era_boosting_example.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "era_boosting_example.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyPioUeGTvEulxfb/MZqtUts",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/numerai/example-scripts/blob/master/era_boosting_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a0GI-eLUns1j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c57f3446-a24b-4d1d-862c-d0832968d368"
      },
      "source": [
        "from xgboost import XGBRegressor\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "TRAINING_DATAPATH = 'https://numerai-public-datasets.s3-us-west-2.amazonaws.com/latest_numerai_training_data.csv.xz'\n",
        "df = pd.read_csv(TRAINING_DATAPATH)\n",
        "features = [c for c in df if c.startswith(\"feature\")]\n",
        "\n",
        "def ar1(x):\n",
        "    return np.corrcoef(x[:-1], x[1:])[0,1]\n",
        "\n",
        "def autocorr_penalty(x):\n",
        "    n = len(x)\n",
        "    p = ar1(x)\n",
        "    return np.sqrt(1 + 2*np.sum([((n - i)/n)*p**i for i in range(1,n)]))\n",
        "\n",
        "def smart_sharpe(x):\n",
        "    return np.mean(x)/(np.std(x, ddof=1)*autocorr_penalty(x))\n",
        "\n",
        "def spearmanr(target, pred):\n",
        "    return np.corrcoef(\n",
        "        target,\n",
        "        pred.rank(pct=True, method=\"first\")\n",
        "    )[0, 1]\n",
        "\n",
        "def era_boost_train(X, y, era_col, proportion=0.5, trees_per_step=10, num_iters=200):\n",
        "    model = XGBRegressor(max_depth=5, learning_rate=0.01, n_estimators=trees_per_step, n_jobs=-1, colsample_bytree=0.1)\n",
        "    features = X.columns\n",
        "    model.fit(X, y)\n",
        "    new_df = X.copy()\n",
        "    new_df[\"target\"] = y\n",
        "    new_df[\"era\"] = era_col\n",
        "    for i in range(num_iters-1):\n",
        "        print(f\"iteration {i}\")\n",
        "        # score each era\n",
        "        print(\"predicting on train\")\n",
        "        preds = model.predict(X)\n",
        "        new_df[\"pred\"] = preds\n",
        "        era_scores = pd.Series(index=new_df[\"era\"].unique())\n",
        "        print(\"getting per era scores\")\n",
        "        for era in new_df[\"era\"].unique():\n",
        "            era_df = new_df[new_df[\"era\"] == era]\n",
        "            era_scores[era] = spearmanr(era_df[\"pred\"], era_df[\"target\"])\n",
        "        era_scores.sort_values(inplace=True)\n",
        "        worst_eras = era_scores[era_scores <= era_scores.quantile(proportion)].index\n",
        "        print(list(worst_eras))\n",
        "        worst_df = new_df[new_df[\"era\"].isin(worst_eras)]\n",
        "        era_scores.sort_index(inplace=True)\n",
        "        era_scores.plot(kind=\"bar\")\n",
        "        print(\"performance over time\")\n",
        "        plt.show()\n",
        "        print(\"autocorrelation\")\n",
        "        print(ar1(era_scores))\n",
        "        print(\"mean correlation\")\n",
        "        print(np.mean(era_scores))\n",
        "        print(\"sharpe\")\n",
        "        print(np.mean(era_scores)/np.std(era_scores))\n",
        "        print(\"smart sharpe\")\n",
        "        print(smart_sharpe(era_scores))\n",
        "        model.n_estimators += trees_per_step\n",
        "        booster = model.get_booster()\n",
        "        print(\"fitting on worst eras\")\n",
        "        model.fit(worst_df[features], worst_df[\"target\"], xgb_model=booster)\n",
        "    return model\n",
        "\n",
        "boost_model = era_boost_train(df[features], df[\"target\"], era_col=df[\"era\"], proportion=0.5, trees_per_step=10, num_iters=20)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[19:20:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 0\n",
            "predicting on train\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:39: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "getting per era scores\n",
            "['era103', 'era68', 'era91', 'era58', 'era60', 'era69', 'era41', 'era27', 'era112', 'era67', 'era85', 'era19', 'era65', 'era104', 'era89', 'era18', 'era42', 'era14', 'era49', 'era66', 'era84', 'era106', 'era40', 'era75', 'era33', 'era9', 'era119', 'era25', 'era107', 'era113', 'era50', 'era73', 'era74', 'era7', 'era57', 'era101', 'era100', 'era2', 'era110', 'era114', 'era71', 'era8', 'era26', 'era31', 'era82', 'era46', 'era118', 'era117', 'era88', 'era59', 'era29', 'era35', 'era21', 'era24', 'era13', 'era86', 'era62', 'era79', 'era70', 'era81']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.19056347331593365\n",
            "mean correlation\n",
            "0.06904305002044078\n",
            "sharpe\n",
            "2.3394072131223305\n",
            "smart sharpe\n",
            "1.9240690220670957\n",
            "fitting on worst eras\n",
            "[19:20:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 1\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era34', 'era32', 'era56', 'era30', 'era3', 'era54', 'era37', 'era78', 'era28', 'era53', 'era36', 'era99', 'era105', 'era63', 'era11', 'era108', 'era116', 'era77', 'era83', 'era51', 'era4', 'era45', 'era94', 'era80', 'era90', 'era93', 'era102', 'era43', 'era15', 'era10', 'era72', 'era92', 'era97', 'era61', 'era42', 'era44', 'era91', 'era120', 'era98', 'era115', 'era76', 'era16', 'era87', 'era5', 'era12', 'era1', 'era111', 'era6', 'era64', 'era74', 'era47', 'era109', 'era25', 'era7', 'era20', 'era52', 'era96', 'era23', 'era48', 'era9']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "-0.16737907776919278\n",
            "mean correlation\n",
            "0.07622352639933644\n",
            "sharpe\n",
            "2.811446637288859\n",
            "smart sharpe\n",
            "3.3103404647211874\n",
            "fitting on worst eras\n",
            "[19:21:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 2\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era103', 'era58', 'era60', 'era69', 'era104', 'era66', 'era41', 'era106', 'era91', 'era107', 'era73', 'era85', 'era67', 'era40', 'era112', 'era27', 'era101', 'era89', 'era18', 'era33', 'era50', 'era26', 'era113', 'era119', 'era100', 'era49', 'era17', 'era46', 'era55', 'era21', 'era31', 'era19', 'era84', 'era70', 'era114', 'era117', 'era62', 'era110', 'era118', 'era59', 'era65', 'era42', 'era75', 'era82', 'era32', 'era116', 'era57', 'era13', 'era9', 'era88', 'era14', 'era71', 'era102', 'era3', 'era79', 'era34', 'era87', 'era35', 'era39']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.07521072267409357\n",
            "mean correlation\n",
            "0.07730581832197374\n",
            "sharpe\n",
            "1.9225643981567548\n",
            "smart sharpe\n",
            "1.7766925069858786\n",
            "fitting on worst eras\n",
            "[19:21:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 3\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era103', 'era58', 'era66', 'era85', 'era60', 'era29', 'era81', 'era8', 'era91', 'era99', 'era86', 'era74', 'era106', 'era41', 'era100', 'era19', 'era69', 'era50', 'era46', 'era28', 'era104', 'era54', 'era80', 'era78', 'era93', 'era40', 'era53', 'era18', 'era24', 'era117', 'era49', 'era67', 'era56', 'era89', 'era25', 'era37', 'era95', 'era119', 'era107', 'era98', 'era63', 'era5', 'era27', 'era45', 'era22', 'era82', 'era31', 'era2', 'era84', 'era26', 'era9', 'era34', 'era97', 'era112', 'era114', 'era7', 'era55', 'era43', 'era111']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.0012629639878276196\n",
            "mean correlation\n",
            "0.0911637053798566\n",
            "sharpe\n",
            "5.481831258880421\n",
            "smart sharpe\n",
            "5.452109793286549\n",
            "fitting on worst eras\n",
            "[19:21:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 4\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era103', 'era32', 'era11', 'era58', 'era65', 'era14', 'era75', 'era59', 'era35', 'era3', 'era21', 'era57', 'era51', 'era118', 'era42', 'era91', 'era116', 'era15', 'era94', 'era77', 'era85', 'era33', 'era101', 'era73', 'era44', 'era79', 'era113', 'era110', 'era1', 'era96', 'era52', 'era70', 'era81', 'era17', 'era90', 'era109', 'era47', 'era36', 'era10', 'era49', 'era72', 'era66', 'era106', 'era40', 'era9', 'era41', 'era50', 'era12', 'era13', 'era29', 'era62', 'era120', 'era61', 'era115', 'era20', 'era100', 'era102', 'era30', 'era8']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "-0.10091594322834774\n",
            "mean correlation\n",
            "0.09846038217708765\n",
            "sharpe\n",
            "6.869412013093646\n",
            "smart sharpe\n",
            "7.563288793612459\n",
            "fitting on worst eras\n",
            "[19:22:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 5\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era60', 'era103', 'era69', 'era19', 'era74', 'era58', 'era119', 'era7', 'era71', 'era80', 'era107', 'era84', 'era67', 'era117', 'era46', 'era91', 'era89', 'era112', 'era104', 'era34', 'era78', 'era25', 'era18', 'era54', 'era114', 'era4', 'era82', 'era55', 'era42', 'era31', 'era86', 'era27', 'era99', 'era88', 'era21', 'era108', 'era93', 'era75', 'era38', 'era53', 'era98', 'era6', 'era3', 'era23', 'era57', 'era26', 'era45', 'era85', 'era113', 'era41', 'era66', 'era87', 'era56', 'era101', 'era76', 'era116', 'era97', 'era33', 'era37']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.04981221444548435\n",
            "mean correlation\n",
            "0.10311402013396469\n",
            "sharpe\n",
            "6.534276831179925\n",
            "smart sharpe\n",
            "6.193128605297869\n",
            "fitting on worst eras\n",
            "[19:22:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 6\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era65', 'era59', 'era9', 'era32', 'era35', 'era81', 'era68', 'era63', 'era64', 'era11', 'era111', 'era39', 'era2', 'era118', 'era106', 'era40', 'era79', 'era70', 'era7', 'era103', 'era14', 'era49', 'era50', 'era51', 'era94', 'era117', 'era77', 'era110', 'era83', 'era28', 'era24', 'era36', 'era48', 'era29', 'era95', 'era119', 'era107', 'era92', 'era52', 'era43', 'era100', 'era58', 'era34', 'era60', 'era53', 'era25', 'era46', 'era47', 'era84', 'era44', 'era18', 'era17', 'era96', 'era91', 'era42', 'era22', 'era62', 'era69', 'era15', 'era102']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.035587477887545314\n",
            "mean correlation\n",
            "0.11088895529563943\n",
            "sharpe\n",
            "8.615414467940663\n",
            "smart sharpe\n",
            "8.281825108332987\n",
            "fitting on worst eras\n",
            "[19:23:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 7\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era19', 'era103', 'era80', 'era74', 'era67', 'era68', 'era114', 'era12', 'era73', 'era41', 'era89', 'era66', 'era85', 'era54', 'era71', 'era75', 'era3', 'era112', 'era27', 'era55', 'era82', 'era100', 'era101', 'era31', 'era57', 'era8', 'era21', 'era91', 'era90', 'era115', 'era7', 'era99', 'era104', 'era113', 'era35', 'era60', 'era16', 'era87', 'era116', 'era2', 'era78', 'era105', 'era14', 'era33', 'era32', 'era9', 'era10', 'era88', 'era93', 'era98', 'era58', 'era108', 'era4', 'era43', 'era70', 'era45', 'era1', 'era72', 'era107', 'era109']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vZrea2dL8Prm7xRdCCDFchnT8ZjYZmAOcDswAzjGzGU3J3gdscPdjgS8An8nwR4A3ufuLgPcAl3Wr4EIIITqjyoz/JKDP3Ve6+w7gCuDMpjRnAt9K+WrgFDMzd1/k7qszfBlwgJnt142CCyGE6Iwqjv9I4MHS7/4Ma5nG3QeAjcChTWneCix09+2dFVUIIUQ3mDIaSszshcTyz6lt4s8FzgU45phjsNEolBBC1JQqM/5VwNGl30dlWMs0ZjYFOBh4NH8fBXwfeLe739NKgbtf7O4z3X3m1KlTh1cDIYQQw6KK458PHGdm081sX+BsYG5TmrnEzVuAWcAN7u5mdghwDXCBu9/YrUILIYTonCEdf67Znw9cDywHrnL3ZWY228zOyGSXAIeaWR/wAaDY8nk+cCxwoZndlp/Du14LIYQQlam0xu/u84B5TWEXluRtwFkt8l0EXLSXZRRCCNFF9OSuEELUDDl+IYSoGXL8QghRM+T4hRCiZsjxCyFEzZDjF0KImiHHL4QQNUOOXwghaoYcvxBC1Aw5fiGEqBly/EIIUTPk+IUQombI8QshRM2Q4xdCiJohxy+EEDVDjl8IIWqGHL8QQtQMOX4hhKgZcvxCCFEz5PiFEKJmyPELIUTNqOT4zew0M1thZn1mdkGL+P3M7MqMv9nMppXiPpThK8zsDd0ruhBCiE4Y0vGb2WRgDnA6MAM4x8xmNCV7H7DB3Y8FvgB8JvPOAM4GXgicBnwljyeEEGKMqDLjPwnoc/eV7r4DuAI4synNmcC3Ur4aOMXMLMOvcPft7n4v0JfHE0IIMUaYuw+ewGwWcJq7/0H+fhfwCnc/v5Tm9kzTn7/vAV4BfBy4yd0vz/BLgGvd/eomHecC5+bP5wMrgMOARzKskFuFVZFHI18vl011ki3GW9lUp+GnPcjdp1IFdx/0A8wCvl76/S7gy01pbgeOKv2+JwvyZeD3SuGXALOG0plpFzTLrcKqyKORr5fLpjrJFuOtbKpT5zqqfKos9awCji79PirDWqYxsynAwcCjFfMKIYQYRao4/vnAcWY23cz2JW7Wzm1KMxd4T8qzgBs8hqG5wNm562c6cBxwS3eKLoQQohOmDJXA3QfM7HzgemAycKm7LzOz2cTlxVxiCecyM+sD1hODA5nuKuAOYAD4Y3ffVbFsF7eQW4VVkUcjXy+XrdN8vVy20c7Xy2XrNF8vl63TfL1ctk7zDVfHkAx5c1cIIcTEQk/uCiFEzZDjF0KImiHHL4QQNUOOX9QKMzt8rMsw2pjZoWNdBtFb9NzN3XzVw0nAkcAngJ8CuzN6FbFF1DN+ADjZ3T+W7wX6U+AH7j7PzL5NvGPoYOCPgB8A04FfzWN9zd1/YGZ/RmxB3QksBu4EvgO8Cnhz6nka8TRxP/AQ8BPifURX5+fvgf2AB4HPAbcBrwN+G/hrwLLMfcTDbXcSO6G+BpyS9bgTWJ6fov4Qz0NcCdwA/Apwn7uvN7M/IR6QOwh4I7CZeOr5DmAZ8Li7P5YvzHtbHutEYN883j0lHe8AznJ3N7N93H2nmZ0ArHD33WZ2BHBEk+5vAMcDB+QxX95G9/8CTgY2ZnueBrw9bfVZYLG7P0wJM3uau29K+Vnuvj7lM3IXGWb2LKJfHAa8BLjL3ZcW+YFXAvsDjwH3p30MWEQ8hGjA8g51T3P3+8zs2EF0v4zYCbeS2A13GfBi4AHg74DrRsje3wB+RDxUuQ/wf/N7P+BLGXdLFd3E7ryvp75m3euAuU197E+A/wK+5e4/ajqXIc7fW4ApqXcS8Hx3X96izu8v6X5j2vJlg+g+HDg1y/3HwKXE2wMeBC4itpg/2dbldh6qrYndjy8hzs273H2g1NavA84Cfh2YCuwAHib81k+LOnfB3s1t/Sbg1dmffuzudzIchvO010h8slLF5/3ZUDcSHXc34XAfAj4NXEA433XZCNsJh313hm3L38uBTcBWwuFsA3alXDjXNUQnfBj4LrCWcL6fJJztTdkQv5Fpvk9sVV2Ux9lBDEIbCMf9QH4/kuV+CHgiy7Ml5bXZEf6JcEZ3EM7wvky3I78vAT6U+gbSJo8Dd2WdL8nwi1O/p84ledzHsi5/nzZYlWnWZTl3ZVkuJTraxkyzLsu/KNOsSV3b85iP5e9dKd+dx91OdPJWurdl2R4u/b4306zL78VZ9oezPjuJAXNx2mRz2uOhtMFDeQzP461Ie6wFzkt7b8n4nfnZnOX0LMeWPMYTebwf5HF2pO7NGd5Kt2e6R4m+2k53X6bfQQwAVxD96uERtPfOTL8sy74t016V+q5ro/vxFrofz3SP5TEK3ffkcR/lqX3skUyzmei/NxB97OvE+TZA9LdFeexdJb3l/v1ISbdnunVp761tdN+fbbg97b0cuDWP0dzWO9NGrdp6e36vpnFuLUo9m7OcRVsPZJp7017bgJ9nmtu6ZO9yW99P+IuiPz+Ruv8TOLqy3+0Bx78rO8dPszK/SHkLsDXTLE+DLsnv7dngd2aj7CZmsXfTmFWvyXRnpLFvSwOtT+M+nrqfmTq+QpygX8qOuTbz7yh1vCLPrtT5Dxm/HnhBdqTl2aluz7AtxIxrRZapKPcu4PjUPT/T3UU8CLc90/9SHtPTNhfScBq7sj5fzbxvKtV5KzA707ygZMPH8/v30laricFjbXbKjaX67wLekro3ZtrP0zjJvgp8LHUtIwbMp+hO289I3TuIWeEPgQ/ksYrvR4gB7qc0TqYbiRP8sax3MZBen3XaljZ7LNugaJ/fzviLU+eLiAHvhrTZB/KzEvj3rNuGLMMTqXsTMTA+RTewNI/3SJb1emJCMgD8VuounmlZnvW7Om37JRpOYCTsvTzr+3jG/V7a+0up80ttdBeDxtqS7oG031ezToXudcQ5tpU4D07O+DuIGSlps9WEQ+0nrpIfTnuvT70baQySG9mzf28odBN9qDh/thN+4CFa9O+UdwI3l9q5v0VbDxBX+q3aehfRF6/P8n0k6zyQdv2Toq2JyclppbZ+sNTW67tk73Jbr8nyTiVWMOYSvub1wA+r+t1eWONfDpzn7r9JGODVKT9ANBDAswgDvSnT9BMd7zSioTYBzyYc5S6isYpZx6FE4y7KsNVEg60j7nHcnTrend+3Eo1bdIgPEs7nrsx3B9HxVhFLQOsz3cXEstKk1HMYMTo/AfwOjVnmQuD/EEsNP06dz8jvbXn8B4HnEksG22lcTRyU5X+YvJJw9z9M+edEp7gr0zw9y3Jpydb7EJetVxAd7JlZtklZtm00ZkiPE1dZq4iTexLw0dTtxPua/jaPM+DuH2mjexJx0u4DbHL3fyQu/T+VZXk6sRRi+fs/sxwDxGXuZuKkLQaHncTyETSupu4jXg2yMY9zmZl9x93PJfrDhannyCz7p7LuBxL9Yr/U+R8ZtjrrPL+N7p1E/+snTsxLsw0nl8q2f+o8LNvwhcDvEyfpthG091eI5cb9UsdJxBPz5xGD0a1tdK/OY+8GHnb3D6TtdhBLihS6iX72wswD8G0z+5885j4Z9jhx/h5JnL/7px3mpv0eI5ZL9y3Vudy/C8f/ZRoz6rVEX3o/0VfK/fsxYD8zOyt/H0G080uIPtHc1tuIJbdWbT0AbHT3N6QNPke09R1EH/tNGm29mTj/DyDO/+U02npHN+zd1NZTUtcPCL9wFIC7/4jGktrQ9MCMfxaxzgdxebWIuPz6NNH4d9JwiNelEd9PjOgHEuvx+wDfTsNtzu8BorNcSXS4+UQHepRwwquB72WjFTPw/8pynEzjsusmGks1y4h7BNcCn8+0F9HotKuITl0sZ/SXGncXMYNdR3Tsz9G4ZHXisu/lROfYmPqKGezS1Hsmjc5RXNJfQQw+99CYtd5IdIzCOc0hOtyDaePbiJNoGbH2/EjW8ZNZp0VZ/qOIDvcEjYG0qM/tafP1GfeNNrrn57FWEDOea9NOKzLu7VnPi4n1frKcf0oMyptpLNVsznrcQcxy12W570wdP8963QR8E/gfoD+P+f9oLJksJU7M+4hZ3ZdLuh8i+sajWYZWutdknmbd69Jef0iclBuJNfU1aZtlxJp74QxGwt5zsvyFc9lKY8Lzt8Ry6WC6t6ad7k+9m1vovjvtsyD19hFLoZuItWuIwXYbcS4vI65W+tP2xXk+Ke1U1Lncv3eUdK9P/a10F3Xuz/TXEj5hRx6rnxj09mhroo+9uE1bbyX8x6eyTf+HOGfXElcuXy619Sey7LtTx3IaS5jzsv57a+9yW9+f9V5CrI4sJZbxDgTurOp3e/Hm7glEByhGr0mEgfoJh1GMlBvcfXtT3l8nHMrbiLeFfjjDX0bMLA4n1hknA7j7ajN7JTH4LAOudPctpeP9EnveZH3Y3be2KPOR7r7KzA4ibqC+wN2/mnHPITuUu2/JP6Ixj1dhTCWuWiYDi9x9cUnv6wkndLK7f6ik6xTi0vMgGn9ycxbRGQ4mOuupxGDy70THfEPa8Pfd/Ym0x9uJmVjR6dYCt7r7YjM7EVjq7tvM7GDgfOBy4NiS7tcQA+R7ic57LXEFM5TuN6e9C7vcSzjI72f7/Bh4urs/bGbHAH9DXCV9kOjc5xAn/5nEVdp2YgnnkKzDZYQjeBXRZ97u7n1Zj/OIAXRq6p4MXOPuXzCzMwrdxKzwb9z9f5vZH7XQ/Y/Eifj8QXQ/CJyduk9MO+1Twd6HEDcnm+39WmKmOVx7vzT7SZW2LnR/L+3+CmLzwcNNuq9Pe2xOvRdlOcp6DwB+jTgfnpe690l7f79VH3P3Tzb177cR58Ds7CPHt9Ddqs4HEP37DVnvHcQsudzWSzxu0B9Tauv3E0tDBwJ/kPp+N499WOoq7N2un51ELNcOp63/jbi6a2fvcltPIq5WX5d1Kwaz3cDh7n4/Fegpx29mbyQc2f6l4OtpON9DiFlXWT4uf9/dJn64aZ+8C9+mjC9w9zuL73JYFbli2l9x99ub9B7m7o/QBVrZ2d1nd+PYvai3nW5iLXVtxh/u7muL73JYys/23BXSKs0g+Q4nnF9P2BuY4+6PZvyhg8lDxQ+Wllim7Jm2nuj9e9iM5TJP+UPcxPk2MVP6GI275NfmZydxCVW+7C7u9K+lcRlfxA837cbUcx1x6XpqqWzXluQHyt/DlQeLJ0b5/izfD4FphW5gYZvyPEUeLJ5YUlpKXMp+P+3RR/x95q3ETKaVfAWNnS/vI5biVhDrnIPlu4zYLno7cUm6iZjRrKBxk/NaYvlpfbZ7cTNvfX6K+HLaR5vklS3y/TuxZfTRtO/qLPedxBJgP/BSYomtn9guWA57IOWXNcn9TXJz2APEjbdfJi7zryAmFMWy3HrgZ8C/EJfsreSVxG6zy4klhHuIZaah8i3JfHcTSy2bid0v64klibuzDR7Kdi/kDTSWuR4nzrf+YaZdk2HFDcxNwBeJWesCog98NO2ycBjyUPme1+RHrqFx439ztsEmos/vzPBiZ14reWemX1cx32KiTxf3QNZn/Yt7UFtK5SjLOzP9AHHlOljadvkeJ5Y23zssfzvWDr/UYEuIk+2u/L4vG/zl5I2dTNeXn+XEVsvlTXIf8VeRw037O6nn5cQNqI2EE1lBY9Ap7uwX37e1kZvTDpXvLhrbA+fROGHWECfZO7Js7yAugzcAH24jf6SUpyy/M3U+QawZDhAn6V2EQ1lNnCyz2sg30XAaa9IuKyvkm5X2LHa/rMrPE1nfv8t0awjnVuys2ET0ibtK8X/XRm6XbxWNnVbF7q97aWzR8xH8FNtIixuT24mB6zPEcuPmtOFz28i3p82LgeyHab+h8j03820jBrjVxAC4mVhOhHAed5RlYjJwU8qbgLcSzno4aYuwJcQEbicxCBZbFosbwxtobFNcVUGuku8W4M9T/y1p8z6izxdp/4XoF8VAdU4buS/Lu7pivrVp7x+kfGuW4xHC1xT3KsvyfxPnzy/y2PcTE99Wadvl+xQx2fkn4q9vPzUeHf8tNPba30hjlPspe27tLLZs3k2s27WS+zpIu6ukrxiBi73VxTarnxGzlx3Z0Be3kZvTDpVvd+rYRjipYgvpRhqOZIDGnv2yc2kle1PaDfkptqL+lMYNwwuzbEuJjn5jG3kTsUOqkC/K71LOq24AAAyYSURBVKHy3Zj5dmT6BRm3o2T7HcQAuIhwFLcBOzLutiK+nLZiviLsFmKdfoC4L/FXNK4EryO2Pz6acn+mHUweMl+pX+/O75vSzvsRfW9LqR5PkYnZ7G352VKq06D5Suk2E33ppmznrSV7byXWmZ+US2mXZtiUklw1bRF2S5Z/C3EvpzyTXkjs3BogHHKxpfM/iauIVnKVfEX8DmJSsZLGtts+Gv1iUaZZVOpDe8g0+tOiivkeSHsvLtlgCbAt4xfT8F+L02aLifX6Qp5P45mWrVXyZXiRbxLDuLnbC9s5C/6DmLF8hLiMKx70uI6YMe5jZvOImyyH0liu2UR0gGJGcRhwaAdpi+cJriMMfCnxFOJbCef/c2JHxEMpr/fYLvgUuUXaofIVO0VuJ5Z7ipnqzAzfRnSiZ2b4Q/k9g8aWwrL8UDmtuz8zf6/O8v0ujWcP/oiYTR5PnGBfayM/TgxghfyWLNdQ+b6W+Qq7H55lnUzsvoAYlCbl59v5XWzlnVSKp43cLl8R9h/EEtca4orqQmLXzxTipJ2d5flT4n7PkUPIQ+Yzs8+b2dMBzOzDxMm5PzEDXUluNjCzD7aSiRuiR6e8r5l9oJAHy5fyM2hc2e1PtLMBh1k8qd4PTC/LRB88PvMuJmar24aZdhuNQeA5hFNamHn+IcM/TMzGlxDLgd8n+uW9ab9WcpV8RfxFNJagTsi4hwE3s2/Q2Dp9eGnr5x4ycXP3acT28Cr59i3Vf2na+wRiUkDG0yRvBv6SmIDtm9/WJm3LfHmTej1xr7bIX4meuLmbj26/kugsS4llnv3zd7HD58WE8yiMeSjhWA7L348Qd9LL8cNJexBxubWMcPiLswzbiLvpPyOc7xaL1wW0lYt8VdKmPCvrfTSwzuPufxF2AtHpXubuf57hhxOXlEX8lCb5cHf/SpHW3b+Sdn4LMZB9k+jYrwZ+5vHqiu8Cr3L3o1rJhJNeT+OkK+TbBsuX8unElc03CEfwWWLnyCfcfZOZ/SOxvW0p8fDLhYRzKeQ1xC6ttxRpy/Ig+S4g1vQ/n/b6NLHk9Rbgz4h7KOcSTuP5xMSjHFZFHirfi4nlHYhB/TZicDqV2IX13TbyWuLqoJDvIC75Nw+R77vZti9z998ys9/N+m8kdiwdQGONuFmelDqLZ1EGOki7kZhgTSF29UwCvuPubzWzK9z9bIDhyFXS5u/CjxT3NZ6W+o8gzv198/eqLHtxBdtK3kEMHk+vkO8+YiJwYtZ7FzG4G3FOP5jpDizJxbMXk4krqP2IAbKvRdp2+W4F/oJ4VuNfgXPc/UtUoCccP4CZLXL3l411OSY6Y2XnsWzfVrpzy98vu/vthUzcK9gjrIo8RL6F7n7CaNY369dT9p7ouseb/+olx/9Z4obFv7m7D7Y1ysyudffT28lDxQ+VNnV/jsZSxFN0D0fHcPK10k2M7EXY8cQ+9BtL8rr8nkLMjpvjy/LTiNnDHXncwsat0raTh6OvkH+deOjujlKdBtPdiY52+Z6ss7vPbupbI6FvPNq7U309a+8m3S8eIX2jbe92+f4Lqm8fHfI/d0eR84i78rvMDOIyaTKxRPDbwD0W/+k7HXhlrptaC/kj+f2OYaY1YKaZXU0spRxHdJaTUvfAEDqGq296C90/Ji4rjyOWuU4h2qh47P5NxEM9+xAPbxxLdPRijX8jsSupHN8s70Ncghc3jKcQ64tPDJGvU32FfAix4wQau2qKy9Vu6WiXr6jzbjP7KI2+desI6Rtv9u5UXy/bu6x7CrF0OxL6RtPe7fLdTjzEeQtVqXoXeKQ/xLrVu4i12SXsucum2LXS6a6WKmmLXS+bmuIeK6UfaufM3u64Kcf9lLivUN6FsyQbfHNJLnaN3J7hzfHN8iTiqcRit8JyYnfTUPk61VeW30XcfNsMHJN6u6mjXb6izvdn2mbd3dY33uzdqb5etndZ9/YR1Dea9m6X778z7L+r+tte2tUzh7gxcw6NV+06cTPuDmC1d7irpUpab+x6Kd57vTPL8Gs0bvoOunOmk7I16d5KzO53ZL1fD+z0eGld8W6e4lUDhVy8FmIzMbNpjm+Wv0G8tqC42ttE4/UJg+XrVF8h70dspZyV4U8QN926qaNdvqLORxB9q1l3t/WNN3t3qq+X7V3WPWUE9Y2mvdvlOyrDjqAqYz3TL834F3pjv+zfEO9h6afxpOCcjJ9FvKRtFrGj4s0t5PcPN22mf3NJ99dS71pi296gOjotW5PufyW2cH4m672GmO0fkmHF+8SvKslLabzNcHuL+GZ5Zx73iTzuW4nONFS+TvUVcvG0585Sne7rso52+Yo6r8n2bdbdbX3jzd6d6utle5d1bxhBfaNp73b5iocqZ1f2t2Pt8EuO72ZiRFtIzLKn0niN6sGjVIZJwK+Vfo+17kOKsIx/LY1XP7+2iC+lbY7fQ87j3EKsExZvUTyceMCmbb5O9TXJNxPbAG9rat9u6WiZr1TnZ7boW13XNw7t3am+XrZ3WXdh767rG2V7t8t3MB34qTF3+CVH907iQa1ilr+C+DtAiL9e+yti/f9y4tH1slx8/6RN/HDSrsnfFzbpHo6OTsv2pO6S3nLYopK9FpW/hyEXdt5B/MHDCuDeqsfoQN+iwdq3yzqGqnNL3SOgb1zZu1N9PW7vsu7K9t4LW4y4vdvl6+Qz5g5/j8LEE29/TLyCtPizkvLL2+bTuLQp5J00Hutf1yJ+uGlvJNb0Lynp3jQMHZ2Wrax7eanO5fJ8llgqsJLcKqytXLLzj4nlrBOGeYxh6WuSW7VvN3UMVed2uruqbxzau1N9vWzvsu4R0zfK9m6Zb9w7/tIA8ASNP2TYVfo9UrsQymkLXbtLun0YOvbmjn2h22n8CYuXyuNd+Dxe1Klk424ct5d1F3qL77HQLXuPne6Jbm8v6lzVx/bSPv4yBxOXTtOJt2bOIt6xMlK7EMppjycef/8s8UTmLODOUdgVUNZ9sbs/w8wWEks8U7M8U4mZxAnEO3YK+efEK4G/S+yMao5/Unb38yz+fOI5xLMB04ktaK2O206urG8vdHeko1W+kt4jiCusd7bQ3TV949TenerrVXuXdT+PeIVC1/WNgb1b5buKWBKuvKunVx3/HPJPnImnVY14Au65xLLHXOB0GtssF9F4iVFz/HDT3k2MoFuIl3sZsc2yqo5Oy1bWvcPi33m+SLzqtfiP1jnEQxzvTPvsQ/xLT/HHz+/K45Tjm+XziNnBXKKjnUx0mFZpW8nD1deJ7k51tMtX6P0e8XbQom8dMUL6xpu9O9XXy/Yu634H4UdGQt9o2rtdvtcQE8bvEe8LGpqxXtZps9RT3tpZ7GpZzAjsQuCpuwIWZfgiGi+Zuruijr3dcbMo095VssXWUnkWlsIKeXcp75YW8XvIpeNuKeVrmbYb+jrU3amOlvlKehezZ98aEX3j0N6d6utle5d1bxkpfaNs73b5iv+LXlzVx/bSA1xldlr8N60Dcyz+m3Y38EV33+jxCtIvuvtjxP7+7Sk3xw8rbeou3hvvxAz7UODIijo6LVtZ9xPEAxmU6l2UZ6eZPbtZTltNTvkp8U1ycVwv2bhd2m7o60h3pzra5Cvbcmcr3V3WN+7s3am+Hrb3HrpHUN+o2rtNvt2lOleiVx3/l2j8+fZ2YmfMp4CfmNlbzcwKuVVYO7li2la6f1xVR6dla9K9y+KdQT8Hvl4qT/FvU1tL8hZiF9AJxNJSc/wespl9suJx28nD0teh7k51tMxX0vuppvYdEX3j0N6d6utle5d1bxspfaNs73b5VpbqXImeeTtnM2b2AuIlZZ+j8Y/1+4yC6ieIUXQSsb6/D6M3QBa6DyRG9+JfuYry/CXRyNOzbIX8S5l+bZv4Zvkn7r68ZOPB0nZDXye6O9XRLt9P3H057NG3RlLfeLN3p/p61t5NukdS32jau12+J+tchZ51/AUWf67QaldA13Yh0MO7AjxeLXsMcIS739wdqwoh6kyv7uopU97hs7c7QKqk7bVdAbNL5al2x14IIQZhPMz4F7r7y81sEeApbwWWp7zb3Sdl/PPd/cCm+GGldfcDUu9iYFeGb8m0VXR0Wray7if1ef6rj5ktdveXjKrxhRATkl69uVum5a4AursLoZd3BZTLI4QQe814WOop7wpYQPu729OJPa1V7oS3TZt35mcBHyUevCrvCqiio9OylXU/uSugqTxCCLHX9PxSD7TdFdDtXQi9vCtgWHfshRBiMMaF4xdCCNE9xsMavxBCiC4ixy+EEDVDjl8IIWqGHL8QQtQMOX4hhKgZ/x9VvbPj+W7J4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.09680482207056058\n",
            "mean correlation\n",
            "0.11190238782394825\n",
            "sharpe\n",
            "8.405774229706095\n",
            "smart sharpe\n",
            "7.602223952290934\n",
            "fitting on worst eras\n",
            "[19:24:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 8\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era65', 'era59', 'era40', 'era81', 'era69', 'era50', 'era111', 'era5', 'era110', 'era106', 'era117', 'era13', 'era26', 'era49', 'era39', 'era34', 'era18', 'era11', 'era79', 'era46', 'era53', 'era86', 'era84', 'era118', 'era25', 'era37', 'era119', 'era42', 'era47', 'era61', 'era68', 'era120', 'era63', 'era52', 'era56', 'era51', 'era89', 'era107', 'era6', 'era36', 'era28', 'era44', 'era57', 'era17', 'era103', 'era20', 'era94', 'era77', 'era9', 'era24', 'era38', 'era29', 'era32', 'era64', 'era19', 'era102', 'era97', 'era96', 'era112', 'era15']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.00807034610554681\n",
            "mean correlation\n",
            "0.12181267511411689\n",
            "sharpe\n",
            "8.249787657165236\n",
            "smart sharpe\n",
            "8.149854522735907\n",
            "fitting on worst eras\n",
            "[19:24:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 9\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era91', 'era41', 'era66', 'era60', 'era85', 'era67', 'era68', 'era80', 'era104', 'era58', 'era73', 'era103', 'era69', 'era114', 'era101', 'era62', 'era54', 'era100', 'era7', 'era74', 'era55', 'era31', 'era27', 'era35', 'era23', 'era113', 'era75', 'era3', 'era33', 'era21', 'era71', 'era116', 'era40', 'era117', 'era87', 'era12', 'era22', 'era34', 'era19', 'era106', 'era70', 'era99', 'era82', 'era26', 'era84', 'era14', 'era65', 'era88', 'era49', 'era76', 'era30', 'era46', 'era48', 'era43', 'era98', 'era2', 'era10', 'era16', 'era78', 'era118']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FLeIPMLMrM/4mM5ue4a82s1vMbFl+n9rd4gshhBgtIzp+M5sKzAXOAGYC55rZzKZk7wY2uPvxwOeAT2X4Q8Dr3f15wDuBy7pVcCGEEJ1RZcZ/CtDv7qvcfQdwBXBWU5qzgG+kfDVwmpmZuy929zUZvhw4yMwO6EbBhRBCdEYVx380cH/p90CGtUzj7kPARuDwpjRvAha5+/ZmBWZ2npktNLOFg4ODVcsuhBCiA8bl5q6ZPZdY/jm/Vby7X+zus9x9Vl9f33gUSQghaksVx78aOLb0+5gMa5nGzKYBhwIP5+9jgO8C73D3u/e2wEIIIfaOKo5/AXCCmc0ws/2Bc4B5TWnmETdvAWYD17u7m9lhwPeBC939hm4VWgghROeM6Phzzf4C4DpgBXCVuy83szlmdmYmuwQ43Mz6gfcCxZbPC4DjgYvM7Nb8HNn1WgghhKjMtCqJ3H0+ML8p7KKSvA04u0W+jwEf28syCiGE6CJ6clcIIWqGHL8QQtQMOX4hhKgZcvxCCFEz5PiFEKJmyPELIUTNkOMXQoiaIccvhBA1Q45fCCFqhhy/EELUDDl+IYSoGXL8QghRM+T4hRCiZsjxCyFEzZDjF0KImiHHL4QQNUOOXwghaoYcvxBC1Aw5fiGEqBmVHL+ZnW5mK82s38wubBF/gJldmfE3mdn0Utz7M3ylmb22e0UXQgjRCSM6fjObCswFzgBmAuea2cymZO8GNrj78cDngE9l3pnAOcBzgdOBL+XxhBBCTBBVZvynAP3uvsrddwBXAGc1pTkL+EbKVwOnmZll+BXuvt3dfwH05/GEEEJMEObuwycwmw2c7u5/kL/fDrzE3S8opbkt0wzk77uBlwAfAW5098sz/BLgGne/uknHecB5+fPZwErgCOChDCvkVmFV5PHI18tlU51ki8lWNtVp9GkPcfc+quDuw36A2cBXS7/fDnyxKc1twDGl33dnQb4I/F4p/BJg9kg6M+3CZrlVWBV5PPL1ctlUJ9lispVNdepcR5VPlaWe1cCxpd/HZFjLNGY2DTgUeLhiXiGEEONIFce/ADjBzGaY2f7Ezdp5TWnmAe9MeTZwvccwNA84J3f9zABOAG7uTtGFEEJ0wrSRErj7kJldAFwHTAUudfflZjaHuLyYRyzhXGZm/cB6YnAg010F3A4MAX/s7rsqlu3iFnKrsCryeOTr5bJ1mq+Xyzbe+Xq5bJ3m6+WydZqvl8vWab7R6hiREW/uCiGE2LfQk7tCCFEz5PiFEKJmyPELIUTNkOMXtcLMjpzoMow3Znb4RJdB9BY9d3M3X/VwCnA08FHgJ8DujF5NbBH1jB8CTnX3D+d7gf4U+J67zzezbxLvGDoU+CPge8AM4FfzWF9x9++Z2Z8RW1B3AkuAO4BvAS8D3pB6nkQ8TTwAPAD8mHgf0dX5+XvgAOB+4DPArcCrgN8G/hqwLHM/8XDbHcROqK8Ap2U97gBW5KeoP8TzEFcC1wO/Atzj7uvN7E+IB+QOAV4HbCaeer4dWA486u6P5Avz3pzHOhnYP493d0nHW4Gz3d3NbD9332lmJwEr3X23mR0FHNWk+2vAicBBecwXt9H9v4BTgY3ZnqcDb0lbfRpY4u4PUsLMnuTum1J+mruvT/nM3EWGmT2N6BdHAC8A7nT3ZUV+4KXAgcAjwL1pHwMWEw8hGrCiQ93T3f0eMzt+GN0vInbCrSJ2w10GPB+4D/g74NoxsvfXgB8SD1XuB/zf/D4A+ELG3VxFN7E776upr1n3IDCvqY/9CfBfwDfc/YdN5zLE+XszMC31TgGe7e4rWtT5PSXdr0tbvmgY3UcCr8ly/zFwKfH2gPuBjxFbzB9v63I7j9TWxO7HFxDn5p3uPlRq61cBZwO/DvQBO4AHCb/1k6LOXbB3c1u/Hnh59qcfufsdjIbRPO01Fp+sVPF5TzbUDUTH3U043AeATwIXEs53MBthO+Gw78qwbfl7BbAJ2Eo4nG3ArpQL57qW6IQPAt8G1hHO9+OEs70xG+I3Ms13ia2qi/M4O4hBaAPhuO/L74ey3A8Aj2V5tqS8LjvCPxHO6HbCGd6T6Xbk9yXA+1PfUNrkUeDOrPMlGX5x6vfUuTSP+0jW5e/TBqszzWCWc1eW5VKio23MNINZ/sWZZm3q2p7HfCR/70r5rjzudqKTt9K9Lcv2YOn3LzLNYH4vybI/mPXZSQyYS9Imm9MeD6QNHshjeB5vZdpjHXB+2ntLxu/Mz+Ysp2c5tuQxHsvjfS+PsyN1b87wVro90z1M9NV2uvsz/Q5iALiC6FcPjqG9d2b65Vn2bZn2qtR3bRvdj7bQ/WimeySPUei+O4/7ME/sYw9lms1E/72e6GNfJc63IaK/Lc5j7yrpLffvh0q6PdMNpr23ttF9b7bh9rT3CuCWPEZzW+9MG7Vq6+35vYbGubU49WzOchZtPZRpfpH22gb8LNPc2iV7l9v6XsJfFP35sdT9n8Cxlf1uDzj+Xdk5fpKV+XnKW4CtmWZFGnRpfm/PBr8jG2U3MYu9i8asem2mOzONfWsaaH0a99HU/dTU8SXiBP1Cdsx1mX9HqeMVeXalzn/I+PXAc7IjrchOdVuGbSFmXCuzTEW5dwEnpu4Fme5O4kG47Zn+l/KYnra5iIbT2JX1+XLmfX2pzluBOZnmOSUbPprfv5e2WkMMHuuyU24s1X8X8MbUvTHTfpbGSfZl4MOpazkxYD5Bd9p+ZureQcwKfwC8N49VfD9EDHA/oXEy3UCc4I9kvYuB9Lqs07a02SPZBkX7/HbGX5w6n0cMeNenzd6bn1XAv2fdNmQZHkvdm4iB8Qm6gWV5vIeyrNcRE5Ih4LdSd/FMy4qs39Vp2y/QcAJjYe8VWd9HM+730t5fSJ1faKO7GDTWlXQPpf2+nHUqdA8S59hW4jw4NeNvJ2akpM3WEA51gLhKfjDtvT71bqQxSG5kz/69odBN9KHi/NlO+IEHaNG/U94J3FRq54EWbT1EXOm3autdRF+8Lsv3wazzUNr1T4q2JiYnp5fa+v5SW6/vkr3Lbb02y9tHrGDMI3zNq4EfVPW7vbDGvwI4391/kzDAy1O+j2gggKcRBnp9phkgOt7pRENtAp5OOMpdRGMVs47DicZdnGFriAYbJO5x3JU63pHftxCNW3SI9xHO587MdzvR8VYTS0DrM93FxLLSlNRzBDE6Pwb8Do1Z5iLg/xBLDT9KnU/J7215/PuBZxJLBttpXE0ckuV/kLyScPc/TPlnRKe4M9M8OctyacnW+xGXrVcQHeypWbYpWbZtNGZIjxJXWauJk3sK8KHU7cT7mv42jzPk7h9so3sKcdLuB2xy938kLv0/kWV5MrEUYvn7P7McQ8Rl7mbipC0Gh53E8hE0rqbuIV4NsjGPc5mZfcvdzyP6w0Wp5+gs+yey7gcT/eKA1PkfGbYm67ygje6dRP8bIE7MS7MNp5bKdmDqPCLb8LnA7xMn6bYxtPeXiOXGA1LHKcQT8+cTg9EtbXSvyWPvBh509/em7XYQS4oUuol+9tzMA/BNM/ufPOZ+GfYocf4eTZy/B6Yd5qX9HiGWS/cv1bncvwvH/0UaM+p1RF96D9FXyv37EeAAMzs7fx9FtPMLiD7R3NbbiCW3Vm09BGx099emDT5DtPXtRB/7TRptvZk4/w8izv8VNNp6Rzfs3dTW01LX9wi/cAyAu/+QxpLayPTAjH82sc4HcXm1mLj8+iTR+HfQcIjXphHfQ4zoBxPr8fsB30zDbc7vIaKzXEl0uAVEB3qYcMJrgO9koxUz8P/KcpxK47LrRhpLNcuJewTXAJ/NtB+j0WlXE526WM4YKDXuLmIGO0h07M/QuGR14rLvxUTn2Jj6ihnsstR7Fo3OUVzSX0EMPnfTmLXeQHSMwjnNJTrc/WnjW4mTaDmx9vxQ1vHjWafFWf5jiA73GI2BtKjPbWnz9Rn3tTa6F+SxVhIznmvSTisz7i1Zz4uJ9X6ynH9KDMqbaSzVbM563E7Mcgez3Hekjp9lvW4Evg78DzCQx/x/NJZMlhEn5j3ErO6LJd0PEH3j4SxDK91rM0+z7sG01x8SJ+VGYk19bdpmObHmXjiDsbD33Cx/4Vy20pjw/C2xXDqc7q1pp3tT7+YWuu9K+yxMvf3EUugmYu0aYrDdRpzLy4mrlYG0fXGeT0k7FXUu9+8dJd3rU38r3UWdBzL9NYRP2JHHGiAGvT3amuhjz2/T1lsJ//GJbNP/Ic7ZdcSVyxdLbf3RLPvu1LGCxhLm/Kz/3tq73Nb3Zr2XEqsjy4hlvIOBO6r63V68uXsS0QGK0WsKYaABwmEUI+UGd9/elPfXCYfyZuJtoR/I8BcRM4sjiXXGqQDuvsbMXkoMPsuBK919S+l4v8SeN1kfdPetLcp8tLuvNrNDiBuoz3H3L2fcM8gO5e5b8o9ozONVGH3EVctUYLG7LynpfTXhhE519/eXdJ1GXHoeQuNPbs4mOsOhRGd9DTGY/DvRMV+bNvx9d38s7fEWYiZWdLp1wC3uvsTMTgaWufs2MzsUuAC4HDi+pPsVxAD5LqLzXkNcwYyk+w1p78IuvyAc5HezfX4EPNndHzSz44C/Ia6S3kd07nOJk/8s4iptO7GEc1jW4TLCEbyM6DNvcff+rMf5xADal7qnAt9398+Z2ZmFbmJW+Dfu/r/N7I9a6P5H4kR89jC67wfOSd0np532q2Dvw4ibk832fiUx0xytvV+Y/aRKWxe6v5N2fwmx+eDBJt3XpT02p96PZTnKeg8Cfo04H56VuvdLe3+3VR9z94839e83E+fAnOwjJ7bQ3arOBxH9+7VZ7x3ELLnc1ks9btAfV2rr9xBLQwcDf5D6fjePfUTqKuzdrp+dQizXjqat/424umtn73JbTyGuVl+VdSsGs93Ake5+LxXoKcdvZq8jHNmBpeDraDjfw4hZV1k+IX/f1SZ+tGkfvwvfpozPcfc7iu9yWBW5YtpfcffbmvQe4e4P0QVa2dnd53Tj2L2ot51uYi11XcYf6e7riu9yWMpP99wV0irNMPmOJJxfT9gbmOvuD2f84cPJI8UPl5ZYpuyZtt7X+/eomchlnvKHuInzTWKm9GEad8mvyc9O4hKqfNld3OlfR+MyvogfbdqNqeda4tL1NaWyXVOS7yt/j1YeLp4Y5QeyfD8Aphe6gUVtyvMEebh4YklpGXEp+920Rz/x95m3EDOZVvIVNHa+vJtYiltJrHMOl+8yYrvobcQl6SZiRrOSxk3Oa4jlp/XZ7sXNvPX5KeLLaR9ukle1yPfvxJbRh9O+a7LcdxBLgAPAC4kltgFiu2A57L6UX9QkDzTJzWH3ETfefpm4zL+CmFAUy3LrgZ8C/0JcsreSVxG7zS4nlhDuJpaZRsq3NPPdRSy1bCZ2v6wnliTuyjZ4INu9kDfQWOZ6lDjfBkaZdm2GFTcwNwGfJ2atC4k+8KG0y6JRyCPle1aTH/k+jRv/m7MNNhF9fmeGFzvzWsk7M/1gxXxLiD5d3ANZn/Uv7kFtKZWjLO/M9EPEletwadvle5RY2nzXqPztRDv8UoMtJU62O/P7nmzwF5M3djJdf35WEFstVzTJ/cRfRY427e+knhcTN6A2Ek5kJY1Bp7izX3zf2kZuTjtSvjtpbA+cT+OEWUucZG/Nsr2VuAzeAHygjfzBUp6y/LbU+RixZjhEnKR3Eg5lDXGyzG4j30jDaaxNu6yqkG922rPY/bI6P49lff8u060lnFuxs2IT0SfuLMX/XRu5Xb7VNHZaFbu/fkFji56P4afYRlrcmNxODFyfIpYbN6cNn9lGvi1tXgxkP0j7jZTvmZlvGzHArSEGwM3EciKE87i9LBOTgRtT3gS8iXDWo0lbhC0lJnA7iUGw2LJY3BjeQGOb4uoKcpV8NwN/nvpvTpv3E32+SPsvRL8oBqpz28j9Wd41FfOtS3t/L+VbshwPEb6muFdZlv+bOH9+nse+l5j4tkrbLt8niMnOPxF/ffuJyej4b6ax1/4GGqPcT9hza2exZfMuYt2uldzfQdpdJX3FCFzsrS62Wf2UmL3syIa+uI3cnHakfLtTxzbCSRVbSDfScCRDNPbsl51LK9mb0m7IT7EV9Sc0bhhelGVbRnT0G9rIm4gdUg5jRj0AAAydSURBVIX8sfweKd8NmW9Hpl+YcTtKtt9BDICLCUdxK7Aj424t4stpK+Yrwm4m1umHiPsSf0XjSvBaYvvjwykPZNrh5BHzlfr17vy+Me18ANH3tpTq8QSZmM3emp8tpToNm6+UbjPRl27Mdt5asvdWYp35cbmUdlmGTSvJVdMWYTdn+bcQ93LKM+lFxM6tIcIhF1s6/5O4imglV8lXxO8gJhWraGy77afRLxZnmsWlPrSHTKM/La6Y776095KSDZYC2zJ+CQ3/tSRttoRYry/kBTSeadlaJV+GF/mmMIqbu72wnbPgP4gZyweJy7jiQY9riRnjfmY2n7jJcjiN5ZpNRAcoZhRHAId3kLZ4nuBawsCXEk8hvolw/j8jdkQ8kPJ6j+2CT5BbpB0pX7FT5DZiuaeYqc7K8G1EJ3pqhj+Q3zNpbCksyw+U07r7U/P3mizf79J49uCPiNnkicQJ9pU28qPEAFbIb8xyjZTvK5mvsPuRWdapxO4LiEFpSn6+md/FVt4ppXjayO3yFWH/QSxxrSWuqC4idv1MI07aOVmePyXu9xw9gjxiPjP7rJk9GcDMPkCcnAcSM9BV5GYDM3tfK5m4IXpsyvub2XsLebh8KT+FxpXdgUQ7G3CExZPqA8CMskz0wRMz7xJitrptlGm30RgEnkE4pUWZ5x8y/APEbHwpsRz4XaJf/iLt10qukq+I/xiNJaiTMu5BwM3sazS2Th9Z2vq5h0zc3H0SsT28Sr79S/VflvY+iZgUkPE0yZuBvyQmYPvnt7VJ2zJf3qReT9yrLfJXoidu7uaj2y8lOssyYpnnwPxd7PB5PuE8CmMeTjiWI/L3Q8Sd9HL8aNIeQlxuLScc/pIswzbibvpPCee7xeJ1AW3lIl+VtCnPznofCwx63P0vwk4iOt2L3P3PM/xI4pKyiJ/WJB/p7l8q0rr7l9LObyQGsq8THfvlwE89Xl3xbeBl7n5MK5lw0utpnHSFfOtw+VI+g7iy+RrhCD5N7Bz5qLtvMrN/JLa3LSMefrmIcC6FvJbYpfXGIm1ZHibfhcSa/mfTXp8klrzeCPwZcQ/lPMJpPJuYeJTDqsgj5Xs+sbwDMajfSgxOryF2YX27jbyOuDoo5NuJS/7NI+T7drbti9z9t8zsd7P+G4kdSwfRWCNulqekzuJZlKEO0m4kJljTiF09U4BvufubzOwKdz8HYDRylbT5u/AjxX2NJ6X+o4hzf//8vTrLXlzBtpJ3EIPHkyvku4eYCJyc9d5FDO5GnNP3Z7qDS3Lx7MVU4grqAGKA7G+Rtl2+W4C/IJ7V+FfgXHf/AhXoCccPYGaL3f1FE12OfZ2JsvNEtm8r3bnl75fd/bZCJu4V7BFWRR4h3yJ3P2k865v16yl77+u6J5v/6iXH/2nihsW/ubsPtzXKzK5x9zPaySPFj5Q2dX+GxlLEE3SPRsdo8rXSTYzsRdiJxD70G0ryYH5PI2bHzfFl+UnE7OH2PG5h41Zp28mj0VfIv048dHd7qU7D6e5ER7t8j9fZ3ec09a2x0DcZ7d2pvp61d5Pu54+RvvG2d7t8/wXVt4+O+J+748j5xF35XWYGcZk0lVgi+G3gbov/9J0BvDTXTa2F/MH8fuso0xowy8yuJpZSTiA6yympe2gEHaPVN6OF7h8Rl5UnEMtcpxFtVDx2/3rioZ79iIc3jic6erHGv5HYlVSOb5b3Iy7BixvG04j1xcdGyNepvkI+jNhxAo1dNcXlard0tMtX1Hm3mX2IRt+6ZYz0TTZ7d6qvl+1d1j2NWLodC33jae92+W4jHuK8mapUvQs81h9i3ertxNrsUvbcZVPsWul0V0uVtMWul01NcY+U0o+0c2Zvd9yU435C3Fco78JZmg2+uSQXu0Zuy/Dm+GZ5CvFUYrFbYQWxu2mkfJ3qK8tvJ26+bQaOS73d1NEuX1HnezNts+5u65ts9u5UXy/bu6x7+xjqG097t8v33xn231X9bS/t6plL3Jg5l8ardp24GXc7sMY73NVSJa03dr0U773emWX4NRo3fYfdOdNJ2Zp0byVm9zuy3q8Gdnq8tK54N0/xqoFCLl4LsZmY2TTHN8tfI15bUFztbaLx+oTh8nWqr5APILZSzs7wx4ibbt3U0S5fUeejiL7VrLvb+iabvTvV18v2LuueNob6xtPe7fIdk2FHUZWJnumXZvyLvLFf9m+I97AM0HhScG7GzyZe0jab2FHxhhbye0abNtO/oaT7K6l3HbFtb1gdnZatSfe/Els4P5X1XkvM9g/LsOJ94leV5GU03ma4vUV8s7wzj/tYHvdNRGcaKV+n+gq5eNpzZ6lO93RZR7t8RZ3XZvs26+62vslm70719bK9y7o3jKG+8bR3u3zFQ5VzKvvbiXb4Jcd3EzGiLSJm2X00XqN66DiVYQrwa6XfE637sCIs419J49XPryziS2mb4/eQ8zg3E+uExVsUjyQesGmbr1N9TfJNxDbAW5vat1s6WuYr1fmpLfpW1/VNQnt3qq+X7V3WXdi76/rG2d7t8h1KB35qwh1+ydG9jXhQq5jlryT+DhDir9f+ilj/v5x4dL0sF98/bhM/mrRr8/dFTbpHo6PTsj2uu6S3HLa4ZK/F5e9RyIWddxB/8LAS+EXVY3Sgb/Fw7dtlHSPVuaXuMdA3qezdqb4et3dZd2V774Utxtze7fJ18plwh79HYeKJtz8mXkFa/FlJ+eVtC2hc2hTyThqP9Q+2iB9t2huINf1LSro3jUJHp2Ur615RqnO5PJ8mlgqsJLcKayuX7PwjYjnrpFEeY1T6muRW7dtNHSPVuZ3uruqbhPbuVF8v27use8z0jbO9W+ab9I6/NAA8RuMPGXaVfo/VLoRy2kLX7pJuH4WOvbljX+h2Gn/C4qXyeBc+jxZ1Ktm4G8ftZd2F3uJ7InTL3hOne1+3txd1rupje2kff5lDiUunGcRbM2cT71gZq10I5bQnEo+/f5p4InM2cMc47Aoo677Y3Z9iZouIJZ6+LE8fMZM4iXjHTiH/jHgl8LeJnVHN8Y/L7n6+xZ9PPIN4NmAGsQWt1XHbyZX17YXujnS0ylfSexRxhfW2Frq7pm+S2rtTfb1q77LuZxGvUOi6vgmwd6t8VxFLwpV39fSq459L/okz8bSqEU/APZNY9pgHnEFjm+ViGi8xao4fbdq7iBF0C/FyLyO2WVbV0WnZyrp3WPw7z+eJV70W/9E6l3iI421pn/2If+kp/vj57XmccnyzfD4xO5hHdLRTiQ7TKm0rebT6OtHdqY52+Qq93yHeDlr0raPGSN9ks3en+nrZ3mXdbyX8yFjoG097t8v3CmLC+B3ifUEjM9HLOm2WespbO4tdLUsYg10IPHFXwOIMX0zjJVN3VdSxtztuFmfaO0u22Foqz6JSWCHvLuXd0iJ+D7l03C2lfC3TdkNfh7o71dEyX0nvEvbsW2OibxLau1N9vWzvsu4tY6VvnO3dLl/xf9FLqvrYXnqAq8xOi/+mdWCuxX/T7gY+7+4bPV5B+nl3f4TY37895eb4UaVN3cV7452YYR8OHF1RR6dlK+t+jHggg1K9i/LsNLOnN8tpq6kpPyG+SS6O6yUbt0vbDX0d6e5UR5t8ZVvubKW7y/omnb071dfD9t5D9xjqG1d7t8m3u1TnSvSq4/8CjT/f3k7sjPkE8GMze5OZWSG3CmsnV0zbSvePqurotGxNundZvDPoZ8BXS+Up/m1qa0neQuwCOolYWmqO30M2s49XPG47eVT6OtTdqY6W+Up6P9HUvmOibxLau1N9vWzvsu5tY6VvnO3dLt+qUp0r0TNv52zGzJ5DvKTsMzT+sX6/cVD9GDGKTiHW9/dj/AbIQvfBxOhe/CtXUZ6/JBp5RpatkH8p069rE98s/9jdV5RsPFzabujrRHenOtrl+7G7r4A9+tZY6pts9u5UX8/au0n3WOobT3u3y/d4navQs46/wOLPFVrtCujaLgR6eFeAx6tljwOOcvebumNVIUSd6dVdPWXKO3z2dgdIlbS9titgTqk81e7YCyHEMEyGGf8id3+xmS0GPOWtwIqUd7v7lIx/trsf3BQ/qrTuflDqXQLsyvAtmbaKjk7LVtb9uD7Pf/UxsyXu/oJxNb4QYp+kV2/ulmm5K4Du7kLo5V0B5fIIIcReMxmWesq7AhbS/u72DGJPa5U74W3T5p352cCHiAevyrsCqujotGxl3Y/vCmgqjxBC7DU9v9QDbXcFdHsXQi/vChjVHXshhBiOSeH4hRBCdI/JsMYvhBCii8jxCyFEzZDjF0KImiHHL4QQNUOOXwghasb/B5W5u4adzS/MAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.09806512485409276\n",
            "mean correlation\n",
            "0.12341863835760118\n",
            "sharpe\n",
            "8.905984830206075\n",
            "smart sharpe\n",
            "8.044461941861982\n",
            "fitting on worst eras\n",
            "[19:25:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 10\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era81', 'era95', 'era25', 'era111', 'era59', 'era50', 'era83', 'era11', 'era9', 'era39', 'era92', 'era18', 'era56', 'era32', 'era109', 'era42', 'era79', 'era90', 'era53', 'era93', 'era63', 'era57', 'era110', 'era35', 'era37', 'era119', 'era89', 'era51', 'era13', 'era107', 'era47', 'era94', 'era29', 'era45', 'era5', 'era61', 'era41', 'era36', 'era1', 'era52', 'era65', 'era91', 'era86', 'era31', 'era17', 'era54', 'era112', 'era7', 'era96', 'era85', 'era120', 'era77', 'era8', 'era23', 'era105', 'era28', 'era72', 'era4', 'era102', 'era34']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AXwN/N/KiCiGE6Aajvbj7MeAL7r5hd4nM7EwzW2xmiwcGBka5SEIIUW+mVUizBji89PuwDGuVpt/MpgH7A48ArwDmmtk/AAcAO81si7t/uZzZ3c8DzgOYPXu2P9xJTYQQQlSiiuNfBBxlZjMJB38qcHpTmgXAe4BfAnOBa9zdgVcXCczsY8CGZqcvhBBibBnS8bv7oJmdDVwFTAUucPcVZjYPWOzuC4DzgQvNrA9YR1wchBBC9CBVRvy4+0JgYVPYuSV5C3DKEMf4WAflE0II0WX05K4QQtQMOX4hhKgZcvxCCFEz5PiFEKJmyPELIUTNkOMXQoiaIccvhBA1Q45fCCFqhhy/EELUDDl+IYSoGXL8QghRM+T4hRCiZsjxCyFEzZDjF0KImiHHL4QQNUOOXwghaoYcvxBC1IxKjt/MTjCzVWbWZ2bntIjfy8wuyfjrzWxGhr/ezG40s+X5fVx3iy+EEGK4DOn4zWwqMB84EZgFnGZms5qSvRd41N2PBL4AfCbDHwbe7O4vIv6M/cJuFVwIIURnVBnxzwH63H21u28DLgZObkpzMvDtlC8Djjczc/el7n5/hq8A9jGzvbpRcCGEEJ1RxfEfCtxX+t2fYS3TuPsgsB44sCnN24Al7r61WYGZnWlmi81s8cDAQNWyCyGE6IAxWdw1sxcS0z9ntYp39/Pcfba7z54+ffpYFEkIIWpLFce/Bji89PuwDGuZxsymAfsDj+Tvw4AfAO929ztHWmAhhBAjo4rjXwQcZWYzzWxP4FRgQVOaBcTiLcBc4Bp3dzM7ALgcOMfdr+1WoYUQQnTOkI4/5+zPBq4CVgKXuvsKM5tnZidlsvOBA82sD3g/UGz5PBs4EjjXzG7Kz8Fdr4UQQojKTKuSyN0XAgubws4tyVuAU1rk+wTwiRGWUQghRBfRk7tCCFEz5PiFEKJmyPELIUTNkOMXQoiaIccvhBA1Q45fCCFqhhy/EELUDDl+IYSoGXL8QghRM+T4hRCiZsjxCyFEzZDjF0KImiHHL4QQNUOOXwghaoYcvxBC1Aw5fiGEqBly/EIIUTMqOX4zO8HMVplZn5md0yJ+LzO7JOOvN7MZpbgPZfgqM3tj94ouhBCiE4Z0/GY2FZgPnAjMAk4zs1lNyd4LPOruRwJfAD6TeWcRf87+QuAE4Ct5PCGEEONElRH/HKDP3Ve7+zbgYuDkpjQnA99O+TLgeDOzDL/Y3be6+11AXx5PCCHEOGHuvvsEZnOBE9z9D/P3u4BXuPvZpTS3ZJr+/H0n8ArgY8B17n5Rhp8PXOHulzXpOBM4M38+H1gFHAQ8nGGF3CqsijwW+Xq5bKqTbDHRyqY6DT/tfu4+nSq4+24/wFzgG6Xf7wK+3JTmFuCw0u87syBfBn6/FH4+MHconZl2cbPcKqyKPBb5erlsqpNsMdHKpjp1rqPKp8pUzxrg8NLvwzKsZRozmwbsDzxSMa8QQogxpIrjXwQcZWYzzWxPYrF2QVOaBcB7Up4LXONxGVoAnJq7fmYCRwE3dKfoQgghOmHaUAncfdDMzgauAqYCF7j7CjObR9xeLCCmcC40sz5gHXFxINNdCtwKDAJ/4u47KpbtvBZyq7Aq8ljk6+WydZqvl8s21vl6uWyd5uvlsnWar5fL1mm+4eoYkiEXd4UQQkwu9OSuEELUDDl+IYSoGXL8QghRM+T4Ra0ws4PHuwxjjZkdON5lEL1Fzy3u5qse5gCHAh8HfgbszOg1xBZRz/hB4Dh3/2i+F+jPgB+5+0Iz+w7xjqH9gT8GfgTMBH49j/V1d/+Rmf05sQV1O7AMuA34LvAq4C2p52nE08T9wIPA1cT7iC7Lzz8AewH3AZ8DbgJeB/wO8NeAZZn7iIfbbiN2Qn0dOD7rcRuwMj9F/SGeh7gEuAb4NeBud19nZn9KPCC3H/AmYCPx1POtwArgcXd/LF+Y9/Y81rHAnnm8O0s6TgdOcXc3sz3cfbuZHQOscvedZnYIcEiT7m8CRwP75DFf3kb3/waOA9Zne54AvCNt9Vlgmbs/RAkze5q7b0j5We6+LuWTchcZZvYsol8cBLwEuN3dlxf5gVcCewOPAfekfQxYSjyEaMDKDnXPcPe7zezI3eh+GbETbjWxG+5C4MXAvcDfA1eOkr2/CfyEeKhyD+Af83sv4EsZd0MV3cTuvG+kvmbdA8CCpj72p8B/Ad929580ncsQ5+8NwLTUOwV4vruvbFHn95V0vylt+bLd6D4YeEOW+0+AC4i3B9wHfILYYv5kW5fbeai2JnY/voQ4N29398FSW78OOAX4TWA6sA14iPBbPyvq3AV7N7f1m4FXZ3/6qbvfxnAYztNeo/HJShWf92VDXUt03J2Ew30Q+DRwDuF8B7IRthIO+44M25K/VwIbgM2Ew9kC7Ei5cK4PEJ3wIeB7wFrC+X6ScLbXZUP8r0zzA2Kr6tI8zjbiIvQo4bjvze+Hs9wPAk9keTalvDY7wr8QzuhWwhnenem25ff5wIdS32Da5HHg9qzz+Rl+Xur31HlzHvexrMs/pA3WZJqBLOeOLMsFREdbn2kGsvxLM80DqWtrHvOx/L0j5TvyuFuJTt5K95Ys20Ol33dlmoH8XpZlfyjrs524YC5Lm2xMezyYNngwj+F5vFVpj7XAWWnvTRm/PT8bs5ye5diUx3gij/ejPM621L0xw1vp9kz3CNFX2+nuy/TbiAvAxUS/emgU7b0906/Ism/JtJemvivb6H68he7HM91jeYxC95153Ed4ah97ONNsJPrvNUQf+wZxvg0S/W1pHntHSW+5fz9c0u2ZbiDtvbmN7nuyDbemvVcCN+Yxmtt6e9qoVVtvze/7aZxbS1PPxixn0daDmeautNcW4BeZ5qYu2bvc1vcQ/qLoz0+k7v8EDq/sd3vA8e/IzvGzrMwvU94EbM40K9OgN+f31mzw27JRdhKj2DtojKofyHQnpbFvSgOtS+M+nrqfmTq+QpygX8qOuTbzbyt1vCLPjtT5Txm/DnhBdqSV2aluybBNxIhrVZapKPcO4OjUvSjT3U48CLc10/9KHtPTNufScBo7sj5fzbxvLtV5MzAv07ygZMPH8/v301b3ExePtdkp15fqvwN4a+pen2k/T+Mk+yrw0dS1grhgPkV32n5W6t5GjAp/DLw/j1V8P0xc4H5G42S6ljjBH8t6FxfSq7JOW9Jmj2UbFO3zOxl/Xup8EXHBuyZt9v78rAZ+mHV7NMvwROreQFwYn6IbWJ7HezjLehUxIBkEfjt1F8+0rMz6XZa2/RINJzAa9l6Z9X08434/7f2l1PmlNrqLi8baku7BtN9Xs06F7gHiHNtMnAfHZfytxIiUtNn9hEPtJ+6SH0p7r0u962lcJNeza/9+tNBN9KHi/NlK+IEHadG/U94OXF9q5/4WbT1I3Om3ausdRF+8Ksv3kazzYNr1T4u2JgYnJ5Ta+r5SW6/rkr3Lbf1Alnc6MYOxgPA1rwd+XNXv9sIc/0rgLHf/LcIAr075XqKBAJ5FGOjNmaaf6HgnEA21AXg24Sh3EI1VjDoOJBp3aYbdTzTYALHGcUfqeHd+30g0btEhPkA4n9sz361Ex1tDTAGty3TnEdNKU1LPQcTV+Qngd2mMMpcA/5eYavhp6nxGfm/J498HPJeYMthK425ivyz/Q+SdhLv/Ucq/IDrF7Znm6VmWC0q23oO4bb2Y6GDPzLJNybJtoTFCepy4y1pDnNxTgL9J3U68r+nv8jiD7v6RNrqnECftHsAGd/9n4tb/U1mWpxNTIZa//zPLMUjc5m4kTtri4rCdmD6Cxt3U3cSrQdbncS40s++6+5lEfzg39RyaZf9U1n1fol/slTr/I8PuzzovaqN7O9H/+okT84Jsw6mlsu2dOg/KNnwh8AfESbplFO39FWK6ca/UMYd4Yv4s4mJ0Yxvd9+exdwIPufv703bbiClFCt1EP3th5gH4jpn9Tx5zjwx7nDh/DyXO373TDgvSfo8R06V7lupc7t+F4/8yjRH1WqIvvY/oK+X+/Riwl5mdkr8PIdr5JUSfaG7rLcSUW6u2HgTWu/sb0wafI9r6VqKP/RaNtt5InP/7EOf/Shptva0b9m5q62mp60eEXzgMwN1/QmNKbWh6YMQ/l5jng7i9Wkrcfn2aaPzbaDjEK9OI7yOu6PsS8/F7AN9Jw23M70Gis1xCdLhFRAd6hHDC9wPfz0YrRuD/leU4jsZt13U0pmpWEGsEVwCfz7SfoNFp1xCdupjO6C817g5iBDtAdOzP0bhldeK27+VE51if+ooR7PLUezKNzlHc0l9MXHzupDFqvZboGIVzmk90uPvSxjcRJ9EKYu754azjJ7NOS7P8hxEd7gkaF9KiPrekzddl3Dfb6F6Ux1pFjHiuSDutyrh3ZD3PI+b7yXL+GXFR3khjqmZj1uNWYpQ7kOW+LXX8Iut1HfAt4H+A/jzm/6MxZbKcODHvJkZ1Xy7pfpDoG49kGVrpfiDzNOseSHv9EXFSrifm1B9I26wg5twLZzAa9p6f5S+cy2YaA56/I6ZLd6d7c9rpntS7sYXuO9I+i1NvHzEVuoGYu4a42G4hzuUVxN1Kf9q+OM+npJ2KOpf797aS7nWpv5Xuos79mf4Kwidsy2P1Exe9Xdqa6GMvbtPWmwn/8als0/8hztm1xJ3Ll0tt/fEs+87UsZLGFObCrP9I7V1u63uy3jcTsyPLiWm8fYHbqvrdXlzcPYboAMXVawphoH7CYRRXykfdfWtT3t8kHMrbibeFfjjDX0aMLA4m5hmnArj7/Wb2SuLiswK4xN03lY73K+y6yPqQu29uUeZD3X2Nme1HLKC+wN2/mnHPITuUu2/KP6Ixj1dhTCfuWqYCS919WUnv6wkndJy7f6ik63ji1nM/Gn9ycwrRGfYnOusbiIvJD4mO+ca04R+4+xNpj3cQI7Gi060FbnT3ZWZ2LLDc3beY2f7A2cBFwJEl3a8hLpBnEJ33CuIOZijdb0l7F3a5i3CQP8j2+SnwdHd/yMyOAP6WuEv6ANG5TyNO/pOJu7StxBTOAVmHCwlH8Cqiz7zD3fuyHmcRF9DpqXsqcLm7f8HMTip0E6PCv3X3/2Nmf9xC9z8TJ+Lzd6P7PuDU1H1s2mmPCvY+gFicbLb3a4mR5nDt/dLsJ1XautD9/bT7K4jNBw816b4q7bEx9X4iy1HWuw/wG8T58LzUvUfa+wet+pi7f7Kpf7+dOAfmZR85uoXuVnXeh+jfb8x6byNGyeW2vtljgf6IUlu/j5ga2hf4w9T3e3nsg1JXYe92/WwOMV07nLb+d+Lurp29y209hbhbfV3WrbiY7QQOdvd7qEBPOX4zexPhyPYuBV9Fw/keQIy6yvJR+fuONvHDTfvkKnybMr7A3W8rvsthVeSKaX/N3W9p0nuQuz9MF2hlZ3ef141j96LedrqJudS1GX+wu68tvsthKT/bc1dIqzS7yXcw4fx6wt7AfHd/JOMP3J08VPzu0hLTlD3T1pO9fw+b8ZzmKX+IRZzvECOlj9JYJb8iP9uJW6jybXex0r+Wxm18ET/ctOtTz5XEresbSmW7oiTfW/4erry7eOIq35/l+zEwo9ANLGlTnqfIu4snppSWE7eyP0h79BF/n3kjMZJpJV9MY+fLe4mpuFXEPOfu8l1IbBe9hbgl3UCMaFbRWOS8gph+WpftXizmrctPEV9O+0iTvLpFvh8SW0YfSfven+W+jZgC7AdeSkyx9RPbBcth96b8sia5v0luDruXWHj7VeI2/2JiQFFMy60Dfg78K3HL3kpeTew2u4iYQriTmGYaKt/Nme8OYqplI7H7ZR0xJXFHtsGD2e6F/CiNaa7HifOtf5hpH8iwYgFzA/BFYtS6mOgDf5N2WTIMeah8z2vyI5fTWPjfmG2wgejz2zO82JnXSt6e6Qcq5ltG9OliDWRd1r9Yg9pUKkdZ3p7pB4k7192lbZfvcWJq84xh+dvxdvilBruZONluz++7s8FfTi7sZLq+/KwktlqubJL7iL+KHG7a3009LycWoNYTTmQVjYtOsbJffN/URm5OO1S+22lsD1xI44R5gDjJTs+ynU7cBj8KfLiN/JFSnrL8ztT5BDFnOEicpLcTDuV+4mSZ20a+joGVLsoAAA3dSURBVIbTeCDtsrpCvrlpz2L3y5r8PJH1/ftM9wDh3IqdFRuIPnF7Kf7v28jt8q2hsdOq2P11F40tej6Kn2IbabEwuZW4cH2GmG7cmDZ8bhv5lrR5cSH7cdpvqHzPzXxbiAvc/cQFcCMxnQjhPG4ty8Rg4LqUNwBvI5z1cNIWYTcTA7jtxEWw2LJYLAw/SmOb4poKcpV8NwB/kfpvSJv3EX2+SPuvRL8oLlSntZH7srz3V8y3Nu39o5RvzHI8TPiaYq2yLP83cf78Mo99DzHwbZW2Xb5PEYOdfyH++vZTE9Hx30Bjr/21NK5yP2PXrZ3Fls07iHm7VnJfB2l3lPQVV+Bib3WxzernxOhlWzb0eW3k5rRD5duZOrYQTqrYQrqehiMZpLFnv+xcWsnelPbR/BRbUX9GY8Hw3CzbcqKjX9tG3kDskCrkT+T3UPmuzXzbMv3ijNtWsv024gK4lHAUNwHbMu6mIr6ctmK+IuwGYp5+kFiX+Csad4JXEtsfH0m5P9PuTh4yX6lf78zv69LOexF9b1OpHk+RidHsTfnZVKrTbvOV0m0k+tJ12c6bS/beTMwzPymX0i7PsGkluWraIuyGLP8mYi2nPJJeQuzcGiQccrGl8z+Ju4hWcpV8Rfw2YlCxmsa22z4a/WJpplla6kO7yDT609KK+e5Ney8r2eBmYEvGL6Phv5alzZYR8/WFvIjGMy2bq+TL8CLfFIaxuNsL2zkL/oMYsXyEuI0rHvS4khgx7mFmC4lFlgNpTNdsIDpAMaI4CDiwg7TF8wRXEga+gHgK8W2E8/8FsSPiwZTXeWwXfIrcIu1Q+YqdIrcQ0z3FSHV2hm8hOtEzM/zB/J5FY0thWX6wnNbdn5m/78/y/R6NZw/+mBhNHk2cYF9vIz9OXMAK+a1ZrqHyfT3zFXY/OMs6ldh9AXFRmpKf7+R3sZV3SimeNnK7fEXYfxBTXA8Qd1TnErt+phEn7bwsz58R6z2HDiEPmc/MPm9mTwcwsw8TJ+fexAh0NbnZwMw+0EomFkQPT3lPM3t/Ie8uX8rPoHFntzfRzgYcZPGkej8wsywTffDozLuMGK1uGWbaLTQuAs8hnNKSzPNPGf5hYjR+MzEd+AOiX96V9mslV8lXxH+CxhTUMRn3EOBm9k0aW6cPLm393EUmFnefRmwPr5Jvz1L9l6e9jyEGBWQ8TfJG4C+JAdie+W1t0rbMl4vU64i12iJ/JXpicTcf3X4l0VmWE9M8e+fvYofPiwnnURjzQMKxHJS/HyZW0svxw0m7H3G7tYJw+MuyDFuI1fSfE853k8XrAtrKRb4qaVOem/U+HBjwWP0vwo4hOt3L3P0vMvxg4payiJ/WJB/s7l8p0rr7V9LObyUuZN8iOvargZ97vLrie8Cr3P2wVjLhpNfROOkK+abd5Uv5ROLO5puEI/gssXPk4+6+wcz+mdjetpx4+OVcwrkU8gPELq23FmnL8m7ynUPM6X8+7fVpYsrrrcCfE2soZxJO4/nEwKMcVkUeKt+LiekdiIv6TcTF6Q3ELqzvtZHXEncHhXwrccu/cYh838u2fZm7/7aZ/V7Wfz2xY2kfGnPEzfKU1Fk8izLYQdr1xABrGrGrZwrwXXd/m5ld7O6nAgxHrpI2fxd+pFjXeFrqP4Q49/fM32uy7MUdbCt5G3HxeHqFfHcTA4Fjs947iIu7Eef0fZlu35JcPHsxlbiD2ou4QPa1SNsu343AB4lnNf4NOM3dv0QFesLxA5jZUnd/2XiXY7IzXnYez/ZtpTu3/P2qu99SyMRawS5hVeQh8i1x92PGsr5Zv56y92TXPdH8Vy85/s8SCxb/7u6+u61RZnaFu5/YTh4qfqi0qftzNKYinqJ7ODqGk6+VbuLKXoQdTexDv7YkD+T3NGJ03Bxflp9GjB5uzeMWNm6Vtp08HH2F/JvEQ3e3luq0O92d6GiX78k6u/u8pr41Gvomor071dez9m7S/eJR0jfW9m6X77+g+vbRIf9zdww5i1iV32FmELdJU4kpgt8B7rT4T9+ZwCtz3tRayB/J79OHmdaA2WZ2GTGVchTRWeak7sEhdAxX38wWun9K3FYeRUxzHU+0UfHY/ZuJh3r2IB7eOJLo6MUc/3piV1I5vlneg7gFLxaMpxHzi08Mka9TfYV8ALHjBBq7aorb1W7paJevqPNOM/sbGn3rxlHSN9Hs3am+XrZ3Wfc0Yup2NPSNpb3b5buFeIjzBqpSdRV4tD/EvNW7iLnZm9l1l02xa6XTXS1V0ha7XjY0xT1WSj/UzpmR7rgpx/2MWFco78K5ORt8Y0kudo3ckuHN8c3yFOKpxGK3wkpid9NQ+TrVV5bfRSy+bQSOSL3d1NEuX1HnezJts+5u65to9u5UXy/bu6x76yjqG0t7t8v33xn231X9bS/t6plPLMycRuNVu04sxt0K3O8d7mqpktYbu16K915vzzL8Bo1F393unOmkbE26NxOj+21Z79cD2z1eWle8m6d41UAhF6+F2EiMbJrjm+VvEq8tKO72NtB4fcLu8nWqr5D3IrZSzs3wJ4hFt27qaJevqPMhRN9q1t1tfRPN3p3q62V7l3VPG0V9Y2nvdvkOy7BDqMp4j/RLI/4l3tgv+7fEe1j6aTwpOD/j5xIvaZtL7Kh4Swv5fcNNm+nfUtL99dS7lti2t1sdnZatSfe/EVs4P5P1foAY7R+QYcX7xC8tyctpvM1wa4v4Znl7HveJPO7biM40VL5O9RVy8bTn9lKd7u6yjnb5ijo/kO3brLvb+iaavTvV18v2Lut+dBT1jaW92+UrHqqcV9nfjrfDLzm+64kr2hJilD2dxmtU9x+jMkwBfqP0e7x1H1CEZfxrabz6+bVFfCltc/wuch7nBmKesHiL4sHEAzZt83Wqr0m+ntgGeFNT+3ZLR8t8pTo/s0Xf6rq+CWjvTvX1sr3Lugt7d13fGNu7Xb796cBPjbvDLzm6dxIPahWj/FXE3wFC/PXaXxHz/xcRj66X5eL76jbxw0n7QP4+t0n3cHR0WrYndZf0lsOWluy1tPw9DLmw8zbiDx5WAXdVPUYH+pburn27rGOoOrfUPQr6JpS9O9XX4/Yu665s7xHYYtTt3S5fJ59xd/i7FCaeePsT4hWkxZ+VlF/etojGrU0hb6fxWP9Ai/jhpr2WmNM/v6R7wzB0dFq2su6VpTqXy/NZYqrASnKrsLZyyc4/JaazjhnmMYalr0lu1b7d1DFUndvp7qq+CWjvTvX1sr3LukdN3xjbu2W+Ce/4SxeAJ2j8IcOO0u/R2oVQTlvo2lnS7cPQMZIV+0K30/gTFi+Vx7vwebyoU8nG3ThuL+su9Bbf46Fb9h4/3ZPd3l7UuaqP7aV9/GX2J26dZhJvzZxLvGNltHYhlNMeTTz+/lniicy5wG1jsCugrPs8d3+GmS0hpnimZ3mmEyOJY4h37BTyL4hXAn+P2BnVHP+k7O5nWfz5xHOIZwNmElvQWh23nVxZ3wh0d6SjVb6S3kOIO6x3ttDdNX0T1N6d6utVe5d1P494hULX9Y2DvVvlu5SYEq68q6dXHf988k+ciadVjXgC7rnEtMcC4EQa2yyX0niJUXP8cNPeQVxBNxEv9zJim2VVHZ2Wrax7m8W/83yReNVr8R+t84mHON6Z9tmD+Jee4o+f35XHKcc3y2cRo4MFREc7jugwrdK2koerrxPdnepol6/Q+33i7aBF3zpklPRNNHt3qq+X7V3WfTrhR0ZD31jau12+1xADxu8T7wsamvGe1mkz1VPe2lnsalnGKOxC4Km7ApZm+FIaL5m6o6KOke64WZppby/ZYnOpPEtKYYW8s5R3U4v4XeTScTeV8rVM2w19HeruVEfLfCW9y9i1b42Kvglo70719bK9y7o3jZa+MbZ3u3zF/0Uvq+pje+kBrjLbLf6b1oH5Fv9NuxP4oruv93gF6Rfd/TFif//WlJvjh5U2dRfvjXdihH0gcGhFHZ2Wraz7CeKBDEr1Lsqz3cye3Synraam/JT4Jrk4rpds3C5tN/R1pLtTHW3ylW25vZXuLuubcPbuVF8P23sX3aOob0zt3SbfzlKdK9Grjv9LNP58eyuxM+ZTwNVm9jYzs0JuFdZOrpi2le6fVtXRadmadO+weGfQL4BvlMpT/NvU5pK8idgFdAwxtdQcv4tsZp+seNx28rD0dai7Ux0t85X0fqqpfUdF3wS0d6f6etneZd1bRkvfGNu7Xb7VpTpXomfeztmMmb2AeEnZ52j8Y/0eY6D6CeIqOoWY39+DsbtAFrr3Ja7uxb9yFeX5S6KRZ2bZCvlXMv3aNvHN8tXuvrJk492l7Ya+TnR3qqNdvqvdfSXs0rdGU99Es3en+nrW3k26R1PfWNq7Xb4n61yFnnX8BRZ/rtBqV0DXdiHQw7sCPF4tewRwiLtf3x2rCiHqTK/u6ilT3uEz0h0gVdL22q6AeaXyVFuxF0KI3TARRvxL3P3lZrYU8JQ3AytT3unuUzL++e6+b1P8sNK6+z6pdxmwI8M3ZdoqOjotW1n3k/o8/9XHzJa5+0vG1PhCiElJry7ulmm5K4Du7kLo5V0B5fIIIcSImQhTPeVdAYtpv7o9k9jTWmUlvG3aXJmfC/wN8eBVeVdAFR2dlq2s+8ldAU3lEUKIEdPzUz3QdldAt3ch9PKugGGt2AshxO6YEI5fCCFE95gIc/xCCCG6iBy/EELUDDl+IYSoGXL8QghRM+T4hRCiZvx/8HwhY5zRZXQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.06080064476339463\n",
            "mean correlation\n",
            "0.13507749460506374\n",
            "sharpe\n",
            "10.910114003486044\n",
            "smart sharpe\n",
            "10.228103768232755\n",
            "fitting on worst eras\n",
            "[19:26:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 11\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era69', 'era60', 'era103', 'era58', 'era104', 'era67', 'era66', 'era73', 'era40', 'era21', 'era101', 'era27', 'era114', 'era62', 'era113', 'era91', 'era55', 'era80', 'era87', 'era3', 'era33', 'era100', 'era74', 'era75', 'era41', 'era106', 'era84', 'era107', 'era116', 'era70', 'era44', 'era88', 'era97', 'era115', 'era46', 'era71', 'era117', 'era26', 'era6', 'era19', 'era108', 'era49', 'era12', 'era38', 'era112', 'era22', 'era15', 'era119', 'era9', 'era14', 'era85', 'era42', 'era18', 'era24', 'era50', 'era82', 'era7', 'era20', 'era89']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.10118176790406452\n",
            "mean correlation\n",
            "0.13308254654930052\n",
            "sharpe\n",
            "7.252120710884461\n",
            "smart sharpe\n",
            "6.530171764681238\n",
            "fitting on worst eras\n",
            "[19:27:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 12\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era81', 'era78', 'era32', 'era64', 'era99', 'era30', 'era39', 'era65', 'era111', 'era93', 'era95', 'era35', 'era53', 'era63', 'era11', 'era59', 'era37', 'era57', 'era76', 'era56', 'era25', 'era45', 'era34', 'era54', 'era79', 'era92', 'era90', 'era10', 'era31', 'era83', 'era47', 'era29', 'era40', 'era2', 'era51', 'era69', 'era36', 'era21', 'era114', 'era118', 'era94', 'era116', 'era4', 'era98', 'era48', 'era13', 'era110', 'era16', 'era8', 'era43', 'era28', 'era1', 'era5', 'era105', 'era23', 'era86', 'era58', 'era80', 'era102', 'era109']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.036655273098730644\n",
            "mean correlation\n",
            "0.14982115486770964\n",
            "sharpe\n",
            "12.688791229155695\n",
            "smart sharpe\n",
            "12.184554932875534\n",
            "fitting on worst eras\n",
            "[19:28:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 13\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era60', 'era103', 'era104', 'era91', 'era67', 'era69', 'era41', 'era66', 'era58', 'era73', 'era27', 'era107', 'era33', 'era17', 'era113', 'era106', 'era101', 'era62', 'era3', 'era75', 'era70', 'era117', 'era84', 'era100', 'era46', 'era40', 'era18', 'era9', 'era87', 'era112', 'era74', 'era72', 'era55', 'era49', 'era44', 'era42', 'era119', 'era26', 'era50', 'era88', 'era71', 'era19', 'era97', 'era85', 'era15', 'era61', 'era52', 'era6', 'era32', 'era115', 'era22', 'era38', 'era14', 'era89', 'era12', 'era81', 'era108', 'era7', 'era24']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.08117139245359586\n",
            "mean correlation\n",
            "0.1454708017643731\n",
            "sharpe\n",
            "8.405798480595836\n",
            "smart sharpe\n",
            "7.721963279197698\n",
            "fitting on worst eras\n",
            "[19:29:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 14\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era77', 'era96', 'era65', 'era120', 'era39', 'era59', 'era35', 'era82', 'era111', 'era53', 'era10', 'era99', 'era34', 'era57', 'era11', 'era116', 'era93', 'era78', 'era25', 'era30', 'era54', 'era20', 'era56', 'era21', 'era114', 'era79', 'era95', 'era63', 'era64', 'era2', 'era37', 'era29', 'era45', 'era90', 'era31', 'era47', 'era110', 'era36', 'era4', 'era80', 'era51', 'era60', 'era117', 'era68', 'era103', 'era81', 'era13', 'era92', 'era76', 'era102', 'era91', 'era32', 'era48', 'era46', 'era98', 'era94', 'era16', 'era43', 'era106', 'era5']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.07013180122499489\n",
            "mean correlation\n",
            "0.1624429872487469\n",
            "sharpe\n",
            "14.048830653580088\n",
            "smart sharpe\n",
            "13.04879227453485\n",
            "fitting on worst eras\n",
            "[19:30:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 15\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era104', 'era69', 'era60', 'era67', 'era103', 'era107', 'era41', 'era66', 'era27', 'era118', 'era40', 'era58', 'era33', 'era113', 'era101', 'era70', 'era62', 'era28', 'era73', 'era17', 'era9', 'era3', 'era87', 'era75', 'era84', 'era100', 'era112', 'era1', 'era74', 'era55', 'era72', 'era88', 'era44', 'era18', 'era71', 'era119', 'era19', 'era50', 'era49', 'era85', 'era12', 'era42', 'era86', 'era97', 'era6', 'era8', 'era26', 'era52', 'era14', 'era7', 'era59', 'era21', 'era91', 'era15', 'era89', 'era115', 'era61', 'era83', 'era10']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.1630503196177299\n",
            "mean correlation\n",
            "0.16103749927638536\n",
            "sharpe\n",
            "11.517973198959853\n",
            "smart sharpe\n",
            "9.743531774916525\n",
            "fitting on worst eras\n",
            "[19:31:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 16\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era105', 'era39', 'era96', 'era35', 'era116', 'era65', 'era24', 'era109', 'era11', 'era23', 'era53', 'era77', 'era25', 'era111', 'era82', 'era120', 'era78', 'era30', 'era29', 'era38', 'era34', 'era56', 'era95', 'era79', 'era99', 'era93', 'era57', 'era32', 'era54', 'era117', 'era22', 'era114', 'era64', 'era108', 'era2', 'era45', 'era63', 'era31', 'era47', 'era81', 'era110', 'era20', 'era51', 'era90', 'era4', 'era102', 'era80', 'era106', 'era5', 'era36', 'era3', 'era92', 'era37', 'era9', 'era76', 'era46', 'era43', 'era1', 'era48', 'era17']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.21615710136544933\n",
            "mean correlation\n",
            "0.17728037415602016\n",
            "sharpe\n",
            "11.394617561430895\n",
            "smart sharpe\n",
            "9.126925005858286\n",
            "fitting on worst eras\n",
            "[19:33:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 17\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era68', 'era69', 'era103', 'era60', 'era58', 'era107', 'era104', 'era40', 'era41', 'era67', 'era62', 'era113', 'era27', 'era73', 'era66', 'era91', 'era70', 'era33', 'era101', 'era16', 'era88', 'era75', 'era87', 'era55', 'era112', 'era13', 'era74', 'era89', 'era21', 'era119', 'era18', 'era19', 'era84', 'era100', 'era28', 'era94', 'era7', 'era98', 'era118', 'era49', 'era42', 'era71', 'era85', 'era59', 'era50', 'era12', 'era10', 'era26', 'era6', 'era44', 'era72', 'era52', 'era97', 'era65', 'era114', 'era14', 'era106', 'era8', 'era116', 'era86']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.20368991871805295\n",
            "mean correlation\n",
            "0.16966177885855122\n",
            "sharpe\n",
            "9.202598118756194\n",
            "smart sharpe\n",
            "7.467024260732863\n",
            "fitting on worst eras\n",
            "[19:34:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "iteration 18\n",
            "predicting on train\n",
            "getting per era scores\n",
            "['era39', 'era35', 'era96', 'era53', 'era24', 'era29', 'era111', 'era61', 'era11', 'era105', 'era117', 'era34', 'era15', 'era32', 'era77', 'era25', 'era47', 'era23', 'era79', 'era99', 'era54', 'era82', 'era57', 'era78', 'era31', 'era56', 'era64', 'era30', 'era83', 'era120', 'era81', 'era2', 'era109', 'era93', 'era63', 'era38', 'era3', 'era115', 'era107', 'era9', 'era102', 'era110', 'era70', 'era22', 'era51', 'era95', 'era46', 'era5', 'era90', 'era45', 'era80', 'era43', 'era108', 'era92', 'era40', 'era17', 'era27', 'era69', 'era13', 'era1']\n",
            "performance over time\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vMLMvEO8YOgb4PeCbwEzgF/NYn3H3b5rZHxFLUPcDi4G7gS8DvwS8Ie08hXiaeAB4GPgu8T6i6/PzN8CRwEPAx4BFwK8Cvw78X8Ayz6uJh9vuJlZCfQZ4ZZbjbmBFforyQzwP8RXge8DPA/e7+2Yz+wPiAbknA68DdhJPPS8HlgHb3P2xfGHeb+WxXgQckce7t2TjLcAF7u5mNtXd95vZ6cBKdz9oZicAJzTZ/jxwGvCkPOYL29j+X8ArgK1Zn+cCb05ffRRY7O4bKGFmT3H3HRl+urtvzvB5uYoMM3s60S6mAc8H7nH3pUV64CXAUcBjwAPpHwMWEg8hGrCiS9sz3P1+M3vWELbPJFbCrSFWw30ReB7wIPDXwE0j5O/PA7cQD1VOBf42v48Ersp9d1SxTazO+2zaa7a9CZjb1Mb+APgB8C/ufkvTuQxx/t4BTEm7k4Bnu/uKFmV+d8n269KXZw5h+3jgnMz37wPXEG8PeAj4ILHE/PG6LtfzcHVNrH58PnFu3uPug6W6/lXgAuClwHRgH7CB0K1bizL3wN/Ndf164GXZnr7j7nfTCZ087TUSnyxU8Xl3VtRtRMM9SAjuw8CHgcsI8d2UlbCXEOxVuW1P/l4B7AB2E4KzBziQ4UJc1xONcAPwVWAjIb4fIsT29qyI/5Fxvk4sVV2Yx9lHXIS2EML9YH4/kvl+GNie+dmV4Y3ZEP6REKPlhBjen/H25ffngL9Ie4Ppk23APVnmz+X2q9O+p80ledzHsix/kz5Ym3E2ZT4PZF6uIRra1oyzKfO/MOOsT1t785iP5e8DGV6Vx91LNPJWtvdk3jaUft+XcTbl9+LM+4Ysz37igrk4fbIz/fFw+uDhPIbn8VamPzYCl6S/d+X+/fnZmfn0zMeuPMb2PN438zj70vbO3N7Ktme8R4m22s726oy/j7gAXEu0qw0j6O/9GX9Z5n1Pxr0u7d3Uxva2Fra3ZbzH8hiF7XvzuI/yxDb2SMbZSbTf7xFt7LPE+TZItLeFeewDJbvl9v1IybZnvE3p791tbD+Qdbg3/b0CuDOP0VzX+9NHrep6b36vo3FuLUw7OzOfRV0PZpz70l97gB9lnEU98ne5rh8g9KJoz9vT9veBkyvrbh8I/4FsHLdmYf4rw7uA3RlnRTp0SX7vzQq/OyvlINGLXUWjV70+452Xzl6UDtqczt2Wto9NG58iTtCrsmFuzPT7Sg2vSHMgbf597t8MPCcb0opsVHfltl1Ej2tl5qnI9wHgtLQ9L+PdQzwItzfj/0we09M3l9MQjQNZnk9n2teXyrwbmJ1xnlPy4bb8flv6ah1x8diYjXJrqfwHgDem7a0Z9+M0TrJPA1ekrWXEBfMJttP3Z6TtfUSv8NvAe/JYxfcjxAXuVhon023ECf5Ylru4kN6cZdqTPnss66Con1/P/VenzV8gLnjfS5+9Jz9rgG9k2bZkHran7R3EhfEJtoGlebxHMq83Ex2SQeBVabt4pmVFlu/69O1VNERgJPy9Isu7Lfe9Lf19Vdq8qo3t4qKxsWR7MP336SxTYXsTcY7tJs6DV+T+5USPlPTZOkJQB4i75A3p781pdyuNi+RWDm3fWwrbRBsqzp+9hA48TIv2neH9wE9K9TzQoq4HiTv9VnV9gGiLN2f+3pdlHky//kFR10Tn5NxSXT9UquvNPfJ3ua7XZ36nEyMYcwmteTXw7aq62w9j/CuAS9z91wgHvCzDDxIVBPB0wkGvzzgDRMM7l6ioHcBPE0J5gKisotdxHFG5C3PbOqLCNhFzHKvSxv/M7zuJyi0axJ8Q4nNPpltONLy1xBDQ5ox3NTGsNCntTCOuztuB36DRy1wA/B9iqOE7afOn8ntPHv8h4BnEkMFeGncTT878byDvJNz9dzP8I6JR3JNxnpp5uabk66nEbeu1RAM7NvM2KfO2h0YPaRtxl7WWOLknAe9P2068r+mv8jiD7v6+NrYnESftVGCHu/8Dcet/ZeblqcRQiOXv72c+Bonb3J3ESVtcHPYTw0fQuJu6n3g1yNY8zhfN7MvufjHRHi5POydm3q/Msh9NtIsj0+Z/5LZ1WeZ5bWzvJ9rfAHFiXpN1OLmUt6PS5rSsw+cCv02cpHtG0N+fIoYbj0wbZxNPzF9CXIzubGN7XR77ILDB3d+TvttHDClS2Cba2XMzDcAXzOzHecypuW0bcf6eSJy/R6Uf5qb/HiOGS48olbncvgvh/ySNHvVGoi29m2gr5fb9GHCkmV2Qv08g6vn5RJtorus9xJBbq7oeBLa6+2vSBx8j6no50cZ+jUZd7yTO/ycR5/8KGnW9rxf+bqrrKWnrm4QunATg7rfQGFIbnj7o8c8ixvkgbq8WErdfHyYq/24agnhTOvHdxBX9aGI8firwhXTczvweJBrLV4gGN49oQI8SIrwO+FpWWtED/0Hm4xU0brtupzFUs4yYI7gR+HjG/SCNRruWaNTFcMZAqXIPED3YTUTD/hiNW1YnbvteSDSOrWmv6MEuTbvn02gcxS39tcTF514avdbbiIZRiNMcosE9lD5eRJxEy4ix50eyjB/KMi3M/J9ENLjtNC6kRXnuSp9vzn2fb2N7Xh5rJdHjuTH9tDL3vTnLeTUx3k/m8w+Ji/JOGkM1O7Mcy4le7qbM991p40dZrtuBfwZ+DAzkMf8/jSGTpcSJeT/Rq/tkyfbDRNt4NPPQyvb6TNNse1P663eJk3IrMaa+Pn2zjBhzL8RgJPw9J/NfiMtuGh2evyKGS4eyvTv99EDa3dnC9qr0z/y0u5oYCt1BjF1DXGz3EOfyMuJuZSB9X5znk9JPRZnL7XtfyfbmtN/KdlHmgYx/I6EJ+/JYA8RF75C6JtrY89rU9W5CP67MOv0xcc5uJO5cPlmq6w9k3g+mjRU0hjBvyPIfrr/Ldf1AlnsJMTqylBjGOxq4u6ru9uPk7ulEAyiuXpMIBw0QglFcKbe4+96mtC8lBOW3iLeFvje3n0n0LI4nxhknA7j7OjN7CXHxWQZ8xd13lY73Mxw6ybrB3Xe3yPOJ7r7WzJ5MTKA+x90/nft+lmxQ7r4r/4jGPF6FMZ24a5kMLHT3xSW7ryZE6BXu/hclW68kbj2fTONPbi4gGsMxRGM9h7iYfINomK9JH/62u29Pf7yZ6IkVjW4jcKe7LzazFwFL3X2PmR0DXAp8CXhWyfbLiQvkO4nGeyNxBzOc7Tekvwu/3EcI5Nezfr4DPNXdN5jZKcBfEndJf0I07ouIk/984i5tLzGE87QswxcJIfglos282d1XZzkuIS6g09P2ZOBb7v53ZnZeYZvoFf6lu/9vM/u9Frb/gTgRnz2E7YeAC9P2i9JPUyv4+2nE5GSzv3+F6Gl26u8XZDupUteF7a+l319MLD7Y0GT75vTHzrT7wcxH2e6TgF8mzodnpu2p6e+vt2pj7v6hpvb9W8Q5MDvbyGktbLcq85OI9v2aLPc+opdcruslHhP0p5Tq+t3E0NDRwO+kvd/MY09LW4W/27Wzs4nh2k7q+t+Ju7t2/i7X9STibvVXs2zFxewgcLy7P0AF+kr4zex1hJAdVdp8Mw3xfRrR6yqHT83fq9rs7zTu47PwbfL4HHe/u/gub6sSrhj35939ria709z9EXpAKz+7++xeHLsf7bazTYylbsz9x7v7xuK7vC3DP+25KqRVnCHSHU+IX1/4G5jj7o/m/uOGCg+3f6i4xDBl39T1RG/fHTOWwzzlDzGJ8wWip3QFjVnyG/Ozn7iFKt92FzP9G2ncxhf7O427Ne3cRNy6nlPK242l8IPl707DQ+0nrvIDmb9vAzMK28CCNvl5Qnio/cSQ0lLiVvbr6Y/VxN9n3kn0ZFqFr6Wx8uVdxFDcSmKcc6h0XySWi95F3JLuIHo0K2lMct5IDD9tznovJvM256fYX477aFN4TYt03yCWjD6a/l2X+b6bGAIcAF5ADLENEMsFy9sezPCZTeGBpnDztgeJibefI27zryU6FMWw3Gbgh8C/ErfsrcJriNVmXyKGEO4lhpmGS7ck060ihlp2EqtfNhNDEquyDh7Oei/CW2gMc20jzreBDuOuz23FBOYO4BNEr3U+0Qben35Z0EF4uHTPbNKRb9GY+N+ZdbCDaPP7c3uxMq9VeH/G31Qx3WKiTRdzIJuz/MUc1K5SPsrh/Rl/kLhzHSpuu3TbiKHNd3akt2Mt+KUKW0KcbPfk9/1Z4S8kJ3Yy3ur8rCCWWq5oCq8m/iqy07i/kXZeSExAbSVEZCWNi04xs198L2oTbo47XLp7aCwPvIHGCbOeOMneknl7C3EbvAV4b5vw+0ppyuG3ps3txJjhIHGS3kMIyjriZJnVJnw7DdFYn35ZUyHdrPRnsfplbX62Z3n/OuOtJ8StWFmxg2gT95T2/3WbcLt0a2mstCpWf91HY4mej+CnWEZaTEzuJS5cHyGGG3emD5/RJnxX+ry4kH07/Tdcumdkuj3EBW4dcQHcSQwnQojH8nKY6AzcnuEdwJsIse4kbrFtCdGB209cBIsli8XE8BYayxTXVghXSXcH8Mdp/470+WqizRdx/5VoF8WF6qI24dWZ33UV021Mf38zw3dmPh4htKaYqyyH/5M4f/4rj/0A0fFtFbdduiuJzs4/En99e+V4FP47aKy1v43GVe5WDl3aWSzZXEWM27UKr+4i7oGSveIKXKytLpZZ/ZDovezLir66Tbg57nDpDqaNPYRIFUtIt9IQkkEaa/bL4tIq7E1xt+SnWIp6K40Jw8szb0uJhn5bm/AOYoVUEf5gfg+X7rZMty/jz899+0q+30dcABcSQrEI2Jf7FhX7y3Erpiu23UGM0w8S8xJ/TuNO8CZi+eOjGR7IuEOFh01XatcH8/v29PORRNvbVSrHE8JEb3ZRfnaVyjRkulK8nURbuj3reXfJ37uJcebHw6W4S3PblFK4atxi2x2Z/13EXE65J72AWLk1SAhysaTz+8RdRKtwlXTF/n1Ep2INjWW3q2m0i4UZZ2GpDR0SptGeFlZM92D6e3HJB0uAPbl/MQ39Wpw+W0yM1xfheTSeadldJV1uL9JNooPJ3X5YzlnwH0SP5X3EbVzxoMdNRI9xqpndQEyyHEdjuGYH0QCKHsU04Lgu4hbPE9xEOPga4inENxHi/yNiRcTDGd7ssVzwCeEWcYdLV6wUuYsY7il6qmfl9j1EIzo2tz+c32fQWFJYDj9cjuvux+bvdZm/36Tx7MHvEb3J04gT7DNtwtuIC1gRfmPma7h0n8l0hd+Pz7xOJlZfQFyUJuXnC/ldLOWdVNpPm3C7dMW2/yCGuNYTd1SXE6t+phAn7ezMzx8S8z0nDhMeNp2ZfdzMngpgZu8lTs6jiB7oGnKxgZn9SaswMSF6coaPMLP3FOGh0mX4p2jc2R1F1LMB0yyeVB8AZpbDRBs8LdMuJnqrezqMu4fGReBnCVFakGn+Pre/l+iNLyGGA79OtMv70n+twlXSFfs/SGMI6vTctwFwM/s8jaXTx5eWfh4SJiZ3n0IsD6+S7ohS+Zemv08nOgXkfprCO4E/IzpgR+S3tYnbMl1OUm8m5mqL9JXoi8ndfHT7JURjWUoM8xyVv4sVPs8jxKNw5nGEsEzL348QM+nl/Z3EfTJxu7WMEPzFmYc9xGz6Dwnx3WXxuoC24SJdlbgZnpXlPhnY5DH7X2w7nWh0Z7r7H+f244lbymL/lKbw8e7+qSKuu38q/fxG4kL2z0TDfhnwQ49XV3wV+CV3P6lVmBDpzTROuiK8aKh0GX4tcWfzeUIIPkqsHPmAu+8ws38glrctJR5+uZwQlyK8nlil9cYibjk8RLrLiDH9j6e/PkwMeb0R+CNiDuViQjSeTXQ8ytuqhIdL9zxieAfior6IuDidQ6zC+mqb8Ebi7qAILydu+XcOk+6rWbdnuvurzOw3s/xbiRVLT6IxRtwcnpQ2i2dRBruIu5XoYE0hVvVMAr7s7m8ys2vd/UKATsJV4ubvQkeKeY2npP0TiHP/iPy9NvNe3MG2Cu8jLh5PrZDufqIj8KIs9wHi4m7EOf1Qxju6FC6evZhM3EEdSVwgV7eI2y7dncCfEs9q/BtwkbtfRQX6QvgBzGyhu5851vmY6IyVn8eyflvZzhOFNHsAAAizSURBVCV/P+fudxVhYq7gkG1VwsOkW+Dup49mebN8feXviW57vOlXPwn/R4kJi393dx9qaZSZ3ejur20XHm7/cHHT9sdoDEU8wXYnNjpJ18o2cWUvtp1GrEO/rRTelN9TiN5x8/5y+ClE72F5Hrfwcau47cKd2CvCLyUeulteKtNQtrux0S7d42V299lNbWsk7I1Hf3drr2/93WT7eSNkb7T93S7dD6D68tFh/3N3FLmEmJU/YGYQt0mTiSGCXwfutfhP35nAS3Lc1FqE35ffb+kwrgFnmdn1xFDKqURjOTttDw5jo1N7M1vY/g5xW3kqMcz1SqKOisfuX0881DOVeHjjWURDL8b4txKrksr7m8NTiVvwYsJ4CjG+uH2YdN3aK8JPI1acQGNVTXG72isb7dIVZT5oZu+n0bbuHCF7483f3drrZ3+XbU8hhm5Hwt5o+rtduruIhzjvoCpVZ4FH+kOMW72dGJtdwqGrbIpVK92uaqkSt1j1sqNp32Ol+MOtnDncFTflfbcS8wrlVThLssJ3lsLFqpG7cnvz/ubwJOKpxGK1wgpiddNw6bq1Vw6/nZh82wmcknZ7aaNduqLMD2TcZtu9tjfe/N2tvX72d9n23hG0N5r+bpfuP3Pbf1bV235a1TOHmJi5iMardp2YjFsOrPMuV7VUieuNVS/Fe6/3Zx5+mcak75ArZ7rJW5Pt3UTvfl+W+9XAfo+X1hXv5ileNVCEi9dC7CR6Ns37m8OfJ15bUNzt7aDx+oSh0nVrrwgfSSylnJXbtxOTbr200S5dUeYTiLbVbLvX9sabv7u118/+LtueMoL2RtPf7dKdlNtOoCpj3dMv9fgXeGO97F8S72EZoPGk4JzcP4t4SdssYkXFG1qE391p3Iz/hpLtz6TdjcSyvSFtdJu3Jtv/Rizh/EiWez3R239abiveJ35dKbyUxtsM97bY3xzen8fdnsd9E9GYhkvXrb0iXDztub9Upvt7bKNduqLM67N+m2332t5483e39vrZ32XbW0bQ3mj6u1264qHK2ZX1dqwFvyR8PyGuaAuIXvZ0Gq9RPWaU8jAJ+OXS77G2/bRiW+7/FRqvfv6VYn8pbvP+Q8J5nDuIccLiLYrHEw/YtE3Xrb2m8E+IZYCLmuq3VzZapiuV+dgWbavn9sahv7u118/+Ltsu/N1ze6Ps73bpjqELnRpzwS8J3VuJB7WKXv5K4u8AIf567c+J8f8vEY+ul8PF93fb7O8k7vr8fXmT7U5sdJu3x22X7Ja3LSz5a2H5u4Nw4ed9xB88rATuq3qMLuwtHKp+e2xjuDK3tD0C9saVv7u11+f+Ltuu7O/D8MWI+7tdum4+Yy74h2Qmnnj7feIVpMWflZRf3jaPxq1NEd5P47H+TS32dxr3NmJM/3Ml2zs6sNFt3sq2V5TKXM7PR4mhAiuFW21rGy75+TvEcNbpHR6jI3tN4Vb120sbw5W5ne2e2huH/u7WXj/7u2x7xOyNsr9bphv3wl+6AGyn8YcMB0q/R2oVQjluYetgybZ3YONwZuwL207jT1i8lB/vwWdbUaaSj3tx3H62XdgtvsfCtvw9drYnur+9KHNVje2ndfxljiFunWYSb82cRbxjZaRWIZTjnkY8/v5R4onMWcDdo7AqoGz7anf/KTNbQAzxTM/8TCd6EqcT79gpwj8iXgn8VWJlVPP+x8PufonFn0/8LPFswExiCVqr47YLV7Z3GLa7stEqXcnuCcQd1ltb2O6ZvXHq727t9au/y7afSbxCoef2xsDfrdJdRwwJV17V06/CP4f8E2fiaVUjnoB7BjHsMRd4LY1llgtpvMSoeX+ncVcRV9BdxMu9jFhmWdVGt3kr295n8e88nyBe9Vr8R+sc4iGOt6Z/phL/0lP88fPb8zjl/c3hS4jewVyiob2CaDCt4rYKd2qvG9vd2miXrrD7NeLtoEXbOmGE7I03f3drr5/9Xbb9FkJHRsLeaPq7XbqXEx3GrxHvCxqesR7WaTPUU17aWaxqWcwIrELgiasCFub2hTReMrWqoo3DXXGzMOPeU/LF7lJ+FpS2FeGDpbS7Wuw/JFw67q5SupZxe2GvS9vd2miZrmR3MYe2rRGxNw793a29fvZ32faukbI3yv5ul674v+jFVTW2nx7gKrPf4r9pHZhj8d+0B4FPuPtWj1eQfsLdHyPW9+/NcPP+juKm7eK98U70sI8DTqxoo9u8lW1vJx7IoFTuIj/7zeynm8Ppq8kZfsL+pnBxXC/5uF3cXtjryna3NtqkK/tyfyvbPbY37vzdrb0+9vchtkfQ3qj6u026g6UyV6Jfhf8qGn++vZdYGXMl8F0ze5OZWRFuta1duGLcVra/U9VGt3lrsn3A4p1BPwI+W8pP8W9Tu0vhXcQqoNOJoaXm/YeEzexDFY/bLtyRvS5td2ujZbqS3Sub6ndE7I1Df3drr5/9Xba9Z6TsjbK/26VbUypzJfrm7ZzNmNlziJeUfYzGP9ZPHQXT24mr6CRifH8qo3eBLGwfTVzdi3/lKvLzZ0Qlz8y8FeGfyfgb2+xvDn/X3VeUfDxU3F7Y68Z2tzbapfuuu6+AQ9rWSNobb/7u1l7f+rvJ9kjaG01/t0v3eJmr0LfCX2Dx5wqtVgX0bBUCfbwqwOPVsqcAJ7j7T3rjVSFEnenXVT1lyit8DncFSJW4/bYqYHYpP9Vm7IUQYgjGQ49/gbu/0MwWAp7h3cCKDB9090m5/9nufnTT/o7iuvuT0u5i4EBu35Vxq9joNm9l24/b8/xXHzNb7O7PH1XnCyEmJP06uVum5aoAersKoZ9XBZTzI4QQh814GOoprwqYT/vZ7ZnEmtYqM+Ft4+bM/Czg/cSDV+VVAVVsdJu3su3HVwU05UcIIQ6bvh/qgbarAnq9CqGfVwV0NGMvhBBDMS6EXwghRO8YD2P8QggheoiEXwghaoaEXwghaoaEXwghaoaEXwghasZ/A33vUIq7IwjVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "autocorrelation\n",
            "0.11689719155675654\n",
            "mean correlation\n",
            "0.19118296172904847\n",
            "sharpe\n",
            "14.7134728481647\n",
            "smart sharpe\n",
            "13.041466610798759\n",
            "fitting on worst eras\n",
            "[19:36:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}