ML_approach_in_stock_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/prasunna09/Machine-Learning-mini-project/blob/main/ML_approach_in_stock_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
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      "outputs": []
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        "df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/ml_mini_project/stock portfolio performance data set (1).xlsx', sheet_name='3rd period')\n",
        "df.head()"
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      "execution_count": 16,
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              "  Unnamed: 0  ...    Unnamed: 12\n",
              "0         ID  ...  Rel. Win Rate\n",
              "1          1  ...           0.65\n",
              "2          2  ...           0.65\n",
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              "0  Large B/P   Large ROE   Large S/P   ... Total Risk Abs. Win Rate Rel. Win Rate\n",
              "1           1           0           0  ...        0.8      0.457143          0.65\n",
              "2           0           1           0  ...   0.460754      0.285714          0.65\n",
              "3           0           0           1  ...    0.67053      0.457143         0.725\n",
              "4           0           0           0  ...   0.764019           0.2         0.275\n",
              "5           0           0           0  ...   0.333465      0.371429         0.575\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLmmlYXOh9lo",
        "outputId": "d5ab71f1-24d7-42a4-cbe4-97fc8da8fe6d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 63 entries, 1 to 63\n",
            "Data columns (total 12 columns):\n",
            " #   Column                                   Non-Null Count  Dtype \n",
            "---  ------                                   --------------  ----- \n",
            " 0    Large B/P                               63 non-null     object\n",
            " 1    Large ROE                               63 non-null     object\n",
            " 2    Large S/P                               63 non-null     object\n",
            " 3    Large Return Rate in the last quarter   63 non-null     object\n",
            " 4    Large Market Value                      63 non-null     object\n",
            " 5    Small systematic Risk                   63 non-null     object\n",
            " 6   Annual Return                            63 non-null     object\n",
            " 7   Excess Return                            63 non-null     object\n",
            " 8   Systematic Risk                          63 non-null     object\n",
            " 9   Total Risk                               63 non-null     object\n",
            " 10  Abs. Win Rate                            63 non-null     object\n",
            " 11  Rel. Win Rate                            63 non-null     object\n",
            "dtypes: object(12)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuU1gjsViSjr",
        "outputId": "b3741463-985c-4d7d-c5ea-90e55627341e"
      },
      "source": [
        "pattern = df.shape[0]\n",
        "features = df.shape[1]-6\n",
        "print('number of features {}'.format(features))\n",
        "print('number of patterns {}'.format(pattern))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of features 6\n",
            "number of patterns 63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dl064WaziXNW",
        "outputId": "1654f697-2e20-447e-dd04-613a68a96a32"
      },
      "source": [
        "for col in df.columns:\n",
        "    print(col)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Large B/P \n",
            " Large ROE \n",
            " Large S/P \n",
            " Large Return Rate in the last quarter \n",
            " Large Market Value \n",
            " Small systematic Risk\n",
            "Annual Return\n",
            "Excess Return\n",
            "Systematic Risk\n",
            "Total Risk\n",
            "Abs. Win Rate\n",
            "Rel. Win Rate\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df[' Large B/P '], df['Annual Return'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "g05qcaV9-Jnp",
        "outputId": "700ae5c7-3001-4d55-e7a3-7f66d1b5fc95"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f747dcfcd10>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df[' Large B/P '], df['Excess Return'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "zDmbHU2U-Pap",
        "outputId": "bf738363-5044-4156-98ce-cdf030bee339"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f747dc5b990>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df[' Large ROE '], df['Annual Return'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "vHVAO2Uq-ivh",
        "outputId": "40152862-b192-449b-ebba-106eb1826834"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f747dbae990>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXK4KoFHiaxz"
      },
      "source": [
        "X = df.iloc[:, 0:6]\n",
        "Y = df.iloc[:, 6:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "713Qz6s4idlV"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KZ2imAUWih_P"
      },
      "source": [
        "model1 = LinearRegression().fit(X,Y.iloc[:,0])  #Annual Return\n",
        "model2 = LinearRegression().fit(X,Y.iloc[:,1])  #Excess Return\n",
        "model3 = LinearRegression().fit(X,Y.iloc[:,2])  #Systematic Risk\n",
        "model4 = LinearRegression().fit(X,Y.iloc[:,3])  #Total Risk\n",
        "model5 = LinearRegression().fit(X,Y.iloc[:,4])  #Abs. Win Rate\n",
        "model6 = LinearRegression().fit(X,Y.iloc[:,5])  #Rel. Win Rate"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhWXOcI8iyl_",
        "outputId": "2742db3b-285b-4bb9-ca3d-d14401ac0828"
      },
      "source": [
        "print(model1.score(X,Y.iloc[:,0]))\n",
        "print(model2.score(X,Y.iloc[:,1]))\n",
        "print(model3.score(X,Y.iloc[:,2]))\n",
        "print(model4.score(X,Y.iloc[:,3]))\n",
        "print(model5.score(X,Y.iloc[:,4]))\n",
        "print(model6.score(X,Y.iloc[:,5]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7357532024358292\n",
            "0.7818623825607572\n",
            "0.3918334102793113\n",
            "0.5739961988350175\n",
            "0.32999680517437235\n",
            "0.5683377362773634\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vPOaWWnWO2Jh"
      },
      "source": [
        "--------------------X-------------------------X-------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9r6W6Rk9O8a2"
      },
      "source": [
        "# Annual return"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "mAqWbNE3NOaG",
        "outputId": "ad12fb72-12df-49bb-8a43-375b4ba63433"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=3)\n",
        "m1 = LinearRegression().fit(x_train,y_train.iloc[:,0])\n",
        "y_p1 = m1.predict(x_test)\n",
        "print(y_p1)\n",
        "print(m1.score(x_train, y_train.iloc[:,0]))\n",
        "print(m1.score(x_test, y_test.iloc[:, 0]))\n",
        "print(\"mse :\",mean_squared_error(y_test.iloc[:, 0], y_p1))\n",
        "plt.scatter(y_p1, y_test.iloc[:, 0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.73481932 0.45134164 0.60194108 0.52091796 0.56753672 0.66392599\n",
            " 0.64173952 0.51850924 0.58655782 0.50242916 0.46332753 0.56683308\n",
            " 0.57420302 0.63414304 0.33444292 0.56433585 0.54978886 0.71849722\n",
            " 0.6482152 ]\n",
            "0.7275748824861061\n",
            "0.6882607901977171\n",
            "mse : 0.0054040030485757885\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d7fd1d10>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzzUd3drPFN9"
      },
      "source": [
        "#Excess Return"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "U4h2-hThPIoo",
        "outputId": "b7326037-11f4-4005-bc7d-4ab6e31f955c"
      },
      "source": [
        "m2 = LinearRegression().fit(x_train,y_train.iloc[:,1])\n",
        "y_p2 = m2.predict(x_test)\n",
        "print(y_p2)\n",
        "print(m2.score(x_train, y_train.iloc[:,1]))\n",
        "print(m2.score(x_test, y_test.iloc[:, 1]))\n",
        "print(\"mse :\",mean_squared_error(y_test.iloc[:, 1], y_p2))\n",
        "plt.scatter(y_p2, y_test.iloc[:, 1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.72279087 0.44652085 0.60346469 0.52047838 0.57421458 0.67617536\n",
            " 0.64503939 0.54217499 0.56999056 0.46740341 0.46712616 0.56092103\n",
            " 0.58128551 0.6502235  0.30553356 0.58113614 0.56833983 0.73210718\n",
            " 0.64306593]\n",
            "0.7686687334922737\n",
            "0.7795659369817366\n",
            "mse : 0.003856585964903499\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d7fb4fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_onWJiGDRdM5"
      },
      "source": [
        "# Systamatic Risk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "hxwkuV52QDgq",
        "outputId": "ec525de9-e0e1-4803-d09b-7628d44fef2f"
      },
      "source": [
        "m3 = LinearRegression().fit(x_train,y_train.iloc[:,2])\n",
        "y_p3 = m3.predict(x_test)\n",
        "print(y_p3)\n",
        "print(m3.score(x_train, y_train.iloc[:,2]))\n",
        "print(m3.score(x_test, y_test.iloc[:, 2]))\n",
        "print(\"mse :\",mean_squared_error(y_test.iloc[:, 2], y_p3))\n",
        "plt.scatter(y_p3, y_test.iloc[:, 2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.37744453 0.3687317  0.43100425 0.41745347 0.46769359 0.51533671\n",
            " 0.47170465 0.57806166 0.32603267 0.17952075 0.43238968 0.41441675\n",
            " 0.48206039 0.54845225 0.21649297 0.52919145 0.53713974 0.55976476\n",
            " 0.41818231]\n",
            "0.45050345086521326\n",
            "-0.6137044557234455\n",
            "mse : 0.017643284929782642\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d7eaca10>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3horB6CpSQGc"
      },
      "source": [
        "# Total Risk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "Wg8a2h9dSWAo",
        "outputId": "ac54ea6c-8705-4bd5-f9a2-07ab5dd9453e"
      },
      "source": [
        "m4 = LinearRegression().fit(x_train,y_train.iloc[:,3])\n",
        "y_p4 = m4.predict(x_test)\n",
        "print(y_p4)\n",
        "print(m4.score(x_train, y_train.iloc[:, 3]))\n",
        "print(m4.score(x_test, y_test.iloc[:, 3]))\n",
        "print(\"mse :\",mean_squared_error(y_test.iloc[:, 3], y_p4))\n",
        "plt.scatter(y_p4, y_test.iloc[:, 3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.41313992 0.38695525 0.50495854 0.44318149 0.52308454 0.60800296\n",
            " 0.51206629 0.64532966 0.3175592  0.14088133 0.45082257 0.39582789\n",
            " 0.53065931 0.63243182 0.1235693  0.62118582 0.61323507 0.65162856\n",
            " 0.45062125]\n",
            "0.5892587932110146\n",
            "0.16034375224911734\n",
            "mse : 0.013049473167342413\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d827b3d0>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YF8BT_UlSnt6"
      },
      "source": [
        "# Abs. Win Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "nkrsWzXlSw14",
        "outputId": "48cb8446-51aa-4532-dafd-0405bbc20312"
      },
      "source": [
        "m5 = LinearRegression().fit(x_train,y_train.iloc[:,4])\n",
        "y_p5 = m5.predict(x_test)\n",
        "print(y_p5)\n",
        "print(m5.score(x_train, y_train.iloc[:, 4]))\n",
        "print(m5.score(x_test, y_test.iloc[:, 4]))\n",
        "print(\"mse :\",mean_squared_error(y_test.iloc[:, 4], y_p5))\n",
        "plt.scatter(y_p5, y_test.iloc[:, 4])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.66151652 0.45425926 0.49851687 0.49630904 0.48773038 0.54756523\n",
            " 0.52788061 0.41479755 0.60169715 0.59457428 0.48861409 0.59756592\n",
            " 0.50613489 0.50001269 0.53062367 0.42916515 0.49172214 0.50830324\n",
            " 0.57405264]\n",
            "0.34511707906330324\n",
            "-0.0505502609396975\n",
            "mse : 0.015907025942115932\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d8172090>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tgvdl2QHTBPg"
      },
      "source": [
        "# Rel. Win Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "aqqU18grTgLl",
        "outputId": "413b7987-bd3f-404f-f06e-7cdb92a3a3a8"
      },
      "source": [
        "m6 = LinearRegression().fit(x_train,y_train.iloc[:,5])\n",
        "y_p6 = m6.predict(x_test)\n",
        "print(y_p6)\n",
        "print(m6.score(x_train, y_train.iloc[:, 5]))\n",
        "print(m6.score(x_test, y_test.iloc[:, 5]))\n",
        "print(\"mse :\",mean_squared_error(y_test.iloc[:, 5], y_p6))\n",
        "plt.scatter(y_p6, y_test.iloc[:, 5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.78542454 0.58503051 0.58949811 0.61141962 0.57142709 0.61529492\n",
            " 0.62173954 0.46395963 0.76712399 0.81263139 0.60934408 0.7441829\n",
            " 0.59849704 0.55527386 0.77138975 0.48803022 0.54851776 0.56202996\n",
            " 0.69325975]\n",
            "0.5748062811216503\n",
            "0.47209167525274687\n",
            "mse : 0.006679283305493291\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d7f6f510>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aMlLdstjIW1"
      },
      "source": [
        "--------------X------------X---------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uxADpEAjL-_"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1_fObDujU5C",
        "outputId": "d0f93937-956d-47c6-b38d-0376e18fd9af"
      },
      "source": [
        "lr = LinearRegression()\n",
        "lr.fit(x_train, y_train)\n",
        "y_pred = lr.predict(x_test)\n",
        "print(\"predicted values are : \")\n",
        "print(y_pred)\n",
        "\n",
        "print(\"\\n test set values are :\")\n",
        "print(np.asarray(y_test)[:])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted values are : \n",
            "[[0.73481932 0.72279087 0.37744453 0.41313992 0.66151652 0.78542454]\n",
            " [0.45134164 0.44652085 0.3687317  0.38695525 0.45425926 0.58503051]\n",
            " [0.60194108 0.60346469 0.43100425 0.50495854 0.49851687 0.58949811]\n",
            " [0.52091796 0.52047838 0.41745347 0.44318149 0.49630904 0.61141962]\n",
            " [0.56753672 0.57421458 0.46769359 0.52308454 0.48773038 0.57142709]\n",
            " [0.66392599 0.67617536 0.51533671 0.60800296 0.54756523 0.61529492]\n",
            " [0.64173952 0.64503939 0.47170465 0.51206629 0.52788061 0.62173954]\n",
            " [0.51850924 0.54217499 0.57806166 0.64532966 0.41479755 0.46395963]\n",
            " [0.58655782 0.56999056 0.32603267 0.3175592  0.60169715 0.76712399]\n",
            " [0.50242916 0.46740341 0.17952075 0.14088133 0.59457428 0.81263139]\n",
            " [0.46332753 0.46712616 0.43238968 0.45082257 0.48861409 0.60934408]\n",
            " [0.56683308 0.56092103 0.41441675 0.39582789 0.59756592 0.7441829 ]\n",
            " [0.57420302 0.58128551 0.48206039 0.53065931 0.50613489 0.59849704]\n",
            " [0.63414304 0.6502235  0.54845225 0.63243182 0.50001269 0.55527386]\n",
            " [0.33444292 0.30553356 0.21649297 0.1235693  0.53062367 0.77138975]\n",
            " [0.56433585 0.58113614 0.52919145 0.62118582 0.42916515 0.48803022]\n",
            " [0.54978886 0.56833983 0.53713974 0.61323507 0.49172214 0.54851776]\n",
            " [0.71849722 0.73210718 0.55976476 0.65162856 0.50830324 0.56202996]\n",
            " [0.6482152  0.64306593 0.41818231 0.45062125 0.57405264 0.69325975]]\n",
            "\n",
            " test set values are :\n",
            "[[0.7949826944163363 0.7619931010445204 0.2332489634999982\n",
            "  0.3181021920632392 0.7142857142857144 0.7249999999999999]\n",
            " [0.4285048322719283 0.41949155737321403 0.326461892785946\n",
            "  0.33076060342727087 0.2857142857142858 0.575]\n",
            " [0.6852811075297494 0.6818841442537507 0.3890124762538785\n",
            "  0.48559277900815484 0.6285714285714286 0.6500000000000001]\n",
            " [0.5949172724783864 0.5761730360318277 0.23857378657126044\n",
            "  0.2823450171083174 0.7142857142857144 0.7249999999999999]\n",
            " [0.6803740002345079 0.6599283867261911 0.21951324218467197\n",
            "  0.33036685702845725 0.5428571428571427 0.7249999999999999]\n",
            " [0.7078872044845801 0.7077354439547989 0.4372271184636829\n",
            "  0.5223347436598758 0.6285714285714286 0.49999999999999994]\n",
            " [0.7595405275633962 0.7326969266589429 0.22676286411556756\n",
            "  0.3448349947372646 0.7142857142857144 0.6500000000000001]\n",
            " [0.5252079433337624 0.5282714981762116 0.35530373092344525\n",
            "  0.44797195427038505 0.5428571428571427 0.42500000000000004]\n",
            " [0.592999339924922 0.5713185234829189 0.20295107553779768\n",
            "  0.24319105533265298 0.7142857142857144 0.7249999999999999]\n",
            " [0.38307083306316025 0.3622264019069168 0.24564776405731412 0.2\n",
            "  0.3714285714285714 0.8]\n",
            " [0.5102838360163464 0.5000647439002324 0.3369946314780787\n",
            "  0.3246439593199398 0.4571428571428572 0.6500000000000001]\n",
            " [0.5684356148438111 0.5733877075548326 0.4974866354553649\n",
            "  0.48182091680657974 0.5428571428571427 0.7249999999999999]\n",
            " [0.6865353997833581 0.6874462671121802 0.44016659387882895\n",
            "  0.5107917600128739 0.6285714285714286 0.575]\n",
            " [0.6891129976617067 0.6995452239545259 0.4613902999089394\n",
            "  0.5933711215776576 0.5428571428571427 0.42500000000000004]\n",
            " [0.27369840984654464 0.2625719953987181 0.406379224392682\n",
            "  0.33346546822165635 0.3714285714285714 0.575]\n",
            " [0.6296169969361016 0.6454758806993273 0.4831524137570902\n",
            "  0.5928692614664304 0.5428571428571427 0.49999999999999994]\n",
            " [0.6052685774578304 0.6079321695370308 0.405901475793917\n",
            "  0.47565850814831007 0.6285714285714286 0.49999999999999994]\n",
            " [0.6616763448386038 0.6831060488005657 0.554108676904187\n",
            "  0.6570610561552733 0.4571428571428572 0.49999999999999994]\n",
            " [0.7537277444252053 0.7409186281088698 0.3823983309332547\n",
            "  0.4564584526899647 0.6285714285714286 0.7249999999999999]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mj4kCzNojXYv",
        "outputId": "10ca0d0e-3377-4051-8b15-773dd1d77398"
      },
      "source": [
        "print(\"Training Accuracy:\",lr.score(x_train,y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.5737184978098757\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').\n",
            "  \"multioutput='uniform_average').\", FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5kp6dM8jgnd",
        "outputId": "297d05a7-09a3-42b0-b945-369fc993fdfb"
      },
      "source": [
        "print(\"validation Accuracy:\",lr.score(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "validation Accuracy: 0.2980899823467963\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:434: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').\n",
            "  \"multioutput='uniform_average').\", FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoWwCVdQi7xo",
        "outputId": "e811478a-0484-4f78-eb5a-fbfee9140ec5"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "print(\"MSE:- \",mean_squared_error(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE:-  0.010423276059702313\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ptNz72VZjm91",
        "outputId": "a0c25603-4e1d-4add-8b7e-cfad3200e186"
      },
      "source": [
        "plt.scatter(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f73d80cc850>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsNiiVQ0jrV7"
      },
      "source": [
        "------------------X---------------X-----------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwafC3HqRH-H"
      },
      "source": [
        "# Using SGD(Hyperparameter tuning)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "16l5DW0uRLht",
        "outputId": "4559862b-cdf3-49d6-eaac-fa05f8183446"
      },
      "source": [
        "df1 = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/ml_mini_project/stock portfolio performance data set (1).xlsx', sheet_name='2nd period')\n",
        "df1.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>the weight of the stock-picking concept</th>\n",
              "      <th>Unnamed: 2</th>\n",
              "      <th>Unnamed: 3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>Unnamed: 5</th>\n",
              "      <th>Unnamed: 6</th>\n",
              "      <th>the normalized  investment performance indicator</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "      <th>Unnamed: 9</th>\n",
              "      <th>Unnamed: 10</th>\n",
              "      <th>Unnamed: 11</th>\n",
              "      <th>Unnamed: 12</th>\n",
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              "      <td>Large B/P</td>\n",
              "      <td>Large ROE</td>\n",
              "      <td>Large S/P</td>\n",
              "      <td>Large Return Rate in the last quarter</td>\n",
              "      <td>Large Market Value</td>\n",
              "      <td>Small systematic Risk</td>\n",
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              "      <td>Excess Return</td>\n",
              "      <td>Systematic Risk</td>\n",
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              "      <td>0.6</td>\n",
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              "      <td>0.285714</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.732604</td>\n",
              "      <td>0.490854</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.6</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f94b8d4d-19f0-4ca3-bd02-91ef577e1987')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f94b8d4d-19f0-4ca3-bd02-91ef577e1987 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-f94b8d4d-19f0-4ca3-bd02-91ef577e1987');\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",
              "  "
            ],
            "text/plain": [
              "  Unnamed: 0  ...    Unnamed: 12\n",
              "0         ID  ...  Rel. Win Rate\n",
              "1          1  ...            0.4\n",
              "2          2  ...            0.6\n",
              "3          3  ...            0.4\n",
              "4          4  ...            0.6\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dau72n77S_Lz"
      },
      "source": [
        "new_header = df1.iloc[0] #grab the first row for the header\n",
        "df1 = df1[1:] #take the data less the header row\n",
        "df1.columns = new_header #set the header row as the df header"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFhPiDZSTG2S"
      },
      "source": [
        "df1 = df1.drop('ID', axis=1)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "rtyoxTnAT6Vl",
        "outputId": "deb4dd0e-13e1-4915-bb2b-e71dc14f50fc"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Large B/P</th>\n",
              "      <th>Large ROE</th>\n",
              "      <th>Large S/P</th>\n",
              "      <th>Large Return Rate in the last quarter</th>\n",
              "      <th>Large Market Value</th>\n",
              "      <th>Small systematic Risk</th>\n",
              "      <th>Annual Return</th>\n",
              "      <th>Excess Return</th>\n",
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              "      <th>Abs. Win Rate</th>\n",
              "      <th>Rel. Win Rate</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>1</th>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.310197</td>\n",
              "      <td>0.325059</td>\n",
              "      <td>0.491529</td>\n",
              "      <td>0.42591</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.4</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.732604</td>\n",
              "      <td>0.490854</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.542857</td>\n",
              "      <td>0.6</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0.632961</td>\n",
              "      <td>0.634952</td>\n",
              "      <td>0.46601</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.3</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d262c0e2-95d7-4320-a729-6ab3dc1427d8 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-d262c0e2-95d7-4320-a729-6ab3dc1427d8');\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",
              "  "
            ],
            "text/plain": [
              "0  Large B/P   Large ROE   Large S/P   ... Total Risk Abs. Win Rate Rel. Win Rate\n",
              "1           1           0           0  ...   0.316609      0.371429           0.4\n",
              "2           0           1           0  ...   0.367016      0.542857           0.6\n",
              "3           0           0           1  ...    0.42591      0.285714           0.4\n",
              "4           0           0           0  ...        0.8      0.542857           0.6\n",
              "5           0           0           0  ...        0.2           0.8           0.3\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YksM6cBXUSIM"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMuzT4F2Zdnk"
      },
      "source": [
        "# ANNUAL RETURN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GFfiwmTUYk7",
        "outputId": "c5fd525a-1f0c-4f28-a475-27837770fb7f"
      },
      "source": [
        "x = df1.iloc[:, 0:6]\n",
        "y1 = df1.iloc[:, 6]\n",
        "\n",
        "x_train,x_r,y_train,y_r = train_test_split(x,y1,train_size=0.6,random_state=3)\n",
        "x_valid, x_test, y_valid, y_test = train_test_split(x_r,y_r,test_size=0.5,random_state=3)\n",
        "#lr = 0.1 #learning rate #mse: 0.013758178600912298\n",
        "#rho = 0.5 #stopping criteria\n",
        "#itr = 500 #epoch\n",
        "lr = 0.01 #mse :  0.004625348510260119\n",
        "rho = 0.00001\n",
        "itr = 500\n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr, learning_rate = 'constant', eta0 = lr, tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse1 = mean_squared_error(y_valid, SGDReg_1.predict(x_valid))\n",
        "print(\"mse : \", mse1)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.004597122054510231\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQpCe38Ca5jg",
        "outputId": "af391a53-c80f-4b5d-c264-e9dbf19ad0b1"
      },
      "source": [
        "lr = 0.01\n",
        "rho = 0.00001\n",
        "itr = 500\n",
        "print(\"results of linear reg for test set: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr, learning_rate = 'constant', eta0 = lr, tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse1 = mean_squared_error(y_test, SGDReg_1.predict(x_test))\n",
        "print(\"test mse : \", mse1)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg for test set: \n",
            "test mse :  0.007935330316995358\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwUz7N-XXuba",
        "outputId": "872baaa5-076f-410e-afa8-cd9503a50347"
      },
      "source": [
        "print(\"mse : \", mse1)\n",
        "print(\"Actual Values : \", y_test)\n",
        "print(\"Predicted values : \", SGDReg_1.predict(x_test))\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse :  0.007935330316995358\n",
            "Actual Values :  59    0.384899\n",
            "19    0.274221\n",
            "61    0.378291\n",
            "17         0.2\n",
            "37    0.481079\n",
            "10    0.368398\n",
            "49     0.33381\n",
            "50    0.545977\n",
            "55    0.795741\n",
            "34     0.51703\n",
            "5     0.632961\n",
            "48    0.567642\n",
            "47    0.398972\n",
            "Name: Annual Return, dtype: object\n",
            "Predicted values :  [0.42143594 0.3124017  0.42000287 0.29020661 0.4429807  0.47489263\n",
            " 0.39364716 0.5101233  0.56900143 0.44536675 0.5254257  0.49902576\n",
            " 0.3954385 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbQiWE9rbh5c"
      },
      "source": [
        "# Excess Return"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBo4rU99blz6",
        "outputId": "0c0c4cac-7b0e-4009-9e54-3d7efe710e78"
      },
      "source": [
        "x = df1.iloc[:, 0:6]\n",
        "y2 = df1.iloc[:, 7]\n",
        "\n",
        "x_train,x_r,y_train,y_r = train_test_split(x,y2,train_size=0.6,random_state=3)\n",
        "x_valid, x_test, y_valid, y_test = train_test_split(x_r,y_r,test_size=0.5,random_state=3)\n",
        "lr = 0.1 #learning rate #mse :  0.006619851092433992\n",
        "rho = 0.5 #stopping criteria\n",
        "itr = 500 #epoch\n",
        "#lr = 0.001\n",
        "#rho = 0.0001\n",
        "#itr = 500\n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr, learning_rate = 'constant', eta0 = lr, tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse2 = mean_squared_error(y_valid, SGDReg_1.predict(x_valid))\n",
        "print(\"mse : \", mse2)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.00712187188638916\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BSstbDgPoGlk",
        "outputId": "ed9c4aaf-8bc9-472a-da0a-e12fe2f7deac"
      },
      "source": [
        "lr = 0.1 #learning rate\n",
        "rho = 0.5 #stopping criteria\n",
        "itr = 500 #epoch\n",
        "print(\"results of linear reg for test set: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr, learning_rate = 'constant', eta0 = lr, tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse2 = mean_squared_error(y_test, SGDReg_1.predict(x_test))\n",
        "print(\"mse : \", mse2)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg for test set: \n",
            "mse :  0.010434532064699503\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBk74pzfokOa",
        "outputId": "d661159d-a19a-409a-a1fb-cecab3095daa"
      },
      "source": [
        "print(\"mse : \", mse2)\n",
        "print(\"Actual Values : \", y_test)\n",
        "print(\"Predicted values : \", SGDReg_1.predict(x_test))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse :  0.010434532064699503\n",
            "Actual Values :  59    0.515604\n",
            "19    0.318641\n",
            "61    0.558443\n",
            "17    0.465028\n",
            "37    0.511664\n",
            "10    0.699995\n",
            "49    0.495608\n",
            "50    0.578402\n",
            "55    0.703839\n",
            "34     0.53918\n",
            "5     0.634952\n",
            "48         0.8\n",
            "47    0.563954\n",
            "Name: Excess Return, dtype: object\n",
            "Predicted values :  [0.51345901 0.3918763  0.50363109 0.4730472  0.45737381 0.58015017\n",
            " 0.48601324 0.50241707 0.54355007 0.47373729 0.57410252 0.54300252\n",
            " 0.49829813]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-WkxwYIpD3v"
      },
      "source": [
        "# Systematic Risk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AJUrTEcnpC9a",
        "outputId": "508434f4-631a-4393-8244-ff2708b943a7"
      },
      "source": [
        "x = df1.iloc[:, 0:6]\n",
        "y3 = df1.iloc[:, 8]\n",
        "\n",
        "x_train,x_r,y_train,y_r = train_test_split(x,y3,train_size=0.6,random_state=3)\n",
        "x_valid, x_test, y_valid, y_test = train_test_split(x_r,y_r,test_size=0.5,random_state=3)\n",
        "lr = 0.1 #learning rate  #mse :  0.013758178600912298\n",
        "rho = 0.5 #stopping criteria\n",
        "itr = 500 #epoch\n",
        "#lr = 0.0001\n",
        "#rho = 0.001\n",
        "#itr = 2000\n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr, learning_rate = 'constant', eta0 = lr, tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse3 = mean_squared_error(y_valid, SGDReg_1.predict(x_valid))\n",
        "print(\"mse : \", mse3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.013758178600912298\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txA8Nf1Fp2EO",
        "outputId": "3658577b-bc83-4a7e-f872-8aa288f91158"
      },
      "source": [
        "lr = 0.1 \n",
        "rho = 0.5 \n",
        "max_itr = 500 \n",
        "\n",
        "print(\"results of linear reg for test set: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr, learning_rate = 'constant', eta0 = lr, tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse3 = mean_squared_error(y_test, SGDReg_1.predict(x_test))\n",
        "print(\"mse : \", mse3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg for test set: \n",
            "mse :  0.008229190784099592\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1Gj28CspyiQ",
        "outputId": "6efd7b91-768b-46a1-8b55-865990ea4ca2"
      },
      "source": [
        "print(\"mse : \", mse3)\n",
        "print(\"Actual Values : \", y_test)\n",
        "print(\"Predicted values : \", SGDReg_1.predict(x_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse :  0.008229190784099592\n",
            "Actual Values :  59    0.383468\n",
            "19    0.462653\n",
            "61    0.336303\n",
            "17     0.25177\n",
            "37    0.483168\n",
            "10         0.2\n",
            "49     0.36207\n",
            "50     0.47854\n",
            "55    0.582114\n",
            "34    0.485741\n",
            "5      0.46601\n",
            "48    0.295212\n",
            "47    0.362573\n",
            "Name: Systematic Risk, dtype: object\n",
            "Predicted values :  [0.40897519 0.44805195 0.42734891 0.3425177  0.50308667 0.41523472\n",
            " 0.43164333 0.50782185 0.50336169 0.47249443 0.41017121 0.45505473\n",
            " 0.40867618]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBZReTZdqSb0"
      },
      "source": [
        "# Total Risk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btLIigpkqaOW",
        "outputId": "eec04bca-2e7c-4f00-bb22-b945f5729ae4"
      },
      "source": [
        "x = df1.iloc[:, 0:6]\n",
        "y4 = df1.iloc[:, 9]\n",
        "\n",
        "x_train,x_r,y_train,y_r = train_test_split(x,y4,train_size=0.6,random_state=3)\n",
        "x_valid, x_test, y_valid, y_test = train_test_split(x_r,y_r,test_size=0.5,random_state=3)\n",
        "lr = 0.1 #learning rate  #mse :  0.007897522852629752\n",
        "rho = 0.5 #stopping criteria\n",
        "itr = 500 #epoch\n",
        "#lr = 0.0001 #mse :  0.11210510398936405\n",
        "#rho = 0.001\n",
        "#itr = 2000\n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr,learning_rate = 'constant',eta0 = lr,tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse4 = mean_squared_error(y_valid, SGDReg_1.predict(x_valid))\n",
        "print(\"mse : \", mse4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.007897522852629752\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyyMWRH6raRU",
        "outputId": "6074749e-c532-4f72-cdb0-8163dbaf98ed"
      },
      "source": [
        "lr = 0.1 \n",
        "rho = 0.5 \n",
        "itr = 500 \n",
        "print(\"results of linear reg for test set: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr,learning_rate = 'constant',eta0 = lr,tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse4 = mean_squared_error(y_test, SGDReg_1.predict(x_test))\n",
        "print(\"mse : \", mse4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg for test set: \n",
            "mse :  0.0035295332332010458\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_PFU0r54rvUS",
        "outputId": "6264e825-8cd0-4964-9d04-ff0ece1dfabf"
      },
      "source": [
        "print(\"mse : \", mse4)\n",
        "print(\"Actual Values : \", y_test)\n",
        "print(\"Predicted values : \", SGDReg_1.predict(x_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse :  0.0035295332332010458\n",
            "Actual Values :  59    0.353574\n",
            "19    0.411816\n",
            "61    0.325086\n",
            "17    0.301101\n",
            "37    0.416726\n",
            "10    0.259765\n",
            "49    0.386663\n",
            "50    0.393196\n",
            "55    0.419277\n",
            "34    0.394577\n",
            "5          0.2\n",
            "48    0.304955\n",
            "47    0.388801\n",
            "Name: Total Risk, dtype: object\n",
            "Predicted values :  [0.29850724 0.34143501 0.33859867 0.31565949 0.40654576 0.40488342\n",
            " 0.37315922 0.38916178 0.35820071 0.33979354 0.20035647 0.37627402\n",
            " 0.32304493]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOoe1mpBsv9l"
      },
      "source": [
        "# Abs. Win Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WexkWnvHsvl1",
        "outputId": "922263e8-2aac-45e5-9457-40fdeb43e1dc"
      },
      "source": [
        "x = df1.iloc[:, 0:6]\n",
        "y5 = df1.iloc[:, 10]\n",
        "\n",
        "x_train,x_r,y_train,y_r = train_test_split(x,y5,train_size=0.6,random_state=3)\n",
        "x_valid, x_test, y_valid, y_test = train_test_split(x_r,y_r,test_size=0.5,random_state=3)\n",
        "lr = 0.1 #learning rate  #mse :  0.006229052646036139\n",
        "rho = 0.5 #stopping criteria\n",
        "itr = 500 #epoch\n",
        "#lr = 0.0001 #mse :  0.24755807284476367\n",
        "#rho = 0.001\n",
        "#itr = 2000\n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr,learning_rate = 'constant',eta0 = lr,tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse5 = mean_squared_error(y_valid, SGDReg_1.predict(x_valid))\n",
        "print(\"mse : \", mse5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.006229052646036139\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YOGiYyPtb3_",
        "outputId": "e5006409-ba04-41dc-972e-e4f50cca22d3"
      },
      "source": [
        "lr = 0.1\n",
        "rho = 0.5 \n",
        "itr = 500 \n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr,learning_rate = 'constant',eta0 = lr,tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse5 = mean_squared_error(y_test, SGDReg_1.predict(x_test))\n",
        "print(\"mse : \", mse5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.011350579485838595\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "irlf-9x-to9u",
        "outputId": "74541cad-4be5-401a-8d88-cb05fc3d55da"
      },
      "source": [
        "print(\"mse : \", mse5)\n",
        "print(\"Actual Values : \", y_test)\n",
        "print(\"Predicted values : \", SGDReg_1.predict(x_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse :  0.011350579485838595\n",
            "Actual Values :  59    0.542857\n",
            "19    0.285714\n",
            "61    0.285714\n",
            "17    0.285714\n",
            "37    0.371429\n",
            "10    0.457143\n",
            "49         0.2\n",
            "50    0.457143\n",
            "55    0.457143\n",
            "34    0.371429\n",
            "5          0.8\n",
            "48    0.457143\n",
            "47    0.371429\n",
            "Name: Abs. Win Rate, dtype: object\n",
            "Predicted values :  [0.41035833 0.31691226 0.40575215 0.35771709 0.37227728 0.43380086\n",
            " 0.37535656 0.4057326  0.46182757 0.37994658 0.52733449 0.42613501\n",
            " 0.38111429]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAywZrfPtzMr"
      },
      "source": [
        "# Rel. Win Rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynf00JLGt2a2",
        "outputId": "d0fede05-9422-43ba-d085-576d0f11660a"
      },
      "source": [
        "x = df1.iloc[:, 0:6]\n",
        "y6 = df1.iloc[:, 11]\n",
        "\n",
        "x_train,x_r,y_train,y_r = train_test_split(x,y6,train_size=0.6,random_state=3)\n",
        "x_valid, x_test, y_valid, y_test = train_test_split(x_r,y_r,test_size=0.5,random_state=3)\n",
        "lr = 0.1 #learning rate  #mse :  0.006815174199087365\n",
        "rho = 0.5 #stopping criteria\n",
        "itr = 500 #epoch\n",
        "#lr = 0.0001 #mse :  0.22999264321094445 \n",
        "#lr = 0.1 #mse :  0.006966749572783009\n",
        "#rho = 0.001\n",
        "#itr = 2000\n",
        "print(\"results of linear reg: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr,learning_rate = 'constant',eta0 = lr,tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse6 = mean_squared_error(y_valid, SGDReg_1.predict(x_valid))\n",
        "print(\"mse : \", mse6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg: \n",
            "mse :  0.006815174199087365\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WO6oJEFbu1P8",
        "outputId": "442f971c-6543-47c7-edc1-b205e9a12b84"
      },
      "source": [
        "lr = 0.1 \n",
        "rho = 0.5 \n",
        "itr = 500 \n",
        "\n",
        "print(\"results of linear reg for test set: \")\n",
        "SGDReg_1 = linear_model.SGDRegressor(max_iter = itr,learning_rate = 'constant',eta0 = lr,tol = rho)\n",
        "SGDReg_1.fit(x_train, y_train)\n",
        "mse6 = mean_squared_error(y_test, SGDReg_1.predict(x_test))\n",
        "print(\"mse : \", mse6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "results of linear reg for test set: \n",
            "mse :  0.010948480720243382\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C30NjJ31vPLY",
        "outputId": "b0c2b5f9-5a8a-4006-ca01-a4216193f7f1"
      },
      "source": [
        "print(\"mse : \", mse6)\n",
        "print(\"Actual Values : \", y_test)\n",
        "print(\"Predicted values : \", SGDReg_1.predict(x_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse :  0.010948480720243382\n",
            "Actual Values :  59    0.6\n",
            "19    0.4\n",
            "61    0.4\n",
            "17    0.4\n",
            "37    0.5\n",
            "10    0.6\n",
            "49    0.3\n",
            "50    0.5\n",
            "55    0.6\n",
            "34    0.6\n",
            "5     0.3\n",
            "48    0.7\n",
            "47    0.5\n",
            "Name: Rel. Win Rate, dtype: object\n",
            "Predicted values :  [0.48250558 0.47682945 0.44650149 0.46571904 0.4769558  0.42062722\n",
            " 0.44872833 0.52212012 0.53499998 0.53690261 0.4375941  0.51656492\n",
            " 0.49373345]\n"
          ]
        }
      ]
    }
  ]
}