = pd.read_csv('Nifty50.csv')\n",
        "ndf.Date = pd.to_datetime(ndf['Date'])\n",
        "ndf = ndf.set_index('Date')\n",
        "\n",
        "# calling heromoto as stock\n",
        "hdf = pd.read_csv('HEROMOTOCO.csv')\n",
        "dhf = hdf[hdf[\"Series\"] == \"EQ\"]\n",
        "hdf.Date = pd.to_datetime(hdf['Date'])\n",
        "hdf = hdf.set_index('Date')\n",
        "\n",
        "# ______________________ Daily Beta ______________________________\n",
        "\n",
        "index = ndf[\"Open\"].where(hdf.index == ndf.index)\n",
        "inedexReturn = (index.pct_change()*100).dropna().values.reshape(-1, 1)\n",
        "\n",
        "stock = hdf[\"Open Price\"].where(hdf.index == ndf.index)\n",
        "stockReturn = (stock.pct_change()*100).dropna().values.reshape(-1, 1)\n",
        "\n",
        "reg_for_beta_d = LinearRegression().fit(inedexReturn[len(inedexReturn)-60:].reshape(-1, 1), stockReturn[len(stockReturn)-60:].reshape(-1, 1))\n",
        "print('3 Month\\'s Daily Beta = ', reg_for_beta_d.coef_.tolist())\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3 Month's Daily Beta =  [[0.8213591446879909]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBMGQ2_p_zDm",
        "colab_type": "code",
        "colab": {},
        "outputId": "9cc27ffa-0239-4cb6-9f51-015d268db192"
      },
      "source": [
        "# Using covarience\n",
        "daily_covMat = np.cov(inedexReturn[len(inedexReturn)-60:].reshape(1, -1), stockReturn[len(stockReturn)-60:].reshape(1, -1))\n",
        "print('3 Month\\'s Daily Beta = ', daily_covMat[0][1] / daily_covMat[0][0])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3 Month's Daily Beta =  0.8213591446879912\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-G0NljjO_zDp",
        "colab_type": "code",
        "colab": {},
        "outputId": "c3425c08-bc24-48f6-de0f-2ae3e3819c53"
      },
      "source": [
        "# _______________________ Monthly Beta ___________________________\n",
        "\n",
        "index_m_last = index.loc[index.groupby(index.index.to_period('M')).apply(lambda x: x.index.max())]\n",
        "inedexReturn_m_lats = (index_m_last.pct_change()*100).dropna().values.reshape(-1, 1)\n",
        "\n",
        "stock_m_last = stock.loc[stock.groupby(stock.index.to_period('M')).apply(lambda x: x.index.max())]\n",
        "stockReturn_m_last = (stock_m_last.pct_change()*100).dropna().values.reshape(-1, 1)\n",
        "\n",
        "reg_for_beta_m = LinearRegression().fit(inedexReturn_m_lats.reshape(-1, 1), stockReturn_m_last.reshape(-1, 1))\n",
        "print('Monthly Beta = ', reg_for_beta_m.coef_.tolist())\n",
        "\n",
        "# Using covarianece\n",
        "monthly_covMat = np.cov(inedexReturn_m_lats.reshape(1, -1), stockReturn_m_last.reshape(1, -1))\n",
        "print('Monthly Beta = ', monthly_covMat[0][1] / monthly_covMat[0][0])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Monthly Beta =  [[0.5179140593697137]]\n",
            "Monthly Beta =  0.5179140593697137\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "csZ0FkHq_zDt",
        "colab_type": "code",
        "colab": {},
        "outputId": "bfd8fba0-0556-40f8-a9a4-f8efc3c69977"
      },
      "source": [
        "print(\"_\"*20, \"Write Up\", \"_\"*20, end=\"\\n\")\n",
        "print(\"\"\"\n",
        "\\tIn both type of calculations (Monthly and daily) the Beta values are less than 1\\\n",
        "if the beta value happens to be negative then it is indication of that the stock as\\\n",
        "a whole loosing or in loss while index or market is in profit or gaining.\n",
        "\\n\\tThe beta vaule of last Heromoto for daily last 3 months is ~0.82 which sugget that stock in less\\\n",
        "voletile than the market. while the monthly beta value (~0.52) suggest that the stock is even less voletile\\\n",
        "than the market or index. The daily beta value matches to the original beta value for Heromoto corp.\\\n",
        "with less than 0.02 error.\n",
        "\"\"\")\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "____________________ Write Up ____________________\n",
            "\n",
            "\tIn both type of calculations (Monthly and daily) the Beta values are less than 1if the beta value happens to be negative then it is indication of that the stock asa whole loosing or in loss while index or market is in profit or gaining.\n",
            "\n",
            "\tThe beta vaule of last Heromoto for daily last 3 months is ~0.82 which sugget that stock in lessvoletile than the market. while the monthly beta value (~0.52) suggest that the stock is even less voletilethan the market or index. The daily beta value matches to the original beta value for Heromoto corp.with less than 0.02 error.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}