chatgpt_skater_arima.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOx1dT+m71NbX3bNL9uZPJn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/microprediction/timemachines/blob/main/chatgpt_skater_arima.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install statsmodels"
      ],
      "metadata": {
        "id": "FgSf62ZFoSDz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install microprediction"
      ],
      "metadata": {
        "id": "JTUfM9uMpMQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A \"Skater\" written by ChatGPT\n",
        "(I admit this one took some trial and error)"
      ],
      "metadata": {
        "id": "s_ePQeXC0hu8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime, timedelta\n",
        "import pytz\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "from microprediction import MicroReader\n",
        "from numpy.linalg import LinAlgError\n",
        "\n",
        "# Set up reader and writer objects\n",
        "reader = MicroReader()\n",
        "\n",
        "# Define the skater function with a buffer of 15\n",
        "def skater(y, s, k, a=None, t=None, e=1000, r=0.1):\n",
        "    if len(s.get('history', [])) < 15:\n",
        "        if len(s.get('history', [])) == 0:\n",
        "            s['history'] = [y]\n",
        "        x = [s['history'][-1]] * k\n",
        "        x_std = [0] * k\n",
        "    else:\n",
        "        try:\n",
        "            arima_order = (2, 0, 0)\n",
        "            arima_model = sm.tsa.ARIMA(s['history'], order=arima_order)\n",
        "            arima_result = arima_model.fit()\n",
        "            x = arima_result.forecast(steps=k)[0]\n",
        "            x_std = np.sqrt(arima_result.forecast(steps=k)[1])\n",
        "        except LinAlgError:\n",
        "            # Use the last value as the prediction\n",
        "            x = [s['history'][-1]] * k\n",
        "            x_std = [0] * k\n",
        "    s['history'].append(y)\n",
        "    s['history'] = s['history'][-15:]  # Keep last 15 observations\n",
        "    return x, x_std, s\n",
        "\n",
        "# Read the data\n",
        "lagged_values = reader.get_lagged_values(name='finance-futures-gold-log-return.json', count=1000)\n",
        "y = lagged_values[::-1]\n",
        "\n",
        "# Generate predictions and submit them to the stream\n",
        "s = {}\n",
        "x = list()\n",
        "for yi in y:\n",
        "    xi, x_std, s = skater(y=yi, s=s, k=1, e=1000)\n",
        "    x.append(xi)\n",
        "\n",
        "# Create a plot of the data and predictions\n",
        "ndx = list(range(len(x)))\n",
        "plt.plot(ndx, y, label='Data')\n",
        "plt.plot(ndx, x, label='Predictions')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "Psy7Zxj_zhav",
        "outputId": "9e9040b5-0464-44fe-f1dd-ecf3d53945f8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
            "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:547: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
            "  warnings.warn('Inverting hessian failed, no bse or cov_params '\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
            "/usr/local/lib/python3.8/dist-packages/statsmodels/base/model.py:566: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}