Rongjing's_Submission_Cubist.ipynb

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      "name": "Index (1)-Copy1 (1).ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jiarongj/Python/blob/master/Rongjing's_Submission_Cubist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
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        "colab": {},
        "outputId": "0af836c0-bbb7-4f72-ec28-cc9ba63746b1"
      },
      "source": [
        "!pip install yfinance --upgrade --no-cache-dir"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: yfinance in /srv/conda/envs/notebook/lib/python3.6/site-packages (0.1.54)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from yfinance) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: pandas>=0.24 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from yfinance) (1.0.4)\n",
            "Requirement already satisfied, skipping upgrade: requests>=2.20 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: multitasking>=0.0.7 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from pandas>=0.24->yfinance) (2020.1)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from requests>=2.20->yfinance) (2020.4.5.2)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from requests>=2.20->yfinance) (2.9)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from requests>=2.20->yfinance) (1.25.9)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5 in /srv/conda/envs/notebook/lib/python3.6/site-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.15.0)\n"
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    {
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      "metadata": {
        "id": "qvkVuKGJV7yK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf\n",
        "\n",
        "%config InlineBackend.figure_format = 'svg'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pd3mrEqwV7yO",
        "colab_type": "code",
        "colab": {},
        "outputId": "4b171a4e-b9ed-4cc5-82a9-b18cc10f939e"
      },
      "source": [
        "tickers = ['MSFT', 'ORCL', 'IBM', 'AAPL', 'BMRC']\n",
        "start_date = '2018-10-01'\n",
        "end_date = '2018-12-31'\n",
        "data = yf.download(tickers = tickers, start = start_date, end = end_date)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  5 of 5 completed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "zuxOba9FV7yU",
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        "colab": {},
        "outputId": "ab1335db-c7ba-4c5a-d09f-1fee40031bd3"
      },
      "source": [
        "data = yf.download(tickers, start_date, end_date)\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  5 of 5 completed\n"
          ],
          "name": "stdout"
        },
        {
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"5\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
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              "      <td>153.750000</td>\n",
              "      <td>115.150002</td>\n",
              "      <td>50.230000</td>\n",
              "      <td>...</td>\n",
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              "      <td>41.404999</td>\n",
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              "      <th>2018-10-03</th>\n",
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              "      <th>2018-10-04</th>\n",
              "      <td>222.701340</td>\n",
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              "      <th>2018-10-05</th>\n",
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            "text/plain": [
              "             Adj Close                                                \\\n",
              "                  AAPL       BMRC         IBM        MSFT       ORCL   \n",
              "Date                                                                   \n",
              "2018-10-01  221.988266  39.983109  140.783600  112.812271  50.263626   \n",
              "2018-10-02  223.961426  39.717392  141.473694  112.363396  48.740192   \n",
              "2018-10-03  226.686707  40.292305  140.986008  112.382904  48.274426   \n",
              "2018-10-04  222.701340  40.775425  139.228516  110.060501  48.002735   \n",
              "2018-10-05  219.087158  40.654644  137.130585  109.416473  47.886288   \n",
              "\n",
              "                 Close                                                ...  \\\n",
              "                  AAPL       BMRC         IBM        MSFT       ORCL  ...   \n",
              "Date                                                                  ...   \n",
              "2018-10-01  227.259995  41.380001  153.000000  115.610001  51.799999  ...   \n",
              "2018-10-02  229.279999  41.105000  153.750000  115.150002  50.230000  ...   \n",
              "2018-10-03  232.070007  41.700001  153.220001  115.169998  49.750000  ...   \n",
              "2018-10-04  227.990005  42.200001  151.309998  112.790001  49.470001  ...   \n",
              "2018-10-05  224.289993  42.075001  149.029999  112.129997  49.349998  ...   \n",
              "\n",
              "                  Open                                                \\\n",
              "                  AAPL       BMRC         IBM        MSFT       ORCL   \n",
              "Date                                                                   \n",
              "2018-10-01  227.949997  42.000000  151.710007  114.750000  51.220001   \n",
              "2018-10-02  227.250000  41.404999  152.990005  115.300003  51.650002   \n",
              "2018-10-03  230.050003  41.080002  154.000000  115.419998  49.590000   \n",
              "2018-10-04  230.779999  41.724998  151.690002  114.610001  49.680000   \n",
              "2018-10-05  227.960007  42.150002  150.899994  112.629997  49.419998   \n",
              "\n",
              "              Volume                                      \n",
              "                AAPL   BMRC      IBM      MSFT      ORCL  \n",
              "Date                                                      \n",
              "2018-10-01  23600800  41200  3757400  18883100  19217800  \n",
              "2018-10-02  24788200  24600  3029900  20787200  22406800  \n",
              "2018-10-03  28654800  25200  3315300  16648000  21207600  \n",
              "2018-10-04  32042000  17200  4237900  34821700  19034000  \n",
              "2018-10-05  33580500  21600  4602200  29068900  16900200  \n",
              "\n",
              "[5 rows x 30 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 10
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      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultiIndex([('Adj Close', 'AAPL'),\n",
              "            ('Adj Close', 'BMRC'),\n",
              "            ('Adj Close',  'IBM'),\n",
              "            ('Adj Close', 'MSFT'),\n",
              "            ('Adj Close', 'ORCL'),\n",
              "            (    'Close', 'AAPL'),\n",
              "            (    'Close', 'BMRC'),\n",
              "            (    'Close',  'IBM'),\n",
              "            (    'Close', 'MSFT'),\n",
              "            (    'Close', 'ORCL'),\n",
              "            (     'High', 'AAPL'),\n",
              "            (     'High', 'BMRC'),\n",
              "            (     'High',  'IBM'),\n",
              "            (     'High', 'MSFT'),\n",
              "            (     'High', 'ORCL'),\n",
              "            (      'Low', 'AAPL'),\n",
              "            (      'Low', 'BMRC'),\n",
              "            (      'Low',  'IBM'),\n",
              "            (      'Low', 'MSFT'),\n",
              "            (      'Low', 'ORCL'),\n",
              "            (     'Open', 'AAPL'),\n",
              "            (     'Open', 'BMRC'),\n",
              "            (     'Open',  'IBM'),\n",
              "            (     'Open', 'MSFT'),\n",
              "            (     'Open', 'ORCL'),\n",
              "            (   'Volume', 'AAPL'),\n",
              "            (   'Volume', 'BMRC'),\n",
              "            (   'Volume',  'IBM'),\n",
              "            (   'Volume', 'MSFT'),\n",
              "            (   'Volume', 'ORCL')],\n",
              "           )"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZKMtmi-DV7yb",
        "colab_type": "code",
        "colab": {},
        "outputId": "fe236d8e-8d39-4838-f242-7991744611be"
      },
      "source": [
        "df = data['Adj Close']\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>AAPL</th>\n",
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              "      <th>2018-10-01</th>\n",
              "      <td>221.988266</td>\n",
              "      <td>39.983109</td>\n",
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              "      <th>2018-10-02</th>\n",
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            "text/plain": [
              "                  AAPL       BMRC         IBM        MSFT       ORCL\n",
              "Date                                                                \n",
              "2018-10-01  221.988266  39.983109  140.783600  112.812271  50.263626\n",
              "2018-10-02  223.961426  39.717392  141.473694  112.363396  48.740192\n",
              "2018-10-03  226.686707  40.292305  140.986008  112.382904  48.274426\n",
              "2018-10-04  222.701340  40.775425  139.228516  110.060501  48.002735\n",
              "2018-10-05  219.087158  40.654644  137.130585  109.416473  47.886288"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jp9VlXiSV7yf",
        "colab_type": "code",
        "colab": {},
        "outputId": "9fea4ba7-0305-4968-8bed-78dd4c16489c"
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed2a4dd978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
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              "<Figure size 432x288 with 1 Axes>"
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      "metadata": {
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        "colab_type": "code",
        "colab": {},
        "outputId": "5c562da1-130e-47ab-d734-2b6852c6f9db"
      },
      "source": [
        "w = [32, 100, 150,350, 210]\n",
        "res_data = df.pct_change()[1:]\n",
        "res_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>BMRC</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>ORCL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-10-02</th>\n",
              "      <td>0.008889</td>\n",
              "      <td>-0.006646</td>\n",
              "      <td>0.004902</td>\n",
              "      <td>-0.003979</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-03</th>\n",
              "      <td>0.012169</td>\n",
              "      <td>0.014475</td>\n",
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              "      <td>0.000174</td>\n",
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              "      <th>2018-10-04</th>\n",
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              "      <td>0.011990</td>\n",
              "      <td>-0.012466</td>\n",
              "      <td>-0.020665</td>\n",
              "      <td>-0.005628</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-05</th>\n",
              "      <td>-0.016229</td>\n",
              "      <td>-0.002962</td>\n",
              "      <td>-0.015068</td>\n",
              "      <td>-0.005852</td>\n",
              "      <td>-0.002426</td>\n",
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              "    <tr>\n",
              "      <th>2018-10-08</th>\n",
              "      <td>-0.002318</td>\n",
              "      <td>-0.004278</td>\n",
              "      <td>-0.004295</td>\n",
              "      <td>-0.011415</td>\n",
              "      <td>0.000203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-21</th>\n",
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              "      <td>-0.008496</td>\n",
              "      <td>-0.018404</td>\n",
              "      <td>-0.032312</td>\n",
              "      <td>-0.048443</td>\n",
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              "    <tr>\n",
              "      <th>2018-12-24</th>\n",
              "      <td>-0.025874</td>\n",
              "      <td>-0.012984</td>\n",
              "      <td>-0.030377</td>\n",
              "      <td>-0.041739</td>\n",
              "      <td>-0.029773</td>\n",
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              "      <th>2018-12-26</th>\n",
              "      <td>0.070421</td>\n",
              "      <td>0.029992</td>\n",
              "      <td>0.035512</td>\n",
              "      <td>0.068310</td>\n",
              "      <td>0.044507</td>\n",
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              "      <th>2018-12-27</th>\n",
              "      <td>-0.006490</td>\n",
              "      <td>-0.008174</td>\n",
              "      <td>0.021456</td>\n",
              "      <td>0.006166</td>\n",
              "      <td>0.008522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28</th>\n",
              "      <td>0.000512</td>\n",
              "      <td>0.048931</td>\n",
              "      <td>-0.006592</td>\n",
              "      <td>-0.007808</td>\n",
              "      <td>-0.003336</td>\n",
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              "<p>61 rows × 5 columns</p>\n",
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            ],
            "text/plain": [
              "                AAPL      BMRC       IBM      MSFT      ORCL\n",
              "Date                                                        \n",
              "2018-10-02  0.008889 -0.006646  0.004902 -0.003979 -0.030309\n",
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              "2018-10-08 -0.002318 -0.004278 -0.004295 -0.011415  0.000203\n",
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              "2018-12-26  0.070421  0.029992  0.035512  0.068310  0.044507\n",
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              "\n",
              "[61 rows x 5 columns]"
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          "execution_count": 14
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      "metadata": {
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        "outputId": "1ff4ce8d-02ef-4a54-e32b-1907219f3c7f"
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      "source": [
        "weighted_returns = (w * res_data)\n",
        "weighted_returns"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "    .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>AAPL</th>\n",
              "      <th>BMRC</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>ORCL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-10-02</th>\n",
              "      <td>0.284434</td>\n",
              "      <td>-0.664572</td>\n",
              "      <td>0.735271</td>\n",
              "      <td>-1.392636</td>\n",
              "      <td>-6.364863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-03</th>\n",
              "      <td>0.389393</td>\n",
              "      <td>1.447510</td>\n",
              "      <td>-0.517078</td>\n",
              "      <td>0.060766</td>\n",
              "      <td>-2.006784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-04</th>\n",
              "      <td>-0.562590</td>\n",
              "      <td>1.199038</td>\n",
              "      <td>-1.869858</td>\n",
              "      <td>-7.232782</td>\n",
              "      <td>-1.181888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-05</th>\n",
              "      <td>-0.519322</td>\n",
              "      <td>-0.296210</td>\n",
              "      <td>-2.260238</td>\n",
              "      <td>-2.048053</td>\n",
              "      <td>-0.509429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-08</th>\n",
              "      <td>-0.074183</td>\n",
              "      <td>-0.427807</td>\n",
              "      <td>-0.644181</td>\n",
              "      <td>-3.995360</td>\n",
              "      <td>0.042592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-21</th>\n",
              "      <td>-1.244658</td>\n",
              "      <td>-0.849645</td>\n",
              "      <td>-2.760568</td>\n",
              "      <td>-11.309268</td>\n",
              "      <td>-10.172985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-24</th>\n",
              "      <td>-0.827965</td>\n",
              "      <td>-1.298380</td>\n",
              "      <td>-4.556536</td>\n",
              "      <td>-14.608552</td>\n",
              "      <td>-6.252293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-26</th>\n",
              "      <td>2.253485</td>\n",
              "      <td>2.999227</td>\n",
              "      <td>5.326782</td>\n",
              "      <td>23.908415</td>\n",
              "      <td>9.346455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-27</th>\n",
              "      <td>-0.207673</td>\n",
              "      <td>-0.817359</td>\n",
              "      <td>3.218426</td>\n",
              "      <td>2.157980</td>\n",
              "      <td>1.789632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28</th>\n",
              "      <td>0.016397</td>\n",
              "      <td>4.893092</td>\n",
              "      <td>-0.988751</td>\n",
              "      <td>-2.732792</td>\n",
              "      <td>-0.700486</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                AAPL      BMRC       IBM       MSFT       ORCL\n",
              "Date                                                          \n",
              "2018-10-02  0.284434 -0.664572  0.735271  -1.392636  -6.364863\n",
              "2018-10-03  0.389393  1.447510 -0.517078   0.060766  -2.006784\n",
              "2018-10-04 -0.562590  1.199038 -1.869858  -7.232782  -1.181888\n",
              "2018-10-05 -0.519322 -0.296210 -2.260238  -2.048053  -0.509429\n",
              "2018-10-08 -0.074183 -0.427807 -0.644181  -3.995360   0.042592\n",
              "...              ...       ...       ...        ...        ...\n",
              "2018-12-21 -1.244658 -0.849645 -2.760568 -11.309268 -10.172985\n",
              "2018-12-24 -0.827965 -1.298380 -4.556536 -14.608552  -6.252293\n",
              "2018-12-26  2.253485  2.999227  5.326782  23.908415   9.346455\n",
              "2018-12-27 -0.207673 -0.817359  3.218426   2.157980   1.789632\n",
              "2018-12-28  0.016397  4.893092 -0.988751  -2.732792  -0.700486\n",
              "\n",
              "[61 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikBJE-ikV7yp",
        "colab_type": "code",
        "colab": {},
        "outputId": "40cb8162-aaad-4b14-cefe-7de6545df3bb"
      },
      "source": [
        "portf_returns = weighted_returns.sum(axis=1)\n",
        "portf_returns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2018-10-02    -7.402365\n",
              "2018-10-03    -0.626193\n",
              "2018-10-04    -9.648081\n",
              "2018-10-05    -5.633252\n",
              "2018-10-08    -5.098939\n",
              "                ...    \n",
              "2018-12-21   -26.337124\n",
              "2018-12-24   -27.543726\n",
              "2018-12-26    43.834364\n",
              "2018-12-27     6.141006\n",
              "2018-12-28     0.487460\n",
              "Length: 61, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGj1NrTUV7yr",
        "colab_type": "code",
        "colab": {},
        "outputId": "a64a34a5-2ab8-43e6-e64d-0d6b4c60191a"
      },
      "source": [
        "fig = plt.figure()\n",
        "ax1 = fig.add_axes([0.1,0.1,0.8,0.8])\n",
        "ax1.hist(portf_returns, bins = 60)\n",
        "ax1.set_xlabel('Portfolio returns')\n",
        "ax1.set_ylabel(\"Freq\")\n",
        "ax1.set_title(\"Portfolio Returns by days\")\n",
        "plt.show(); "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpoVFygNV7yv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Display consensus sell-side earnings estimates for each of the stocks at the beginning and at the end of the holding period"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8q2CkFIoV7yy",
        "colab_type": "code",
        "colab": {},
        "outputId": "a8fe4f8a-b7a0-4f8c-8dbd-17eaeecb30f9"
      },
      "source": [
        "# tickers = yf.Tickers(\"MSFT ORCL IBM AAPL BMRC\")\n",
        "# # ^ returns a named tuple of Ticker objects\n",
        "\n",
        "# # access each ticker using (example)\n",
        "# tickers.msft.info\n",
        "# tickers.aapl.history(period=\"1mo\")\n",
        "# tickers.goog.actions\n",
        "msft = yf.Tickers(\"MSFT\")\n",
        "msft.info"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'Tickers' object has no attribute 'info'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-b9d49cd2b56a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# tickers.goog.actions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mmsft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0myf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTickers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"MSFT\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmsft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'Tickers' object has no attribute 'info'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiR8Uwp9V7y1",
        "colab_type": "code",
        "colab": {},
        "outputId": "29c5b8e9-a736-46d8-aef4-29c1f26c2975"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"5\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Close</th>\n",
              "      <th>...</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Open</th>\n",
              "      <th colspan=\"5\" halign=\"left\">Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>BMRC</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>BMRC</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>...</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>BMRC</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>ORCL</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>BMRC</th>\n",
              "      <th>IBM</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>ORCL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>2018-10-01</th>\n",
              "      <td>221.988266</td>\n",
              "      <td>39.983109</td>\n",
              "      <td>140.783600</td>\n",
              "      <td>112.812271</td>\n",
              "      <td>50.263626</td>\n",
              "      <td>227.259995</td>\n",
              "      <td>41.380001</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>115.610001</td>\n",
              "      <td>51.799999</td>\n",
              "      <td>...</td>\n",
              "      <td>227.949997</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>151.710007</td>\n",
              "      <td>114.750000</td>\n",
              "      <td>51.220001</td>\n",
              "      <td>23600800</td>\n",
              "      <td>41200</td>\n",
              "      <td>3757400</td>\n",
              "      <td>18883100</td>\n",
              "      <td>19217800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-02</th>\n",
              "      <td>223.961426</td>\n",
              "      <td>39.717392</td>\n",
              "      <td>141.473694</td>\n",
              "      <td>112.363396</td>\n",
              "      <td>48.740192</td>\n",
              "      <td>229.279999</td>\n",
              "      <td>41.105000</td>\n",
              "      <td>153.750000</td>\n",
              "      <td>115.150002</td>\n",
              "      <td>50.230000</td>\n",
              "      <td>...</td>\n",
              "      <td>227.250000</td>\n",
              "      <td>41.404999</td>\n",
              "      <td>152.990005</td>\n",
              "      <td>115.300003</td>\n",
              "      <td>51.650002</td>\n",
              "      <td>24788200</td>\n",
              "      <td>24600</td>\n",
              "      <td>3029900</td>\n",
              "      <td>20787200</td>\n",
              "      <td>22406800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-03</th>\n",
              "      <td>226.686707</td>\n",
              "      <td>40.292305</td>\n",
              "      <td>140.986008</td>\n",
              "      <td>112.382904</td>\n",
              "      <td>48.274426</td>\n",
              "      <td>232.070007</td>\n",
              "      <td>41.700001</td>\n",
              "      <td>153.220001</td>\n",
              "      <td>115.169998</td>\n",
              "      <td>49.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>230.050003</td>\n",
              "      <td>41.080002</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>115.419998</td>\n",
              "      <td>49.590000</td>\n",
              "      <td>28654800</td>\n",
              "      <td>25200</td>\n",
              "      <td>3315300</td>\n",
              "      <td>16648000</td>\n",
              "      <td>21207600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-04</th>\n",
              "      <td>222.701340</td>\n",
              "      <td>40.775425</td>\n",
              "      <td>139.228516</td>\n",
              "      <td>110.060501</td>\n",
              "      <td>48.002735</td>\n",
              "      <td>227.990005</td>\n",
              "      <td>42.200001</td>\n",
              "      <td>151.309998</td>\n",
              "      <td>112.790001</td>\n",
              "      <td>49.470001</td>\n",
              "      <td>...</td>\n",
              "      <td>230.779999</td>\n",
              "      <td>41.724998</td>\n",
              "      <td>151.690002</td>\n",
              "      <td>114.610001</td>\n",
              "      <td>49.680000</td>\n",
              "      <td>32042000</td>\n",
              "      <td>17200</td>\n",
              "      <td>4237900</td>\n",
              "      <td>34821700</td>\n",
              "      <td>19034000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-10-05</th>\n",
              "      <td>219.087158</td>\n",
              "      <td>40.654644</td>\n",
              "      <td>137.130585</td>\n",
              "      <td>109.416473</td>\n",
              "      <td>47.886288</td>\n",
              "      <td>224.289993</td>\n",
              "      <td>42.075001</td>\n",
              "      <td>149.029999</td>\n",
              "      <td>112.129997</td>\n",
              "      <td>49.349998</td>\n",
              "      <td>...</td>\n",
              "      <td>227.960007</td>\n",
              "      <td>42.150002</td>\n",
              "      <td>150.899994</td>\n",
              "      <td>112.629997</td>\n",
              "      <td>49.419998</td>\n",
              "      <td>33580500</td>\n",
              "      <td>21600</td>\n",
              "      <td>4602200</td>\n",
              "      <td>29068900</td>\n",
              "      <td>16900200</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-21</th>\n",
              "      <td>147.747253</td>\n",
              "      <td>37.364574</td>\n",
              "      <td>103.382050</td>\n",
              "      <td>96.266937</td>\n",
              "      <td>42.866879</td>\n",
              "      <td>150.729996</td>\n",
              "      <td>38.509998</td>\n",
              "      <td>110.940002</td>\n",
              "      <td>98.230003</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>156.860001</td>\n",
              "      <td>38.889999</td>\n",
              "      <td>112.500000</td>\n",
              "      <td>101.629997</td>\n",
              "      <td>46.080002</td>\n",
              "      <td>95744600</td>\n",
              "      <td>147700</td>\n",
              "      <td>10386500</td>\n",
              "      <td>111242100</td>\n",
              "      <td>58768400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-24</th>\n",
              "      <td>143.924454</td>\n",
              "      <td>36.879440</td>\n",
              "      <td>100.241623</td>\n",
              "      <td>92.248878</td>\n",
              "      <td>41.590611</td>\n",
              "      <td>146.830002</td>\n",
              "      <td>38.009998</td>\n",
              "      <td>107.570000</td>\n",
              "      <td>94.129997</td>\n",
              "      <td>42.689999</td>\n",
              "      <td>...</td>\n",
              "      <td>148.149994</td>\n",
              "      <td>38.500000</td>\n",
              "      <td>109.900002</td>\n",
              "      <td>97.680000</td>\n",
              "      <td>43.770000</td>\n",
              "      <td>37169200</td>\n",
              "      <td>39000</td>\n",
              "      <td>3821400</td>\n",
              "      <td>43935200</td>\n",
              "      <td>17230400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-26</th>\n",
              "      <td>154.059814</td>\n",
              "      <td>37.985538</td>\n",
              "      <td>103.801392</td>\n",
              "      <td>98.550377</td>\n",
              "      <td>43.441681</td>\n",
              "      <td>157.169998</td>\n",
              "      <td>39.150002</td>\n",
              "      <td>111.389999</td>\n",
              "      <td>100.559998</td>\n",
              "      <td>44.590000</td>\n",
              "      <td>...</td>\n",
              "      <td>148.300003</td>\n",
              "      <td>38.330002</td>\n",
              "      <td>108.000000</td>\n",
              "      <td>95.139999</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>58582500</td>\n",
              "      <td>30400</td>\n",
              "      <td>6645800</td>\n",
              "      <td>51634800</td>\n",
              "      <td>21428900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-27</th>\n",
              "      <td>153.059998</td>\n",
              "      <td>37.675060</td>\n",
              "      <td>106.028572</td>\n",
              "      <td>99.158005</td>\n",
              "      <td>43.811893</td>\n",
              "      <td>156.149994</td>\n",
              "      <td>38.830002</td>\n",
              "      <td>113.779999</td>\n",
              "      <td>101.180000</td>\n",
              "      <td>44.970001</td>\n",
              "      <td>...</td>\n",
              "      <td>155.839996</td>\n",
              "      <td>38.799999</td>\n",
              "      <td>109.989998</td>\n",
              "      <td>99.300003</td>\n",
              "      <td>43.950001</td>\n",
              "      <td>53117100</td>\n",
              "      <td>50300</td>\n",
              "      <td>6045600</td>\n",
              "      <td>49498500</td>\n",
              "      <td>19048700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-12-28</th>\n",
              "      <td>153.138428</td>\n",
              "      <td>39.518536</td>\n",
              "      <td>105.329666</td>\n",
              "      <td>98.383781</td>\n",
              "      <td>43.665752</td>\n",
              "      <td>156.229996</td>\n",
              "      <td>40.730000</td>\n",
              "      <td>113.029999</td>\n",
              "      <td>100.389999</td>\n",
              "      <td>44.820000</td>\n",
              "      <td>...</td>\n",
              "      <td>157.500000</td>\n",
              "      <td>38.990002</td>\n",
              "      <td>114.220001</td>\n",
              "      <td>102.089996</td>\n",
              "      <td>45.139999</td>\n",
              "      <td>42291400</td>\n",
              "      <td>47600</td>\n",
              "      <td>5117500</td>\n",
              "      <td>38196300</td>\n",
              "      <td>39141200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>62 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             Adj Close                                                \\\n",
              "                  AAPL       BMRC         IBM        MSFT       ORCL   \n",
              "Date                                                                   \n",
              "2018-10-01  221.988266  39.983109  140.783600  112.812271  50.263626   \n",
              "2018-10-02  223.961426  39.717392  141.473694  112.363396  48.740192   \n",
              "2018-10-03  226.686707  40.292305  140.986008  112.382904  48.274426   \n",
              "2018-10-04  222.701340  40.775425  139.228516  110.060501  48.002735   \n",
              "2018-10-05  219.087158  40.654644  137.130585  109.416473  47.886288   \n",
              "...                ...        ...         ...         ...        ...   \n",
              "2018-12-21  147.747253  37.364574  103.382050   96.266937  42.866879   \n",
              "2018-12-24  143.924454  36.879440  100.241623   92.248878  41.590611   \n",
              "2018-12-26  154.059814  37.985538  103.801392   98.550377  43.441681   \n",
              "2018-12-27  153.059998  37.675060  106.028572   99.158005  43.811893   \n",
              "2018-12-28  153.138428  39.518536  105.329666   98.383781  43.665752   \n",
              "\n",
              "                 Close                                                ...  \\\n",
              "                  AAPL       BMRC         IBM        MSFT       ORCL  ...   \n",
              "Date                                                                  ...   \n",
              "2018-10-01  227.259995  41.380001  153.000000  115.610001  51.799999  ...   \n",
              "2018-10-02  229.279999  41.105000  153.750000  115.150002  50.230000  ...   \n",
              "2018-10-03  232.070007  41.700001  153.220001  115.169998  49.750000  ...   \n",
              "2018-10-04  227.990005  42.200001  151.309998  112.790001  49.470001  ...   \n",
              "2018-10-05  224.289993  42.075001  149.029999  112.129997  49.349998  ...   \n",
              "...                ...        ...         ...         ...        ...  ...   \n",
              "2018-12-21  150.729996  38.509998  110.940002   98.230003  44.000000  ...   \n",
              "2018-12-24  146.830002  38.009998  107.570000   94.129997  42.689999  ...   \n",
              "2018-12-26  157.169998  39.150002  111.389999  100.559998  44.590000  ...   \n",
              "2018-12-27  156.149994  38.830002  113.779999  101.180000  44.970001  ...   \n",
              "2018-12-28  156.229996  40.730000  113.029999  100.389999  44.820000  ...   \n",
              "\n",
              "                  Open                                                \\\n",
              "                  AAPL       BMRC         IBM        MSFT       ORCL   \n",
              "Date                                                                   \n",
              "2018-10-01  227.949997  42.000000  151.710007  114.750000  51.220001   \n",
              "2018-10-02  227.250000  41.404999  152.990005  115.300003  51.650002   \n",
              "2018-10-03  230.050003  41.080002  154.000000  115.419998  49.590000   \n",
              "2018-10-04  230.779999  41.724998  151.690002  114.610001  49.680000   \n",
              "2018-10-05  227.960007  42.150002  150.899994  112.629997  49.419998   \n",
              "...                ...        ...         ...         ...        ...   \n",
              "2018-12-21  156.860001  38.889999  112.500000  101.629997  46.080002   \n",
              "2018-12-24  148.149994  38.500000  109.900002   97.680000  43.770000   \n",
              "2018-12-26  148.300003  38.330002  108.000000   95.139999  43.000000   \n",
              "2018-12-27  155.839996  38.799999  109.989998   99.300003  43.950001   \n",
              "2018-12-28  157.500000  38.990002  114.220001  102.089996  45.139999   \n",
              "\n",
              "              Volume                                         \n",
              "                AAPL    BMRC       IBM       MSFT      ORCL  \n",
              "Date                                                         \n",
              "2018-10-01  23600800   41200   3757400   18883100  19217800  \n",
              "2018-10-02  24788200   24600   3029900   20787200  22406800  \n",
              "2018-10-03  28654800   25200   3315300   16648000  21207600  \n",
              "2018-10-04  32042000   17200   4237900   34821700  19034000  \n",
              "2018-10-05  33580500   21600   4602200   29068900  16900200  \n",
              "...              ...     ...       ...        ...       ...  \n",
              "2018-12-21  95744600  147700  10386500  111242100  58768400  \n",
              "2018-12-24  37169200   39000   3821400   43935200  17230400  \n",
              "2018-12-26  58582500   30400   6645800   51634800  21428900  \n",
              "2018-12-27  53117100   50300   6045600   49498500  19048700  \n",
              "2018-12-28  42291400   47600   5117500   38196300  39141200  \n",
              "\n",
              "[62 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WepZd1Xg3DGJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "db919f64-c8cb-4a90-e2d7-b3767c8181b2"
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = \"jiarongj\" # username from the json file\n",
        "os.environ['KAGGLE_KEY'] = \"5960b07b5c6fd08473cc59802eb61d53\" # key from the json file\n",
        "# Find the JSON content file from the Kaggle web account page, the file content was like '{\"username\":\"jiarongj\",\"key\":\"5960b07b5c6fd08473cc59802eb61d53\"}'\n",
        "!kaggle datasets download -d tsaustin/us-historical-stock-prices-with-earnings-data # through api copied the file from kaggle"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading us-historical-stock-prices-with-earnings-data.zip to /content\n",
            " 99% 418M/424M [00:03<00:00, 160MB/s]\n",
            "100% 424M/424M [00:03<00:00, 122MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ti9Jnu_l50Oh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "e973542b-53e2-4373-bee1-313c1367e8d6"
      },
      "source": [
        "!mkdir file_destination\n",
        "!unzip /content/us-historical-stock-prices-with-earnings-data.zip -d file_destination"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/us-historical-stock-prices-with-earnings-data.zip\n",
            "  inflating: file_destination/dataset_summary.csv  \n",
            "  inflating: file_destination/stocks_latest/dividends_latest.csv  \n",
            "  inflating: file_destination/stocks_latest/earnings_latest.csv  \n",
            "  inflating: file_destination/stocks_latest/stock_prices_latest.csv  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcTE8ZSx6Ptu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "d9db5a1b-5e51-476f-982a-054bc2e2c519"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/file_destination/stocks_latest/earnings_latest.csv')\n",
        "#df[df['symbol'] == 'AAPL'][(df['date']<='2019-01-01') & ('2018-10-01'<=df['date'])]\n",
        "df[df['symbol'] == 'AAPL'][df['qtr'].isin(['12/2018','11/2018','10/2018','09/2018'])]"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "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>symbol</th>\n",
              "      <th>date</th>\n",
              "      <th>qtr</th>\n",
              "      <th>eps_est</th>\n",
              "      <th>eps</th>\n",
              "      <th>release_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2018-11-01</td>\n",
              "      <td>09/2018</td>\n",
              "      <td>2.79</td>\n",
              "      <td>2.91</td>\n",
              "      <td>post</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>375</th>\n",
              "      <td>AAPL</td>\n",
              "      <td>2019-01-29</td>\n",
              "      <td>12/2018</td>\n",
              "      <td>4.17</td>\n",
              "      <td>4.18</td>\n",
              "      <td>post</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    symbol        date      qtr  eps_est   eps release_time\n",
              "374   AAPL  2018-11-01  09/2018     2.79  2.91         post\n",
              "375   AAPL  2019-01-29  12/2018     4.17  4.18         post"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4BXfNMh6CAc",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}