monteprediction_entry.ipynb

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        "<a href=\"https://colab.research.google.com/github/microprediction/monteprediction_colab_examples/blob/main/monteprediction_entry.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": "markdown",
      "source": [
        "# Sector Monte Carlo Game\n",
        "\n",
        "I'm looking for people to help blow away traditional index investing as the world knows it, using a marvellous monte-mutuel meritocracy. Those who choose to be part of this new venture submit one million scenarios for next week's sector returns, and do so every weekend. Each scenario comprises 11 numbers, one for each financial sector. Every week the following are updated:\n",
        "\n",
        "*   [Weekly likelihood scores](https://www.monteprediction.com/weekly)\n",
        "*   [Wealth](https://www.monteprediction.com/)\n",
        "\n",
        "Loosely speaking you are rewarded based on how many of your samples are close to the ground truth (and in the case of wealth, how many of your competitors are also close - precise details below).\n",
        "\n",
        "You can participate by running this notebook.\n",
        "\n",
        "* Change the email and name below and, prefereably,\n",
        "* Modify this example notebook first to *improve the algorithm* (and I mean *completely replace it* as you see fit).  \n",
        "\n",
        "Once you get the joke, you'll need to script this up to run it every week - I would suggest on a Saturday. The ground truth is calculated Monday close to Monday close, but to allow for various uses of the data, the cutoff for submissions is Sunday mightnight UTC.\n",
        "\n"
      ],
      "metadata": {
        "id": "8wu0RqylugX9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn\n",
        "!pip install scipy\n",
        "!pip install --upgrade monteprediction\n",
        "\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "import numpy as np\n",
        "from datetime import datetime, timedelta\n",
        "import time\n",
        "from monteprediction import SPDR_ETFS\n",
        "from monteprediction.calendarutil import get_last_wednesday\n",
        "from monteprediction.submission import send_in_chunks\n",
        "\n",
        "# Tournament settings, don't change these.\n",
        "num_samples_per_chunk = int(1048576/8)\n",
        "num_chunks = 8\n",
        "num_samples = num_chunks*num_samples_per_chunk"
      ],
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      "execution_count": 1,
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            "Installing collected packages: monteprediction\n",
            "Successfully installed monteprediction-1.0.17\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1. Create a dataframe with just over one million hypothetical weekly returns for each sector.   \n",
        "\n",
        "Do this however you like this is just an example. One column per sector."
      ],
      "metadata": {
        "id": "PWvz4C7vBVVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This example uses Quasi-Monte Carlo on the empirical covariance\n",
        "# There is absolutely no requirement you follow this pattern\n",
        "\n",
        "from scipy.stats.qmc import MultivariateNormalQMC\n",
        "from sklearn.covariance import EmpiricalCovariance\n",
        "\n",
        "# Get historical weekly returns\n",
        "last_wednesday = get_last_wednesday()\n",
        "num_weeks = int(52+4*52*np.random.rand())\n",
        "start_date = last_wednesday - timedelta(weeks=num_weeks)\n",
        "data = yf.download(SPDR_ETFS, start=start_date, end=last_wednesday, interval=\"1wk\")\n",
        "weekly_prices = data['Adj Close']\n",
        "weekly_returns = weekly_prices.pct_change().dropna()\n",
        "\n",
        "# Use cov estimation to generate samples\n",
        "cov_matrix = EmpiricalCovariance().fit(weekly_returns).covariance_\n",
        "qmc_engine = MultivariateNormalQMC(mean=np.zeros(len(SPDR_ETFS)), cov=cov_matrix)\n",
        "samples = qmc_engine.random(num_samples)\n",
        "df = pd.DataFrame(columns=SPDR_ETFS, data = samples)\n",
        "print(df[:3])\n",
        "\n",
        "# Verify submission\n",
        "assert len(df.index)==num_samples,f'Expecting exactly {num_samples} samples'\n",
        "assert list(df.columns)==SPDR_ETFS,'Columns should match SPDR_ETFS in order'\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZtSzrXdBL__",
        "outputId": "a4c02fa2-e46e-4480-ee55-195cf31e9a63"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  11 of 11 completed\n"
          ]
        },
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          "output_type": "stream",
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          "text": [
            "        XLB       XLC       XLE       XLF       XLI       XLK       XLP  \\\n",
            "0 -0.005739 -0.027379  0.055381 -0.005150 -0.007348 -0.037388  0.036084   \n",
            "1  0.017681  0.012907 -0.007036  0.046401  0.040037  0.062786  0.021175   \n",
            "2  0.051049  0.020684  0.054569  0.026398  0.034945  0.029755  0.021246   \n",
            "\n",
            "       XLRE       XLU       XLV       XLY  \n",
            "0  0.005877  0.045887  0.014344 -0.043577  \n",
            "1  0.032060  0.006658 -0.002639  0.068654  \n",
            "2  0.022588  0.006153  0.004474  0.042195  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2. Submit the dataframe"
      ],
      "metadata": {
        "id": "UlqcDSD5DXNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "YOUR_EMAIL = 'monteprediction_entry@monteprediction.com'  # Be sure to change this\n",
        "YOUR_NAME  = 'Original gangster'    # The handle to be used on the leaderboard\n",
        "response = send_in_chunks(df, num_chunks=num_chunks, email=YOUR_EMAIL, name=YOUR_NAME)\n",
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WwTq_rwDVB1",
        "outputId": "eec33079-bb18-43aa-976c-a3182f10b497"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chunk 0 of 8 sent successfully.\n",
            "Chunk 1 of 8 sent successfully.\n",
            "Chunk 2 of 8 sent successfully.\n",
            "Chunk 3 of 8 sent successfully.\n",
            "Chunk 4 of 8 sent successfully.\n",
            "Chunk 5 of 8 sent successfully.\n",
            "Chunk 6 of 8 sent successfully.\n",
            "Chunk 7 of 8 sent successfully.\n",
            "{'expiry': '2024_05_20', 'message': 'Submission from monteprediction_entry@monteprediction.com received for week concluding 2024_05_20', 'name': 'Original gangster'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3. (Later, optional) verify the submission\n",
        "A more detailed check of your submission will be performed within 10-20 minutes of your submitting."
      ],
      "metadata": {
        "id": "M05VMSwPFSuD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from monteprediction.verification import get_verification_status\n",
        "import time\n",
        "time.sleep(15*60)\n",
        "print(get_verification_status(email=YOUR_EMAIL))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fk3tZHQfFame",
        "outputId": "b092d7f9-c672-4782-aee2-adf079c11bd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "verified\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Explaining the game ...\n",
        "Here's how the reward system works.\n",
        "\n",
        "### Weekly likelihood scores\n",
        "\n",
        "We will assume you are participant $i$. Let's write your samples as $\\{x_{ik}\\}_{k=0}^{n-1}$, where each $x_{ik}$ for $k=0,\\dots,2^{20}-1$ is an 11-vector. This will be used to imply an unnormalized prediction density for $z \\in \\mathbf{R}^{11}$ as:\n",
        "\n",
        "$$\\rho_i(z) = \\frac{1}{n} \\sum_{k=0}^{n-1} \\exp(-a \\|x_{ik}-z \\|_2) $$\n",
        "\n",
        "where $a$ is a system parameter set at approximately $a=300$. This is almost your score and approximately one week from now it will appear in the [weekly scoring](https://www.monteprediction.com/weekly). There is also an overall likelihood score although only available through the [api](https://www.monteprediction.com/api/scores) at time of writing.\n",
        "\n",
        "### Accumulated wealth\n",
        "\n",
        "There is also an [overall wealth](https://www.monteprediction.com/) computed for each player based on their longitudinal performance. To see how your wealth is updated each week, let us suppose you have an initial wealth $W_i$.\n",
        "\n",
        "A system parameter $b_i=0.1$ determines the fraction of your total wealth you deploy that week. You are considered to invest $\\Omega_i = b_i W_i$ and similarly for other participants yielding a total investment of $\\Omega = \\sum_i \\Omega_i$. This pot will be split when the truth $z$ is revealed.\n",
        "\n",
        "To this end your 'mass' is $Q_i(z) = \\Omega_i \\rho_i(z)$ represents loosely how many of your samples are close to $z$ weighted by your wealth. The total mass near $z$ supplied by all participants is $Q(z) = \\sum_i Q_i(z)$. Your payout is $\\Omega \\frac{Q_i(z)}{Q(z)}$ which may be thought of as your share of the mass near the truth. Your net profit is $\\delta_i(z) = \\Omega \\frac{Q_i(z)}{Q(z)} - \\Omega_i$ and that is what dictates how your wealth ticks up or down from one week to the next.   \n",
        "\n",
        "### Incentive\n",
        "\n",
        "It should be apparent that $Q$ plays the role of an unnormalized market probability (i.e. risk-neutral density) and further, that a participant with perfect knowledge of the true density $P$ will at worst break even against any opponents' play, subject only to the ability to approximate $P$ with a collection of Monte Carlo paths in this fashion.\n",
        "\n",
        "(Because this entry has been included in the mix, and is not particularly clever, there is a subsidy for participation for anyone taking even a moment to reflect statistically upon the problem ... for instance, by applying shrinkage to the covariance estimation or fixing the 1-margins).\n",
        "\n",
        "### Minor change to scoring Apr '24\n",
        "\n",
        "What's above isn't *entirely* true.\n",
        "\n",
        "In April it was decided by the community that it would be better to compute likelihoods using not only the likelihood for the 11-dimensional outcome, but also adding in the likelihoods for each 10-dimensional subspace with a lower weighting.\n",
        "\n",
        "It is hoped that this will reward more accurate predictions with greater statistical power (i.e. more skill versus luck). For avoidance of doubt you can always refer to the scoring code which is transparent and appears in the monteprediction package [here](https://github.com/microprediction/monteprediction/blob/main/monteprediction/scoring.py)."
      ],
      "metadata": {
        "id": "S38mSrPNDbsa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ... here's how you can verify your score...\n",
        "from monteprediction.truth import get_most_recent_truth\n",
        "from monteprediction.scoring import compute_score\n",
        "z = get_most_recent_truth()\n",
        "print({'z':z})\n",
        "score = compute_score(samples=df.values,z=z)\n",
        "print(f\"Score using your samples above last week: {score}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tNFHQsO4FW8j",
        "outputId": "db63d91b-3ca1-4065-baf7-73d63c10ed2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%%**********************]  11 of 11 completed\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'z': array([-0.03072112, -0.01442068, -0.01988178, -0.03632423, -0.02204183,\n",
            "       -0.0049891 , -0.0127809 , -0.02970295, -0.01473307, -0.03009355,\n",
            "       -0.00967343])}\n",
            "Score using your samples above last week: 1.2029812637019566\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Some suggestions\n",
        "\n",
        "There are many critiques of this example entry, one that has been created solely to provide a template and illustrate the submission process, that might provoke you to create better entries.\n",
        "\n",
        "\n",
        "1.   The distributions for each sector are assumed normal\n",
        "2.   The joint distribution is assumed multivariate normal\n",
        "3.   The empirical distribution is used to estimate a covariance matrix, which is typically not a brilliant idea. See instead LedoitWolf or ShrunkCovariance from [sklearn.covariance](https://scikit-learn.org/stable/modules/covariance.html), for example. You can also avail yourself of dozens of other covariance estimation methods in the [precise](https://github.com/microprediction/precise/tree/main/precise/skaters/covariance) package.\n",
        "\n",
        "To emphasize, do not treat the above as normative in any way. If you prefer to create samples using a water computer, or copulas, or machine learning generative models (quite popular of late as per [this paper](https://cs230.stanford.edu/projects_fall_2019/reports/26259829.pdf) for example) then go for it.\n",
        "\n"
      ],
      "metadata": {
        "id": "kRDDZo6iyfMW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Leaderboard\n",
        "\n",
        "As noted, to go [www.monteprediction.com](www.monteprediction.com) for the weekly likelihood scores and accumulated wealth.\n",
        "\n",
        "## Scheduling\n",
        "\n",
        "This is a long-running tournament (perhaps forever) and the wealth will be taken rather seriously. Only those who contribute longitudinally will be rewarded (details to follow). So, it is very important to submit each and every week. I strongly suggest setting up a cron job to run on Saturdays, or otherwise ensuring that your entry is sent. There is a wealth penalty if you fail to submit.\n",
        "\n",
        "The cutoff is Sunday mightnight UTC. If you submit after that, your scenarios will apply to the week after.\n",
        "\n",
        "## Rules\n",
        "\n",
        "The official rules are, or will be, at https://www.monteprediction.com/rules and superceed anything written here.\n",
        "\n",
        "## Participant likelihood scores\n",
        "\n",
        "The complete list of participant scores can be retrieved [directly](https://www.monteprediction.com/api/weekly) or using the helper provided:\n"
      ],
      "metadata": {
        "id": "xrJu9OJYoFIX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from monteprediction.api import get_weekly\n",
        "import datetime\n",
        "import pytz\n",
        "\n",
        "scores_df = get_weekly()\n",
        "\n",
        "# You may wish to convert from epoch ...\n",
        "\n",
        "def epoch_to_new_york(epoch_time):\n",
        "  tz_new_york = pytz.timezone('America/New_York')\n",
        "  date_time_utc = datetime.datetime.utcfromtimestamp(epoch_time)\n",
        "  date_time_new_york = date_time_utc.replace(tzinfo=datetime.timezone.utc).astimezone(tz_new_york).date()\n",
        "  return date_time_new_york\n",
        "\n",
        "scores_df['date'] = scores_df['date'].apply(epoch_to_new_york)\n",
        "scores_df"
      ],
      "metadata": {
        "id": "D_bPfZ4l727b",
        "outputId": "fffd68f8-c926-40f5-f21e-2c3e6b8dc46a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "          date                            name     score\n",
              "0   2024-05-05             Empirical benchmark  2.491296\n",
              "1   2024-05-05            joakim_dot_arvidsson  2.017710\n",
              "2   2024-05-05                          at3325  1.608274\n",
              "3   2024-05-05                     thomaswcole  0.493504\n",
              "4   2024-05-05  ovvo_dot_financial_dot_systems  0.252178\n",
              "..         ...                             ...       ...\n",
              "65  2024-02-04                    langchain101  0.316611\n",
              "66  2024-01-28               Original gangster  0.218671\n",
              "67  2024-01-28                          nobody  0.163634\n",
              "68  2024-01-28         ladsf987asdf87as987afsd  0.150153\n",
              "69  2024-01-28                       empirical  0.149701\n",
              "\n",
              "[70 rows x 3 columns]"
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024-05-05</td>\n",
              "      <td>Empirical benchmark</td>\n",
              "      <td>2.491296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024-05-05</td>\n",
              "      <td>joakim_dot_arvidsson</td>\n",
              "      <td>2.017710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024-05-05</td>\n",
              "      <td>at3325</td>\n",
              "      <td>1.608274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2024-05-05</td>\n",
              "      <td>thomaswcole</td>\n",
              "      <td>0.493504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2024-05-05</td>\n",
              "      <td>ovvo_dot_financial_dot_systems</td>\n",
              "      <td>0.252178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2024-02-04</td>\n",
              "      <td>langchain101</td>\n",
              "      <td>0.316611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>2024-01-28</td>\n",
              "      <td>Original gangster</td>\n",
              "      <td>0.218671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>2024-01-28</td>\n",
              "      <td>nobody</td>\n",
              "      <td>0.163634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>2024-01-28</td>\n",
              "      <td>ladsf987asdf87as987afsd</td>\n",
              "      <td>0.150153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>2024-01-28</td>\n",
              "      <td>empirical</td>\n",
              "      <td>0.149701</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>70 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-520cedb3-6bd5-43b7-9699-242a15dbea75')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-520cedb3-6bd5-43b7-9699-242a15dbea75 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-520cedb3-6bd5-43b7-9699-242a15dbea75');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f5d22ec4-d7f6-4e6a-b1f3-2a3b5da2a5ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5d22ec4-d7f6-4e6a-b1f3-2a3b5da2a5ad')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5d22ec4-d7f6-4e6a-b1f3-2a3b5da2a5ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "scores_df",
              "summary": "{\n  \"name\": \"scores_df\",\n  \"rows\": 70,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-01-28\",\n        \"max\": \"2024-05-05\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"2024-03-03\",\n          \"2024-02-18\",\n          \"2024-05-05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 24,\n        \"samples\": [\n          \"gredin\",\n          \"david_dot_russell_dot_hopkins\",\n          \"Empirical benchmark\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.945658184560975,\n        \"min\": 1.4171e-05,\n        \"max\": 20.5977663785,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          0.0001415336,\n          2.4912957025,\n          0.4841413278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Official ground truth\n",
        "\n",
        "You can retrieve the official ground truth returns [directly](https://www.monteprediction.com/api/truth/2024_04_01) or using the helpers as shown below."
      ],
      "metadata": {
        "id": "IRQT8ZrF9Tuu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from monteprediction.api import get_truth\n",
        "get_truth('2024-04-01')"
      ],
      "metadata": {
        "id": "YHC4GfjR9WI9",
        "outputId": "90e219d1-21ea-4118-d2d7-86743496db8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ticker    return\n",
              "0     XLB  0.020288\n",
              "1     XLC  0.000745\n",
              "2     XLE  0.030010\n",
              "3     XLF  0.020729\n",
              "4     XLI  0.009099\n",
              "5     XLK -0.006174\n",
              "6     XLP  0.016951\n",
              "7    XLRE  0.030532\n",
              "8     XLU  0.036523\n",
              "9     XLV  0.019413\n",
              "10    XLY  0.008595"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0385966f-1b6a-4033-83ce-6fa23517ef55\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ticker</th>\n",
              "      <th>return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XLB</td>\n",
              "      <td>0.020288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XLC</td>\n",
              "      <td>0.000745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XLE</td>\n",
              "      <td>0.030010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XLF</td>\n",
              "      <td>0.020729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>XLI</td>\n",
              "      <td>0.009099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>XLK</td>\n",
              "      <td>-0.006174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XLP</td>\n",
              "      <td>0.016951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>XLRE</td>\n",
              "      <td>0.030532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>XLU</td>\n",
              "      <td>0.036523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>XLV</td>\n",
              "      <td>0.019413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>XLY</td>\n",
              "      <td>0.008595</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0385966f-1b6a-4033-83ce-6fa23517ef55 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0385966f-1b6a-4033-83ce-6fa23517ef55');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-13b6b7a5-2b56-4f7e-b566-c99c6d17378e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13b6b7a5-2b56-4f7e-b566-c99c6d17378e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-13b6b7a5-2b56-4f7e-b566-c99c6d17378e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"get_truth('2024-04-01')\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"ticker\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"XLK\",\n          \"XLB\",\n          \"XLV\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"return\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01303227037242534,\n        \"min\": -0.0061744652,\n        \"max\": 0.0365234903,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          -0.0061744652,\n          0.0202875461,\n          0.0194130687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Community moments\n",
        "\n",
        "A side benefit of being part of this obscure prediction club is access to the community covariance matrix. There is a [list of dates](https://www.monteprediction.com/api/moments) for which these are available and you can access them at URL's such as [this](https://www.monteprediction.com/api/moments/covariance/2024_04_01). There are helpers in the [Python client](https://github.com/microprediction/monteprediction/blob/main/monteprediction/api.py) for other quantities derivable from the covariance matrix.\n"
      ],
      "metadata": {
        "id": "KCqh9ZhK9Aqy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from monteprediction.api import get_mean, get_std\n",
        "get_mean('2024-04-01')"
      ],
      "metadata": {
        "id": "5RmOhE2m_034",
        "outputId": "1636c285-27e7-4b72-9a79-a6fb75925b9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        XLB      XLC       XLE       XLF       XLI       XLK       XLP  \\\n",
              "0  0.011614  0.01426  0.019381  0.014757  0.013132  0.017884  0.010922   \n",
              "\n",
              "      XLRE       XLU       XLV       XLY  \n",
              "0  0.01691  0.016301  0.013361  0.020174  "
            ],
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              "\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>XLB</th>\n",
              "      <th>XLC</th>\n",
              "      <th>XLE</th>\n",
              "      <th>XLF</th>\n",
              "      <th>XLI</th>\n",
              "      <th>XLK</th>\n",
              "      <th>XLP</th>\n",
              "      <th>XLRE</th>\n",
              "      <th>XLU</th>\n",
              "      <th>XLV</th>\n",
              "      <th>XLY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.011614</td>\n",
              "      <td>0.01426</td>\n",
              "      <td>0.019381</td>\n",
              "      <td>0.014757</td>\n",
              "      <td>0.013132</td>\n",
              "      <td>0.017884</td>\n",
              "      <td>0.010922</td>\n",
              "      <td>0.01691</td>\n",
              "      <td>0.016301</td>\n",
              "      <td>0.013361</td>\n",
              "      <td>0.020174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f190b80-e7ea-4bb3-a7c4-61831770cf25 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4f190b80-e7ea-4bb3-a7c4-61831770cf25');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"get_mean('2024-04-01')\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"XLB\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0116141018,\n        \"max\": 0.0116141018,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0116141018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.014260267,\n        \"max\": 0.014260267,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.014260267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.019380646,\n        \"max\": 0.019380646,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.019380646\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.014756958,\n        \"max\": 0.014756958,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.014756958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0131318229,\n        \"max\": 0.0131318229,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0131318229\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLK\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0178844257,\n        \"max\": 0.0178844257,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0178844257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0109216747,\n        \"max\": 0.0109216747,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0109216747\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLRE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0169103375,\n        \"max\": 0.0169103375,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0169103375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLU\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0163012494,\n        \"max\": 0.0163012494,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0163012494\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0133608621,\n        \"max\": 0.0133608621,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0133608621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XLY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.020174225,\n        \"max\": 0.020174225,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.020174225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from monteprediction.api import get_correlation, get_covariance\n",
        "corr = get_correlation('2024-04-01')\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm', center=0, linewidths=0.5)"
      ],
      "metadata": {
        "id": "IQlm6rVU0Trp",
        "outputId": "8f6da387-2a6c-48fd-eb38-bf72691117a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}