Chapter_02_Python_for_DataSciences.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EN-TA Chapter 02: Python for DataSciences.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "nJYyIbZ_WbFT",
        "DuY3oXKNWd1h",
        "iEQQbsVFWkKi",
        "JUULnsP_Xemv",
        "cGqtdDyYX1Dk",
        "EMAdDk_6YXgw",
        "Kd-SYHI-ZF85",
        "2UHCKDk8bJEf",
        "W9Vc62veaNKg",
        "7xKs11J6akHQ",
        "wwObVh1w9Wl9",
        "-9ElsbKP-xvK",
        "emrr2xJjhmKd",
        "4NWfx4mRhpgX",
        "S0TnQtBMfRXB",
        "F8rax_dDgnFI",
        "oYh-hMSTB1Ur",
        "KE9L44nED-JK",
        "Qy6T6HOiGHaN",
        "7ilLgwf1HIMw",
        "MLRfDnNGHUkI",
        "EdYvgavpJPGK",
        "xLPPB2NOJUSq",
        "XM6mdryCRCAm",
        "D5KoCwIFT27I",
        "aH-i3Tn3UjJ_",
        "C6_cmiswU2AW",
        "asuSI8Jg0EaC",
        "ppony41B0G1i"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwIk5lNgltIS"
      },
      "source": [
        "<p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://www.quantreo.com/wp-content/uploads/2021/10/Original-on-Transparent.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
        "\n",
        "\n",
        "# PREREQUISITES  - **Python for DataSciences**\n",
        "\n",
        "This chapter will give you the python basics for data sciences you need to follow the rest of the course. These are only reminders of what will be helpful. That's why I advise you to follow free quick Python training for your projects because if you are total strangers to it, it can be hard to do your project.\n",
        "\n",
        "\n",
        "* Numpy:\n",
        "> * Numpy table creation\n",
        "> * Random with Numpy\n",
        "> * Indexing and slixing\n",
        "\n",
        "* Pandas:\n",
        "> * Series and dataframe \n",
        "> * Useful pandas functions\n",
        "> * Indexing and slixing\n",
        "\n",
        "* Matplotlib\n",
        "> * Graphs\n",
        "> * Point clouds\n",
        "> * Useful functions\n",
        "\n",
        "💰Join our community: https://discord.gg/wXjNPAc5BH\n",
        "\n",
        "📚Read our book: https://www.amazon.com/gp/product/B09HG18CYL \n",
        "\n",
        "🖥️Quantreo's YouTube channel: https://www.youtube.com/channel/UCp7jckfiEglNf_Gj62VR0pw\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6gHyLh1Bzh5"
      },
      "source": [
        "### Numpy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oYh-hMSTB1Ur"
      },
      "source": [
        "##### Numpy table creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy75aQjfBqei",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1fa254c-8c00-4e88-8f63-8afa08ba7816"
      },
      "source": [
        "# Import of numpy\n",
        "import numpy as np\n",
        "\n",
        "# 1D array\n",
        "arr = np.array([1,2,3])\n",
        "arr"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO2_F5w7krek",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02783509-43f0-43b0-c67d-f91bcdb3c3d9"
      },
      "source": [
        "# Linspace function\n",
        "np.linspace(0, 50, 51)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.,\n",
              "       13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,\n",
              "       26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,\n",
              "       39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50.])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfR1z0KWk6H7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62af2f78-ea9c-490c-f6bf-de660c7cf7f2"
      },
      "source": [
        "# Arrange function\n",
        "np.arange(0,51)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOMnsG3ZCWXi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcb31d39-a108-4ffd-9a8d-9039074eac74"
      },
      "source": [
        "# 2D array\n",
        "arr_bis = np.array([[1,2,3],\n",
        "                    [4,5,6],\n",
        "                    [7,8,9]])\n",
        "arr_bis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6],\n",
              "       [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTRSkxrJCtgC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a633dee-5891-4abc-b653-979a020561b8"
      },
      "source": [
        "# Null matrix\n",
        "O = np.zeros([3,5])\n",
        "O"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVFbAMe9C_fw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0acf0ccd-6bba-4eb8-80ec-46a7d559e71f"
      },
      "source": [
        "# Identity matrix\n",
        "I = np.identity(5)\n",
        "I"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WiNy3cSuDM9r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90f927e9-b5e0-4275-d7c4-e18d474aa466"
      },
      "source": [
        "# Ones matrix\n",
        "One = np.ones([3,5])\n",
        "One"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_D2LA0o9DXA6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cdc9f4f-71c2-465a-8d8b-c1fe7c950b31"
      },
      "source": [
        "# Add two matrix\n",
        "O + One"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYAJNct5DoYy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30ef4cbc-9abb-466a-c60d-06c3c68a8f10"
      },
      "source": [
        "# Soubstract two matrix\n",
        "One-O"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwQe3BGYDuYx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e732a9a-aa6e-487b-f428-c3c885693cf9"
      },
      "source": [
        "# Scalar multiplication\n",
        "One * 10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10., 10., 10., 10., 10.],\n",
              "       [10., 10., 10., 10., 10.],\n",
              "       [10., 10., 10., 10., 10.]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE9L44nED-JK"
      },
      "source": [
        "##### Random"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXJBsigRENMh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1436642-7737-4079-e0a2-8ca7b8de0535"
      },
      "source": [
        "# Random 1d array in the value 0 and 1 (Ideal to simulate a random weight of a wallet for example)\n",
        "ran = np.random.rand(6)\n",
        "ran"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.72662387, 0.45319368, 0.57995897, 0.62486661, 0.99064826,\n",
              "       0.27565271])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwdBtqLAE3gi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c917f93-eb2e-43d3-a42e-0bf78ca681b7"
      },
      "source": [
        "# BONUS: How to find the shape of a array and transorfm 1d array to 2d array\n",
        "print(f\"Shape is {np.shape(ran)}\")\n",
        "\n",
        "# Reshape\n",
        "ran = ran.reshape(-1,1)\n",
        "\n",
        "# New shape\n",
        "print(f\"Shape is {np.shape(ran)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape is (6,)\n",
            "Shape is (6, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxwK8rYLFGkC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b82e7018-a6a2-440e-bf43-b2015f77a3b7"
      },
      "source": [
        "# Random integer value\n",
        "arr_int = np.random.randint(100, size=(5,5))\n",
        "arr_int"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[43, 90,  4, 39,  6],\n",
              "       [16, 70, 56, 43, 26],\n",
              "       [70, 97, 20, 91, 85],\n",
              "       [23, 11, 51, 37, 24],\n",
              "       [ 5, 93, 18, 42, 46]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8wZZsWzlgHG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4297289d-b485-4e05-8c03-fbf064368804"
      },
      "source": [
        "# Normal 1 Dim\n",
        "np.random.normal(loc=0, scale=0.1, size=(6,))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.02792517, -0.06330768,  0.00584247, -0.0955621 ,  0.11126624,\n",
              "        0.12832733])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BYKsDhxlz14",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fdedd351-9d84-4f1c-d341-b2de1c014a5f"
      },
      "source": [
        "# Normal 2 Dim\n",
        "np.random.normal(loc=0, scale=0.1, size=(6,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.05230276,  0.08787387, -0.11834145,  0.06524513,  0.02068725,\n",
              "        -0.11967343],\n",
              "       [ 0.11096312, -0.07396374,  0.04309298,  0.00750952, -0.1475431 ,\n",
              "        -0.0757121 ],\n",
              "       [ 0.20948655,  0.12048306,  0.04484228, -0.06027274,  0.01692653,\n",
              "        -0.11665756],\n",
              "       [ 0.02583059,  0.17314193, -0.14847655,  0.00140238, -0.25792286,\n",
              "        -0.12068108],\n",
              "       [ 0.02087147,  0.08278935, -0.13664915,  0.07510462,  0.01903448,\n",
              "        -0.04705392],\n",
              "       [-0.24383125, -0.0717745 ,  0.01337392, -0.02585569,  0.00696296,\n",
              "         0.1629074 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PP4qi331l_Lt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40a80094-9fc5-4ff6-ff73-b97d2740b24d"
      },
      "source": [
        "# Set the seed\n",
        "print(np.random.rand(3))\n",
        "print(np.random.rand(3))\n",
        "\n",
        "np.random.seed(seed=56)\n",
        "print(np.random.rand(3))\n",
        "\n",
        "np.random.seed(seed=56)\n",
        "print(np.random.rand(3))\n",
        "\n",
        "np.random.seed(seed=56)\n",
        "print(np.random.rand(3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.19639042 0.35444646 0.81336595]\n",
            "[0.24784955 0.45861133 0.87730142]\n",
            "[0.98419185 0.33341227 0.67370162]\n",
            "[0.98419185 0.33341227 0.67370162]\n",
            "[0.98419185 0.33341227 0.67370162]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qy6T6HOiGHaN"
      },
      "source": [
        "##### Indexing Slicing Transformation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74oL7u6IGJMo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e600707-6801-4fbc-b0cc-8bff20b08eef"
      },
      "source": [
        "# Choose one value in a matrix\n",
        "arr_bis = np.array([[1,2,3],\n",
        "                    [7,1,6],\n",
        "                    [9,6,3]])\n",
        "arr_bis[0][0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkhGlrwiO0HK"
      },
      "source": [
        "arr_bis[0,0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSF6ccF1Ggx6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "315fbe4e-fcba-4bfd-9877-45c63742649c"
      },
      "source": [
        "# Choose sub matrix in the matrix\n",
        "arr_bis[0:2,0:1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1],\n",
              "       [7]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks__IvR_GqHI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70428dc1-d065-4e71-87d3-d265f9f7d206"
      },
      "source": [
        "# Choose one columns or one row\n",
        "print(arr_bis[1,:])\n",
        "print(arr_bis[:,1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7 1 6]\n",
            "[2 1 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0AG2UdRAng9d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d3d220a-fc7e-42a3-be5a-6e5391308ee9"
      },
      "source": [
        "# Max\n",
        "# On the matrix\n",
        "print(arr_bis.max())\n",
        "\n",
        "# By the rows\n",
        "print(arr_bis.max(axis=1))\n",
        "\n",
        "# By the columns\n",
        "print(arr_bis.max(axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9\n",
            "[3 7 9]\n",
            "[9 6 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omomB6aDoBnN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91c19669-1341-41b6-fcdf-2c80ee1f8e8e"
      },
      "source": [
        "# Min\n",
        "# On the matrix\n",
        "print(arr_bis.min())\n",
        "\n",
        "# By the rows\n",
        "print(arr_bis.min(axis=1))\n",
        "\n",
        "# By the columns\n",
        "print(arr_bis.min(axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "[1 1 3]\n",
            "[1 1 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-xixa41oE4X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61513186-365b-4c25-a31a-b5eadc613136"
      },
      "source": [
        "# Mean\n",
        "# On the matrix\n",
        "print(arr_bis.mean())\n",
        "\n",
        "# By the rows\n",
        "print(arr_bis.mean(axis=1))\n",
        "\n",
        "# By the columns\n",
        "print(arr_bis.mean(axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.222222222222222\n",
            "[2.         4.66666667 6.        ]\n",
            "[5.66666667 3.         4.        ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzvvyXXJoOeW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3924a40-7a78-4b60-ff70-39fc3c3b51da"
      },
      "source": [
        "# Std\n",
        "# On the matrix\n",
        "print(arr_bis.std())\n",
        "\n",
        "# By the rows\n",
        "print(arr_bis.std(axis=1))\n",
        "\n",
        "# By the columns\n",
        "print(arr_bis.std(axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.698879511442471\n",
            "[0.81649658 2.62466929 2.44948974]\n",
            "[3.39934634 2.1602469  1.41421356]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QRCdS8BoYL_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7aa32a6e-da5b-4f69-8e39-90e82b82b82f"
      },
      "source": [
        "# Other way available \n",
        "np.std(arr_bis, axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.39934634, 2.1602469 , 1.41421356])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeSWgJteotgR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eccc4e43-0fb6-4d34-d144-91ac1f15c64b"
      },
      "source": [
        "# Log function \n",
        "np.log(arr_bis)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.69314718, 1.09861229],\n",
              "       [1.94591015, 0.        , 1.79175947],\n",
              "       [2.19722458, 1.79175947, 1.09861229]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvtkDDK9o4DB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb5fd174-1d6a-4b6e-ab36-f0d462c24ba7"
      },
      "source": [
        "# Exponential function \n",
        "np.exp(arr_bis)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.71828183e+00, 7.38905610e+00, 2.00855369e+01],\n",
              "       [1.09663316e+03, 2.71828183e+00, 4.03428793e+02],\n",
              "       [8.10308393e+03, 4.03428793e+02, 2.00855369e+01]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPpAz_CWo4eH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6ffc2e6-8b18-43cc-b072-c0055b3b4f2a"
      },
      "source": [
        "# Squared root function \n",
        "np.sqrt(arr_bis)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 1.41421356, 1.73205081],\n",
              "       [2.64575131, 1.        , 2.44948974],\n",
              "       [3.        , 2.44948974, 1.73205081]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fo7zIRfn39RL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05b71d3f-8c6f-4693-f94a-4fe4ccf05448"
      },
      "source": [
        "# Concatenate \n",
        "# Params: tuple of arrays and the axis of the concatenation\n",
        "# Concat 1 dimension\n",
        "arr1 = arr_bis[:,1]\n",
        "arr2 = arr_bis[:,2]\n",
        "\n",
        "print(f\"ARR1 {arr1}\")\n",
        "print(f\"ARR2 {arr2}\")\n",
        "\n",
        "print(np.concatenate((arr1, arr2), axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARR1 [2 1 6]\n",
            "ARR2 [3 6 3]\n",
            "[2 1 6 3 6 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSZV-n-X8-xx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de866f53-cfbb-41dc-aeae-4be90c9686d5"
      },
      "source": [
        "# Concat 2 dimension\n",
        "arr1 = arr1.reshape(-1,1)\n",
        "arr2 = arr2.reshape(-1,1)\n",
        "\n",
        "print(f\"ARR1 {arr1}\")\n",
        "print(f\"ARR2 {arr2}\")\n",
        "\n",
        "print(np.concatenate((arr1, arr2), axis=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARR1 [[2]\n",
            " [1]\n",
            " [6]]\n",
            "ARR2 [[3]\n",
            " [6]\n",
            " [3]]\n",
            "[[2]\n",
            " [1]\n",
            " [6]\n",
            " [3]\n",
            " [6]\n",
            " [3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aq0-jNt38_eq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "482ff022-61a7-42b0-a042-cf23087cd523"
      },
      "source": [
        "# Concat 2 dimension\n",
        "print(np.concatenate((arr1, arr2), axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2 3]\n",
            " [1 6]\n",
            " [6 3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ilLgwf1HIMw"
      },
      "source": [
        "### Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxZBaJicHJBJ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MLRfDnNGHUkI"
      },
      "source": [
        "##### Series and dataframes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdYvgavpJPGK"
      },
      "source": [
        "###### **Series**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YG-fUIdLIuaA"
      },
      "source": [
        "labels = [\"label 1\", \"label 2\", \"label 3\"]\n",
        "list_values = [1,2,3]\n",
        "arr_values = np.array(list_values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNo-p9PIJA3x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1da5e24d-eb4c-40c8-bd36-1874526b87eb"
      },
      "source": [
        "# Create a serie from a list\n",
        "pd.Series(list_values, index=labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label 1    1\n",
              "label 2    2\n",
              "label 3    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_trjK8YSJHqR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c148b8c-29f2-4e9d-884a-4cacd456c143"
      },
      "source": [
        "# Create a serie from a array\n",
        "pd.Series(arr_values, index=labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label 1    1\n",
              "label 2    2\n",
              "label 3    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLPPB2NOJUSq"
      },
      "source": [
        "###### **dataframes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSqejMNzJJ2h"
      },
      "source": [
        "list_columns = [\"col_1\", \"col_2\", \"col_3\"]\n",
        "list_index = [\"row_1\", \"row_2\", \"row_3\"]\n",
        "\n",
        "arr_bis = np.array([[1,2,3],\n",
        "                    [7,1,6],\n",
        "                    [9,6,9]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8If1H15K8UA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9425a0a5-54d9-454a-f577-d7094a9c1177"
      },
      "source": [
        "# Create a dataframe from a array\n",
        "pd.DataFrame(arr_bis).values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [7, 1, 6],\n",
              "       [9, 6, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0B8yIH3vQ6i-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "632142a2-269b-4077-cd79-993b3f9a2df2"
      },
      "source": [
        "# Specify rows/columns\n",
        "pd.DataFrame(arr_bis, index=list_index, columns=list_columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>col_1</th>\n",
              "      <th>col_2</th>\n",
              "      <th>col_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>row_1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_2</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>row_3</th>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       col_1  col_2  col_3\n",
              "row_1      1      2      3\n",
              "row_2      7      1      6\n",
              "row_3      9      6      9"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XM6mdryCRCAm"
      },
      "source": [
        "##### Cleaning and selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLc1A64XRGgz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "90364f02-b9c1-4cf8-abf6-e8fde0057506"
      },
      "source": [
        "# Import of csv\n",
        "assets = pd.read_csv(\"/content/assets.csv\", parse_dates=True, index_col=\"time\")\n",
        "assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 MSFT</th>\n",
              "      <th>High MSFT</th>\n",
              "      <th>Low MSFT</th>\n",
              "      <th>Close MSFT</th>\n",
              "      <th>Volume MSFT</th>\n",
              "      <th>Open GOOG</th>\n",
              "      <th>High GOOG</th>\n",
              "      <th>Low GOOG</th>\n",
              "      <th>Close GOOG</th>\n",
              "      <th>Volume GOOG</th>\n",
              "      <th>Open GOLD</th>\n",
              "      <th>High GOLD</th>\n",
              "      <th>Low GOLD</th>\n",
              "      <th>Close GOLD</th>\n",
              "      <th>Volume GOLD</th>\n",
              "      <th>Open SP500</th>\n",
              "      <th>High SP500</th>\n",
              "      <th>Low SP500</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>Volume SP500</th>\n",
              "      <th>Open DJI30</th>\n",
              "      <th>High DJI30</th>\n",
              "      <th>Low DJI30</th>\n",
              "      <th>Close DJI30</th>\n",
              "      <th>Volume DJI30</th>\n",
              "      <th>Open AAPL</th>\n",
              "      <th>High AAPL</th>\n",
              "      <th>Low AAPL</th>\n",
              "      <th>Close AAPL</th>\n",
              "      <th>Volume AAPL</th>\n",
              "      <th>Open EUR/USD</th>\n",
              "      <th>High EUR/USD</th>\n",
              "      <th>Low EUR/USD</th>\n",
              "      <th>Close EUR/USD</th>\n",
              "      <th>Open USD/CAD</th>\n",
              "      <th>High USD/CAD</th>\n",
              "      <th>Low USD/CAD</th>\n",
              "      <th>Close USD/CAD</th>\n",
              "      <th>Open USD/AUD</th>\n",
              "      <th>High USD/AUD</th>\n",
              "      <th>Low USD/AUD</th>\n",
              "      <th>Close USD/AUD</th>\n",
              "      <th>Open USD/CNY</th>\n",
              "      <th>High USD/CNY</th>\n",
              "      <th>Low USD/CNY</th>\n",
              "      <th>Close USD/CNY</th>\n",
              "      <th>Open USD/GBP</th>\n",
              "      <th>High USD/GBP</th>\n",
              "      <th>Low USD/GBP</th>\n",
              "      <th>Close USD/GBP</th>\n",
              "      <th>Open AMZN</th>\n",
              "      <th>High AMZN</th>\n",
              "      <th>Low AMZN</th>\n",
              "      <th>Close AMZN</th>\n",
              "      <th>Volume AMZN</th>\n",
              "      <th>Open CAC40</th>\n",
              "      <th>High CAC40</th>\n",
              "      <th>Low CAC40</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Volume CAC40</th>\n",
              "      <th>Open DAX30</th>\n",
              "      <th>High DAX30</th>\n",
              "      <th>Low DAX30</th>\n",
              "      <th>Close DAX30</th>\n",
              "      <th>Volume DAX30</th>\n",
              "      <th>Open RUSSEL2000</th>\n",
              "      <th>High RUSSEL2000</th>\n",
              "      <th>Low RUSSEL2000</th>\n",
              "      <th>Close RUSSEL2000</th>\n",
              "      <th>Volume RUSSEL2000</th>\n",
              "      <th>Open EUROSTOCK50</th>\n",
              "      <th>High EUROSTOCK50</th>\n",
              "      <th>Low EUROSTOCK50</th>\n",
              "      <th>Close EUROSTOCK50</th>\n",
              "      <th>Volume EUROSTOCK50</th>\n",
              "      <th>Open INTEL</th>\n",
              "      <th>High INTEL</th>\n",
              "      <th>Low INTEL</th>\n",
              "      <th>Close INTEL</th>\n",
              "      <th>Volume INTEL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>138.0</td>\n",
              "      <td>146.0000</td>\n",
              "      <td>135.02</td>\n",
              "      <td>140.40</td>\n",
              "      <td>80794307.0</td>\n",
              "      <td>1056.51</td>\n",
              "      <td>1106.50</td>\n",
              "      <td>1037.28</td>\n",
              "      <td>1096.80</td>\n",
              "      <td>4220935.0</td>\n",
              "      <td>17.08</td>\n",
              "      <td>17.98</td>\n",
              "      <td>15.445</td>\n",
              "      <td>15.88</td>\n",
              "      <td>30208367.0</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2453.5701</td>\n",
              "      <td>2280.5200</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>901816472.0</td>\n",
              "      <td>20188.6895</td>\n",
              "      <td>20489.3301</td>\n",
              "      <td>18917.4609</td>\n",
              "      <td>19898.9199</td>\n",
              "      <td>871360000.0</td>\n",
              "      <td>239.77</td>\n",
              "      <td>250.00</td>\n",
              "      <td>237.12</td>\n",
              "      <td>246.67</td>\n",
              "      <td>74377540.0</td>\n",
              "      <td>1.1015</td>\n",
              "      <td>1.1045</td>\n",
              "      <td>1.0801</td>\n",
              "      <td>1.0933</td>\n",
              "      <td>1.4231</td>\n",
              "      <td>1.4651</td>\n",
              "      <td>1.4162</td>\n",
              "      <td>1.4481</td>\n",
              "      <td>1.6697</td>\n",
              "      <td>1.7528</td>\n",
              "      <td>1.6591</td>\n",
              "      <td>1.7269</td>\n",
              "      <td>7.0132</td>\n",
              "      <td>7.0478</td>\n",
              "      <td>7.0073</td>\n",
              "      <td>7.0475</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.8703</td>\n",
              "      <td>0.8244</td>\n",
              "      <td>0.8589</td>\n",
              "      <td>1750.00</td>\n",
              "      <td>1841.66</td>\n",
              "      <td>1745.00</td>\n",
              "      <td>1830.00</td>\n",
              "      <td>9512897.0</td>\n",
              "      <td>3905.5300</td>\n",
              "      <td>3908.7200</td>\n",
              "      <td>3726.4500</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>214274700.0</td>\n",
              "      <td>8613.3496</td>\n",
              "      <td>8670.4102</td>\n",
              "      <td>8400.1797</td>\n",
              "      <td>8441.7100</td>\n",
              "      <td>207558900.0</td>\n",
              "      <td>1057.6499</td>\n",
              "      <td>1062.8826</td>\n",
              "      <td>966.2156</td>\n",
              "      <td>991.1641</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2507.3301</td>\n",
              "      <td>2507.3301</td>\n",
              "      <td>2365.7000</td>\n",
              "      <td>2385.8201</td>\n",
              "      <td>98214900.0</td>\n",
              "      <td>30.24</td>\n",
              "      <td>32.4550</td>\n",
              "      <td>28.01</td>\n",
              "      <td>29.20</td>\n",
              "      <td>192337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>140.0</td>\n",
              "      <td>147.4998</td>\n",
              "      <td>135.00</td>\n",
              "      <td>146.57</td>\n",
              "      <td>81059817.0</td>\n",
              "      <td>1093.11</td>\n",
              "      <td>1130.86</td>\n",
              "      <td>1056.01</td>\n",
              "      <td>1119.80</td>\n",
              "      <td>3861489.0</td>\n",
              "      <td>16.21</td>\n",
              "      <td>18.59</td>\n",
              "      <td>16.130</td>\n",
              "      <td>17.71</td>\n",
              "      <td>32043530.0</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2437.2174</td>\n",
              "      <td>2421.7897</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>510792.0</td>\n",
              "      <td>20487.0508</td>\n",
              "      <td>21379.3496</td>\n",
              "      <td>19882.2598</td>\n",
              "      <td>21237.3809</td>\n",
              "      <td>793060000.0</td>\n",
              "      <td>247.51</td>\n",
              "      <td>257.61</td>\n",
              "      <td>238.40</td>\n",
              "      <td>252.86</td>\n",
              "      <td>81013965.0</td>\n",
              "      <td>1.1168</td>\n",
              "      <td>1.1189</td>\n",
              "      <td>1.0954</td>\n",
              "      <td>1.1015</td>\n",
              "      <td>1.3992</td>\n",
              "      <td>1.4277</td>\n",
              "      <td>1.3959</td>\n",
              "      <td>1.4228</td>\n",
              "      <td>1.6329</td>\n",
              "      <td>1.6775</td>\n",
              "      <td>1.6273</td>\n",
              "      <td>1.6699</td>\n",
              "      <td>6.9932</td>\n",
              "      <td>7.0233</td>\n",
              "      <td>6.9870</td>\n",
              "      <td>7.0047</td>\n",
              "      <td>0.8149</td>\n",
              "      <td>0.8329</td>\n",
              "      <td>0.8148</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>1775.47</td>\n",
              "      <td>1857.78</td>\n",
              "      <td>1689.24</td>\n",
              "      <td>1807.84</td>\n",
              "      <td>10917129.0</td>\n",
              "      <td>4041.3301</td>\n",
              "      <td>4042.4600</td>\n",
              "      <td>3759.0000</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>225787000.0</td>\n",
              "      <td>9141.1699</td>\n",
              "      <td>9145.9297</td>\n",
              "      <td>8423.5596</td>\n",
              "      <td>8939.0996</td>\n",
              "      <td>220092600.0</td>\n",
              "      <td>1063.1651</td>\n",
              "      <td>1063.2460</td>\n",
              "      <td>1051.0800</td>\n",
              "      <td>1057.6499</td>\n",
              "      <td>779778.0</td>\n",
              "      <td>2475.1799</td>\n",
              "      <td>2555.4099</td>\n",
              "      <td>2376.2300</td>\n",
              "      <td>2530.5000</td>\n",
              "      <td>106905300.0</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.8000</td>\n",
              "      <td>28.79</td>\n",
              "      <td>32.40</td>\n",
              "      <td>273967.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>140.0</td>\n",
              "      <td>149.3500</td>\n",
              "      <td>135.00</td>\n",
              "      <td>135.42</td>\n",
              "      <td>87905868.0</td>\n",
              "      <td>1096.00</td>\n",
              "      <td>1152.27</td>\n",
              "      <td>1074.44</td>\n",
              "      <td>1084.33</td>\n",
              "      <td>4252365.0</td>\n",
              "      <td>13.21</td>\n",
              "      <td>17.15</td>\n",
              "      <td>12.650</td>\n",
              "      <td>16.33</td>\n",
              "      <td>36592679.0</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2434.2419</td>\n",
              "      <td>2412.7850</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>292505.0</td>\n",
              "      <td>20917.5293</td>\n",
              "      <td>21768.2793</td>\n",
              "      <td>20116.4609</td>\n",
              "      <td>20188.5195</td>\n",
              "      <td>770130000.0</td>\n",
              "      <td>241.95</td>\n",
              "      <td>259.08</td>\n",
              "      <td>240.00</td>\n",
              "      <td>242.21</td>\n",
              "      <td>80605865.0</td>\n",
              "      <td>1.1157</td>\n",
              "      <td>1.1237</td>\n",
              "      <td>1.1084</td>\n",
              "      <td>1.1168</td>\n",
              "      <td>1.3846</td>\n",
              "      <td>1.4019</td>\n",
              "      <td>1.3786</td>\n",
              "      <td>1.3991</td>\n",
              "      <td>1.6207</td>\n",
              "      <td>1.6442</td>\n",
              "      <td>1.6086</td>\n",
              "      <td>1.6327</td>\n",
              "      <td>7.0020</td>\n",
              "      <td>7.0236</td>\n",
              "      <td>6.9925</td>\n",
              "      <td>6.9931</td>\n",
              "      <td>0.8063</td>\n",
              "      <td>0.8194</td>\n",
              "      <td>0.8051</td>\n",
              "      <td>0.8152</td>\n",
              "      <td>1641.51</td>\n",
              "      <td>1759.45</td>\n",
              "      <td>1626.03</td>\n",
              "      <td>1689.15</td>\n",
              "      <td>8917265.0</td>\n",
              "      <td>3886.8201</td>\n",
              "      <td>3962.0100</td>\n",
              "      <td>3632.0601</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>311603900.0</td>\n",
              "      <td>8728.4805</td>\n",
              "      <td>8967.1104</td>\n",
              "      <td>8255.6504</td>\n",
              "      <td>8742.2500</td>\n",
              "      <td>302202400.0</td>\n",
              "      <td>1054.1179</td>\n",
              "      <td>1070.6257</td>\n",
              "      <td>1053.2714</td>\n",
              "      <td>1063.1651</td>\n",
              "      <td>805538.0</td>\n",
              "      <td>2557.7100</td>\n",
              "      <td>2557.7100</td>\n",
              "      <td>2302.8401</td>\n",
              "      <td>2450.3701</td>\n",
              "      <td>137137000.0</td>\n",
              "      <td>30.11</td>\n",
              "      <td>32.8450</td>\n",
              "      <td>28.62</td>\n",
              "      <td>29.01</td>\n",
              "      <td>97928.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>147.5</td>\n",
              "      <td>161.9100</td>\n",
              "      <td>140.73</td>\n",
              "      <td>158.83</td>\n",
              "      <td>92727446.0</td>\n",
              "      <td>1179.00</td>\n",
              "      <td>1219.76</td>\n",
              "      <td>1117.14</td>\n",
              "      <td>1219.73</td>\n",
              "      <td>3700125.0</td>\n",
              "      <td>17.45</td>\n",
              "      <td>17.61</td>\n",
              "      <td>15.060</td>\n",
              "      <td>15.67</td>\n",
              "      <td>31086787.0</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2422.0944</td>\n",
              "      <td>2399.4778</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>869276.0</td>\n",
              "      <td>21973.8203</td>\n",
              "      <td>23189.7598</td>\n",
              "      <td>21285.3691</td>\n",
              "      <td>23185.6191</td>\n",
              "      <td>843080000.0</td>\n",
              "      <td>264.89</td>\n",
              "      <td>279.92</td>\n",
              "      <td>252.95</td>\n",
              "      <td>277.97</td>\n",
              "      <td>92683032.0</td>\n",
              "      <td>1.1176</td>\n",
              "      <td>1.1221</td>\n",
              "      <td>1.1053</td>\n",
              "      <td>1.1105</td>\n",
              "      <td>1.3923</td>\n",
              "      <td>1.3995</td>\n",
              "      <td>1.3778</td>\n",
              "      <td>1.3804</td>\n",
              "      <td>1.5888</td>\n",
              "      <td>1.6320</td>\n",
              "      <td>1.5806</td>\n",
              "      <td>1.6179</td>\n",
              "      <td>7.0283</td>\n",
              "      <td>7.0334</td>\n",
              "      <td>6.9773</td>\n",
              "      <td>7.0087</td>\n",
              "      <td>0.7948</td>\n",
              "      <td>0.8146</td>\n",
              "      <td>0.7921</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>1755.00</td>\n",
              "      <td>1786.31</td>\n",
              "      <td>1680.62</td>\n",
              "      <td>1785.00</td>\n",
              "      <td>8809725.0</td>\n",
              "      <td>4234.3701</td>\n",
              "      <td>4438.5098</td>\n",
              "      <td>4055.1899</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>326461800.0</td>\n",
              "      <td>9480.7803</td>\n",
              "      <td>9985.7402</td>\n",
              "      <td>9064.6797</td>\n",
              "      <td>9232.0801</td>\n",
              "      <td>325900900.0</td>\n",
              "      <td>1058.5250</td>\n",
              "      <td>1061.8434</td>\n",
              "      <td>1052.2232</td>\n",
              "      <td>1054.1179</td>\n",
              "      <td>550756.0</td>\n",
              "      <td>2584.3601</td>\n",
              "      <td>2793.2700</td>\n",
              "      <td>2537.2700</td>\n",
              "      <td>2586.0200</td>\n",
              "      <td>136547100.0</td>\n",
              "      <td>33.87</td>\n",
              "      <td>35.0077</td>\n",
              "      <td>31.00</td>\n",
              "      <td>33.00</td>\n",
              "      <td>126388.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>145.3</td>\n",
              "      <td>153.4700</td>\n",
              "      <td>138.58</td>\n",
              "      <td>139.06</td>\n",
              "      <td>93226366.0</td>\n",
              "      <td>1126.00</td>\n",
              "      <td>1193.87</td>\n",
              "      <td>1113.30</td>\n",
              "      <td>1114.91</td>\n",
              "      <td>4226748.0</td>\n",
              "      <td>16.97</td>\n",
              "      <td>18.00</td>\n",
              "      <td>15.470</td>\n",
              "      <td>16.88</td>\n",
              "      <td>33496744.0</td>\n",
              "      <td>2423.1280</td>\n",
              "      <td>2429.1220</td>\n",
              "      <td>2406.5553</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>418898.0</td>\n",
              "      <td>22184.7109</td>\n",
              "      <td>22837.9492</td>\n",
              "      <td>21154.4609</td>\n",
              "      <td>21200.6191</td>\n",
              "      <td>908260000.0</td>\n",
              "      <td>255.94</td>\n",
              "      <td>270.00</td>\n",
              "      <td>248.00</td>\n",
              "      <td>248.23</td>\n",
              "      <td>104618517.0</td>\n",
              "      <td>1.1259</td>\n",
              "      <td>1.1334</td>\n",
              "      <td>1.1054</td>\n",
              "      <td>1.1176</td>\n",
              "      <td>1.3771</td>\n",
              "      <td>1.3963</td>\n",
              "      <td>1.3704</td>\n",
              "      <td>1.3922</td>\n",
              "      <td>1.5427</td>\n",
              "      <td>1.6090</td>\n",
              "      <td>1.5412</td>\n",
              "      <td>1.5907</td>\n",
              "      <td>6.9770</td>\n",
              "      <td>7.0367</td>\n",
              "      <td>6.9685</td>\n",
              "      <td>7.0298</td>\n",
              "      <td>0.7807</td>\n",
              "      <td>0.8002</td>\n",
              "      <td>0.7782</td>\n",
              "      <td>0.7949</td>\n",
              "      <td>1721.98</td>\n",
              "      <td>1765.00</td>\n",
              "      <td>1675.00</td>\n",
              "      <td>1676.61</td>\n",
              "      <td>11346214.0</td>\n",
              "      <td>4374.6699</td>\n",
              "      <td>4404.2598</td>\n",
              "      <td>4025.8899</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>371403200.0</td>\n",
              "      <td>9863.9902</td>\n",
              "      <td>9932.5596</td>\n",
              "      <td>9139.1201</td>\n",
              "      <td>9161.1299</td>\n",
              "      <td>390477000.0</td>\n",
              "      <td>1068.1112</td>\n",
              "      <td>1069.0863</td>\n",
              "      <td>1050.3913</td>\n",
              "      <td>1058.5250</td>\n",
              "      <td>612402.0</td>\n",
              "      <td>2883.2500</td>\n",
              "      <td>2883.2500</td>\n",
              "      <td>2535.8899</td>\n",
              "      <td>2545.2300</td>\n",
              "      <td>167329900.0</td>\n",
              "      <td>35.29</td>\n",
              "      <td>36.0432</td>\n",
              "      <td>32.33</td>\n",
              "      <td>32.43</td>\n",
              "      <td>145336.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-07-04</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6501.4199</td>\n",
              "      <td>6504.1499</td>\n",
              "      <td>6459.2900</td>\n",
              "      <td>6494.7700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6958.3999</td>\n",
              "      <td>6976.0801</td>\n",
              "      <td>6881.1401</td>\n",
              "      <td>6944.3599</td>\n",
              "      <td>36053500.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5185.8701</td>\n",
              "      <td>5185.8701</td>\n",
              "      <td>5185.8701</td>\n",
              "      <td>5185.8701</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6185.9902</td>\n",
              "      <td>6237.0200</td>\n",
              "      <td>6181.8301</td>\n",
              "      <td>6205.1299</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6956.1401</td>\n",
              "      <td>7029.7998</td>\n",
              "      <td>6956.1401</td>\n",
              "      <td>7016.6602</td>\n",
              "      <td>17218400.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5084.6899</td>\n",
              "      <td>5084.6899</td>\n",
              "      <td>5084.6899</td>\n",
              "      <td>5084.6899</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6218.0695</td>\n",
              "      <td>6286.7488</td>\n",
              "      <td>6212.6309</td>\n",
              "      <td>6256.7038</td>\n",
              "      <td>536096.0</td>\n",
              "      <td>7136.3048</td>\n",
              "      <td>7159.9406</td>\n",
              "      <td>7122.5503</td>\n",
              "      <td>7137.0685</td>\n",
              "      <td>760006.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5180.1184</td>\n",
              "      <td>5207.4892</td>\n",
              "      <td>5132.2034</td>\n",
              "      <td>5170.3844</td>\n",
              "      <td>722995.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5979.1299</td>\n",
              "      <td>5994.0400</td>\n",
              "      <td>5928.0400</td>\n",
              "      <td>5967.2798</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7568.3398</td>\n",
              "      <td>7590.5298</td>\n",
              "      <td>7447.7100</td>\n",
              "      <td>7590.5298</td>\n",
              "      <td>24404000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>5033.3301</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5834.7500</td>\n",
              "      <td>5857.3599</td>\n",
              "      <td>5740.1899</td>\n",
              "      <td>5842.7798</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7177.4800</td>\n",
              "      <td>7297.0400</td>\n",
              "      <td>7112.9702</td>\n",
              "      <td>7258.8999</td>\n",
              "      <td>44872800.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>4884.5000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5732 rows × 80 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open MSFT  High MSFT  ...  Close INTEL  Volume INTEL\n",
              "time                              ...                           \n",
              "2020-03-18      138.0   146.0000  ...        29.20      192337.0\n",
              "2020-03-17      140.0   147.4998  ...        32.40      273967.0\n",
              "2020-03-16      140.0   149.3500  ...        29.01       97928.0\n",
              "2020-03-13      147.5   161.9100  ...        33.00      126388.0\n",
              "2020-03-12      145.3   153.4700  ...        32.43      145336.0\n",
              "...               ...        ...  ...          ...           ...\n",
              "2000-07-04        NaN        NaN  ...          NaN           NaN\n",
              "2000-05-29        NaN        NaN  ...          NaN           NaN\n",
              "2000-04-21        NaN        NaN  ...          NaN           NaN\n",
              "2000-02-21        NaN        NaN  ...          NaN           NaN\n",
              "2000-01-17        NaN        NaN  ...          NaN           NaN\n",
              "\n",
              "[5732 rows x 80 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryhsmwSMRvNn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "05931a2c-f365-42e0-f385-a51a0ee4d4bf"
      },
      "source": [
        "# Ordonning\n",
        "assets = assets.sort_index(ascending=True)\n",
        "assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 MSFT</th>\n",
              "      <th>High MSFT</th>\n",
              "      <th>Low MSFT</th>\n",
              "      <th>Close MSFT</th>\n",
              "      <th>Volume MSFT</th>\n",
              "      <th>Open GOOG</th>\n",
              "      <th>High GOOG</th>\n",
              "      <th>Low GOOG</th>\n",
              "      <th>Close GOOG</th>\n",
              "      <th>Volume GOOG</th>\n",
              "      <th>Open GOLD</th>\n",
              "      <th>High GOLD</th>\n",
              "      <th>Low GOLD</th>\n",
              "      <th>Close GOLD</th>\n",
              "      <th>Volume GOLD</th>\n",
              "      <th>Open SP500</th>\n",
              "      <th>High SP500</th>\n",
              "      <th>Low SP500</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>Volume SP500</th>\n",
              "      <th>Open DJI30</th>\n",
              "      <th>High DJI30</th>\n",
              "      <th>Low DJI30</th>\n",
              "      <th>Close DJI30</th>\n",
              "      <th>Volume DJI30</th>\n",
              "      <th>Open AAPL</th>\n",
              "      <th>High AAPL</th>\n",
              "      <th>Low AAPL</th>\n",
              "      <th>Close AAPL</th>\n",
              "      <th>Volume AAPL</th>\n",
              "      <th>Open EUR/USD</th>\n",
              "      <th>High EUR/USD</th>\n",
              "      <th>Low EUR/USD</th>\n",
              "      <th>Close EUR/USD</th>\n",
              "      <th>Open USD/CAD</th>\n",
              "      <th>High USD/CAD</th>\n",
              "      <th>Low USD/CAD</th>\n",
              "      <th>Close USD/CAD</th>\n",
              "      <th>Open USD/AUD</th>\n",
              "      <th>High USD/AUD</th>\n",
              "      <th>Low USD/AUD</th>\n",
              "      <th>Close USD/AUD</th>\n",
              "      <th>Open USD/CNY</th>\n",
              "      <th>High USD/CNY</th>\n",
              "      <th>Low USD/CNY</th>\n",
              "      <th>Close USD/CNY</th>\n",
              "      <th>Open USD/GBP</th>\n",
              "      <th>High USD/GBP</th>\n",
              "      <th>Low USD/GBP</th>\n",
              "      <th>Close USD/GBP</th>\n",
              "      <th>Open AMZN</th>\n",
              "      <th>High AMZN</th>\n",
              "      <th>Low AMZN</th>\n",
              "      <th>Close AMZN</th>\n",
              "      <th>Volume AMZN</th>\n",
              "      <th>Open CAC40</th>\n",
              "      <th>High CAC40</th>\n",
              "      <th>Low CAC40</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Volume CAC40</th>\n",
              "      <th>Open DAX30</th>\n",
              "      <th>High DAX30</th>\n",
              "      <th>Low DAX30</th>\n",
              "      <th>Close DAX30</th>\n",
              "      <th>Volume DAX30</th>\n",
              "      <th>Open RUSSEL2000</th>\n",
              "      <th>High RUSSEL2000</th>\n",
              "      <th>Low RUSSEL2000</th>\n",
              "      <th>Close RUSSEL2000</th>\n",
              "      <th>Volume RUSSEL2000</th>\n",
              "      <th>Open EUROSTOCK50</th>\n",
              "      <th>High EUROSTOCK50</th>\n",
              "      <th>Low EUROSTOCK50</th>\n",
              "      <th>Close EUROSTOCK50</th>\n",
              "      <th>Volume EUROSTOCK50</th>\n",
              "      <th>Open INTEL</th>\n",
              "      <th>High INTEL</th>\n",
              "      <th>Low INTEL</th>\n",
              "      <th>Close INTEL</th>\n",
              "      <th>Volume INTEL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1469.2500</td>\n",
              "      <td>1478.0000</td>\n",
              "      <td>1438.3600</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>9.318000e+08</td>\n",
              "      <td>11501.8496</td>\n",
              "      <td>11522.0098</td>\n",
              "      <td>11305.6904</td>\n",
              "      <td>11357.5098</td>\n",
              "      <td>169750000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6024.3799</td>\n",
              "      <td>6102.1201</td>\n",
              "      <td>5901.7700</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6961.7202</td>\n",
              "      <td>7159.3301</td>\n",
              "      <td>6720.8701</td>\n",
              "      <td>6750.7598</td>\n",
              "      <td>43072500.0</td>\n",
              "      <td>504.7600</td>\n",
              "      <td>510.9600</td>\n",
              "      <td>489.9500</td>\n",
              "      <td>496.4200</td>\n",
              "      <td>9318000.0</td>\n",
              "      <td>4849.2202</td>\n",
              "      <td>4849.2202</td>\n",
              "      <td>4849.2202</td>\n",
              "      <td>4849.2202</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>1397.4301</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>1.009000e+09</td>\n",
              "      <td>11349.7500</td>\n",
              "      <td>11350.0596</td>\n",
              "      <td>10986.4502</td>\n",
              "      <td>10997.9297</td>\n",
              "      <td>178420000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5922.2300</td>\n",
              "      <td>5925.0698</td>\n",
              "      <td>5657.2002</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6747.2402</td>\n",
              "      <td>6755.3599</td>\n",
              "      <td>6510.4600</td>\n",
              "      <td>6586.9502</td>\n",
              "      <td>46678400.0</td>\n",
              "      <td>497.0500</td>\n",
              "      <td>497.0500</td>\n",
              "      <td>478.2000</td>\n",
              "      <td>478.3800</td>\n",
              "      <td>10090000.0</td>\n",
              "      <td>4657.8301</td>\n",
              "      <td>4657.8301</td>\n",
              "      <td>4657.8301</td>\n",
              "      <td>4657.8301</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>1413.2700</td>\n",
              "      <td>1377.6801</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>1.085500e+09</td>\n",
              "      <td>10989.3701</td>\n",
              "      <td>11215.0996</td>\n",
              "      <td>10938.6699</td>\n",
              "      <td>11122.6504</td>\n",
              "      <td>203190000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5521.8301</td>\n",
              "      <td>5589.5000</td>\n",
              "      <td>5461.5898</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6585.8501</td>\n",
              "      <td>6585.8501</td>\n",
              "      <td>6388.9102</td>\n",
              "      <td>6502.0698</td>\n",
              "      <td>52682800.0</td>\n",
              "      <td>478.3800</td>\n",
              "      <td>479.4800</td>\n",
              "      <td>467.5600</td>\n",
              "      <td>478.8300</td>\n",
              "      <td>10855000.0</td>\n",
              "      <td>4541.7500</td>\n",
              "      <td>4541.7500</td>\n",
              "      <td>4541.7500</td>\n",
              "      <td>4541.7500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>1411.9000</td>\n",
              "      <td>1392.1000</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>1.092300e+09</td>\n",
              "      <td>11113.3701</td>\n",
              "      <td>11313.4502</td>\n",
              "      <td>11098.4502</td>\n",
              "      <td>11253.2598</td>\n",
              "      <td>176550000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5485.9302</td>\n",
              "      <td>5530.2598</td>\n",
              "      <td>5388.8501</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6501.4502</td>\n",
              "      <td>6539.3101</td>\n",
              "      <td>6402.6299</td>\n",
              "      <td>6474.9199</td>\n",
              "      <td>41180600.0</td>\n",
              "      <td>478.8300</td>\n",
              "      <td>480.1900</td>\n",
              "      <td>474.4000</td>\n",
              "      <td>475.3400</td>\n",
              "      <td>10923000.0</td>\n",
              "      <td>4500.6899</td>\n",
              "      <td>4500.6899</td>\n",
              "      <td>4500.6899</td>\n",
              "      <td>4500.6899</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>1400.7300</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>1.225200e+09</td>\n",
              "      <td>11247.0596</td>\n",
              "      <td>11528.1396</td>\n",
              "      <td>11239.9199</td>\n",
              "      <td>11522.5596</td>\n",
              "      <td>184900000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5423.8799</td>\n",
              "      <td>5561.6899</td>\n",
              "      <td>5423.8799</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6489.9399</td>\n",
              "      <td>6791.5298</td>\n",
              "      <td>6470.1401</td>\n",
              "      <td>6780.9600</td>\n",
              "      <td>56058900.0</td>\n",
              "      <td>475.3400</td>\n",
              "      <td>488.3100</td>\n",
              "      <td>475.3400</td>\n",
              "      <td>488.3100</td>\n",
              "      <td>12252000.0</td>\n",
              "      <td>4648.2700</td>\n",
              "      <td>4648.2700</td>\n",
              "      <td>4648.2700</td>\n",
              "      <td>4648.2700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.1105</td>\n",
              "      <td>1.1200</td>\n",
              "      <td>1.1047</td>\n",
              "      <td>1.1157</td>\n",
              "      <td>1.3799</td>\n",
              "      <td>1.3899</td>\n",
              "      <td>1.3726</td>\n",
              "      <td>1.3846</td>\n",
              "      <td>1.6166</td>\n",
              "      <td>1.6354</td>\n",
              "      <td>1.5855</td>\n",
              "      <td>1.6214</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.8144</td>\n",
              "      <td>0.8151</td>\n",
              "      <td>0.8046</td>\n",
              "      <td>0.8062</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>140.0</td>\n",
              "      <td>149.3500</td>\n",
              "      <td>135.00</td>\n",
              "      <td>135.42</td>\n",
              "      <td>87905868.0</td>\n",
              "      <td>1096.00</td>\n",
              "      <td>1152.27</td>\n",
              "      <td>1074.44</td>\n",
              "      <td>1084.33</td>\n",
              "      <td>4252365.0</td>\n",
              "      <td>13.21</td>\n",
              "      <td>17.15</td>\n",
              "      <td>12.650</td>\n",
              "      <td>16.33</td>\n",
              "      <td>36592679.0</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2434.2419</td>\n",
              "      <td>2412.7850</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2.925050e+05</td>\n",
              "      <td>20917.5293</td>\n",
              "      <td>21768.2793</td>\n",
              "      <td>20116.4609</td>\n",
              "      <td>20188.5195</td>\n",
              "      <td>770130000.0</td>\n",
              "      <td>241.95</td>\n",
              "      <td>259.08</td>\n",
              "      <td>240.00</td>\n",
              "      <td>242.21</td>\n",
              "      <td>80605865.0</td>\n",
              "      <td>1.1157</td>\n",
              "      <td>1.1237</td>\n",
              "      <td>1.1084</td>\n",
              "      <td>1.1168</td>\n",
              "      <td>1.3846</td>\n",
              "      <td>1.4019</td>\n",
              "      <td>1.3786</td>\n",
              "      <td>1.3991</td>\n",
              "      <td>1.6207</td>\n",
              "      <td>1.6442</td>\n",
              "      <td>1.6086</td>\n",
              "      <td>1.6327</td>\n",
              "      <td>7.0020</td>\n",
              "      <td>7.0236</td>\n",
              "      <td>6.9925</td>\n",
              "      <td>6.9931</td>\n",
              "      <td>0.8063</td>\n",
              "      <td>0.8194</td>\n",
              "      <td>0.8051</td>\n",
              "      <td>0.8152</td>\n",
              "      <td>1641.51</td>\n",
              "      <td>1759.45</td>\n",
              "      <td>1626.03</td>\n",
              "      <td>1689.15</td>\n",
              "      <td>8917265.0</td>\n",
              "      <td>3886.8201</td>\n",
              "      <td>3962.0100</td>\n",
              "      <td>3632.0601</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>311603900.0</td>\n",
              "      <td>8728.4805</td>\n",
              "      <td>8967.1104</td>\n",
              "      <td>8255.6504</td>\n",
              "      <td>8742.2500</td>\n",
              "      <td>302202400.0</td>\n",
              "      <td>1054.1179</td>\n",
              "      <td>1070.6257</td>\n",
              "      <td>1053.2714</td>\n",
              "      <td>1063.1651</td>\n",
              "      <td>805538.0</td>\n",
              "      <td>2557.7100</td>\n",
              "      <td>2557.7100</td>\n",
              "      <td>2302.8401</td>\n",
              "      <td>2450.3701</td>\n",
              "      <td>137137000.0</td>\n",
              "      <td>30.11</td>\n",
              "      <td>32.845</td>\n",
              "      <td>28.62</td>\n",
              "      <td>29.01</td>\n",
              "      <td>97928.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>140.0</td>\n",
              "      <td>147.4998</td>\n",
              "      <td>135.00</td>\n",
              "      <td>146.57</td>\n",
              "      <td>81059817.0</td>\n",
              "      <td>1093.11</td>\n",
              "      <td>1130.86</td>\n",
              "      <td>1056.01</td>\n",
              "      <td>1119.80</td>\n",
              "      <td>3861489.0</td>\n",
              "      <td>16.21</td>\n",
              "      <td>18.59</td>\n",
              "      <td>16.130</td>\n",
              "      <td>17.71</td>\n",
              "      <td>32043530.0</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2437.2174</td>\n",
              "      <td>2421.7897</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>5.107920e+05</td>\n",
              "      <td>20487.0508</td>\n",
              "      <td>21379.3496</td>\n",
              "      <td>19882.2598</td>\n",
              "      <td>21237.3809</td>\n",
              "      <td>793060000.0</td>\n",
              "      <td>247.51</td>\n",
              "      <td>257.61</td>\n",
              "      <td>238.40</td>\n",
              "      <td>252.86</td>\n",
              "      <td>81013965.0</td>\n",
              "      <td>1.1168</td>\n",
              "      <td>1.1189</td>\n",
              "      <td>1.0954</td>\n",
              "      <td>1.1015</td>\n",
              "      <td>1.3992</td>\n",
              "      <td>1.4277</td>\n",
              "      <td>1.3959</td>\n",
              "      <td>1.4228</td>\n",
              "      <td>1.6329</td>\n",
              "      <td>1.6775</td>\n",
              "      <td>1.6273</td>\n",
              "      <td>1.6699</td>\n",
              "      <td>6.9932</td>\n",
              "      <td>7.0233</td>\n",
              "      <td>6.9870</td>\n",
              "      <td>7.0047</td>\n",
              "      <td>0.8149</td>\n",
              "      <td>0.8329</td>\n",
              "      <td>0.8148</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>1775.47</td>\n",
              "      <td>1857.78</td>\n",
              "      <td>1689.24</td>\n",
              "      <td>1807.84</td>\n",
              "      <td>10917129.0</td>\n",
              "      <td>4041.3301</td>\n",
              "      <td>4042.4600</td>\n",
              "      <td>3759.0000</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>225787000.0</td>\n",
              "      <td>9141.1699</td>\n",
              "      <td>9145.9297</td>\n",
              "      <td>8423.5596</td>\n",
              "      <td>8939.0996</td>\n",
              "      <td>220092600.0</td>\n",
              "      <td>1063.1651</td>\n",
              "      <td>1063.2460</td>\n",
              "      <td>1051.0800</td>\n",
              "      <td>1057.6499</td>\n",
              "      <td>779778.0</td>\n",
              "      <td>2475.1799</td>\n",
              "      <td>2555.4099</td>\n",
              "      <td>2376.2300</td>\n",
              "      <td>2530.5000</td>\n",
              "      <td>106905300.0</td>\n",
              "      <td>29.06</td>\n",
              "      <td>33.800</td>\n",
              "      <td>28.79</td>\n",
              "      <td>32.40</td>\n",
              "      <td>273967.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>138.0</td>\n",
              "      <td>146.0000</td>\n",
              "      <td>135.02</td>\n",
              "      <td>140.40</td>\n",
              "      <td>80794307.0</td>\n",
              "      <td>1056.51</td>\n",
              "      <td>1106.50</td>\n",
              "      <td>1037.28</td>\n",
              "      <td>1096.80</td>\n",
              "      <td>4220935.0</td>\n",
              "      <td>17.08</td>\n",
              "      <td>17.98</td>\n",
              "      <td>15.445</td>\n",
              "      <td>15.88</td>\n",
              "      <td>30208367.0</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2453.5701</td>\n",
              "      <td>2280.5200</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>9.018165e+08</td>\n",
              "      <td>20188.6895</td>\n",
              "      <td>20489.3301</td>\n",
              "      <td>18917.4609</td>\n",
              "      <td>19898.9199</td>\n",
              "      <td>871360000.0</td>\n",
              "      <td>239.77</td>\n",
              "      <td>250.00</td>\n",
              "      <td>237.12</td>\n",
              "      <td>246.67</td>\n",
              "      <td>74377540.0</td>\n",
              "      <td>1.1015</td>\n",
              "      <td>1.1045</td>\n",
              "      <td>1.0801</td>\n",
              "      <td>1.0933</td>\n",
              "      <td>1.4231</td>\n",
              "      <td>1.4651</td>\n",
              "      <td>1.4162</td>\n",
              "      <td>1.4481</td>\n",
              "      <td>1.6697</td>\n",
              "      <td>1.7528</td>\n",
              "      <td>1.6591</td>\n",
              "      <td>1.7269</td>\n",
              "      <td>7.0132</td>\n",
              "      <td>7.0478</td>\n",
              "      <td>7.0073</td>\n",
              "      <td>7.0475</td>\n",
              "      <td>0.8281</td>\n",
              "      <td>0.8703</td>\n",
              "      <td>0.8244</td>\n",
              "      <td>0.8589</td>\n",
              "      <td>1750.00</td>\n",
              "      <td>1841.66</td>\n",
              "      <td>1745.00</td>\n",
              "      <td>1830.00</td>\n",
              "      <td>9512897.0</td>\n",
              "      <td>3905.5300</td>\n",
              "      <td>3908.7200</td>\n",
              "      <td>3726.4500</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>214274700.0</td>\n",
              "      <td>8613.3496</td>\n",
              "      <td>8670.4102</td>\n",
              "      <td>8400.1797</td>\n",
              "      <td>8441.7100</td>\n",
              "      <td>207558900.0</td>\n",
              "      <td>1057.6499</td>\n",
              "      <td>1062.8826</td>\n",
              "      <td>966.2156</td>\n",
              "      <td>991.1641</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2507.3301</td>\n",
              "      <td>2507.3301</td>\n",
              "      <td>2365.7000</td>\n",
              "      <td>2385.8201</td>\n",
              "      <td>98214900.0</td>\n",
              "      <td>30.24</td>\n",
              "      <td>32.455</td>\n",
              "      <td>28.01</td>\n",
              "      <td>29.20</td>\n",
              "      <td>192337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0944</td>\n",
              "      <td>1.0949</td>\n",
              "      <td>1.0857</td>\n",
              "      <td>1.0858</td>\n",
              "      <td>1.4481</td>\n",
              "      <td>1.4668</td>\n",
              "      <td>1.4452</td>\n",
              "      <td>1.4571</td>\n",
              "      <td>1.7256</td>\n",
              "      <td>1.8120</td>\n",
              "      <td>1.7228</td>\n",
              "      <td>1.7671</td>\n",
              "      <td>7.0534</td>\n",
              "      <td>7.0836</td>\n",
              "      <td>7.0531</td>\n",
              "      <td>7.0836</td>\n",
              "      <td>0.8611</td>\n",
              "      <td>0.8715</td>\n",
              "      <td>0.8604</td>\n",
              "      <td>0.8714</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5732 rows × 80 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open MSFT  High MSFT  ...  Close INTEL  Volume INTEL\n",
              "time                              ...                           \n",
              "2000-01-03        NaN        NaN  ...          NaN           NaN\n",
              "2000-01-04        NaN        NaN  ...          NaN           NaN\n",
              "2000-01-05        NaN        NaN  ...          NaN           NaN\n",
              "2000-01-06        NaN        NaN  ...          NaN           NaN\n",
              "2000-01-07        NaN        NaN  ...          NaN           NaN\n",
              "...               ...        ...  ...          ...           ...\n",
              "2020-03-15        NaN        NaN  ...          NaN           NaN\n",
              "2020-03-16      140.0   149.3500  ...        29.01       97928.0\n",
              "2020-03-17      140.0   147.4998  ...        32.40      273967.0\n",
              "2020-03-18      138.0   146.0000  ...        29.20      192337.0\n",
              "2020-03-19        NaN        NaN  ...          NaN           NaN\n",
              "\n",
              "[5732 rows x 80 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ByJ7yg-TeOD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "62990367-6482-4f29-efe2-eebcb4086f17"
      },
      "source": [
        "# Select a column\n",
        "assets = assets[[\"Close DJI30\", \"Close CAC40\", \"Close SP500\"]]\n",
        "assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-15</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-19</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5732 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500\n",
              "time                                             \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700\n",
              "...                 ...          ...          ...\n",
              "2020-03-15          NaN          NaN          NaN\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001\n",
              "2020-03-19          NaN          NaN          NaN\n",
              "\n",
              "[5732 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppgUESj5SAKm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "f04a3f81-a21a-48fd-f75b-7a56a91e4edd"
      },
      "source": [
        "# Missing values NAN\n",
        "assets = assets.dropna()\n",
        "assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500\n",
              "time                                             \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700\n",
              "...                 ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001\n",
              "\n",
              "[5047 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSbtnwn8R4P3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "a056be79-b6bf-4704-e106-0a79426a1df3"
      },
      "source": [
        "# Reset index\n",
        "assets.reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5042</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5043</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5044</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5045</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5046</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Close DJI30  Close CAC40  Close SP500\n",
              "0      11357.5098    5917.3701    1455.2200\n",
              "1      10997.9297    5672.0200    1399.4200\n",
              "2      11122.6504    5479.7002    1402.1100\n",
              "3      11253.2598    5450.1099    1403.4500\n",
              "4      11522.5596    5539.6099    1441.4700\n",
              "...           ...          ...          ...\n",
              "5042   21200.6191    4044.2600    2420.8827\n",
              "5043   23185.6191    4118.3599    2418.0191\n",
              "5044   20188.5195    3881.4600    2426.6181\n",
              "5045   21237.3809    3991.7800    2436.5000\n",
              "5046   19898.9199    3754.8401    2398.1001\n",
              "\n",
              "[5047 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaUD8C16_Y_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "outputId": "ee0cc71d-481f-48d1-a27c-1be7fc3f9fe9"
      },
      "source": [
        "# Rolling \n",
        "assets[\"SMA 15\"] = assets[[\"Close SP500\"]].rolling(15).mean()\n",
        "assets"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2445.100287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2444.029520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2443.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2442.964567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>2440.551540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200          NaN\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200          NaN\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100          NaN\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500          NaN\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700          NaN\n",
              "...                 ...          ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827  2445.100287\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191  2444.029520\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181  2443.003900\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000  2442.964567\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001  2440.551540\n",
              "\n",
              "[5047 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0V3RpnD_bxL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46dbc8b6-1a70-4f33-d5cb-daf65d5705ab"
      },
      "source": [
        "# Shift\n",
        "assets[\"Close SP500\"].shift(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "time\n",
              "2000-01-03          NaN\n",
              "2000-01-04    1455.2200\n",
              "2000-01-05    1399.4200\n",
              "2000-01-06    1402.1100\n",
              "2000-01-07    1403.4500\n",
              "                ...    \n",
              "2020-03-12    2423.1280\n",
              "2020-03-13    2420.8827\n",
              "2020-03-16    2418.0191\n",
              "2020-03-17    2426.6181\n",
              "2020-03-18    2436.5000\n",
              "Name: Close SP500, Length: 5047, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFzrct4WA-7i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "439959e8-722e-4301-a67e-fad099b44f20"
      },
      "source": [
        "# Groupby by mean\n",
        "arr = [[1,11],\n",
        "       [1,10],\n",
        "       [3,15],\n",
        "       [3,15],\n",
        "       [3,10]]\n",
        "\n",
        "df = pd.DataFrame(arr, columns=[\"num\",\"value\"])\n",
        "\n",
        "df.groupby(by=\"num\").mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.333333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         value\n",
              "num           \n",
              "1    10.500000\n",
              "3    13.333333"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg9Z3NYyCvbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "206509f0-2855-4511-e357-2cb541004b0e"
      },
      "source": [
        "# Groupby by sum\n",
        "df.groupby(by=\"num\").sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     value\n",
              "num       \n",
              "1       21\n",
              "3       40"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l-xwc-HC2MU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2c8f0317-6e0b-44cc-9351-aa96896a87fb"
      },
      "source": [
        "# Groupby by std\n",
        "df.groupby(by=\"num\").std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.707107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.886751</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        value\n",
              "num          \n",
              "1    0.707107\n",
              "3    2.886751"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5KoCwIFT27I"
      },
      "source": [
        "##### Iloc & Loc"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4Jy9QxKT5R9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "0fc27afa-de4e-492c-c54f-6688cb209e8b"
      },
      "source": [
        "# Slicing\n",
        "assets.iloc[0:1500, 0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-13</th>\n",
              "      <td>10823.7197</td>\n",
              "      <td>4693.3999</td>\n",
              "      <td>1267.4301</td>\n",
              "      <td>1261.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-14</th>\n",
              "      <td>10883.5098</td>\n",
              "      <td>4674.8501</td>\n",
              "      <td>1272.7400</td>\n",
              "      <td>1261.800013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-15</th>\n",
              "      <td>10881.6699</td>\n",
              "      <td>4673.1401</td>\n",
              "      <td>1270.9399</td>\n",
              "      <td>1262.155340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-16</th>\n",
              "      <td>10875.5898</td>\n",
              "      <td>4704.4102</td>\n",
              "      <td>1267.3199</td>\n",
              "      <td>1262.093333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-19</th>\n",
              "      <td>10836.5303</td>\n",
              "      <td>4694.8599</td>\n",
              "      <td>1259.9200</td>\n",
              "      <td>1262.257333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1500 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200          NaN\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200          NaN\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100          NaN\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500          NaN\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700          NaN\n",
              "...                 ...          ...          ...          ...\n",
              "2005-12-13   10823.7197    4693.3999    1267.4301  1261.032680\n",
              "2005-12-14   10883.5098    4674.8501    1272.7400  1261.800013\n",
              "2005-12-15   10881.6699    4673.1401    1270.9399  1262.155340\n",
              "2005-12-16   10875.5898    4704.4102    1267.3199  1262.093333\n",
              "2005-12-19   10836.5303    4694.8599    1259.9200  1262.257333\n",
              "\n",
              "[1500 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0KS0q6zUCTn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "a39d6099-5265-43dc-eaf7-b03acee41297"
      },
      "source": [
        "# Conditonal by dates\n",
        "assets.loc[\"2010\":\"2012\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>10583.9600</td>\n",
              "      <td>4013.9700</td>\n",
              "      <td>1132.9900</td>\n",
              "      <td>1116.254013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>10572.0195</td>\n",
              "      <td>4012.9099</td>\n",
              "      <td>1136.5200</td>\n",
              "      <td>1118.261347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>10573.6797</td>\n",
              "      <td>4017.6699</td>\n",
              "      <td>1137.1400</td>\n",
              "      <td>1119.796680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>10606.8604</td>\n",
              "      <td>4024.8000</td>\n",
              "      <td>1141.6899</td>\n",
              "      <td>1122.047333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>10618.1904</td>\n",
              "      <td>4045.1399</td>\n",
              "      <td>1144.9800</td>\n",
              "      <td>1124.433993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-19</th>\n",
              "      <td>13251.9697</td>\n",
              "      <td>3664.5901</td>\n",
              "      <td>1435.8101</td>\n",
              "      <td>1420.719333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-20</th>\n",
              "      <td>13311.7197</td>\n",
              "      <td>3666.7300</td>\n",
              "      <td>1443.6899</td>\n",
              "      <td>1422.568660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-21</th>\n",
              "      <td>13190.8398</td>\n",
              "      <td>3661.3999</td>\n",
              "      <td>1430.1500</td>\n",
              "      <td>1423.499987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-27</th>\n",
              "      <td>13096.3096</td>\n",
              "      <td>3674.2600</td>\n",
              "      <td>1418.1000</td>\n",
              "      <td>1424.075987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-12-28</th>\n",
              "      <td>12938.1104</td>\n",
              "      <td>3620.2500</td>\n",
              "      <td>1402.4301</td>\n",
              "      <td>1423.767993</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>746 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2010-01-04   10583.9600    4013.9700    1132.9900  1116.254013\n",
              "2010-01-05   10572.0195    4012.9099    1136.5200  1118.261347\n",
              "2010-01-06   10573.6797    4017.6699    1137.1400  1119.796680\n",
              "2010-01-07   10606.8604    4024.8000    1141.6899  1122.047333\n",
              "2010-01-08   10618.1904    4045.1399    1144.9800  1124.433993\n",
              "...                 ...          ...          ...          ...\n",
              "2012-12-19   13251.9697    3664.5901    1435.8101  1420.719333\n",
              "2012-12-20   13311.7197    3666.7300    1443.6899  1422.568660\n",
              "2012-12-21   13190.8398    3661.3999    1430.1500  1423.499987\n",
              "2012-12-27   13096.3096    3674.2600    1418.1000  1424.075987\n",
              "2012-12-28   12938.1104    3620.2500    1402.4301  1423.767993\n",
              "\n",
              "[746 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CU9dKTkVUMF8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "8e78e689-376d-4246-ef0e-30d97e553a63"
      },
      "source": [
        "# Conditonal by values\n",
        "assets.loc[assets[\"Close DJI30\"] > 15000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-05-07</th>\n",
              "      <td>15056.2002</td>\n",
              "      <td>3921.3201</td>\n",
              "      <td>1625.9600</td>\n",
              "      <td>1583.837320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-05-08</th>\n",
              "      <td>15105.1201</td>\n",
              "      <td>3956.2800</td>\n",
              "      <td>1632.6899</td>\n",
              "      <td>1587.711987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-05-09</th>\n",
              "      <td>15082.6201</td>\n",
              "      <td>3928.5801</td>\n",
              "      <td>1626.6700</td>\n",
              "      <td>1592.689320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-05-10</th>\n",
              "      <td>15118.4902</td>\n",
              "      <td>3953.8301</td>\n",
              "      <td>1633.7000</td>\n",
              "      <td>1598.828653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-05-13</th>\n",
              "      <td>15091.6797</td>\n",
              "      <td>3945.2000</td>\n",
              "      <td>1633.7700</td>\n",
              "      <td>1604.063320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2445.100287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2444.029520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2443.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2442.964567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>2440.551540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1687 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2013-05-07   15056.2002    3921.3201    1625.9600  1583.837320\n",
              "2013-05-08   15105.1201    3956.2800    1632.6899  1587.711987\n",
              "2013-05-09   15082.6201    3928.5801    1626.6700  1592.689320\n",
              "2013-05-10   15118.4902    3953.8301    1633.7000  1598.828653\n",
              "2013-05-13   15091.6797    3945.2000    1633.7700  1604.063320\n",
              "...                 ...          ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827  2445.100287\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191  2444.029520\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181  2443.003900\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000  2442.964567\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001  2440.551540\n",
              "\n",
              "[1687 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oXYx23B-wmb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "0570b78b-f691-4263-e6f0-652df6b2b6c1"
      },
      "source": [
        "# Concat some dataframes\n",
        "df1 = assets.iloc[:,:3]\n",
        "df2 = assets.iloc[:,3:]\n",
        "\n",
        "pd.concat((df1,df2), axis=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-03</th>\n",
              "      <td>11357.5098</td>\n",
              "      <td>5917.3701</td>\n",
              "      <td>1455.2200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-04</th>\n",
              "      <td>10997.9297</td>\n",
              "      <td>5672.0200</td>\n",
              "      <td>1399.4200</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-05</th>\n",
              "      <td>11122.6504</td>\n",
              "      <td>5479.7002</td>\n",
              "      <td>1402.1100</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-06</th>\n",
              "      <td>11253.2598</td>\n",
              "      <td>5450.1099</td>\n",
              "      <td>1403.4500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-01-07</th>\n",
              "      <td>11522.5596</td>\n",
              "      <td>5539.6099</td>\n",
              "      <td>1441.4700</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-12</th>\n",
              "      <td>21200.6191</td>\n",
              "      <td>4044.2600</td>\n",
              "      <td>2420.8827</td>\n",
              "      <td>2445.100287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-13</th>\n",
              "      <td>23185.6191</td>\n",
              "      <td>4118.3599</td>\n",
              "      <td>2418.0191</td>\n",
              "      <td>2444.029520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-16</th>\n",
              "      <td>20188.5195</td>\n",
              "      <td>3881.4600</td>\n",
              "      <td>2426.6181</td>\n",
              "      <td>2443.003900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-17</th>\n",
              "      <td>21237.3809</td>\n",
              "      <td>3991.7800</td>\n",
              "      <td>2436.5000</td>\n",
              "      <td>2442.964567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-18</th>\n",
              "      <td>19898.9199</td>\n",
              "      <td>3754.8401</td>\n",
              "      <td>2398.1001</td>\n",
              "      <td>2440.551540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2000-01-03   11357.5098    5917.3701    1455.2200          NaN\n",
              "2000-01-04   10997.9297    5672.0200    1399.4200          NaN\n",
              "2000-01-05   11122.6504    5479.7002    1402.1100          NaN\n",
              "2000-01-06   11253.2598    5450.1099    1403.4500          NaN\n",
              "2000-01-07   11522.5596    5539.6099    1441.4700          NaN\n",
              "...                 ...          ...          ...          ...\n",
              "2020-03-12   21200.6191    4044.2600    2420.8827  2445.100287\n",
              "2020-03-13   23185.6191    4118.3599    2418.0191  2444.029520\n",
              "2020-03-16   20188.5195    3881.4600    2426.6181  2443.003900\n",
              "2020-03-17   21237.3809    3991.7800    2436.5000  2442.964567\n",
              "2020-03-18   19898.9199    3754.8401    2398.1001  2440.551540\n",
              "\n",
              "[5047 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YAkHm8yCm28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "6ebe3966-f382-4e73-919a-15fde3ee42f4"
      },
      "source": [
        "# Concat some dataframes\n",
        "df1 = assets.iloc[1500:,:]\n",
        "df2 = assets.iloc[:1500,:]\n",
        "\n",
        "pd.concat((df1,df2), axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close DJI30</th>\n",
              "      <th>Close CAC40</th>\n",
              "      <th>Close SP500</th>\n",
              "      <th>SMA 15</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-12-20</th>\n",
              "      <td>10805.5498</td>\n",
              "      <td>4703.4800</td>\n",
              "      <td>1259.6200</td>\n",
              "      <td>1262.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-21</th>\n",
              "      <td>10833.7305</td>\n",
              "      <td>4752.4102</td>\n",
              "      <td>1262.7900</td>\n",
              "      <td>1263.287333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-22</th>\n",
              "      <td>10889.4404</td>\n",
              "      <td>4751.9600</td>\n",
              "      <td>1268.1200</td>\n",
              "      <td>1263.517333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-23</th>\n",
              "      <td>10883.2695</td>\n",
              "      <td>4757.7402</td>\n",
              "      <td>1268.6600</td>\n",
              "      <td>1263.756000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-27</th>\n",
              "      <td>10777.7695</td>\n",
              "      <td>4769.3799</td>\n",
              "      <td>1256.5400</td>\n",
              "      <td>1263.386000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-13</th>\n",
              "      <td>10823.7197</td>\n",
              "      <td>4693.3999</td>\n",
              "      <td>1267.4301</td>\n",
              "      <td>1261.032680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-14</th>\n",
              "      <td>10883.5098</td>\n",
              "      <td>4674.8501</td>\n",
              "      <td>1272.7400</td>\n",
              "      <td>1261.800013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-15</th>\n",
              "      <td>10881.6699</td>\n",
              "      <td>4673.1401</td>\n",
              "      <td>1270.9399</td>\n",
              "      <td>1262.155340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-16</th>\n",
              "      <td>10875.5898</td>\n",
              "      <td>4704.4102</td>\n",
              "      <td>1267.3199</td>\n",
              "      <td>1262.093333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-12-19</th>\n",
              "      <td>10836.5303</td>\n",
              "      <td>4694.8599</td>\n",
              "      <td>1259.9200</td>\n",
              "      <td>1262.257333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5047 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Close DJI30  Close CAC40  Close SP500       SMA 15\n",
              "time                                                          \n",
              "2005-12-20   10805.5498    4703.4800    1259.6200  1262.400000\n",
              "2005-12-21   10833.7305    4752.4102    1262.7900  1263.287333\n",
              "2005-12-22   10889.4404    4751.9600    1268.1200  1263.517333\n",
              "2005-12-23   10883.2695    4757.7402    1268.6600  1263.756000\n",
              "2005-12-27   10777.7695    4769.3799    1256.5400  1263.386000\n",
              "...                 ...          ...          ...          ...\n",
              "2005-12-13   10823.7197    4693.3999    1267.4301  1261.032680\n",
              "2005-12-14   10883.5098    4674.8501    1272.7400  1261.800013\n",
              "2005-12-15   10881.6699    4673.1401    1270.9399  1262.155340\n",
              "2005-12-16   10875.5898    4704.4102    1267.3199  1262.093333\n",
              "2005-12-19   10836.5303    4694.8599    1259.9200  1262.257333\n",
              "\n",
              "[5047 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aH-i3Tn3UjJ_"
      },
      "source": [
        "### Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvdLwfXmk91w"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKpXgOj6k99N"
      },
      "source": [
        "import matplotlib as mpl\n",
        "from matplotlib import cycler\n",
        "\n",
        "colors = cycler('color',\n",
        "                ['#669FEE', '#66EE91', '#9988DD',\n",
        "                 '#EECC55', '#88BB44', '#FFBBBB'])\n",
        "plt.rc('figure', facecolor='#313233')\n",
        "plt.rc('axes', facecolor=\"#313233\", edgecolor='none',\n",
        "       axisbelow=True, grid=True, prop_cycle=colors,\n",
        "       labelcolor='gray')\n",
        "plt.rc('grid', color='474A4A', linestyle='solid')\n",
        "plt.rc('xtick', color='gray')\n",
        "plt.rc('ytick', direction='out', color='gray')\n",
        "plt.rc('legend', facecolor=\"#313233\", edgecolor=\"#313233\")\n",
        "plt.rc(\"text\", color=\"#C9C9C9\")\n",
        "plt.rc('figure', facecolor='#313233')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6_cmiswU2AW"
      },
      "source": [
        "##### Graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBorfFcRwi42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ebbe9be-a84d-49c8-9acb-5a7c4bee6adc"
      },
      "source": [
        "# data simulation\n",
        "arr = np.random.randint(0,15,size=(15,))\n",
        "\n",
        "# square of arr\n",
        "square = arr**2\n",
        "\n",
        "# Mutltiplication of arr\n",
        "mut = arr*3\n",
        "\n",
        "print(arr)\n",
        "print(square)\n",
        "print(mut)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[10 14  9 11  7  6  4  9 10  7  1  8 12  2 14]\n",
            "[100 196  81 121  49  36  16  81 100  49   1  64 144   4 196]\n",
            "[30 42 27 33 21 18 12 27 30 21  3 24 36  6 42]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noAG7_QvvTk1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "11266eb7-a4ba-49b9-d75e-b25979507bf5"
      },
      "source": [
        "# Plot the data\n",
        "plt.plot(arr)\n",
        "plt.plot(square)\n",
        "plt.plot(mut)\n",
        "\n",
        "# Change the xlabel \n",
        "plt.xlabel(\"Random numbers\")\n",
        "\n",
        "# Change the ylabel \n",
        "plt.ylabel(\"Values\")\n",
        "\n",
        "# Change the title\n",
        "plt.title(\"Graph\")\n",
        "\n",
        "# Put a legend\n",
        "plt.legend([\"Normal\", \"Square\", \"Multiplication\"])\n",
        "\n",
        "# Show the chart\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "asuSI8Jg0EaC"
      },
      "source": [
        "##### Scatter"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6Nrf3rvwCk7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "89af6c47-705b-41b0-da84-9978725ad560"
      },
      "source": [
        "# Point clouds\n",
        "plt.scatter(arr, mut)\n",
        "plt.scatter(arr, square)\n",
        "\n",
        "\n",
        "# Change the xlabel \n",
        "plt.xlabel(\"Random Numbers\")\n",
        "\n",
        "# Change the ylabel \n",
        "plt.ylabel(\"Values\")\n",
        "\n",
        "# Change the title \n",
        "plt.title(\"Scatter\")\n",
        "\n",
        "# Put a legend\n",
        "plt.legend([\"Mut\", \"Square\"])\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppony41B0G1i"
      },
      "source": [
        "##### Tools "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO9JX-7fzXL0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "9d8b68d3-0f8e-4582-a989-d27ec38671ed"
      },
      "source": [
        "# Adapt the size\n",
        "plt.figure(figsize=(15,8))\n",
        "\n",
        "# Plot the line\n",
        "plt.plot(arr, alpha=0.6, color=\"turquoise\")\n",
        "\n",
        "# Plot the line\n",
        "plt.plot(square, \"-o\", linewidth=3)\n",
        "\n",
        "# Plot the line\n",
        "plt.plot(mut, \"o\")\n",
        "\n",
        "# Change the ylabel \n",
        "plt.ylabel(\"Values\")\n",
        "\n",
        "# Change the title \n",
        "plt.title(\"Graph\")\n",
        "\n",
        "# Put a legend\n",
        "plt.legend([\"Normal\", \"Square\", \"Multiplication\"])\n",
        "\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guvz3JE09_Go"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}