bt_simple_backtests.ipynb

bt_simple_backtests.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMBPr2VILWcys4b76I1SuTP",
      "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/gist/Curt-Park/f2cd9b854e79c24ceedcacaad3c12b09/bt_simple_backtests.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "usyKd72R-YLW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "981e2bed-b508-4928-d8a6-071d64a43edf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: bt==0.2.9 in /usr/local/lib/python3.7/dist-packages (0.2.9)\n",
            "Requirement already satisfied: ffn>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from bt==0.2.9) (0.3.6)\n",
            "Requirement already satisfied: pyprind>=2.11 in /usr/local/lib/python3.7/dist-packages (from bt==0.2.9) (2.11.3)\n",
            "Requirement already satisfied: future>=0.15 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (0.16.0)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (0.9.0)\n",
            "Requirement already satisfied: numpy>=1.5 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (1.21.6)\n",
            "Requirement already satisfied: tabulate>=0.7.5 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (0.8.10)\n",
            "Requirement already satisfied: scikit-learn>=0.15 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.15 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (1.7.3)\n",
            "Requirement already satisfied: decorator>=4 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (4.4.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=1 in /usr/local/lib/python3.7/dist-packages (from ffn>=0.3.5->bt==0.2.9) (3.2.2)\n",
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            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1->ffn>=0.3.5->bt==0.2.9) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1->ffn>=0.3.5->bt==0.2.9) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1->ffn>=0.3.5->bt==0.2.9) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1->ffn>=0.3.5->bt==0.2.9) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->ffn>=0.3.5->bt==0.2.9) (2022.5)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pandas-datareader>=0.2->ffn>=0.3.5->bt==0.2.9) (2.23.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from pandas-datareader>=0.2->ffn>=0.3.5->bt==0.2.9) (4.9.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1->ffn>=0.3.5->bt==0.2.9) (1.15.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->ffn>=0.3.5->bt==0.2.9) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->ffn>=0.3.5->bt==0.2.9) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->ffn>=0.3.5->bt==0.2.9) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pandas-datareader>=0.2->ffn>=0.3.5->bt==0.2.9) (2022.9.24)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.15->ffn>=0.3.5->bt==0.2.9) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.15->ffn>=0.3.5->bt==0.2.9) (1.2.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: finance-datareader==0.9.50 in /usr/local/lib/python3.7/dist-packages (0.9.50)\n",
            "Requirement already satisfied: requests-file in /usr/local/lib/python3.7/dist-packages (from finance-datareader==0.9.50) (1.5.1)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from finance-datareader==0.9.50) (4.9.1)\n",
            "Requirement already satisfied: pandas>=0.19.2 in /usr/local/lib/python3.7/dist-packages (from finance-datareader==0.9.50) (1.3.5)\n",
            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from finance-datareader==0.9.50) (2.23.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from finance-datareader==0.9.50) (4.64.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader==0.9.50) (2022.5)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader==0.9.50) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader==0.9.50) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19.2->finance-datareader==0.9.50) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader==0.9.50) (2022.9.24)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader==0.9.50) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader==0.9.50) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader==0.9.50) (1.24.3)\n"
          ]
        }
      ],
      "source": [
        "! pip install bt==0.2.9\n",
        "! pip install finance-datareader==0.9.50"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import bt\n",
        "import pandas as pd\n",
        "import FinanceDataReader as fdr\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "0m1YE5ys-m5U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spy = fdr.DataReader(\"SPY\")\n",
        "agg = fdr.DataReader(\"AGG\")\n",
        "dfs = [spy.Close, agg.Close]\n",
        "data = pd.concat(dfs, axis=1, join=\"inner\")\n",
        "data.columns = [\"SPY\", \"AGG\"]\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c4-FFp9R-tuX",
        "outputId": "65decea3-a112-4612-e56d-0053dcad9531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   SPY         AGG\n",
            "Date                              \n",
            "2003-09-29  100.930000  102.169998\n",
            "2003-09-30   99.949997  102.699997\n",
            "2003-10-01  102.080002  102.650002\n",
            "2003-10-02  102.449997  102.489998\n",
            "2003-10-03  103.389999  101.750000\n",
            "...                ...         ...\n",
            "2022-10-31  386.209991   94.900002\n",
            "2022-11-01  384.519989   94.860001\n",
            "2022-11-02  374.869995   94.699997\n",
            "2022-11-03  371.010010   94.349998\n",
            "2022-11-04  376.350006   94.339996\n",
            "\n",
            "[4811 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# baselines\n",
        "st_spy = bt.Strategy(\n",
        "    \"SPY\", \n",
        "    [\n",
        "        bt.algos.RunOnce(),\n",
        "        bt.algos.SelectAll(),\n",
        "        bt.algos.SelectRegex(r\"SPY\"),\n",
        "        bt.algos.WeighEqually(),\n",
        "        bt.algos.Rebalance(),\n",
        "    ]\n",
        ")\n",
        "st_agg = bt.Strategy(\n",
        "    \"AGG\", \n",
        "    [\n",
        "        bt.algos.RunOnce(),\n",
        "        bt.algos.SelectAll(),\n",
        "        bt.algos.SelectRegex(r\"AGG\"),\n",
        "        bt.algos.WeighEqually(),\n",
        "        bt.algos.Rebalance(),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "hTzUsVwz-vN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a simple strategies\n",
        "st_equal = bt.Strategy(\n",
        "    \"spy:agg=5:5\",\n",
        "    [\n",
        "        bt.algos.RunMonthly(),\n",
        "        bt.algos.SelectAll(),\n",
        "        bt.algos.WeighEqually(),\n",
        "        bt.algos.Rebalance(),\n",
        "    ],\n",
        ")\n",
        "\n",
        "st_more_spy = bt.Strategy(\n",
        "    \"spy:agg=7:3\",\n",
        "    [\n",
        "        bt.algos.RunMonthly(),\n",
        "        bt.algos.WeighSpecified(**pd.Series([0.7,0.3], index = data.columns)),\n",
        "        bt.algos.Rebalance(),\n",
        "    ],\n",
        ")"
      ],
      "metadata": {
        "id": "AzqbOK2j-w1e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a backtest\n",
        "bt_spy = bt.Backtest(st_spy, data)\n",
        "bt_agg = bt.Backtest(st_agg, data)\n",
        "bt_equal = bt.Backtest(st_equal, data)\n",
        "bt_more_spy = bt.Backtest(st_more_spy, data)"
      ],
      "metadata": {
        "id": "76kOuFU3-yXZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# run\n",
        "res = bt.run(bt_spy, bt_agg, bt_equal, bt_more_spy)\n",
        "res.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "kiZp6Cn5-zqR",
        "outputId": "d014e2af-e0db-429e-8b4d-72702b668a65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1ec024be50>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res.display()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKLVbeOv-1EI",
        "outputId": "f863069c-be4c-476b-bb75-7a4250ad7e2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stat                 SPY         AGG         spy:agg=5:5    spy:agg=7:3\n",
            "-------------------  ----------  ----------  -------------  -------------\n",
            "Start                2003-09-28  2003-09-28  2003-09-28     2003-09-28\n",
            "End                  2022-11-04  2022-11-04  2022-11-04     2022-11-04\n",
            "Risk-free rate       0.00%       0.00%       0.00%          0.00%\n",
            "\n",
            "Total Return         272.86%     -7.66%      99.20%         160.44%\n",
            "Daily Sharpe         0.46        -0.06       0.42           0.44\n",
            "Daily Sortino        0.70        -0.08       0.65           0.69\n",
            "CAGR                 7.13%       -0.42%      3.67%          5.14%\n",
            "Max Drawdown         -56.47%     -21.73%     -32.77%        -43.27%\n",
            "Calmar Ratio         0.13        -0.02       0.11           0.12\n",
            "\n",
            "MTD                  -2.55%      -0.59%      -1.58%         -1.97%\n",
            "3m                   -9.13%      -9.17%      -9.07%         -9.08%\n",
            "6m                   -12.28%     -8.43%      -10.21%        -11.01%\n",
            "YTD                  -20.76%     -17.30%     -18.74%        -19.48%\n",
            "1Y                   -19.40%     -17.80%     -18.25%        -18.63%\n",
            "3Y (ann.)            6.98%       -5.74%      1.01%          3.50%\n",
            "5Y (ann.)            7.78%       -2.95%      2.77%          4.87%\n",
            "10Y (ann.)           10.25%      -1.71%      4.45%          6.82%\n",
            "Since Incep. (ann.)  7.13%       -0.42%      3.67%          5.14%\n",
            "\n",
            "Daily Sharpe         0.46        -0.06       0.42           0.44\n",
            "Daily Sortino        0.70        -0.08       0.65           0.69\n",
            "Daily Mean (ann.)    8.73%       -0.29%      4.07%          5.89%\n",
            "Daily Vol (ann.)     19.16%      5.12%       9.61%          13.25%\n",
            "Daily Skew           -0.09       -2.31       -0.03          -0.05\n",
            "Daily Kurt           15.14       60.47       20.70          16.35\n",
            "Best Day             14.52%      3.87%       8.73%          10.92%\n",
            "Worst Day            -10.94%     -6.84%      -6.63%         -7.77%\n",
            "\n",
            "Monthly Sharpe       0.54        -0.09       0.49           0.53\n",
            "Monthly Sortino      0.88        -0.14       0.78           0.85\n",
            "Monthly Mean (ann.)  8.05%       -0.36%      3.93%          5.60%\n",
            "Monthly Vol (ann.)   14.84%      4.00%       7.98%          10.64%\n",
            "Monthly Skew         -0.60       -0.04       -0.73          -0.65\n",
            "Monthly Kurt         1.45        3.63        2.33           1.85\n",
            "Best Month           12.70%      5.84%       7.45%          9.64%\n",
            "Worst Month          -16.52%     -4.34%      -9.57%         -12.34%\n",
            "\n",
            "Yearly Sharpe        0.48        -0.06       0.41           0.45\n",
            "Yearly Sortino       0.83        -0.07       0.65           0.74\n",
            "Yearly Mean          8.11%       -0.29%      3.88%          5.57%\n",
            "Yearly Vol           16.94%      5.00%       9.56%          12.44%\n",
            "Yearly Skew          -1.23       -2.22       -1.45          -1.37\n",
            "Yearly Kurt          2.06        7.26        2.04           2.01\n",
            "Best Year            29.69%      5.52%       16.91%         21.60%\n",
            "Worst Year           -38.28%     -17.30%     -19.39%        -27.35%\n",
            "\n",
            "Avg. Drawdown        -1.93%      -1.74%      -1.01%         -1.42%\n",
            "Avg. Drawdown Days   26.62       124.16      24.05          25.32\n",
            "Avg. Up Month        3.11%       0.83%       1.66%          2.22%\n",
            "Avg. Down Month      -3.65%      -0.86%      -1.86%         -2.58%\n",
            "Win Year %           73.68%      57.89%      78.95%         78.95%\n",
            "Win 12m %            80.45%      53.64%      81.36%         83.18%\n"
          ]
        }
      ]
    }
  ]
}